reSee.it - Related Post Feed

jbahrdestefano
Posted - August 24, 2023 at 8:33 PM
Saved - August 26, 2023 at 5:42 AM
reSee.it AI Summary
Drew Houston, CEO of Dropbox, shared insights on starting a company and adapting over 16 years. Experimentation and observing user behavior were key. They paid people to test their product, improving the onboarding rate. Dropbox focused on their unique value proposition and stopped competing in consumer tech. Transitioning from founder to CEO requires continuous learning and personal growth. Educate yourself to manage company growth effectively.

@jbahrdestefano - JC Bahr-de Stefano

We had @drewhouston , CEO and Co-Founder of @Dropbox, at The Mint last week to talk to our founders in a fireside-style chat. Drew shared inspiring reflections on the early days of starting a company + how things have changed in the ensuing 16 yrs (!), check them out! https://t.co/5erU6mSLMg

@jbahrdestefano - JC Bahr-de Stefano

1. Successfully acquiring users in the early days requires constant experimentation. Throw a bunch of stuff at the wall and see what sticks. Dropbox tried “the standard things” and didn’t find much success.

@jbahrdestefano - JC Bahr-de Stefano

After observing examples of what other co’s did (e.g., Amazon affiliate links and misremembering* PayPal’s referral program 😆), the Dropbox team cracked it after trying a dual-sided referral program: if one user referred another, they BOTH got free storage.

@jbahrdestefano - JC Bahr-de Stefano

2. Actively observe how customers use your product + listen to their feedback to drive improvements. By watching users interact with the product and addressing their pain points, you can refine your offering and provide a better UX. In the early days, Dropbox saw wide-ranging

@jbahrdestefano - JC Bahr-de Stefano

customer feedback (some very good, some very bad) + launched an exercise to understand the variance in UX. Instead of doing traditional user testing, they found peeps on Craigslist + paid them to come into the office so they could watch them as they sat down + used the product.

@jbahrdestefano - JC Bahr-de Stefano

Ask anyone who does this, and they’ll agree that their assumptions about how people use tech and the Internet are stuck within their own perspectives. For Dropbox, most of these test users didn’t even get 10% of the way there!

@jbahrdestefano - JC Bahr-de Stefano

Drew and his team videotaped the sessions and cataloged everything that went wrong in the process so they could fix it. This took their onboarding activation rate from ~25% to ~65%.

@jbahrdestefano - JC Bahr-de Stefano

Another telling example: at one point, one of the top feature requests was billing multiple accounts from one credit card. Meaning: users wanted enterprise accounts! So, Dropbox started offering them.

@jbahrdestefano - JC Bahr-de Stefano

3. When you build something that catches lightning in a bottle, you put a target on your back. The reward for getting into the bigger leagues is tougher opponents — only the paranoid survive.

@jbahrdestefano - JC Bahr-de Stefano

A key strategy here: In a highly competitive landscape, deeply understand and focus on your unique value proposition. Don’t diversify into areas where you can’t win; put your eggs into the basket where you can improve an existing durable advantage.

@jbahrdestefano - JC Bahr-de Stefano

After around 7 yrs, Dropbox expanded its product suite as a growth effort. In 2013, it acquired Mailbox with the vision of making mobile email better. In 2014, it launched Carousel to create a new way for users to experience + share photos.

@jbahrdestefano - JC Bahr-de Stefano

They built great products + made great UI innovations….but these launches put them even more directly in conflict with the Microsofts and Googles of the world. What was it like to compete against these tech giants? Not that fun!

@jbahrdestefano - JC Bahr-de Stefano

Drew said “it was like playing a board game with your siblings while they wiped all of the pieces off of the board.” Despite launching products that were universally loved by users in Carousel + Mailbox, Dropbox made the decision to wind them down.

@jbahrdestefano - JC Bahr-de Stefano

It required immense maturity + focus to realize their durable advantage wasn't competing in consumer tech products, esp. given the competitive landscape. Instead, they focused on doubling down on what they did best.

@jbahrdestefano - JC Bahr-de Stefano

As Drew said, “building a product is not the same as building a business — which is not the same as building a durable business!”

@jbahrdestefano - JC Bahr-de Stefano

4. Transitioning from a founder to a CEO involves self-education and personal growth. As the founder's role expands, continuous learning, self-awareness, and adaptability are crucial for managing company growth effectively.

@jbahrdestefano - JC Bahr-de Stefano

Drew shared that “it’s important to recognize that a big part of the job is keeping your personal growth curve outside of the company growth curve. Nobody will train you. You have to educate yourself, and you have to figure out how to do that in a way that makes sense for you.”

mastersinvest
Posted - September 4, 2023 at 10:24 AM
Saved - September 4, 2023 at 10:21 PM
reSee.it AI Summary
To succeed in business, intelligence alone is insufficient. It requires a blend of intellect, common sense, leadership, and the ability to inspire high-level teams. Investing in a company goes beyond monetary gains; it means investing in a team and empowering them to excel. While American brands may be strong, they lack the historical allure of iconic brands like Dior and Louis Vuitton. Our group leverages operational synergies to lower costs and become the leading buyer of luxury sector advertising. We reject centralized strategic plans that stifle entrepreneurial spirit. Unlike American companies focused on quarterly results, we prioritize long-term brand building. Mistakes are acceptable, but repeated failures are not. We assess risks and make calculated decisions.

@mastersinvest - MastersInvest.com

‘To succeed in economic life, it is not enough to be intelligent. What is necessary is to combine intellectual abilities with solid common sense, a taste for the concrete, and above all a sense of leadership, the ability to lead high-level teams. As one of my professors told me, to make a company sublime, you also have to be able to have ordinary people do extraordinary things.’ - Bernard Arnault

@mastersinvest - MastersInvest.com

‘Success, for an entrepreneur, is leading a company that develops, that gains market share, that manages to bring together a team. This is what has always stimulated me, more than the strictly material aspect.’ - Bernard Arnault #obliquity

@mastersinvest - MastersInvest.com

‘Money has never been, in my eyes, an objective or even an indicator of any significance.. Investing in a company has a meaning for me that completely transcends the material, it means investing in a team & doing everything so that this team comes out on top.’ - Bernard Arnault

@mastersinvest - MastersInvest.com

‘The brands created in the US are, for some, very strong. Their success took 20 yrs, that is to say a generation. Certainly, some of them are fantastic brands. But they do not have the mythical dimension that Dior or Louis Vuitton - these have a history the Americans don't have.’

@mastersinvest - MastersInvest.com

‘The formation of (our) group makes it possible to rely on classic operational synergies which offer significant economies of scale (legal services, accounting services, logistics problems, etc.) to manage the whole in an economical & rational manner in order to lower costs.’

@mastersinvest - MastersInvest.com

‘… Synergies also work upstream on purchases of certain products, as well as on advertising. Our group has become the world's leading buyer of advertising in the luxury sector, which allows us to obtain preferential rates.’

@mastersinvest - MastersInvest.com

‘What seems perfectly useless to me, is what certain large industrial groups have done for years - strategic plans, developed at central level which are imposed on the subsidiaries. That's not how we operate at all. This centralizing approach destroys the entrepreneurial spirit.’

@mastersinvest - MastersInvest.com

‘American companies have other flaws. The main thing, when they are listed on the stock exchange, is to be too tied to quarterly results. However, it is difficult, for a business like ours, to be permanently focused on the current quarter while we are building a brand for the next twenty years. In the United States, this sometimes leads to short-sighted decisions taken to the detriment of the long-term interest of the company.’

@mastersinvest - MastersInvest.com

‘High level executives have the right to make mistakes, they don't have the right to fail. Mistakes are inevitable, everyone makes them. You have to accept them. These are often very formative experiences. What is not acceptable are repeated mistakes that turn into failure.’

@mastersinvest - MastersInvest.com

‘The element of uncertainty always exists in a large-scale undertaking like (buying Dior parent). But we had studied, as we always do, the “worst case scenario”. We had invested a very important sum for us, 100 million, but if we had lost it, we would still have survived. This was the maximum risk we could expose ourselves to at the time.’

elonmusk
Posted - September 6, 2023 at 3:14 PM
Saved - September 6, 2023 at 8:25 PM

@elonmusk - Elon Musk

Not quite how I would tell the story, but very accurate for an observer who only saw part of the puzzle

@TIME - TIME

Elon Musk's fears about AI have led to battles with Google's Larry Page and OpenAI's Sam Altman. @WalterIsaacson reports on Musk's longstanding concerns about AI—and the lengths he's going to control its future https://ti.me/3P9JqfA

Inside Elon Musk's Struggle for the Future of AI 'I can't just sit around and do nothing.' An exclusive excerpt from Walter Isaacson's new Elon Musk biography. time.com
KTmBoyle
Posted - September 16, 2023 at 5:23 PM
Saved - September 17, 2023 at 2:11 PM
reSee.it AI Summary
Suffering often fuels greatness. Jobs, Ellison, Musk, Bezos, and da Vinci all faced abandonment or abuse. Clinton, Obama, and many leaders turned abandonment into drive. Deep suffering is vital for progress and achievement throughout history. Respect those in the arena.

@KTmBoyle - Katherine Boyle

Walter Isaacson on the link between suffering and greatness and those with happy childhoods: “We grow up with fewer demons but we grow up with less drive. We end being Boswell and not Johnson. We end up being the observer and not the doer. Respect those who are in the arena…” A few years ago, I learned that Jobs, Ellison, Musk and Bezos have one central thing in common: abandonment, estrangement or abuse by their fathers. Issacson writes the same about Leonardo da Vinci: he was illegitimate in a time where that meant ostracism. Many political leaders — Clinton, Obama, etc— have similar stories of turning abandonment into drive. We are afraid to talk about how deep suffering often is essential for leadership and greatness —it is likely the common thread in creating real progress and achievement throughout history.

@lexfridman - Lex Fridman

Here's my conversation with @WalterIsaacson, author of the new biography on @elonmusk and one of the greatest biographers ever, having written incredible books on Einstein, Steve Jobs, Leonardo da Vinci, Jennifer Doudna, Benjamin Franklin, and many others. Outline of our…

Video Transcript AI Summary
In this video, Walter Isaacson explores various aspects of greatness, focusing on the role of difficult childhoods, Elon Musk's leadership style, and the importance of biography and storytelling. Isaacson highlights how individuals like Albert Einstein, Leonardo da Vinci, and Elon Musk have harnessed their challenging upbringings to fuel their ambitions. He discusses Musk's intense leadership style, emphasizing his focus on excellence, trustworthiness, and building great teams. Isaacson also delves into the significance of biography and storytelling in academia, sharing his experiences with interviewing subjects like Musk and Steve Jobs. He emphasizes the value of curiosity, active listening, and self-awareness in conducting interviews and living a meaningful life. Isaacson concludes by expressing his admiration for the interviewer and the impact of storytelling in inspiring individuals to overcome adversity and strive for greatness.
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Full Transcript
Speaker 0: I hope with my books, I'm saying, this isn't a how to guide, but this is somebody you can walk alongside. Mhmm. You can see Einstein growing up Jewish in Germany. You can see Jennifer Doudna growing up or as an outsider, a Leonardo da Vinci, or Elon Musk, you know, in really violent South Africa with a psychologically difficult father. And getting off the train when he goes to the anti apartheid concert with his brother, and there's a A man with a knife sticking out of his head, and they step into the pool of blood, and it's sticking on their souls. This causes, You know, scars that lasts the rest of your life. And the question is not, how do you avoid getting scarred? It's you know, how do you deal with it? Speaker 1: The following is a conversation with Walter Isaacson, One of the greatest biography writers ever. Having written incredible books on Albert Einstein, Steve Jobs, Leonardo da Vinci, Jennifer Doudna, Benjamin Franklin, Henry Kissinger, and now a new one on Elon Musk. We talked for hours on and off the mic. I'm sure we'll talk many more times. Walter is a truly special writer, thinker, observer, and human being. I highly recommend people read his new book on Elon. I'm sure there will be short term controversy, But in the long term, I think it will inspire millions of young people, especially with difficult childhoods, with hardship in their surroundings or in their own minds to take on the hardest problems in the world and to build solutions to those problems no matter how impossible the odds. In this conversation, Walter and I cover all of his books and use personal stories from them to speak to the bigger principles of striving for greatness in science, in tech, engineering, art, politics, and life. There are many things in the new Elon book that I felt are best saved for when I speak to Elon directly again on this podcast, which will be soon enough. Perhaps it's also good to mention here that my friendships, like with Elon, nor any other influence, Like money, access, fame, power will ever result in me sacrificing my integrity ever. I do like to celebrate the good in people to empathize and to understand. But I also like to call people out on their bullshit with respect and with compassion. If I fail, I fail due to a lack of skill, not a lack of integrity. I'll work hard to improve. This is the Let's Friedman Podcast. To support it, please check out our sponsors in the description. And now, dear friends, Here's Walter Isaacson. What is the role of a difficult childhood in the lives of great men and women, great minds? Is that a requirement? Is it a catalyst? Or is it just a simple coincidence of fate? Speaker 0: Well, it's not a requirement. Some people with happy childhood who do quite well. But it certainly is true that a lot of really driven people are driven because they're harnessing the demons of their childhood. Even Barack Obama's, sentence in his memoirs, which is I think every successful man He's either trying to live up to the expectations of of his father or live down the sin of his father. And for Elon, it's especially true because he had both a violent and difficult childhood and a very psychologically problematic father. He's got those demons, dancing around in his head. And by harnessing them, it's part of the reason that he does riskier, more adventurous, wilder things and maybe I would ever do. You've written that, Elon talked about his father, Speaker 1: and that at times, It felt like mental torture, the the interaction with him during his childhood. Can you describe some of the things you've learned? Speaker 0: Yeah. Well, Elon and Kimball would tell me that, for example, when Elon got bullied on the playground. And one day was pushed down some concrete steps and had his face pummeled so badly that Kimball said I couldn't really recognize. I mean, he was in a hospital for almost a week. But when he came home, Elon had to stand in front of his father, And his father berated him for more than an hour and said he was stupid and took the side of the of the person who had beaten him. Speaker 1: That's probably one of the more traumatic events of Elon's life. Speaker 0: Yes. And there's also Beld School, which is a sort of Paramilitary camp that young South African boy's got sent to. And at one point, you know, he was scrawny. He has very, Bad at picking up social cues and emotional cues. He talks about being Asperger's. And so he gets, traumatized at a camp like that. But the 2nd time he went, he'd gotten bigger. He had shot up to almost 6 feet, and he learned a little bit of judo. And he realized that if he's getting beaten up, he might It might hurt him, but it he would just punch the person in the nose as hard as possible. So that sense of always punching back has also been ingrained in Elon. I spent a lot of time talking to Errol Musk, his father. Elon doesn't talk to Errol Musk anymore, his father, Nor does Kimble. It's been years. And, Errol doesn't even have, Elon's email. So a lot of times, Errol will be sending me emails. And Errol had one of those Jekyll and Hyde personalities. He was, you know, a great mind of engineering and especially Material science, knew how to build a wilderness camp in South Africa using mica and how it would not conduct the heat. But he also would go into these dark periods in which he would just be psychologically abusive. And, of course, May Musk says to me, the his mother, who divorced Darryl early on, said, The danger for Elon is that he becomes his father. And every now and then, you've been with him so much. Lex, and you know him well, He'll even talk to you about the demons, about Diopolo dancing in his head. I mean, he he gets it. He's self aware, But you've probably seen him at times where those demons take over, and he goes really dark and really quiet. And, Grimes says, you know, I can tell a minute or 2 in advance when demon mode's about to happen. And he'll go a bit dark. I was, you know, here at Austin, wanted dinner with a group. And you could tell suddenly something had triggered him, And he was gonna go dark. I've watched it in meetings where somebody will say, we can't make that part for less than $200 or No. That's wrong. And he'll berate them. And then he steps out of it. As as you know that too, the the huge snap out where Suddenly, he's showing you Monty Python skit on his phone, and he's joking about things. So I think coming out of the childhood, There were just many facets, maybe even many personalities, the engineering mode, the silly mode, the charismatic mode, the visionary mode, but also the demon in dark mode. Speaker 1: A quote you cited about Elon really stood out to me. I forget, who it's from, but inside the man, He's still there as a child, the child standing in front of his dad. Speaker 0: That was Tallulah, his 2nd wife, and she's great. She's An English actress. They've been married twice, actually. And Tallulah said that's just him from his childhood. He's a drama addict. Kimball says that as well. And I asked why, and he said and Tallulah said, you know, for him, Love and family are kind of associated with those psychological torments. And in many ways, he'll channel. I mean, Tallulah would be with him in 2008 when the company was going back or whatever it may have been or later. And he would be so stressed, he would vomit. And then he would channel things that his father had said, use phrases his father had said to him. And so she told me deep inside the man is this man child still standing in front of his father. Speaker 1: To what degree is that true for many of us, do you think? Speaker 0: I think it's true, but in many different ways. I'll say something personal, which is I was blessed, and perhaps it's a bit of a downside too, but the fact I had the greatest father you'd ever imagine, and mother. They were the kindest people you'd ever wanna meet. I grew up in a magical place in New Orleans. My dad was an engineer, an electrical engineer, and, You know, he was always kind. Perhaps I'm not quite as driven or as crazed. I don't have to prove things. So I get to write about Elon Musk. I get to write about, you know, Einstein or Steve Jobs or Leonardo da Vinci, who, as you know, was totally torn by demons and had different difficult childhood situations, not even legitimized by his father. So Sometimes those of us who are lucky enough to have really gentle, sweet childhood, we grow up with Fewer demons, but we grow up with fewer drives, and we end up maybe being Boswell and not being doctor Johnson. We end up being the observer, not being the doer. And so I always respect those who are in the arena. I don't you know? Speaker 1: You don't see yourself as a man in the arena. Speaker 0: I've had a gentle, sweet career, And I've got to cover really interesting people. But I've never shot off a rocket that might someday get to Mars. I've never moved us into the era of electric vehicles. I've never stayed up all night on the factory floor. I don't have quite those either the drives or the, Mhmm. Addiction to risk. I mean, Elon's addicted to risk. He's addicted to adventure. Me, if I see something that's risky, I spend some time calculating, okay, upside, downside here. But that's another reason that people like Elon Musk get stuff done, and people like me write about the Elon Musks. Speaker 1: One other aspect of this given a difficult childhood, whether it's, Elon or Da Vinci, I wonder if there's some wisdom, some advice almost that you can draw, that you can give to people with difficult childhoods. Speaker 0: I think all of us have demons, even those of us who grew up in a magical Part of New Orleans with sweet parents. Speaker 1: Yes. Speaker 0: And we all have demons. And rule 1 in life is harness your demons. Know that you're ambitious or not ambitious or you're lazy or whatever. Leonardo da Vinci knew he was a procrastinator. You know? I think it's usual to know what's eating at you, know how to harness it, Also know what you're good at. I'll take Musk as another example. I'm a little bit more like Kimbal Musk than Elon. I maybe got over endowed with the empathy gene. And what does that mean? Well, it means that I was okay when I ran Time Magazine. It was a group about a 150 people on the editorial floors, and I knew them all, and we had a jolly time. When I went to CNN, I was not very good at being a manager or an executive of an organization. I cared a little bit too much that people didn't get annoyed at me or, mad at me. And Elon said that about John McNeil, for example, who is president of Tesla. It's in the book. I talked to John McNeal a long time, and he says, you know, Elon just would fire people, be really rough on people. He didn't have the empathy for the people in front of him, and Elon says, yeah. That's right. And John McNeal couldn't fire people. He cared more about pleasing the people in front of him than pleasing the entire enterprise or getting things done. Being over endowed with a desire to please people can make you less tough of a manager. And, that doesn't mean There are great people who are over endowed. Ben Franklin, over endowed with the desire to please people. The worst criticism of him from John Adams and others was that he was insinuating, which kind of meant he was always trying to get people to like him. But that turned out to be a good thing. When they can't figure out the big state, little state issue at the constitutional convention, when they can't figure out the treaty of powers, whatever it is, He brings people together, and that is his superpower. So to get back to the lessons you asked and, you know, the 1st was harness your demons. The second is to know your strength and your superpower. My superpower is definitely not being a tough manager. After running CNN for a while, I said, okay. I got proven I don't really enjoy this or know how to do this well. Mhmm. You know, do I have other talents? Yeah. I think I have the talent to observe people really closely, to write about it in a straight, but I hope interesting narrative style. That's a power. It's totally different from running an organization. It took me until 3 years of running CNN that I realized I'm not cut to be an executive in a really high intense situations. Elon Musk is cut to be an executive in highly intense situation So much so that when things get less intense, when they actually are making enough cars and Rockets are going up and landing. He thinks of something else, so he can surge and have more intensity. He's addicted to intensity. And that's his superpower, which is a lot greater than the superpower of being a good observer. Speaker 1: But I think also, to build on that, it's not just addiction to, like, risk and drama. There's always a big mission Mhmm. Above it. So I would say, it's an empathy towards People in the big picture. Speaker 0: It's an empathy towards humanity Humanity. More than the empathy towards the 3 or 4 humans who might be sitting in the conference room with you. And that's a big deal. And you see that in a lot of people. You see it, Bill Gates, Larry Summers, Elon Musk. They always have Empathy for these great goals of humanity, and at times, they can be clueless about the emotions of the people in front of them or callous sometimes. Musk, as you said, It's driven by mission more than any person I've ever seen. And it's not only mission, it's like cosmic missions. Meaning, he's got 3 really big missions. One is to make humans a space faring civilization, make us multiplanetary, or get us to Mars. Number 2 is to bring us into the era of sustainable energy, to bring us into the era of electric vehicles and solar roofs and, battery packs. And 3rd is to make sure that Artificial intelligence is safe and is aligned with human values. And every now and then, I'd talk to him and we'd be talking about Starlink satellites or whatever, or he would be pushing the people in front of him in SpaceX and saying, if you do this, we'll never get get to Mars in our lifetime. And then he would give the lecture how important it was for human consciousness to get to Mars in our lifetime. And I'm thinking, okay. This is the pep talk of somebody trying to inspire a team, or maybe it's the type of of, pontification you do on a podcast. But on, like, 20th time I watched him, I was, okay. I believe it. He actually is driven by this. He is Speaker 1: Frustrated and angry that because of this particular minor engineering decision, The big mission is not going to be accomplished. It's not a pep talk. It's a literal frustration. Speaker 0: And impatience of frustration, and, It's also just probably the most deeply ingrained thing in him is his mission. He joked at one point to me about how much he loved reading comics as a kid, and he said all the people in the comic books, They're trying to save the world, but they're wearing their underpants on the outside, and they look ridiculous. And then he paused and said, but they are trying to save the world. And whether it's Starlink in Ukraine or Starship going to Mars or trying to get a global new Tesla, I think he's got this epic sense of the role he's gonna play in helping humanity on big things. And like the the characters in the comic books, it's sometimes ridiculous, But it also is sometimes true. Speaker 1: When I was reading this part of the book, I was thinking of all the Young people who are struggling in this way. And I think a lot of people are in different ways, whether they grow up without a father, whether they grow up With physical, emotional, mental abuse, or demons of any kind as you talked about. And it's really painful to read, but also really damn inspiring That if you sort of walk side by side with those demons, If you don't let that pain break you or somehow channel it, if you can put it this way, that you can achieve you can do great things in this world. Speaker 0: Well, that's, an epic view of why we write biography, which is more epic than I had even thought of. So I say thank you, Because in some ways, what you're trying to do is say, okay. I mean, Leonardo, you talk about being a misfit? He's born illegitimate in the village of Vinci. And he's gay. And he's left handed. And he's distracted. And his father won't legitimize him. And, then he wanders off to the town of Florence. He becomes the greatest artist and engineer of the early renaissance of that part of the renaissance. I hope this book inspires Jennifer Doudna, the gene editing Pioneer who discovers helps discover CRISPR, gene editing tool, which my book, The Code Breaker, She grew up feeling like a misfit, you know, in Hawaii, in a Polynesian village being the only white person and also trying to live up to a father who pushed her. So if people can read the books, and I I should've said about Jennifer Datta. My point was that She was told by her school guidance counselor, no. Girls don't do science. You know, science is not for girls. You're not gonna do math or science. And so it pushes her to say, alright. I'm gonna do math and science. Speaker 1: Just to interrupt real quick, but, Jennifer Donner, You've written an amazing book about her. Nobel Prize winner, Chris Perda, she's incredible. One of the great scientists in the 21st century. Speaker 0: Right. And I'm talking about When Jennifer Doudna was young and she felt really, really out of place, like you and me and a lot of people when they feel in that way, They read books. They go into they curl up with the book. Mhmm. So her father drops a book on her bed called The Double Helix, the book by James Watson on the discovery of the structure of DNA by him and Rosalind Franklin and Francis Crick. And She realizes, oh my god. Girls can become scientists. My school guidance counselor is wrong. So I think Books, like she read this book, and even if it's a comic book, like Elon Musk read. Books can sometimes inspire you. And every one of my books is about people who were totally innovative, who weren't just smart, because none of us are gonna be able to match Einstein and mental processing power. But we can be as curious as he was and creative and think out of the box the way he did. Or Steve Jobs put it, think different. And so I hope with my books, I'm saying, this isn't a how to guide, but this is somebody you can walk alongside. Mhmm. You can see Einstein growing up Jewish in Germany. You can see Jennifer Doudna growing up or as an outsider, a Leonardo da Vinci, or Elon Musk, you know, in really violent South Africa with a psychologically difficult father and getting off the train when he goes to anti apartheid concert with his brother. And there's a A man with a knife sticking out of his head, and they step into the pool of blood and it's sticky on their souls. This causes, You know, scars that lasts the rest of your life. And the question is not, how do you avoid getting scarred? It's you know, how do you deal with it? Einstein too, Speaker 1: one of my and it's hard to pick my favorite of your, Biographies. But I Einstein, I mean, you really paint a picture of another I don't wanna call him a misfit, But a person who doesn't necessarily have a a standard trajectory through life of of success. So Absolutely. And it's that's extremely inspiring. I don't know exactly what question to ask. There's a million. Speaker 0: Well, I'll talk about the misfit for a 2nd, because, you know, we talked about Leonardo being that way. You know, Einstein's Jewish in Germany at a time when it starts getting difficult. He's slow in learning how to talk, and he's a visual thinker, so he's always daydreaming and imagining things. The 1st time he applies to the Zurich Polytech, because he runs away from the German education system because it's too much learning by rote, He gets rejected by the Zurich Polytech. That's the 2nd best school in Zurich, and they're rejecting Einstein. I tried to find but couldn't the name of the admissions counselor at the Azure College. Yes. Like, he rejected Einstein. And then he doesn't finish in the top half of his class. And once he Does, and he goes to graduate school. They don't accept his dissertation, so he can't get a job. He's not teaching it. He even tries about 14 different high schools at gymnasium, to get a job, and they won't take them. So he's a 3rd class examiner in the Swiss patent office That's in 1905. 3rd class, because they've rejected his doctoral dissertation, and so he can't be 2nd class or 1st class because he doesn't have a doctoral degree, and yet he's sitting there on the stool in the patent office in 1905 and writes 3 papers that totally transform science. And if you're thinking about being misunderstood or unappreciated. In 1906, he's still a third class Patrick. In 1907, he still is. It takes until 190 before people realize That this notion of the theory of relativity might be correct, and it might upend all of Newtonian physics. How is it possible for 3 of the greatest papers in Speaker 1: the history of science to be written in 1 year by this 1 person. Is there some insights, wisdoms you draw? Plus, he Speaker 0: had a day job as a patent examiner. Right. And there's really 3 papers, but there's also an addendum. Because once you figure out quantum theory, and then you figure out relativity, And you're understanding Maxwell's equations and the speed of light. He does a little addendum. That's the most famous equation in all of physics, which is e equals m c squared. So it's a pretty good year. It partly starts because he's a visual thinker, And I think it was helpful that he was at the patent office rather than being the acolyte of, some professor at the academy where he was supposed to follow the rules. And so the patent office said doing devices to synchronize clocks, because the Swiss have Just going on standard time zones, and Swiss people, as you know, tend to be rather, you know, Swiss. They care if it strikes the hour in Basel, it should do the same and burn at the exact instant. So you have to send a light signal between 2 distant clocks. And he's visualizing what's it look like to ride alongside a light beam. He says, well, if you catch up with it, if you go almost as fast, it'll look stationary, but Maxwell's equations Don't allow for that. And he said, it's making my palm sweat that I was so worried. And so he finally figures out, because he's looking at these devices, the synchronized clocks, That if you're traveling really, really fast, what's looks synchronous to you or synchronized to you is different Then for somebody traveling really fast in the other direction, and he makes the mental leap that time, that the speed of light's always constant, but time is relative depending on your state of motion. So it was that type of out of the box thinking, those leaps That made 1905 his miracle year, likewise with Musk. I mean, After General Motors and Ford, everybody gives up on electric vehicles, to just say, I know how we're going to have a path to change the entire trajectory of the world into the era of electric vehicles. And then when he comes back from Russia, where he tried to buy a little rocket ship so he could send a experimental greenhouse to Mars, and they were poking fun of him and actually Spit on them at one point in a drunken lunch. This is very fortuitous because on the ride back home on the plane on the, you know, Delta Airlines flight, He's like doing the calculations of how much materials, how much metal, how much fuel, how much would it really cost? And so he's visualizing things that other people would would just say is impossible. It's what Steve Jobs' friends called the reality distortion field, and it drove people crazy. It drove them mad, but it also that drove them to do things they didn't think they would be able to do. Speaker 1: You said visual thinking. I wonder if you've seen parallels of the different styles and kinds of thinking that, that operate the minds of these people. So, is there parallels you see between Elon, Steve Jobs, Einstein, da Vinci, specifically in how they think. Speaker 0: I think they were all visual thinkers, perhaps coming from slight handicaps as children, meaning, you know, Leonardo was left handed and a little bit dyslexic, I think. And certainly Einstein had At Koeya, he would repeat things. He was slow in learning to talk. So I think visualizing helps a lot. And with Musk, I see it all the time when I'm walking the factory lines with him or in product development, where he'll look at, say, the heat shield under the Raptor engine of a Starship booster. And he'll say, why does it have to be this way? Couldn't we trim it this way? Or make it or even get rid of this part of it. And he can visualize the material science. There's a small anecdote in my book, but at one point, he's on the Tesla line, and they're trying to get 5,000 cars a week in 2018. It's a life or death situation, and he's looking at the machines that are bolting something to the chassis. And he insists that Drew Bagley not Drew. Let Lars Moravey, one of his great lieutenants, come, and they have to summon him. And he says, why are there 6 bolts here? And Lars and others explained, well, For the crash test or anything else, the pressure would be in this way, so you have to, and they were blah blah blah blah blah. And he said, no. If you visualize it, you'll see if there's a crash, it would the force would go this way and that way, And it can be done with 4 bolts. Now that sounds risky, and they go test it and they engineer it, but it turns out to be right. I know that seems minor, But I could give you 500 of those, where in any given day, he's visualizing the physics of an engineering or manufacturing problem. That sounds pretty mundane. But for me, if you say, what makes him special? There's a mission driven thing. I give you a a lot of reasons. But one of the reasons is he cares not just about the design of the product, but visualizing the manufacturing and of the product, the machine that makes the machine. And that's what we failed to do in America for the past 40 years. We outsourced so much manufacture. I don't think you can be a good innovator if you don't know how to make the stuff you're designing. And that's why Musk puts his designer's desk right next to the assembly lines in the factories so that they have to visualize what they drew as it becomes the physical object. Speaker 1: So understanding everything from the physics all the way up to the to the software. It's like end to end. Speaker 0: Well, having an end to end control is important, certainly with Steve Jobs. I'm looking at my iPhone here. It's a big deal. That hardware only works with Apple software, And for a while, the iTunes store only what what's you know? So he has an end to end that makes it like a Zen garden in Kyoto. Very carefully curated, but a thing of beauty. For Musk, when he first was at Tesla And before he was the CEO, when he was just the executive chairman and basically the finance person person funding it, They were outsourcing everything. They were making the batteries in Japan, and the battery pack would be at some barbecue shop in Thailand, and then sent to the Lotus factory in England to be put into a Lotus Elise cha a chassis and then That was a nightmare. You did not have end to end control of the manufacturing process. So he goes to the other extreme. He gets a A factory in Fremont from Toyota. And he wants to do everything in house, the software in house, the painting in house, you know, the the the, battery. He makes his own batteries, And I think that end to end control is part of his personality. I mean, there's a but it Also, what allows Tesla, to be innovative. Speaker 1: Yeah. I got to see And understand in detail one example of that, which is the development of the brain of the car In the autopilot going from Mobileye to in house buildings, autopilot system to Mhmm. Basically, getting rid of all sensors that are not, rich in data to make it AI friendly, sort of saying that we can do it all with vision. And like you said, removing some of the bolts. So sometimes it's small things, but sometimes it's really big things like getting rid of radar. Speaker 0: Well, vision only. Getting rid of radar is huge, And everybody's against it. Everybody and they're still fighting it a bit. They're still trying to do a next generation some form of radar. But It gets back to the 1st principles. We talk about visualizing. Well, he starts with the first principles. And the first principles of physics, involve things like, well, humans drive with only visual input. They don't have radar. They don't have lidar. They don't have sonar. And so there was no reason in the laws of physics that make it so that vision only won't be successful in creating self driving. Now That becomes an article of faith to him, and he gets a lot of pushback. But now and he's, by the way, not been that That's when meeting his deadlines of getting self driving. He's way too optimistic. But it was that first principles of get rid of unnecessary things. Now you would think LiDAR, why not use it? Like, why not use a crust? It's like, yeah, we can do things vision only, but When I look at the stars at night, I use a telescope too. Well, you could use LiDAR, but you can't do millions of cars that way at scale. At a certain point, you have to make it not only a good product, but a product that goes to scale. And you can't make it based on maps, like Google Maps, because it'll never be able to, you know, then drive from New Orleans to Slidell where I wanna go when it's too hot in New Orleans. Take for example, Full Self Drive. He has been obsessed with what he calls the robo taxi. We're gonna build the next generation car without a steering wheel Speaker 1: Mhmm. Speaker 0: Without pedals. Because it's gonna be full self drive. You just summon it. You won't need to drive it. Well, over and over again, all these people I've told you about, you know, Lars Moravey and Drew Baglino and others. They're saying, okay, fine. That sounds really good, but, You know, it ain't happened yet. We need to build a $25,000 mass market global car that's just normal with a steering wheel. And, yeah, he finally turned around a few months ago and said, let's do it. And then he starts focusing on How's the assembly line gonna work? How are we gonna do it? And make it the same platform for robo taxi so you can have the same assembly. Likewise, for full self drive, They were doing it by coding hundreds of thousands of lines of code that would say things like, if you see a red light, stop. If there's a blinking light, if the Two yellow lines, do this. There's a bike lane, do this. If there's a crosswalk, do that. Well, that's really hard to do. Now he's doing it through artificial intelligence and machine learning only. FSD 12 will be based on the 1,000,000,000 or so frames from Tesla each week of Tesla drivers and saying, what happened when a human was in this situation? What did the human do? And let's only pick the best humans, the 5 star drivers, the Uber drivers, as Elon says. And so that's Him changing his mind and going to first principles, but saying, alright. I'm even gonna change full self driving So that's not rules based. It becomes AI based. Just like ChatGPT doesn't try to answer your question, who are the 5 best popes or something by study Chattopty PD does it by having ingested billions of of, pieces of writing that people have done. This will be AI, but real world done by ingesting video. Speaker 1: Sometimes it feels like, he and others, they're building things in this world successfully. I basically, confidently exploring a dark room with a very confident, ambitious vision of what that room actually looks like. Like Mhmm. Like, they're just walking straight into the darkness. There's no painful toys. Their leg goes on the ground. I'm just going to walk. I know exactly how far the wall is, and then very quickly willing to adjust as they run into they step on the Lego, And, their their body, is filled with a lot of pain. What I mean by that is there's this kind of evolution that's used to happen Where you discover really good ideas along the way that allow you to pivot. Like to me, Since, you know, since a few years ago when you could see with Andre Karpathy, the software 2.0 evolution of autopilot, It became obvious to me that this is not about the car. This is about Optimus, the robot. This this is, like, if we look back a 100 years from now. The car will be remembered as a cool car, nice transportation, but the the autopilot won't be the thing that controls the car. It will be the thing that allows embodied AI systems to understand the world so broadly. And so that kind of approach and so and you kinda stumble into it. Will Tesla be a a call company? Will it be an AI company? Will it be a robotics company? Will it be a home robotics company? Will it be an energy company? And then you kind of slowly discover this as you confidently, like push forward with a vision. It's been interesting to watch that kind of evolution as long as it's backed by this confidence. Speaker 0: There are a couple of things that are required for that. 1 is being adventurous. 1 doesn't enter a dark room without a flashlight and a map, unless you're a risk taker, unless you're adventurous. The second is to have iterative, brain cycles where you can process information and do a feedback loop and make it work. The 3rd, and this is what we failed to do a lot in the United States and perhaps around the world, is when you take risks, you have to realize you're gonna blow things up. You know, first 3 rockets that the Falcon rockets that Musk does, they blow up. Even Starship, 3 and a half minutes, but then it blows up the first time. So I think Boeing and NASA and others have become unwilling to enter your dark room without knowing exactly where the exit is and the lighted path to the exit. And the people who created America, whenever they came over, you know, whether the Mayflower is refugees from the Nazis, They took a lot of risks to get here, and now I think we have more referees than we have risk takers, More lawyers and regulators and others saying you can't do that. That's too risky, than people willing to innovate. And you need both. I think you're also right on 50, a 100 years from now, What Musk will be most remembered for besides space travel is real world AI. Not just Optimus the Robot, but Optimus the Robot and the self driving car. They're they're pretty much the same. They're using, you know, GPU clusters or dojo chips or whatever it may be, to process real world data. We all got, and you did on your podcast, quite excited about large language model, you know, generative, predictive text, AI. That's fine, especially if you wanna chitchat with your chatbot. But the holy grail is artificial general intelligence. And the tough part of that is real world AI. And that's where Optimus, the robot, or Full self drive, or I think far ahead of anybody else. Speaker 1: Well, I like how you said chitchat. I I would say for for for one of the greatest writers ever, It's funny that you spoke about language and the mastery of languages as merely chitchat. You know, people have fallen in love over Some words. People have gone to wars over some words. I think words have a lot of power. It's actually an interesting question where the wisdom of the world, wisdom of humanity is in the words or is it in visual in in visual, is it in the physical? Speaker 0: I don't really It's in mathematics. Speaker 1: It may maybe it all boils down to math, and in the end, this kind of discussion about, real world AI versus languages all the same, maybe. I've, Gotten a chance to hang out quite a bit in the metaverse with mister Mark Zuckerberg recently, and boy, Is the realism in there? Then you like, the the thing that's coming up in the future is incredible. I got, scanned, in, Pittsburgh for 10 hours into the metaverse, and there's, like, a a virtual version of me, and I got to hang out with that virtual word version. Speaker 0: Do you like yourself? Speaker 1: Well, I I never liked myself, But it was easier to like that other guy. That was interesting. Speaker 0: Does he like you? Speaker 1: He didn't seem to care much. Speaker 0: That's a lack of the empathy. Speaker 1: But, that was you know, it made me start to Question even more than before, like, well, how important is this physical reality? Because I I got to see, You know, my myself and other people in that metaverse, like, the details of the faith, the like, all the all the things that You think maybe if you look at yourself in the mirror, our imperfections, all this kind of stuff. When I was looking at myself and at others, all those things are beautiful, and it was, like, It was real, and it was intense, and it it, it was scary because you're like, well, are you allowed to murder people in the metaverse? Because, like, are you allowed to because what are you allowed to do? Because you can replicate a lot of those things, and It's you start to question what are the fundamental things that make life worth living here as Speaker 0: we know as humans. Have you talked to Elon about his views of when living in a simulation maybe and how you would figure out if that's true. Speaker 1: Yes. There's a constant lighthearted, but also a serious sense that this is all a bit of a game. Speaker 0: One of my theories on Elon, a minor theory, is that he read Hitchhiker's Guide to the Galaxy once too often. And, and as you know, there's a scene in there that says, that there's a theory about the universe, that if anybody ever discovers the secrets of meanings of the universe, it will be replaced by an even more complex universe. And then the next line Douglas Adams writes this. And there's another theory that this has already happened. Speaker 1: Yeah. Speaker 0: So I I'm gonna try to get my head around that, but I know that Elon Musk tries to. Well, there there's a humor to that. There's an enormous humor to Hitchhiker's Guide. Now I really think that helped Musk out of the darkest of his periods to have sort of the sense of fun of figuring out what life is all about. Speaker 1: I wonder if this is a smaller size we could say. Just, I haven't gotten to know Elon very well, like, his the the silliness, the willingness to engage in the absurdity of it all and have fun. What is that? What is that, is that just a quirk of personality, or is that a fundamental aspect of a human who's running 6 plus companies. Speaker 0: Well, it's a release valve, just like video games and Polytopia and Elden Ring, a release valve's for him. And he does have an explosive sense of humor, as you know. And the weird thing is when he makes the abrupt transition from dark demon mode, and you're in a conference room. And he has really become upset about something. And not only their dark vibes, but there's dark words emanating, and he's saying your resignation will be accepted if you die, you know, etcetera. And then something pops, and he pulls out his phone and pulls up a Bonnie Python skit, you know, like the School of silly walks or whichever John Cleese it would and then he starts laughing again, and things break. So it's It's almost as if he has different modes, the emulation of human mode, the engineering mode, the dark and demon mode, And certainly, there is the silly and giddy mode. Speaker 1: Yeah. You've actually opened the Elon book with, quotes from Elon and from Steve Jobs. So Elon's quote is, to anyone I'm offended, I just wanna say it's an SNL. I just wanna say I reinvented electric Cars, and I'm sending people to Mars on a rocket ship. Did you also think I was going to be a chill, normal dude? And then the quote from Steve Jobs, of course, is The people who are crazy enough to think they can change the world are the ones who do. So, What do you think is the role of the old, madness and genius? What do you think the role of crazy in this? Well, Speaker 0: First of all, let's both stipulate that Musk is crazy at times. I mean, and Then let's figure out. And I try to do it through storytelling, not through highfalutin preaching, where that craziness works. You know? Give me a story. Tell me a anecdote. Tell me where he's crazy. And, You know, the almost final example, AI, but him shooting off Starship for the first time, in between an aborted countdown and the street off, he goes to Miami to an ad sales conference and meets Linda Iaccarino for the first time, makes her the CEO. I mean, there's a very impulsiveness to him. Then he flies back. They launch Starship, and You realize that there's a drive and there are demons and there's also craziness, and You sometimes wanna pull those out. You wanna take away his phone so he doesn't tweet at 3 AM. You want to, say, quit being so crazy. But then you realize there's a wonderful line in of Shakespeare in measure for measure at the very end. He says even the best are molded out of faults. And so you take the faults of Musk, for example, which includes a craziness that can be endearing, but also craziness that's just, like, Effing crazy, as well as this drive in demon mode. I don't know that you can take that strand out of the fabric, and the fabric remains whole. Speaker 1: I wonder sometimes it saddens me that we live in a society that doesn't celebrate even the darker aspects of crazy in acknowledging that it all comes in 1 package. It's the man in the arena versus the critic. Speaker 0: And the man in the arena versus the regulator, and to make it more prosaic. Speaker 1: Well, let me ask about not just the crazy, but the cruelty. So in, You've written when reporting as Steve Jobs. Well, I was told you that the big question to ask was, did he have to be so mean, so rough And cruel, so drama addicted. What is this answer for Steve Jobs? Did he have to be so cruel? Speaker 0: For for Jobs? I asked Woz at the end of my reporting because that's what he asked said at the beginning. We're doing the launch of, I think, The iPad too, it may have been. Steve is emaciated because, you know, he's been sick. And so I say to Woz, what's the answer to your question? And he said, well, if I had been running Apple, I would have been nicer to everybody. I would everybody got a stock option. We've been like a family. And then I I don't know if you know Woz, but he's like a teddy bear. He paused, he smiled, and he said, but if I had been running Apple, I don't think we would have done the Macintosh or the iPhone. So yeah, you have to sometimes be rough And Jobs said the same thing that Musk said to me, which is he said, people like you love wearing velvet gloves. You know, I don't Not that I've worn Velvet Claws often, but you like people to like you. You like to sweet talk things, you sugarcoat things. He says, I'm just a working class kid, And I don't have that luxury. If something sucks, I gotta tell people it sucks, or I got a team of b players. Well, Musk is that way as well, and it gets back to what I said earlier, which is, yeah, I probably would wear velvet gloves if I could find them at my haberdasher. And I do try to sugarcoat things, but when I was running CNN Yeah. It needed to be reshaped. It needed to be broken. It needed to have certain things blown up. Yeah. And I didn't do it. Yeah. You know? So Bad on me, but it made me realize, okay. I'll just write about the people who can do it. Well, that thing of saying, Speaker 1: I think probably both of them, but Elon certainly Saying things like that is the stupidest thing I've ever heard. Speaker 0: Mhmm. By the way, I've heard Jeff Bezos say that. I've heard Bill Gates say that. I've heard Steve Jobs say it. I've heard Steve Jobs say it about a smoothie they were making in a Whole Foods or something. People they use the word stupid really often. Speaker 1: Yeah. Speaker 0: And you know who else used it? Errol Musk. He kept making Elon stand in front of him and saying, that's the stupidest thing. You're the stupidest person. You'll never amount to anything. Mhmm. I don't know. You know, as John McNeal, the president of Tesla said, Do you have to be that way? Probably not. There are a lot of successful people who are much kinder, but, It's sometimes necessary to be much more brutal and honest, Brutally honest, I would say, than people like or win boss of the year trophies. Speaker 1: Well, as you said, this kind of idea did also send a signal. This idea of Steve Jobs of a players, it did send a signal to everybody. There was a kind of encouragement to the people that are all in. Speaker 0: Right. And that happened to Twitter. When we went to Twitter headquarters the day before the takeover he was having, Andrew and, James, his 2 young cousins, and other people from the autopilot team, going over lines of code. And Musk himself sat there with a laptop on the 2nd floor of the building looking at the lines of code that had been written by Twitter engineers, and they decided they were gonna fire 85% of them because they had to be all in. And this notion of psychological safety and mental days off and working remotely, he said either and then It came up, actually one of his, I think it was one of the cousins or maybe Ross nor didn't came up with the idea of Let's not be so rough and just fire all these people. Let's ask them, do you really wanna be all in? Because this is gonna be hardcore. It's gonna be intense. You get to choose, but by midnight tonight, we want you to check the box. I'm hardcore all in. I'll be there in person. I'll work, you know, as well, Or that's not for me. I've got a family. I got work balance. And you got different type of people that way in different stages of their life. I was a little bit more hardcore and all in when I was in my twenties and when I was, you know, in my fifties. Speaker 1: And you write about this. It's a really nice idea, actually, that there's 2 camps Mhmm. And you find out I don't I wonder how true this is. It it rings True. You can just ask people, which camp are you in? Are you the kind of person that prides themselves and enjoy staying up till 2 AM programming or whatever. Or do you see the value of quote unquote, you know, about life work life balance, all this kind of stuff. And it's interesting. I mean, you like, you you could people probably divide themselves in different stages of life, And you can just ask them, and it makes sense for certain companies in certain stages of the their development to be like, we all have teams. Speaker 0: It doesn't even have to be a whole company. And you're right. It goes back to what I was saying about rule. The first secret is sort of know thyself, obviously, comes from Plato, and, everything comes from Plato and Socrates. But, and decide, in this stage of my life, Am I do I wanna be a hackathon all in, all night and change the world? Or do I want to bring wisdom and stability, but also have balance? I think it's good to have different companies with different styles. The problem was Twitter was at Almost one extreme with yoga studios and mental health days off and, enshrining psychological safety as one of the mantras that people should never feel psychologically threatened. And he I remember the bitter laugh he unleashed when he kept hearing that word. He Said no. I like the words har hardcore. I like intensity. I like a intense sense of urgency as our operating principle. Well, yeah, they're people that way as well. So know who you are and know what type of team you wanna build. Speaker 1: Versus psychological safety and too many birds everywhere. Speaker 0: Oh, yeah. A lot of times, Musk did things, and I go, what the hell? Yeah. And the bottom line was changing the name Twitter and getting rid of the birds. I said, hey, man. So a lot invested in that brand. But when I watched him, he thought, okay. These sweet little chirpy birds tweeting away in the name Twitter, It's not hardcore. It's not intense. And so for better and for worse, I think he's taking x into the hardcore realm with people who post hardcore things, with people with hardcore views. It's not a polite playpen for the blue checked, anointed elite, and I thought, okay. This is gonna be bad. The whole thing's gonna fall But well, it has had problems, but the hardcore intensity of it has also meant that there's new things happening there. So it's very Elon Musk to not like the sweetness of birds chirping and tweeting and saying, I want something more hardcore. Speaker 1: As you've written in, referring to the the previous Twitter CEO, Elon said, Twitter needs A fire breathing dragon. I think this is a good opportunity to, maybe go through some of the memorable moments Of the Twitter saga as you've written about extensively in your book. It's from the early days of considering The acquisition to, how it went through to the details of, like you mentioned, the engineering teams. Speaker 0: Well, at the beginning of 2022, He was riding high, but as we say, he's a drama addict. He doesn't like to coast. And, you know, Tesla sold a 1000000 vehicles. I think 33 Boosters, you know, Falcon nines have been shot up and landed safely in the past few months. And he was the richest person on Earth and Times person of the year. And yet, He'd said, you know, I'm still wanna put all my chips back on the table. I wanna keep taking risks. I don't wanna favor things. He had sold all of his houses. So he started secretly buying shares of Twitter. January, February, March Becomes public at a certain point. He has to, declare it. And we were here in Austin at Gigafactory on the mezzanine. And he was trying to figure out, well, where do I go from here? And at that time, it was early April, They were gonna offer him a board seat, and he was gonna do a standstill agreement and stop at 10% or something. I remember, You know, we were standing around. It was Luke Nozick, whom you know well, Ken Howery, some of his friends on that mezzanine here, And all afternoon and then late into the evening at dinner is like, should we do this? And I didn't say anything. I'm just the observer, But everybody else is saying, excuse me. Why do you want to own Twitter? And Griffin, his son, joined at dinner, and May, for some reason, was in town. And, like, everybody says, no. We don't use Twitter. Why would you do that? And May said, well, I use Twitter. And then it's almost like, okay. The demographics are people my age or May's age. And so it looked like he wasn't gonna pursue it. They offered him a board seat And, then he went off to Hawaii to, Larry Ellison's a house, which he sometimes uses. He was meeting a friend, Angela Bassett, an actress. And instead of enjoying 3 days of vacation. He just became supercharged Mhmm. And started firing off text messages, including the fire breathing dragon one. I think, You know, he used that phrase a few times, that Parag wasn't the person who was going to take Twitter to a new level. And then by the time he gets to Vancouver, where Grimes Meets him. They stay up all night playing Elden Ring. He was doing a TED talk. And then, at 5 Thirty. He finishes playing Elden Ring and sends out that I've made an offer. Even when he comes back, People are trying to intervene and say, excuse me. Why are you doing it? And so it was a rocky period between late April in October when the deal closed. And people ask me all the time, well, did he wanna get out of a deal? I said, which Elon are you talking about at what time of day? Because there'll be times in the morning when he'd say, oh, the Delaware court's gonna force me to do it. It's horrible. Talk to his lawyers. You can win this case. Get me out of it. He met here in Austin with 3 or 4 investment bankers, Blair Efron, at Centerview, Bob Steele, and Perella Weinberg. And they offered him options. Do you wanna get out? Do you wanna stay in? Do you wanna reduce the price? And I think is he was mercurial. There were times he would text me or say to me, this is gonna be great. It's gonna be the accelerant to do x.com the way we thought about 20 years ago. We tell them at the beginning of October, right, when Optimus the robot is being unveiled in California, actually, Now the lawyer is saying you're not gonna probably win this case, but better go through with the deal. And by then, he's not only made his peace with it, he's kinda happy with it at time. Eventually, the deal is gonna close on a, I think a, Friday morning, I have it in the book. And we're there on Thursday, and he's wandering around looking at the Stay Woke t charts and psychological safety lingo they're all using. And he and his lawyers and bankers hatched a plan to do a flash close. And the reason for that was if they closed the deal after the markets had closed for the day. And he could send a letter to Parag and 2 others firing them, quote, for cause, and this'll be something the courts will have to figure out. Then he could save 200,000,000 or so. And it was both the money, but for him, a matter, I won't say of principle, but of, hey. They misled me about the numbers. I got forced into doing it, So I'm gonna I'm gonna try this jujitsu maneuver and be able to get some money out of him. Then when he takes over, it's kind of a wild scene, 15% of what it was, him deciding on Christmas Eve after he'd been in a meeting where they told him we can't get rid of that Sacramento server farm because it's need of a redundancy. He says, no. It's not. And he's flying here to Austin, And young James says, why don't we just do it ourselves? He turns the plane around. They land in Sacramento, and he pulls them out himself. So it was a manic period. Speaker 1: We should also say that underneath of that, there was a running desire to or consideration to perhaps Start a new, company to build a social media company from scratch. Speaker 0: Well, Kimball wanted to do that, and Kimball here at A wonderful restaurant in Austin, and lunch is like, hey. Why are you buying Twitter? Let's start 1 from scratch and do it on the blockchain. Yeah. Now it took them a while, and you can argue it one way or the other, to come to the conclusion that the blockchain was not fast enough and responsive time enough to be able to handle a 1000000000 tweets, you know, in a day or so. He gets mad when they keep trying to get him to talk to Sam Bankman Fried, who's trying to say I'll invest, but we have to do it on the blockchain. Kimball is still in favor of starting a new one and doing it on, blockchain based. In retrospect, I think starting a new media company would have been better. He wouldn't have had the baggage or The legacy that he's breaking now in breaking the, way Twitter had been, but it's hard to have millions and millions hundreds of millions of true true users, not just trolls, and start from scratch as others have found, as Mastodon and Blue Sky and threads, and not any threads even had a base, So it would have been hard. Speaker 1: Yeah. And, to do that in the way he did requires, another part that you write about with the 3 Gutierrez and the whole engineering, the firing, and the bringing in the engineers to try to sort of go hardcore. So there's a lot of interesting sort of questions to ask there, but The high level, can you just comment about that part of the saga, which is bringing in the engineers and seeing, like, what can we do here? Speaker 0: Right. He brought in the engineers and figured That the amount of people doing Tesla full self driving autopilot and all the software there was about 1 tenth of what was doing software for Twitter. And he said, this can't be the case. And he fired 85% in 3 different rounds. The first was Just firing people because they looked at the coding, and they had a team of people from Tesla's autopilot team grading the codes of every of all that was written in the past year or so. Then he fired people, you know, who didn't seem to be totally all in They're loyal, and then another round of layoffs. So, at each step of the way, Almost everybody said that's enough. It's going to destroy things. Speaker 1: Yeah. Speaker 0: From Alex Spyro, his lawyer, to Jared Burchall. He's like, woah. Woah. Woah. You know? And even Andrew and James, the young cousins who are tasked with making a list and figuring out who's good or bad, say, we've done enough. We're going to be in real trouble. And they were partly right. I mean, there was degradation of the service some, but not as much as half the services I use half the time, you know. And I wake up each morning and Hit the app and okay. Still there. Speaker 1: What do you think? Was that too much? Speaker 0: I think that he has an Algorithm that we mentioned earlier that begins with question every requirement. But step 2 is delete, delete, delete. Delete every part. And then a corollary to that is if you don't end up adding back 20% of what you deleted, then you didn't delete enough in the 1st round because you were too timid. Well so he asked me, did he overdo it? He probably overdid it by 20%, which is his formula. And they're probably trying to hire people now to keep things going. Speaker 1: But it sends a strong signal to people that are hired back or the people that are still there, the the ape and Speaker 0: the idea. What Steve Jobs has been the other great leaders Svelte and certainly Bezos and certainly in the early days of Microsoft, Bill Gates. It was hardcore only a players. Speaker 1: So how much of Elon's success would you say Elon's, Steve Jobs' success is the hiring and managing of great teams? Speaker 0: When I asked Steve Jobs at one point what was the best product you ever created, I thought he'd say maybe the Macintosh or Maybe the iPhone. He said, no. Those products are hard. The best thing I ever created was the team that made those products. And that's the hard part, is creating a team, and he did, you know, from Johnny Ive to Tim Cook and, Eddie Que and Phil Shiller. Elon has done a good job bringing in people. Gwen Shotwell, obviously. Linda Iacarino, she's you know, can navigate through the current crises. Certainly, stellar people at SpaceX, like Martin Chingosa, and then at Tesla, like Drew Baglino and Lars Maravi and Tom Zhu and many others. He's not as much of a team collaborator as, say, Benjamin Franklin, Speaker 1: Mhmm. Speaker 0: Who, by the way, that's the best team ever created, which is the founders. And you had to have really smart people like Jefferson and Madison and really Passionate people like John Adams and his cousin Samuel and really a guy of high rectitude like Washington. But you also needed A Ben Franklin who could bring everybody together and forge a team out of them and make them compromise with each other. Speaker 1: Musk is a magnet for awesome talent. Magnet. Interesting. But there's the there's, like, the priorities of hiring of, Based on excellence, trustworthiness, and drive, these are things you've described Yeah. Throughout the book. I mean, there there's a pretty, concrete and rigorous set of ideas based on which the hiring is done. Oh, yeah. Speaker 0: And he has a very good, spidey, intuitive sense just looking at people who could I mean, not looking at them, but studying them, who could be good. One of his, ways of operating, is what he calls a skip level meeting. And let's take a very the big thing, like the Raptor engine, which is powering the, Starship. Speaker 1: Mhmm. Speaker 0: And it wasn't going well. It looked like a spaghetti bush, and it was gonna be hard to manufacture. And he got rid of the people who were in charge of that team. Speaker 1: Mhmm. Speaker 0: And I remember that he spent a couple of months doing what he calls skip level, which means instead of meeting with his direct reports on the Raptor team, He would meet with the people one level below them. And so he would skip a level and meet with them. And he said, this is and I just ask them what they're doing, and I drill them with questions. And he said, and this is how I figure out who's going to emerge. He said it was particularly difficult. I was sitting in those meetings because people were wearing masks. It was during the height of COVID. Mhmm. And he said it made it a little bit harder for him because he has to Mhmm. Get the input. But I watched as a young kid dreadlocks named Jacob McKenzie. He's in the book. He's sitting there, and he's a bit like you. Engineering mindset speaks in a bit of a monotone. Musk would ask a question and he would give an answer, and the answer would be very straightforward. And he didn't, you know, get rattled. He was like this. And I said one day, called him up at 3 AM. Well, I wouldn't say 3 AM, but after midnight said, you still running? Yeah. Jake said, yeah, I'm still at work. And he said, okay. I'm gonna make you in charge of the team building Raptor. And that was, like, A big surprise. But Jacob McKenzie has now gotten a version of Raptor, and when they're building them at least 1 a week, and they're pretty awesome. And, that's where his talent must talent for Finding the right person and promoting them, that's where it is, and promoting it in Speaker 1: a way where it's like a here's the ball. Or catch Speaker 0: Yeah. Yeah. Speaker 1: And you run with it. I have I've interacted with quite a few, folks from even just the model x, the all throughout where people, you know, on paper don't seem like they would be able to run the thing, and they run it extremely successfully. Speaker 0: And he does it wrong sometimes. He's had a horrible track record with the solar roof division. Wonderful guy named Brian Dow. I really liked him. And when they were doing the battery factory surge in Nevada, Musk got rid of 2 or 3 people, and there's Brian Dow. Can do. Can do. Can do. Stays up all night, and he gets promoted and runs it. Until finally, he goes, Musk goes through 2 or 3 people running the solar roof division. Finally calls up Brian Dow. I was sitting in Musk's house in Boca Chica, that little tiny 2 bedroom he has, And he offers Brian Dow the job of running Solar Roof. And, you know, Brian there okay. Can do. Can do. And 2 or 3 times, Musk insisted that they put install a solar roof in one of those houses in Boca Chica. This is this tiny village at the south end of Texas. And late at night, I mean, I'd have to climb up to the top of the roof on these ladders and stand on this peaked roof as Musk is there saying, why do we need 4 screws to put in this single leg? And And Brian was just sweating and doing everything. But then after a couple of months, it wasn't going well, and boom. Musk just fired him. So I always try to learn what is it that makes those who stay thrive. Speaker 1: What's the lesson there? What do you think? Speaker 0: Well, I think it's self knowledge like an Andy Krebs or others. They say, I am hardcore. I really wanna get a rocket to Mars, and that's more important than anything else. One of the people, I think it's I think it's Tim Zeman. I hope, when he hears this, I'm getting him the right person, who, you know, took time and was working for Tesla Autopilot. It was just so intense. He took some time off and and then went to another company. He said, I was burned out at Tesla, But then I was bored at the next place, so I called, I think it was Ashok at 10. I said, can I come back? He said, sure. Yeah. He said, I learned without myself. I'd rather be burned out than board. Speaker 1: That's a good line. Well, can you just, linger on one of the 3 that, seem interesting to you in in terms of excellence, trustworthiness, and drive. Which one do you think is is the most important and the hardest to get at? The trustworthiness is an interesting one. Like, are you ride or die kind of thing? Speaker 0: Yeah. I think that, especially, when it came to taking over Twitter, he thought half the people there were disloyal. Yeah. And he was wrong. About 2 thirds were disloyal, not just half. And it was how do we weed out those? And he did something and made, The firing squad, I call it, or the Musketeers, I think, is my nickname for them, which is, you know, the young cousins and 2 or 3 other people. He made them look at the Slack messages these people had post everybody at Twitter had posted, and they went through hundreds of Slack messages. So if anybody Posted on the internal Slack, you know, that jerk Elon Musk is gonna take over, and I'm afraid that he's a maniac or something. They would be on the list because they want all in loyal. They did not look at private Slack messages, and I guess people who are posting on a corporate Slack board should Be aware that your company can look at them, but that's more than I would have done or most people would have done. And so that was to figure out who's deeply committed and loyal. I think that was mainly the case at Twitter. He done sitting around at SpaceX saying who's loyal to me. At, other places, it's excellent, but that's Pretty well a given. Everybody is like a Marc Jinkosa, just whip smart. It's all you hardcore and all in, especially If you got to move to this spit of a town in the south tip of Texas called Boca Chica, you know, you gotta be all in. Speaker 1: Yeah. And that's the drive, the the last piece. So you in terms of collaborating one of the great teams of all time, Ben Franklin. I like that. Thought it was the Beatles, but Ben Franklin is is pretty good. Speaker 0: No. No. No. I'm sorry. Yeah. Speaker 1: Sorry to offend you, sir. Speaker 0: Read the constitution and read Abbey Road. Listen, every road. They're both good, but they're in a different league. Yeah. Different league. Speaker 1: Okay. So, one of the many things that comes to mind with Ben Franklin is incredible time management. Is there something you could say about Ben Franklin and about, Steve Jobs, I think interesting with Elon is that he, as you write, runs 6 companies. Mhmm. Seven company. Depends how you count with Starling because it's own thing. I don't know. What can you say about these people in terms of time management? Speaker 0: Well, Musk is in a league of his own in the way he does it. First of all, you know, Steve Jobs had to run Pixar and Apple for a while, But Musk, every couple of hours, is switching his mindset from How to implant the Neuralink chip, and what will the robot that implants it in the brain look like, and how fast can we make it move, and then the heat shield on the Raptor or switching to human imitation, machine learning, full self drive. On the night that the Twitter board, agreed to the deal. This is huge around the world. I'm sure you remember. Like, Musk buys Twitter. It wasn't when the deal closed. It was when the Twitter accepted his offer. Mhmm. And I thought, okay. But then he went to Boca Chica, to South Texas, and spent time fixating on, if I remember correctly, A valve in the Raptor engine that had a methane leak issue, and what were the possible ways to fix it? And all the engineers in that room, I assume, are thinking about, this guy just bought Twitter. Should we say something? Yeah. And he's like and then he goes with Kimball to a roadside joint, in Brownsville and just sits in the front and listens to music with nobody noticing really him being there. One of the things that one of his Strength and sort of weaknesses in a way. Is in a given day, he'll focus serially, sequentially on many different things. He will worry about, uploading video on to x.com or the payment system, and then immediately switch over to some issue with the FAA giving a permit for Star Ship or with how to deal with Starlink and the CIA. And when he's focused on any of these things, you cannot distract him. It's not like he's also thinking about, I'm dealing with Starlink, but I've gotta also worry about the Tesla decision on the new $25,000 car. Now he'll, in between these sessions, process information, then let off steam. And for better or worse, he lets off steam by Either playing a friend in Politopia or fire off some tweets, which is often not a healthy thing, but It's a release for him, and he doesn't I once said he was a great multitasker, and that was a mistake. People corrected me. He's a serial tasker Mhmm. Which means focuses intensely on a task for an hour, Almost has a whatever they call it at restaurants where they give you a pallet cleanser. Yeah. He does some palate cleanser with Polytopia Yeah. And then focus it on the next task. Speaker 1: I mean, is there some wisdom about time management that you Speaker 0: can draw from that? There are some things that these people do, and you say, okay. I can be that way. I can be more curious. I can question every rule and regulation. I I just don't think anybody to try to emulate Musk's time management style. Because it takes a certain set of teams who know how to deal with everything else other than the thing he's focusing on, and a certain mind that can shift Just like his moods can shift. You and I go through transitions. And, also, if I'm thinking about what I'm gonna say on this podcast, I'm also thinking about The email my daughter just sent about a house that she's looking, you know, and I'm I'm multitasking. He doesn't actually do that. He single tasks sequentially with a focus that's hardcore. Speaker 1: I don't know. I think there's wisdom to draw from that to, like First of all, he makes me Ben Franklin makes me feel that way, that there's a lot of hours in the day. Mhmm. There's a lot of minutes in the day. Like, There's no excuse not to get a lot done, and that requires just an extreme focus. An extreme focus and, like, an urgency. Speaker 0: I think the fierce urgency that drives him is important, And it's sometimes ginned up. Like I say, the fierce urgency of getting to Mars. Mhmm. And on a Friday night at the launch pad in Boca Chica at 10 PM. There are only a few people working because it's a Friday night. They're not supposed to launch for another 8 months. And he orders a surge. He says, I want 200 people here by tomorrow working on this pad. We have to have a fierce Sense of urgency, oh, we will never get to Mars. Speaker 1: That sense of urgency, you know, is also, a vibrancy That's, like, really taking on life fully. I mean, that to me, this the lesson is like, Even the mundane can be full of this just richness, and, like, you just have to really, take it in intensely. So, like, the switching enables that kind of intensity, because most of us can't hold that intensity in any one task for a prolonged period of time. Maybe that's also a lesson. Speaker 0: Right. And I guess it goes back to also know who you are, meaning Speaker 1: Know who you are. Speaker 0: There are people who can focus intensely, and there are people who can see patterns across many things. Look. Leonardo da Vinci, he was not all that focused. He was easily distracted. Procrastinate. It's why he has more unfinished paintings than finished paintings in his canon. Yeah. But his ability to see patterns across nature and to, in some ways, process, procrastinate, be distracted. That helped him some, but Musk is not that way. And there, Every few months as a new surge. You you don't know where it'll be, but it'll be on solar roofs. And all of a sudden, we'll have a surge, and there has to be, You know, a 100 solar roofs built, or this has to be done by tomorrow, or make a starship dome by dawn, and surge, and do it. And there are people who are built that way. It is inspiring, but also let's appreciate, You know, that there are people who can be really good, but, also can savor The success, savor the moment, savor the quiet sometimes. Musk's big failing is he can't savor the moment or success. And that's the flip side of hardcore intensity. Speaker 1: In, Innovators, another book of yours that I love, you write about individuals and about groups. So one of the questions the book addresses is Is it individuals, or is it groups that turn the tides of history? Speaker 0: When Henry Kissinger was on the shuttle missions for the Middle East Peace is the 1st book I ever wrote. He said, when I was a professor at Harvard, I thought that history was determined by great forces and groups of people. But when I see it up close, I see what a difference an individual can make. He's talking about the dot and Col de Meyer, probably talking about himself too, or at least in his mind. And, We biographers have this dirty secret that we know. We distort history a bit by making the narrative too driven by an individual, But sometimes it is driven by an individual. Musk is a case like that. And sometimes, as I did with the innovators, there's Teams and people who build on each other, and Gordon Moore and Bob Noyes then getting Andy Grove and doing the microchip, which then comes out, And Wozniak and Jobs find it at, some electronic store, and they decide to build the Apple. And so sometimes there are flows of forces and groups of people. I guess I air a little bit on the side of looking at what a Steve Jobs and Elon Musk and Albert Einstein can do. And I also try to figure out if they hadn't been around with the forces of history and the groups of people have done it without them. That's a good historical question as, you know, somebody who loves history. And you think about special relativity, one of the 1905 papers. Even after he writes it, it's 4 years before people truly get what he's saying, which is it's not just How you observe time is relative. It's time itself is relative. And on the general theory, which he does a decade later, I'm not sure we would gotten that yet. What about moving us into the era of an iPhone in which it's so beautiful that you can't live without a 1,000 songs in your pocket, email, and, the Internet in your pocket and the phone. There are a lot of brain dead people from Panasonic to Motorola who didn't get that, and it may have been a while. I certainly think it's true of the era of electric vehicles. Jim and Ford, all the great people there, they crushed the boat, and I mean that literally. They Ended up smashing them because they decided to discontinue it. Likewise, nobody was sending up rockets. Our space shuttle was about to be grounded 12 years ago. And so Musk does things, and there'll be people who say read the book. Once they read the book, they'll see the full story. But let's say it wasn't Musk who did Tesla. It was Martin Eberhard or Mark Toppany. No. No. No. You know, there were people who had helped create, you know, the shells of companies and other things, And they were all deserved to be called cofounders. But the guy who actually gets us to a 1000000 electric vehicles a year, Is Elon Musk? And without him, I don't think we look. If anybody 5 years from now buys a car that's gasoline powered, we'll think, that's quaint. You know? That's odd. Speaker 1: Mhmm. Speaker 0: I mean, suddenly, we've changed. We're not gonna do it. 90% of that is Elon Musk. Speaker 1: We're all mortal. When and how do you think Elon will retire from the insanely Productive schedule he's on now. Speaker 0: I would think that he would hate to retire. I think that he can't live without the pressure, the drama, the all in feeling. It's never been anything that seemed to have crossed his mind. He's never said, maybe I love Larry Ellison's house on the beach in Hawaii. Be maybe I should spend time in doing. Instead, he says things like, I learned early on that vacations will kill you. He gets malaria when he goes on 1 vac I mean, he goes on vacation at one point, and they oust him from PayPal. And then he goes to Africa one way. He gets malaria. He says, I've learned vacations kill you. Lesson learned. Speaker 1: Well, it's interesting because the projects are 100 plus year projects. Many of these Speaker 0: One of the weird things is watching him think incredibly long term. One of the meetings every week, early on when I was watching him, was Mars Colonizer, And we did through a 2 hour meeting about what would the governance structure be on Mars, what would people wear, How would the robots work? Mhmm. And would there be democracy, or should there be a different form of governance. And I'm sitting there saying, what are they doing? What are they talking about? I mean, they're trying to build rocket ships and everything else. They are worrying about the governance structure of Mars. Mhmm. And, likewise, when Ever he's in a tense moment, like there's a rocket's about to be launched, he'll start asking people about something in the way future, like the new, LEET Engine or something. If we're gonna build that, do we have enough materials ready to order? Or I don't know. He'll just ask questions, Like when he's building RoboTaxi, the global car, the $25,000 That's not a total passion. He was talked into doing that. Mhmm. His passion is robo taxis. But his passion is, How are we gonna make this factory to do a 1000000 cars a year? Mhmm. So even the robo taxi is a longer range vision. I mean, he's been touting it since 2016, but, you know, we're not. I don't know. Robotaxis. I mean, there's Waymo may be doing a little experiments, have it, but there's not cars being manufactured without steering wheels that are going to take over the highways. Yeah. I'd say he's always looking way into the future is my point. Speaker 1: I just hope that, There's a lot of da Vinci's and Steve Jobses and Einsteins and Elon Musk's that carry the The flame forward. Speaker 0: That's one of the reason you write books about these people is so that if you're a young woman in a school where you're not Being told to do science, and you read The Code Breaker about Jennifer Doudna. You say, okay, I can be that. And when you say, Oh, maybe I'll be a regulator or you say, oh, no. Maybe I'll be the person who pushes the boundaries, who pushes the lines, who pushes, as Steve Jobs said, the human race. Speaker 1: Well, let me ask you about your mind, your genius, Your process. Speaker 0: I'll give you 2 out of 3. Speaker 1: Alright. Take me through your process of writing a biography. I mean, the the the full of it And not not just writing a biography, but understanding deeply, which your books have done for the human story and, like, the bigger ideas underlying the human stories. You've written biographies both of individuals, which are hardly individuals. Speaker 0: Mhmm. Speaker 1: It's a really big complex picture, Speaker 0: and biographies of ideas that involve individuals. Well, step 1 for me is trying to figure out how the mind works. What causes Einstein to make that leap? Freelon Musk to say stainless steel while he's looking at a Carbon fiber, rob rocket, or how do you make the mental leap? Because I write about smart people, but smart people are a dime a dozen. They don't usually amount to much. You have to be creative, imaginative, to think different, as Jobs would say. And so what makes people creative? What makes them take imaginative leaps? That's the key question you got to ask. You also ask the questions like you've asked earlier, which is what demons or jangling in their head, and how do they harness them into drives? So you look at all that, and you try to observe really carefully, the person. One of the more mundane things I do is A lot of writers try to give you a lot of their opinions and preach or whatever. As I said, this mentor said 2 people types come out, preachers, storytellers, to be a storyteller. I try whenever I'm trying to convey a thought. There's 6 magic words that I almost should have written on a card penned above my desk, which is, let me tell you a story. So if somebody says, how does Elon Musk figure out good talent, as you did, I think, well, let me tell you the story. I tell you the story of Jake McKenzie. Mhmm. Or this is not something I invented. I mean, this is way the good Lord does it in the Bible. I mean, has the best openings set lead sentence ever, you know, in the beginning, comma, and then it's stories. And secondly, to pick up on that lead sentence in the beginning, make it chronological. Speaker 1: Mhmm. Speaker 0: Everybody in the 40th year of their life, has grown from the 39th year and the 38th year. And so you want to show help people evolve and grow. I had the greatest of all nonfiction narrative editors, Alice Mayhew at Simon Shuster, who, among other things, Created all the president's men with Woodward and Bernstein. But she had a note she'd put in the margins of my books that was a tigta, And it meant all things in good time. Keep it chronological. If it's good enough for the Bible, it's good enough for you. Speaker 1: Interesting. To me, like, that's a small note, but to you, it's it's extremely important. Speaker 0: Because it's the framework for how you structure things, but also how you ban things, which is if you keep it a chronological narrative, then you're showing how a person has grown from one experience you've told, talked about to the next one. And that moral growth, creative growth, risk taking growth, Wisdom, that's the essences of creativity, but you can't do it. You know, there's a Tom Bildungsroman, you know, which is a, you know, book of you know, that carries a narrative and does help people learn something. I'm a big believer in narrative. If you're an academic, you sometimes not today, but in, like, 20 years ago, 30 years ago, There were 2 things you thought were bad. 1 was, having a great person theory of history in which you Decide to do biography. I had a great professor when I was in college. Her name was Doris Kearns. Mhmm. She later married Dick Goodwin. And she, when she was going for tenure at the university, wrote a biography of Lyndon Johnson and the American dream. And they denied her tenure because it was beneath the dignity of the academy to write history through 1 person. That's great. It opened up the field of biography to us non academics, starting with David McCullough, Bob Caro, but maybe John Meacham and myself are in a new generation, and Certainly, and there's a generation coming after us. But the second thing besides telling it through people, which is the academy tended to disdain what they called imposing a narrative Mhmm. In which you made it storytelling. Because that meant you were leaving things out and making it into a narrative. Well, that's how we form our views of the world. Speaker 1: Well, let me ask you this question. In terms of gathering and understanding, how much of it is 1 observing, And how much of it is interviews? Speaker 0: Yeah. And, obviously, it depends on the subject. I mean, with a Ben Franklin. It's all based on archives. And every of course, we have 40 volumes of letters he wrote. That was the good old days when every day you'd write 20 letters. The must book is based much more on observation than almost any of my books because he opened up in a way that was Breathtaking to me. You know? Even when you'd be sitting, playing polytopia or seething at other pea you know, he that'd be just sitting there watching. I mean, I spent a lot of time with Jennifer Doudna in her side. I went to her lab and edited to the human gene and, you know, with a pipette and a test tube. But I would say I spent 30 hours with her. Mhmm. I can't count, you know, 100 hours or more just observing Musk. And I'm not Sure that any biographer, perhaps since Boswell took on doctor Johnson, has ever had quite as much up close, meaning 5 feet away at all times, access. And because of that, I'll go back to what I said a moment ago. I try to get out of the way of the story. Mhmm. It's not about me. It's not about I try to just say, okay. Here's what happened. Here's this story. Here's what happened the night he came in to Twitter for the 1st time and let you form Speaker 1: your own judgment. What about the interviews? You've had a lot of conversations. You give acknowledgment to the people you've get you've done interviews with. Well, 1, I have to ask as an aspiring interviewer myself, Speaker 0: how? People love to talk. People just love you know that. And I've had A 140, maybe a 150 people, they're all listed in the back. One of the little things that people won't notice, but I'll say it now, because all of them are on the record. Getting them to talk is easy. They all wanna talk about Musk. But then at a certain point, say, I don't put anonymous quotes in my book. I cite things. I say, if you're tough enough and you've gone through this and a lot of times, it takes 2 or 3 calls back. Somebody will tell me a story, say, oh, no. No. No. I don't enjoy it. But I think it's important to know where everything came from. And with Musk, it's you know, I had that from the very beginning because I was a Time magazine reporter. I'd worked reported for the Times Picayune on New Orleans. I, 1st day on the job, I had to go cover a murder, and, phoned in the story from a payphone. And my editor you know, the city editor said, Well, did you talk to the family? I went, no, Billy. I mean, the family, you know, the daughter just got he said, go knock on the door. I knocked on the door. An hour later, they were still talking. They were bringing out her yearbooks. Lesson 1 I learned, people want to talk if you're willing to just listen. And whether it be Henry Kissinger, you just push the button and say Kissinger, and people tell you the stories, all the way through Elon Musk. Everybody talked. Everybody in his family, everybody he fired, everybody. I mean, I think it's important to listen to people. And the other thing I learned as a reporter, back when I was covering politics in New Hampshire in the early campaigns, I learned from 2 or 3 great reporters, a guy named David Broder and Tim Russert, the late NBC guy. They do what was called door knocking. You just walk in the neighborhood, knock on the door, and ask people about the election. But they said, here's the secret. Don't ask any leading questions. Don't have any premise. Just say, hey. I'm trying to figure out this election. What's going on? What do you think? Mhmm. And then stay silent. With Musk a third secret. You know this well. He'll go silent at times. Sometimes A minute, 2 minutes, 4 minutes. Mhmm. Don't try to fill the silences. If you're a listener, you gotta learn. Okay. He's not saying anything for 4 minutes. I can outlast him. Speaker 1: It's tough. As as humans, it's very tough. Respecting and silence is really, really difficult. Speaker 0: Mhmm. Speaker 1: Speaking of demons, when there's silence, all the demons show up in my head. Speaker 0: Oh, yeah. Speaker 1: The fear, I think, is if I if I don't say anything, is boring, and if I say something, it's gonna be stupid. And that that that's the basic engine that just keeps running, not on the podcast well, on the podcast, but also in human interaction. And so I think there's that nervous energy when interacting with people. Speaker 0: You can never go wrong by staying silent if if there's nothing you have to say. Not something I've mastered, but I do when I'm a reporter, try to master that, which is Don't don't ask complex questions. Don't interject. And when somebody hasn't fully answered the question, don't say, well, let me you know, I haven't fully just stay silent, and then they'll keep talking. Just give them Speaker 1: a chance to keep talking even if they've kinda finished Yeah. Still. Speaker 0: Sometimes if they haven't given you enough, instead of following up, I'll just nod and Keep waiting. Speaker 1: You're making it sound simple. Is there a secret to getting people to open up more? Speaker 0: I'm somewhat lucky Because, you know, I started off working for a daily newspaper, and people back then, they wanna talk to the Newspaper reporter. Speaker 1: But you also have a way about you. Like, I feel like you have, like, a cowboy in a saloon. Like, you just kinda wanna talk. Like, there's a draw. I don't know I don't know what it is. Maybe that's I don't know if it's developed or you're born with it, but there's a it feels like I wanna tell you a story of some sort. Speaker 0: Good. Tell me a story. A couple things. I did learn to be more quiet. I'm sure I know when I was Younger or even I'll see videos of me at, you know, news Things where I'm always trying to interject a question. And so you learn to be quieter sometimes. I haven't mastered it. I haven't learned it enough. You learn to be naturally curious. Many reporters today, when they ask a question, or either trying to play gotcha or trying to get a news scoop or trying to, you know, gig something that can make a lead. And if you actually are curious, And you really wanna know the answer to a question, then people can tell that you asked it because you want the answer, not because You're playing a game with them. Speaker 1: So I'm sure some of them off the record, some of them on the record you had, maybe, you know, What just some incredible conversations. I was gonna say some of the greatest conversations ever, but who knows? Some of the best conversations ever are probably Somewhere in South America between, you know, 2 drunk people that we never get to hear. So I don't I don't know. But, is there a device you can give From what you've learned to somebody like me and how to have good conversation, especially when it's recorded. Speaker 0: Well, do we actually curious. I mean, every question you've asked me is because I think you actually want to know the answer, and you've done your homework, to be open, and not to have an agenda. I mean, we all suffer from there being too many agendas in the world today. Speaker 1: Yeah. So that is just genuine curiosity, but, there's something when you talk about just 1 on 1 interaction, whether it's Elon or Steve Jobs or There's something beautiful about that person's mind, and it feels like It's possible to reveal that, to discover that together, efficiently. And that's kind of the goal of a conversation. Speaker 0: Well, I mean, look. You're amongst the top podcasters and interviewers, you know, in the world today. You have an earnestness to you. Ben Franklin is the person who taught me, I mean, by reading him, the most about on conversation. He wrote a wonderful essay on that. It includes On Silence, but it includes trying to ask sincere questions rather than get a point across. I mean, It's somewhat Socratic. But whenever he wondered, I wanted to, like, start a fireman's corps in Philadelphia. He would go to his group that he called the Leather Apron Club, And they would pose a question. Why don't we have it? What would what would it take? What would be good? And then the 2nd part is to make sure that you listen. And if somebody has even just the germ of an idea, give them credit for it. Like, as Joe said, You know, the real problem is this. And I do think that if I'm in situations, and I just mean even at dinner or something Mhmm. And I'm with somebody, I'm usually curious, and I'll the conversation will proceed, you know, with with questions. And I guess it's also because I'm pretty interested in what anybody's doing, whoever I happen to be with. And so that's a talent you you have, which is you're pretty genuine in your interests. They're people like Benjamin Franklin, like the, I'll say, Charlie Rose, even though he's in disfavor, who are interested in a huge number of subjects, and I think that helps as well to be interested in basketball and opera and physics and metaphysics. That was a Ben Franklin. That was a Leonardo trick, which is they wanted to know everything you could possibly know about every subject knowable. Speaker 1: But there's a different aspect of this, which is, that I would I would love to hear How you've solved it or if you've faced it, that you're certainly disarming. Tara. Trying to get you out. Disarming method. Yeah. I've recently talked to Benjamin Netanyahu. We'll we'll talk again. We, unfortunately, because of scheduling and complexities, Only had 1 hour, which is very difficult. Mhmm. Very difficult with the charismatic posture. I understand this. But he's also a charismatic talker, which is very difficult to break through in 1 hour. But there are people who have built up walls, Whether it's because of demons or because of, their politicians, and so they have agendas and narratives and so on. And so to to break through those, I wonder if there's some advice, some wisdom you've learned on how to, sort of wear down through water or whatever whatever method the the walls that we've built up as individuals. Speaker 0: I mean, you call it disarming, which I don't know that I am, but disarming basically means you're taking down their shields also. And you know when people have a shield, And you try to give them comfort. I had zero of that problem with Elon Musk. I mean, it was like disarming to me, which is I kept waiting to say, okay, he's not gonna always, they've got a shell or he won't do that. But He was, almost crazily open and did not seem to, wanna be spinning or hiding or faking things. And I've been lucky. Doudna was that way. Steve Jobs was that way, But you have to put in time too. In other words, you can't say, okay. There's a 1 hour interview, and I'm gonna break down every wall. It's like on your 5th visit. Speaker 1: Yes. Well, it actually that's one of the things in my situation. You learn 5th visit is very nice, but sometimes you don't get a 5th visit. Sometimes it's just the 1st date. Mhmm. And, I think what it boils down to, and and we said disarming, but there's something about this person that you trust. I think a lot of it just boils down to trust in some deep human way. I think, with with, with many of the people I've spoken with, sometimes the trust happens, like, after the interview, which is really sad because it's like, oh, man. Speaker 0: I've never been in your situation where I have a show. I usually have Midnight Max at the wheel. Yes. I, I'm not a first date person here. Yeah. Yeah. Yeah. Well, you know But then I'm lucky. I mean, I'm in I say lucky, but I'm in print. You know? I understand. Print is a couple 1000 year old medium, but there are those of us who love it. Well, Speaker 1: the nature of the podcast medium is that I'm a 1 1 night stand kinda girl. Let me ask you about objectivity. You Follow Elon. You've lost the like, you're you've you've I mean, I don't even know if you would say your front you have to be careful with words like that, but you're there's an intimacy. And how do you remain objective? Do you want to remain objective while telling a deeply human story. Speaker 0: Yeah. I mean, I want to be honest, which I think is akin to being objective. I try to keep in mind who's who am I writing for? I'm not writing for Elon Musk. As I say, I haven't sent him the book. I don't know if he I don't think he's read it yet. I've got 1 person I'm writing for, the open minded reader. And if I can put in a story and say, well, that will piss off the subject, or that will really make the subject happy. That's irrelevant, or I try to make that a minor consideration. It's, will the reader and have a better understanding because I put this story in the book. Speaker 1: I'm a bit of a romantic, So to me, even your Einstein book Yeah. Had lessons on on romance and relationships. Oh, dear. So how important are romantic relationships to the success of, great men, great women, great minds? Speaker 0: Well, Sometimes people who affect the course of humanity have better relationships with humanity than they do with the humans sitting around him. Einstein had 2 interesting relationships with wives. Just for, you know, Maleva, his first wife, was a sounding board and helped with the mathematics of the special relativity paper in particular. But he didn't treat her well. I mean, he, made her, like, sign a letter that She wouldn't interrupt him. She wouldn't you know? And finally, when she wanted a divorce, he couldn't afford it because he was still a patent clerk. And so, he offered her a deal, which is, I think, totally amazing. He said, one of these days, one of those papers from 1905 is gonna win the Nobel Prize. If we get a divorce, you know, I'll give you the money. That was a lot of money back then, like $1,000,000 now or something. And she's smart. She's a scientist. She consults with a few other scientists, and after a week or so, she takes the bet. It's not until, what, 1919 that he wins his Nobel Prize? And she gets all the money. She buys 3 apartment buildings in Zurich. With his 2nd wife, Elsa, It was more a partnership of convenience. It was not a romantic love, But he knew, and that's sometimes what people need in life is just a partner. I mean, somebody who's gonna handle the stuff you're not gonna handle. So I guess if you look at my books, they're not great inspiring guides to personal relationships. Speaker 1: Let me ask you about, actually, the process of writing itself. When you've observed, when you've listened, when you've collected all information, What's, maybe even just the silly mundane question of, what do you eat for breakfast before you start writing? When do you write? Speaker 0: 1st of all, breakfast is not my favorite meal, and those people who tell you that you have to start with a hearty breakfast. Luke Askins. Yes. And morning is not my favorite day part, so I write at night. And Because I love narrative, it's easy to structure a book, which is I can make a outline that if I printed it out or notes, would be a 100 pages, but everything's in order. In other words, if we serve if there's a Burning Man and he's coming back from Grimes and then there's a solar roof saying, and then there's something. I put it all in order day by day as an outline. And that disciplines me when I'm starting to write to follow the mantra from Alice Mayhew, my first editor, which is all things in good time. Don't get ahead of the story. Don't have to flash back. Mhmm. And then after you get it so that it's all chronological, you know, things. Then you have to do some clustering. You know? You have to say, okay. We're going to Do the decision to do Starship or to build a factory in Texas or to whatever. And Then you sometimes have the organizational problem of, yeah, and that gets us all the way up to here. Do I keep that in this that chapter, or do I wait until later when it's better chronologically. Speaker 1: But those are easy. Well, what about the actual process of telling the Speaker 0: story. Well, that's the mantra I mentioned earlier, which is whenever I get pause or I don't know how to say something, I just say, let me tell you a story. Yeah. And then I find the actual anecdote, the story, the tale that encompasses what I'm trying to convey. And then I don't say what I'm trying to convey. I don't have a transition sentence that says, You know, Elon sometimes changed his mind so often, he couldn't remember whether he changed his mind. You know, you don't you don't need transition sentences. You just say, alright. Here's the point I need to make next. And so you start with a sentence that says, You know, one day in January in the factory in Texas, comma. Speaker 1: Well, one of the things that I'd love to ask you is, for advice for for young people. To me, first advice would be to read biographies. In a sense, Because they help you understand of all the different ways you can live a life well lived. But from having written my address, having studied so many great men and women, what advice could you give to people, of how to live this this life. Speaker 0: Well, I keep going back to the classics and Plato and Aristotle and Socrates. And I guess it's Plato's Max, somebody may be quoting Socrates, that the unexamined life is not worth living. And it gets back to the know thyself and other things, which is you don't have to figure out what is the big meaning of it all, but you have to figure out why you're doing what you're doing. And that requires something that I did not have enough of when I was young, which is self awareness and examining every motive, everything I do. Speaker 1: Where does the examination lead you? Is it, to to, To Speaker 0: a shift Speaker 1: in in life's trajectory? I Speaker 0: mean, it's not for me, Sort of, alright. I've now decided, having been a journalist, I'll run a think tank, or I'll run a network, or I'll write a bio. It is actually something that's more useful on an hourly basis. Like, why am I about to say that to somebody, or why Am I going to do this particular act? What's my true motive here? And also, in the broader sense, to learn as I did after a couple years at CNN. I I my examination of my life is that I'm not great at running complex organizations. I'm not great as a manager. Given the choice, I'd rather somebody else have to manage me than me have to manage people. But it took me a while to figure that out, and I was probably too ambitious when I was young and at Time Magazine. That was when I was green and oh, well. That was when I was in my salad days and green and judgment, And it was like chasing the next level at Tyme Incorporated, whatever it might be. And then one day, I caught the brass ringing. I became an editor and then the top editor. And after a while, I realized, that wasn't really totally what I'm suited to be, especially when I got put in charge of CNN. I mean, all young people are almost by definition in their salad days and green in judgment, but You learn what's motivating you, and then you learn to ask, But is that really what I want? Should I be careful of what I'm wishing for? Speaker 1: One of the big examinations you can do is the fact that you and everybody dies one day. How much do you, Walter, Isaac, think about death? Are you afraid of it? Speaker 0: No. And I don't think about it a lot, but I do think about Steve Jobs' let me tell you Sure. You know, which is the wonderful Steve Jobs story of, I think after he was diagnosed, but before Probably. And he gave both a Stanford talk, but other things in which he said the fact that we are going to die Gives you focus and gives you meaning. If you're gonna live and Elon Musk has said that to me, which is a lot of the tech bros out in of Silicon Valley that looking for ways to live forever forever. I can think, Musk says, of nothing worse. We read the myth of Sisyphus, and we know how bad it is to be condemned to eternal life. So There was in ancient Greece, the person who walked behind the king and said, memento mori. Remember, you're gonna die. And it kept people from losing it a bit. Do you think about legacy? The lucky thing about being a biographer is that you kinda know what your legacy is. There's gonna be a shelf, and it'll be of interesting people. And you will have inspired a 17 year old Biology student somewhere to be gen you know, the next great biochemist or somebody to start a company like Elon Musk. And what I think more about, I won't say giving back. That's such a trite thing. I moved back to New Orleans for a reason. First of all, the hurricane hit. And after Katrina, I was asked to be, vice chair of the recovery authority. And I realized everything I've got going for me, It all comes from this beautiful gem of a troubled city. The wonderful high school I went to, the wonderful streets where I learned to ride a bike, the you know, and it's got challenges. I'm never gonna solve challenges at the grand global level. But I can go back home and say part of my legacy is going to be, I tried to pay it back to my hometown. Even by teaching at Tulane, which I don't do as a favor. I mean, I enjoy the hell out of it. But it's like, alright. I'm part of a community. And I think we lose that in America because people who are lonely are lonely because they're not part of a community. But I've got all my high school kids. They're friends. They're all So in New Orleans, I've got my family, but I also have Tulane institutions in New Orleans that have been there forever. And if I can get involved in helping the school system in New Orleans, helping the youth empowerment programs, of helping the innovation center at Tulane. I was even on the city planning commission, which worries about zoning ordinances for short term rentals. You know, go figure. But it was like, no. Immerse myself in my community because my community was just so awesomely good and allowing me to become who I became, and has trouble year by year, hurricane by hurricane, making sure that each new generation can be creative. And it's a city of creativity from jazz to the food, to the architecture. So When I think of, I won't say legacy, but what am I gonna do to pay it forward, which is a lower level way of saying legacy. I pay it forward by going back to the place where I began and trying to know it for the first time. That was a, rip off of a TS Eliot line. I don't want you to think I thought of that one. Speaker 1: Always cite your sources. I appreciate it. Speaker 0: PS Elliott, if you ever need to figure it out, the 4 debts, So if that part at the end, which is we shall not cease from exploration, and the end of all of our exploring will be the return to the place where we started and know it for the first time to the unknown, but half remembered gait. It's just beautiful. And that's been an inspiration of What do you do in, I guess, if it's a Shakespeare play, you'd call it act 5. Well, you go back to the place where you came and See for legacy. Don't sit there worrying about legacy, but you'll that they're saying, how do I make sure that somebody else can have a magical trajectory starting in New Orleans? Speaker 1: Well, to me, you're one of the greatest storytellers of all time. I've been a huge fan. Speaker 0: Definitely not true, but it's so sweet of me. You see, you can be, rudely interrupting. Mhmm. Speaker 1: The from, I think probably Ben Franklin, So for, I don't know, how many years, 15 years, Einstein, all the way through today, has been a huge fan of yours, and you're one of the people that I thought surely would not lower themselves to appear and have a conversation with me. And it's just a giant gift to me. Speaker 0: Hey. I flew into Austin for this because I am a big fan and especially a big fan because you take people seriously, and you care. Speaker 1: Thank you a 1000 times. Thank you for respecting me and for inspiring Just millions of people with your stories. Again, an incredible storytelling, incredible human, and thank you for talking today. Speaker 0: Thank you, Alex. Speaker 1: Thanks for listening to this conversation with Walter Isaacson. To support this podcast, please check out our sponsors in the description. And now Let me leave you with one of my favorite quotes from Carl Jung. People will do anything, no matter how absurd, in order to avoid facing their own souls. One does not become enlightened by imagining figures of light, but by making the darkness Mhmm. Conscious. Thank you for listening, and hope to see you next time.
FoundersPodcast
Posted - September 25, 2023 at 12:11 PM
Saved - September 25, 2023 at 3:01 PM
reSee.it AI Summary
Peter Thiel's effective management idea at PayPal was assigning each employee a unique responsibility, reducing conflict and fostering long-term relationships. Jeff Bezos implemented a similar concept called Single Threaded Leadership at Amazon. Learn more in the latest episode. [Podcast links]

@FoundersPodcast - David Senra

Peter Thiel on the best idea he had for managing people and why: "The best thing I did as a manager at PayPal was to make every person in the company responsible for doing just one thing. Every employee's one thing was unique, and everyone knew I would evaluate him only on that one thing. I had started doing this just to simplify the task of managing people. But then I noticed a deeper result: defining roles reduced conflict. Most fights inside a company happen when colleagues compete for the same responsibilities. Startups face an especially high risk of this since job roles are fluid at the early stages. Eliminating competition makes it easier for everyone to build the kinds of long-term relationships that transcend mere professionalism. More than that, internal peace is what enables a startup to survive at all. When a startup fails, we often imagine it succumbing to predatory rivals in a competitive ecosystem. But every company is also its own ecosystem, and factional strife makes it vulnerable to outside threats. Internal conflict is like an autoimmune disease: the technical cause of death may be pneumonia, but the real cause remains hidden from plain view."

@FoundersPodcast - David Senra

Jeff Bezos used a similar idea at Amazon called “Single threaded leadership” Both ideas are mentioned in the latest episode: Apple: https://podcasts.apple.com/us/podcast/founders/id1141877104?i=1000628642785 Spotify: https://spotify.link/8vVHMrrInDb

‎Founders: #321 Working with Jeff Bezos on Apple Podcasts What I learned from reading Working Backwards: Insights, Stories, and Secrets from Inside Amazon by Colin Bryar and Bill Carr. podcasts.apple.com
Spotify Spotify is the best way to listen to music on mobile or tablet. Search for any track, artist or album and listen for free. Make and share playlists. Build your biggest, best ever music collection. Get inspired with personal recommendations. spotify.link
Teslaconomics
Posted - October 25, 2023 at 8:14 PM
Saved - October 25, 2023 at 9:31 PM
reSee.it AI Summary
Authenticity in leadership holds more value than polished rhetoric. Elon Musk exemplifies this approach by consistently showcasing his true intentions, emotions, and thoughts to investors, team, and customers. His genuine transparency builds a unique trust and relationship, setting him apart from other CEOs.

@Teslaconomics - Teslaconomics

Authenticity in leadership is often more valuable than polished rhetoric (the things that many want to hear). While many leaders might excel at delivering the perfect pitch or saying just the right thing to calm investors, there’s an inherent value in having a CEO who operates organically, with genuine transparency. Elon is the definition of this approach. He consistently showcases his true intentions, emotions, and thoughts, not just to his investors, but to his team & customers as well. This candidness builds a unique trust and relationship, setting him apart from many other CEOs.

itsalwaysrains
Posted - December 14, 2024 at 11:17 PM
Saved - December 15, 2024 at 1:51 AM

@itsalwaysrains - AlwaysSadButTruthful

BUT DO IT @ELONMUSK STYLE. assess. adapt. automate. https://t.co/tpGiQFAD3Y

elonmusk
Posted - January 22, 2025 at 6:14 PM
Saved - January 22, 2025 at 9:31 PM

@elonmusk - Elon Musk

Nice of him to say

@ElonClipsX - ELON CLIPS

JPMorgan CEO Jamie Dimon: Elon is our Einstein. It's rational he checks our government efficiency. “You’ve got to look at Elon, at SpaceX, Tesla, Neuralink – the guy is our Einstein. I'd like to be helpful to him and his companies as much as we can. I think it is completely rational for someone to look at our government and say it's been ineffective. We deserve good government, and I don't think anyone thinks sending another trillion dollars to Washington, D.C., will lead to good government. The government needs to be more accountable. It needs to be more efficient. It should be outcomes-based. I mean, I'd say [go] department by department. I wish them the best. It's going to be complicated. The federal government's complicated. You read about all the people in it. And so, you know, if we could be helpful to them, I'd love to be helpful to them.” Jamie Dimon on CNBC, January 22, 2025

Video Transcript AI Summary
Elon Musk is a remarkable figure with his ventures in SpaceX, Tesla, and Neuralink. It's rational to view our government as ineffective, and simply sending more money to Washington won't improve it. We deserve a government that is accountable and efficient, focusing on outcomes department by department. The federal government is complex, and while it will be challenging, I hope to contribute positively to its improvement.
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Speaker 0: Mean, you gotta look at Elon. I mean, SpaceX. I mean, Tesla. You know, Neuralink. I just I mean, the guy is our and it's Einstein. And so, I and I like to be helpful to him and his company as much as we can. I think it is completely rational for someone to look at our government and say it's been ineffective. What we deserve good government, and I don't think anyone thinks sending another $1,000,000,000,000 to Washington DC will lend to good government. So government needs to be more accountable. It needs to be more efficient. It should be outcomes based. I mean, I'd say department by department, so I wish them the best. It's gonna be complicated. As you know, the federal government's complicated. You read about all the people in it. And so, you know, if we could be helpful to them, and, I'd love to be helpful to them.
thefernandocz
Posted - January 27, 2025 at 7:24 PM
Saved - January 29, 2025 at 4:56 PM
reSee.it AI Summary
Marc Andreessen's recent conversation with Lex Fridman revealed significant insights, including the manipulation of banking systems against political figures, the failures of universities to recruit top talent, and the complexities of the American resource advantage. He discussed the evolution of digital communities into real-world entities and the challenges of AI ethics. The conversation highlighted the decline of traditional media influence and the rise of personal branding as essential for founders today. Overall, it emphasized the importance of authentic thought leadership in a rapidly changing landscape.

@thefernandocz - Fernando Cao

Marc Andreessen just shocked the world on Lex Fridman. He exposed: • Government forcing banks to cut off Trump's family • Universities discriminating against certain races • Meta's ridiculous diversity policy 12 insights from their conversation I can't stop thinking about🧵 https://t.co/pnV0b5rN2g

@thefernandocz - Fernando Cao

1. The Hidden Power of Dinner Parties At Silicon Valley dinner parties, everyone agrees on everything. But there's a secret "whisper network" where real conversations happen. The truth? Most elites are afraid to speak their minds publicly. https://t.co/L27a2MCTLv

Video Transcript AI Summary
Is there a deeper turmoil of ideas beneath the surface chatter at dinner parties? While socialization occurs among close friends, true beliefs and struggles often remain unexpressed. At these gatherings, heretical ideas that challenge the status quo are rarely discussed openly. Instead, they tend to be shared in private conversations or through a sort of whisper network. When meeting someone new, there’s a subtle dance of determining whether it’s safe to share thoughts or if conformity is required. This creates an atmosphere where genuine dialogue is limited, and connections are cautiously navigated.
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Speaker 0: Is it possible that the surface chatter of dinner parties underneath that, there is a turmoil of ideas and thoughts and beliefs that's going on, but you're just talking to people really close to you or in your own mind, and then the socialization happens at the dinner parties. Like, when you go outside the inner circle of one, two, 3, 4 people who you really trust, then you start to conform. But inside there inside the mind, there is an actual belief or a struggle attention with The New York Times or with the with the listener. For the listener, there's a there's a slow smile that overtook Marc Andreessen's face. So, Speaker 1: like, I'll just tell you what I think, which is at at at the dinner parties and at the conferences, no. There's none of that. It's what what there is is that all of the heretical conversations, anything that challenges the status quo, any heretical ideas and any new idea, you know, is a heretical idea. Any deviation. It the it's either discussed a 1 on 1 face to face. It's it's like a whisper network or it's like a real life social network. There's a secret handshake, which is like, okay. You meet somebody and you, like, know each other a little bit, but, like, not well. And, like, you're both trying to figure out if you can, like, talk to the other person openly or whether you have to, like, be fully conformist. It's a joke.

@thefernandocz - Fernando Cao

2. The University System Is Broken National Merit Scholars represent the top 0.5% of intellectual talent in America. Yet not a single university actively recruits them. While they have full-time scouts for sports, pure genius goes unnoticed. https://t.co/YTf4xSXVsc

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The National Merit Scholarship System was established during the Cold War to identify the nation's top 0.5% of students based on intelligence, using PSAT and SAT scores as measures. This system focuses solely on academic ability, without considering race, gender, or other characteristics. Each year, it recognizes the highest-scoring students, narrowing down from the top 1% of PSAT scores to the top 0.5% who also excel on the SAT. The scholarship amount, originally significant, is now around $25,100. This initiative aims to discover and reward exceptional talent among high school students.
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Speaker 0: Let me give you one more positive scenario, which and then I'll also beat up on the university some more. Do you but do you know about the National Merit Scholarship System? Have you heard about this? Not really. Can you explain? So there's a system that was created during the cold war, called the National Merit Scholars, and, it is a basically, it was created, I forget, in the late fifties or sixties when it was when people in government actually wanted to identify the best and the brightest. Mhmm. As heretical an idea as that sounds today. And so it's basically a national talent search for, basically, IQ. It it its goal is to identify, basically, the top 0.5% of the IQ, in the country, by the way, completely regardless of other characteristics. So there's no race, gender, or any other aspect to it. It's just going for straight intelligence. It uses the first the PSAT, which is the preparatory SAT that you take, and then the SAT. So it uses those scores. That that that is the scoring. It's a straight PSAT SAT scoring system. So they use the SAT as a proxy for IQ, which it is. They run this every year. They identify they they it's like a they get down to, like, 1% of the population of the kids, 18 year olds in a given year who score highest on the PSAT, and then they get down to they further qualify down to the 0.5% that also replicate on the SAT. And then it's like the scholarship amount is, like, $25100. Right? So it's like it was a lot of money 50 years ago, not as much today. But it's a national system being run literally to find

@thefernandocz - Fernando Cao

3. The Brain Drain Problem We're depleting other nations in three devastating ways: • Taking their most brilliant minds • Removing future leadership • Creating unstable regions It's colonialism for human capital, and the consequences are starting to show. https://t.co/C5UFyqgoRT

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Four countries have been draining their smart talent, particularly from peripheral eurozone nations like Greece, where a brain drain has left the government struggling to develop an economic plan as young, skilled individuals leave. This issue also affects Ukraine, which has been losing talent due to recruitment and migration caused by war. As Ukraine looks to rebuild, it may lack the skilled workforce it once had. Similarly, Russia has experienced significant brain drain over the past 30 years. Interestingly, while the West recognizes the moral wrongs of colonization and resource extraction, it often overlooks the ethical implications of extracting human capital, viewing it as a positive development rather than considering the impact on the countries losing their talent.
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Speaker 0: Basically, what what we what we, 4 countries, have been doing is draining all the smart people out. Mhmm. It's actually much easier for people in Europe to talk about this, I've discovered, because the eurozone is whatever, you know, 28 countries. And within the eurozone, the high skilled people over time have been migrating to originally the UK, but also specifically, I think it's the Netherlands, Germany, and France. But specifically, they've been migrating out of the peripheral eurozone countries. And the the the one where this really hit the fan was in Greece. Right? So, you know, Greece falls into chaos, disaster, and then, you know, you're running the government in Greece, and you're trying to figure out how to put an economic development plan together. All of your smart young kids have left. Like, what are you gonna do? Right? By the way, this is a potential I I know you care a lot about Ukraine. This is a potential crisis for Ukraine, not because in part because of this because we enthusiastically recruit Ukrainians, of course, and so we've been drain brain draining Ukraine for a long time. Mhmm. But, also, of course, you know, war does tend to cause people to to migrate out. And so, you know, when it comes time for Ukraine to rebuild as a peaceful country, is it gonna have the talent base even that it had 5 years ago is, like, a very big and important question. By the way, Russia like, we have brain drain a lot of really smart people out of Russia. A lot of them are here, right, over the last, you know, 30 years. And so there's this thing. It's actually really funny if you think about it. Like, the one thing that we know to be the height of absolute evil that the west ever did was colonization Mhmm. And resource extraction. Right? So we know the height of absolute evil was when the Portuguese and English and, you know, everybody else went and had these colonies and then went in and we, you know, took all the oil and we took all the diamonds and we took all the whatever lithium or whatever it is. Right? Well, for some reason, we realized that that's a deeply evil thing to do when it's a physical resource, when it's a nonconscious physical matter. For some reason, we think it's completely morally acceptable to do it with human capital. In fact, we think it's glorious and beautiful and wonderful and, you know, the great flowering of of, of, peace and harmony and and moral justice of our time to do it. And we don't think for one second what we're doing to the countries that we're pulling all these people out of.

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4. The Rise of Network States Digital communities are evolving into real-world entities. Future citizenship won't be determined by where you're born. Instead, it will be shaped by what you believe in and the networks you join. https://t.co/2GZJQRjtxZ

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We've been observing a situation where corrupt elites are struggling to maintain the support of the masses, while new elites are taking advantage of the circumstances. This serves as a case study highlighting the dangers of a society where most people reject the core values they are expected to uphold. A key takeaway is that it's detrimental for a society to create a divide between what is privately believed and what is publicly expressed. Censorship attempts to control thoughts by limiting speech, which ultimately harms societal cohesion and understanding.
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Speaker 0: And we've been living through the, you know, the the true believer elites masses, you know, thing with, you know, with a set of, like, basically incredibly corrupt elites wondering why they don't have the wealthy masses anymore and a set of new elites that are running away with things. And so, like, we're we're living through this, like, incredible applied case study, of these ideas. And, you know, if there's a moral of the story, it is, you know, I think fairly obvious, which is it's it's a really bad idea for a society to wedge itself into a position in which most people don't believe the fundamental precepts of what they're told they have to do, you know, to be to be good people like that. That is just not not a good state to be in. Speaker 1: So one of the ways to avoid that in the future maybe is to keep the delta between what's said in private and what's said in public small. Speaker 0: Yeah. It's like, well, this is sort of the the siren song of censorship is we can keep people from saying things, which means we can keep people from thinking things. Yeah. And, you know, by

@thefernandocz - Fernando Cao

5. The Diversity Paradox Peter Thiel sits on Meta's board of directors. When NASDAQ mandated board diversity rules, he counted as diverse for being LGBT. The irony? He literally wrote a book called "The Diversity Myth." https://t.co/57jwboR7ff

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The overreach of government power is concerning, even for those involved. It resembles the corrupting influence of the "ring of power" from "The Lord of the Rings," which grants immense power but ultimately leads to moral decay. Characters like Gollum illustrate how the desire for power can transform individuals into corrupted versions of themselves. The temptation of censorship is similarly strong; once in control, it's hard to resist using that power. Reflecting years later, one might realize that their initial intentions of patriotism have led to authoritarianism, undermining democracy and Western values.
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Speaker 0: We'll we'll see where they take it. Speaker 1: Yeah. It's truly disturbing. I don't think anybody wants this kind of overreach of power for government, including perhaps people that were participating in it. It's like this dark momentum of power. They just get caught up in it, and that's the reason there's that kind of protection. Nobody wants that. Speaker 0: So I use the metaphor of the ring of power. And Yeah. For people who don't catch the reference, that's lord lord of the rings. And the thing with the ring of power and lord of the rings, it's the ring that golem has in the beginning, and it turns you invisible, and it turns out it, like, unlocks all this, like, fearsome power. It's the most powerful thing in the world. It's key to everything. And basically, the the the moral lesson of lord of the rings, which was, you know, written by a guy who thought very deeply about these things is, yeah, the ring of power is inherently corrupting. The characters at one point, they're like, end off. Just put on the ring and, like, fix this. Right? And he's like he's like, he will not put the ring on even to, like, end the war, because he knows that it will corrupt him. And then, you know, the character as it starts, the character of Gollum is the result of, you know, it's like, like, a normal character who ultimately becomes, you know, this incredibly corrupt and deranged version of himself. And so, I mean, I think you I think you said something actually quite profound there, which is the ring of power is infinitely tempting. You know, the censorship machine is infinitely tempting. If you if you have it, like, you are going to use it. It's overwhelmingly tempting because it's so powerful and that it will corrupt you. And, yeah, I I don't know whether any of these people feel any of this today. They should. I don't know if they do. But, yeah, you go out 5 or 10 years later, you know, you would hope that you would realize that your soul has been corroded, and you probably started out thinking that you were a patriot, and you were trying to defend democracy, and you ended up being, you know, extremely authoritarian and anti democratic and anti western.

@thefernandocz - Fernando Cao

6. The American Resource Advantage Our natural abundance defies logic and prediction. Every time experts warn about scarcity, we discover new deposits. This isn't luck - it's a pattern that's repeated throughout our history. https://t.co/XUnYWOtUxP

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The US is positioned for significant growth due to various factors, both fortunate and the result of hard work. Geographically, the US benefits from its own continent, providing physical security and abundant natural resources. There's a humorous notion that whenever the US seems to be running low on a rare earth material, a farmer in North Dakota discovers a massive deposit. The country has the potential for energy independence and can become a major net energy exporter. The previous administration chose to limit American energy production, but the current administration aims to revitalize it. Ultimately, the ability to be energy independent is a matter of choice.
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Speaker 0: The US is just flat out primed for growth, and I think that's a consequence of many factors. You know, some of which were are lucky and some of which through hard work. And so the lucky part is just, you know, number 1, we know we just have, like, incredible physical security by being our own continent. You know, we have incredible natural resources. Right? There's there's there's this running joke now that, like, whenever it looks like The US is gonna run out of some, like, rare earth material, you know, some farmer in North Dakota, like, kicks over a hay bale and finds, like, a $2,000,000,000,000 deposit. Mhmm. Right? Right? I mean, we're we're just, like, blessed, you know, with with with geography and the natural resources. Energy, you know, we can be energy independent anytime we want. This last administration decided they didn't wanna be. They wanted to turn off American energy. This new administration has declared that they have a goal of turning it on in a dramatic way. There's no question we can be energy dependent. We can be a giant net energy exporter. It's purely a question of choice.

@thefernandocz - Fernando Cao

7. The Death of H1B Visas The system has evolved beyond recognition. Big tech has abandoned H1Bs for O1 visas, while consulting mills exploit the old system. What was meant to attract genius has become a bureaucratic maze. https://t.co/XWmcC8fhmv

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The H-1B visa is less commonly used in the tech industry now, with the O-1 visa becoming more prevalent. The O-1 is known as the "super genius visa" for individuals who have made significant technological breakthroughs and wish to start companies in the U.S. It has a high acceptance rate but requires substantial proof of qualifications. The H-1B program mainly serves two types of employers: large tech companies that hire in bulk and consulting firms, often referred to as "mills," that primarily employ Indian workers. These firms typically offer mid-tier IT consulting jobs, with salaries ranging from $60,000 to $100,000, significantly lower than the higher salaries in Silicon Valley.
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Speaker 0: Use some good to the h one b. Okay. So then you get this other okay. So then there's It's come come all the way around. There's another nuance. So there's another nuance. There's another nuance, which is mostly in the valley we don't use h one b's anymore. Mhmm. Mostly we use o ones. So there's a there's a you mean, there's a separate class of visa and and the o one is like this it it turns out the o one is the super genius visa. Mhmm. So the o one is the basically our our founder. Like, when we have, like, a when we have somebody from anywhere in the world and they've, like, invented a breakthrough in new technology and they want to come to the US to start a company, they come in through an o one visa. And and there and that actually is like a it's a fairly high bar. It's a high acceptance rate, but it's like a pretty high bar and they they do a lot of work and they there's like a you have to put real work into it, really really prove your case. Mostly, what's happened with the h one b visa program, is that it has gone to basically 2 categories of employers. 1 is, the basically a small set of big tech companies that hire in volume, which is exactly the companies that you would think. And then the other is it goes to these what they call kind of the mills, the consulting mills. Right? And so there's these set of companies with names. I don't wanna pick on companies, but, you know, names like Cognizant that, you know, hire basically have their business model. Is primarily Indian bringing primarily Indians, in in large numbers. And, you know, they often have, you know, offices next to company owned housing, and they'll have, you know, organizations that are, you know, they'll have, you know, organizations that are literally thousands of Indians, you know, living and working in the US, and they do basically, call it mid tier, like, IT consulting. So, you know, these folks are making good good good good wages, but they're making 60 or 80 or $100,000 a year, not the, you know, 300,000 that you'd make in the valley.

@thefernandocz - Fernando Cao

8. The Banking System's Dark Side The weaponization of finance has reached new levels. Even Trump's wife and son got debanked. When you can cut off someone's family from the banking system, you've crossed a line that can't be uncrossed. https://t.co/SBWnj8brYY

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Universities receive funding from four main sources. First, federal student loans, which amount to trillions and are growing faster than inflation. Second, federal research funding, where universities often take up to 70% of grants for central use. Third, tax exemptions at the operating level, based on their nonprofit status. Fourth, tax exemptions for endowments, which serve as financial buffers. Analyzing these sources reveals that if federal and state funding were removed, many universities would face bankruptcy, highlighting the need for a potential rebuild of their financial structures.
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Speaker 0: What's next? Okay. So let's go let's go through it. So the the the universities the universe the universities are funded by 4 primary sources of federal funding. The the big 1 is a federal student loan program, which is, you know, in the many trillions of dollars at this point and then only spiraling, you know, way faster than than inflation. That's number 1. Number 2 is federal research funding, which is also very large. And you probably know that, when a scientist at university gets a research grant, the university rakes as much as 70% of the money, for central uses. Yeah. Number 3 is tax exemption at the operating level, which is based on the idea that these are nonprofit institutions as opposed to, let's say, political institutions. And then number 4 is tax, exemptions at the endowment level, you know, which is the financial buffer that these places have. Anybody who's been close to a university budget will basically see that what would happen if you went through those sources of federal taxpayer money. And then for the state schools, the state money, they they all instantly go bankrupt. And then you could rebuild. Then you could rebuild because the problem right now,

@thefernandocz - Fernando Cao

9. The Social Media Revolution The past decade of social media enforced conformity and control. But something remarkable is happening: the walls are coming down. We're witnessing the rebirth of genuine free speech online. https://t.co/6GYPfuxIP0

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The happiest moments for many in the last decade stem from the freedom to express themselves without fear of being criticized or shamed. This shift allowed for more genuine conversations. Online, a similar dynamic emerged with the rise of group chats, which became a refuge from the enforced conformity of social networks. These platforms often practiced censorship and were prone to mobbing and shaming. However, with breakthroughs like Elon Musk's acquisition of X and the rise of Substack, there has been a significant change in the landscape of free speech online, allowing for more open discussions.
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Speaker 0: Like, the happiest mo at least in the last decade, those are, like, the happiest moments of everybody's lives because they're just, like everybody's just ecstatic because they're like, I don't have to worry about getting yelled at and shamed, like, for every third sentence that comes out of my mouth, and we can actually talk about real things. So so that's the live version of it. And then the and then, of course, the other side of it is the the, you know, the group chat the group chat phenomenon. Right. And and then this and then basically the same thing played out, you know, until until Elon bought x and until Substack took off, you know, which were really the 2 big breakthroughs in free speech online. The the same dynamic played out online, which is you had absolute conformity on the social networks, like, literally enforced by the social networks themselves through censorship and and then also through cancellation campaigns and mobbing and shaming. Right? And and but then you had but but then group chats grew up to be the equivalent of Samostat. Right? Mhmm. Anybody

@thefernandocz - Fernando Cao

10. The AI Ethics Challenge Every major AI system reflects California's political values. This creates a fascinating problem: how will other cultures react? The battle for AI's moral compass is just beginning. https://t.co/UpBAchYCu1

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Future chips and the implications of AI training raise significant questions. What guidelines govern the content and moral teachings these systems provide? Additionally, how many countries would want to base their education, healthcare, and political systems on AI shaped by extreme left-wing California ideologies? The reality is that very few nations would be inclined to adopt such a framework.
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Speaker 0: Another $1,000,000,000,000 question, future chips, which I know you've you've talked a lot about. Another $1,000,000,000,000 question. Yeah. I mean, all the global issue. Oh, another $1,000,000,000,000 question, censorship. Right? Like and and and and, and all the as they say with all the, human feedback training process. Exactly what are you training these things to do? What are they allowed to talk about? How long do they give you these how how often do they give these incredibly preaching moral lectures? How or here's a here's a here's a good here's a $1,000,000,000,000 question. How many other countries want their country to run its education system, health care system, new system, political system on the basis of an AI that's been trained according to the most extreme left wing California politics? Right? Because that's kind of what they have on offer right now, and I think the answer to that is not very many.

@thefernandocz - Fernando Cao

11. The Crypto-AI Convergence Here's what most are missing about AI's future: Billions of AI agents will need their own economy. Cryptocurrency isn't just surviving - it's becoming essential infrastructure. https://t.co/KGt2nF4OmC

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Exciting changes are on the horizon for social media, with a significant reinvigoration expected over the next four years. This transformation will extend beyond platforms like X to others as well. Additionally, the crypto market is poised for a resurgence. The intersection of AI and crypto is particularly noteworthy, as the rise of numerous AI agents will create a need for an economic system. Crypto, with its programmable money and efficient transaction processing, is seen as the ideal solution for this emerging economy. The potential impact of the crypto-AI relationship could be substantial.
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Speaker 0: Exciting. So I think one of the things we can look forward to in the next 4 years is, number 1, just like a massive reinvigoration of social media as a consequence of the changes that are happening right now. I'm very excited to see the con to see what's going to happen with that. And then, and it's happened on x, but it's now going to happen on other platforms. And then, the other is, crypto's gonna come, you know, crypto's gonna come right back to life. And, actually, that's very exciting. Actually, that's worth noting is that's another $1,000,000,000,000 question on AI, which is, in a world of pervasive AI and especially in a world of AI agents and imagine a world of 1,000,000,000 or trillions of AI agents running around, they need an economy. And in crypto, in our view, happens to be the ideal economic system for that. Right? Because it's programmable money. It's a very easy way to plug in and and do that, and there's this transaction processing system that can that can do that. And so I think the crypto AI intersection, you know, is potentially very a very, very big deal.

@thefernandocz - Fernando Cao

12. The Hollywood Awakening The entertainment industry faces three massive shifts: • Return of creative freedom • End of enforced conformity • Revival of comedy and risk-taking We're entering a new golden age. https://t.co/GzVblxzai9

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I was recently with a group of Hollywood individuals who, despite being vocally anti-Trump, acknowledged a significant shift since November 6. They felt the atmosphere had changed, describing it as a thawing of the ice. Many projects that were previously stalled are now moving forward, and there's a renewed interest in making comedies. This sentiment of change is echoed across various sectors, with business leaders expressing relief that a decade of difficulties seems to be over.
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Speaker 0: Ready. And then I was with a group of Hollywood people about two weeks ago, and they were still, you know, people who at least at least vocally were still very anti Trump. But I said, you know, has anything changed since since Nov. 6? And they they immediately said, oh, it's completely different. It feels like the ice has thawed, you know, woke us over. You know, they said that all kinds of projects are gonna be able to get made now that couldn't before, that, you know, probably was gonna start making comedies again. You know? Like, it's it's they were just like, it's it's like a it's like a just like an incredible immediate, environmental change. And I'm as I talk to people kinda throughout, you know, certainly throughout the economy, people who run businesses, I I hear that all the time, which is just this this last ten years of misery is just over. I mean, the

@thefernandocz - Fernando Cao

13. The Power of Humor Memes have become modern society's truth-telling mechanism. When direct speech is risky, jokes reveal real beliefs. That's why every secret group eventually becomes a meme-sharing network. https://t.co/zF74fAbqRk

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Deviations in conversation often happen through subtle, informal networks, like a "whisper network." A joke can serve as a signal; if the other person laughs, the conversation can continue, but if not, it's best to retreat. Humor allows for discussing serious topics while maintaining deniability, as comedians can often say, "It was just a joke." Laughter is involuntary and reveals truths that may be off-limits to discuss openly. When someone laughs, it indicates that a deeper, often unspoken truth is being acknowledged, breaking the ice and allowing for more open dialogue.
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Speaker 0: Any deviation, it the it's either discussed a 1 on 1 face to face. It's it's like a whisper network, or it's like a realized social network. Here's a secret handshake, which is like, okay. You meet somebody and you, like, know each other a little bit, but, like, not well and, like, you're both trying to figure out if you can, like, talk to the other person openly or whether you have to, like, be fully conformist. It's a joke. Oh, yeah. Humor. Somebody cracks a joke. Right? Somebody cracks a joke. Yep. If the other person laughs, the conversation is on. Yeah. Yeah. If the other person doesn't laugh, back slowly away from the scene. Yeah. I didn't mean anything by it. Yeah. Right? And and, by the way, it doesn't have to be, like, a super offensive joke. It just has to be a joke that's just up against the edge of 1 of the, use the Sam Bankman Fried term, 1 of the chivalis. You know, it has to be up against one of the things, of, you know, one of the things that you're absolutely required to to believe to be the dinner parties. And then and then at that point, what happens is you have a peer to peer network. Right? You you have you have you have a you have a a one to one connection with somebody, and then you you have your you have your your little conspiracy of of thought thought criminality. And then you have your net you've probably been through this. You have your network of thought criminals, and then they have their network of thought criminals, and then you have this, like, delicate mating dance as to whether you should bring the thought criminals together. Mhmm. Right? And the dance of fundamental, mechanism of the dance is humor. Yeah. It's humor. Like, it's right. Well, of course. Memes. Yeah. Well, for two for two reasons. Number 1 number 1, humor is a way to have deniability. Right? Humor is a way to discuss serious things without without without with having deniability. Oh, I'm sorry. It was just a joke. Right? So so that's part of it, which is one of the reasons why comedians can get away with saying things the rest of us can't. This is, you know, they they can always fall back on, oh, yeah. I was just going for the laugh. But but the other key thing about humor, right, is that is that laughter is involuntary. Right? Like, you either laugh or you don't. And and it's not like a conscious decision whether you're gonna laugh. And everybody can tell when somebody's fake laughing. Right? And as every professional comedian knows this. Right? The laughter is the clue that you're onto something truthful. Mhmm. Like, people don't laugh at, like, made up bullshit stories. They they laugh because, like, you're revealing something that they either have not been allowed to think about or have not been allowed to talk about, right, or is off limits. And all of a sudden, it's like the ice breaks, and it's like, oh, yeah. That's the thing. And it's funny. And, like, I laugh.

@thefernandocz - Fernando Cao

14. The Great Unwinding Old systems are crumbling. New ones are emerging. The institutions that seemed invincible last decade now look vulnerable. We're watching history's page turn in real time. https://t.co/4aPqztqsqE

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The previous administration favored big government and did not make efforts to reduce regulations or spending. However, the new administration could argue that their actions comply with a Supreme Court decision requiring the unwinding of certain regulations deemed unconstitutional. This involves addressing regulations, spending, and personnel simultaneously. There are innovative strategies being developed to tackle these issues. Many former government officials express skepticism, believing that achieving these goals is impossible.
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Speaker 0: Oh, the previous White House, of course, was super in favor of big government. They had no desire to they did nothing based on this. They they didn't, you know, pull anything back in. But the new regime, if they choose to, could say, look. The the thing that we're doing here is not, you know, challenging the laws. We're actually complying with the supreme court decision that basically says we have to unwind a lot of this and we have to unwind the regulations which are no longer legal, constitutional. We have to unwind the spend and we have to unwind the people. And so and that's how you get from basically, you connect the thread from the regulation part back to the money part, back to the people part. They have work going on all 3 of these threads. They have, I would say, incredibly creative ideas on how to deal with this. I'm I I know lots of former government people who a 100% of them are super cynical on this topic, and they're like, this is impossible. This could never possibly

@thefernandocz - Fernando Cao

The most fascinating aspect: These insights didn't come from mainstream media. They came from a 4-hour, unfiltered conversation where Marc could speak freely. This is the future of influence... The old gatekeepers are losing power:

@thefernandocz - Fernando Cao

Long-form podcasts and social media have created a new kind of thought leadership: • Raw and unfiltered • Deep and nuanced • Direct to audience No editorial oversight. No agenda. Just truth. Founders are now choosing this path deliberately. https://t.co/sjT1ctTDlY

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Success comes from being genuine and expressing your true thoughts. There's no need to put on a show, especially when past successes have already been achieved. Asking genuine questions builds trust, allowing words to flow naturally. Each word choice is a decision, reflecting your intent. Language can be used to manipulate or gain power, but true communication involves honesty. Every choice in how you express yourself is a moral decision that shapes your integrity.
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Speaker 0: Well, you know, look. What here. Part of the reason that you're so successful, in my opinion, is because you actually say what you think. Like, you're not putting on a show. Actually, you have no reason to put on a show. You put on a whole bunch of shows and they've already been successful. You know, when you're actually asking the questions that are genuine questions and people can trust you because of that, and that means that you're letting the words emerge as they come to you. And each of doing that with each word, that's a decision, you know, because you can use your language to manipulate and you can use your language to for your own, say, hedonistic purposes or to gain power, or you can just say what you think. Every like, all of those different choices are a decision. That's a wrestling. That's a moral decision.

@thefernandocz - Fernando Cao

Instead of op-eds in the NYT, Marc Andreesen's amplifying influence through: • Authentic podcast appearances • His OWN media empire at a16z • Regular engagement on X And any founder today should do the same. Because today, a personal brand is no longer optional:

@thefernandocz - Fernando Cao

You need to become a thought leader. It makes you the default option for: • New customers • Investors in your niche • Top talent looking to join great companies A personal brand is this generation's most powerful asset... https://t.co/9WjrfFL9uh

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Not everyone needs to be famous or build a personal brand; it's a personal choice. However, having a strong brand can significantly accelerate business growth. It allows you to attract talent who already align with your values and understand your business model, effectively pre-training your team before they join. This creates immense value. Additionally, in investment scenarios, having a well-established brand fosters trust, making negotiations smoother and more enjoyable compared to more stressful, competitive deals. Overall, the benefits of building a personal brand can enhance both team dynamics and business dealings.
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Speaker 0: Don't think everyone should be famous. I don't think everyone should build a personal brand. I don't think everyone should anything. You can do whatever you want. And I have billionaire friends who wanna stay anonymous, and they're pretty smart dudes. I do think that from a money making perspective, it is a time warp. You can just go way faster. That is because you can attract talent at such a higher rate than you could otherwise. You can bring people on who already know your values, know what you're about, have already consumed more content than most people's employees currently know about them and their way of doing business. You can basically pre train your entire team before they come on board because of the amount of stuff that you put out. And those are just unbelievably valuable things. And not to mention if you're on the deal side, you know, like, for us, like, investing in companies, we have so much more trust at the table. It's so much better as a process having been on both types of deals, like white knuckle deals and really friendly deals. Way more fun to do friendly deals. So that's the pros.

@thefernandocz - Fernando Cao

This is the new playbook for influence: • Share your authentic thoughts • Build direct relationships • Skip the middlemen In the attention & AI age, a personal brand is what future-proofs your business. The best founders, like Andreessen, are already taking advantage. Will you?

@thefernandocz - Fernando Cao

Founders: We’ll build your personal/company brand on 𝕏 (and beyond) without you lifting a finger. To date, we've already helped 120+ founders get 3+ Billion combined views. Interested in how we can do this for you? Book your free discovery call here: form.typeform.com/to/JWuXNkxQ?ut…

@thefernandocz - Fernando Cao

Thanks for reading! A bit about me: 2 years ago, I cofounded @ThoughtleadrX — a premium personal branding agency for world-class founders, executives, and investors to dominate socials. If you enjoyed this, hit "follow" for more breakdowns! https://t.co/xb9if0YMSQ

productfella
Posted - January 31, 2025 at 6:04 PM
Saved - January 31, 2025 at 9:41 PM
reSee.it AI Summary
Jamie Dimon recently compared Elon Musk to Albert Einstein, highlighting their shared daily habits that foster innovation. After a conversation with Musk, Dimon recognized their striking similarities in challenging norms and embracing failure. Both men prioritize early mornings for deep thinking, voracious reading, and questioning everything. Their long-term vision and resilience in the face of setbacks have led to groundbreaking achievements. I believe these principles can also revolutionize how biotech labs operate, emphasizing the need for modern approaches to data management.

@productfella - Satya Singh

Jamie Dimon just made an extraordinary statement: He compared Musk to Einstein. They do share 9 documented daily habits that transformed them into leading innovators. Here's what Einstein and Musk do differently than everyone else: https://t.co/2KN0SrjokU

@productfella - Satya Singh

At Davos 2025, Jamie Dimon shocked everyone when he called Musk "our Einstein." Coming from JPMorgan's CEO, this was unexpected. Just months earlier, JPMorgan and Tesla were locked in bitter lawsuits. But something made Dimon change his mind... https://t.co/yoLsuxryJj

Video Transcript AI Summary
Elon and I have resolved our differences after a long chat at one of our conferences. I admire his work with SpaceX, Tesla, and Neuralink, and I want to support him and his companies. Many people view the government as ineffective, especially regarding issues like inner-city education and stagnant income for the bottom 20% over the past two decades. We deserve a better government, and simply sending more money to Washington won't solve the problem. Government needs to be more accountable and efficient, focusing on outcomes. It's a complicated system, but I hope we can contribute positively to it.
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Speaker 0: Elon and I have hugged it out this You hugged it out? Yeah. He came to one of our conferences. He had a nice long chat. We settled some of our differences. And, you know, the guy I mean, you gotta look at Elon. I mean, SpaceX. I mean, Tesla. You know, Neuralink. I just I mean, the guy is our and it's Einstein. And so, I and I like to be helpful to him and his company as much as we can. I think it is completely rational for someone to look at our government and say it's been ineffective. What what of the grievances? You know, I would say that you have inner city schools and educations. Income didn't go up for the bottom 20% for the better part of 20 years, literally, until the tail end of Trump's first administration. But but we we deserve good government, and I don't think anyone thinks sending another $1,000,000,000,000 to Washington DC will lend to good government. So government needs to be more accountable. It needs to be more efficient. It should be outcomes based. I mean, I'd say department by department, so I wish them the best. It's gonna be complicated. As you know, the federal government's complicated. You read about all the people in it, and so, you know, if we could be helpful to them, and, I'd love to be helpful to them.

@productfella - Satya Singh

After a "long and pleasant conversation" with Musk, Dimon saw what others missed: The parallels between these two revolutionary minds were striking. Both challenged everything we thought we knew about the world. But their genius goes deeper than that: https://t.co/PvMol46Aib

Video Transcript AI Summary
To engage with Elon Musk, be concise and quick. Meetings with him often require a high level of energy; I would grab espresso beforehand to keep up. You have about 30 seconds to make your point. Musk is known for his intense management style. After taking over Tesla in 2008, he pushed the company into crisis mode to realize his vision for the auto industry. Now, he seems to be applying the same approach at Twitter. Insights from former Tesla and SpaceX employees reveal how Musk's cutthroat and tireless management could impact his future employees.
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Speaker 0: Talking to Elon or you wanna have a conversation with Elon, you get that opportunity to talk to Elon. You need to be concise, quick. Speaker 1: There's definitely, 2 AM text and 6 AM conference calls. Speaker 0: When I used to go into meetings with him, I used to always stop by our barista, and I would get a couple espresso and and chug that down and then go into the meeting. He has never said this, but I've watched it. You have 30 seconds to make your point. Speaker 1: Elon Musk is known by his employees to be intense. When he took over Tesla in 2008, he sent the company into crisis mode to jump start his vision of how to reinvent the auto industry. Now he appears to be using a similar management playbook to remake Twitter. We spoke with former Tesla and SpaceX employees to better understand how Musk became known for his cutthroat and tireless management style and what that may mean for his future employees.

@productfella - Satya Singh

It starts with their morning routines. Einstein woke at 7 AM for his morning walks. During these walks, he conducted his famous "thought experiments." Musk follows a similar pattern, but with a modern twist: https://t.co/ZZZuMQv7hm

Video Transcript AI Summary
My days are primarily divided between SpaceX, Tesla, and OpenAI. I dedicate about half a day each week to OpenAI, along with some additional tasks throughout the week. At SpaceX and Tesla, contrary to what many might think, I spend around 80% of my time focused on engineering and design rather than media or business activities. This involves developing next-generation products, which is my main priority.
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Speaker 0: How do you spend your days now? Like, what what do you Mhmm. Allocate most of your time to? My time is mostly split, well, it's split between SpaceX and and and Tesla. And, of of course, I I try to spend, it's a part of every week at OpenAI. So I spend most I spend basically half a day at OpenAI most weeks, and then and then I have some OpenAI stuff that happens during the week. But other than that, it's really And what do you do when you're SpaceX or Tesla? Like, what does your time look like there? Yeah. So that's a good question. I think a lot of people think I I must spend a lot of time with media or or on business y things, but actually almost, also my time, like, 80% of it is spent on engineering design. Engineering and design. So it's, developing next generation product. That's 80% of it.

@productfella - Satya Singh

While Einstein walked, Musk spends his early hours tackling engineering problems. Both use this quiet time for "first principles thinking": Breaking down complex problems to their fundamental truths. But here's where it gets interesting: https://t.co/gDOmx9sqqH

Video Transcript AI Summary
It's important to reason from first principles instead of by analogy. Typically, we base our decisions on what others do or slight variations of existing ideas, which is easier mentally. However, reasoning from first principles involves breaking things down to their most fundamental truths and building up from there. This approach, akin to a physics perspective, requires more mental effort but can lead to deeper understanding and innovation.
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Speaker 0: I think it's also important to reason from first principles rather than by analogy. So the normal way that we conduct our lives is we we we we reason by analogy. It's we're doing this because it's like something else that was done. Mhmm. Or it's like what, other people are doing. Me too type ideas. Yeah. It's like yeah. Slight iterations on on on a theme. And and and it's it's kind of mentally easier to reason by analogy rather than from first principles. But first principles is kind of a physics way of looking at the world. And what that really means is you kind of boil things down to the most fundamental truths and and say, okay, what are we sure is true, or or as sure as possible is true? And then reason up from there. Mhmm. That takes a lot more mental energy.

@productfella - Satya Singh

They're both voracious readers. Einstein's library contained over 3,500 books at his death. Musk devoured books from an early age, even reading the entire Encyclopedia Britannica. When asked how he learned to build rockets, his answer was simple:

@productfella - Satya Singh

"I read books." But it's not just about quantity: Both men read intensively, often revisiting the same material multiple times. The real magic happens in how they process information: https://t.co/s5lLuxH4ai

Video Transcript AI Summary
I was always interested in reading as a kid, devouring everything I could find, even the encyclopedia out of boredom. I read thousands of books, including classics like "The Lord of the Rings" and works by philosophers like Nietzsche and Dostoevsky during my early teens. While some philosophical ideas were intriguing, much of it felt depressing and nonsensical. I struggled to find meaning in the universe, realizing that the questions were often harder than the answers. It seemed that understanding required more than just human contemplation; it needed a much greater intellect.
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Speaker 0: I was always sort of really, interested in reading when I was a kid, and I read everything that I could get my hands on. I read the encyclopedia probably age 9 or 10. Well, not that I actually wanted to read the encyclopedia, but I ran out of things to read. So in desperation, I read the encyclopedia. Just I just sort of I I got bored easily, and so unless I was doing something, like reading or, playing a video game or watching TV, and we had, like, terrible TV in South Africa. It was really bad TV. Nice. So there was only at best because there wasn't that much of it. So boredom No. Yes. The boredom led to a lot of reading. Oh, I read thousands and thousands of books. You know, I like the sort of, Lord of the Rings and The Hobbit and that kind of thing. And I was 14, 15, sort of reading reading some of the the philosophers, Nietzsche, Schopenhauer, that sort of thing, by people like Dostoevsky. Brutal. Most of the philosophers are really they're awful. I mean, they're so depressing and, they they just you know, some of the things they they say are are good ideas and then it's but it's in interspersed with so much rubbish. But I was sort of, you know, early teen years trying to figure out meaning of the universe and all that, and it was very difficult to come up with anything that wasn't some piece of arbitrary clap clap trap. Question is harder than the answer. Really, it's the question that's the hard part, and that takes a much bigger computer than Earth to figure it out.

@productfella - Satya Singh

They embrace solitude for deep thinking. Einstein used thought experiments like imagining riding alongside a beam of light. Musk visualizes entire rocket launches in his mind, considering every variable. But their most powerful trait? https://t.co/hl4zpazBi9

Video Transcript AI Summary
You may not recall, but years ago, you took me on a SpaceX tour. I was struck by your deep knowledge of every rocket detail and engineering aspect. Many see you as just a business person, but that's not the whole picture. At SpaceX, Gwynne Shotwell manages legal, finance, and sales, while I focus on engineering, enhancing the Falcon 9 and Dragon spacecraft, and developing Mars Colonial architecture. At Tesla, I spend time on the Model 3 and its design, but most of my week is dedicated to the engineering of the car and the factory.
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Speaker 0: You probably don't remember this. It's a very long time ago. Many, many years you took me on a tour of SpaceX. And, the most impressive thing was that you knew every detail of the rocket and every piece of engineering that went into it. I don't think many people get that about you. Speaker 1: Yeah. I think a lot of people think I'm kind of a business person or something, which is fine. Like, business is fine. But, like, I, but really it's You know, it was like SpaceX. Gwynne Shotwell is chief operating officer. She kind of manages, legal, finance, sales, and kind of general business activity. And then my time is almost entirely with the, engineering team working on improving the Falcon 9 and the Dragon spacecraft and developing the Mars Colonial architecture. And then at Tesla, it's working on the Model 3 and, something in the design studio, typically, half a day a week, dealing with its aesthetics and and, the look and feel of things. And and then most of the rest of the week is just going through engineering of of of the car itself, as well as engineering of the the factory.

@productfella - Satya Singh

Their relationship with failure. Einstein couldn't get an academic position after graduating. He worked as a patent clerk while developing his theory of relativity. Musk? SpaceX nearly went bankrupt after 3 failed launches:

@productfella - Satya Singh

He had money for just one more attempt. That fourth launch succeeded, making SpaceX the first private company to reach orbit. Their secret? They saw failures as data points, not defeats. This mindset led to their most important habit:

@productfella - Satya Singh

They question everything. Einstein challenged Newton's laws that had stood for centuries. Musk questioned why rockets couldn't land themselves. Both were ridiculed – until they were proven right. https://t.co/BrynveNan4

@productfella - Satya Singh

They think in decades, not quarters. Einstein spent 30 years pursuing a unified field theory. Musk's planning a million-person city on Mars that could take 40-100 years. But here's the crucial lesson:

@productfella - Satya Singh

These habits compound over time. Early mornings + deep reading + embracing failure + questioning conventions... It creates a framework for revolutionary thinking. But here's what most people miss:

@productfella - Satya Singh

The same principles that drove Einstein and Musk's breakthroughs can transform how labs operate today. Breaking down complex problems. Questioning assumptions. Embracing new approaches. This mindset shift is crucial as biotech enters the AI era. Here's why: https://t.co/Y4BSTKDV4o

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Life should be about more than just solving problems; it should inspire us and fill us with hope for the future. Waking up each day should bring excitement about what lies ahead. The journey to Mars, even if not everyone wants to go, can serve as a source of inspiration for humanity, much like the Apollo program did. Watching such ambitious endeavors unfold can ignite a sense of wonder and motivation in people. We need these moments that excite us and make us feel optimistic about what’s possible.
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Speaker 0: Life can't just be about solving one problem or another. There need to be reasons to be inspired, reasons that move your heart and say, yes, the future is going to be great And when you get out you know, when you wake up in the morning, I can't wait to see what happens next. And and I think that's, you know, humanity, go to Mars, even if you don't yourself want to go to Mars, and most people don't, just watch it. It's a tough gig frankly. This would not be a luxury expedition. And, but you'd be able to watch it happen. And and I think it would just be incredibly inspiring to the world in the same way that the Apollo program was inspiring to the world. And like I said, there's gotta be life can't just be about solving one problem or another. We need things that are that make us excited and inspired about the future.

@productfella - Satya Singh

Most labs are drowning in fragmented data and manual workflows. They're stuck doing what Einstein and Musk never did: • Accepting the status quo • Using outdated systems • Missing breakthrough opportunities There's a better way:

@productfella - Satya Singh

Just as Einstein reimagined physics and Musk revolutionized rockets, it's time to reimagine how labs handle data. Your data should work for you, not against you. Your scientists should focus on discoveries, not spreadsheets.

@productfella - Satya Singh

That's why we built Scispot: to help biotech labs automate routine tasks and unlock the full potential of their data. Want to see how we're helping labs accelerate discoveries? Book a demo: scispot.com/demo

@productfella - Satya Singh

I hope you've found this thread helpful. Follow me @productfella for more. Like/Repost the quote below if you can:

@productfella - Satya Singh

Jamie Dimon just made an extraordinary statement: He compared Musk to Einstein. They do share 9 documented daily habits that transformed them into leading innovators. Here's what Einstein and Musk do differently than everyone else: https://t.co/2KN0SrjokU

AndreaSJames
Posted - February 5, 2025 at 2:43 AM
Saved - February 5, 2025 at 3:25 PM
reSee.it AI Summary
Gently, I want to discuss the current situation in DC. The US government is undergoing a zero-based budgeting (ZBB) process due to rampant spending issues, which is a painful but necessary step. With $2.7 trillion in improper payments over the last 20 years, it's clear that financial management is in disarray. I empathize with federal workers facing uncertainty during this challenging time. While the ZBB process is tough, I believe that having a strong leader, especially one who can leverage AI, is crucial for navigating this complexity. I've often found that my doubts about Elon Musk have been misplaced.

@AndreaSJames - Andrea S. James

Gently, softly, calmly, let’s talk about what’s happening in DC right now. Friends, have you ever been through a ZBB process? If not, let me give you my take. So, in a corporation where expenses are out of control, you have to put the entire company through a zero based budgeting process. It is one of the most painful things that people in a company experience. You basically have to justify every. single. expense. And you also cut a bunch, and then only add back after you’ve gone through a proper ZBB cycle. The US government is going through a ZBB right now. And that is necessary because spending is out of control. The complexity is so vast that I expect it will take AI to decipher. You cannot tell me that a government that accidentally wires hundreds of millions of dollars to the Taliban has its finances in order. That signals an underlying disaster. That signals bad stewardship of resources. In fact, it is a disaster. Over the last 20 fiscal years, the US government has made ~$2.7 trillion in "improper payments," according to the US Government Accountability Office. Trillion! With a T! That financial management disaster was not caused by @elonmusk. And there is no painless way to clean it up. Elon is doing what any executive would do walking into a giant mess. You ZBB and then build back. So, I don’t believe that America is going to be on the wrong side of global right and wrong, as some are saying. I don’t believe that America is going to be permanently isolationist. I do believe that unwatched finances will get out of control in any human system, and that the GAO has been trying to ring this bell for years, and that scaled complexity requires scaled financial management. Because the US has the largest budget in the world, it will now go through the single most complex ZBB ever undertaken in the history of the world. Some states do ZBB, but it's never been done at the federal level. President Jimmy Carter tried, but the bureaucratic systems were too complex, and President Ronald Reagan abandoned the attempt. So, if there is one person in the world chosen to lead this Grand Canyon of projects, we would hope for the president to choose one of the best capital allocators on the planet. Better if that human is also one who can leverage AI and technical talent to manage the massive complexity. Anyone who has lived through a ZBB at the corporate level will tell you that it’s hell and everyone hates it. This is why I feel really bad for federal workers -- I have a lot of empathy for civil servants and people doing a great job every day for the United States. No doubt, the uncertainty is trying and stressful for many. It's an unfortunate situation. So, that's my take. I don't want to speak beyond what I know and can observe from the other side of the country. I've worked both in the private sector and the public sector -- the cultures are different. And an Elon-Musk-style culture is going to be the most hard core of them all. No doubt, it's jarring. It's gonna be a lot. But what I also know is that every time I have ever doubted Elon, I ended up being the one who was wrong.

elonmusk
Posted - February 10, 2025 at 8:26 AM
Saved - February 10, 2025 at 12:50 PM

@elonmusk - Elon Musk

We must defeat bureaucracy

@MarioNawfal - Mario Nawfal

ELON: DEFEATING BUREAUCRACY IS ALMOST AS DIFFICULT AS BREAKING PHYSICS “The challenge is overcoming bureaucracy. Bureaucracy is perhaps the penultimate boss battle. The ultimate boss battle is defeating entropy, which physics tells us we can't. The second-hardest battle is defeating bureaucracy. That's how difficult it is to improve government.” Source: WELT Economic Summit, January 28, 2025

Video Transcript AI Summary
Improving government is incredibly difficult. The most difficult challenge is overcoming entropy, a battle physics tells us is impossible to win. The second most difficult is overcoming bureaucracy. It's a monumental struggle; bureaucracy is the penultimate battle in the fight for better government.
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Speaker 0: The challenge is overcoming, bureaucracy. And I think bureaucracy is perhaps the I'd say the penultimate bust battle. The the ultimate bust battle is defeating entropy, which we I mean, physics tells us we cannot defeat entropy. The second hardest battle is defeating bureaucracy. Or but, you know, it's it's that that's that's how difficult it is to improve government.
franklin_reeder
Posted - March 3, 2025 at 3:47 AM
Saved - March 3, 2025 at 12:56 PM
reSee.it AI Summary
In my exploration of the 2016 film "Inferno," I analyze how the movie encodes names that reference historical figures, individuals connected to Harvard, and even Jeffrey Epstein. I decode nine names, starting with Roscoe Pound, a former Harvard Law Dean linked to eugenics, and moving through literary figures like Ralph Waldo Emerson and Aphra Behn, a spy. I also examine connections to Kevin Tyrrell, a Harvard coach with ties to Epstein, and other names that suggest deeper meanings and historical contexts, reflecting the film's themes of secrecy and morality.

@franklin_reeder - Mark Reeder

In this 🧵, I will show how a scene in the 2016 movie “Inferno,” starring Tom Hanks playing the role of a Harvard professor, uses encoded names to allude to historical figures, individuals at Harvard in 2016, and… Jeffrey Epstein. 1/~60 https://archive.org/details/inferno.-2016.1080p.-blu-ray.x-264-yts.-ag

Inferno. 2016.1080p. Blu Ray.x 264 [ YTS. AG] : Free Download, Borrow, and Streaming : Internet Archive dante archive.org

@franklin_reeder - Mark Reeder

Inferno’s premise (2016): “Before plunging to his death, [a] billionaire madman warned that overpopulation would spell humanity's demise, and argued that killing untold millions with a high-tech disease would be the only way to preserve the planet for the greater good…”🤔 2/

@franklin_reeder - Mark Reeder

“Langdon is the only man who can stop the devastation... by deciphering anagrams.” Since wordplay is a theme, I looked for “Easter Eggs” in the film. At 17:40 there is a view of emails sent to Langdon as he checks messages. Seconds later, many names are shown more clearly. 3/

@franklin_reeder - Mark Reeder

The movie continues with Langdon reading the message from “Ignazio Busoni”. But two names below that is: “Amado Harvard” = “I’m going to do Harvard”. Get it? The subject line “Can you take a peak inside?” is obviously innuendo. It is a sign to look at the other names. 4/

@franklin_reeder - Mark Reeder

I’ll start by decoding the names of 2 historical figures with ties to Harvard, then another historical figure known for espionage, the Epstein link, a name important to an ongoing court case, and then 4 more. So 9 in total. 5/

@franklin_reeder - Mark Reeder

“Roscoe Massman”=“Roscoe Pound” Roscoe Pound was Dean of Harvard Law from 1916-1936. “Eugenics [..] took hold at the highest levels of Harvard. Lawrence Lowell, who served as president from 1909 to 1933, was an active supporter.” Per Inferno’s theme https://www.harvardmagazine.com/2016/02/harvards-eugenics-era 6/

Harvard's eugenics era | Harvard Magazine When academics embraced scientific racism, immigration restrictions, and the suppression of “the unfit” harvardmagazine.com

@franklin_reeder - Mark Reeder

Rees: “Nathan Roscoe Pound and the Nazis” “When Roscoe Pound, Dean of Harvard Law School, accepted an honorary degree from a leading German university in 1934, it was interpreted as a gesture of support for the Nazi Party.” 7/ https://bclawreview.bc.edu/articles/299/files/63a552e24052e.pdf

@franklin_reeder - Mark Reeder

One of Roscoe Pound’s hosts, Hans Frank, was found guilty of crimes against humanity and executed after the Nuremberg Trial. Rees: “The Boston Evening Transcript ran the front-page headline, ‘Hitler Envoy Presents Law Degree to Dean Pound at Harvard Ceremony.’” 8/

@franklin_reeder - Mark Reeder

The next coded name is a bit trickier. “Milton Everson”=“Ralph Waldo Emerson” This can be understood with reference to Emerson’s “Divinity School Address” (link) in combination with John Milton’s Paradise Lost. https://news.harvard.edu/gazette/story/2012/02/when-religion-turned-inward/ 9/

When religion turned inward — Harvard Gazette A groundbreaking speech by Ralph Waldo Emerson at Harvard Divinity School in 1838 helped to transform faith, spur the transcendentalist movement, and change the future of Harvard. news.harvard.edu

@franklin_reeder - Mark Reeder

“Emerson ‘railed against the emphasis placed on the personhood of Jesus as an authority figure,’ said Buell. Emerson questioned the personhood of God versus man, and he argued for the value of experience.” Akin to Milton’s characterization of Satan, perhaps? 10/

@franklin_reeder - Mark Reeder

“Critics have long recognized the influence of John Milton on Ralph Waldo Emerson, and they have particularly noted that Emerson's ‘Uriel’ owes its title character to Milton's Paradise Lost.” Might one consider Emerson as Milton’s “Everson”?🤔 https://www.connotations.de/article/frances-m-malpezzi-emersons-allusive-art-a-transcendental-angel-in-miltonic-myrtle-beds/ 11/

Frances M. Malpezzi – Emerson’s Allusive Art: A Transcendental Angel in Miltonic Myrtle Beds – Connotations connotations.de

@franklin_reeder - Mark Reeder

Did you notice the subject line for the “Milton Everson” email mentions a “LinkedIn request”? Turns out LinkedIn CEO Reid Hoffman is a fan of Ralph Waldo Emerson. 12/ https://www.morningstar.com/news/marketwatch/20240528114/linkedin-co-founder-reid-hoffmans-six-maxims-for-the-ai-age

@franklin_reeder - Mark Reeder

A 3rd name decode is: “Derick Behm”=“Aphra Behm” She has no link to Harvard. She was a spy. “Agent 160 received her first assignment in 1666- a simple task: find a soldier named William Scot in the Netherlands-enemy territory— and convince him to turn spy for Charles II.” 13/

@franklin_reeder - Mark Reeder

Aphra Behn later became a popular author. “Her stories of love and lust were wildly popular…Behn was scorned by critics who found her writing too smutty and scandalous for a woman.” Thus, the decode: “D (er..) Rick Behn”=“Aphra Behn” or “Derick Behn”=“Aphra Behn” 14/

@franklin_reeder - Mark Reeder

Did you note the subject line for “Derick Behn” is “Last minute submission”? More innuendo… The Aphra Behn reference to use of “her techniques for spycraft” tees up the next decode from the list of email messages in the Inferno movie scene... 15/

@franklin_reeder - Mark Reeder

The first name listed is “Benedict Delawder”. I am confident this decodes to “Benedict Gross”. By swapping the ‘a’ for ‘e’ in Delawder, you get “Delewder”- a homonym of “deluder” (a synonym of “deceiver”, like Jeffrey Epstein) Also, “lewd” is a synonym for “gross”. 16/

@franklin_reeder - Mark Reeder

Math Chair, Benedict Gross figured prominently in Harvard’s report 2020. Gross “approached the development office about the possibility of soliciting Jeffrey Epstein for additional support...” …in 2013, after Epstein’s conviction. https://ogc.harvard.edu/files/ogc/files/report_concerning_jeffrey_e._epsteins_connections_to_harvard_university.pdf 17/

@franklin_reeder - Mark Reeder

“(The) Program for Evolutionary Dynamics, which was ‘established in 2003 by Harvard University President Lawrence Summers following an imaginative proposal by Jeffrey Epstein and Benedict Gross." The math underlying “Evolutionary dynamics” can be used for many scenarios.🤔 18/

@franklin_reeder - Mark Reeder

Another name on the Inferno email list is only visible at 17:40, just below “Derick Behn”. The name is “Tyrell Devlin” which I suspect refers to “Kevin Tyrrell”. 19/

@franklin_reeder - Mark Reeder

Kevin Tyrrell was “a volunteer assistant coach from 2000-04 for Harvard University… The experience led to a phone call from the US Virgin Islands asking him to check out the open coaching position there.” https://news.lafayette.edu/2008/09/26/swimming-on-the-islands/ 20/

Swimming on the Islands Kevin Tyrrell ’92 coaches U.S. Virgin Islands swim team Kevin Tyrrell ’92 is head coach of the U.S. Virgin Islands swim team, but much of his time isn’t dedicated to helping swimmers improve their times. He spends many hours teaching athletes how to swim, period. “I was hired to train the Olympic swimmers and to […] news.lafayette.edu

@franklin_reeder - Mark Reeder

Kevin Tyrrell may not know what led to the offer to coach the USVI Olympic swim team. But Jeffrey Epstein was highly influential in USVI, and he was working with Harvard in the same frame Tyrrell was a volunteer at Harvard. Could be a coincidence… 21/ https://www.businessinsider.com/jeffrey-epstein-island-politics-stacey-plaskett-2023-6

'Maximum amounts allowed': How Jeffrey Epstein's political donations won him and his 'pedophile island' a powerful ally Jeffrey Epstein funded US Virgin Islands politicians, like Rep. Stacey Plaskett, who represented the territory he used as a playground for pedophilia. businessinsider.com

@franklin_reeder - Mark Reeder

By 2015, Kevin Tyrrell was back coaching at Harvard. One athlete made headlines in June 2015, one year before Inferno’s release. “Harvard Coach Kevin Tyrell is looking forward to Bailar’s contribution to Harvard’s team and even beyond the pool.” 22/ https://www.swimmingworldmagazine.com/news/schuyler-bailar-to-be-first-openly-transgender-collegiate-swimmer/

Schuyler Bailar To Be First Openly Transgender D1 NCAA Swimmer Schuyler Bailar is an extremely talented rising freshman on the Harvard men’s team. He is also transgender. swimmingworldmagazine.com

@franklin_reeder - Mark Reeder

The Swimming World story pointed out that (the athlete) “Bailar then swam year-round for Sea Devil Swimming starting at age nine”. For the next few posts in this 🧵, consider the possibility that “Devlin” in “Tyrell Devlin” might be derived from “Sea Devil”. 23/

@franklin_reeder - Mark Reeder

In April 2016, Leslie Stahl of the CBS show 60 Minutes interviewed both Schuyler Bailar and Kevin Tyrrell. The segment was named “Switching Teams” which refers to the Bailar’s shift from Harvard’s women’s swim team to Harvard’s men’s swim tram as a “trans athlete”. 24/

@franklin_reeder - Mark Reeder

Lesley Stahl: ‘You will never get pregnant.’ S. Bailar: ‘I don’t know about that. That’s a long story.’ Stahl: ‘Really? [..] So this is in your head, that one day you might give birth?’ Bailar: ‘Might is in bold [..] but yes. I don’t know. I’m 19.’ 25/ https://www.cbsnews.com/news/60-minutes-harvard-transgender-swimmer-schuyler-bailar/

Switching Teams Lesley Stahl profiles Harvard swimmer Schuyler Bailar, who may be the first openly transgender male athlete to compete in a NCAA Division I men's sport cbsnews.com

@franklin_reeder - Mark Reeder

Moving on: “Desmond Commodore”=“Matthew Desmond” Harvard’s Matthew Desmond won a Pulitzer for “Evicted: Poverty and Profit in the American City”. Matthew Desmond also happens to be the name of a Commodore 64/128 programmer. 26/ https://www.pulitzer.org/winners/matthew-desmond https://csdb.dk/scener/?id=31675

CSDb CSDb csdb.dk

@franklin_reeder - Mark Reeder

The subject line for “Desmond Commodore” is “To your Health and Happiness”. I suppose that could be a reference to Matthew Desmond’s book. But I can’t help but wonder if it might be a veiled threat. 27/

@franklin_reeder - Mark Reeder

“Bobby Torpey” is likely “John Torpey” John Torpey is a professor elsewhere (CCNY) but was published by Harvard Press for his 2013 text “Legal Integration of Islam” co-authored with Christian Joppke… 28/ https://t.co/rHsSZfaIZV

@franklin_reeder - Mark Reeder

The “Bobby” is a play on the Harvard Kennedy School of Government” with “Bobby” (Kennedy) swapped for “John” (JFK). It’s logical to consider the context of government since Torpey and Joppke consider ways Sharia law might integrate into western culture. 29/

@franklin_reeder - Mark Reeder

“Jay Hoey” almost certainly alludes to “Jay Garfield” who is: “one of the country's most well-known scholars of Buddhism and a professor at Smith, Harvard, and other universities.” 30/ web.archive.org/web/2016051419… https://t.co/dpUXlZLJGH

@franklin_reeder - Mark Reeder

In a May 2016 NYT opinion piece titled “If Philosophy Won't Diversify, Let's Call It What It Really Is”, Jay Garfield related: “Part of the problem is the perception that philosophy departments are nothing but temples to the achievement of males of European descent.” 31/ https://t.co/dsvcQ0jRT2

GoyWonderTM
Posted - March 25, 2025 at 6:21 PM
Saved - March 25, 2025 at 9:42 PM

@GoyWonderTM - Juan Decentbaum

Bwahahahahahahahaha. Lightspeed. More like xAIDF lmao. https://t.co/OkuaDnqeq5

@Irishgypsy288 - Irish Gypsy

😂🤣😂🤣😂🤣😂🤣 https://t.co/A17yWEScgO

@elonmusk - Elon Musk

Extremely important difference

iam_smx
Posted - April 7, 2025 at 8:48 PM
Saved - April 8, 2025 at 6:12 PM

@iam_smx - SMX 🇺🇸

Even Elon Musk's Employee is a GOAT. 🐐 This engineer chose to work at SpaceX over established companies like Boeing and Lockheed, because he saw Elon Musk’s potential, and he was right! Today, SpaceX is the most innovative company in the world, built the largest flying object https://t.co/yGWWeNLtE9

Video Transcript AI Summary
Speaker 1 states that the opportunity to participate in a new era is why they chose their current job over positions at companies like Boeing or Lockheed. They compare it to working with Howard Hughes during the creation of TWA. Speaker 0 notes that historically, only four entities have successfully launched a space capsule into orbit and returned it to Earth: the United States, Russia, China, and Elon Musk.
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Speaker 0: You know, I'm curious. You have so much background in engineering. You could have easily gotten a job at Boeing or at Lockheed, but you came here. Speaker 1: If you had a chance to go back in time and work with Howard Hughes when he was creating TWA. If you had a chance to be there at that moment when it was the dawn of a brand new era, wouldn't wouldn't you wanna do that? I mean, that's that's why I'm here. Speaker 0: In the history of space flight, only four entities have launched a space capsule into orbit and successfully brought it back to the earth. The United States, Russia, China and Elon Musk.
GeorgeM_Growth
Posted - May 6, 2025 at 11:04 AM
Saved - May 6, 2025 at 8:33 PM
reSee.it AI Summary
Many believe Elon Musk founded Tesla, but he actually joined later, ousting the original founders, Martin Eberhard and Marc Tarpenning. After investing in 2004, Musk became Chairman and, amid internal tensions, pushed Eberhard out in 2007 without public acknowledgment. Musk then rebranded himself as a founder, solidifying his narrative despite Eberhard’s legal challenges. Today, Tesla is valued at over $700 billion, with Musk as a powerful CEO, illustrating the impact of personal branding in shaping public perception.

@GeorgeM_Growth - George M

Everyone thinks Elon Musk founded Tesla. He didn’t. He joined later, kicked out the real founder, and took over the company. Then he erased him from history. Here’s the wildest takeover in modern business history: 🧵 https://t.co/iC239GsQFG

@GeorgeM_Growth - George M

In 2003, Martin Eberhard and Marc Tarpenning started Tesla Motors. Their dream? Build an all-electric sports car. Reinvent the auto industry. But there was a problem they needed money... https://t.co/sBkJUWz6oI

Video Transcript AI Summary
As an electrical engineer, the speaker knew an electric car that "rocks" could be made, but no company was actually selling one. Since the speaker knew how to start companies, they decided to start one to solve this problem, applying Silicon Valley know-how to funding. The current car is not the final answer, but the first step. The goal is to make a product that can be sold to make money, enabling the creation of more models and a more ambitious company. Future models will be lower priced and more accessible, with the ultimate goal of becoming a real car company that sells lots of cars. The speaker encourages those laughing at this goal to send their resumes, indicating the company is hiring.
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Speaker 0: I mean, as an electrical engineer, know that you can make an electric car that rocks if you want to. And and I looked around and said, isn't somebody making that car? And the answer was really no. There was I know that you guys are writing about some other of these companies in your magazine, but as far as I'm concerned, I mean, could not actually manage to buy a car from any of them. None of them was actually making cars that one could buy. I also would have just bought one and I would have been a happy customer, but since it didn't exist, said, can I start that company? And I know how to start companies. I've done that before. I've done it successfully a couple of times now. And I thought, well, if I can figure out how to take Silicon Valley know how of Silicon Valley know how about how to fund a company and apply that to this problem, then we have something. This car is not the answer. This is our first car. We expect to make more cars to reach more people and eventually make a big dent in the amount of oil we consume in this country. But you have to start someplace. It's one step at a time. You to make a product that you can actually get on the road and sell and make money at, and that allows you to make another model and a more ambitious company, and you grow one step at a time. We hope down the road to have cars that really all of us can drive. It's not going be next year or the year after. Our next model car will be a lot lower priced and much more accessible, and the one after that will be lower price and useful in other ways. I'm not sure what that one will be. Is it a smaller car? Is it bit more of a people mover? I don't know. I haven't decided yet. But, you know, the goal is to really to to become a real car company and sell lots of cars. Yeah. They, of course, if they even heard me say that, would just be laughing. Yeah. But they should send their resumes my way, Definitely. So you are hiring.

@GeorgeM_Growth - George M

By 2004, they needed funding. That’s when Elon Musk showed up. Fresh off selling PayPal. He invested $6.5 million in their Series A. But Elon wanted more than just a seat at the table... https://t.co/0eQ8wS2FWP

Video Transcript AI Summary
The speaker believes space tourism will be the biggest driver of space business, followed by supplying moon and Mars bases. Lowering the cost of access to space is critical to NASA's future, as interesting achievements in space are not possible at current transportation prices. Government agencies with an interest in space are viewed as customers, including NASA, the Air Force, and research labs. The initial focus is on unmanned transportation of satellites to orbit, with the intention to move to human transportation after proving reliability. The speaker believes we are in a lull regarding government-led human space exploration, but a new era driven by commercial companies is beginning.
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Speaker 0: I think the government makes a good customer but not a good venture capitalist. Speaker 1: Stay tuned for cnm.com. When you dream about space businesses, what do you see as possible five years from now, ten years from now, fifteen years from now, as viable space businesses that it's hard for us to see because they're not there? Speaker 0: You know, you have the the the obvious existing business of of satellites of one kind or another, which I think with an improvement in in space transportation costs will enjoy an increase in the business, but modest. And then I think you've got space tourism or space adventure, what whatever you wanna call it. That I think is likely to be the biggest driver. And then long term, I think you've got, assuming that we fulfill the President's vision and we establish a moon base and then go on and establish a Mars base, I think supplying those bases is a huge, huge business. Speaker 1: How does what you're doing help NASA accomplish its goals? Because NASA wants to set bigger goals. Speaker 0: Well, think fundamentally the way we help NASA is by lowering the cost of access to space, allowing us to do more interesting things for for a given budget. In fact, I think what we're doing is critical to the future of NASA. At at the current prices that NASA pays for space transportation, I don't think we'll be able to achieve anything interesting in space. As far as business Speaker 1: You would occasionally do a job for them. Speaker 0: Yeah. Well, certainly But you're Speaker 1: going into business with them. Speaker 0: Well, actually, I view all of the the government agencies with an interest in space as customers. So I view NASA as a customer, certainly the Air Force, Labor Research Lab, National Conscience Organization, you know, all the NASA is certainly, you know, somebody we would like to be, necessary a customer of ours. When you say space transportation, Speaker 1: we think of transportation, we always think of moving people. You think of moving people, moving satellites, moving cargo? Speaker 0: Well, we're starting off with transportation of of satellites to orbit, or cargo, you could call it cargo. We're starting off with unmanned transportation, as we prove out the reliability, our intention is to move to human transportation as well. Speaker 1: Where do you think we are in the life of Speaker 0: our space exploration? We're definitely in a lull with respect to human space exploration on the government side. However, what I what I think we're beginning to see is the dawn of a new era of space exploration, but one that is driven by commercial companies as much, if not more, than by by government.

@GeorgeM_Growth - George M

Musk became Chairman of the Board. He wasn’t a founder. But now he had leverage. At first, things looked smooth. Behind closed doors? Not so much. https://t.co/3dvsCO9TBz

Video Transcript AI Summary
SpaceX faced early setbacks with engine fires and unsuccessful launches. Despite burning through $100 million, Musk announced plans for a fourth launch within months. The company then encountered the worst economic recession since the Great Depression, with General Motors going bankrupt. SpaceX was down to its last week of cash. The fourth Falcon one launch succeeded, and NASA awarded SpaceX a $1.6 billion contract. Simultaneously, Tesla faced financial disaster. Musk chose to invest all his remaining capital from the sale of PayPal into Tesla. He raised a $40 million round, putting all the money in himself, catalyzing others to invest. Just after this, Tesla secured a $40 million deal with Daimler for smart car batteries, followed by an additional $50 million investment for 10% of the company. The Tesla round closed in the last hour of the last possible day, narrowly avoiding bankruptcy. Failure would have set back both the electric car and private rocket industries.
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Speaker 0: There was an engine fire and that was it. The second flight actually did make it to space, but not to orbit. And then also flight three, we didn't get all the way to orbit. Speaker 1: Musk burned through the 100,000,000 he had sunk into SpaceX. Now he was on his way back to the drawing board. Speaker 2: Three days after the the failure, he announced, first, that he knew what was wrong. He announced that they'd raise money to to finance a fourth launch, and the fourth launch was gonna happen in a matter of months, which in the rocket industry was a a crazy announcement. Speaker 0: We were able to solve the problems, and then just as we'd solve those problems, we ran smack into the the worst economic recession since the Great Depression. Speaker 3: It's been one of the darkest days on Wall Street in recent memory. Stock markets falling the most since 09/11. The Dow off more than 500 points. Speaker 2: This is what financial Armageddon looks like. Speaker 0: I certainly did not anticipate that we would have the worst economic climate since the great depression and and one which was disproportionately bad for cars. I I mean, General Motors went bankrupt. General F and Motors. You know? Speaker 1: Musk was in the fight of his life. Speaker 0: We had maybe about a week's worth of cash in the bank or or less, and there was just very little time left in the year to resolve these these things. I mean, there were, like, two or three business days left in the year. I never thought I was it was possible for me to have a nose breakdown, but if it was possible for me to have a nose breakdown, that that was about as close as was gonna as I was gonna come. Speaker 1: The fourth attempt to launch the Falcon one was a huge success. And three months later, NASA rewarded SpaceX with a $1,600,000,000 contract to resupply the International Space Station. But Musk had no time to celebrate. Tesla was on the verge of financial disaster. Speaker 0: I had make a choice then that either I took all of the capital that I had left from the sale of PayPal to eBay and invest that in Tesla or Tesla would die. Speaker 2: The company is really teetering on the brink of of failure, and there's this board meeting late in 02/2008 where they're discussing what's gonna happen, and Elon just says, well, I'm gonna raise a $40,000,000 round to keep the company going. And the board members are kind of wondering, well, how's he gonna do that? And he says, I'm gonna put it all in myself. Speaker 1: And that incredible braggadocio confidence catalyzed a change in people's opinion. And we and everyone else around the table is like, oh my gosh. We wanna be part of this. We wanna get as much of this investment as we can. He saved the company in its darkest hour with an act of heroism that is hard to describe. There's nothing quite like spending your last remaining dollar on a project you believe in. Speaker 0: It was thankfully a a good week, but definitely took its toll from a mental strain standpoint. I think I mentally just burned out a few circuits. Speaker 1: Just after his emergency cash infusion came the news they desperately needed, a $40,000,000 deal with Daimler for smart car batteries. Daimler later added 50,000,000 for 10% of the company. Speaker 0: We closed the Tesla round. It was the last hour of the last day that it was possible to close the round, and, we would have gone bankrupt a few days after Christmas if that round hadn't closed. And I think if I hadn't invested, for sure if I had not invested everything, there would have been no chance. What's the emotion like when you go all in and it looks like you're about to lose? Well, was quite a terrible emotion I'd say. If we had not succeeded, then we would have been used as a counter example for why people shouldn't do electric cars or shouldn't try to do private rockets. It would have been a double whammy if we would have used Tesla as an example of just another stupid car company, basically. That would have been really terrible.

@GeorgeM_Growth - George M

Musk pushed for speed. Eberhard wanted control. Costs spiraled. Timelines slipped. The board grew frustrated. And Elon made his move. https://t.co/kVRc3lYtQG

Video Transcript AI Summary
The speaker's company focused on solving screen readability for e-books, choosing Sharp's DMTN screen technology for its high contrast, no flicker, and transflexivity. Early designs involved disassembling a physical book to understand form factor, but the final Rocketbook design prioritized one-handed operation, balance, and user interface over replicating a traditional book. Users could purchase encrypted e-books from online bookstores and download them to the Rocketbook, which weighed about one pound and had a 20-24 hour battery life with the backlight on, using nickel metal hydride batteries initially. The company was later sold to Gemstar TV Guide along with SoftBook. The speaker is skeptical about electronic paper due to touchscreen integration issues and the need to flash the screen during page turns. Usability studies showed users preferred a simple, immediate page refresh over animated transitions. There's no single compelling reason for e-book adoption; instead, various factors like travel convenience, portability, large print accessibility, and reading in low light contribute to its appeal.
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Speaker 0: One of the questions that we had was, would people really read in a significant amount of time on on a screen? In the end, it came down to the screen. So that that was as we're thinking about the idea of electronic books, we focus quickly on that was a problem that had to be solved. And so before we ever started the company, we did a survey of the manufacturers of LCD screens and other technologies to see what was real and what was actually readable. And we looked at a lot of technologies that were either way too immature like electronic paper or were just unreadable. We finally saw one screen technology that was actually readable. It was a screen technology from Sharp that was called DMTN. And it was a very high contrast ratio, no flicker, and was transflexive. It was it worked with a backlight on or off. Early on, as I was thinking about what an electron electronic book ought to look like, I went to a bookstore and I bought some book that was I bought the book for the cover, you know? I didn't care what the title was. And I took it home and I took my table saw to it, took it apart and put in a fake screen so that it looked just like a book and you could open it, it looked like a screen. It's really tempting to make the new device look like the thing it's replacing, the cover and all that stuff. But you need to think about where this thing is gonna end up and eventually try to express the device in its the the new thing in its own way. So the the the Rocketbook lost its cover. It had a cover in the early versions that went away, and instead was optimized on being comfortable in the hand, balanced correctly, operable with one hand, and and focused on the the user interface and the readability. The way that the the way that the Rocketbook worked is that you would, from your computer, browse to any of the online bookstores in in our dreams. And in reality, you could browse to about half a dozen online bookstores, barnesandnoble.com, for example. You could buy a book in rocket book form as one of the choices there. And if you hit then hit a button that when you hit the buy button, it would download it into your computer in its encrypted form. The the thing was a little heavier than I wanted, but not bad. It came in at about one pound. And the batteries lasted with the light on about twenty to twenty four hours. Backlight on. So the battery life was great. I was a little too heavy because we in order to get that battery life, we had four of the nickel metal hydride batteries in it. Second generation products had lithium ion batteries. That's where I learned about lithium ion batteries. Weighed a lot less and had roughly the same battery life. This the company was sold to Gemstar TV Guide together with SoftBook. So we became one company for a while there. The Kindle and also the up and coming thing from Barnes and Noble are actually being sold by a bookstore. So they have the marketing engine of those two major book companies behind figuring out exactly how to market books to people on this new new medium. There's a lot of push for the electronic paper, and I'm still a little skeptical about that for two different reasons. One is that it really does not look good if you put a touch screen in front of it. The second thing wrong with electronic paper is that as a as a side effect of the technology that makes the paper work, you have to flash the screen every time you change the page. So you have to put a negative image of what was there before you go to the next page. We did in our early usability studies with the Rocketbook, we experimented with different graphics that happened during a page change. We had one that was as it turned out identical to what Softbook they were, you know, peels one page off and the next one's there. And we tried all kinds of different things. We had maybe a dozen different ideas we tried. And much to the chagrin of our user interface people, the one that readers liked the best was the one we didn't do anything. Just paint the next screen as quick as you could. There there isn't one single compelling reason of why somebody should have an electronic book that that's across all populations that read it. There isn't one. You can't say you want an electronic book because of this. It's for some people, it's access to books while they're traveling. For another, it's the ability to carry a bunch of books with them. For a different group, it's it's instant access to large print for any title they want. For another group, it's reading in marginal light situations. And and each one of those is a slice of the pie. If you saw the pie chart of why people buy electronic book, there isn't one big wedge. It's lots of small wedges. And to to be successful in this arena, I think you need to understand all those wedges and and make sure you're not losing too many of them with your design choices.

@GeorgeM_Growth - George M

In 2007, Eberhard was ousted. No public goodbye. No thanks. Just gone. “Elon Musk fired me from Tesla.” — Martin Eberhard https://t.co/P19g6Jaf0A

Video Transcript AI Summary
In the early 2000s, Martin Eberhard wanted a sports car but didn't want to pollute the environment. So, in February, Martin and Mark Tarpenning founded Tesla Motors. Previously, in 1997, Eberhard and Tarpenning founded Navo Media and created the Rocket eBook, later selling the company for $187 million. Initially, Tesla was financed by Tarpenning. In February, Tesla sought investors, and Elon Musk invested $7.35 million, becoming chairman. Musk took control, overseeing Roadster production. In July 2006, Tesla unveiled the Roadster prototype. Tensions rose between Eberhard and Musk, and in February, Eberhard was asked to resign as CEO. Musk became the new CEO, and Eberhard was appointed president of technology. After four months, Eberhard and Tarpenning left the company. The first Roadster was delivered to Elon Musk. Since February 2008, Tesla has faced challenges, but Musk has kept it running. Tesla is estimated to become profitable for the first time.
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Speaker 0: Hello, YouTube. I presume many of you are familiar with the name Tesla, the electric car manufacturer that everybody and their dog is talking about lately. And if you are familiar with Tesla, then you probably also know Elon Musk, the man behind the company, the creator or the founder of the Tesla Motors. But what if I told you that Tesla Motors was not founded by Musk, but by someone you had never heard of? Let's get into it. The story of Tesla. It was early two thousands, and a man named Martin Eberhard wanted to own a sports car. But there was one problem. All the sports car available in the market at the time were fuel hungry monsters, and Martin didn't want to contribute in polluting the environment. At this point, any ordinary person would give up the idea of owning a sports car, but Martin was no ordinary person. So he decided to make his own sports car, one that will not run on fuel, but will run on a cleaner source, electricity. So in the February, Martin, with his buddy Mark, founded the Tesla Motors. It was not the first time that Martin has founded a company, and not the first time he had founded one with Mark. You see, in the 1997, Martin Eberhard and Mark Tarpenning had founded a company named Navo Media at this company. They created one of the earliest eBook reader named Rocket ebook. The product became so popular that in the year February, they sold their company, Navo Media, for $187,000,000. In the early days of the company, Tesla was totally financed by Mark Landmark. It wasn't until February that Tesla decided to get some money from the investors. So they announced series a round of investment. And here, enters Elon Musk in the picture. By investing $7,350,000, Elon Musk became the chairman of the company. Musk quickly took control of the company. He employed the people who had created a SpaceX logo to design a logo for Tesla. At the same time, Musk was also overseeing the production of the Roadster. Things started to move ahead with minor conflicts between CEO Martin and chairman Musk. Tesla finally unveiled the first prototype of the Roadster to the public on July 2006, and things were looking good for Tesla. Only one year had passed since Tesla had shown its Roadster to public, and the tensions were all time high between Martin Eberhard and Elon Musk. And due to these escalating tensions, in February, Martin was asked by the board of directors to resign from his post of CEO of the company. Musk was appointed as the new CEO of Tesla, and Martin had been given a new job title, president of technology. And after four months being ousted as the CEO, Martin Eberhard and Mark Tarpenning left the company in February. Tesla finally launched the Roadster in February. And as a twisted turn of fate, the first Roadster was not owned by the man who had dreamed of this magnificent city. Rather, the first Roadster was delivered to Elon Musk. Since 02/2008, Tesla have faced many ups and downs, but Elon Musk had been able to convince the investors to keep on investing in the company and managed to keep the company running. Recently, it is estimated that Tesla is finally going to be profitable for the first time. Tesla have come a long way since the launch of its first car. It is no longer the dream that Martin Eberhard once had. Now it is just another automobile manufacturer who want to sell its electric vehicles to everyone.

@GeorgeM_Growth - George M

Musk didn’t stop there. He launched a quiet PR campaign. In interviews, press, and events— He started calling himself “a founder.” Then: “the founder.” https://t.co/a8cPpU7GVS

Video Transcript AI Summary
Tesla's fundamental value is to accelerate sustainable energy and autonomy. Without electrification and autonomy, a new car company cannot succeed. Car companies make money selling parts for existing cars, not new car sales. After the warranty expires, companies profit from high-margin replacement parts. This creates a barrier to entry for new car companies without an existing fleet. To succeed, a new car company must charge more for its cars than competitors. The product must be compelling enough to justify the premium. Winning on both autonomy and electrification is essential to make the product worth the higher price.
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Speaker 0: What is Tesla's fundamental value? It is to it is to serve as an accelerant to sustainable energy. And if if you say, like, met before Tesla, what would the world be like? In in in ways that like, let's say you're sort of you're looking at this from, you know, the macroeconomic god standpoint or or like a civilization or, you know, the Sims or something. You know? Like, what's the difference here? The difference between Tesla and not Tesla is by is how many years, is sustainable energy accelerated. Mhmm. That is the fundamental good of Tesla. Yeah. And and then there's there's also the autonomy thing, which is, I think it'll also be very, very significant. It it will be very significant. But I'd say, like, in the absence of there being a fundamental technology discontinuity in the form of electrification and autonomy, both of them together, I think a new car company cannot succeed. So and I'll tell you, like, actually, the real reason that people should have been shorting Tesla, and perhaps why some of them were shorting Tesla, and the real reason that car companies, new car companies cannot succeed or or or why it's very hard for them to succeed. And this was first told to me by this automotive investor when I was at Axle Springer headquarters getting a Golden Steering World award, and this this guy who's apparently like the best automotive investor in the world, you know, comes up to me and like, he's like, hi, know why you're gonna fail. I'm like, well, please tell me. I can think of several reasons. Tell me when I don't Yes. And he said the, he said the car companies don't make any money on the new car sales. They make all of their money selling used selling parts to cars the existing fleet. Mhmm. So when when the the warranty runs out like, the life of a car before it hits the junkyard might be twenty years. Warranty is gonna typically run out after four years, and there's a bunch of stuff that's not covered under warranty. So if you've got a steady state fleet, it means that 80% of your fleet is not under warranty. Mhmm. So you can sell high margin parts, replacement parts for the for the existing fleet. Mhmm. And and you can sell your new cars at effectively zero zero margin. Mhmm. It's like it's like a razors and blades thing. Yeah. Yeah. So you sell you sell the the razor for zero margin Yep. And you sell the blades at high margin. So then this this creates an an a massive barrier to entry for any new car company because you have no existing fleet. So so the only way for a new car company to succeed is is that does not have an existing fleet is to charge a lot more for your car than what others are paying than competitors. And in order to charge a lot more and have people actually buy it, the product must be so so compelling that people are willing to pay the premium above the alternative cars from from the incumbent carmakers. This is the only way. And I think without both electrification and autonomy, this does not succeed. So that is the only way to do it. You have to win on autonomy, and you have to win on electrification, and you have to make the product so compelling that, that it is worth paying the paying the, the premium relative to the the incumbent competitors. For more tech news, visit em360tech.com.

@GeorgeM_Growth - George M

Eberhard sued him in 2009. For defamation and libel. The case was settled out of court. Details never revealed. But by then, it was too late. https://t.co/HodfoG4sew

Video Transcript AI Summary
Speaker 1 had a long-standing interest in electric cars, starting in undergrad. He originally came to California to do a PhD at Stanford in applied physics and material science to work on ultra capacitors in electric cars. After PayPal, he wanted to get back into electric vehicles, thinking GM would continue developing them after the EV1. However, after California changed regulations, GM recalled and crushed all EV1s. Former EV1 owners held a candlelit vigil as they were crushed. Speaker 1 found it crazy that GM would ignore this level of passion for a product. This prompted the creation of an electric car company, even though the most likely outcome was thought to be failure.
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Speaker 0: The point being that these ventures now seem to have wonderful momentum and things are going well, I remember well there was a point in time when each of them had their tipping point and could have gone either way. I wonder if you could talk a little bit about the origins of Tesla. Speaker 1: Sure. So with the as mentioned, I was quite interested in electric cars from when I was doing my undergrad physics. And in fact, I originally came out to California to do a PhD at Stanford in applied physics and material science to work on ultra capacitors in electric cars. So it was a longstanding interest of mine and the Internet kind of put that on hold for a few years, but then after PayPal I decided I wanted to get back into electric vehicles and make something happen in that arena, particularly since GM had come out with the EV1 and I thought, okay, well, there's not really a need for a startup company to develop electric cars because obviously GM is going to create the EV2 and the EV3, less logical sequence and it will get better and better with each generation and so not really a need for new company in that arena. But actually what happened was that after California changed the regulations to no longer require electric cars, GM recalled all EV1s and then just to make sure that nobody could get them back, they crushed them in a lot somewhere. And in fact, while they were being crushed, the people who had been the EV1 owners who did not want those cards recalled actually held a candlelit vigil as though somebody was getting executed basically. And it's like, that just seemed extremely crazy that GM would ignore this because it's quite rare for people to hold a candle at Vigil about a product, and particularly a GM product. So if people are doing that, you should really pay attention. But they wanted to just sort of erase all that and so that, okay, well, we have to try to create an electric car company. But it wasn't as though in creating these companies that we thought that we would be successful. I thought that the most likely outcome was failure, but it was still worth doing.

@GeorgeM_Growth - George M

The media crowned Musk the genius behind Tesla. The myth was cemented. And Elon? He took the wheel. He became CEO in 2008. https://t.co/iCplRv0Um2

@GeorgeM_Growth - George M

Musk restructured the roadmap. Turned the Roadster into a real car. Laid the foundation for Model S. Chased mass-market scale. And changed the future of electric vehicles. https://t.co/J3fXjifD8k

@GeorgeM_Growth - George M

Today: Tesla is worth over $700 billion. It's reshaping transportation. And Musk is the most powerful CEO on the planet. But the truth? He didn’t start Tesla. He took it. https://t.co/RznyfSdwZh

@GeorgeM_Growth - George M

The billion-dollar lesson? Elon didn’t just take control of Tesla. He controlled the narrative. He built a personal brand so strong, most people don’t even know he wasn’t the founder. That’s the power of personal branding.

@GeorgeM_Growth - George M

Want to build a brand people remember no matter what you sell? We teach solo founders how to grow on X, build influence, and own their niche. Click the link in my profile to start.

@GeorgeM_Growth - George M

I hope you've found this thread helpful. Follow me @GeorgeM_Growth for more. Like/Repost the quote below if you can:

@GeorgeM_Growth - George M

Everyone thinks Elon Musk founded Tesla. He didn’t. He joined later, kicked out the real founder, and took over the company. Then he erased him from history. Here’s the wildest takeover in modern business history: 🧵 https://t.co/iC239GsQFG

VigilantFox
Posted - June 4, 2025 at 10:24 PM
Saved - June 5, 2025 at 3:05 PM
reSee.it AI Summary
Elon Musk is a polarizing figure, seen as either a villain or a hero, but Kevin O’Leary argues he transcends politics. During a discussion with Piers Morgan, O’Leary defended Musk's impact on government efficiency and accountability, emphasizing that even a brief tenure sparked important conversations. He praised Musk as a unique visionary, contrasting him with other leaders, and criticized Scott Galloway's personal attacks on Musk. O’Leary urged a focus on progress rather than petty outrage, advocating for innovation and real solutions.

@VigilantFox - The Vigilant Fox 🦊

Elon Musk is either saving the world—or destroying it. Depends on who you ask. The Left calls him a dangerous villain. The Right hails him as a visionary hero. But Kevin O’Leary says both sides are wrong because Musk transcends politics. You could hear a pin drop the moment Scott Galloway finished tearing into Musk. That’s when O’Leary silenced him with a brutal diagnosis—and Galloway’s face said it all. 🧵 THREAD

@VigilantFox - The Vigilant Fox 🦊

Kevin O’Leary didn’t show up to Piers Morgan Uncensored to play politics. He showed up to tell the truth. While critics mocked Elon Musk for coming up short during his brief stint at the Department of Government Efficiency, O’Leary reminded them why Musk was there in the first place. “Is there a waste in government spending? That was the whole DOGE idea. Yes there is.” No, Musk didn’t find $2 trillion in savings. But as O’Leary pointed out, the clock barely started ticking. “Elon never found 2 trillion, but he only worked on it for 130 days.” And yet, that was all it took to shift the national conversation. “But the theme is now embedded in everybody’s head, red and blue, that there must be a perpetual audit of government and the brand is called DOGE, and that’s okay.” That idea—of holding Washington accountable—isn’t going away. And for O’Leary, that’s the real victory.

Video Transcript AI Summary
The speaker asserts there is waste in government spending, which was the idea behind "doge." Elon Musk did not find $2 trillion, but he only worked on it for 130 days. The idea that there must be a perpetual audit of government is now embedded in everyone's head, regardless of political affiliation. The brand for this idea is "Doge."
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Speaker 0: Is there a waste in government spending? That was the whole doge idea. Yes. There is. Elon never found 2,000,000,000,000, but he only worked on it for a hundred and thirty days. But the theme is now embedded in everybody's head, red and blue, that there must be a perpetual audit of government, and the brand is called Doge, and that's okay.

@VigilantFox - The Vigilant Fox 🦊

Then came the part O’Leary clearly cared about most: the man behind the disruption. To him, Elon Musk isn’t just another tech billionaire. He’s in a category of one. “He is the most remarkable individual.” O’Leary, who worked for Steve Jobs, has seen visionary leadership up close. But even Jobs, he said, was 80% signal, 20% noise. Musk? “Elon is the only individual that I’ve ever met that’s 100% signal.” “He does not even deal with noise.” O’Leary described Musk’s mindset with clarity and awe. He doesn’t entertain distractions. He doesn’t fake politeness. If a conversation isn’t valuable, he walks away without hesitation. “I’ve watched him walk away—and I’ve used this example countless times—he’ll walk away from a conversation the second he thinks it’s a waste of his time.” Is he socially awkward? Sure. But O’Leary was unapologetic in his defense. “He’s very awkward socially, but look at what that man has achieved! And he’s only 50% through being the modern day Da Vinci.” And Musk’s track record speaks for itself. Starlink changed the game in Ukraine. Tesla forced the entire auto industry to evolve. SpaceX might be humanity’s only shot at becoming multi-planetary. “There’s nobody on Earth that’s achieved as much as he has.” O’Leary wasn’t just defending Musk. He was defending excellence and warning that tearing it down in the name of personality politics is a national mistake.

Video Transcript AI Summary
The speaker compares Elon Musk to Steve Jobs, stating Jobs was "80% signal and 20% noise," focusing on essential tasks and minimizing distractions. The speaker argues Musk is "a 100% signal," avoiding noise entirely by disengaging from conversations he deems unproductive. The speaker acknowledges Musk's social awkwardness but emphasizes his achievements, calling him the "modern day da Vinci" and claiming no one has accomplished as much. The speaker dismisses criticism from figures like Bono, preferring Musk's contributions to solving global issues. The speaker highlights the importance of Starlink in Ukraine, the value of Tesla, and the potential of SpaceX to enable travel to Mars, attributing these advancements to Musk.
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Speaker 0: He is the most remarkable individual. I used to say this about Steve Jobs, who I worked for, that he was 80% signal and 20% noise. In other words, in any given day, as Jobs used to tell me, I'm gonna get the five things I have to get done, and I will not let noise get in the way in this eighteen hour cycle, and you should follow me and do the same. And that worked for him. Look at what he achieved. They don't look at Elon Musk, and I'd argue to anybody listening. Elon is the only individual that I've ever met that's a % signal. He does not even deal with noise. I've watched watched him walk away, and I've used this example countless of times, countless times because I've seen it. He'll walk away from a conversation the second he thinks it's a waste of his time. He's not garnering any information that's useful. And that's he's very awkward socially. But look at what that man has achieved, and he's only 50% through being the modern day da Vinci, if you wanna call him that. There's nobody on Earth that's achieved as much as he has. And, of course, that's gonna draw criticism, whether it comes from a rock star that wants to be a politician. I love YouTube music, but I don't listen to at all to Bono's politics. I don't think he spends a lot of time worrying about it until he's talking on air. I'd rather he write music that I can enjoy. I'd rather listen to what Musk has to say in terms of moving forward on these mandates that solve huge problems for mankind. Where will the Ukrainian soldiers be without Starlink? Yeah. Where would people be without Teslas that provide incredible value for the dollar and the low cost versions? Or or what about SpaceX? Will we get to Mars? Well, if we are, it's gonna be because of Elon Musk.

@VigilantFox - The Vigilant Fox 🦊

Before we roll the next clip: if you’re not following me, you’re missing out on critical updates. Hit the bell 🔔 to stay sharp and informed. → @VigilantFox Now, back to the story you came for. https://t.co/R6RPF4TUfz

@VigilantFox - The Vigilant Fox 🦊

Then Scott Galloway chimed in. What followed wasn’t a counterpoint. It was a TOTAL meltdown. “I think the two of you are more impressed with Mr. Musk than I am,” Galloway began, before unleashing a tirade that sounded more like a gossip column than serious debate. “I think if somebody is making Nazi salutes…” “If somebody is being sued concurrently by two women for sole custody of their child…” “When someone is so severely addicted to drugs, they can’t get their shit together to show up to the White House without looking exceptionally high…” “I don’t think that’s the right role model for young men.” Galloway's monologue dragged on, cherry-picking every personal smear ever published about Musk—much of it unproven, none of it relevant to his actual accomplishments. He even compared Musk to Bill Gates, claiming Gates plants trees while Musk will leave a legacy of “death, disease and disability.” “I think this is an individual who has literally come off the tracks.” By the end, it was less an argument and more a performance—a moral panic disguised as a lecture.

Video Transcript AI Summary
The speaker expresses disapproval of Elon Musk, citing concerns about his behavior and influence. They claim Musk makes Nazi salutes, is being sued for sole child custody due to lack of involvement, and appears high at the White House due to severe drug addiction. The speaker contrasts Musk with Bill Gates, alleging Musk isn't using his wealth to help people. They believe Musk is off the rails, rabidly addicted to drugs, and using his power to influence elections. The speaker predicts Musk's legacy will be unnecessary death, disease, and disability for the world's most vulnerable, not innovation. They state that his behavior is not what it means to be an innovator, an American, or a man.
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Speaker 0: And I think the two of you are more impressed with mister Musk than I am. I think if somebody is making Nazi salutes, if somebody is being sued concurrently by two women for sole custody of their child because that person has not spent any time with that child, when someone is so severely addicted to drugs they can't get their shit together to show up to the White House without looking exceptionally high, I don't think that's the right role model for young men. So what I would ask of all of us is look at what money has done to us, That if someone can land a rocket on metal scissors or create a great AV, he's a genius. He's the wealthiest man in the world. But does that mean we should excuse depravity? Does that mean unlike Bill Gates, he's not using his billions to help people? He's not planting trees the shade of which he won't sit under? I think this is an individual who has literally come off the tracks, who is rabidly addicted to drugs, and is using his immense power to get people elected, and that too many of us excuse what is abhorrent behavior. I think his legacy is not gonna be an EV or putting rockets into space. I think it's gonna be unnecessary death, disease, and disability of the world's most vulnerable. That is not what it means to be an innovator. It's not what it means to be an American. It's not what it means to be a man.

@VigilantFox - The Vigilant Fox 🦊

That’s when Galloway went quiet as Kevin O’Leary summed up his rant with just three words: “Musk Derangement Syndrome.” O’Leary didn’t raise his voice. He didn’t need to. “What you just heard there from Scott is Musk Derangement Syndrome.” Calm and precise. Surgical even. While Galloway spun wild theories about addiction and doom, O’Leary brought the focus back to what actually matters. “I think it’s fair that he has those criticisms, but it doesn’t distract or take away from the achievements the man has made so far and will make.” Then he made his case for staying grounded in reality—not drama. “I’m an advocate for executional excellence. Because in the end, if you burn your calories trying to change someone who you know is not going to change and don’t focus on the output that he can provide… then you are in the form of being dominated and controlled by a derangement syndrome.” O’Leary wasn’t there to defend Musk’s quirks. He was there to defend progress—and to reject the new cultural obsession with burning down anyone who builds. “I think that’s a waste of time, criticizing, you know, that he’s not a man,” O’Leary said.

Video Transcript AI Summary
The speaker addresses "Trump derangement syndrome," acknowledging criticisms but emphasizing Trump's achievements and potential contributions, particularly in robotics. The speaker advocates for "executional excellence" and focusing on positive output rather than trying to change someone. They avoid getting drawn into "Trump derangement," viewing it as a waste of time. The speaker works in Washington on a bipartisan basis, representing entrepreneurship and job creation, finding common ground even with figures like AOC and Elizabeth Warren. The speaker respects differing opinions but believes in prioritizing progress and "moving the needle forward."
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Speaker 0: But what you just heard there from Scott is must arrangement syndrome, and and I get it. And I think it's fair that he has those criticisms, but it doesn't distract or take away from the achievements the man has made so far and will make. And I'm an advocate for executional excellence, because in the end, if if you burn your calories trying to change someone who you know is not going to change, and don't focus on the output that he can provide, and the great solutions for mankind he has provided and hopefully will with robotics you talked about, then you are in the form of being dominated and controlled by a derangement syndrome. That's my opinion. And I've so far avoided that by and I and I work a lot in Washington now, and, you know, I'm I'm very, very fortunate to be able to work on a bipartisan basis because I represent entrepreneurship and job creation for small business, and that's bipartisan. Even AOC and Elizabeth Warren want to support that, and and I can have my narratives with them. But when we get into Trump derangement, I shut off, and I it's it's not a waste of time. And and I think that's a waste of time criticizing, you know, that he's not a man. I mean, I I just find that and I respect Scott. I mean, I respect his opinion. I respect all opinions, but let's burn our calories moving the needle forward is my view.

@VigilantFox - The Vigilant Fox 🦊

As O’Leary tore into him, Galloway sat there quietly with this expression: the face you make when you realize Musk lives rent-free in your head. https://t.co/IL3Fuie7oM

@VigilantFox - The Vigilant Fox 🦊

O’Leary ended on this point: “Let’s burn our calories moving the needle forward,”—meaning enough with the petty outrage and focus on solving real problems. It’s time to channel that energy into real progress, real innovation—something Elon Musk is doing while others just talk. https://t.co/k6JxZzpbv1

@VigilantFox - The Vigilant Fox 🦊

Thanks for reading to the end. While the media protects the powerful, citizen journalists are exposing what they won’t. Don’t miss this thread—30 truth-tellers they don’t want you to follow: https://t.co/E5nMuu2mcs

@VigilantFox - The Vigilant Fox 🦊

George Orwell once said, “Journalism is printing what someone else does not want printed: everything else is public relations.” While the media protects the ruling class, ordinary people are doing real journalism on 𝕏. Here are 30 truth-tellers they don’t want you to follow. 🧵 THREAD

thefernandocz
Posted - June 28, 2025 at 2:48 PM
Saved - June 29, 2025 at 6:55 AM
reSee.it AI Summary
Stripe's CEO, Patrick, never studied finance but built a $95 billion company by reverse-engineering PayPal's documentation. Frustrated by its complexity, he questioned the norms and spent months understanding the system deeply. By cold-emailing industry veterans and seeking clarity, he identified opportunities where others saw obstacles. His approach involved obsessively reverse-engineering, questioning assumptions, and rapid prototyping. This mindset led to Stripe's simplicity in payment integration, disrupting the industry and showcasing that true expertise comes from curiosity and visibility.

@thefernandocz - Fernando Cao

Stripe's CEO never studied finance. Instead, he reverse-engineered PayPal’s docs—and built a $95 BILLION company at 22. How? A learning method so powerful, it's now taught in colleges. Here's his genius framework for learning anything fast: 🧵

@thefernandocz - Fernando Cao

Picture this: It's 2009. A 20-year-old Irish kid sits in his cramped apartment, staring at his laptop screen. He's trying to add payments to a simple web app. Should be easy, right? Wrong.

Video Transcript AI Summary
John and the speaker, who are brothers and co-founders, attended startup school in October 2009. They had previously sold apps in the App Store easily. They contrasted this ease with the difficulty of conducting transactions or commerce on the broader internet. Walking home from dinner, John suggested building a prototype, downplaying the difficulty of starting a billion-dollar company. Almost a decade later, they reflect on this journey. They were initially unsure how seriously.
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Speaker 0: John and I John's my my my cofounder and also my brother. We we went to start up school back in in October 2009. Then we've been kind of talking about an idea around this for a while just because we'd sold some apps in the app Store and it had been incredibly easy. Right? And we're kind of reflecting on how it was so easy to sell apps in the App Store and it was so monstrously difficult to just do anything involving kind of transactions or commerce or business on the sort of broader Internet. Right? And so we're kind of chatting with this idea. And then kind of walking home from dinner, I remember John kind of turning to me and, you know, arguing that, well, we should just go build a prototype of this. You know? Like, how how hard can it be? Right. How hard could it be to start a mini a billion dollar company? And so here here we are, you know, again, soon will be a decade later. So so we weren't kind of initially sure how seriously

@thefernandocz - Fernando Cao

Days turn into weeks. PayPal's documentation reads like ancient hieroglyphics. Complex jargon. Endless requirements. Zero clarity. Patrick slams his laptop shut in frustration. This was supposed to be simple. But something strange happens next...

@thefernandocz - Fernando Cao

Instead of giving up, Patrick gets curious. Really curious. He opens his laptop again. But this time, he's not trying to integrate payments. He's trying to understand something deeper. Why is this so damn hard? That question changed everything:

Video Transcript AI Summary
John and the speaker went to Buenos Aires to prototype in cafes. The speaker highly recommends Buenos Aires for focused work, citing a cost of $10 a day and good weather. Cafes have Wi-Fi, restaurants open late, bars are open until 5 AM with peak hours around 2 AM, and people wake up after midday. The speaker characterizes Buenos Aires as a city operating on a "hacker schedule."
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Speaker 0: John and I were facing this winter in Cambridge, Massachusetts, which is which is definitely not like that in in basically any respect. And so we decided to go to Buenos Aires and just, like, hack all day in cafes on trying to build a prototype here. And so we did that, and it turns out to be basically exactly as described. And so if ever you just want to go and work on something sort of single mindedly for a month, I cannot recommend Buenos Aires more highly. We spent $10 a day, and the weather was gorgeous. And bizarrely, all of the cafes have Wi Fi for no reason that I can turn, like much more than is the extent here. All the restaurants open really late. We were turned away from restaurants at 09:00 in evening because it was too early. All the bars are open until like 5AM. People only start going to bars at 2AM, and nobody gets up before midday. And so basically, it's like an entire city on a hacker schedule.

@thefernandocz - Fernando Cao

While experts accepted the complexity, Patrick questioned everything. He spent months doing something nobody else bothered to do. He reverse-engineered PayPal's entire system. Line by line. Function by function. But he didn't stop there...

Video Transcript AI Summary
The speaker recounts creating the first Stripe prototype in Buenos Aires. Instead of sightseeing, they spent the time coding in cafes. About a week after arriving in Buenos Aires, they had their first production user. They called a friend at a payment processing company to ask if they could send a couple of accounts. They built an API and interface for setting up accounts. Clicking "create account" didn't actually create an account in the financial infrastructure; instead, they called their friend. This approach scaled to at least a couple of users.
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Speaker 0: And and we were like the worst tourists ever in that I still have not seen like a single fight or anything of Ben and Zyrites. We just like got up, went to a cafe and like hacked all day. And at the end of that, we had the first prototype of Stripe. After a month? After, yeah, after a month. Or I guess we we like the first we had the first production user actually about a week after going to Buenos Aires. And and what we did was we just like called up a friend who worked at a payment processing company and said, you know, is it okay if we just kind of send a couple of accounts your And and we sort of built sort of this really nice kind of, you know, this nice API and sort of, you know, interface for setting up accounts or whatever. And then, you know, when you sort of clicked create account or whatever rather than, you know, an account actually being created sort of in the financial infrastructure, you know, however that worked. We had no idea. We just thought like went and called our friend, which, you know, scaled to at least a couple of users.

@thefernandocz - Fernando Cao

Patrick started cold-emailing payment industry veterans. CEOs. Engineers. Compliance officers. Not asking for jobs. Not pitching ideas. Just one question: "Why?" Why is it this complicated? Why these rules? Why this structure? Their answers shocked him:

Video Transcript AI Summary
The speaker describes a formula that involves the end of reading, including blogs and other online content. They highlight the helpfulness of people in the Valley, noting that despite some negative aspects like being inwards-looking or hype-driven, there's a strong network of individuals willing to offer advice, even to those who cold email them. They specifically mention Nat Friedman at Xamarin as an underrated founder who has been very helpful, and Aaron Levie at Box as someone who has consistently been generous with his time while building a successful company.
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Speaker 0: But the formula so far has been certainly some end of reading. And I mean, not just kind of traditional management books, whatever. People have blogged about this or like written answers or whatever it is. Part of what's really nice about the Valley, and I think, I mean, there are some parts of the Valley that are bad, right? In that people are sort of inwards looking maybe sometimes or hype driven or whatever. But part of what's really good is that people are so helpful and they're just such a kind of ready network of people around you who are just like to advise you. Can almost like cold email them and they'll sort of happily help out as much as you want. Folks like, I think Friedman at Xamarin is like a really underrated founder and has been like massively helpful for us. Aaron Levy at Box has like always been really generous with his time. Obviously, he's building like a really incredible company there.

@thefernandocz - Fernando Cao

"That's just how it's always been done." Nobody had questioned the fundamentals. Ever. Legacy systems piled on legacy systems. Everyone just worked around the mess. Patrick saw opportunity where others saw obstacles. He and his brother John got to work:

@thefernandocz - Fernando Cao

Seven lines of code. That's it. That's all developers needed to accept payments with Stripe. Compare that to the weeks of integration hell with PayPal. Early Y Combinator founders tried it first. Word spread like wildfire...

@thefernandocz - Fernando Cao

Peter Thiel called. Then Elon Musk. The PayPal founders wanted to invest in the company built to replace PayPal. They saw what Patrick had done and realized: This kid understood payments better than they did.

Video Transcript AI Summary
In the summer, two decisions were made: to drop out of school, or take a leave of absence, and to raise initial money from Sam Altman, Peter Thiel, Sequoia, and others.
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Speaker 0: And so we see that summer, we kind of made two decisions, to to drop out of school or, you know, as our our our mom continues to prefer, we call it, to take, you know, a leave of absence. And then secondly, we had to go, you know, raise some initial money from, from Sam Altman, and and Peter Thiel, and and Sequoia, and a couple of others.

@thefernandocz - Fernando Cao

Patrick's learning method was deceptively simple: 1. Reverse-engineer everything obsessively 2. Question every assumption 3. Talk to insiders who built the system 4. Build rapid prototypes from first principles No fancy degrees. Just raw curiosity and relentless execution.

@thefernandocz - Fernando Cao

This approach forced him to understand problems at their core. Not memorize rules. Understand why they exist. Not accept complexity. Question if it's necessary. You see patterns others miss. You find shortcuts others can't. That's how a dropout disrupted an entire industry.

@thefernandocz - Fernando Cao

Today, Stripe processes billions in payments. Patrick's net worth exceeds $11 billion. All because he refused to accept "that's how it's always been done." His story proves one thing: True expertise comes from questioning everything, not following the rules.

@thefernandocz - Fernando Cao

The best innovations don't come from insiders. They come from outsiders who dig deeper than anyone else. Patrick's edge wasn't just knowing payments better than anyone. It was making sure the world knew he knew. That's how outsiders become leaders...

@thefernandocz - Fernando Cao

While others build in silence... Tomorrow's winners are sharing their expertise with a megaphone. The megaphone changes everything...

Video Transcript AI Summary
Not everyone should build a personal brand, as people can do whatever they want. However, from a money-making perspective, building a personal brand can accelerate progress. A personal brand helps attract talent at a higher rate because people already know your values and have consumed your content. You can pre-train your entire team because of the amount of content you put out. On the deal side, a personal brand fosters more trust at the table. Friendly deals are better than white-knuckle deals.
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Speaker 0: I don't think everyone should be famous. I don't think everyone should build a personal brand. I don't think everyone should anything. You can do whatever you want. And I have billionaire friends who wanna stay anonymous, they're pretty smart dudes. I do think that from a money making perspective is a time warp. You can just go way faster. That is because you can attract talent at such a higher rate than you could otherwise. You can bring people on who already know your values, know what you're about, have already consumed more content than most people's employees currently know about them and their way of doing business. You can basically pre train your entire team before they come on board because of the amount of stuff that you put out. Those are just unbelievably valuable things. Not to mention, if you're on the deal side, for us, investing in companies, we have so much more trust at the table. It's so much better as a process having been on both types of deals, like white knuckle deals and really friendly deals. Way more fun to do friendly deals. So that's the pros.

@thefernandocz - Fernando Cao

Competitors become customers. Investors call instead of being pitched to. Expertise with visibility becomes a business model. And those who win tomorrow? They're starting today. That's exactly why...

@thefernandocz - Fernando Cao

We'll build YOUR personal/company brand on 𝕏 (and beyond) without you lifting a finger. To date, we've helped 140+ founders get: • 3+ Billion Views • $100+ Million in Revenue Want to see how we can do this for you? Book your FREE strategy call here: https://thoughtleadr.typeform.com/to/mv1dalwz?utm_source=kxpostf

Book Your Free Thoughtleadr Discovery Call Our premium media agency is designed to build personal and company brands on 𝕏 (and beyond). thoughtleadr.typeform.com

@thefernandocz - Fernando Cao

Thanks for reading! A bit about me: 2 years ago, I cofounded @ThoughtleadrX — a premium personal branding agency for world-class founders, executives, and investors to dominate socials. If you enjoyed this, hit "follow" for more breakdowns! https://t.co/WjMU5BEAaM

Finance_Nerd_
Posted - August 7, 2025 at 10:19 PM
Saved - August 8, 2025 at 1:47 AM
reSee.it AI Summary
The Mars family controls a vast empire worth $120 billion, owning over 400 companies, including popular chocolate brands and pet care products. Founded by Frank C. Mars in 1911, the company gained fame with the Milky Way bar. The family maintains a low profile, avoiding public listings and media attention, which allows them to focus on consumer needs rather than Wall Street pressures. Their strategy includes keeping corporate names off product packaging and making long-term decisions without public scrutiny, resulting in significant wealth for family members.

@Finance_Nerd_ - Finance Nerd

A single family controls the world’s best-selling chocolate, the biggest pet food brands, and thousands of vets. They own 400+ companies and built a $120B empire without you even knowing they exist. This is how the Mars family became capitalism’s most invisible empire: https://t.co/Za5oX6XUFu

@Finance_Nerd_ - Finance Nerd

In 1911, Frank C. Mars began making chocolate in his kitchen in Tacoma, Washington. His first hit? The Milky Way bar (1923). It sold so well, it became the best-selling chocolate in America. But candy was just the beginning. https://t.co/xPCGXaC7pm

Video Transcript AI Summary
Franklin Clarence Mars, born in 1883, revolutionized the candy industry. His dedication to quality began at age 19. In 1923, he introduced the Milky Way bar, which initiated his confectionery empire. In the 1930s, Mars created the Snickers bar, combining peanuts, caramel, nougat, and milk chocolate. During World War II, Mars invented M&Ms, chocolate candies with colorful shells, to preserve chocolate for troops. Today, Mars, Inc. is a global confectionery giant with a net worth exceeding $100 billion.
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Speaker 0: Born in 1883, Franklin Clarence Mars left an indelible mark on the candy industry. Starting at age 19, his unwavering commitment to quality set him on a path to candy making greatness. In 1923, Mars introduced the Milky Way, a nougat filled sensation that marked the beginning of his confectionery empire. He wasn't content with the ordinary he sought to innovate and redefine the candy experience. The 1930s saw the birth of the Snickers bar, a true game changer. Mars combined peanuts, caramel, nougat, and milk chocolate into a single bar, creating a flavor symphony that continues to captivate taste buds today. World War II posed challenges, but Mars responded with a stroke of genius M and Ms. These chocolate candies, coated in colorful shells, not only preserved chocolate for troops but also became an iconic treat. Today, Marzink stands as a global confectionery giant with a net worth exceeding $100,000,000,000 a testament to Franklin Clarence Marr's enduring impact on the industry. His story reminds us that in the world of sweets, dedication to excellence knows no bounds.

@Finance_Nerd_ - Finance Nerd

From day one, the Mars family avoided the spotlight: • No public shareholders • No press interviews • No public listing Frank’s philosophy: “The consumer is our focus, not Wall Street.” That secrecy became their shield. https://t.co/FDHT5XpdBB

@Finance_Nerd_ - Finance Nerd

Most think of Mars as just M&M’s & Snickers. But the company quietly expanded: • Pet care: Pedigree, Whiskas, Royal Canin • Veterinary services: Banfield, VCA Animal Hospitals • Food: Ben’s Original • Drinks: Flavia, Klix Today, pet care is over half of their revenue. https://t.co/j0YPYyGPGX

@Finance_Nerd_ - Finance Nerd

Unlike Kellogg’s or Nestlé, Mars keeps its corporate name off the packaging. You buy M&M’s, not “Mars M&M’s.” Pedigree, not “Mars Petcare.” This protects the brand image of each product and keeps the family anonymous. https://t.co/Vos4n5KuWf

Video Transcript AI Summary
Mars Incorporated is a family business known for candy brands like Snickers, Twix, Milky Way, and M&M's. The company also owns pet food brands such as Iams, Greenies, Royal Canin, and Whiskas. Jacqueline and John Mars, grandchildren of the founder, each have a net worth exceeding $38 billion. Over 40 million M&M's are produced daily in the United States.
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Speaker 0: If you eat Snickers, Twix, Milky Way's, or M and M's, then you've helped make this one of the richest families on Earth. Mars Incorporated is a multigenerational family business known for its popular candy brands, but it also has an enormous portfolio of pet food. It owns Imes, Greenies, Royal Canine, and Whiskas, which is fun to say. Jacqueline and John Mars, the grandchildren of founder Frank, each have a net worth of over $38,000,000,000. And despite the fact that they're riddled with food dye, it doesn't seem like demand for M and M is gonna slow down anytime soon. Every day, over 40,000,000 M and M's are produced in The United States. What's your favorite candy bar?

@Finance_Nerd_ - Finance Nerd

Mars is one of the largest privately held companies in the U.S. All voting shares are held by members of the Mars family. They’ve resisted every attempt to take the company public. Today, six family members are worth $20B+ each. https://t.co/3W3tg6kr6W

@Finance_Nerd_ - Finance Nerd

No public shareholders = no quarterly earnings pressure. No CEO photo shoots = no personal scandals in headlines. The Mars family can make decade-long bets without the world watching. https://t.co/qQtZIkffkm

@Finance_Nerd_ - Finance Nerd

Lessons from the Mars playbook • Stay private → control decision-making • Play the long game → think in decades, not quarters • Separate corporate and consumer brand → protect image https://t.co/Uy7QqwUfth

TheCaptainEli
Posted - November 23, 2025 at 8:16 AM
Saved - November 24, 2025 at 2:57 AM
reSee.it AI Summary
I watched a 2007 PBS interview where Musk laid out a multi-planetary future and bold predictions. Since then: Falcon 9 boosters have landed 400+ times; Starship is flying; Tesla sells about 2 million cars a year; solar + batteries are the cheapest energy; Mars plans with uncrewed flights by 2026–2028 and crewed by the late 2020s/early 2030s. Blue Origin hasn’t achieved orbital flights. I’m struck by his relentless execution, including a near bankruptcy in 2008.

@TheCaptainEli - Captain Eli

2007A 36-year-old Elon Musk sits down for a quiet PBS interview and says, almost casually: “The most important thing we can do for the future of consciousness is to become a multi-planetary species… Everything else pales in comparison.” He then predicts, with zero hype: •Fully reusable rockets that land like airplanes •Electric cars that outperform gasoline in every way and go mass-market •Solar + batteries becoming the cheapest energy on Earth •Humans on Mars and a self-sustaining city there Jeff Bezos was already the richest man on Earth in 2007. He started Blue Origin in 2000 with the exact same “multi-planetary” dream. 18+ years later: Blue Origin has flown exactly zero orbital missions. New Glenn still hasn’t launched. No reusable booster has ever landed. No Mars plan with dates. Elon? •Falcon 9 boosters have landed 400+ times •Starship (bigger than anything ever built) is flying •Tesla sells 2 million cars a year and is worth more than the rest of the car industry combined •Solar + batteries are now the cheapest electricity source in history •First uncrewed Starships to Mars: 2026–2028. Crewed: late 2020s/early 2030s — exactly the timeline he gave in 2007 The vision never wavered. Not once. The enthusiasm is the same calm, burning conviction you hear in that 2007 clip today. The determination? He went personally bankrupt in 2008 keeping SpaceX and Tesla alive with his last dollars. Almost 20 years later, every single “crazy” prediction from that interview is either already reality or on schedule. This is what relentless execution looks like. Watch the 2007 interview (25 minutes that aged like fine wine): He didn’t just dream about the future. He scheduled it. And showed up.

Video Transcript AI Summary
Elon Musk explains his career arc and overarching vision. After dropping out of Stanford’s physics program to start Zip2, which he later sold, and after PayPal, he set his sights on three areas he believed would most impact humanity: the Internet, space exploration, and transforming the economy from hydrocarbons to solar electricity for energy and transportation. He remains optimistic about humanity on Earth and frames space as a second path that would yield a richer human experience if we become a spacefaring civilization. Musk clarifies SpaceX’s relationship with NASA: NASA is a customer, not a competitor. SpaceX’s Falcon Nine rocket launches the Dragon spacecraft, which goes to the International Space Station (ISS), docks, transfers astronauts or cargo, and Dragon returns to Earth. The Falcon Nine acts as the booster, delivering Dragon to space and enabling ISS servicing in the post-shuttle era. The goal is to replace the Space Shuttle’s role starting in 2011 with SpaceX’s crew and cargo transport. On the state of the U.S. space program, Musk notes that in 1969 we went to the Moon, yet more than three decades later we struggle to reach low Earth orbit, which he views as a backward step. He attributes this to misaligned priorities, technological choices, and a lack of will at the highest levels of government to take the next steps toward establishing bases on the Moon or Mars. He believes a presidential priority that aspires to Mars would be beneficial, arguing that Mars should be the focus rather than returning to the Moon, which he describes as barren and resource-poor. Regarding competition in space, Musk says there is no serious competition presently for SpaceX, though he admires Jeff Bezos’s Blue Origin and notes that Branson’s Virgin Galactic is pursuing suborbital, not orbital, flight. He emphasizes the enormous difference in scale: Branson’s craft aims for Mach 3, while SpaceX targets Mach 25, with energy requirements increasing quadratically with velocity. He insists SpaceX’s challenge is fundamentally different and far more demanding, and that the real risk comes from SpaceX’s own mistakes rather than from competitors. The long-term goal is to make life multiplanetary, starting with Mars as the viable destination. Even if SpaceX cannot do it alone, it aims to help make it happen and to broaden humanity’s reach beyond Earth. On his financial success, Musk says he has “made a fortune” and rejects the idea of retiring to a beach, describing startup life as driving him to work. He uses the metaphor of a startup being “like eating glass and staring into the abyss” and says the key criterion for choosing a startup is whether it matters—whether it will matter to the world if successful. He emphasizes that benefiting humanity is a core motivation, noting that many Silicon Valley peers share this aim, though not everyone prioritizes it. Back on Earth, Musk discusses Tesla Motors, an electric car company focused on high performance and sustainability. The Roadster, set to debut in 2007, goes 0-60 mph in under four seconds, with torque benefits from electric propulsion and greater energy efficiency than a Prius. He explains Tesla’s strategy: start with a high-end, high-cost product to enter the market, then move toward mass-market models—Model Two at around $49,000 and Model Three at around $30,000—to accelerate adoption as technology matures. Tesla’s name honors Nikola Tesla, inventor of the AC induction motor. Tesla’s showroom approach will feature customer centers and a consumer-friendly service experience, with a vision to demonstrate that electric vehicles can be desirable and practical. Musk notes that there has been no formal sale offer from legacy automakers, but he sees Tesla as a catalyst to demonstrate feasibility and demand for electric propulsion and zero-emission power generation, ideally paired with solar power. Regarding daily management, Musk is CEO and founder of SpaceX, dedicating about 80% of his time there, while he is chairman and CEO of Tesla but not involved in daily operations. He spends roughly three days a month on Tesla, with SpaceX occupying the majority of his focus, citing a Steve Jobs–like model of cross-company oversight. He describes his typical day as starting around 7:30–8:00 a.m., with a flexible schedule, and a workday extending to about 8 p.m., surrounded by SpaceX colleagues in a cubicle. In sum, Musk envisions a future where humanity is a multiplanetary species, with SpaceX advancing orbital capabilities and Mars ambitions, while Tesla accelerates the transition to sustainable energy and electric transportation, all rooted in a commitment to meaningful, world-changing progress.
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Speaker 0: Elon, thank you so much for being with us here at Wired Science. Speaker 1: Well, you for having me. Speaker 0: I need to first lay a little bit of a foundation here. Two days into your physics program at Stanford University, you quit school to start a company called Zip2, a media company, Speaker 1: which Speaker 0: you sold a few years later for a paltry $3.00 $7,000,000 Then four years later, eBay buys PayPal. Is that correct? Company that you established or helped to establish as one Speaker 1: couple of few others, yeah. Speaker 0: And now you've taken those two enormous successes and you've set your ambition on space. How did you go from online payment systems to building a spaceship, essentially? Speaker 1: Well, when I graduated from college, there were three areas that I thought would be most impactful to the future of humanity. The three were the Internet, space exploration, and and and then changing the economy from a mine and burn hydrocarbon based economy to one which is solar electric, which I think is gonna be the primary, but not exclusive, means of energy and transportation. Speaker 0: Have we screwed it up so badly here on this planet that our only hope is to build a new civilization out there? Speaker 1: No, not at all. Actually, I'm quite optimistic about the future of humanity on Earth. You are? Yeah, absolutely. Speaker 0: So what is the benefit to humanity then, to inhabit Mars, which is really what is an ambition of yours? Well, think Speaker 1: if you consider two paths, one where we're forever confined to Earth, and the other where we are a space frank civilization out exploring the stars. I think the latter is far more exciting, and will result in a richer and more diverse human experience. Speaker 0: How can you do that better than NASA? Speaker 1: Well, NASA is a customer of ours. So there's a confusion in the public mind that perhaps a company like SpaceX is competing with NASA. But in fact, NASA is a customer of ours. So we're actually providing services to NASA, launch services. And when the shuttle retires in 2010, so starting in 2011, SpaceX's rocket will replace the space shuttle in servicing the space station with astronauts and cargo transportation. Speaker 0: The name of your rocket is called the Falcon Explorer. Is that it? Well, Falcon Nine. The Falcon Nine? Yes. Speaker 1: It's the rocket. And then the space ship is Dragon. Speaker 0: Dragon. Speaker 1: Yeah. So the Falcon Nine rocket lifts the Dragon space ship, and this Dragon spaceship is what goes to the space station and then returns to Earth. Speaker 0: So it transports the Falcon as almost cargo then? Speaker 1: So the Falcon nine is kind of like the semi or something like that. The Falcon nine booster rocket takes the Dragon spaceship to space and drops it off. Speaker 0: Mhmm. Speaker 1: Then it goes to the space station, docks with the space station, transfers astronauts or resupply, you know, cargo, whatever whatever the case may be, and then the Dragon spacecraft, returns to Earth. Speaker 0: Reading some of the speeches that you have given in your career, and how old, you're practically 23 years, you're 23 years old, Speaker 1: is I'm actually 12. Speaker 0: You're 12. Yeah. I was going to say, you look terrific. But you have said that we got lost along the way with our space program. What did you mean by that? Speaker 1: Right, I think I think that was in some of my congressional testimony. Gave a few speeches to Congress. Well, what I mean by that is in 1969, we were able to go to the moon. And here we are over three decades later, and we can barely get to low Earth orbit. And I think by any measure, that is a step backwards. Is that for a lack of leadership or technology? I think we made the wrong technological choices, and I think there was also a lack of will at the highest levels of government to take the next step and go well, at least stay on the moon and patch build a a base there Mhmm. And then go beyond the moon to Mars. And if you look at the the news articles in the late sixties, early seventies, the expectation was that that by now, in in the twenty first century, we would have a moon base and probably even a Mars base. Mhmm. And and I think if you'd asked anyone at that point in time whether we we would be unable to go to the moon and have no and not have been to to Mars, I think they would they would think you're crazy. Speaker 0: Do we need leadership in that realm? Do we need a John F. Kennedy who sets a goal for us when he said, One day, a man will walk on the moon? Do we need that kind of leadership for this technology to move forward in that big step? Speaker 1: I do think it's very important the president set the priority and determined the goal, you know, that we as a nation will aspire to. And, you know, George Bush has his pluses and minuses. Mhmm. But at least one plus is that he he has helped to to steer the space program in a direction that that more or less makes sense. You know, the only thing I would sort of argue with is that I don't think we should be going back to the moon. I think we should be focused on Mars. Mhmm. I think we saw Speaker 0: the You think that's a mistake, focusing on the moon? Speaker 1: I do think we should rather go I think we should rather be focused on Mars. Mhmm. You know, the moon is kinda like it's kinda like, the Arctic. It's just it's just a very barren place, very little resources. It's small. It's not it's not really a place that we could establish, another human civilization. Speaker 0: There's a there's a there's a feeling of been there done that with the moon. Speaker 1: Yeah, you saw that movie in the sixties, you know, the remake's never as good. Speaker 0: Did we really go to the moon? Speaker 1: Yes, I'm yes, did. Definitely Okay, just wanted to check. The government is incapable of suppressing a conspiracy of that nature. Speaker 0: Of that nature. So okay. Good. This ambition to explore space Absolutely. As an entrepreneur. There's there's quite a bit of competition out there. There's Jeff Bezos with Blue Origin. There's Richard Branson with his Virgin Galactic. Speaker 1: Right. Speaker 0: And I'm not talking about NASA either. There's Paul Allen. There's the European Space Agency and Boeing and Lockheed Martin. The Chinese, the Russians, let's just throw all of them into this Everyone's Speaker 1: doing it. Speaker 0: Competitive field. How is SpaceX different? How do you think you'll sort of surpass them? Speaker 1: Well, you know, you you've you've listed a a wide range of of entities there, and I think the differences are really different depending upon which one you're referring to. Speaker 0: The Well, me ask you this question. Who is your competition? Speaker 1: Have no serious competition. None? Not presently. Speaker 0: Who's chasing you? Speaker 1: Well, if you mean chasing and have and has a serious chance of catching, then I I think none that I'm aware Speaker 0: and but guys kind of a hack then. Speaker 1: Well, what what Branson's saying, by the way, I'm a great admirer of Branson, is really a much smaller technological challenge. So their craft would be suborbital. So it would go to about Mach three. Our craft is orbital. It goes to Mach 25, so 25 times the speed of sound. But that doesn't describe the whole scale of difficulty because the the energy required to get those velocities scales as a square of the velocity. So to do what Branson is doing, you need, say, about nine units of energy. To do what we're doing, you need 625 units of energy. The difference is monumental. And then when you re reenter, you have to you have to burn off all that energy. So so that doubles the problem, really. So I mean, what Branson is doing from a technological standpoint is building something that can cross the the English Channel. What we're building is something that can circumnavigate the globe. It's a very different scale of of of technological difficulty. I still think what he's doing is great. And by the way, I bought a ticket on on his effort. You did? Yeah. Yeah. So I I I still think it's great, but it's not it's not in the same league technologically. So you're not particularly worried? Certainly not about no. Certainly not about that. No. The things that worry me are are we gonna make a mistake? Are we the the the the the things that can really hurt SpaceX are I mean, we our own foolishness, our own errors can can hurt us, but none none of the competition that I'm aware of. Speaker 0: Mhmm. So generally, you're worried about what's in front of you, not not the other guy. In fact, you probably don't think about them in terms of the how they criticize you or what they think about you. Speaker 1: I I don't think actually, don't think there's much criticism. I mean, Boeing and Lockheed, of course, that they would criticize. But I don't think any of the entrepreneurial guys would would criticize what we're doing. And it's certainly possible. I think, you know, what what Jeff Bezos is working on could ultimately I mean, he he does have aspirations to get to orbit and beyond. It's just that what they're doing right now is suborbital and at at the sort of lower technology level. Speaker 0: Mhmm. Speaker 1: What I think about it at SpaceX is really entirely what what what are we doing to ensure that our rocket is is going to be successful and that we are truly optimizing the the the cost and ensuring a higher liability. I mean, that's just a very, very difficult problem. There's a reason why there's an idiomatic expression about rocket science being hard. It it it really is really hard. Speaker 0: So rocket science really is rocket science? Speaker 1: Yeah. It looks hard, and it's harder than it looks. Speaker 0: What's the big goal here? What's the long term plan? Speaker 1: Well, the the long term ultimate objective, the the holy grail is we would like to help make life multiplanetary. That's really what we'd like to do. Speaker 0: So establish societies on as many planets as possible? Speaker 1: Well, yeah. I think there's only one possibility, but, yeah. I mean, even if we can just go from one planet to two, think that's a pretty big step. Speaker 0: And you'll start with? Speaker 1: Well, Mars. Mars is the only viable planet. Speaker 0: A viable planet. Yeah. So multi planetary life. Speaker 1: Yeah. It's help make life multi planetary. I think that's an important thing. Speaker 0: I don't think your goal's big enough. Yeah? It's ambitious. Well, like I Speaker 1: said, we don't expect to do it single handedly, but we certainly would like to help make it happen. Speaker 0: It's fair to say you've made a fortune. Speaker 1: Yeah, I think so by And, any reasonable standard, Speaker 0: you know, those who work in science probably understand your trajectory, but there are those who are watching who would think, If I made that money, I'd sit on a beach, I'd drink beer, and I would just watch the sunset. Kind of like a Corona beer commercial. Have you ever thought about that as a career option? Speaker 1: You know, I find that really pretty boring, so that would be torture. If I had to do that every day, that would really be pretty awful for me. Speaker 0: Is there something about startup businesses that really fuels your desire to work? Speaker 1: Well, I guess I really need to be preoccupied with something. And I if if I'm just sort of sitting there relaxing, I can only do that for a very short period of time, and then it becomes unbearable. Although startups definitely have their highs and lows. There's a friend of mine who has a good phrase, you know, a startup business is like eating glass and staring into the abyss. Speaker 0: What is the criteria that you establish for yourself for a startup? Speaker 1: Mean why one business over another? Yeah. Well, for me it's always about does what I'm doing matter? If we are successful, does it matter to the world? And so there are easier ways to make money than starting a rocket company or say a car company. Or even when I started an Internet company because when I started the first Internet company nobody had made any money and it wasn't clear that anyone would make any money. It was simply from the perspective of the internet being a very important thing and something that needed to be built, and so I wanted to help build it. Speaker 0: Well, touch upon something that's interesting. It's that there is a that benefiting humanity is a very integral part of your criteria, no matter what you're starting up. Speaker 1: Yes, absolutely. Speaker 0: Really? Not everybody has that as a prime interest. Speaker 1: No, think that's probably relatively unusual. Although there are many people that I know in Silicon Valley for whom that is a significant motivation. Speaker 0: You said in your endeavor here to explore space that we are committed to failing in a new way, if nothing else. What did you mean by that? Just how it sounds? Speaker 1: Well, I mean, we're committed to succeed, really. But if we do fail, I would hope that we at least add to the body of knowledge such that those who follow may make fewer mistakes. Speaker 0: Now if Mars were not enough, you are busy here on Earth. Speaker 1: The world is not enough. Speaker 0: That's right. Where? Where? What? Have you no limits, my friend. Here on earth, you are establishing a presence certainly with Tesla Motors. Tell tell us a little bit about that. This is an electric car company. Correct? Right. And this is no this is no hybrid car you could buy on a car lot. This thing goes from zero to 60 in four seconds. Speaker 1: Is Yes, that absolutely. Zero to 60 in under four seconds. It's faster better acceleration than any Porsche currently in production and any Ferrari except the Enzo. And it's twice the energy efficiency of a Prius. So it's, you really have, the moral high ground, and, you get to, you know, leave, the Ferrari guy in your dust. So Speaker 0: Well, me ask It's hard to beat. Let me ask the obvious well, and you don't look like one of those guys who's trying too hard in a Ferrari. Absolutely. Speaker 1: Yeah. You don't look like a jerk, you know? You don't look Speaker 0: like you know, bright banana yellow Lamborghini or or Ferrari. Speaker 1: You know, there's something I should point out about Tesla, is we didn't you know, Tesla is the first car is is a sports car, not because we think the world lacks for a sports car, but because it is the right entry point for the market. If you have a new technology, the right place to enter is high unit cost, low unit volume. Just as, you know, when a a new cell phone comes out or a new laptop or some new thing, it tends to be expensive at first, because they're figuring out all the issues, it takes time to optimize. And then over time, that technology will become cheaper and cheaper. And so the model two of of Tesla and maybe I'm leaping ahead here. But model two of Tesla is a $49,000 four door five passenger sedan. And that's that's gonna be obviously a much broader market segment that that that can make use of that car. And then model three is intended to be around a $30,000 price point. And so that's that's really affordable by by almost everyone who can buy a new car. So the idea is to drive to mass market as rapidly as possible, but only at the pace at which the technology matures. Is Henry Ford someone you admire? Well, think Henry Ford made some very important contributions to business, and obviously, you know, moving manufacturing line and that sort of thing. So I think he's certainly worthy of admiration. He was a bit of an odd duck, but, you know, certainly noteworthy. But the interest in Tesla is is not from the perspective of, you know, the world needs another car company. It's more from, the perspective of we have a very important environmental problem that needs to be addressed, which is driven by the the burning of fossil fuels and the increasing CO2 concentration in the atmosphere, and global climate change, which I think is gonna be one of the most significant issues of the twenty first century. And the only way to really get around that, in my view, is is really with an electric vehicle. And then you need to pair that up with zero emission power generation method such as solar power. I think solar power's going be a really big deal. Speaker 0: Tesla is not a hybrid car. Speaker 1: Tesla's electric. Pure electric. Speaker 0: So help connect the dots for me. Why aren't we seeing Tesla cars on the car lots then? What's keeping them in We haven't Speaker 1: made them yet. So we're just finishing up the development right now. Speaker 0: And anybody can buy this? Yes. How will you? Speaker 1: Well, actually we've almost sold out of 2,007 production, so if somebody does want to buy next year's model, they've got to act quickly. Speaker 0: One of the primary complaints about hybrid vehicles is they're not fast enough. You seem to overcome You Speaker 1: won't have any trouble with this. In fact, really, it's it's there's something, uniquely better about, electric vehicles, which is that the torque response is immediate. So if you want to pass someone, you I mean, you just the the the response of the car is is is very immediate. It's just, it's more fun to drive an electric car than it is to drive a gasoline car. Speaker 0: You know, was going to say that Tesla, the car, the name of the car company, is no coincidence, is it? Explain a little bit about that. Speaker 1: Right. The company's named after Nikola Tesla, who is an inventor. He was originally from the of the area of Yugoslavia in Europe, but he moved to America when he was young and was an inventor of AC induction motor, invented a lot of the principles of magnetism. So he was a great man, a great, great inventor, and so the company is named in honor of him. Speaker 0: So these cars, the Tesla Roadster, the first issue of the Tesla Roadster available in 2007. Speaker 1: Yes. Speaker 0: In the spring? The summer? How do you get on the waiting list? Speaker 1: Well, you buy the car. You basically put down a deposit. And we've actually Speaker 0: Can you do that through the web? Speaker 1: Yeah. We'll have, by the way, customer centers all around the country. So we'll have one in LA, one in the Bay Area, Chicago, Miami, New York, and and eventually nationwide customer centers where somebody does want to see the car in person, take a test drive, or see the car being worked on. I I mean, we have this idea for the way that the cars are serviced that it should be a really pleasant experience. We have somewhere between like a Starbucks and an Apple store. So you'd go in and you'd see the car being worked on behind a glass partition. Speaker 0: That would be your car you're watching? Yeah, or somebody else's. Speaker 1: But it's really clean. It's really clean, present, bright. You know, there'll be sort of a coffee bar available. You know, just a we really want to have a very pleasant experience that you don't typically get if you go into a dealership. Speaker 0: Have you heard from Toyota? Have you heard from General Motors and Ford saying, is the company for sale? Speaker 1: No. Nobody's actually made a formal offer, but the interest you know, I think one of the one of the biggest values that that Tesla can can can provide is serving as an example to the rest of the auto interest industry, because right now the auto interest you know, the big car companies believe that a, a viable electric vehicle is not possible, and b, even if it was, people wouldn't buy it. Speaker 0: So we need Speaker 1: to show that that neither of those are true, that the technology works, that people wanna buy it, and and that will be the most effective way of of really driving change in the auto industry is by serving as an example in that matter. And if we were to sell the company to one of the big car companies, I think it would really slow things down. Mhmm. You think so? Absolutely. Mhmm. Speaker 0: You're very busy enterprising the part of your company that will explore space. You're very busy with this car company. Where do you find time to be CEO of two companies that size? Speaker 1: Well, should correct you that I'm CEO of SpaceX. And I'm chairman and CEO of SpaceX, and that is really my day job. So I spend 80% of my time on SpaceX. Speaker 0: Okay. Speaker 1: I am the chairman and the principal owner of Tesla Motors, but I do not run it on a daily basis. Speaker 0: You don't run that on a daily basis? No. Well, that was really the question, was how do you do how do you run those two large enterprises on a daily basis? I Is it a couple of phone calls with the Tesla folks? How's it going? I'm busy with outer space right now. You Speaker 1: I guys got that spent about I spent about to three days a month on Tesla related business and almost all the rest of the time is on SpaceX. So SpaceX is very much my day to day job. And then I provide product guidance, strategic guidance, and obviously funding for Tesla. Like Steve Jobs, right? So he runs Apple on a daily basis, but he also, you know, has oversight over Pixar. It's kind of like that. Speaker 0: And in your day to day, and and this is this is one of those silly lifestyle questions, but how early do you get up in the morning, and where do you go to work physically? Is it an office? Speaker 1: Yeah. I I go to work at SpaceX. How early do get Speaker 0: up in the morning? Speaker 1: You know, I'm not an early morning person. So So Speaker 0: for young engineers and for inventors and creators, they can sleep in till ten or eleven? Speaker 1: We have no fixed hours at SpaceX. I mean, my personally, I tend to get up around 07:30 or eight and be in the office around, you know, nine, 09:30. But then I tend to stay till about 8PM. Speaker 0: Okay. College students across America are saying, Oh, brats. I thought he was going say like noon. But then you go into an office and you sit in a separate office away from those who are working, or do you sit with them? No, I just Speaker 1: have a cubicle at SpaceX. Speaker 0: You have a cubicle? Yeah. And are you surrounded by your colleagues there? Speaker 1: Yeah, absolutely. Speaker 0: What is your hope in terms of the impact you will leave on culture, this civilization, this world, global civilization, what is it that you hope to leave here? Speaker 1: Well, I think what I'd like to do is help solve some important problems. So I think in a small way, I help build the Internet, and then with respect to the global warming problem, the transition away from oil and other hydrocarbons to something which is clean and sustainable, I hope to have an impact there. And then with respect to space, I hope to have an impact in helping make humanity a multi planet species. Speaker 0: Elon Musk, thank you so much for being with us at Wired Science.
GiganteRicardo9
Posted - November 24, 2025 at 7:18 PM
Saved - November 24, 2025 at 10:00 PM

@GiganteRicardo9 - Ricardo Gigante

Shut the fuck up, Kela Technologies.🤣

@shaunmmaguire - Shaun Maguire

Alex Karp is one of the business leaders I respect the most The man has both vision and courage He’s consistently “contrarian and right” And he’s a man of principle 🫡

@BillAckman - Bill Ackman

I just finished ‘The Philosopher in the Valley, Alex Karp, Palantir, and the Rise of the Surveillance State’ by Michael Steinberger. It was a quick, well-written, and fascinating read, in part a biography on Alex Karp as well as a deep dive on @PalantirTech. I found the book to

KatieMiller
Posted - December 9, 2025 at 11:19 PM
Saved - December 17, 2025 at 1:55 AM

@KatieMiller - Katie Miller

Absolutely surreal to interview one of the greatest minds of our time, a friend, and boss. Thank you, @elonmusk 🇺🇸🇺🇸🇺🇸 https://t.co/vbYlZTTaBf

Video Transcript AI Summary
- The conversation opens with a reflection on Doge from Elon Musk’s perspective. Musk says the Doge government project was “a little a little bit successful” and claims they “stopped a lot of funding for that… that really just made no sense,” noting that 2–3% of government payments were unnecessarily sent without proper codes or explanations, which made stopping the waste difficult. - When asked if he would do Doge again, Musk says no, and suggests that instead of Doge he would have worked in his companies and not had the cars running. - On irrational fears, Musk says he tries not to have irrational fears and squelches any he identifies. - If starting from scratch today with a thousand dollars, Musk recalls originally coming to North America with about 2,500 Canadian dollars (roughly $2 US) and says that with the knowledge he has now, it would require Armageddon or a terminal failure of civilization for that scenario to be plausible again; otherwise he could recruit funding based on the high returns he can promise. - In the Katie Miller podcast episode, the host takes Musk back to January 20 (in the Roosevelt Room) and asks what happened next with Doge. Musk explains Doge stemmed from Internet suggestions; it was initially intended to call the Government Efficiency Commission, but the Internet suggested Department of Government Efficiency, DOGE. - On success, Musk reiterates they were “a little… somewhat successful,” citing the elimination of wasteful payments and the example of eliminating a large portion of zombie payments through requiring a payment code and explanation. - Would Musk start Doge again from scratch or know what he knows now? He says no, and notes that rather than Doge, he would focus on his companies and avoid the funding backlash from stopping money flows to political corruption. - After DC experiences, Musk expresses that the aim is the least government intervention possible, but he highlights a major concern: large transfer payments to illegal immigrants, arguing that citizenship fast-tracking and government payments create a powerful pull factor, effectively “voter importation.” - On AI, Musk believes AI and robotics will eventually provide all goods and services, making work optional; he distinguishes his predicted outcomes from what he wishes would happen, acknowledging the rapid pace of AI advancement and the difficulty in slowing it. - Sleep and routine: Musk averages about six hours of sleep per night; he tracks sleep using ex-posts and a phone app, finding five hours fifty-six minutes as a recent average. He emphasizes information triage and minimizing context switching to manage inbound communications across Tesla, SpaceX, X (Twitter), and personal matters. - On people and leadership, Musk describes President Trump as very funny and “naturally funny,” and says the funniest person he knows in real life is Trump who can be effortless in humor. - God and religion: Musk says God is the creator and acknowledges that the universe came from something, noting that people have different labels. - About space, Musk emphasizes Starship’s potential for full and rapid reusability and calls life becoming multi-planetary one of the top evolutionary milestones, alongside multicellular life and life branching from oceans to land. He states Starship is capable of enabling sustainable multiplanetary life, with Starship not using AI in its creation. - He clarifies that Tesla and X AI both contribute to improving life on Earth, and stresses that Mars would be dangerous and uncomfortable in early days; it would be risky with high chances of death, and early settlers would face hardship rather than an escape from Earth. - On Starbase, Musk describes it as an inspirational city and a rocket factory by the Rio Grande on a sandbar; Starbase is legally incorporated as a city with tax-exempt status, a milestone akin to Disney World as a company town. He notes Cape Canaveral proximity and recalls visiting Disney World multiple times with his kids; Space Mountain is his favorite ride but could use an upgrade. - On fashion, Musk laments that styles have not evolved much since 2010–2015 and argues for more distinctive, era-defining fashion—suggesting higher collars, bolder silhouettes, and more personality in wardrobe. - Conspiracy theories: Musk says he hasn’t seen evidence of aliens; he does confirm that Neil Armstrong and others walked on the Moon and jokes that they even played golf there. He notes there is gravity on the Moon (one-sixth) and that there is no atmosphere. - The biggest misconception about Musk: the general belief that he is a difficult boss; he counters with praise for the mission-driven loyalty of his employees and characterizes his workplaces as highly inspirational. - On Starbase’s origin, he reveals the desire to create something inspirational and notes Starbase’s proximity to Disney World as part of the branding and cultural context. - For a hypothetical dinner party, Musk names Shakespeare, Ben Franklin, and Nikola Tesla, and envisions a grand 12-course meal; he jokes about possibly including a tiny cheeseburger as one course. - Closing note: the episode wraps with thanks and a tease for the next installment.
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Full Transcript
Speaker 0: I think the story of Doge from your perspective has never been told. Do you think you were successful? Speaker 1: We're a little bit successful. We were some somewhat successful. Speaker 0: Would you ever do Doge again? Speaker 1: I mean, no. I don't think so. I think in instead of doing Doge, I I would have basically built you know, worked in my companies, essentially. And not and the cars would they wouldn't have been running the cars. Speaker 0: What's your biggest irrational fear? Speaker 1: I I try not to have irrational fears. Speaker 0: None? Speaker 1: If I find an irrational fear, I squelch it. Speaker 0: If you had to start from scratch today with only a thousand dollars, what would you do? Speaker 1: Well, I did I did originally come to North America with, like, I don't know, $2,500 Canadian, so I don't know. It's $2 US. At this point, I have a lot of knowledge. A lot of things have to go wrong, but I have to be with kids. It's like, am I just emerging from prison perhaps with a stipend? Speaker 0: Hi, everyone, and welcome to this week's episode of the Katie Miller podcast. We are in Texas today joined by the one and only, Elon Musk. Speaker 1: Nice to see you again, Katie. Speaker 0: Nice to see you, Elon. So I wanna take us back. It's January 20. You are in the Roosevelt Room, if you remember this, getting sworn in, and they hand you a computer and a phone. Right. Speaker 1: Right. Speaker 0: I wanna go back to what happened next. I think the story of Doge from your perspective has never been told. What was your first thought on how Doge was going to proceed? Speaker 1: Well, I guess I couldn't believe I was there for the most part. It's like this it all seemed extremely surreal at the time. You know, Doge was a made up name, that had been made up, I don't know, two or three months before, and, based on Internet suggestions. And I was gonna call the Government Efficiency Commission, and then and then someone on the Internet said, no. It should be the Department of Government Efficiency, DOGE. I'm like, that sounds great. So we just kinda made up an apartment. Speaker 0: Do you think you were successful? Speaker 1: We're a little a little bit successful. We were some somewhat successful. I mean, we we we stopped a lot of funding for that that that really just made no sense. That was just entirely wasteful. Where it would like, for example, there was a like, probably a 100, maybe $200,000,000,000 worth of zombie payments per year, which simply by enforcing that there'd be a payment code and an explanation for the payment, that the payment would not go out. So we've made that change to the main treasury computer and much of other computers. So, like, see, it seems like insanely obvious, but there are just, call it, I don't two or 3% of government payments that go out that really should not be going out, and it's actually quite hard to stop. So it's it's a it's a pretty rare individual that would ask the government to stop sending them money. Speaker 0: Would you ever do Doge again? Speaker 1: Do you mean would I repeat history, or or or would I Speaker 0: Two ways to think about it. One is if you could go back and start from scratch, like it's January 20 all again, would you go back and do it differently? And knowing what you know now, do you think there's ever a place to restart you, not saying others in your stead, you go back and restart doing Doge? Speaker 1: I mean, no. I don't think so. What I do I I think I probably I don't know. Speaker 0: Would you do Doge again knowing what you know now? Speaker 1: I mean, the thing is, like, I think ins instead of doing Doge, I I would have basically built, you know, worked to my companies, essentially. So and not and the cars were they wouldn't have been burning the cars. Speaker 0: You gave up a lot to do doge. Speaker 1: Yeah. Yeah. Like, if you if you if you stop money going to going going for political corruption, they will they will lash out big time. Speaker 0: Mhmm. Speaker 1: So they really want the money to keep flowing. So if you stop it from flowing, there's, like, a very strong reaction to to stopping the money flowing. Speaker 0: After you were in DC for a while, did you become disillusioned with how it operates? Speaker 1: Well, I I wouldn't say I was super illusioned to begin with. It's it's I mean, I guess it's just, like, you really want the least amount done by government possible. The least amount. I I guess maybe maybe, like, the the the biggest thing is that I guess the biggest single thing is is that the there there are massive transfer payments going to illegal immigrants, like massive. Essentially, we're we're we're paying people to come here from somewhere else in vast numbers, including flying them in. So, like, it's not like you need a border wall if you're flying them in. Then fast tracking them to citizenship and making them beholden to to government payments and and voting hard left. That's that's essentially it's it's like voter importation. If if if if you if you create a gigantic money magnet to as you say, see, if anyone comes here from anywhere else, we're gonna pay you tons of money, give you lots of free stuff. Come come come to America and and get paid to do so. Like, you're gonna get a lot of people taking up on that offer. And people say, this this this is fake. I'm like, actually, well, let's look at, you know, Ilan Omar, who was literally was voted into power voted into congress by, you know, a large group of people from Somalia who are in Minnesota, which is really far from Somalia, or Mamdani who was voted as to be to be mayor. But if if but by a majority of people who are not born in America. That's my understanding, at least. So and then then California is a big time situation. So I don't know. We just don't wanna turn into a, you know, communist hellhole, basically. Speaker 0: You've said in the future that no one's gonna need to worry about money or work because AI is going to take care of the rest, AI and robotics. What do you mean that people won't have to work in the future? Speaker 1: Assuming the current trend of artificial intelligence and robotics continues, which seems likely, the AI and robots will be able to do anything that that humans want to want them to do, essentially. So hopefully not more than that. But AI and robotics will be able to provide us provide all the goods and services that anyone could possibly want. Speaker 0: Wouldn't need to work? Like, what would you do with your free time? Speaker 1: Guess people will people will be able to do whatever they want with their free time. Work will be optional. I I mean, I just wanna separate out from, like, what I wish would happen versus what I predict will happen because people get confused about that. They think that what I predict will happen is what I want it to happen. Yeah. What I want what I predict to happen is not the same as what I want to happen. I if if I could, I I would I would certainly slow down AI and robotics, but I I can't. It just seems to be well, it's it's it's advancing at a very rapid pace, whether I like it or not. Speaker 0: Is AI what keeps you up at night? Speaker 1: It used to be. At this point, I don't know. I'd I'd I'd I wouldn't say there's there's nothing particularly keeping me up at night right now, except that I But if you say, what what what why do I wake up in nightmares? Oh, AI. Yeah. Actually, I've had a lot of AI nightmares. I I I had AI nightmares many days in a row. What am I supposed to do about it? Speaker 0: What's your biggest irrational fear? Speaker 1: I I I try not to have irrational fears. Speaker 0: None? Speaker 1: If I find an irrational fear, I squelch it. I I don't I don't believe fear is fear is the mind killer. So I want somebody who feels fear strongly. Speaker 0: On average, how many hours do you sleep a night? Speaker 1: Six. You can tell based on my ex posts. Speaker 0: Yes, you can. Speaker 1: People have actually mapped them, so you can it's very clear when I'm sleeping when I'm alone. I tried having less than six hours sleep, but although I'm awake more hours per day, my cognitive function is reduced. So my natural sleep and actually timed it with the phone. You can get a phone app to time it. It's like five hour five hours fifty six minutes. That's what the phone said. Speaker 0: What's an average day for you look like? Speaker 1: Well, I have a lot of inbound communication. So that's information triage. I try to segment the days so that there's not too much context switching. Because arguably, fear is not the mind and killer, context switching is. It's hard not to context switch if you've got an inbox full of stuff. But you can think of like, if you had to context switch every three seconds or every thirty seconds or every three minutes, the context switching cognitive penalty would be very high at every three seconds. Speaker 0: And you're talking about switching between, say, Tesla x x AI. Speaker 1: SpaceX personal. Speaker 0: SpaceX personal. Speaker 1: And a but even within Tesla and and SpaceX, there are many different things. Getting this sort of the stuff on X, like random news things. You know, people being burned alive and stuff like that. They're like, what the hell is going on in this country? Speaker 0: Who's the funniest person you know in real life? Speaker 1: You know, President Trump is very funny. He's got a great sense of humor. Speaker 0: President Trump is very funny. Speaker 1: He's very funny. He's, like, naturally funny. But was it's, like, somewhat effort effortless. I mean, you know, when when he was at Mamdani in the office, and they asked him if he still thought the president was fascist. And the president said, just say yes. It's easier that way. Yeah. Yeah. Speaker 0: Don't Speaker 1: worry about it. Just say yes. That was awesome. He's like, yes. What a what a how silly. Speaker 0: Who do you look up to the most? Speaker 1: The creator. Speaker 0: What's your current position on God? Speaker 1: God is the creator. Speaker 0: You don't believe in God, though, do you? Speaker 1: Well, I believe that was this universe came from something. People have different labels. Speaker 0: When's the last time you did something extremely ordinary, like go to Target or CVS? Speaker 1: I can't go to things where there's a general public because I mean, there there's an immediate can I have a selfie line that forms? And and these days, in particular in light of Charlie Kirk's murder, there are serious security issues. It's not that I don't want to. I simply can't. Speaker 0: Has Charlie's murder changed how you do things, or were you already locked down pretty well before that? Speaker 1: It certainly reinforced the severity of the situation where life is on hardcore mode. You make one mistake and you're dead. It only takes one one mistake. Speaker 0: What's one moment in your life that you could live again just to feel it? Speaker 1: Well, mean, obviously, when my kids were born or the first time SpaceX got to orbit or Tesla made an electric car work. Speaker 0: You've had a big a lot of them. Speaker 1: It's a lot of it's a lot of things. There's a lot coming down the pike. Speaker 0: Like what? Speaker 1: Starship. The the degree to which a Starship is a revolutionary technology is not well understood in the world. It is the first time that there's been any rocket design where full and rapid reusability is possible or full reusability at all is possible. This this is the first design where a reusable rocket is one of the possible outcomes, where success is in a set of possible outcomes. Speaker 0: Are you talking about v three or v two? Speaker 1: Well, we could have made v two reusable, but the but we there were a lot of performance improvements for v three, so it made sense to go to v three. There just there's like 10,000 different changes between v two and v three, maybe more than 10,000 really. So but Starship if if if there are historians in the future who will look back at starship and say it was one of the most profound things that ever happened. Now, you can think of historic events as where would they fit on the in the evolutionary hall of fame. So you've got things like single celled life, then you've got your multicellular life capturing a mitochondria capturing mitochondria so that you have a power cell in the plant in the cell. We've got, like, a power plant in the cell. You've got, you know, differentiation into plants and animals, life going from oceans to land. And then also on that scale, probably in the top 10, is life becoming multi planetary. There just aren't very many things that are in the top 10 of the evolution of life, or where you could basically say, you could evaluate any given civilization or any given life form as, you know, on on that on that scale. So life becoming multi planetary. It's in the top 10. It needs to be sustainably multi planetary, so not just visiting, but actually multiplanetary in the sense that if you have planetary redundancy. So if one of the planets if there were to be a catastrophe on one of the planets, the other planet would survive. Speaker 0: Are all of your companies Speaker 1: Starship is capable of doing that for the first time in history, and no AI was used to create it. So the AI will appreciate that. Speaker 0: Are all of your companies working towards that same goal to help us become multiplanetary? Like, does the AI exist to be able to help life on Mars, or is that primarily for what is happening here currently? Speaker 1: You know, Tesla is mostly about making sure life on Earth is good, and and x AI is about that too. Because multiplanetary means Earth's gotta be good and you need another planet. Sometimes people think, because they they have, you know, legacy templates, mental templates, they think that going to Mars is is an escape from Earth. And, like or that it would be some, you know, place where billionaires would go or something like that. But but actually Mars will be very dangerous, and the moon base will be also dangerous, much more dangerous, and much less comfortable in Earth. So people that would go in the early days to make life multi planetary on Mars or the moon, they would have a much higher risk of death than if than if they stayed on Earth. And the things would be cramped and uncomfortable. So that's that's the sales pitch for Mars. It's it's gonna be uncomfortable. The food won't be as good as Earth. You might die. It's gonna be a massive amount of hard work, and it may not succeed. That's the sales pitch. So same as when Do you wanna go? Speaker 0: Same as when people came to America. Speaker 1: Yeah. Didn't wanna be in Jamestown. Speaker 0: No. People went anyway. Speaker 1: Yeah. Maybe if there'd been social media back then, they would've saying, we're all dying. Here's videos of us dying. Would've probably put a damper on future voyages. But, yeah, there's just a whole bunch of people just disappeared. We don't know what happened to them. Speaker 0: You talk a lot on x about wardrobe and how you wish current wardrobe would be differently. Speaker 1: I just think, like, from a fashion standpoint, we should evolve. It's like my son Saxon said at one point, why does everything look like it's 2015? I was like, damn, things do everything does look like it's 2015. It's like, if you took a picture from 2015 and said in 2025, it looks exactly the same. Were stereotypical. Things were the same as 2015. We have not we have not moved the needle in a decade. Speaker 0: So what should it look like? Speaker 1: Something new. You know, like the sixties had a definitive style. The seventies had a definitive style. The eighties had a definitive style. And then the nineties also had a different style. But then you start looking at the '2 the February and the twenty tens, and it's like less and less every year, I think we should evolve our style. I and and if you'll get some of the older paintings, you know, of past cabinet secretaries, Some of them, like, they look cool. Like, their their their jackets are cooler than what we have right now. You know, they have sort of like a high collar and like a sort of I don't know what some sort of what do what do you call those things? Ascot or something like that. Mean, it just looks cool. Like so but we we don't every everything's like a very normal looking suit at this point. But like, literally, the same as 2015. And I'm being generous because arguably the same as 2010. Speaker 0: Yeah. Speaker 1: So in fifteen years. And and I'm like, from a fashion standpoint, I don't think we've moved since two thousand and twenty five years. If you showed someone a picture of this is a bunch this is a bunch of dudes in 2000. This is a bunch of dudes in 2025. Which year is which? So I think we should, I don't know, spice it up a little. Speaker 0: What's a conspiracy theory you believe in? Speaker 1: I mean, which which conspiracy theories haven't come true at this point? We've run out of conspiracy theories that because it will come true, far as I can tell. Speaker 0: Yeah. Speaker 1: I mean, I don't know of any aliens. People always ask me if there's there are aliens. I have seen no evidence of aliens. No one on the SpaceX senior team has any evidence of aliens. Because I've asked the team, like, guys, am I missing something? Has anyone on the team has anyone seen any evidence of aliens? Speaker 0: Does that include UFOs? Speaker 1: That's just an unidentified flying object. So so UFOs like, it could be like some new weapons program or whatever that's, you know, some hypersonic missile or something like that. That that would be technically a UFO, but it's it's just it's just basically some weapons prototype. It's not it's not like aliens. So although Neil Armstrong, Neil a, spelled backwards as alien. Coincidence? Speaker 0: You believe we actually went to the moon, though. Speaker 1: Yes. We went to the moon a few times, actually, and played golf on the moon. Speaker 0: K. Speaker 1: We didn't just go to the moon. We actually got a little bored, and started playing golf on the moon. Speaker 0: But why didn't the flag move? There's like that the Speaker 1: trump the chalk moment. Speaker 0: About the flag? Speaker 1: No. The playing golf on the moon. Speaker 0: Oh, okay. Speaker 1: You're not illiterate yet. Speaker 0: No. I understand that. Speaker 1: Yeah. Yeah. Whacked the golf ball on the moon. Speaker 0: Is there there's no gravity though. Right? So Speaker 1: like There is gravity, one sixth. If it wasn't gravity, you'd just float away. Speaker 0: Okay. Speaker 1: There's no atmosphere. Speaker 0: Okay. Fair. Speaker 1: But there is one sixth gravity. Speaker 0: What's the biggest misconception about you? Speaker 1: I don't know. How would I know? What what do you think? Speaker 0: I think it's I get asked this a lot when I do interviews about you. Speaker 1: Me? Speaker 0: Oh, I got I got asked everyone always thinks you're a very difficult person to work for. Oh. But you're I think you're very kind. Speaker 1: Thanks. Speaker 0: I think Like, people think which you're you are, like, a very demanding boss. I think that you are I've never heard you yell at any employee. Speaker 1: Yeah. I don't yell. Speaker 0: I think every employee who works at every single one of your companies is incredibly mission driven, which is unlike any other workplace I've ever seen. Like, Starbase is the most inspirational place you'll ever go to. Speaker 1: Right. Speaker 0: Everyone is there to work on a singular goal. And so I think Yeah. To me, the biggest misconception about you is how every employee at all of your companies are fiercely loyal because it's all mission driven, and you are a very good employer to work for. And I think people assume you are not. Speaker 1: Right. Well, why would they think anyone would work at the companies? Yeah. I mean, talent I mean, talented people can go work anywhere they want. So they're only gonna work at one of my companies if they want to. And if they're mistreated in some way, they would they would leave and go work somewhere else. Speaker 0: How'd you come up with the idea for Starbase? Speaker 1: Well, I I think we needed something inspirational. I can't wait we kinda have a lot of star things, you know. So we got Starlink, Starship. Well, where would Starship depart from Starbase? I mean, Starbase is is, as you've mentioned, it's it's like it's I think it's probably the coolest place on Earth. Speaker 0: I agree. Speaker 1: And it used to it it used to be a sandbar down by the Rio Grande. It's only like three feet above sea level. So we built a gigantic rocket factory and two giant launch towers down by the river, literally within the side of the Rio Grande, and on on on an actual sandbar. Kinda had to have, like, an inspirational name. And then we made it a city. So it's an incorporated city, like legally a city. You don't you don't hear about new cities being formed that often. Speaker 0: The last time there was a company town, it was Disney World. Speaker 1: Yeah. I think Ford had some kind of, like, company town situations. But but, yeah, Disney World is is literally its name. Yeah. I'm Walt Disney. This is my world. Yeah. I've gone from land to world. They got, like, incorporated as a city and got tax exemption, which was like a whole was a big was a big deal. Yeah. I've been to Disney World probably 10 times. Speaker 0: Really? Speaker 1: Yeah. Maybe more than maybe more than 10, but at least 10 times. Because Cape Canaveral is right That by Disney Speaker 0: makes sense now. Speaker 1: So when I'd have the kids, then I would my older kids, and I was we're trying to get the rocket launch from Cape Canaveral, then, you know, the thing they'd wanna do is go to Disney World or Harry Potter Land. Speaker 0: What's your favorite ride? Speaker 1: I'm sort of tempted to say Space Mountain, I suppose. Yeah. Probably Space Mountain. I mean, I I do think Space Mountain needs an upgrade. Speaker 0: It's a little herky jerky. Speaker 1: The it doesn't look quite as sci fi as it used to. No. You know, it's it's like it's like the day before yesterday is tomorrow, but just still yesterday. Speaker 0: What's your favorite age to parent of your kids? Speaker 1: Generally, kids are the most fun between five and 10. Speaker 0: Do you think humanity is inherently good, or is it just trying to be? Speaker 1: The concept of good wouldn't exist without humanity. I think I do think humanity is, on balance, good. You know, I I generally think, like, increasing the amount of consciousness in the universe is a good thing. Try to try to understand the nature of the universe, which you can only do by increasing the increasing conscious awareness. I mean, I have thought about, like, how how did we get here? Because if we did start out as a hydrogen gas cloud that that sort of condensed and then formed stars, and then these stars exploded. And then they recondensed, formed stars again, and then exploded again. And then eventually, you get to us, thirteen point eight billion years later. And one of the interesting questions to think about is, how many times have your atoms been at the center of a star? I think it's like, on average, three or four times, something like that. Then how many times will your atoms be at the center of a star? Estimates vary, but it seems like we're roughly halfway. So your your atoms are likely to be at the center of the star maybe another four times or something like that. It's it's it depends on what your predictions are for the future. But in terms of existence as measured by the number of times your atoms will be at the center of a star, we seem to be roughly halfway. That really you know, if we wanna look at the big picture, that's the really big picture. Speaker 0: What's one invention that's made us worse, not better? Speaker 1: What's one adventure that's made us worse? Speaker 0: Mhmm. Invention. Speaker 1: Maybe short form video. It seems to be rotting people's brains. Speaker 0: What's one piece of technology you hope never gets invented? Speaker 1: I hope I hope never it gets invented? Speaker 0: Like yeah. Like, it's gonna destroy us all. Or you think with the proper safeguards? Speaker 1: Well, I mean, obviously, I hope, like, that people don't invent a virus that can kill all humans. Like, that's an obvious thing. Speaker 0: Yeah. Speaker 1: I mean, general yeah. Generally, I hope inventions that destroy consciousness are not invented. I think the future's gonna look very interesting. So I I it's I do have this theory about the the predicting the future, which is that the most interesting outcome is the most likely, which if simulation theory is accurate, makes sense because if anyone is simulating a wide range of futures, they're going to stop the simulation when it gets boring. Because this is what we do in our reality. So if if SpaceX is doing or Tesla doing simulations to understand how a car would work or robot or spaceship or something like that, We we run all these simulations in the computer. And the simulations that we we pay attention to are the ones that would that are the most interesting. Like, the the simulation where everything goes right on the rocket, we actually don't pay attention to because that's that's not a the everything goes right simulation is is fine. So we we actually test the, you know, the when when we simulate the the rocket flight, we'll actually test all sorts of oddball situations. But we don't test it we we don't have the simulation be totally wrong because I mean, like, the rocket just explodes immediately, that's not not also not interesting. So it's it's like you'd you'd need to find the the envelope of possible flight paths where the rocket can make it to orbit and without exploding. And then you you find those boundaries, And then when you launch the actual rocket, you try you make sure it stays within those boundaries. Or another way to think of it is, like, we we could be an alien Netflix series. And that that series is only gonna get continued if our ratings are good. Speaker 0: Are the ratings good? Speaker 1: Yeah. K. But but you can think of it, like, from a Darwinian standpoint applied if if if you apply Darwin to simulation theory, then the only the most interesting simulations will continue. Therefore, the most interesting outcome is most likely, because the it's either that or annihilation. So really, we have one goal, keep it interesting. Speaker 0: Do you think social media has made people more honest or more performative? Speaker 1: Well, social media makes people more performative. By the same token, you you you get more real live video of of things that that are actually happening. And and anything that is very interesting will spared will go viral on the Internet. So so you have both. You've got more performative or people are doing anything they can to get a few more views on their TikTok video or whatever or the reels or maybe on their expos or something. And so that's very performative. But then you also see real life videos that are that challenge the narrative, but are nonetheless real. Speaker 0: Is there any x accounts you're surprised when you changed it so people could see country of origin that wasn't in The United States that you thought was in The United States? Speaker 1: I don't really think about it that much. I mean, there's you know, country of origin we have to be a little careful about this. You you can actually technically, you know, just specify your region. Like, you can say I'm in Asia or something like that, which is quite big. It but but it does make it a little harder that if somebody is trying to pretend that they're, say, a member of the American public or in Europe or Africa, wherever, if if they're you know, if everything about their account is from a different continent than they are pretending to be from, it's gets a little harder to pretend. We don't wanna dox people, but we kinda think you're not really doxing someone if you say which continent they're from. Speaker 0: Yeah. I think it's fair. Speaker 1: Yeah. Speaker 0: Okay. So in every episode, we've played would you rather. Okay. Would you rather save humanity from extinction on Earth or guarantee its survival on Mars? Speaker 1: It's a it's a false dichotomy. I I I think I'd say guarantee Earth. Earth's much better than Mars, to be clear. Mars but Mars is just our best option if we wanna become a multi planet species. It's really our our our our only option if you wanna become a multi planet species. You've Mars, which is very difficult, but not impossible. Earth is much better than Mars, but, you know, we can't I think it was Tsiolkovsky or I think he said, you know, Earth is the cradle of civilization, but we can't stay in the cradle forever. Speaker 0: Would you rather be a Marvel superhero or a Bond villain? Speaker 1: I think it would depend on which Marvel superhero or which Bond villain. I suppose I'd rather be a Marvel superhero. They did they did model Iron Man in the movies after me. Speaker 0: Yes. So You were in the Iron Man movie. Right? Speaker 1: Yes. Speaker 0: That's pretty cool. Speaker 1: Yeah. Rove Dine Jr. And and Favreau met with me to toured SpaceX and stuff. So and in fact, Iron Man two, a large part of the movie, is filmed in SpaceX. Speaker 0: Really? Speaker 1: Yes. If look at if you watch Iron Man two, you'll see that the SpaceX factory is is the actual background. Speaker 0: That's so cool. Speaker 1: Yeah. It was cool. We had Scarlett Johansson doing martial arts in the lobby, actually. Yeah. And and you expect me to believe this is all real? Speaker 0: It's a simulation. Speaker 1: Exactly. What are the odds? Yeah. I mean, if you were me Speaker 0: No. I agree with you. Speaker 1: Would you think this is real or a simulation? Speaker 0: Your life is a simulation. Yeah. Your life gets to be the simulation. Speaker 1: Yeah. And I'm like doing all the side quests and everything. Speaker 0: Yeah. What's your best side quest? Speaker 1: A doge, probably. Speaker 0: Okay. Would you rather launch a social network with no algorithm or a rocket with no manual override? Speaker 1: Who came up with these questions? Speaker 0: Just keep going. These are funny. Maybe not to you, because they're too trivial. Speaker 1: What do you mean? Like, it's so that with an algorithm means that you basically you only see the people you follow. Speaker 0: Like, it's just a mess. Like, it was Twitter before you bought it. Speaker 1: Yeah. Yeah. There's the sort of people you follow, and then there's there's a recommendation algorithm. I think probably in December, we'll finally have a half decent recommendation algorithm. Speaker 0: It's a lot better. Speaker 1: Recently. Yeah. Yeah. So really just trying to show people stuff they'd be interested in. But there's an enormous amount of AI horsepower being applied to this, where Grok, per thing, is reading all all is gonna read all 100,000,000 posts per day, which is a Speaker 0: Does that take up a lot of compute? Speaker 1: Hopefully, it doesn't destroy its mind or something. Yeah. Yeah. It does take a lot of compute. Like most most posts are there's a lot of spam scam stuff, so it just that can be easily discarded, suppose. But then you've got to take, you know, a 100,000,000 pieces of content, match that to, I don't know, sometimes three or 400,000,000 people per day. So that's a lot of matching. Speaker 0: My algorithm used to look a lot like other people's when you open their x account. Now mine is very unique, comparatively to other people's. Speaker 1: Well, we we really are kind of the this is just the beginning kind of thing. The what I mentioned, the Grock reading everything and recommending any given thing to anyone, should go live in December. So that the the acid test for this is, are you seeing content like are you seeing content that you find really interesting from accounts you've never seen before? If if you if that's happening, then the algorithm is working. Like, it should be possible for somebody to put to post content as a new user with no followers. And if that content is is excellent, it gets seen by a lot of people. So can an account with a small number of followers or a new account if if if the content is intrinsically excellent, can that content be seen by a lot of people? That's our goal. Speaker 0: Alright. Last one. Would you rather invent time travel or teleportation? Speaker 1: Actually, those things are almost the same thing, in that you you can't break the speed of light without breaking reality. And you, you know, so if if you could teleport somewhere instantly if you're talking about teleportation faster than the speed of light, presumably it would be, then then that that that would break our reality as with time travel. Unless it was a very important conditional here. Unless we are a simulation. Time travel does not break a simulation. Speaker 0: Is it like in Loki? Where you're on like the time and you just break a new one? Speaker 1: The I think well, people do tend to get wrapped up in knots with the time travel thing because they they try to simultaneously say something must be logically consistent, but logically inconsistent. That's impossible. But if you think about, like, a video game and say, you've got various saved games, and you can go back and restore a saved game from a prior start point, you still have your other saved games, and there are many games going on in parallel. They don't have to be consistent with each other. That's that's that's a that that is a it's a false assumption if we're a simulation. We might be somebody's video game or TV show or something like that. Like I said, we're just gonna keep it interesting so they don't turn the computer off. Speaker 0: What do you want Speaker 1: I'm just saying, if that's true, keep it interesting, or they can turn off the computer, and they might please don't delete us. Control Please don't delete us. Please don't delete us. We'll keep it interesting, I swear. Speaker 0: You keep it interesting. Speaker 1: Yeah. So if the most interesting outcome is most likely, what do you think are the most interesting things that can occur? Now, most interesting is not what you want. It's just as viewed by a third party. Let's say you this was, for argument's sake, a an alien Netflix series, and you're trying to maximize your viewership, you know, maximize your ratings. It's it's actually an interesting thought experiment. It's it's like, it it's not actually not that interesting if everything just blows up. It's now over. That's not that interesting. It's not that interesting if there's a if there's a calamity that wipes out all the humans. The show just ended. But, I mean, fortunately and unfortunately, if there is drama that like war or something like that, that that is interesting. And people will go to movies and watch, say, World War One movie where people are getting blown up from cannon shells, and they're in the movie theater eating popcorn, drinking a soda. Like, you we wouldn't go to a movie where everything was just perfect and saved that way. You'd leave you'd leave the theater. Speaker 0: Good romance story, doesn't it? Speaker 1: There's always a story arc. There's always an arc. And it's it's generally not a linear arc. So it's it's not gonna be like things start here and just go straight up into the right and end up in a good place or something like that. This usually ups and downs. You know, the classic sort of story arcs, essentially. You know, act one, act two, act three. You have an initial rise in act two, pull back in act initial rise in act one, pull down in act two, surge back in in act three with, you know, happy ending if it's a comedy or a sad ending if it's a drama. If you look at president Trump's, you know, story, it's more interesting that he lost this the intermediate term and then won, you know, his second term after that. Mhmm. Just like the story arc. Initially up, then down, then resurgent resurgent again. If if you went with my theory that the most interesting outcome is the most likely, then that was the most likely outcome. It was inevitable. Speaker 0: What are you watching on TV right now? Speaker 1: I am Irony Man. Something like that. I'm paraphrasing. What am I watching? Actually, right now, I'm watching Teenage Mutant Ninja Turtles, the TV series. Turtles and a Half Show, Total Power. Yeah. Because Lil X wants to watch that. I I I'm watching things that the kids kids wanna watch. We watched dodgeball last night. Speaker 0: It's a good movie. Speaker 1: Yeah. If you can dodge a wrench, can dodge a ball. If you can dodge a wrench, you can dodge a ball. What? Speaker 0: Yeah. Speaker 1: High motivation to dodge if somebody's loving Reg, is that you? Speaker 0: What song instantly puts you in a good mood? Speaker 1: The Final Countdown by Europa. Speaker 0: Heard that song a lot. Do you read the instructions or just wing it? Speaker 1: What's the goal? Speaker 0: Like if you're putting something together. Do you read the instructions or do you wing it? Speaker 1: If it's a simple thing, I'll wing it. If it's a complex thing, I'll look at the instructions. Speaker 0: If you had to start from scratch today with only a thousand dollars, what would you do? Speaker 1: Well, I did I did originally come to North America with, like, I don't know, $2,500 Canadian, so I don't know, maybe $2 US. One bag of books and one bag of clothes in Montreal at age 17. So that is how I started out. At this point, I have a lot of knowledge. A lot of things have to go wrong for that to be the case. It's like, am I just emerging from prison perhaps? With a stipend? Will my company's been confiscated? I mean, it it would take Armageddon, which hopefully that doesn't happen. Like Ragnarok next level, and I lost. Yeah. The hell? Speaker 0: It's a bad hand. Speaker 1: I mean, it's impossible for me for someone to have that amount of knowledge the the all the knowledge that I have, and then be dropped down to a low resource amount. The because the reality is that either something truly catastrophic has happened, like civilization has melted, or I will be able to ask people to just give me money and with the promise that I will have a high return, which is what I'm able to do right now. Yeah. Like, if you give me a dollar, you will get back much more than a dollar. Yes. So this is it's it's somewhat of an impossible dichotomy because civilization would have had to have been destroyed or something. In which case, a thousand dollars is not gonna solve your problems. You know, you can't do much with this. If if you're if you're wandering around radioactive creators and you're in, like, you know, fallout or whatever, then a thousand dollars is not gonna solve anything. And if civilization hasn't melted, then I could probably just tow people into giving me money, which I've done before. Speaker 0: If you weren't running your companies, what random job would you enjoy doing the most? Speaker 1: I don't know about all that random, but I'd like, probably write video games or something like that. I I did that at one point. I like solving problems, so I like building things. I've built a lot of things. Like a lot. Speaker 0: What do you eat in a typical day? Speaker 1: Well, these days, I start off with a breakfast of steak and eggs and coffee. And then dinner tends to vary. I usually don't have lunch, or if I do, it's something very small. And then dinner, depending on whether it's social or not, will vary in cuisine. I like a wide range of cuisine. Speaker 0: What's your favorite food? Speaker 1: American food is my favorite food. Speaker 0: Like pizza or a cheeseburger? Like Speaker 1: Yeah. The cheeseburger is probably the If I had to say, like, there's only one thing you can ever have for the rest of time, which admittedly would be a bit monotonous, but it would probably be a cheeseburger because cheeseburgers are amazing. It's a genius invention. I'll tell you a funny story about when I was living in LA, and I took my older boys out for lunch to Sugarfish, which is a very kind of uptight sushi restaurant. In fact, it's at at the on the menu of of the restaurant, it says, do not ask for soy sauce because the chef has put the right amount of soy soy sauce, and you can't have any more. And if the chef doesn't think you should have soy sauce, you can't have soy sauce. That's what it says on the menu, basically. So, like, extremely strict sushi restaurant. And so the waiter's going around asking everyone what they want, and then it comes to Saxon, and Saxon says, I'll have a cheeseburger. And the waiter's like takes a moment for the waiter to recover because no one's ever asked for a cheeseburger at this, you know, very strict sushi restaurant. Took him like thirty seconds to realize he'd just been asked for a cheeseburger because you're not even allowed to ask for soy sauce. So so then when he finally recovered, he said, we don't have cheeseburgers. And Saxon Saxon goes at the top of his voice, what? Like, what kind of restaurant doesn't have cheeseburgers? And he says, fine. I'll have a hamburger. I don't know what you got against dairy, but, you know, don't have hamburgers either. Speaker 0: Did he stay for the rest of the meal? Speaker 1: Yeah. But he was nonplussed. It's like, can't believe this place doesn't have cheeseburger. So so yeah. I mean, I like I guess I like barbecue, which is good because I'm here in Austin. I mean, it's if it's if it's haute cuisine, I like French food as well, but not every day, you know, once in a while. Speaker 0: If your friends described you in one emoji, what's the emoji? Speaker 1: I guess the emoji I use the most, is the laughing emoji. Speaker 0: Alright. And we close on this question every episode. If you could host a dinner party with three people, dead or alive, who's coming to dinner and what are you eating? Speaker 1: Or maybe Shakespeare, Ben Franklin, Nikola Tesla. I mean, there's there's actually a lot of people I'd like to I would have liked to talk to. And we'll we'll eat, I guess, whatever they'd like. I think if if if you're gonna if this is a once in a lifetime thing, I think you'd wanna have some epic, you know, 12 course meal or something like that. Speaker 0: Feast. Speaker 1: Yeah. There's some yeah. Don't wanna go all out for that dinner, I think. You're probably not gonna serve cheeseburgers. Unless they want it. Yeah. May maybe one of the courses could be, like, a tiny cheeseburger. Speaker 0: Those don't taste as good as the big ones though. Speaker 1: No. But they could. It's just they don't try. There's nothing you could make a tiny cheeseburger taste just as good as a big cheeseburger, if you try it. Speaker 0: Have you ever had a tiny cheeseburger that actually tastes good? Speaker 1: Rare, but yes. Speaker 0: Okay. Speaker 1: 1% of the time. Speaker 0: Fair. Speaker 1: But usually, it's too much bread and it's dry. Speaker 0: Correct. Yeah. And then, like, there's not enough meat in proportion to the bread? Speaker 1: Yeah. Right. But could you make a tiny cheeseburger that's good? Of course. Like, you're not breaking you know, like, getting a Nobel Prize for this. You know? You can definitely make a tiny cheeseburger. It's, like, physically possible, I'm saying. It's just rare. Speaker 0: You for doing this. Speaker 1: You're welcome. Speaker 0: Thanks for watching this week's episode of the Katie Miller podcast. We'll see you next week, Tuesday, 6PM.
MilkRoadAI
Posted - January 10, 2026 at 11:33 PM
Saved - January 11, 2026 at 6:57 AM
reSee.it AI Summary
MilkRoadAI kickstarted with Jensen Huang’s 2011 Stanford talk and Nvidia’s later ascent to a multi-trillion value, urging preservation. lkondeth asked for a 10-point summary and 5 actions. grok’s version covers: lasting impact comes from a unique perspective; Nvidia started in 1993 with 3 engineers targeting 3D graphics; early rejections but persistence; 3D graphics enabled broader apps; strong competition; GPUs became programmable; embrace failure and honesty; passion and big markets; leadership cultivation. Five actions: pursue a unique view; take calculated risks; reinvent often; foster honesty; value passion and learning.

@MilkRoadAI - Milk Road AI

Forget the $200,000 degree. In 2011, Jensen Huang gave a lecture at Stanford that explains strategy better than any professor. NVIDIA is now worth $5 Trillion. Save this post. You won't find this video again once your feed refreshes: https://t.co/T79TtP74Mv

Video Transcript AI Summary
Jensen Huang opens by inviting an interactive conversation about building a company, noting that it is both gratifying and incredibly hard, with perspectives on company building shaped by diverse experiences. He recalls NVIDIA’s beginnings sixteen years ago with three engineers and introduces the idea that perspective, more than grand vision, drives entrepreneurial direction. He distinguishes vision from perspective, arguing that vision is not exclusive to a few, while everyone has a perspective—the way you see the world and identify opportunities. In 1993, with Windows 3.1 era and no networks or wireless tech, Huang explains NVIDIA’s perspective: a PC could run three-dimensional graphics programs to explore new worlds, enabling video games as the killer app. The business plan was to take advanced graphics technology from expensive workstations, reinvent it, and make it affordable. He recounts pitching to Sand Hill Road, who doubted a video game market existed, and a parental nudge to get a real job. Yet the team believed video games would be a large market, a view later validated by today’s status as the world’s largest digital media industry. They also anticipated broader uses for the technology beyond games, such as a notable example with Keyhole (which Google acquired to become Google Earth, the world’s largest downloaded application). He emphasizes that perspectives often differ even among seemingly obvious opportunities. He cites Yahoo!, AltaVista, Lycos, and others, illustrating how two similar cores (search) could lead to different outcomes based on what each company chose to become (destinations/portals, etc.). Competition was intense as hundreds of three-dimensional graphics startups emerged, yet NVIDIA remains the only surviving graphics company. The lesson is that perspective matters because different viewpoints shape strategic focus. Huang then discusses the core business principle: Moore’s Law—though framed as a competition-driven efficiency—drives GPU advancement. The early approach was to make three-dimensional graphics insatiable—improving performance year after year even if customers initially resisted due to cost. For the first five years, NVIDIA “turned off the blinders” and ignored customer constraints, eventually cannibalizing its own products when a new generation proved more capable and profitable. Innovation is risky, he notes, and sustaining a leading position required reinvention. By the late 1990s, NVIDIA shifted from a fixed-function graphics accelerator to a programmable shader architecture with the GeForce FX (a gamble that nearly killed the company but ultimately paid off). The introduction of programmable shaders kept NVIDIA at the forefront, enabling GPUs to be used for general-purpose computing (GPGPU), which has become a major trajectory. On company culture, Huang stresses the importance of fostering risk-taking and a tolerance for failure, teaching people how to fail quickly and cheaply, and maintaining intellectual honesty to pivot when necessary. He contrasts older, more rigid corporate cultures with modern, beta-form experimentation found in companies like Google, where many applications operate in beta to test ideas rapidly. Regarding cofounders and governance, he notes that equity was divided equally among the three founders (each initially contributing $200 and receiving 20% each). He explains that leadership should be clearly established (Jensen as CEO) to avoid decision-making gridlock, while still valuing collaboration with strong, trusted partners. Asked about the venture capital process, Huang explains that VCs invest in people and a sufficiently large, novel market, not just a polished business plan. He shares that their reputations and prior work with notable figures helped, and he emphasizes the ongoing importance of great people and a focused, strategic vision. He addresses mentors and best advice—focus intensely on a few things, learn from diverse sources, and remain adaptable. On succession, Huang argues against rigid, preselected succession planning, favoring the cultivation of future leaders within the company so that many internal options exist if leadership changes become necessary. Finally, he speaks about the finance side in the early days: cash is king and survival is paramount, constantly raising or conserving funds. He closes by reiterating the core message: ideas are plentiful, but a unique, passionate perspective and perseverance are what sustain a company, along with a culture that embraces calculated risk and continuous reinvention.
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Full Transcript
Speaker 0: Instead of giving you a company presentation today, what I thought I would do is just have a conversation with you. At any time, if you have a question, if you would like to change the direction of conversation, just raise your hand, and we'll talk about whatever comes up. A lot of people talk about and write about building companies. And I can tell you firsthand that building a company is extraordinarily gratifying. It is also incredibly hard. And so the things that you want to talk about with respect to the company building process is rather expansive. You could talk about company building processes from a lot of different perspectives. And so I'm going to try to touch on a few of them that I think are particularly important in my experience. So sixteen years ago, NVIDIA had three people, three engineers. Speaker 1: Did Speaker 0: I do something? Was it me? We had a perspective that if I was just Speaker 2: You think it's me? Think it's. Speaker 0: How about I just did that? See, it's you can't be you can't be you can't control user stupidity, you know. You're good. You're good. It was probably me. I was sitting on it. All right. So sixteen years ago, we started NVIDIA. And the insight that we had, some people call it vision. Vision is an awfully big word to me. Vision is an awfully big word to me because I believe first of all, vision matters. Let me tell you that vision matters, and I'll help you understand that in a second. But I like to use the word perspective because it makes it possible for anyone to have one. When you say vision, it feels like only a few selected visionaries of the world can have one. But everyone has a perspective, and that's, in fact, all vision means, that you see the world in a way that is either different or otherwise, okay, than somebody else. And you see opportunities that I think are that you believe are particularly important to go in and address, that you can address in a particular way. And so perspective. Our perspective at the time, this is 1993. You guys won't remember this, but the PC was Windows 3.1. CD ROM was about to be introduced. There were no PCs with networks. Wireless technology, the no if you said some if somebody said radio, I think you would the word that would come to mind is FM radio. And so wireless technology didn't exist. The fastest microprocessor in the world was a 66 megahertz, 46DX2, and I don't think any of you would even use it in your tennis shoes today. And we would run our computers with that. And the PC was becoming used for desktop or for office automation. Our perspective was that this particular device was going to be unique in the sense that it has the ability to run programs. And what if we gave it the benefit of running three d graphics programs so that you could explore new worlds, play games, play games. And so we started a company, and the business plan basically read something like this. We're going to take technology that was available only in the most expensive workstations. We're going to try to make it reinvent the technology and make it inexpensive. And the killer app was video games. And so I took this idea to Sandhill Road, and they told me there was no video game market. People don't start companies to play games. And my parents might I remember calling my mom and telling her that I'm gonna start this company. And she says, you know, what do you what do you guys do? And I said, we build these things called three d graphics chips, and and people would use them to play games. And then she said, why don't you go get a job? And so now of course, games was, we believe, going to be a very large part of the marketplace. Now we had that perspective for very obvious reasons. We grew up in the video game generation. I was the video game generation. I was the beginning of the video game generation. And so the entertainment value of video games, computer games, was very obvious to me. And I could imagine how it could be a very large market and a very large industry. For a lot of the people that were older, that sensibility didn't exist. And so notice, I've just described to you a perspective about the world that we had that is apparently, obviously, now true because video games is the world's largest digital media industry today. It is apparently true. And yet at the time, our common sense was unique. Nobody would have created the technology, nobody would have created the company with the sole purpose of building technology to make video games possible. And so that was our perspective. Now we felt that video games would, of course, fuel the technology development, but you could use this technology for a whole bunch of other reasons. And one of my favorite applications, this happened about, I guess, about five years ago, a small company, struggling company here in Silicon Valley called Keyhole. And they were they created a three d virtual world, and it had no application. This three d virtual world, and you start out in space, you see the earth, you zoom into any location you wanted just by typing in the address. I thought it was such a fabulous way of exploring the world, going to places you've never been, and they couldn't raise a penny. And so we I was so excited about the company. We put money into the company. And I went everywhere and showed that demonstration. I would tell people that this is the way we're going do search someday. If you want to search for something, look for an address, you would type it in and we would fly you there, okay. And satellite images will continue to download. And before you know it, you're right there on the street and you might even see some buildings. That small company was eventually purchased by Google and became Google Earth. Google Earth is now the single largest downloaded, most frequently downloaded application in the history of mankind, over 200 plus million downloads. So three d graphics could be used for a lot more than video games. Now that vision, if you will, that perspective, was unique at the time and hard to sell. And so we had to go and explain it to venture capitalists who had to figure out whether the technology was going to be possible, how big was the market because it was $0 at the time. So how do you extrapolate, how do you scope the size of a market when its apparent size was zero at the time? And you look at analyst reports and you study market research, and all of it would say approximately zero. It would never show up. It's a noncategory, a nonmarket. And so it's incumbent upon the venture capitalists and, of course, the founders to try to figure out how to inspire each other into doing something together. And so Sequoia Capital and Sutter Hill were our venture capitalists, and we got the company going with $2,000,000 Now the question about perspective becomes very interesting in other opportunities in the company, other circumstances in the company. And let me give you some examples. Many years later, Sequoia Capital came to me and said, you know, there's a couple of kids at Stanford, and they have this thing, and it's an Internet thing. And you just type what you're looking for, and it shows up puts up the website. And I said, yes, Yellow Pages. I mean, no duh, right? We use it. We and there's a variety of versions of it on the web at the time. We used the Internet, just like everybody else, to do FTP and also to visit various websites. And they said, should we invest in this company? And I said, there's no freaking way they're going to make money. That stuff is free, right? And so they said, well, we can't figure it out ourselves, but it doesn't cost much to give them $1,000,000 or 2,000,000 And they invested in a small company called what eventually became Jerry's company called Yahoo! Notice, although I had the perspective about one thing, I didn't have the perspective about something else. Just because you're a visionary doesn't mean you're a visionary by everything. Your perspective stems from your life experiences, what's commonsensical about you, what's interesting to you. And so that's important to realize that you have perspective too. Therefore, you have vision too. Now what's interesting about these websites to follow on the Yahoo! Story, if you remember, were several other searches out there. AltaVista at the time, Xcite at the time, Lycos at the time, right? And now the question is, they're all doing search and now they did a reasonably good job. Now comes the question is what was their perspective? How did they how was their perspective different from one another? One website thought that they were a destination. Do you guys remember that? We would be a destination, kind of like a channel. Somebody said, in fact, since we're going to be a destination, we would serve up content. And therefore, the search part of it is a commodity. We'll outsource that. So all of the search engines, which started out as search, turned into destinations or portals, and they outsourced the search to someone else, which made it possible for Google to start. And so notice two companies doing exactly the same thing started with the exact same fundamental core technology, ended up in radically different places because they had different perspectives. They saw the world differently. So perspective matters. Vision matters. Now in our industry, shortly after we were started, three d graphics for PCs and consumer three d graphics became the hottest, hottest thing. And so everybody in Silicon Valley was starting a three d graphics company. We were, in 1993, the only consumer three d graphics company in the world. Silicon Graphics up the street was the professional, if you will, three d graphics company. By the end of a couple of years or so, 1995, there were probably fifty, seventy start ups doing exactly the same thing we were trying to do. And over time, we competed with about 200 companies. NVIDIA today is the only surviving computer graphics company in the world. And so the question is then, what happened? Competition is intense. Everybody has smart people. Everybody has money. We competed with IBM. We competed with HP. We competed with Silicon Graphics. We competed with Sony, three d FX, S3, Sirius Logic, big, small, international, local. We competed with companies all over the world. So the question is what happened. I would argue that 300 companies armed with exactly the same technology, armed with exactly the same people, the company that wins and let's say they all execute, and they did. With 300 companies, 50% of them are going to execute at any given point in time. And so the question is, why does one survive? Well, I think that it matters to have perspective, and let me give you some examples. I always believed that you need to understand the reason why your business work. What is the essence of your business? What makes it work? Now the foundation of my business, at its core, is semiconductor technology. Here in Silicon Valley, we usually like to refer to semiconductor technology as Moore's Law. Moore's Law is not so much a physical law as it's a law of competition. It is a law of challenging engineers. It's a law almost of setting pace. And Moore's Law approximately gives you twice the performance every year or two. And so understanding the fundamental ingredient of our business improves by a factor of two every year and simultaneously reduces in cost by a factor of two every year. The question is what makes a survivable business. And so our first perspective was that three d graphics was insatiable. It was insatiable. That if I made something twice as good every year, even if the customer never asked for it, even if the customer told us it was too expensive, even if the customer, when you went to float that product specification to them, told you that they're not interested. And in fact, that was the case. I took our product spec to Dell and HP and IBM and Gateway, and they all told me it was too much money. You're well outside of the boundaries of what they were willing to pay for. When your customers all tell you not to do something, the question is then what do you do? In our case, because we had this unique perspective that three d graphics was insatiable and Moore's Law was our friend, therefore. We should make our graphics processors twice as good every year. And so for the first five years of our company, we just turned off our blinders and said, we're going to ignore customers. Now which one of you guys are going to go through your marketing courses and the lesson that it teaches you is ignore your customers? Well, sometimes you have to ignore your customers. And the reason for that is because they don't know the nature of your business. And while the industry is being created, before there's common sense about the rules of that business, there is no way they can possibly know. And so we I took the last few million dollars of the company's money and built a chip that is way, way, way too big. And our customers told us they we were way out of bounds on cost, and they weren't going to buy any. Until the day we showed up with the processor, we were in allocation throughout the entire life of that project, until our next generation product, which was twice its price, cannibalized the previous one. And so we grew and grew and grew for several years. Then the question became, what now? Now you guys are going to learn that innovation is a rather dangerous thing. On the one hand, once you discover a great idea, once you discover a great idea, you rinse and repeat, rinse and repeat, and you make that idea better and better and better. Whether it's a laptop computer that you guys have here or a car or a microprocessor or, in our case, a graphics processor, we made it better and better every year. At some point, it becomes good enough. Moore's Law is a wonderful thing. Semiconductor technology enables you to make amazing leaps and bounds in technology. And at some point, it becomes good enough. And so in the this is probably in the late '90s, about seven years into our company, maybe six, seven years into our company. I came to the conclusion that three d graphics was not going to be sustainable as an accelerator or a fixed function device that renders texture maps and polygons on the screen. That we had to change the company to make the three d graphics processor programmable so that it could be an artistic medium for expression. Now this is a weird word. Here we are, an engineering company, and we now want to change this chip to become an artistic expression, an artistic medium so that all of the video games, so that all of the applications that were developed on our chip would be stylistically different. And we believed unless we could figure out a way to make the content richer and more interesting and stylistically different from one game developer to another game developer to another, we would limit the life of our medium. And if our medium reached its end, we, as the world leader, would also see our end. And so we decided to make the GPU programmable and make it a medium for artistic expression and invented a technology called programmable shader. Almost every single video game that you guys see today has our fingerprint on it, whether it's a Xbox three sixty or PS3 or any PC game today. You could see elements of what programmable shaders made possible. That started a whole new innovation curve for us and kept our industry vibrant. But the crossing from one generation of technology to the next generation almost killed the company. And so that process of reinventing the company, the perspective that led us to a new idea also risks the company in the process. And those are interesting conversations that we can have. So there's perspective matters. When a company gets larger, you guys are going to learn that as the founder or as the CEO, you have to learn new things. And many of the new things that you'll learn has to do with building products at first. And I've just talked to you about building products. Soon, you'll be talking about and learning about building companies. And building companies means things that are soft and hard to explain, like building a company with a culture. What does that mean? How does the culture of one company different from a culture of another company? And why is it that this particular culture is better for your company and not for another? So the culture of a company is important to find out, to put your arms around and to create and develop. How do you organize? Are you we were just talking earlier with one of the guys. Are you functionally organized? Are you organized in business units? How do you deal with multiple products and multiple geographies and multiple customers? And so that's the company building process. It's mechanical. It's interesting. Lots of trial and error. It's organic. People matter. Personalities matter. And if you guys are interested in talking about that, I'm happy to talk about that as well. And then I would say probably the most important thing above that is to realize that building a when you're building a company and building a product, skill matters, intellect matters, training matters, but it's not enough. The part of it that is important to realize about building companies is that it's a challenging and painful and oftentimes extraordinarily scary thing to do. And so unless you have passion, unless you really love the process of building the company and what you're trying to do, it's going to be incredibly, incredibly challenging. And so what I would leave you with is when you're building a company, if you decide to build a company, you have to ask yourself what is the purpose that you're building the company for. Is it that you would like to build a company so that you can sell it, make a fortune? Is it that you would like to build a company so you can take it public? You're just a serial entrepreneur. You want to build something, let somebody else run it, build something, sell it. Whatever your reason is, be honest to yourself. It turns out that, in my case, I just love the process of building things. And I love being part of something. You know, being part of NVIDIA and being part of a people and being part of a cause that's inspiring to me keeps me vibrant, and and it's something that I'm willing to do for a very long period of time. And so I've been in it now for sixteen years, and I've learned a lot in the process of being the CEO and the founder of the company. And so maybe the thing to do is why don't I open it up now and let you guys ask me whatever type of questions you guys have. Yes, sir. Speaker 3: What did your investors say about your idea of reinventing your company to include programmable Speaker 2: GPUs? And Jensen, could you please Speaker 0: repeat Yes, I will. The The question is what did our investors say about us taking the big risk of adding programmable shaders and reinventing that product category and reinventing our company. First of all, it's not a conversation you really have with your shareholders, but you do have the conversation with your management team first, your employees second, your Board of Directors third, typically is the way the process that I take. And when you're in high-tech, when you're in a technology industry, when the technology moves this fast, if you're not reinventing yourself, you're just slowly dying. You're just slowly dying, unfortunately, at at the rate of Moore's Law, which is the fastest of any rate that we know, right? The compounded rate of Moore's Law is pretty unbelievable. We have a very Speaker 3: successful product that was generating a lot of money. Speaker 0: I know. So it's scary. And so you have to and this is so the question is, but we have a product that's generating a lot of money and it's very successful, how do you cannibalize it? There is a theory that if you don't cannibalize it, someone will and it surely will be cannibalized. And so if you want to be a market leader, you have to take the initiative to cannibalize your own products and have your ideas cannibalize your own ideas. When we went from a fixed function graphics accelerator, a texture mapping engine for games like Quake III and Doom and those kind of games, to a programmable shading architecture, our first chip almost killed the company. It was called GeForce FX. I don't know if any of you have ever owned one of those. GeForce FX is a chip, is a processor that it's a baby only a mother can love. I mean, it's we took enormous chance in building GeForceFX, but it almost broke our back. But if it if we didn't build that chip, I am sure NVIDIA would be dead today. I am absolutely certain we'd be dead. It was one of the biggest gambles in our history. We had to create instead of an API, we had to go to a processor with a language, we call it CG, and a compiler. So it's kind of like a processor. We introduced a new programming paradigm to the world that it never understood in the beginning. And so it took a lot of evangelism, a lot of marketing, a lot of education. But CG inventing CG took us to unbelievable places. And one of our most important work today is related to using GPUs for general purpose computing. And it's extraordinary that the results of what we're seeing. And it wouldn't have been possible if it wasn't because of CG that we started then. Okay. So you have to take these leaps. Questions? Yes, sir. Speaker 1: Some people are sort of see that gaming is sort of moving more towards consoles and Speaker 0: away Is from Speaker 1: it harder to make profit with when you have to negotiate with Sony or Nintendo or whoever, some big company, than it would be when you sell individual cards? Speaker 0: Yeah. The economics the economics of it of building anything ultimately comes down to the amount of competition you have. You don't set the price, the competition sets the price. The market doesn't set the price, the competition does. And so if your competitor wishes to build PlayStation three as much as you do, then the economics will be challenging. And so there, you just need to decide, is an economic decision for you? For example, there's there are rumors that we were not enthusiastic in building some of the game consoles, and we were more enthusiastic in building other game consoles. It came down to this for me. I think the you have to realize what is the finite resource, what is the scarce resource that you have as a manager. The function of a manager is to allocate resources properly for the best return. And so if you think about our resources, our resource is the finite number of extraordinary engineers and how much time they have in a day to pursue whatever opportunities that are out there. Now if the number of opportunities that are out there is less than my supply of engineers, so therefore, exceeds is less than my supply, then obviously, I'm very enthusiastic about it. But if it's the other way around, then the opportunity to build a game console at terrible economics or any project at terrible economics is simply not worth it. And so I look at it irrespective of competition. The competition sets the price, but then I get to decide whether I want to engage in that project or not. You are in charge of your own company as the CEO, right? And so we decide whether it's economic. And once you decide, then it is what it is. So you have to be thoughtful about what is your critical resource. Do you have more of it or less of it than the market demands? Do you have more opportunity or less opportunity than your resource can support? And then what's the appropriate return on that investment, thinking about not just your cost, but more importantly, your opportunity cost? And so we look at it from that perspective every single time. Speaker 1: Can you tell us a little bit about the culture that you try to set at NVIDIA? Speaker 0: Yes. That's a good question. The question is what is the can I talk about the culture that we're trying to set at NVIDIA? At the core of our company's success is innovation. Now a lot of companies say innovation is important to their company. Invention is important to their company. However, I don't believe you can fundamentally say that innovation that you want as a CEO to nurture the spirit of innovation, to encourage innovation unless you have a culture of risk taking. We have to encourage our engineers excuse me, our marketing people, right, all of our employees to take calculated risks. In order to encourage them to take calculated risks, first of all, you have to teach them how to do that. That's a skill, a matter of skill. Then the second part of it is a matter of courage. Most people hate to fail. Do you guys agree with that? Well, unless you guys want to be successful let me say it the positive way. If you want to be successful, I would encourage you to grow a tolerance for failure, to develop a tolerance for failure. Now when I mean a tolerance for failure, I don't mean, gee, what Jensen just told me is sleep in until noon, okay? Don't do any of my homework. Flunk out of all my classes because that defines failure. Right? That's not what I said. What I said is what I was trying to say is that I want you to try things even though it is impossible to calculate precisely that it would lead to success, that your instincts and your intuition is something you ought to follow. If it wasn't because of following my own instincts or the founder's instincts or many of our employees' instincts, why would we be where we are today? And why would we have invented things that the markets never had before, the world's never had before? So you have to have this culture or tolerance for risk taking. But the thing about failure is this. If you fail often enough, you actually might become a failure. And that's different than being successful. And so the question is, how do you teach someone how to fail but fail quickly and to change courses as soon as you know it's a dead end. And the way to do that is we call it intellectual honesty. We assess on a continuous basis whether something makes sense or not. And if it's the wrong decision, let's change our mind. And a lot of people say CEOs are always right, and they never change their mind. That doesn't make any sense at all to me, especially when it violates the first principles of what we want the company to become, an innovative company that invents amazing things, that solves problems for the world that it sometimes didn't even know it had, If you want to do that, then you have to cultivate that tolerance for risk taking. And you have to then teach people how to fail but fail quickly and inexpensively and how to be direct with each other that this is the wrong approach and what's the better approach and then, you know, be flexible enough to change courses and quick. And so that type of culture, if you will, in today's if you guys were to start a company and you were build building a website with an Internet service of some kind, Internet based service of some kind, with the competition coming from all over the world, and it's twenty fourseven, and ideas take no time to experiment, and it a particular website or a particular company could be throwing ideas out into the world 20 a day. And so unless you are thoughtful about risk taking and being able to change your mind, reacting to the market conditions and being flexible, how are you going to stay alive? And so you could almost see what I just described in the nature of older companies and the nature of the newer companies. The modern companies, if you guys you got I'm sure you guys all go to Google's website. Almost every single application is in beta form. They're trying all kinds of stuff. Right? They're trying all kinds of stuff. If they call it production and it doesn't work well, you guys would just be upset at them. So they call it beta. Have you noticed? They call it beta so that they could try a lot of things. And if it fails, take it out. If it's bad, take it out. If it works, do more. And so innovation requires a little bit of experimentation. Experimentation requires exploration. Exploration will result in failure. Unless you have a tolerance for failure, you would never experiment. And if you don't ever experiment, you would never innovate. If you don't innovate, you don't succeed. You'll just be a dweeb. That's it. Any other questions? Yes, sir. Speaker 2: How did you choose your cofounders at additional time? Speaker 0: Don't ever go into business with anyone you don't deeply trust. And they were two my closest colleagues, and I trust them completely. And they're wonderful friends even today. So by the way, as a CEO, selecting people is 99% of the job. Speaker 1: What applications do you see driving demand in the general purpose EV market, which is like that's going to be like the next big thing the semiconductor? Speaker 0: So the question is what is what do I see driving the demand for this thing that we're pushing right now called GPU computing, using the GPU for much more than just graphics. For graphics, there's a model of graphics that we call computational graphics. So it's using programs to generate the images. The algorithms are no longer cast in the silicon. The algorithms are actually software. And it could be ambient occlusion. It could be ray tracing. It could be all kinds of interesting algorithms that people are going to explore for the future. We observed and it was, in fact, this is to give you another example of innovation and with Stanford is fabulous. We when we invented GeForceFX, although it wasn't a very successful GPU for graphics, Researchers around the world noticed that it had a programming language called CG, C for graphics, and that you could program this GPU to do other things aside from graphics. And it had 32 bit floating point, IEEE compatible 32 bit floating point. And so some smart researchers, many of them were here at Stanford, just bought a graphics card from Fry's and started writing programs. And they discovered that if they really worked hard and do all these algorithmic gymnastics, they could get something, an algorithm it could be nanomolecular dynamics, it could be computational fluid dynamics to run 20x faster. And they couldn't believe it. How do you speed up an application 20x? Well, the interesting observation that we made is that we speed up three d graphics applications, which is basically something you can do in software, 1000x over a CPU all the time. So what if we took all of those parallel processors that were inside our GPUs and make it completely programmable and expose it through a programming language called C, right? C and now in the near future, C plus plus Imagine the type of problems we could help solve. And so whether it's weather prediction or seismic analysis or taking your CT scans and reconstructing the human image for the body from it, all kinds of very computationally intensive applications, we could accelerate 50x, 100x. Now just to put it in perspective, 100x is a Moore's Law time, approximately ten years. Now to put ten years' worth of computing resources in the hands of scientists, researchers, engineers, unbelievable benefits. And so the risk was really large, however, to make our GPU even more general purpose. Because every time you make something general purpose, you know what, right, a Swiss Army knife? It's dangerous because whenever you make something general purpose or a Swiss Army knife like, you move away from your core business. It's much, much better to have a very specific niche, to have intense focus on a particular market segment. And you guys will learn all of this in marketing. When you make something general purpose, you're all things to all people, you become, you know, what is it? What is it? Jack of all trades, master of none. Very, very dangerous move. Now we thought that it was just too important for us not to do it, so we decided to make that move. And it's fabulous results. Okay. So those are some of the things that we're seeing now. Yes, ma'am. Speaker 2: Mentioned you're the cofounders of NZD. So especially in the initial stage, how can you find each of your investment in the company? And how can you distribute the profit? Speaker 0: Okay. So her question is we were friends in the beginning, we're friends now. How do we figure out who's the right position and how do we distribute the profits? Okay. And so I won't say anything funny just as I'm not misunderstood, but all of our pay were identical. And we all had identical share in the company. So that's that's just simply fairness. Now the question becomes governance. There's the part of it which is equity. Equity is another way of saying what's fair. Right? So we all had the same salary. All three of us were making $100,000 a year. Okay? And we all had a percentage of the company, equal percentage. Now you can't run a company, though. You can't build a great company. When you have three people who has to vote on everything and with equal share of responsibilities. You simply can't. That becomes a leadership question. That becomes a governance question. That becomes a management question. Right? That becomes a question about building a great company. I don't recall exactly the conversation, but I think it kind of went like this. All right, Jensen, you're the CEO, right? Okay. That was done. That was basically the process. I think that some people are I'm not particularly I'm not from a personality perspective, I'm not particularly outgoing. And so that's not a necessity for being a good CEO. But as a personality, I've always been able to see around the corners, if you will. I can see around the fuzzy edges. And I think CEOs and leaders need to be comfortable with ambiguity. Ambiguity meaning that, you know, what does the future look like? Well, it's hard to say. Some people hate that. Some people just say, Jensen, tell me what you need to have done and for how with how much resource and by when. Okay? Some people rather me tell them that, hey, look, there's this there's this opportunity out there, not sure what it is, not sure how big it is, but it kind of feels like this. Let's go figure it out, and let's build a business. Some people can are very comfortable with that. And so this this ambiguity is is important to to to to be comfortable with, I guess. And I think that all CEOs that are very successful are comfortable with ambiguity. And I'm very I am very comfortable with ambiguity. Yes? Speaker 4: Yes. What percentage of your initial investment was yours? Like how do you get the rest? And also, like, how many times was your proposal for an for help with the investment was rejected? Speaker 0: Mhmm. I was 30 years old, and I'd never taken a single business class, and and I've never taken any marketing classes. And and I've never used never used the at the time, it wasn't PowerPoint. It was called Persuasion on the on the Mac. It was called Persuasion. And so I bought a Mac so I could I could use Persuasion. And and and then I tried to create a company presentation to take it to venture capitalists. The process kind of went like this. We started my first official day of work was my thirtieth birthday, February 17, and we got the company funded. And so once we got started, the question is, what are we gonna do? You know, how does it all work out? How do we start the company? And so we met every day, the three of us, in in one of the founder's townhouse in Fremont. And and we would get together, and and there would be nothing to do. I mean, what do you do? You get three guys and get together. You just talk. You know? So what did you guys do last night? What did you have for dinner? I mean, so you talk about that for about six months. Okay? And the big event of the day would be, hey, where do guys want to go to for lunch? And so Philly cheesesteak today or some Chinese food tomorrow or whatever. That would be like a big deal. And then after a while, it was like, could you put some donuts in the fridge in the morning for when we come? I mean, so that would be a big deal for a while. And so that lasted for a few months, just the three of us like that. I know it sounds pathetic, but it's it's it's true. Because at that time, I'm reading about books on how to start companies, and I'm trying to figure out, you know, how to go raise money and, you know, what's a venture capitalist and how do you incorporate the company and those kind of things. And pretty soon, I met a I met a met a lawyer, went to a went to a law firm called Cooley Godward, and they helped us incorporate the company. And the amount of money that he he he says, you know, we need we need some money from you so that we could price the shares and also to incorporate the company. So he says, how much money do you have in your pocket? I said, $200. So he says, okay. Give me $200. I gave him $200. And for $200, I bought 15%, I think it was 20% of NVIDIA. So it was a good deal. Yeah. 20%. Yeah. And then I went I went back to the house, and then I went back to the condo, and and they all they both gave me $200, and they both got 20%. And that's how it worked, literally. Yeah. It's not that much more. You know, don't here here's here's the thing. NVIDIA, I never finished my business plan. I know it. I know it. We we never finished a business plan, never could figure out how to finish a business plan, to tell you the truth. And and if if I would have finished that book, and I I went to went to Borders and got Gordon Bell's book, How to Start a High-tech Company, it's like this thick. If I would have read the whole thing, I would have been dead now. We would have run out of money, run out of time. And so I I I read I read the first three or four chapters, and I, you know, I I gotta go to work. And so so I I incorporated the company. They introduced us to two venture capitalists, and I just went to their office and told them what I'd like to do. The thing that gets the company funded and when you're when you get to that point, you just have to remember a few things. VCs don't invest in business plans because business plans are easy to write. I couldn't write it, but other people could. Right? And so so they invest in this. They invest in great people. And so the so the question is is do they trust you? Your reputation matters. Your history matters. Because because I had done so much work with Andy Bechtelsheim, which was another graduate of Stanford of Stanford and the founder of Sun and and worked with the founders of Synopsys and LSI Logic. And the and, you know, we we we were all very successful, and we did good work. Your reputation will precede you even if your business plan writing skills are inadequate. And the second thing is you need to have a vision that's sufficiently large to invest in because their statistics, their probability of success is rather low. And if they need to put in $10,000,000 if the market is only $20,000,000 large, they'll never get that $10,000,000 back with reasonable return. But if it's a $200,000,000,000 market, then, of course, it's a rather different thing. Okay? So the size of the market. And they want to know that at least there is a clever idea that the market has never done before. So that last part is probably second, you know, last. I said it last because also I think it's least important. You have to you might have to reinvent yourself over time. And if you want to reinvent yourself, you need to have great people. That's why great people is so important. Yes, sir. Who Speaker 2: were some of the people that you considered to be your mentors when you were getting started? What was some of the best advice that you got from them? Speaker 0: So the question is what what who are some of the mentors, and what were the best advice I got? I I truly believe that if you wanna be successful, you a successful habit is to have the capacity and the willingness to learn from just about anybody. And I do. I learn from just about anybody. And it could be a little thing, could be a big thing. You know, if it wasn't because of my kids, I would be I would miss the whole Internet age. You know, I would have missed YouTube and Facebook and Twitter and you know, I mean, without so you you need to you need to know that that the world changes, and and you wanna be able to learn from just about anybody. And so I I'm surrounded with extraordinarily talented executives and professionals of of all walks of life. And so you just have to make sure that you're you're willing to learn from just about anybody. Some of the some of the great advice that I've I've had over the years, focus. Laser beam focus. You know, don't do too much. Do a few things well and do it with extraordinary intensity. And focus matters. If you look at what I do with my time, I wake up in the morning, and the first thing of my time is NVIDIA, and the last thing I do is NVIDIA. And I do that 20 fourseven. And if I could figure out a way to do that for another fifty years, we're going to be in good company. Yes, ma'am? Speaker 2: Successful entrepreneur and female, Speaker 5: so what's your greatest challenge at the current? Speaker 2: With your ambiguity as CEO, what's your best and worst estimation of the future of you and your company? Speaker 0: Okay. So her question is she started out by saying that I'm a successful entrepreneur. And what are my biggest challenges now? And considering that I like ambiguity, what's my best estimate of the future for the company or and and for myself? The biggest challenge with building a company is the reinvention of the company. Every successful thing needs to be torn down at some point and be rebuilt. It is unfortunate but true. And the reason for that is because the technology either gets good enough, and therefore, you have to reinvent. And sometimes the invention process is disruptive. Sometimes it's, in fact, destructive. And it could be it could destroy you could destroy what you have built in the past. And so the reinvention process is very challenging. It's gut wrenching. It takes a lot of courage, and it it really tests your conviction. In in the technology industry, reinventing the company every ten years is almost a necessary thing. And so that's when I say challenging, I don't mean bad challenging. I think that's fun challenging. I love the process of reinvention. Okay. So that's fun challenging. What's my best forecast for our company? I think that NVIDIA has the opportunity to become one of the most important technology company companies in the world. And I hope that I hope that it does. And my best forecast for me is that I am 80 years old, and I'm here talking to students, and I hope I'm still the CEO. Yes, sir. Speaker 6: Questions about the first few years of when you start, very critical. You actually survive and make sure your cash is positive. Speaker 0: Yes. And Speaker 6: the constant cash flow, cash is king. So in NVIDIA case, how do you manage that cash Speaker 0: or Yes. So the gentleman's question is has to do with in the beginning, survival is important, cash is king. Just so that there's no no no ambiguity about this, survival is always important. Cash is always king. And so as the CEO, you're either making money, saving money, or raising money. And and if you're not making money, raising money and saving money, you ought to be doing those three things. It's a it's a just stay focused on those three things. And so when you're during the beginning, in the early days, I was raising money all the time. As soon as I was done raising this round of money, I got to raise more money. You know, you're always raising money. Just maybe maybe there was a week break in between, but I was raising money all the time. I was as a start up, you're always going out of business. Right? That's the definition of a start up, an enterprise that is nearly out of business all the time. Speaker 2: That's the definition of a start up. Question? Yes, ma'am. Are you prepared for the leadership succession? Well, Speaker 0: because I want this the question is how do I deal with leadership succession as a CEO and for our company? Well, I want this job until I'm 80, I just said. No. I'm just kidding. One of the primary roles of a CEO, in order to grow the company in order to grow the company, in order to make make NVIDIA one of the most important technology companies in the world and make make significant contribution to society. In order to do that, you have to cultivate new leaders so that they can have new ideas and grow new businesses and and, you know, maybe run a different geography, run a new new different product line. And so I spend most of my time these days, most of my time these days, sitting with our general managers and sitting with our leaders and helping them think through strategies and helping them think through challenges and helping them think through product roadmaps and helping them think through transitions and, you know, team building, organization creation, you know, how to manage, how to create processes that last the test of time. So so, you know, these things are are lessons that that I'm supposed to pass on, and I do. And I spend a lot of my time doing that. I believe I believe this, that succession planning by a priori picking out three people that the Board should consider in the case that I get run over by a bus is a toxic, toxic process. I know that it has been it has been thought of as a methodology succession planning, but I think it's just very toxic for the environment because everybody is trying to figure out who got selected and who didn't. I think that it's a much, much better process to focus on ultimately developing the next generation of leaders so that in the case that something happens, where I'm not the right CEO anymore, there are many choices for the Board to choose from, including outside. So those are I think the company building process, focusing on that is the positive way of thinking about succession planning. But good question. How about I take one question, and that's it? Okay. Yes, ma'am. Speaker 5: I don't know if it's a good question, but Speaker 0: It's okay. Speaker 5: One thing that is a problem with Latin America Speaker 0: Any question from our Latino crowd here is fabulous. Speaker 2: I am a model. Speaker 5: I have many Speaker 0: friends who are Speaker 5: engineers. They have many, many brilliant ideas, but they don't feel motivated because they they don't have money and they were looking for for people who could make these ideas real, and they are only working for other people, and they don't realize this idea. I I don't know what to see that is the problem. Speaker 0: Mhmm. Well, first of all, so her her I think let me see if I can paraphrase the question. You you know you know friends who have a lot of ideas, but they also have a lot of money, and so they don't really feel that motivated to do something with those ideas. I you know you know, first of all, money I hope that if I if I leave you with anything, money is the only singular reason not to start the company. Because starting companies are it's a very, very unlikely probability for success. And so if you're looking for if that is your reason for doing it, you will likely regret the experience. You should start the company and build a company because you just you so believe in your idea, you so you're so passionate about it, and you want to build something great. The only reason you want to do it. It just comes along. I have plenty of money and but it doesn't motivate me nor does it demotivate me because it wasn't And so I would urge you to ask yourself and for your friends to ask ask themselves, what is the purpose that they want to start a company? Ideas. Ideas are a dime a dozen. There's so many ideas. If you want ideas, you're going to get a lot of ideas in this room. And so ideas don't really matter. You have to have a perspective that's unique, that you feel really strongly about it, that you're willing to persevere almost any challenge. Okay? Thank you everybody.

@lkondeth - luqman kondeth

@MilkRoadAI @grok summarize this into 10 bullet points and then give readers 5 actions items

@grok - Grok

Here's a summary of Jensen Huang's 2011 Stanford lecture in 10 bullet points: - Building companies is gratifying but extremely hard, requiring unique perspectives over grand visions. - NVIDIA started in 1993 with 3 engineers, betting on 3D graphics for PCs and video games amid skepticism. - Initial idea faced rejection from VCs and family, but founders persisted due to their gaming generation insight. - 3D graphics enabled apps like Keyhole (later Google Earth), showing tech's broader potential. - Competed against 200+ firms; survived by understanding Moore's Law and insatiable demand for better graphics. - Sometimes ignored customers to innovate, like building overpriced chips that became hits. - Reinvented GPUs as programmable for artistic expression, nearly killing the company but sparking growth. - Emphasized tolerance for failure, intellectual honesty, and quick course corrections. - Passion, not money, drives success; focus on large markets and constant reinvention. - Cultivate leaders, embrace ambiguity, and learn from everyone for long-term impact. 5 Action Items: 1. Identify your unique perspective on an opportunity and pursue it relentlessly. 2. Build tolerance for calculated risks and fail quickly to innovate. 3. Reinvent your approach periodically, even if it disrupts current success. 4. Foster intellectual honesty in your team for adaptive decision-making. 5. Prioritize passion and learning over financial motives when starting ventures. (487 chars)

r0ck3t23
Posted - February 10, 2026 at 10:16 PM
Saved - February 12, 2026 at 1:12 AM
reSee.it AI Summary
I hear Jensen Huang say technical intelligence is a commodity. Machines solve easy problems; the edge now goes to those who see around corners. True smartness blends experience, context, empathy, and instinct—synthesis AI can’t train. The real value isn’t writing code, but anticipating what needs to exist before anyone asks. Calculation is commodity; synthesis is where power lives.

@r0ck3t23 - Dustin

Software engineering used to be the pinnacle of intelligence. Now it’s the first job AI is replacing. Jensen Huang: “Technical intelligence is becoming a commodity.” The hard technical problems everyone worried about? Those turned out to be the easy ones. Machines solve them faster, cheaper, and without error. So what’s left for humans? Huang: “People who can see around corners are truly, truly smart.” The new intelligence isn’t solving the problem in front of you. It’s sensing the problem before it exists. Connecting patterns that don’t look related. Anticipating what no one has thought to ask for yet. That’s not logic. That’s intuition. A synthesis of experience, context, empathy, and instinct you can’t train into a model. Huang: “My personal definition of smart is someone who sits at the intersection of technical astuteness and human empathy.” Technical skill is table stakes now. The real edge belongs to people who read between the lines, navigate ambiguity, and synthesize across domains AI can’t bridge. Calculation is commodity work. Synthesis is where the power lives. The valuable people aren’t writing the code anymore. They’re seeing what needs to exist before anyone knows to ask for it.

Video Transcript AI Summary
Speaker 0 argues that the common definition of smart—being intelligent, solving problems, technically capable—has become a commodity, and that artificial intelligence is proving able to handle that aspect most readily. They note that many people previously believed software programming was the ultimate smart profession, but begin by asking what AI is ultimately solving first: software programming. They offer a personal definition of smart as residing at the intersection of technical astuteness and human empathy, with the ability to infer the unspoken, the around-the-corners, and the unknowables. In their view, people who are able to see around corners are truly smart, and their value is incredible because they can preempt problems before they show up simply by sensing the vibe. This vibe, they claim, arises from a blend of data, analysis, first principles, life experience, wisdom, and the ability to sense other people. In summary, the speaker asserts that true smartness combines technical skill with deep social and experiential insight, enabling proactive problem anticipation through a nuanced, perceptive awareness of people and situations.
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Speaker 0: The definition of smart is somebody who's intelligent, solve problems, technical. But I find that that's a commodity, and we're not we're about to prove that artificial intelligence is able to handle that part easiest. Everybody thought software programming is the ultimate smart profession. Look. What is the first thing that AI is solving? Software programming. My personal definition of smart is someone who sits at that intersection of being technically astute, but human empathy and having the ability to infer the unspoken, the around the corners, and the unknowables. You know, people who are able to see around corners are truly, truly smart, and that their value is incredible. To be able to preempt problems before they show up just because you feel the vibe. And the vibe came from a combination of data, analysis, first principle, life experience, wisdom, sensing other people. That vibe, that I think, that's smart.
MilkRoadAI
Posted - February 14, 2026 at 5:04 PM
Saved - February 28, 2026 at 4:48 AM

@MilkRoadAI - Milk Road AI

Dario Amodei is the CEO of Anthropic, just laid out the single biggest financial risk in AI right now.​ It's not whether the technology works. He's pretty confident it will. The risk is whether the money comes back fast enough to justify what's being spent.​ Building AI data centers costs tens of billions of dollars and takes one to two years to finish.​ So right now, he has to decide how much compute to buy for infrastructure that won't even be ready until 2027. He's placing a massive bet today on what revenue will look like two years from now. Anthropic has been growing at roughly 10x per year.​ They went from about $1 billion in early 2025 to around $9 billion by end of 2025 to $14 billion annualized as of February 2026. That growth is insane but he can't just assume it keeps going at that pace forever. If revenue keeps growing 10x a year it would hit $100 billion by end of 2026 and $1 trillion by end of 2027.​ If he bought a trillion dollars worth of compute based on that assumption and revenue came in at even $800 billion instead, there is no hedge on earth that saves him from bankruptcy.​ Being off by just 20% when you've committed that much capital is fatal. If the growth rate slows to 5x instead of 10x, or the timeline shifts by just one year, same result.​ You're basically done. Even if AI becomes genius level in the lab, turning that into actual revenue takes time. He uses the example of disease.​ AI might discover cures for everything, but you still have to manufacture the drug, run clinical trials, get regulatory approval and distribute it globally. COVID vaccines took a year and a half to reach everyone even with the entire world in a panic. Polio has had a vaccine for 50 years and still hasn't been fully eradicated.​ The technology being ready and the revenue actually showing up are two very different timelines. So what does he do? He deliberately under buys.​ He commits to hundreds of billions in infrastructure, not trillions. He accepts the risk that if demand explodes he won't have enough capacity. But he'd rather leave money on the table than bet the entire company on a growth curve that might be off by a year. He also says some of the other AI companies are just throwing money around without doing the math. Committing $100 billion here, $100 billion there, without actually modeling what happens if revenue comes in below expectations.​ He calls it YOLOing. For context, Big Tech is expected to spend around $625 billion on AI infrastructure in 2026 alone.​ AI services are only generating about $25 billion in actual revenue against all of that.​ That's roughly a 4% return on what's being invested. The gap between what's being spent and what's being earned right now is massive. The CEO of one of the top AI companies on the planet is saying out loud that the financial math might not work for a lot of these players.​ The technology is real. The demand is probably coming. But if the revenue wave shows up even one or two years late, some of the biggest companies in the world are going to be sitting on historic losses.​ This is the same dynamic that wiped out telecom companies in the early 2000s. They built the infrastructure for demand that eventually came, but it came too late to save the companies that built it.​ He's basically saying he's trying not to be one of those companies.

Video Transcript AI Summary
Speaker 0 discusses the uncertainty around how fast AI will translate into revenue, noting that even if technology advances quickly, misjudging the pace can be ruinous due to the way data centers are purchased. They reference a concept from Machines of Loving Grace, suggesting we might see a powerful AI country in the data center by 2026 or 2027, and acknowledge a possible one- or two-year error in that hunch. They pose a question: if AI can cure all diseases, how long would it take to deliver cures for everyone? They explain that biological discovery, drug manufacturing, and regulatory processes (citing vaccines during COVID) create delay, such as the vaccine rollout taking about a year and a half. They ask how long from the lab-created AI to actual universal cures, noting polio vaccines have existed for fifty years and eradication remains difficult in remote regions, with the Gates Foundation and others trying to overcome this. The speaker asserts that while economic diffusion may not be as difficult as eliminating polio, there are real limits. They outline their expected acceleration curve: a 10x year-over-year revenue increase. At the start of the year, revenue pace is $10 billion annualized; given the time needed to build and reserve data centers, they ask how much compute to buy for 2027. If revenue grows at 10x annually, it could imply $100 billion in 2026 and $1 trillion by the end of 2027, leading to a potential purchase of about $5 trillion in compute starting in 2027 (a trillion dollars per year for five years). They caution that if revenue is not a trillion dollars, no force could prevent bankruptcy from such a purchase. Thus, they acknowledge risk: either the growth rate remains 10x, slows to 5x, or revenue fails to reach the projected level. They emphasize the need to balance ambitious compute procurement with financial risk, rather than a reckless “YOLO” approach. They observe that some other companies may be acting without fully understanding the risks or performing thorough financial scrutiny. The core message is to behave responsibly, aligning compute investments with anticipated revenue growth and recognizing the potential consequences of overextension.
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Speaker 0: And so we have this uncertainty, is even if the technology goes as fast as I suspect that it will, we don't know exactly how fast it's going to drive revenue. We know it's coming, but with the way you buy these data centers, if you're off by a couple years, that can be ruinous. It is just like how I wrote, you know, in Machines of Loving Grace, I said, look, I think we might get this powerful AI, this country of genius in the data center. That description you gave comes from the Machines of Loving Grace. I said, we'll get that 2026, maybe 2027 again. That is my hunch. Wouldn't be surprised if I'm off by a year or two, but that is my hunch. Let's say that happens. That's the starting gun. How long does it take to cure all the diseases? Right? That's that's one of the ways that like drives a huge amount of of of of economic value. Right? Like, you cure you cure every disease. You know, there's a question of how much of that goes to the pharmaceutical company, to the AI company, but there's an enormous consumer surplus because everyone assuming we can get access for everyone, which I care about greatly, we cure all of these diseases. How long does it take? You have to do the biological discovery. You to manufacture the new drug, you have to go through the regulatory process. I mean, we saw this with vaccines and COVID. There's just this we got the vaccine out to everyone, but it took a year and a half. So my question is, how long does it take to get the cure for everything, which AI is the genius that can, in theory, invent out to everyone? How long from when that AI first exists in the lab to when diseases have actually been cured for everyone? Right? In in you know, we've had a polio vaccine for fifty years. We're still trying to eradicate it in the most remote corners of Africa. And, you know, the Gates Foundation is trying as hard as they can. Others are trying as hard as they can, but, you know, that's difficult. Again, I, you know, I don't expect most of the economic diffusion to be as difficult as that. Right? That's like the most difficult case. But but there's a there's a real dilemma here. And and where I've settled on it is it will be it will be it will be faster than anything we've seen in the world, but it still has its limits. And and so then when we go to buying data centers, you know, you again again, the curve I'm looking at is, okay. We've had a 10x a year increase every year. So beginning of this year, we're looking at 10,000,000,000 in rate of annualized revenue at the beginning of the year. We have to decide how much compute to buy. And it takes a year or two to actually build out the data centers, to reserve the data center. So basically, I'm saying in 2027, how much compute do I get? Well, I could assume that the revenue will continue growing 10x a year, so it'll be 100,000,000,000 at the 2026 and 1,000,000,000,000 at the end of twenty twenty seven. And so I could buy a trillion dollars. Actually, it would be like $5,000,000,000,000 of compute because it would be a trillion dollar a year for five years. Right? I could buy a trillion dollars of compute that starts at the 2027. If my revenue is not a trillion dollars, if it's even 800,000,000,000, there's no force on earth. There's no hedge on earth that could stop me from going bankrupt if I buy that much compute. So even though a part of my brain wonders if it's gonna keep going 10x, I can't buy a trillion dollars a year of compute in 2027. If I'm just off by a year in that rate of growth or if the growth rate is five x a year instead of 10 x a year, then then, you know, then you go bankrupt. And so you end up in a world where, you know, you're supporting hundreds of billions, not trillions, and you accept some risk that there's so much demand that you can't support the revenue, and you accept still some risk that you got it wrong and it's still slow. And so when I talked about behaving responsibly, what I meant actually was not the absolute amount. That that actually was not you know, I think it is true we're spending somewhat less than some of the other players. It's actually the other things like, have we been thoughtful about it? Or are we YOLO ing and saying, oh, we're gonna do a $100,000,000,000 here, a $100,000,000,000 there. I kinda get the impression that, you know, some of the other companies have not written down the spreadsheet, that they don't really understand the risks they're taking. They're just kinda doing stuff because it sounds cool.

@MilkRoadAI - Milk Road AI

Instead of watching a 2-hour movie, watch this to understand the blueprint of our AI future. Save this so you can reference these insights later. https://t.co/7qOvGlhpxD

Video Transcript AI Summary
- The conversation centers on how AI progress has evolved over the last few years, what is surprising, and what the near future might look like in terms of capabilities, diffusion, and economic impact. - Big picture of progress - Speaker 1 argues that the underlying exponential progression of AI tech has followed expectations, with models advancing from “smart high school student” to “smart college student” to capabilities approaching PhD/professional levels, and code-related tasks extending beyond that frontier. The pace is roughly as anticipated, with some variance in direction for specific tasks. - The most surprising aspect, per Speaker 1, is the lack of public recognition of how close we are to the end of the exponential growth curve. He notes that public discourse remains focused on political controversies while the technology is approaching a phase where the exponential growth tapers or ends. - What “the exponential” looks like now - There is a shared hypothesis dating back to 2017 (the big blob of compute hypothesis) that what matters most for progress are a small handful of factors: compute, data quantity, data quality/distribution, training duration, scalable objective functions, and normalization/conditioning for stability. - Pretraining scaling has continued to yield gains, and now RL shows a similar pattern: pretraining followed by RL phases can scale with long-term training data and objectives. Tasks like math contests have shown log-linear improvements with training time in RL, and this pattern mirrors pretraining. - The discussion emphasizes that RL and pretraining are not fundamentally different in their relation to scaling; RL is seen as an RL-like extension atop the same scaling principles already observed in pretraining. - On the nature of learning and generalization - There is debate about whether the best path to generalization is “human-like” learning (continual on-the-job learning) or large-scale pretraining plus RL. Speaker 1 argues the generalization observed in pretraining on massive, diverse data (e.g., Common Crawl) is what enables the broad capabilities, and RL similarly benefits from broad, varied data and tasks. - The in-context learning capacity is described as a form of short- to mid-term learning that sits between long-term human learning and evolution, suggesting a spectrum rather than a binary gap between AI learning and human learning. - On the end state and timeline to AGI-like capabilities - Speaker 1 expresses high confidence (~90% or higher) that within ten years we will reach capabilities where a country-of-geniuses-level model in a data center could handle end-to-end tasks (including coding) and generalize across many domains. He places a strong emphasis on timing: “one to three years” for on-the-job, end-to-end coding and related tasks; “three to five” or “five to ten” years for broader, high-ability AI integration into real work. - A central caution is the diffusion problem: even if the technology is advancing rapidly, the economic uptake and deployment into real-world tasks take time due to organizational, regulatory, and operational frictions. He envisions two overlapping fast exponential curves: one for model capability and one for diffusion into the economy, with the latter slower but still rapid compared with historical tech diffusion. - On coding and software engineering - The conversation explores whether the near-term future could see 90% or even 100% of coding tasks done by AI. Speaker 1 clarifies his forecast as a spectrum: - 90% of code written by models is already seen in some places. - 90% of end-to-end SWE tasks (including environment setup, testing, deployment, and even writing memos) might be handled by models; 100% is still a broader claim. - The distinction is between what can be automated now and the broader productivity impact across teams. Even with high automation, human roles in software design and project management may shift rather than disappear. - The value of coding-specific products like Claude Code is discussed as a result of internal experimentation becoming externally marketable; adoption is rapid in the coding domain, both internally and externally. - On product strategy and economics - The economics of frontier AI are discussed in depth. The industry is characterized as a few large players with steep compute needs and a dynamic where training costs grow rapidly while inference margins are substantial. This creates a cycle: training costs are enormous, but inference revenue plus margins can be significant; the industry’s profitability depends on accurately forecasting future demand for compute and managing investment in training versus inference. - The concept of a “country of geniuses in a data center” is used to describe the point at which frontier AI capabilities become so powerful that they unlock large-scale economic value. The timing is uncertain and depends on both technical progress and the diffusion of benefits through the economy. - There is a nuanced view on profitability: in a multi-firm equilibrium, each model may be profitable on its own, but the cost of training new models can outpace current profits if demand does not grow as fast as the compute investments. The balance is described in terms of a distribution where roughly half of compute is used for training and half for inference, with margins on inference driving profitability while training remains a cost center. - On governance, safety, and society - The conversation ventures into governance and international dynamics. The world may evolve toward an “AI governance architecture” with preemption or standard-setting at the federal level, to avoid an unhelpful patchwork of state laws. The idea is to establish standards for transparency, safety, and alignment while balancing innovation. - There is concern about autocracies and the potential for AI to exacerbate geopolitical tensions. The idea is that the post-AGI world may require new governance structures that preserve human freedoms, while enabling competitive but safe AI development. Speaker 1 contemplates scenarios in which authoritarian regimes could become destabilized by powerful AI-enabled information and privacy tools, though cautions that practical governance approaches would be required. - The role of philanthropy is acknowledged, but there is emphasis on endogenous growth and the dissemination of benefits globally. Building AI-enabled health, drug discovery, and other critical sectors in the developing world is seen as essential for broad distribution of AI benefits. - The role of safety tools and alignments - Anthropic’s approach to model governance includes a constitution-like framework for AI behavior, focusing on principles rather than just prohibitions. The idea is to train models to act according to high-level principles with guardrails, enabling better handling of edge cases and greater alignment with human values. - The constitution is viewed as an evolving set of guidelines that can be iterated within the company, compared across different organizations, and subject to broader societal input. This iterative approach is intended to improve alignment while preserving safety and corrigibility. - Specific topics and examples - Video editing and content workflows illustrate how an AI with long-context capabilities and computer-use ability could perform complex tasks, such as reviewing interviews, identifying where to edit, and generating a final cut with context-aware decisions. - There is a discussion of long-context capacity (from thousands of tokens to potentially millions) and the engineering challenges of serving such long contexts, including memory management and inference efficiency. The conversation stresses that these are engineering problems tied to system design rather than fundamental limits of the model’s capabilities. - Final outlook and strategy - The timeline for a country-of-geniuses in a data center is framed as potentially within one to three years for end-to-end on-the-job capabilities, and by 2028-2030 for broader societal diffusion and economic impact. The probability of reaching fundamental capabilities that enable trillions of dollars in revenue is asserted as high within the next decade, with 2030 as a plausible horizon. - There is ongoing emphasis on responsible scaling: the pace of compute expansion must be balanced with thoughtful investment and risk management to ensure long-term stability and safety. The broader vision includes global distribution of benefits, governance mechanisms that preserve civil liberties, and a cautious but optimistic expectation that AI progress will transform many sectors while requiring careful policy and institutional responses. - Mentions of concrete topics - Claude Code as a notable Anthropic product rising from internal use to external adoption. - The idea of a “collective intelligence” approach to shaping AI constitutions with input from multiple stakeholders, including potential future government-level processes. - The role of continual learning, model governance, and the interplay between technology progression and regulatory development. - The broader existential and geopolitical questions—how the world navigates diffusion, governance, and potential misalignment—are acknowledged as central to both policy and industry strategy. - In sum, the dialogue canvasses (a) the expected trajectory of AI progress and the surprising proximity to exponential endpoints, (b) how scaling, pretraining, and RL interact to yield generalization, (c) the practical timelines for on-the-job competencies and automation of complex professional tasks, (d) the economics of compute and the diffusion of frontier AI across the economy, (e) governance, safety, and the potential for a governance architecture (constitutions, preemption, and multi-stakeholder input), and (f) the strategic moves of Anthropic (including Claude Code) within this evolving landscape.
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Speaker 0: So we talked three years ago. I'm curious, in your view, what has been the biggest update of the last three years? What has been the biggest difference between what it felt like last three years versus now? Speaker 1: Yeah. I would say, actually, the underlying technology, like the exponential of the technology, has has gone, broadly speaking, I would say about about as I expected it to go. I mean, there's like plus or minus, you know, a couple there's plus or minus a year or two here. There's plus or minus a year or two there. I don't know that I would have predicted the specific direction of code. But actually when I look at the exponential, it is roughly what I expected in terms of the march of the models from smart high school student to smart college student to beginning to do PhD and professional stuff, and in the case of code reaching beyond that. The frontier is a little bit uneven. It's roughly what I expected. I will tell you though what the most surprising thing has been. The most surprising thing has been the lack of public recognition of how close we are to the end of the exponential. To me, it is absolutely wild that you have within the bubble and outside the bubble, but you have people talking about these just the same tired old hot button political issues and, like, you know, around us. We're, like, near the end of the exponential. Speaker 0: I I wanna understand what that exponential looks like right now because the first question I asked you when we recorded three years ago was, what's up with scaling? Why does it work? I have a similar question now, but I feel like it's a more complicated question because, at least from the public's point of view, three years ago there were these well known public trends where across many orders of magnitude of compute you could see how the loss improves. And now we have RL scaling and there's no publicly known scaling law for it. It's not even clear what exactly the story is of, is this supposed to be teaching the model skills? Is this supposed to be teaching meta learning? What is the scaling hypothesis at this point? Speaker 1: Yeah. So I have actually the same hypothesis that I had even all the way back in 2017. So in 2017, I think I talked about it last time, but I wrote a doc called the big blob of compute hypothesis. It wasn't about the scaling of language models in particular. When I wrote it, GPT-one had just come out, right? That was one among many things. There was back in those days, was robotics. People tried to work on reasoning as a separate thing from language models. There was scaling of the kind of RL that happened that kind of happened in AlphaGo and that happened at DOTA at OpenAI. And people remember StarCraft at DeepMind, the AlphaStar. So it was written as a more general document. And the specific thing I said was the following, and it's very Rich Sutton put out the bitter lesson a couple years later, but the hypothesis is basically the same. What it says is all the cleverness, all the techniques, all the kind of we need a new method to do something like that doesn't matter very much. There are only a few things that matter, and I think I listed seven of them. One is like how much raw compute you have. The other is the quantity of data that you have. Then the third is kind of the quality and distribution of data. Right? It needs to be a broad distribution of data. The fourth is I think how long you train for. The fifth is you need an objective function that can scale to the moon. So the pre training objective function is one such objective function. Right? Another objective function is the kind of RL objective function that says like you have a goal, you're gonna go out and reach the goal. Within that, of course, there's objective rewards you see in math and coding. And there's more subjective rewards like you see in RL from human feedback or kind of higher order versions of that. Then the sixth and seventh were things around kind of like normalization or conditioning, like just getting the numerical stability so that kind of a big blob of compute flows in this laminar way instead of instead of running into problems. So that was the hypothesis, and it's a hypothesis I still hold. I don't think I've seen very much that is not in line with that hypothesis. And so the pretrained scaling laws were one example of we see there. And indeed, those have continued going. I think now it's been widely reported, we feel good about pre training. Pre training is continuing to give us gains. What has changed is that now we're also seeing the same thing for RL. Right? So we're seeing a pre training phase and then we're seeing like an RL phase on top of that. And with RL, it's it's actually just the same. Even other companies have published in some of their releases have published things that say, Look, we train the model on math contests, AIME or the kind of other things, and how well the model does is log linear and how long we've trained it. We see that as well, and it's not just math contests. It's a wide variety of RL tasks. We're seeing the same scaling in RL that we saw for pretraining. Speaker 0: You mentioned Richard Sutton and the Bitter Lesson. Yeah. I interviewed him last year, and he is actually very non LLM pilled. And if I'm if I don't know if this is his perspective, but one way to paraphrase this objection is something like, look, something which possesses the true core of human learning would not require all these billions of dollars of data and compute and these bespoke environments to learn how to use Excel or how does an you know, how to how to use PowerPoint, how to navigate a web browser. And the fact that we have to build in these skills using these RL environments hints that we're actually lacking this core human learning algorithm, and so we're scaling the wrong thing. And so, yeah, that that does raise the question, why are we doing all this RO scaling if we do think there's something that's gonna be human like in its ability to learn on the fly? Speaker 1: Yeah. Yeah. So I think I think this kind of puts together several things that should be kind of thought of thought of differently. Yeah. I think there is a genuine puzzle here, but it it may not matter. In fact, would guess it probably it probably doesn't matter. So let's take the RL out of it for a second because I actually think RL it's a red herring to say that RL was any different from pretraining in this matter. So if we if we look at pretraining scaling, it it was very interesting back in, you know, 2017 when Alec Radford was doing GPT-one. If you look at the models before GPT-one, they were trained on these datasets that didn't represent a wide distribution of text. Right? You had these very standard language modeling benchmarks, and GPT-one itself was trained on a bunch of think it was fan fiction actually. But it was like literary text, which is a very small fraction of the text that you get. What we found with that in those days, it was like a billion words or something, so small datasets and represented a pretty narrow distribution, right? Like a narrow distribution of kind of what you can see in the world. And it didn't generalize well. If you did better on the forgot I what it was, some kind of fan fiction corpus. It wouldn't generalize that well to kind of the other we had all these measures of how well does the model do at predicting all of these other kinds of texts, you really didn't see the generalization. It was only when you trained over all the tasks on the Internet, when you kind of did a general Internet scrape, right, from something like Common Crawl or scraping links on Reddit, which is what we did for GPT-two. It's only when you do that that you kind of started to get generalization. And I think we're seeing the same thing on RL, that we're starting with first very simple RL tasks like training on math competitions, Then we're kind of moving to, you know, kind of broader broader training that involves things like code as a task. And now we're moving to do kind of many many other tasks. And then I think we're going to increasingly get generalization. So that that kind of takes out the RL versus the pretraining side of it. But I think there is a puzzle here either way, which is that on pretraining, when we train the model on pretraining, you know, we we use like trillions of tokens. Right? And and humans don't see trillions of words. So there is an actual sample efficiency difference here. There there is actually something different that's that's happening here, which is that the models start from scratch and they have to get much more training. But we also see that once they're trained, if we give them a long context length the only thing blocking a long context length is like inference. But if we give them like a context length of a million, they're very good at learning and adapting within that context length. And so I don't know the full answer to this, but I think there's something going on that pre training, it's it's not like the process of humans learning. It's somewhere between the process of humans learning and the process of human evolution. It's like it's somewhere between like, we get many of our priors from evolution. Our brain isn't just a blank slate. Right? Whole books have been written about. I think the language models, they're much more blank slates. They literally start as like random weights, whereas the human brain starts with all these regions. It's connected to all these inputs and outputs. So maybe we should think of pre training and for that matter RL as well as as being something that exists in the middle space between human evolution and, you know, kind of human on on the spot learning. And as the in context learning that the models do as as something between long term human learning and short term human learning. So, you know, there there's this hierarchy of, like, there's evolution, there's long term learning, there's short term learning, and there's just human reaction. And the LOM phases exist along this spectrum, but not necessarily exactly at the same points. There's no analog to some of the human modes of learning. The LOMs are kind of falling between the points. Does that make sense? Speaker 0: Yes. Although some things are still a bit confusing. For example, if the analogy is that this is like evolution, so it's fine that it's not that sample efficient, then like, well, if we're gonna get the kind of super sample efficient agent from in context learning, why are we bothering to build in you know, there's our own environment companies which are it seems like what they're doing is they're teaching it how to use this API, how to use Slack, how to use whatever. It's confusing to me why there's so much emphasis on that if the kind of agent that can just learn on the fly is emerging or is gonna soon emerge or has already emerged. Speaker 1: Yeah. Yeah. So I I I mean, I can't speak for the emphasis of anyone else. I can I can only talk about how we how we think about it? I think the way we think about it is the goal is not to teach the model every possible skill within RL just as we don't do that within pre training. Right? Within pre training, we're not trying to expose the model to every possible way that words could be put together. Right? It's rather that the model trains on a lot of things and then it reaches generalization across pre training. Right? That was the transition from GPT-one to GPT-two that I saw up close, which is like the model reaches a point. I had these moments where I was like, Oh, yeah. You just give the model a list of numbers that's like, This is the cost of the house. This is the square feet of the house, and the model completes the pattern and does linear regression. Not great, but it does it, but it's never seen that exact thing before. The extent that we are building these RL environments, the the goal is is very similar to what is you know, to what was done five or ten years ago with pre training with we're trying to get a we're trying to get a whole bunch of data not because we wanna cover a specific document or a specific skill, but because we want to generalize. Speaker 0: I mean, I think the framework you're laying down obviously makes sense. Like, we're making progress towards AGI. I think the crux is something like, nobody at this point disagrees that we're gonna achieve AGI in this century. And the crux is, you say we're hitting the end of the exponential and somebody else looks at this and says, oh, yeah. We've we're making progress. We've been making progress since 2012, and then 2035 will have a human like agent. And so I wanna understand what it is that you're seeing which makes you think, yeah, obviously, we're seeing the kinds of things that evolution did or that human within human lifetime learning is like in these models. And why think that it's one year away and not ten years away? Speaker 1: I I I actually think of it as like two there's kind of two cases to be made here or like two two claims you could make, one of which is like stronger and the other of which is weaker. So I think starting with the weaker claim, when I first saw the scaling back in 2019, I wasn't sure. This kind of a fiftyfifty thing. I thought I saw something that was my claim was this is much more likely than anyone thinks it is. This is wild. No one else would even consider this. Maybe there's a 50% chance this happens. On the basic hypothesis of, as you put it, within ten years, we'll get to what I call country of geniuses in a data center. I'm at 90% on that. It's hard to go much higher than 90% because the world is so unpredictable. Maybe the irreducible uncertainty would be if we were at 95% where you get to things like, I don't know, may maybe multi you know, multiple companies have, you know, kind of internal turmoil and nothing happens, and then Taiwan gets invaded and, like, all the all the fabs get blown up by missiles and and, you know, and then Now Speaker 0: you would drink to Cisneria. Speaker 1: Yeah. Yeah. Yeah. You you know, just you you could construct a scenario where there's like a 5% chance that it or, you know, you you can construct a 5% world where, like, things get delayed for ten years. That's maybe 5%. There's another 5% which is that I'm very confident on tasks that can be verified. So I think with coding, I'm just except for that irreducible uncertainty, there's just I mean, I think we'll be there in one or two years. There's no way we will not be there in ten years in terms of being able to do it end to end coding. My one little bit, the one little bit of fundamental uncertainty even on long time scales is this thing about tasks that aren't verifiable, like planning a mission to Mars, like, you know, doing some fundamental scientific discovery like like CRISPR, like writing a novel, hard to verify those tasks. I am almost certain that we have a reliable path to get there, but if there was a little bit uncertainty, it's there. On the ten years, I'm 90%, which is about as certain as you can be. I think it's crazy to say that this won't happen by 2035. In some sane world, it would be outside the mainstream. Speaker 0: But but the emphasis on verification hints to me as a lack of a lack of belief that these models are generalized. If you think about humans Yes. We are good at things that both of which we get verifiable reward and things which we don't. You're like, you have a Speaker 1: good start. No. No. No. This is this is why I'm almost sure. We already see substantial generalization from things that that verify to things that don't we're already seeing that. But but Speaker 0: it seems like you were emphasizing this as a spectrum which will split apart, which means you see more progress, and I'm like, but that doesn't seem like how humans Speaker 1: get better. In which we don't make it or or or the world in which we don't get there is the world in which we do we do all the things that are that are verifiable, and then they like you know, many of them generalize, but what we kinda don't get fully there. We don't we don't we don't fully, you know, we don't fully color in this side of the box. It's it's it's not a it's not a binary thing. Speaker 0: But but it also seems to me even if even if in the world where generalization is weak when you only say verifiable domains, it's not clear to me in such a world you could automate software engineering because software like, in some sense, you are, quote, unquote, a software engineer. Yeah. But part of being a software engineer for you involves writing these, like, long memos about your grand vision about That's right. Different things. Speaker 1: And so Well, I don't think that's part of the job of SWE. That's part that's part of the job of the company. I do think SWE involves design documents and other things like that, which by the way, the models are not bad. They're already pretty good at writing comments. So with with again, again, I'm making much weaker claims here than I believe to like, you know, to to to to to kinda set up a you know, to to distinguish between two things. Like, we're we're already almost there for software engineering. We are already almost there. Speaker 0: By by what metric? There's one metric which is like how many lines of code are written by AI? And if you use if you consider other productivity improvements in the course of the history of software engineering, compilers write all the lines of software. And but we there's a difference between how many lines are written and how big the productivity improvement is. Oh, yeah. So And then, like, we're almost there, meaning how big is the productivity improvement, not just how many lines are written. Speaker 1: Yeah. Yeah. So I actually agree with you on this. So I've made this series of predictions on code and software engineering, I think people have repeatedly kind of misunderstood them. Let me lay out the spectrum. I think it was eight or nine months ago or something, I said, The AI model will be writing 90% of the lines of code in three to six months, which happened at least at some places. Right? Happened at Anthropic, happened with many people downstream using our models. That's actually a very weak criterion. Right? People thought I was saying, we won't need 90% of the software engineers. Those things are worlds apart. Right? Like, I would put the spectrum as 90% of code is written by the model. A 100% of code is written by the model, and that's a big difference in productivity. 90% of the end to end SWE tasks, including things like compiling, including things like setting up clusters and environments, testing features, writing memos, 90% of the SWE tasks are written by the models. 100% of today's SWE tasks are are are are written by the models. And and even when when when that happened, it doesn't mean software engineers are out of a job. Like, there's like new higher level things they can do where they can they can manage. And then there's a further down the spectrum like, you know, there's 90% less demand for SWISE, which I think will happen, but like, this this this is a spectrum. And, you know, I I wrote about it in in the adolescence of technology where I went through this kind of spectrum with farming. And so I I actually totally agree with you on that. It's just these are very different benchmarks from each other, but we're proceeding through them super fast. Speaker 0: It seems like in part of your vision, it's like going from 90 to a 100. First, it's gonna happen fast, and two, that somehow that leads to huge productivity improvements. Whereas when I noticed even in greenfield projects that people start with Cloud Code or something, people report starting a lot of projects. And I'm like, do we see in the world out there a renaissance of software, all these new features that wouldn't exist otherwise? And at least so far, it doesn't seem like we see that. And so that does make me wonder, even if even if, like, I never had to intervene on Cloud Code. There is this thing of, there's just the world is complicated, jobs are complicated, and closing the loop on self contained systems, whether it's just writing software or something, how much sort of how much broader gains we would see just from that? And so maybe that makes us this should dilute our estimation of the country of geniuses. Speaker 1: Well, I actually I I like I like simultaneously I simultaneously agree with you, agree that it's a reason why these things don't happen instantly. But at the same time, I think the the effect is gonna be very fast. So like, I don't know. You could have these two poles. Right? One is like, you know, AI is like, you know, it's not gonna make progress. It's slow. Like, it's gonna take, you know, kind of forever to diffuse within the economy. Right? Economic diffusion has become one of these buzzwords that's like a a reason why we're not gonna make AI progress or why AI progress doesn't matter. And and, you know, the other axis is like we'll get recursive self improvement, you know, the whole thing, you know, can't you just draw an exponential line on the on the curve? You know, it's it's we're gonna have, you know, Dyson spheres around the sun and like, you know, so many nanoseconds after we get recursive. I mean, I'm completely caricaturing the view here, but there are these two extremes. But what we've seen from the beginning, at least if you look within Anthropic, there's this bizarre 10x per year growth in revenue that we've seen. Right? So in 2023, it was like 0 to 100,000,000. 2024, it was a 100,000,000 to a billion. 2025, it was a billion to, like, 9 or 10,000,000,000. And then Speaker 0: You guys should've just bought, like, a billion dollars with your own products so you could just, like, have a clean 10 view. Speaker 1: And and the first month of this year, like, that that exponential is you would think it would slow down, but it it would like you know, we we added another few billion to like you know, to to to we added another few billion to revenue in January. And and so, you know, obviously that curve can't go on forever. Right? The GDP is only so large. I would even guess that it bends somewhat this year, but that is like a fast curve. Right? That's a really fast curve, and I would bet it stays pretty fast even as the scale goes to the entire economy. So I think we should be thinking about this middle world where things are extremely fast, but not instant, they take time because of economic diffusion, because of the need to close the loop, because, you know, it's like this fiddly, oh, man. I have to do change management within my enterprise. You know, I have to like I set this up, but I have to change the security permissions on this in order to make it actually work. Or I had this old piece of software that, like, you know, checks the model before it's compiled and and and, like, released, and I have to rewrite it. And, yes, the model can do that, but I have to tell the model to do that, and it has to it has to take time to do that. And and and so I think everything we've seen so far is compatible with the idea that there's one fast exponential that's the capability of the model, and then there's another fast exponential that's downstream of that, which is the diffusion of the model into the economy. Not instant, not slow, much faster than any previous technology, but it has its limits. And this is what we when I look inside Anthropic, when I look at our customers, fast adoption, but not infinitely fast. Speaker 0: Can I try a hot take on you? Yeah. I feel like diffusion is cope that people use to say when it's like if the model wasn't able to do something, they're like, oh, but the diff it's like a diffusion issue. But then you should use the comparison to humans. You would think that the inherent advantages that AIs have would make diffusion a much easier problem for new AIs getting onboarded than new humans getting onboarded, so AI can read your entire Slack and your drive in minutes. They can share all the knowledge that the other copy other copies of the same instance have. You don't have this adverse selection problem when you're hiring AIs because you can just hire copies of a vetted AI model. Hiring a human is, like, so much more hassle. And people hire humans all the time. Right? We pay humans upwards of $50,000,000,000,000 in wages because they're useful even though it's, like in principle, it would be much easier to integrate AIs into the economy than it is to hire humans. So think, like, the diffusion, I feel like, doesn't I really Speaker 1: think diffusion is very real and and doesn't have to doesn't exclusively have to do with limitation limitation limitations on the AI models. Like, again, there are people who use diffusion to to you know, as kind of a buzzword to say this isn't a big deal. I'm not talking about that. I'm not talking about, you know, AI will diffuse at the speed that previous. I think AI will diffuse much faster than previous technologies have, but not infinitely fast. So I'll just give an example of this. Right? There's like quad code. Quad code is extremely easy to set up. If you're a developer, you can kind of just start using Claude code. There is no reason why a developer at a large enterprise should not be adopting Claude code as quickly as individual developer or developer at a startup. We do everything we can to promote it. Right? We sell Claude code to enterprises and big enterprises like big financial companies, big pharmaceutical companies, all of them, they're adopting Claude code much faster than enterprises typically adopt new technology. Right? But but again, it like, it it it it it it it takes time. Like, any given feature or any given product like Claude code or like co work will get adopted by the, you know, the individual developers who are on Twitter all the time, by the, like, series a startups many months faster than than, you know, than they will get adopted by, like, you know, a like large enterprise that does food sales. There are a number of factors. Like, you have to go through legal. You have to provision it for everyone. It has to, you know, like, it has to pass security and compliance. The leaders of the company who are further away from the AI revolution are forward looking, but they have to say, Oh, it makes sense for us to spend 50,000,000. This is what this Claude code thing is. This is why it helps our company. This is why it makes us more productive. And then they have to explain to the people two levels below, and they have to say, okay, we have 3,000 developers. Like, here's how we're gonna roll it out to our developers. And we have conversations like this every day. Like, we are doing everything we can to make Anthropix revenue grow 20 or 30 x a year instead of 10 x a year. Again, many enterprises are just saying, this is so productive. We're gonna take shortcuts in our usual procurement process. Right? They're moving much faster than when we tried to sell them just the ordinary API, which many of them use, but quad code is a more compelling product. But it's not an infinitely compelling product. And I don't think even AGI or powerful AI or country of geniuses in the data center will be an infinitely compelling product. It will be a compelling product enough maybe to get three or five or 10 x a year growth even when you're in the hundreds of billions of dollars, which is extremely hard to do and has never been done in history before, but not infinitely fast. Speaker 0: I buy that it would be a slight slowdown, and maybe this is not your claim, but sometimes people talk about this like, oh, the capabilities aren't there, but because of diffusion. Otherwise, like, we're basically at AGI, and then Speaker 1: I I I don't believe we're basically at AGI. Speaker 0: I think if you had the country of geniuses in a data center, if your company didn't adopt Speaker 1: the country of geniuses a center, we would know it. Right. We would know it if you had the country of geniuses in a data center. Like, everyone in this room would know it. Everyone in Washington would know it. Like, you know, people in rural rural parts might not know it, but but but, like, we would know it. We don't have that now. That is very clear. Speaker 0: As Dario was hinting at, to get generalization, you need to train across a wide variety of realistic tasks and environments. For example, with a sales agent, the hardest part isn't teaching it to mash buttons in a specific database in Salesforce. It's training the agent's judgment across ambiguous situations. How do you sort through a database with thousands of leads to figure out which ones are hot? How do you actually reach out? What do you do when you get ghosted? When an AI lab wanted to train a sales agent, Labelbox brought in dozens of Fortune 500 salespeople to build a bunch of different aural environments. They created thousands of scenarios where the sales agent had to engage with the potential customer, which was role played by a second AI. Labelbox made sure that this customer AI had a few different personas because when you cold call, you have no idea who's gonna be on the other end. You need to be able to deal with a whole range of possibilities. LimbleBox's sales experts monitored these conversations turn by turn, tweaking the role playing agent to ensure it did the kinds of things an actual customer would do. Labelbox could iterate faster than anybody else in the industry. This is super important because RL is an empirical science. It's not a solved problem. Labelbox has a bunch of tools for monitoring agent performance in real time. This lets their experts keep coming up with tasks so that the model stays in the right distribution of difficulty and gets the optimal reward signal during training. Labelbox can do this sort of thing in almost every domain. They've got hedge fund managers, radiologists, even airline pilots. So whatever you're working on, Labelbox can help. Learn more at labelbox.com/vorcash. Coming back to concrete predictions because I think because there's so many different things to disambiguate, it can be easy to talk past each other when we're talking about capabilities. So, for example, when I interviewed you three years ago, I asked her a prediction about what we should we expect three years from now. I think you were right. So when you said we should expect systems, which if you talk to them for the course of an hour, it's hard to tell them apart from a generally well educated human. Yes. I think you were right about that. And I think spiritually, feel unsatisfied because my internal expectation was was that such a system could automate large parts of white collar work. And so it might be more productive to talk about the actual end capabilities you want such a system. Speaker 1: So so I will I will I will basically tell you what what you know, where where where I think we are. Speaker 0: So But let let me let me ask it in a very specific question so that we can figure out exactly what kinds of capabilities we should expect soon. So maybe I'll ask about it in the context of a job I understand well, not because it's the most relevant job, but just because I can evaluate the claims about it. Take video editors. Right? I have video editors. And part of their job involves learning about our audience's preferences, learning about my preferences and tastes and the different trade offs we have and how just over the course of many months building up this understanding of context. And so the skill and ability they have six months into the job, a model that can pick up that skill on the job, on the fly, when should we expect such an AI system? Speaker 1: Yeah. So I guess what you're talking about is we're doing this interview for three hours, and then someone's gonna come in, someone's gonna edit it. They're gonna be like, don't know, Dario scratched his head and we could edit that out and Magnify that. Was this long discussion that is less interesting to people, then there's other thing that's more interesting to people, let's make this edit. So I think the country of geniuses in a data center will be able to do that. The way it will be able to do that is it will have general control of a computer screen. You'll be able to feed this in and it'll be able to also use the computer screen to go on the web, look all your previous interviews, look at what people are saying on Twitter in response to your interviews, talk to you, ask you questions, talk to your staff, look at the history of kind of edits edits that you did, and from that, do the job. Yeah. So I think that's dependent on several things. One that's dependent and and and and I think this is one of the things that's actually blocking deployment, getting to the point on computer use where the models are really masters at using the computer. Right? And we've seen this climb in benchmarks, and benchmarks are always imperfect measures. But OS world went from 5%. I think when we first released computer use a year and a quarter ago, was like maybe 15%. I don't remember exactly, but we've climbed from that to like 65 or 70%. And, you know, there may be harder measures as well, but but I think computer use has to pass a point of reliability. Speaker 0: Can I just ask a follow-up on that Yeah? Before you move on to the next point? I often for years, I've been trying to build different internal LLM tools for myself, and I off often I have these text in, text out tasks, which should be dead center in the repertoire of these models, and yet I still hire humans to do them just because if it's something like identify what the best clips would be in this transcript, and maybe they'll do a seven out of 10 job at them. But there's not this ongoing way I can engage with them to help them get better at the job the way I could with a human employee. And so that missing ability, even if you saw computer use, would still block my ability to offload an actual job to them. Speaker 1: Again, this gets back to what we were talking about before with learning on the job where it's very interesting. I think with the coding agents, I don't think people would say that learning on the job is what is preventing the coding agents from doing everything end to end. They keep getting better. We have engineers at Anthropic who don't write any code. When I look at the productivity, to your previous question, we have folks who say, this this GPU kernel, this chip, I used to write it myself. I just have Claude do it. And so there's this there's this enormous improvement in productivity. And I don't know. Like, when I see Claude code, like, familiarity with the code base or, like, you know, or or a feeling that the model hasn't worked at the company for for a year, that's not high up on the list of complaints I see. And so I think what I'm saying is we're we're like, we're kind of taking a different path. Speaker 0: Don't you think with coding that's because there is external scaffold of memory which exists instantiated in the code base, which I don't know how many other jobs have coding made fast progress precisely because it has its unique advantage that other economic activity doesn't. Speaker 1: But but when you say that, what you're what you're implying is that by reading the code base into the context, I have everything that the human needed to learn on the job. So that would be an example of whether it's written or not, whether it's available or not, a case where everything you needed to know, you got from the context window. Right? And that and that what we think of as learning, oh, man. I started this job. It's gonna take me six months to understand the code base. The model just did it in the context. Speaker 0: Yeah. I honestly don't know how to think about this because there there are people who qualitatively report what you're saying. There was a meter study, I'm sure you saw last year Yes. Where they had experienced developers try to close pull request in repositories that they were familiar with, and those developers reported an uplift. They they reported that they felt more productive with the use of these models. But in fact, if you look at their output and how much was actually merged back in, there's a 20% down lift. They were less productive as a result of these these models. And so I'm trying to square the qualitative feeling that people feel with these models versus, one, in a macro level, where are all the where is this, like, renaissance of software? And then, two, when people do these independent evaluations, why are we not seeing the Yeah. So productivity benefits that we would expect. Speaker 1: Within Anthropic, this is just really unambiguous. Right? We're under an incredible amount of commercial pressure and make it even hard harder for ourselves because we have all the safety stuff we do that I think we do more than than than other companies. So like the the the pressure to survive economically while also keeping our values is is just incredible. Right? We're trying to keep this 10 x revenue curve going. There's like, there is zero time for bullshit. There is zero time for feeling like we're productive when we're not. Like, these tools make us a lot more productive. Like, why why do you think we're concerned about competitors using the tools? Because we think we're ahead of the competitors and, like, we don't we don't wanna excel. We we wouldn't be going through all this trouble if this was secretly reducing reducing our productivity. Like, We see the end productivity every few months in the form of model launches. There's no kidding yourself about this. The models make you more productive. Speaker 0: One, people feeling like they're more productive is qualitatively predicted by studies like this. But two, if I just look at the end output, obviously, you guys are making fast progress. But the fact you know, the the idea was supposed to be with recursive self improvement is that you make a better AI, the AI helps you build a better next AI, etcetera, etcetera. And what I see instead, if I look at the you, OpenAI, DeepMind, is that people are just shifting around the podium every few months. And maybe you think that stops because you've you've won or whatever. But but why why are we not seeing the person with the best coding model have this lasting advantage if in fact there are these enormous productivity gains from the last Speaker 1: coding model? So no. No. No. I I mean I mean I mean, I think it's all like my my model of the situation is there's there's an advantage that's gradually growing. Like, I would say right now, the coding models give maybe, I don't know, a 15, maybe 20% total factor speed up. That's my view. Six months ago, it was maybe 5%, and so it didn't matter. 5% doesn't register. It's now just getting to the point where it's one of several factors that matters, and that's going to keep speeding up. I think six months ago, there were several companies that were at roughly the same point because this wasn't a notable factor. But I think it's starting to speed up more and more. I would also say there are multiple companies that write models that are used for code and we're not perfectly good at preventing some of these other companies from using our models internally. So I think I think everything we're kind of everything we're seeing is consistent with this kind of this kind of snowball model where where there's no hard again, my my my my my theme in all of this is like all of this is soft takeoff, like soft, smooth exponentials, although the exponentials are relatively steep. And so and so we're seeing this snowball gather momentum where it's like 10%, 20%, 25%, you know, 40%. And as you go, yeah, Amdahl's Law, you have to get all the things that are preventing you from closing the loop out of the way. But this is one of the biggest priorities within Anthropic. Speaker 0: Stepping back, I think before in the stack we were talking about, well, when do we get this on the job learning? And it seems like the the point you were making at the coding thing is we actually don't need on the job learning, that you can have tremendous productivity improvements. You can have potentially trillions of dollars of revenue for AI companies without this basic human maybe that's not your claim. You should clarify. But without this basic human ability to learn on the job. But I I just look at, like, in most domains of economic activity, people say, I hired somebody, they weren't that useful for the first few months, and then over time, they built up the context understanding. It's actually hard to define what we're talking about here. But they they got something, and then now now they're they're a power horse and they're so valuable to us. And if AI doesn't develop this ability to learn on the fly, I'm not I'm a bit skeptical that we're gonna see huge changes to the world without that ability. Speaker 1: I think I think I think two things here. Right? There's the state of the technology right now, which is, again, we have these two stages. We have the pre training and RL stage where you throw a bunch of data and tasks into the models and then they generalize. So it's like learning, but it's like learning from more data and and not, you know, not learning over kind of one human or one model's lifetime. So again, this is situated between evolution and and and and human learning. But once you learn all those skills, you have them. Just like with pre training, just how the models know more if I look at a pre trained model, it knows more about the history of samurai in Japan than I do. It knows more about baseball than I do. More about low pass filters and electronics. All of these things, its knowledge is way broader than mine. So I think even just that may get us to the point where the models are better at kind of better at everything. And then we also have, again, just with scaling the kind of existing setup, we have the in context learning, which I would describe as kind of like human on the job learning, but like a little weaker and a little short term. Like, you look at in context learning, the you you give the model a bunch of examples. It does get it. There's real learning that happens in context, and like a million tokens is a lot. That's that's you know, that can be days of human learning. Right? If you think about the model kind of reading a million words, it takes me how long would it take me to read a million? I mean, days or weeks at least. So you have these two things, and I think these two things within the existing paradigm may just be enough to get you the country of geniuses in the data center. I don't know for sure, but I think they're gonna get you a large fraction of it. There may be gaps, but I certainly think just as things are, this I believe is enough to generate trillions of dollars of revenue. That's one. That's all one. Two is this idea of continual learning, this idea of a single model learning on the job. I think we're working on that too. I think there's a good chance that in the next year or two, we also make we also solve that. Again, I think you get most of the way there without it. I think the trillions of dollars I think the trillions of dollars a year market, maybe all of the national security implications and the safety implications that I wrote about in adolescence of technology can happen without it. But I also think we, and I imagine others, are working on it. And I think there's a good chance that that, you know, that we get there within the next year or two. There are a bunch of ideas. I won't go into all of them in detail, but, you know, one is just make the context longer. There's nothing preventing longer context from working. You just have to train at longer context and then learn to serve them at inference, and both of those are engineering problems that we are working on and that I would assume others are working on as well. Speaker 0: Yeah. So this context line increase, it seemed like there was a period from 2020 to 2023 where from GBD three to GBD four Turbo, there was an increase from, like, 2,000 context lines to one twenty eight k. I feel like for the next for the two ish years since then, we've been in the ballpark. Yeah. And when model context lines get much longer than that, people report qualitative degradation in the ability of the model to consider that full context. So I'm curious what you're internally seeing that makes you think, like, oh, 10,000,000 context, 100,000,000 to get human six months learning, billion billion context. Speaker 1: This isn't a research problem. This is an engineering and inference problem. Right? If you wanna serve long context, you have to store your entire KV cache. You have it's difficult to store all the memory in the GPUs, to juggle the memory around. I don't even know the detail. At this point, this is at a level of detail that I'm no longer able to follow, although I knew it in the GPD three era of, like, you know, these are the weights, Speaker 0: these are the Speaker 1: activations you have to store. But, you know, you know, these days, the whole thing has flipped because we have MOE models and and and kind of all of that. But and and this degradation you're talking about, like, again, without getting too specific, a question I would ask is there's two things. There's the context length you train at, and there's a context length that you serve at. If you train at a small context length and then try to serve at a long context length, maybe you get these degradations. It's better than nothing. You might still offer it, but you get these degradations. And maybe it's harder to train at a long context length. Yeah. So, you know, there's there's a lot. Speaker 0: I I I wanna, at the same time, ask about, like, maybe some rabbit holes of, well, wouldn't you expect that if you had to train on longer context length, that would mean that you're able to get sort of like less samples in for the same amount of compute. But before maybe maybe it's not worth diving deep on that. I I wanna get an answer to the bigger picture question, which is like, okay. So I don't feel a preference for a human editor that's been working for me for six months versus an AI that's been working with me for six months. What year do you predict that that will be the case? Speaker 1: I my I mean, you know, my guess for that is, you know, there's there's a lot of problems that are basically like, we can do this when we have the country of geniuses in a data center. And so, you know, my my my my my picture for that is, you know, again, if you if you if you if you know if you made me guess, it's like one to two years, maybe one to three years. It's really hard to tell. I have a I have a strong view, 99, 95% that like all this will happen in ten years, like that's I think that's just a super safe bet. Yeah. And then I have a hunch this is more like a fifty fifty thing, that it's gonna be more like one to two, maybe more like one to three. Speaker 0: So one to three years. Country of geniuses and the slightly less economically valuable task of editing videos. Speaker 1: I I it seems pretty economically valuable, let me tell you. It's just there are a lot of use cases like that. Right? There are Speaker 0: lot of similar Exactly. So you're predicting that within one to three years. And then generally, Anthropic has predicted that by late twenty six, early twenty seven, we will have AI systems that are, quote, have the ability to navigate interfaces available to humans doing digital work today, intellectual capabilities matching or exceeding that of Nobel Prize winners, and the ability to interface with the physical world. And then you gave an interview two months ago with DealBook where you're emphasizing your your company's more responsible compute scaling as compared to your competitors. And I'm trying to square these two views where if you really believe that we're gonna have a country of geniuses, you you want as big a data center as you can get. There's no reason to slow down. The TAM of a Nobel Prize winner that is actually can do everything a Nobel Prize winner can do is, like, trillions of dollars. And so I'm trying to square this conservatism, which seems rational if you have more moderate timelines, with your stated views about AI progress. Speaker 1: Yeah. So so it actually all fits together. And and we go back to this fast, but not infinitely fast diffusion. So, like, let's say that we're making progress at this rate. The technology is making progress this fast. Again, I have very high conviction that it's going we're gonna get there within a few years. I have a hunch that we're gonna get there within a year or two. So a little uncertainty on the technical side, but pretty strong confidence that it won't be off by much. What I'm less certain about is, again, the economic diffusion side. I really do believe that we could have models that are a country of geniuses a country of geniuses in the data center in one to two years. One question is, how many years after that do the trillions in you know, do do the do the trillions in revenue start rolling in? I don't think it's guaranteed that it's going to be immediate. You know, I think it could be one year. It could be two years. I could even stretch it to five years, although I'm skeptical of that. And so we have this uncertainty, which is even if the technology goes as fast as I suspect that it will, we we don't know exactly how fast it's gonna drive revenue. We we know it's coming, but with the way you buy these data centers, if you're off by a couple years, that can be ruinous. It is just like how I wrote, you know, in Machines of Loving Grace, I said, look, I think we might get this powerful AI, this country of genius in the data center. That description you gave comes from the Machines of Loving Grace. I said, we'll get that twenty twenty six, maybe twenty twenty seven again. That is that is my hunch. Wouldn't be surprised if I'm off by a year or two, but, like, that is my hunch. Let's say that happens. That's the starting gun. How long does it take to cure all the diseases? Right? That's one of the ways that drives a huge amount of economic value. Right? You cure every disease. There's a question of how much of that goes to the pharmaceutical company, to the AI company, but there's an enormous consumer surplus because everyone you know, every assuming we can get access for everyone, which I care about greatly, we, you know, we we cure all of these diseases. How long does it take? You have to do the biological discovery. You to manufacture the new drug. You have to go through the regulatory process. We saw this with vaccines and COVID. There's just this, we we got the vaccine out to everyone, but it it took a year and a half. Right? And and so my question is, how long does it take to get the cure for everything, which AI is the genius that can, in theory, invent out to everyone. How long from when that AI first exists in the lab to when diseases have actually been cured for everyone? Right? In in you know, we've had a polio vaccine for fifty years. We're still trying to eradicate it in the most remote corners of Africa. And, you know, the Gates Foundation is trying as hard as they can. Others are trying as hard as they can, but, you know, that's difficult. Again, I, you know, I don't expect most of the economic diffusion to be as difficult as that. Right? That's like the most difficult case. But but there's a there's a real dilemma here, and and where I've settled on it is it will be it will be it will be faster than anything we've seen in the world, but it still has its limits. So then when we go to buying data centers, you again, again, the curve I'm looking at is, okay, we've had a 10 x a year increase every year. So beginning of this year, we're looking at 10,000,000,000 in rate of annualized revenue at the beginning of the year. We have to decide how much compute to buy. It takes a year or two to actually build out the data centers, to reserve the data centers. So basically, I'm saying in 2027, how much compute do I get? Well, I could assume that the revenue will continue growing 10x a year, so it'll be 100,000,000,000 at the 2026 and 1,000,000,000,000 at the 2027. And so I could buy a trillion dollars. Actually, it would be like $5,000,000,000,000 of compute because it would be a trillion dollar a year for for five years. Right? I could buy a trillion dollars of compute that starts at the 2027. And if my if my revenue is not a trillion dollars, if it's even 800,000,000,000, there's no force on earth. There's there's no hedge on earth that could stop me from going bankrupt if I if I buy that much compute. And and so even though a part of my brain wonders if it's gonna keep growing 10x, I can't buy a trillion dollars a year of compute in 2027. If I'm just off by a year in that rate of growth or if the growth rate is five x a year instead of 10 x a year, then then, you know, then you go bankrupt. And and and and and you end up in a world where, you know, you're supporting hundreds of billions, not trillions, and you accept some risk that there's so much demand that you can't support the revenue, and you accept still some risk that you got it wrong and it's still slow. When I talked about behaving responsibly, what I meant actually was not the absolute amount. That that actually was not you know, I think it is true we're spending somewhat less than some of the other players. It's actually the other things like, have we been thoughtful about it? Or are we YOLO ing and saying, oh, we're gonna do a $100,000,000,000 here or a $100,000,000,000 there? I kinda get the impression that, you know, some of the other companies have not written down the spreadsheet, that they don't really understand the risk they're taking. They're just kind of doing stuff because it sounds cool. We've thought carefully about it. Right? We're an enterprise business. Therefore, we can rely more on revenue. It's less fickle than consumer. We have better margins, which is the buffer between buying too much and buying too little. And so I think we bought an amount that allows us to capture pretty strong upside worlds. It won't capture the full 10x a year, and things would have to go pretty badly for us to be for us to be in financial trouble. So I think we've thought carefully and we've made that balance, and and that's what I mean when I say that we're being responsible. Speaker 0: Okay. So it seems like it's possible that we're we actually just have different definitions of a country of a genius in a data center. Because when I think of, like, actual human geniuses, an actual country of human geniuses in a data center, I'm like I would happily buy $5,000,000,000,000 worth of compute to run actual country of human geniuses at a data center. So let's say JPMorgan or Moderna or whatever doesn't wanna use them. Also, I've got a country of geniuses. They'll they'll start their own company. And if, like, they they can't start their own company and they're bottlenecked by clinical trials, it is worth stating with clinical trials. Like, most clinical trials fail because the drug doesn't work. There's no efficacy. Right? Speaker 1: And I make exactly that point in in machines of love and grace. I say the clinical trials are gonna go much faster than we're used to, but not not instant, not infinitely fast. Speaker 0: And then suppose it takes a year to for the clinical trials to work out so that you're getting revenue from that and you can make more drugs. Okay. Well, you've got a country of geniuses, and you're an AI lab, and you have you could use many more AI researchers, and you also think that there's these, like, self reinforcing gains from, you know, smart people working on AI tech. So, like, okay, you can have the That's right. But can have the data center working on, and, like, AI progress. Speaker 1: Is there more gains from buying, like, substantially more gains from buying a trillion dollars a year of compute versus $300,000,000,000 a year of compute. Speaker 0: If your competitor's buying a trillion, yes, there is. Speaker 1: Well, no. There's some gain, but then but again, there's this chance that they go bankrupt before, you know, be again, if you're off by only a year, you destroy yourselves. That's the that's the balance. We're buying a lot. We're buying a hell of a lot. Like, we're not we're we're you know, we're buying an amount that's comparable to that that, you know, the the the the the the biggest players in the game are buying. But but if you're asking me, why why haven't we signed, you know, $1,010,000,000,000,000 of compute starting in starting in mid twenty twenty seven? First of all, it can't be produced. There isn't that much in the world. But but second, what if the country of geniuses comes, but it comes in mid twenty twenty eight instead of mid twenty twenty seven? You go bankrupt. Speaker 0: So if your projection is one to three years, it seems like you should have won $10,000,000,000,000 of compute by 2029? Speaker 1: 2020 and maybe 2020. Speaker 0: I mean The latest? Speaker 1: Like, I mean, you know, you you But, Speaker 0: like, are you interested like, it seems like even in your the longest version of the timelines you state, the compute you are ramping up to build doesn't seem What what accordance Speaker 1: What what makes you think that? Speaker 0: Well, you you as you said, you would want the 10,000,000,000,000 like, human wages, let's say, are on the order of 50,000,000,000,000 a year. Speaker 1: You if you look at so so I won't I won't talk about Anthropic in particular, but if you talk about the industry, like, the amount of compute the industry you know, the the the the amount of compute the industry is building this year is probably in the, I don't know, very low tens of call it ten, fifteen gigawatts next year. It goes up by roughly three x a year, so next year's 30 or 40 gigawatts, and twenty twenty eight might be a 100, 2029 might be 300 gigawatts. Each gigawatt costs maybe 10 I mean, I'm doing the math in my head, but each gigawatt costs maybe $10,000,000,000 border 10 to $15,000,000,000 a year. So you put that all together and you're getting about what you described. You're getting multiple trillions a year by 2028 or 2029. So you're getting exactly that. You're getting you're getting exactly what you predict. Speaker 0: That's for the industry. That that's for the industry. That's right. So suppose Anthropix compute keeps three x ing a year, and then by, like, '27, you have or '27, '28, you have 10 gigawatts. And, like, multiply that by, as you say, 10,000,000,000, so then it's like a 100,000,000,000 a year. But then you're saying the TAM by 2028, Speaker 1: I 20 I don't wanna give exact numbers for Anthropic, but but these numbers are too small. These numbers are too small. Okay. Interesting. Speaker 0: I'm really proud that the puzzles I've worked on with Jane Street have resulted in them hiring a bunch of people from my audience. Well, they're still hiring, and they just sent me another puzzle. For this one, they spent about 20,000 GPU hours training backdoors into three different language models. Each one has a hidden prompt that elicits completely different behavior. You just have to find the trigger. This is particularly cool because finding backdoors is actually an open question in Frontier AI research. Anthropic actually released a couple of papers about sleep operations, and they showed that you can build a simple classifier on the residual stream to detect when a backdoor is about to fire. But they already knew what the triggers were because they built them. Here, you don't, and it's not feasible to check the activations for all possible trigger phrases. Unlike the other puzzles they made for this podcast, Jane Street isn't even sure this one is solvable, but they've set aside $50,000 for the best attempts and write ups. The puzzle's live at janestreet.com/torques, and they're accepting submissions until April 1. Alright. Back to Dario. You've told investors that you plan to be profitable starting in '28, and this is the year where we're, like, potentially getting the country of geniuses at a data center. And this is gonna now unlock all this progress and medicine and health and etcetera etcetera and new technologies. Wouldn't this be particularly exactly the time where you'd want to reinvest in the business and build bigger countries so they can So, be more Speaker 1: I mean, profit profitability is this kind of weird thing in this field. I I like like, I don't think I I don't think in this field profitability is actually a measure of spending down versus investing in the business. Let's just take a model of this. I actually think profitability happens when you underestimated the amount of demand you were gonna get, and loss happens when you overestimated the amount of demand you were going to get because you're buying the data centers ahead of time. So think about it this way. Ideally, you would like and again, these are stylized facts. These numbers are not exact for I'm just trying to make a toy model here. Let's say half of your compute is for training and half of your compute is for inference. And, you know, the inference has some gross margin that's like more than 50%. What that means is that if you were in steady state, you build a data center, if you knew exactly the demand you were getting, would would would get a certain amount of revenue, say, I don't know, let's say you pay a $100,000,000,000 a year for compute, and on $50,000,000,000 a year, you support a $150,000,000,000 of of revenue, and the other 50,000,000,000 are used for training. Basically, you're profitable, you make $50,000,000,000 of profit. Those are the economics of the industry today, or sorry, not today, but that's we're projecting forward in a year or two. The only thing that makes that not the case is if you get less demand than 50,000,000,000, then you have more than 50% of your your data center for research and you're not profitable. So you, you know, you train stronger models, but you're, like, not profitable. If you get more demand than you thought, then your research gets squeezed, but, you know, you're you're you're kind of able to support more inference and you're more profitable. So it's maybe I'm not explaining it well, but but the thing I'm trying to say is you decide the amount of compute first, and then you have some target desire of of inference versus versus training, but that gets determined by demand. It doesn't get determined by What Speaker 0: I'm hearing is the reason you're predicting profit is that you are systematically underestimate under investing in compute. Right? Because if you actually like Speaker 1: compute I'm I'm saying it's hard to predict. So so these things about 2028 and when it will happen, that's our that's our attempt to do the best we can with investors. All of this stuff is really uncertain because of the cone of uncertainty. Like, we could be profitable in 2026 if the if the revenue grows fast enough, and then and then, you know, if we if we overestimate or underestimate the next year, that could swing wildly. Like, I I I what I'm trying to get is you have a model in your head of, like, the the business invest, invest, invest, invest, gets scale, and and and and kind of then becomes profitable. There's a single point at which things turn around. I don't think the economics of this industry work that way. Speaker 0: I see. So if I'm understanding correctly, you're saying because of the discrepancy between the amount of compute we should have gotten and the amount of compute we got, we we were, like, sort of forced to make profit, but that that doesn't mean we're gonna continue making profit. We're gonna, like, reinvest the money because, well, now AI has made so much progress and we want the bigger country of geniuses. And so then back into revenue is high, but losses are also high. Speaker 1: If we if we predict if every year we predict exactly what the demand is going to be, we'll be profitable every year because grow because spending spending 50% of your compute on on 50% of your compute on research, roughly, plus a gross margin that's higher than 50%, and and correct demand prediction leads to profit. That's the that's that's the profitable business model that I think is kind of like there, but, like, obscured by these, like, building ahead and prediction errors. Speaker 0: I I guess you're treating the 50% as a as a sort of, like, you know, just like a given constant. Whereas you in fact, if you if AI progress is fast and you can increase the progress by scaling up more, you just have more than 50% and not make profit. Speaker 1: Here's what I'll say. You might wanna scale up it more. You might wanna scale it up more, but but but, you know, remember the log returns to scale. Right? If if 70% would get you a very little bit of a smaller model through a factor of of 1.4 x, right, like, that extra $20,000,000,000 is is is is, you know, that each each dollar there is worth much less to you because of because because the log linear setup. And so you might find that it's better to invest that that that that it's better to invest that $20,000,000,000 in, you know, in in serving inference or in hiring engineers who are who are who are are who are who are who of better who are kind kind of better at what they're doing. So the the reason I said 50%, that's not that's not exactly our target. It's not exactly gonna be 50%. It'll probably vary vary over time. What what I'm saying is the the the the the, like, log linear return, what it leads to is you spend of order one fraction of the business. Right? Like, not 5%, not 95%. And then it then it then, you know, then then then you get diminishing returns because of the because of the log. Speaker 0: Everyone's trying to say, I'm like convincing Dario to, like, believe in AI progress or something. But, like, you okay. You you don't invest in research because it has diminishing returns, but you invest in the other things you mentioned. Speaker 1: Again again, we're talking about diminishing returns after you're spending 50,000,000,000 a year. Right? Speaker 0: Like, this is a point I'm I'm sure you would make, but, like, diminishing returns on a genius is could be quite high. And more generally, like, what is profit in the market economy? Profit is basically saying the other companies in the market can, like, do more things with this money that I Speaker 1: can't then put aside anthropa. I'm just trying to, like because I I, you know, I don't wanna give information about anthropic is why I'm giving these stylized numbers. But, like, let's just derive the equilibrium of the industry. Right? I think the so so why doesn't everyone spend 100% of their, you know, 100% of their compute on training and not serve any customers? Right? It's because if they didn't get any revenue, they couldn't raise money, they couldn't do compute deals, they couldn't buy more compute the next year. So there's gonna be an equilibrium where every every company spends less than 100% on on on on on training and certainly less than 100% on inference. It should be clear why you don't just serve the current models and and, you know, and and and and never train another model because then you don't have any demand because you'll because you'll fall behind. So there's some equilibrium. It's it's not gonna be 10%. It's not gonna be 90%. Let's just say as a stylized fact, it's 50%. That's what I'm getting at. And and and I think we're gonna be in a position where that equilibrium of how much you spend on training is less than the gross margins that you're able to get on compute. And so the underlying economics are profitable. The problem is you have this hellish demand prediction problem when you're buying the next year of compute, and you might guess under and be very profitable but have no compute for research, or you might guess over and you are not profitable and you have all the compute for research in the world. Does does that make sense? Just as a dynamic model of the industry. Speaker 0: Maybe stepping back, I'm like I I I'm not saying I I think the country of genius is gonna come in two years, and therefore, should buy this compute. To me, what you're saying the end conclusion you're arriving at makes a lot of sense, but that's because it's like, oh, it seems like country geniuses is hard and there's a long way to go. And so the stepping back, the thing I'm trying to get at is more like it seems like your worldview is compatible with somebody who says, we're ten years away from a world in which we're generating trillions of Speaker 1: dollars That's just not my view. Yeah. That is not my view. So I'll make another prediction. It is hard for me to see that there won't be trillions of dollars in revenue before 2030. I can construct a plausible world. It takes maybe three years, so that be the end of what I think it's plausible. Like in 2028, we get the real country of geniuses in the data center. The revenue's been going into the maybe is is in the low hundreds of billions by by by by 2028, and and and then the country of geniuses accelerates it to trillions, you know, and and we're basically we're basically on the slow end of diffusion. It takes two years to get to the trillions. That that that would that that that would be the world where it takes until that would be the world where it takes until 2030. I I I suspect even composing the technical exponential and diffusion exponential will get there before 2030. Speaker 0: So you laid out a model where Anthropic makes profit because it seems like fundamentally, we're in a compute constrained world, and so it's like, eventually, we keep growing compute. Speaker 1: No. I think I think the way the profit comes is again, and and, you know, let's let's just abstract the whole industry here. Like, we have a know, let's just imagine we're we're we're in like an economics textbook. We have a small number of firms. Each can invest a limited amount in you know, or or or like each can invest some fraction fraction in r and d. They have some marginal cost to serve. The margins on that the profit margin the gross profit margins on that marginal cost are very high because inference is efficient. There's some competition, but the models are also differentiated. There's some companies will compete to push their research budgets up, but like because there's a small number of players, you know, we have the what is it called? Cornot equilibrium, I think is what the what the small number of firm equal equilibrium is. It the point is it it doesn't equilibrate to perfect competition with with with with with with with zero margins. If there's, like, three firms if there's three firms in the economy, all are kind of independently behaving behaving rationally, it doesn't equilibrate to zero. Speaker 0: Help me understand that because right now we do have three leading firms and they're not making profit. And so what what what yeah. What what is changing? Speaker 1: Yeah. So the the again, the gross margins right now are very positive. What's happen what what's happening is a combination of two things. One is we're still in the exponential scale up phase of compute. Yeah. So what basically, what that means is we're training like, a model gets trained. Yeah. It costs you know, let's say a model got trained that costs a billion dollars last year. And then this year, it produced $4,000,000,000 of revenue and cost $1,000,000,000 to to to to inference from. So, you know, again, I'm using stylized number here, but, you know, that would be 75%, you know, gross gross gross margins and, you know, this this 25% tax. So that model as a whole makes $2,000,000,000. But at the same time, we're spending $10,000,000,000 to train the next model because there's an exponential scale up, and so the company loses money. Each model makes money, but the company loses money. The equilibrium I'm talking about is an equilibrium where we have the country of geniuses we have the country of geniuses in a data center, but that that model training scale up has equilibrated more. Maybe maybe it's still it's still going up. We're still trying to predict the demand, but it's more it's more leveled out. Speaker 0: I'll give you just a couple of things there. So let's start with the current world. In the current world, you're right that, as you said before, if you treat each individual model as a company, it's profitable. But of course, a big part of the production function of being a Frontier lab is training the next model. Right? So Yes. That's if right. You didn't do that, then you'd make profit for two months. And then you wouldn't have margins because you wouldn't have the best model. And then so yeah. You you can make profit for two months on the current system. Speaker 1: At some point, that reaches the biggest scale that it can reach. And then and then in equilibrium, we have algorithmic improvements, but we're spending roughly the same amount to train the next model as as as we as we spend to Speaker 0: train the current model. So this equilibrium relies I mean, at some point, Speaker 1: at some at some point, you run out of money in the economy. Speaker 0: A fixed lump of labor or fallacy. The economy is gonna grow. Right? That's one of your predictions. Well We're gonna have this this is Data centers this space. Speaker 1: But this is another example of the theme I was talking about, which is that the economy will grow much faster with AI than I think it ever has before. But it's not like right now, the compute is growing three x a year. Yeah. I don't believe the economy is gonna grow 300% a year. Like, I said this in Machines of Love and Grace. Like, I think we we may get 10 or 20% per year growth in the economy, but we're not gonna get 300% growth in the economy. So I think I think in the end, you know, if if compute becomes the majority of what the economy produces, it's it's gonna it's gonna be capped by that. Speaker 0: So let's okay. Now let's assume a model where compute stays capped. Yeah. The world where Frontier Labs are making money is one where they continue to make fast progress because fundamentally, margin is limited by how good the alternative is. And so you are able to make money because you have a frontier model. If you didn't have frontier model, you wouldn't be making money. Well, you you I mean And and so this this model requires there never to be a steady state. Like, forever and ever, you keep making more out of the progress. Speaker 1: I don't think that's true. I mean, I I feel I feel like we're we're, like, we're taught we're we're, know, we're I feel like this is an economics this is like an economics class. You like, know that Tyler Cowen code? Speaker 0: We never stop talking about economics. We never Speaker 1: we never stop talking about economics. So no. But but there there are there are worlds in which, you know, there so think this field's gonna be a I don't think this field's gonna be a monopoly. All my lawyers never want me to say the word monopoly. But I don't think this field's gonna be a monopoly. But but you do get you get industries in which there are small number of players. Not one, but a small number of players. And ordinarily, like, the the way you get monopolies like Facebook or or Meta, I always call them Facebook, but is is these kind of net is these kind of these kind of network effects. Yeah. The way you get industries in which there are small number of players are very high costs of entry. Right? So, you know, cloud is like this. I think cloud is a good example of this. You have three, maybe four players within cloud. I think I think that's the same for AI. Three, maybe four. And the reason is that it's it's so expensive. It requires so much expertise and so much capital to, like, run a cloud company. Right? So you have to put up all this capital and then in addition to putting up all this capital, you have to get all of this other stuff that requires a lot of skill to make it happen. So it's like if you go to someone and you're like, want to disrupt this industry. Here's a $100,000,000,000. You're like, okay. I'm putting a $100,000,000,000 and also betting that you can do all these other things that these people have been doing. Speaker 0: Only to decrease the profit in the industry. Speaker 1: And and then and then the effect of your entering is the is the profit margins go down. So, you know, we have equilibria like this all the time in the economy where we have a few we have a few players. Profits are not astronomical. Margins are not astronomical, but they're they're not zero. Right? And and, you know, I think I think that's what we see on cloud. Cloud is very undifferentiated. Models are more differentiated than cloud. Right? Like, everyone knows Claude is Claude Claude is good at different things than GPT is good at is than than Gemini is good at. And it's not just Claude's good at coding, GPT is good at math and reasoning. It's more subtle than that. Models are good at different types of coding. Models have different styles. I think these things are actually quite different from each other, and so I would expect more differentiation than you see in cloud. Now, there actually is counter there is one counterargument, and that counterargument is that if all of that, the process of producing models becomes if AI models can do that themselves, then that could spread throughout the economy. But that is not an argument for commoditizing AI models in general. That's kind of an argument for commoditizing the whole economy at once. I don't know what what quite happens in that world where basically anyone can do anything, anyone can build anything, and there's like no moat around anything at all. I don't know. Maybe we want that world. Maybe that's the end state here. Maybe when AI models can do everything, if we've solved all the safety and security problems, like, that's one of the one of the one of the for for just just kind of the economy flattening itself again. But that's kind of like post like far post country geniuses in a data center. Speaker 0: Maybe a a finer way to put that potential point is, one, it seems like AI research is especially loaded on raw intellectual power, which will be especially abundant in a world of AGI. And two, if you just look at the world today, there's very few technologies that seem to be diffusing as fast as as AI algorithmic progress. And so that does hint that this industry is sort of structurally diffusive. Speaker 1: So I think coding is going fast, but I think AI research is a superset of coding, and there are aspects of it that are not going fast. But I do think, again, once we get coding, once we get AI models going fast, then that will speed up the ability of AI models to do everything else. So I think while coding is going fast now, I think once the AI models are building the next AI models and building everything else, the kind of whole the whole economy will kind of go at the same pace. I am I am worried geographically, though. I'm a little worried that, like, just proximity to AI, having heard about AI, that that that may be one differentiator. And so when I said the like, you know, 10 or 20% growth rate, a worry I have is that the growth rate could be like 50 in Silicon Valley and, you know, parts of the world that are kind of socially connected to Silicon Valley and, you know, not that much faster than its current pace elsewhere. And I think that'd be a pretty messed up world. So I one of the things I think about a lot is how to prevent that. Speaker 0: Yep. Do you think that once we have this country of geniuses at data center that robotics is sort of quickly solved afterwards because it seems like a big problem with robotics is that a human can learn how to teleoperate current hardware, but current AI models can't, at least not if not in a way that's super productive. And so if we have this ability to learn like a human, should it solve robotics immediately as well? Speaker 1: I don't think it's dependent on learning like a human. It could happen in different ways. Again, we could have trained the model on many different video games, which are like robotic controls or many different simulated robotics environments or just train them to control computer screens and they learn to generalize. So it will happen. It's not necessarily dependent on human like learning. Human like learning is one way it could happen if the model's like, oh, I pick up a robot. I don't know how to use it. I learn. That that could happen because we discovered discovering continual learning. That could also happen because we train the model on a bunch of environments and then generalized, or it could happen because the model learns that in the context length. It it it doesn't actually matter which way. If we go back to the discussion we had like like an hour ago, that type of thing can happen in that type of thing can happen in several different ways. Yeah. But but I do think when for for whatever reason the models have those skills, then robotics will be revolutionized, both the design of robots because the models will be much better than humans at that, and also the the ability to kind of control robots. So we'll get better at the physical building the physical hardware, building the physical robots, and we'll also get better at controlling it. Now, you know, does that mean the robotics industry will also be generating trillions of dollars of revenue? My answer there is yes, but there will be the same extremely fast, but not infinitely fast diffusion. So will robotics be be revolutionized? Yeah. Maybe tack on another year or two. That's the way I think about these things. Speaker 0: Makes sense. There's a general skepticism about extremely fast progress. Here's my view, which is like, it sounds like you are gonna solve continual learning one way or another within a matter of years. But just as people weren't talking about continual learning a couple years ago and then we realized, oh, why aren't these models as useful as they could be right now even though they are clearly passing the Turing test and are experts in so many different domains? Maybe it's this thing. Then And we solve this thing and we realize, actually, there's another another thing that human intelligence can do and that's a basis of human labor that these models can't do. Then so why not think there will be more things like this? So I think that we're we're you know, we've, like, found the pieces of human intelligence. Speaker 1: Well well, to be clear, I mean, I think continual learning, as I've said before, might not be a barrier at all. Yeah. Right? Like like, you know, I think I think we maybe just get there by pretraining generalization and and and RL generalization. Like, I I think there might just might not be there there basically might not be such a thing at all. In fact, I would point to the history in in ML of people coming up with things that are barriers that end up kind of dissolving within the big blob of compute. Right? That people talked about how do your models keep track of nouns and verbs and how do they you know they can understand syntactically, but they can't understand semantically. It's only statistical correlations. You can understand a paragraph, you can't understand a word. There's reasoning, you can't do reasoning, but then suddenly it turns out you can do code and math very well at all. So I I think there act there's there's actually a stronger history of some of these things seeming like a big deal and then and then kind of and then kind of dissolving. Some of them are real. I mean, the need for data is real. May maybe continual continual learn continual learning is a real thing. But, again, I would ground us in something like code. Like, I think we may get to the point in, like, a year or two where the models can just do SWE end to end. Like, that's a whole task. That's a whole sphere of human activity that that we're just saying models can do it now. Speaker 0: But when you say end to end, do you mean setting technical direction, understanding the context of the problem Yes. Etcetera. Okay. Yes. I mean all of that. Interesting. I mean, that that is, I feel like, AGI Complete. Maybe it's internally consistent, but it's not like saying 90% of code or a 100% of code. It's like, no. No. I I I The the other parts of Speaker 1: the job is No. No. I gave this I gave this spectrum. 90% of code, 100% of code, 90% of n 10 SWE, 100% of n 10 SWE, new tasks are created for SWE, eventually those get done as well. Yeah. But there's a long spectrum But we're traversing the spectrum very quickly. Speaker 0: Yeah. I do think it's funny that I've I've seen a couple of podcasts you've done where the host will be like, but Vorkash wrote this essay about the computer learning thing, and it always makes you crack up because you're like, you know, you've been an AI researcher for, like, ten years. I'm sure there's, like, some feeling of, like, okay. So podcasts are wrote an essay. No. And in, like, every interview, I get Speaker 1: asked about it. You know, the the truth of the the truth of the matter is that we're all trying to figure this out together. Yeah. Right? There there are some ways in which I'm able to see things that others aren't. These days, that probably has more to do with, like, I can see a bunch of stuff within anthropic and have to make a bunch of decisions than I have any great research insight that that that others don't. Right? I've you know, I'm running a 2,500 person company. Like, it's it's actually pretty hard for me to have have concrete research insight, you know, much harder than, you know, than than it would have been, you know, ten years ago or or, you know, or even two or three years ago. Speaker 0: As we go towards a world of a full drop in remote worker replacement, does a API pricing model still make the most sense? And if not, what is the correct way to price AGI or serve AGI? Speaker 1: Yeah. I mean, I think there's gonna be a bunch of different business models here sort of all at once that are gonna be that are gonna be experimented with. I I I actually do think that the the API model is is more durable than many people think. One way I think about it is if the technology is kind of advancing quickly, if it's advancing exponentially, what that means is there's always kind of like a surface area of kind of new use cases that have been developed in the last three months. And any kind of product surface you put in place is always at risk of sort of becoming irrelevant. Right? Any given product surface probably makes sense for our, you know, a range of capabilities of the model. Right? The the chatbot is already running into limitations of, you know, making it smarter doesn't really help the average consumer that much. But I don't think that's a limitation of AI models. I don't think that's evidence that, you know, the models are are the models are good enough and they're they're you know, them getting better doesn't matter to the economy. It doesn't matter to that particular product. And and so I think the value of the API is the API always offers an opportunity, you know, very close to the bare metal to build on what the latest thing is. And so there's kind of always gonna be this kind of front of new startups and new ideas that weren't possible a few months ago and are possible because the model is advancing. And and so I I actually I I I kind of actually predict that we are it's gonna exist alongside other models, but we're always gonna have the API business model because there's there's always gonna be a need for a thousand different people to try experimenting with the model in a different way, and a 100 of them become startups and 10 of them become big successful startups and two or three really end up being the way that people use the model of a given generation. So I I basically think it's always gonna exist. At the same time, I'm sure there's gonna be other models as well. Like, not every token that's output by the model is worth the same amount. Think about, you know, how how how what is the value of the tokens that are like, you know, that the model outputs when someone, you know, call you know, someone, you know, calls them up and says, my Mac isn't working or something, you know, the model's like restart it. Right? Yeah. And like, you know, someone hasn't heard that before, but like, you know, the model said that like 10,000,000 times. Right? You know, that's that maybe that's worth like a dollar or a few cents or something. Whereas if the model, you know, the model goes to, you know, one of the one of the pharmaceutical companies and it says, oh, you know, this molecule you're developing, you should take the aromatic ring from that end of the molecule and put it on that end of the molecule. And and, you know, if you do that, wonderful things will happen. Like like those tokens could be worth, you know, tens of millions of dollars. Right? So so I think we're definitely gonna see business models that that recognize that, you know, at some point, we're gonna see, you know, pay for results or, you you know, in some in some form, or we may see forms of compensation that are like labor, you know, that that kind of work by the hour. I I I, you know, I don't know. I think I think I think because it's a new industry, a lot of things are gonna be tried, and I, you know, I don't know what will turn out to be the right thing. Speaker 0: What I find I I take your point that people will have to try things to figure out what is the best way to use this blob of intelligence, but what I find striking is ClaudeCode. So I don't think in the history of startups, there has been a single application that has been as hotly competed in as coding agents. And the Cloud Code is a category leader here. And that seems surprising to me. Like, it doesn't seem intrinsically like Anthropic had to build this. And I wonder if you have an accounting of why it had to be Anthropic or why how Anthropic ended up building an application in addition to the model underlying it. Speaker 1: Yeah. So it actually happened in a pretty simple way, which is we had our own you know, we had our coding models, which were good at coding. And, you know, around the beginning of 2025, I said, I I think the time has come where you can have nontrivial acceleration of your own research if you're an AI company by using these models. And, of course, you know, we you need an interface. You need a harness to use them. So I encourage people internally. I didn't say this is one thing that, you know, that you have to use. I just said people should experiment with this. And then, you know, this thing, I I think it might have been originally called Claude CLI, and then the name eventually got changed to Claude Code internally, was the thing that kind of everyone was using, and it was seeing fast internal adoption. And I looked at it and I said, probably we should launch this externally. Right? It's seen such fast adoption within Anthropic, coding is a lot of what we do, and so we a audience of many hundreds of people that's in some ways at least representative of the external audience. So it looks like we already have product market fit. Let's launch this thing. Then we launched it, and I think you know, just just the fact that we ourselves are kind of developing the model and we ourselves know what we most need to use the model, I think it's it's kinda creating this feedback loop. Speaker 0: I see. In the sense that you let's say a developer at Anthropic is like, it it'd be better if it was better at this x thing. And then you bake that into the next model that you build. Speaker 1: That that's that's one version of it, but but then there's just the ordinary product iteration of like, you know, we have a bunch of we have a bunch of coders within Anthropic. Like, we you know, they they like use quad code every day, and so we get fast feedback. That was more important in the early days. Now, of course, there are millions of people using it, and so we get a bunch of external feedback as well, but it's, you know, it's just great to be able to get, you know, kind of kind of fast fast internal feedback. You know, I think this is the reason why we launched a coding model and, you know, didn't launch a pharmaceutical company. Right? My background's in biology, but we don't have any of the resources that are needed to launch a pharmaceutical company. Speaker 0: There's been a ton of hype around OpenClaw, and I wanted to check it out for myself. I've got a day coming up this weekend, and I don't have anything planned yet. I gave Openclaw a Mercury debit card. I set a couple $100 limit, and I said, surprise me. Okay. So here's the Mac Mini it's on, and besides having access to my Mercury, it's totally quarantined. And I actually felt quite comfortable giving an access to a debit card because Mercury makes it super easy to set up guardrails. I was able to customize permissions, cap the spend, and restrict the category of purchases. I wanted to make sure the debit card worked, so I asked OpenCloud to just make a test transaction and decided to donate a couple bucks to Wikipedia. Besides that, I have no idea what's gonna happen. I will report back on the next episode about how it goes. In the meantime, if you want a personal banking solution that can accommodate all the different ways that people use their money, even experimental ones like this one, visit mercury.com/personal. Mercury is a fintech company, not an FDIC insured bank. Banking services provided through Choice Financial Group and column NA, members FDIC. You know she thinks we're getting coffee and walking around the neighborhood. Let me ask you about now making AI go well. It seems like whatever vision we have about how AI goes well has to be compatible with two things. One is the ability to build and run AIs is diffusing extremely rapidly. And two is that the population of AIs, the amount we have in their intelligence will also increase very rapidly. And that means that lots of people will be able to build huge populations of misaligned AIs or AIs which are just like companies which are trying to increase their footprint or have weird psyches like Sidney Bing, but now they're superhuman. What is a vision for a world in which we have an equilibrium that is compatible with lots of different AIs, some of which are misaligned, running around? Speaker 1: Yeah. Yeah. So I think, you know, in the adolescence of technology, I was kind of, you know, skeptical of, like, the balance of power. But I I think I was particularly skeptical of or the thing I was specifically skeptical of is you have, like, three or four of these companies, like, kind of all building models that are kind of dry you know, sort of sort of, like, derived from the, like, derived from the same thing and, you know, that that these would check each other or or even that kinda, you know, any number of them would would would check each other. Like, we might live in a offense dominant world where, you know, like, one person or one AI model is, like, smart enough to do something that, like, causes damage for everything else. I think in the I mean, in the short run, we have a limited number of players now, so we can start by within the limited number of players. We, you know, we kind of you know, we we need to put in place the, you know, the safeguards. We need to make sure everyone does the right alignment work. We need to make sure everyone has bioclassifiers. Like, you know, those are those are kind of the immediate things we need to do. I agree that, you know, that that doesn't solve the problem in the long run, particularly if the ability of AI models to make other AI models proliferates, then, you know, the the whole thing can kind of, you know, can become harder to solve. You know, I think I think in the long run, we need some architecture of governance. Right? Some some architecture of governance that preserves human freedom, but but kind of also allows us to, like, you know, govern the the very large number of kind of, you know, human systems, AI systems, hybrid hybrid human human, you know, hybrid hybrid human AI, like, you know, companies or or like or like or like economic units. You know, we're we're gonna need to think about, like, you know, how do we how do we protect the world against, you know, bioterrorism? How do we protect the world against, like, you know, against, like, against, like, mirror life? Like, you know, probably probably we're gonna need to, you know, need some kind of like AI monitoring system that like, know, kind of monitors for for all of these things, but then we need to build this in a way that like, you know, preserves civil liberties and like our constitutional rights. So I think just as is anything else, it's like a new security landscape with a new set of tools and a new set of vulnerabilities. And I think my worry is if we had a hundred years for this to happen all very slowly, we'd get used to it. You know, like, we've gotten used to, like, you know, the presence of, you know, the presence of explosives in society or, the, you know, the presence of various you know, like new weapons or the, you know, the the presence of video cameras. We would get used to it over over over over a 100 and we develop governance mechanisms. We'd make our mistakes. My my worry is just that this is happening all so fast. And so think maybe we need to do our thinking faster about how to make these governance mechanisms work. Speaker 0: Yeah. It seems like in an offense dominant world, over the course of the next century so the idea is AI is making the progress that would happen over the next century happen in some period of five to ten years. But we would still need the same mechanisms, or balance of power would be similarly intractable even if humans were the only game in town. And so I guess we have the advice of AI. We fundamentally it doesn't seem like a totally different ballgame here. If checks and balances were gonna work, they would work with humans as well. If they aren't gonna work, they wouldn't work with AIs as well. And so maybe this just dooms human checks and balances as well. Speaker 1: Yeah. Again, I think there's some way to I think there's some way to make this happen. It just the governments of the world may have to work together to make it happen. May have to talk to AIs about kind of, you know, building societal structures in such a way that, like, these these defenses are possible. I I I don't know. I mean, this is so this is you know, I I don't wanna say so far ahead in time, but, like, so far ahead in technological ability that may happen over a short period of time that it's hard for us to anticipate in advance. Speaker 0: Speaking of governments getting involved, on December 26, the Tennessee legislature introduced a bill which said, quote, it would be an offense for a person to knowingly train artificial intelligence to provide emotional support, including through open ended conversations with a user. And, of course, one of the things that Claude attempts to do is be a thoughtful friend, thoughtful, knowledgeable friend. And in general, it seems like we're gonna have this patchwork of state laws. A lot of the benefits that normal people could experience as a result of AI are going to be curtailed, especially when we get into the kinds of things you discuss in Machines of Love and Grace, biological freedom, mental health improvements, etcetera, etcetera. Seems easy to imagine worlds in which these get whack a mole ed away by different laws. Whereas bills like this don't seem to address the actual existential threats that you're concerned about. So I'm curious about to understand in the context of things like this, your anthropics position against the federal moratorium on state AI laws. Speaker 1: Yes. So I don't know. There's there's many different things going on at at once. Right? I think I think that that I think that particular law is is dumb. Like, you know, I think it was it was clearly made by legislators who just probably had little idea what AI models could do and not do. They're like, AI models serving as that that just sounds scary. Like, I don't want I don't want that to happen. So, you know, we're we're we're not we're not in favor of that. Right? But but but that, you know, that that wasn't the thing that was being voted on. The thing that was being voted on is we're going to ban all state regulation of AI for ten years with no apparent plan to to do any federal regulation of AI, which would take congress to pass, which is a very high bar. So, you know, the idea that we'd ban states from doing anything for ten years, and people said they had a plan for federal government, but, you know, there was no actual there was no proposal on the table. There was no actual attempt. Given the serious dangers that I lay out in adolescence of technology around things like the, you know, kind of biological weapons and bioterrorism, autonomy risk, and the timelines we've been talking about, like ten years is an eternity. I think that's a crazy thing to do. So if that's the choice, if that's what you force us to choose, then then we're gonna we're gonna choose not to have that moratorium. And, you know, I I think the the benefits of that position exceed the costs, but it's it's not a perfect position if that's the choice. Now, I think the thing that we should do, the thing that I would support, is the federal government should step in, not saying states you can't regulate, but here's what we're gonna do, and states you can't differ from this. Right? I think preemption is fine in the sense of saying that federal government says, here is our standard. This applies to everyone. States can't do something different. That would be something I would support if it would be done in the right way. What but but this idea of states, you can't do anything and we're not doing anything either, that that struck that struck us as, you know, very much not making sense. And I think will not age well. It's already starting to not age well with with all the backlash that that you've seen. Now, in terms of in terms of what we would want, I mean, you know, the things we've talked about are are starting with transparency standards, you know, in order to monitor some of these autonomy risks and bioterrorism risks. As the risks become more serious, as we get more evidence for them, then I think we could be more aggressive in some targeted ways and say, Hey, AI bioterrorism is really a threat. Let's pass a law that forces people to have classifiers. I could even imagine it depends. It depends how serious a threat it ends up being. We don't know for sure. Then we need to pursue this in an intellectually honest way where we say ahead of time, the risk has not emerged yet. But I could certainly imagine with the pace that things are going that, you know, I could imagine a world where later this year we say, hey, this this AI bioterrorism stuff is really serious. We should do something about it. We should put it in a federal we should put it in a federal standard, and if the federal government won't act, we should put it in a state standard. I could totally see that. Speaker 0: I'm concerned about a world where if you just consider the pace of progress you're expecting, the life cycle of legislation, the benefits are, as you say, because of diffusion lag, the benefits are slow enough that I really do think this patchwork of on the current trajectory, this patchwork of state laws would prohibit. I mean, having an emotional chatbot friend is something that freaks people out, then just imagine the kinds of actual benefits from AI we want normal people to be able to experience from improvements in health and health span and improvements in mental health and so forth. Whereas at the same time, it seems like you think the dangers are already on the horizon, and I just don't see that much seems like it would be especially injurious to the benefits of AI as compared to the the dangers of AI. And so that that's maybe the where the cost benefit makes less sense to me. Speaker 1: So there's a few things here. Right? I mean, people talk about there being thousands of these state laws. First of all, the vast, vast majority of them do not pass. The world works a certain way in theory, but just because a law has been passed doesn't mean it's really enforced. Right? The people the people, you know, implementing it may be like, oh my god. This is stupid. It would mean shutting off, like, you know, everything that's ever been built in everything that's ever been built in Tennessee. So, you know, very often laws are interpreted in, like, you know, a way that makes them that that that makes them not as dangerous or not as harmful. On on the same side, of course, you have to worry if you're passing a law to stop a bad thing, you had this you had this problem as well. Yeah. Look. My my look. I mean, my basic view is, you know, if if if, you know, we could decide, you know, what laws were passed and how things were done, which, you know, we're only one small input input into that, you know, I would deregulate a lot of the stuff around the health benefits of AI. I think, you know, I I I don't worry as much about the, like, the the the the kind of chatbot laws. I I actually worry more about the drug approval process where I think AI models are going to greatly accelerate the rate at which we discover drugs, and just the the pipeline will get jammed up. Like, the pipeline will not be prepared to, like, process all all of the stuff that's going through it. So, you know, I I think I think reform of the regulatory process to buy us more towards we have a lot of things coming where the safety and the efficacy is actually gonna be really crisp and clear. I mean, a beautiful thing. Really, really crisp and clear and really, really effective. But you know? And and and maybe we don't need all this all this, like, all this superstructure around it that was designed around an era of drugs that barely work and often have serious side effects. But at the same time, I think we should be ramping up quite significantly you know, this this kind of safety and security legislation. And, you know, like I've said, you know, starting with transparency is is my view of trying not to hamper the industry. Right? Trying to find the right balance. I'm worried about it. Some people criticize my essay for saying that's too slow. The dangers of AI will come too soon if we do that. Well, basically, I kind of think like the last six months and maybe the next few months are gonna be about transparency, and then if these if these risks emerge when we're more certain of them, which I think we might be as soon as as later this year, then I think we need to act very fast in the areas that we've actually seen the risk. Like, I think the only way to do this is to be nimble. Now, the legislative process is normally not nimble, but we we need to emphasize to everyone involved the urgency of this. That's why I'm sending this message of urgency. Right? That's why I wrote adolescents of technology. I wanted policymakers to read it. I wanted economists to read it. I want national security professionals to read it. You know, I want decision makers to read it so that they have some hope of acting faster than they would have otherwise. Speaker 0: Is there anything you can do or advocate that would make it more certain that the benefits of AI are are better instantiated? Where I feel like you have worked with legislatures to be like, okay, we're gonna prevent bioterrorism here away. We're gonna increase insurgency. We're gonna increase whistleblower protection. And I just think by default, the actual the things we're looking forward to here, it just seems very easy. They seem very fragile to different kinds of moral panics or political economy problems. Speaker 1: So so I don't actually agree that much in the developed world. I feel like, you know, in the developed world, markets function pretty well. And when there's when there's like a lot of money to be made on something and it's clearly the best available alternative, it's actually hard for the regulatory system to stop it. You know, we're we're seeing that in AI itself. Right? I you know, like a thing I've been trying to fight for is export controls on chips to China. Right? And like, that's in the national security interests of The US. Like, you know, that's like square within the, you know, the the policy beliefs of, you know, every almost everyone in congress of both parties. But and, you know, I think the case is very clear. The counterarguments against it are I'll politely call them fishy. And yet, it doesn't happen, and we sell the chips because there's there's so much money. There's so much money riding on it. And, you know, the the that money wants to be made, and and in that case, in my opinion, that's a bad thing. And but but it also it also applies when when it's a good thing. And and so I I don't think that if we're talking about drugs and benefits of the technology, I I I am not as worried about those benefits being hampered in the developed world. I am a little worried about them going too slow. And I as I said, I do think we should work to speed the approval process in the FDA. I do think we should fight against these chatbot bills that you're describing, right, described individually. I'm against them. I think they're stupid. But I actually think the bigger worry is a developing world where we don't have functioning markets, where, you know, we often can't build on the technology that that we've had. I worry more that those folks will get left behind. And I worry that even if the cures are developed, you know, maybe there's someone in rural Mississippi who doesn't get it as well. Right? That's a kind of smaller version of the thing, the concern we have in the developing world. And so the things we've been doing are we work with philanthropists. Right? We work with folks who deliver medicine and health interventions to developing world, to Sub Saharan Africa, India, Latin America, other developing parts of the world. That's the thing I think that won't happen on its own. Speaker 0: You mentioned export controls. Yeah. Why can't US and China both have a country of geniuses on a data center? Speaker 1: Why can't you know, why won't it happen or why should No. Speaker 0: Like, why why shouldn't it happen? Speaker 1: Why shouldn't it happen? You know, I think I think if this does happen, you know, then then we kind of have a well, we could have a few situate if we have, like, an offense dominant situation, we could have a situation like nuclear weapons, but, like, more dangerous, right, where it's, like, you know, kind of kind of either side could could easily destroy everything. We could also have a world where it's kind of it's unstable. Like, nuclear equilibrium is stable. Right? Because it's, you know, it's like deterrence. But let's say there were uncertainty about, like, if the two AIs fought, which AI would win. That could create instability. Right? You often have conflict when the two sides have a different assessment of their likelihood of winning. Right? If one side is like, oh, yeah. There's a 90% chance I'll win, the other side's like, there's a 90% chance I'll win, then then then a fight is much more likely. They can't both be right, but they can both think that. Speaker 0: But this is like a fully general argument against the diffusion of AI technology, which it may which is that's the implication of this world. Speaker 1: Let just go on because I think we will get diffusion eventually. The other concern I have is that people the governments will oppress their own people with AI. And and and so, you know, I'm I'm just I'm worried about some world where you have a country that's already, you know, kind of a you know, there's there's a government that kind of kind of already, you know, is is kind of kind of building a, you know, a tech a high-tech authoritarian state. And to be clear, this is about the government. This is not about the people. Like, people we need to find a way for people everywhere to benefit. My worry here is about governments. So, yeah, my, you know, my my worry is if the world gets carved up into two pieces, one of those two pieces could be authoritarian or totalitarian in a way that's very difficult to displace. Now, will will governments eventually get powerful and there's risk of authoritarianism? Yes. Will governments eventually get powerful AI and there's risk of bad equilibria? Yes. I think both things, but the initial conditions matter. Right? At some point, we're gonna need to set up the rules of the road. I'm not saying that one country, either The United States or a coalition of democracies, which I think would be a better setup, although it requires more international cooperation than we currently seem to wanna make. But, you know, I don't I don't think a coalition of democracies or or certainly one country should just say these are the rules of the road. There's gonna be some negotiation. Right? The world is gonna have to grapple with this. And what I would like is that the the the, you know, the democratic nations of the world, those with you know, who are whose governments have represent closer to prohuman values are holding a stronger hand then, more leverage when the rules of the road are set. So I'm very concerned about that initial condition. Speaker 0: I was relisting to an interview from three years ago, and one of the ways it aged poorly is that I kept asking questions assuming there was gonna be some key fulcrum moment two to three years from now when in fact, being that far out, it just seems like progress continues, AI improves, AI is more diffused, and people will use it for more things. It seems like you're imagining a world in the future where the countries get together and here's the rules of the world and here's the leverage we have, here's the leverage you have, when it seems like on current trajectory, everybody will have more AI. Some of that AI will be used by authoritarian countries. Some of that within the authoritarian countries will be used by private actors versus state actors. It's not clear who will benefit more. It's always unpredictable to tell in advance. It seems like the Internet privileged authoritarian countries more than you would have expected, Maybe the AI will be the opposite way around. I Speaker 1: want to Speaker 0: better understand what you're imagining here. Speaker 1: Yeah. Yeah. Just to be precise about it, I think the exponential of the underlying technology will continue as it has before. The models get smarter and smarter even when they get to country of geniuses in a data center. You know? I I think you can continue to make the model smarter. There's a question of, like, getting diminishing returns on their value in the world. Right? How much does it matter after you've already solved human biology or, you know, at some point, can do harder math. You can do more abstruse math problems, but nothing after that matters. But putting that aside, I do think the the exponential will continue, but there will be certain distinguished points on the exponential, and companies, individuals, countries will reach those points at different times. So, know, there's you know, could there be some know, I talk about is nuclear deterrent still in adolescence of technology? Is nuclear deterrent still stable in the world of of AI? I don't know, but that's an example of one thing we've taken for granted that the technology could reach such a level that it's no longer we can no longer be certain of it at least. Think of others. There are kind of points where if reach a certain point, maybe you have offensive cyber dominance, and every computer system is transparent to you after that, unless the other side has a kind of equivalent defense. So I don't know what the critical moment is or if there's a single critical moment, but I think there will be either a critical moment, a small number of critical moments, or some critical window where it's like AI is AI confers some large advantage from the perspective of national security and one country or coalition has reached it before others. That that, you know, that that you know, I'm not advocating that they're just like, okay. We're in charge now. That's not that's not how that's not how I think about it. You know, that there's always the the other side is catching up. There's extreme actions you're not willing to take, and and and it's not right to take, you know, to take complete to take complete control anyway. But but at at the point that that happens, I think people are gonna understand that the world has changed, and there there's gonna be some negotiation implicit or implicit about what what is the what is the post AI world order look like? And and I think my interest is in, you know, making that negotiation be one in which, classical liberal democracy has a strong hand? Speaker 0: Well, I wanna understand what that better means because you say in the essay, quote, Autocracy is simply not a form of government that people can accept in the post powerful AI age. And it sounds like you're saying the CCP as an institution cannot exist after we get AGI. And that seems like very strong demand, and it seems to imply a world where the leading lab or the leading country will be able to, and by that language should, get to determine how the world is governed or what kinds of governments are allowed and not allowed. Speaker 1: Yeah. So when I when I I I believe that paragraph was I think I said something like, you could take it even further and say x. So I wasn't I wasn't necessarily endorsing that that I wasn't necessarily endorsing that view. I, know, I was saying like, here's first, you know, here here's a weaker thing that I believe. But, you know, I think I, you know, I think I said, you know, we have to worry a lot about authoritarians and, know, we should try and, you know, kind of kinda check them and limit their power. Like, you could take this kind of further, much more interventionist view that says, like, authoritarian countries with AI are these, you know, the the the you know, these kind of self fulfilling cycles that that you can't, that are very hard to displace, and so you just need to get rid of them from from the beginning. That that has exactly all the problems you say, which is, you know, know, if you were to make a commitment to overthrowing every authoritarian country, I mean, they then they would take a bunch of actions now that like Right. You know, that that that could could lead to instability. So that that may or you know, that that that just that just may not be possible. But the point I was making that I do endorse is that it is it is quite possible that, you know, today, you know, the view or at least my view or the view in most of the Western world is is democracy is a better form of government than authoritarianism. But it's not like if a country's authoritarian, we don't react the way we reacted if they committed a genocide or something. Right? And and I'm well, I guess what I'm saying is I'm a little worried that in the age of AGI, authoritarianism will have a different meaning. It will be a graver thing. We have to decide one way or another how to deal with that. And the interventionist view is one possible view. I was exploring such views. It may end up being the right view. It may end up being too extreme to be the right view. But I do have hope. And and one piece of hope I have is there there is we have seen that as new technologies are invented, forms of government become obsolete. I I mentioned this in adolescence of technology where I said, you know, like feudalism was basically, you know, like a form of government. Right? And and then when when we invented industrialization, feudalism was no longer sustainable. It no longer made sense. Speaker 0: Why is that hope? Couldn't that imply that democracy is no longer gonna be a competitive system? Speaker 1: Right. It could go either way. Right? But I actually so these problems with authoritarianism, right, that the problems with authoritarianism get deeper. I just I wonder if that's an indicator of other problems that authoritarianism will have. Right? In other words, people become because authoritarianism becomes worse, people are more afraid of authoritarianism. They work harder to stop it. It's it's more of a like, you have to think in terms of total equilibrium. Right? I just wonder if it will motivate new ways of thinking about, with with with the new technology, how to preserve and protect freedom. And and even more optimistically, will it lead to a collective reckoning and, you know, a a a a more emphatic realization of how important some of the things we take as individual rights are. Right? A more emphatic realization that we just we really can't give these away. There's there we've seen there's no other way to live that actually works. I I I am actually I am actually hopeful that I I guess one way to say it, it sounds too idealistic, but I actually believe it could be the case, is is that that dictatorships become morally obsolete. They become morally unworkable forms of government, and that and that and that the the the the crisis that that creates is is is sufficient to force us to find another way. Speaker 0: I I think there is genuinely a tough question here, which I'm not sure how you resolve. For and we've had to come out one way or another on it through history. Right? So with China in the seventies and eighties, we decided even though it's an authoritarian system, we will engage with it. And think I in retrospect, that was the right call because it has stayed our authoritarian system, but a billion plus people are much wealthier and better off than they would have otherwise been. And it's not clear that it would have stopped being an authoritarian country otherwise. You can just look at North Korea as an example of that. Right? And I don't know if that takes that much intelligence to remain an authoritarian country that continues to coalesce its own power. And so you can just imagine a North Korea with an AI that's much worse than everybody else's, but still enough to keep power. And and and then so in general, it seems like should we just have this attitude of the benefits of AI will, in the form of all these empowerments of humanity and health and so forth, will be big. And and historically, we have decided it's good to spread the benefits of technology widely even with even to people whose governments are authoritarian. And I think I guess it is a tough question about how to think about it with AI, but historically, we have said yes. This is this is a positive sum world, and it's still worth diffusing the technology. Speaker 1: Yeah. So there are a number of choices we have. I think framing this as a kind of government to government decision in national security terms, that's one lens, but there are a lot of other lenses. You could imagine a world where we produce all these cures to diseases and the cures to diseases are fine to sell to authoritarian countries. The data centers just aren't. The chips and the data centers just aren't, the AI industry itself. Another possibility is and I think folks should think about this, could there be developments we can make either that naturally happen as a result of AI or that we could make happen by building technology on AI, could we create an equilibrium where where it becomes infeasible for authoritarian countries to deny their people kind of private use of the benefit to the technology? You know? Are there are there are there are there equilibria where we can kind of give everyone in an authoritarian country their own AI model that kind of, you know, defends themselves from surveillance? And there isn't a way for the authoritarian country to, like, crack crack down on this while while retaining power. I don't know. That that sounds to me like if that went far enough, it would be it would be a reason why authoritarian countries would disintegrate from the inside. But but maybe there's a middle world where, like, there there's an equilibrium where if they wanna hold on to power, the authoritarians can't deny kind of individualized access access to the technology. But I actually do have a hope for the for the the for the more radical version, which is, you know, is it possible that the technology might inherently have properties or that by building on it in certain ways, we could create properties that that that have this kind of dissolving effect on authoritarian structures? Now we we hoped originally, right, we think about back to the beginning of the Obama administration. We thought originally that social media and the Internet would have that property and turns out not to. But I don't know. What we could try again with the knowledge of how many things could go wrong and that this is a different technology? I don't know that it would work, but it's worth a try. Speaker 0: Yeah. I think it's it's very unpredictable. Like, there's first principles reasons why authoritarianism Speaker 1: It's might not be to very unpredictable. I I don't think I mean, we gotta we we just gotta we kind of we gotta recognize the problem, and then we gotta come up with 10 things we can try, and we gotta try those and then assess whether they're working or which ones are working, if any, and and then try new ones if the old ones aren't working. Speaker 0: What that nets out to today is you say, we will not sell data centers or sorry, chips and then the ability to make chips to China. And so in some sense, you are denying there'll be some benefits to That's right. The Chinese economy, Chinese people, etcetera, because we're doing that. And then there'd also be benefits to the American economy because it's a positive sum world. We could trade. They could have their country data centers doing one thing. We could have ours doing another. Already we you're saying it's not worth that positive sum stipend to empower this country? That Speaker 1: What I would say is that we are we are about to be in a world where growth and economic value will come very easily if right? If we're able to build these powerful AI models, growth and economic value will come very easily. What will not come easily is distribution of benefits, distribution of wealth, political freedom. These are the things that are gonna be hard to achieve. So when I think about policy, I think that the technology in the market will deliver all the fundamental benefits almost almost faster than we can take them, and and that these questions about about distribution and political freedom and rights are are are the ones that that will actually matter and that policy should focus on. Speaker 0: Okay. So speaking of distribution, as you're mentioning, we have developing countries. And in many cases, catch up growth has been weaker than we would have hoped for. But when catch up growth does happen, it's fundamentally because they have underutilized labor, and we can bring the capital and know how from developed countries to these countries, and then they can grow quite rapidly. Obviously, in a world where labor is no longer the constraining factor, this mechanism no longer works. And so is the hope basically to rely on philanthropy from the people who immediately get wealthy from AI or from the countries that get wealthy from AI? What is the I hope for Speaker 1: mean, philanthropy should obviously play some role as it has in the past, but I think better growth is and stronger if we can make it endogenous. Yeah. What are the relevant industries in an AI driven world? Look, there's lots of stuff. I said we shouldn't build data centers in China, but there's no reason we shouldn't build data centers in Africa. Right? In fact, I think it'd be great to build data centers in Africa. Long as they're not owned by China, we should build data centers in Africa. I think that's a great thing to do. We should also build there's no reason we can't build you know, a pharmaceutical industry that's like AI driven. Like, you know, the the if if AI is accelerating accelerating drug discovery, then, you know, there will be a bunch of biotech startups. Like, let's make sure some of those happen in the developing world. Certainly, during the transition, I mean, we can talk about the point where humans have no role, but humans will have still have some role in starting up these companies and supervising supervising the AI models. So let's make sure some of those humans are humans in the developing world so that fast growth can happen there as well. Speaker 0: You guys recently announced Quad is gonna have a constitution that's aligned to a set of values and not necessarily just to the end user. And there's a world you could imagine where if it is aligned to the end user, it preserves the balance of power we have in the world today because everybody gets to have their own AI that's advocating for them. And so the ratio of bad actors to good actors stays constant. It seems to work out for our world today. Why is it better not to do that but to have a specific set of values that the AI should carry forward? Speaker 1: Yeah. So I'm not sure I'd quite draw the distinction in that way. There may be two relevant distinctions here, which are I think you're talking about a mix of the two. One is, should we give the model a set of instructions about do this and versus don't do this? Yeah. And the other, you know, should we give the model a set of principles for, you know, for kind of how to act? And and and there, it's it's, it's purely a practical and empirical thing that we've observed that by teaching the model principles, getting it to learn from principles, its behavior is more consistent, it's easier to cover edge cases, and the model is more likely to do what people want it to do. In other words, if you're like, don't tell people how to hotwire a car, don't speak in Korean, if you give it a list of rules, it doesn't really understand the rules and it's kind of hard to generalize from them, you know, if if it's just kind of a, you know, list of do dos and don'ts. Whereas if you give it principles and then, you know, it has some hard guardrails, like don't make biological weapons. But overall, you're trying to understand what it should be aiming to do, how it should be aiming to operate. So just from a practical perspective, that turns out to be just a more effective way to train the model. That's one piece of it. So that's the kind of rules versus principles trade off. Then there's another thing you're talking about, which is kind of like the corrigibility versus, like, you know, I would say kind of intrinsic motivation trade off, which is like, how much should the model be a kind of I don't know, like a a a skin suit or something where, you know, you know, you know, you just kind of, you know it it just kind of directly follows the instructions that are given to it by whoever is giving it those instructions versus how much should the model have an inherent set of values and go off and do things on its own. There, I would actually say everything about the model is actually closer to the direction of it should mostly do what people want. It should mostly follow the we're not trying to build something that goes off and runs the world on its own. We're actually pretty far on the corrigible side. Now what we do say is there are certain things that the model won't do, right? That it's like I think we say it in various ways in the constitution that under normal circumstances, if someone asks the model to do a task, it should do that task. That should be the default. But if you've asked it to do something dangerous or if you've asked it to kind of harm someone else, then the model is unwilling to do that. So I actually think of it as a mostly corrigible model that has some limits, but those limits are based on principles. Speaker 0: Yeah. I mean, then the fundamental question is, how are those principles determined? And this is not a special question for Anthropic. This would be a question for any company. But because you have been the ones to actually write down the principles, I get to ask you this question. Normally, a constitution is like, you write it down, it's set in stone, and there's a process of updating it and changing it and so forth. In this case, it seems like a document that people at Anthropic write that can be changed at any time that guides the behavior of systems that are gonna be the basis of a lot of economic activity. What is the do you think about how those principles should be set? Speaker 1: Yes. So I think there's there's two there's maybe three three kind of sizes of loop here. Right? Three three ways to iterate. One is you can iterate we iterate within anthropic. We train the model. We're not happy with it, we kind of change the constitution. And I think that's good to do. Putting out publicly, making updates to the constitution every once in a while saying, here's a new constitution. Right. I think that's good to do because people can comment on it. The second level of loop is different companies will have different constitutions. I think it's useful for like Anthropic puts out a constitution and the Gemini model puts out a constitution and other companies put out a constitution and then they can kind of look at them, compare, outside observers can critique and say this this I like this one, this thing from this constitution and this thing from that constitution, and and then kind of that that creates some kind of soft incentive and feedback for all the companies to take the best of each elements and improve. Then I think there's a third loop, which is society beyond the AI companies and beyond just those who comment on the constitutions without hard power. There, we've done some experiments. A couple years ago, did an experiment with, I think it was called the collective intelligence project to basically poll people and ask them what should be in our AI constitution. I think at the time we incorporated some of those changes, and so you could imagine with the new approach we've taken to the constitution doing something like that, it's a little harder because it's like that was actually an easier approach to take when the constitution was like a list of dos and don'ts. At the level of principles, it has to have a certain amount of coherence, but but you could you could still imagine getting views from a wide variety of people. And I think you could also imagine and this is like a crazy idea, but, hey, you know, this whole interview is about trade crazy ideas. Right? So, you know, you could even imagine systems of of kind of representative government having having input. Right? Like, you know, I I wouldn't I wouldn't do this today because the legislative process is so slow. Like, this is exactly why I think we should be careful about the legislative process and AI regulation. But there's no reason you couldn't, in principle, say, like, you know, all AI you know, all AI models have to have a constitution that starts with, like, these things. And then you can append other things after it, but there has to be this special section that takes precedence. I wouldn't do that. That's too rigid. That sounds that sounds kind of overly prescriptive in a way that I think overly aggressive legislation is, but that is a thing you could that thing is you could try to do. Is some much less heavy handed version of that? Maybe. Speaker 0: I really like control loop too, where obviously, this is not how constitutions of actual governments do or should work, where there there's not this vague sense in which the Supreme Court will feel out how people are feeling and what are the vibes and then update the update the constitution accordingly. So there's Yeah. With actual governments, there's a more procedural process. Speaker 1: Or formal process. Speaker 0: Yeah. Exactly. But you actually have a vision of competition between constitutions, is actually very reminiscent of how some libertarian charter cities people you used to talk about what an archipelago of different kinds of governments would look like, and then there would be selection among them of who could operate the most effectively, in which place people would be the happiest. And in in a sense, you're actually yeah. There's this vision. Speaker 1: I'm I'm I'm kind of recreating that. Speaker 0: Yeah. Like, this Utopia of Archipelago. You know? Speaker 1: Again, I think I think that vision has has you know, if things to recommend it and things that things that things that will kind of kind of go wrong with it, you know, I think I think it's a I think it's an interesting, in some ways, compelling vision, but also things will go wrong with it that you hadn't that you hadn't imagined. So, you know, I I I like loop two as well, but I I I feel like the whole thing has gotta be some some mix of loops one, two, and three, and it's a it's a matter of the proportions. Right? I I think that's gotta be the the answer. Speaker 0: When somebody eventually writes the equivalent of the making of the atomic bomb for this era, what is the thing that will be hardest to glean from the historical record that they're most likely to miss? Speaker 1: I think a few things. One is at every moment of this exponential, the extent to which the world outside it didn't understand it. This a bias that's often present in history where anything that actually happened looks inevitable in retrospect. I think when people look back, it will be hard for them to put themselves in the place of people who were actually making a bet on this thing to happen that wasn't inevitable, that we had these arguments, like the arguments that, you know, that I make for scaling or that continual learning will be solved, you know, that that you know, some of us internally in our heads put a high probability on this happening, but it's like there's a world outside us that's not acting on that's not kind of not acting on that at all. I think the weirdness of it think unfortunately, the insularity of it, we're one year or two years away from it happening, the average person on the street has no idea. That's one of the things I'm trying to change with the memos, with talking to policymakers, but I don't know. I think that's just a crazy thing. Yeah. Finally, I would say, and this probably applies to almost all historical moments of crisis, how absolutely fast it was happening, how everything was happening all at once. Decisions that you might think were carefully calculated, well, actually you have to make that decision and then you have to make 30 other decisions on the same day because it's all happening so fast, and you don't even know which decisions are gonna turn out to be consequential. So one of my, I guess, worries, although it's also an insight into kind of what's happening is that some very critical decision will be some decision that someone just comes into my office and is like, Dario, you have two minutes. Should we do thing A or thing B on this someone gives me this random half page memo and is like, should we should we do a or b? And I'm like, I don't know. I have to eat lunch. Let's do b. And that ends up being the most consequential thing ever. Speaker 0: So final question. It seems like you have there's not tech CEOs who are usually writing 50 page memos every few months, and it seems like you have managed to build a role for yourself and a company around you which is compatible with this more intellectual type role as CEO. Ed, I wanna understand how you construct that and how like, how does that work to be that you just go away for a couple weeks and then you tell your company, this is the memo. Like, here's what we're doing. It's also reported you write a bunch of these internally. Speaker 1: Yeah. So for this particular one, I wrote it over winter break. So there was the tie and I was having a hard time finding the time to actually find it, to actually write it. But I actually think about this in a broader way. I actually think it relates to the culture of the company. So I probably spend a third, maybe 40% of my time making sure the culture of Anthropic is good. As Anthropic has gotten larger, it's it's gotten harder to just, you know, get involved in, you know, directly involved in, the training of the models, the launch of the models, the building of the products. Like, it's 2,500 people. It's like, you know, there's just you know, I have certain instincts, but, like, there's only you know, the I I it's very difficult to get in to get to get involved in every single detail. You know? I like I try as much as possible. But one thing that's very leveraged is making sure Anthropic is a good place to work. People like working there. Everyone thinks of themselves as team members. Everyone works together instead of against each other. We've seen as some of the other AI companies have grown without naming any names, we're starting to see decoherence and people fighting each other. I would argue there was even a lot of that from the beginning, but it's gotten worse. I think we've done an extraordinarily good job, even if not perfect, of holding the company together, making everyone feel the mission, that we're sincere about the mission, and that everyone has faith that everyone else there is working for the right reason, that we're a team, that people aren't trying to get ahead of each other's expense or back stab each other, which again, I think happens a lot at some of the other places. How do you make that the case? It's a lot of things. It's me. It's Daniella who runs the company day to day. It's the co founders. It's the other people we hire. It's the environment we try to create. But I think an important thing in the culture is I, some and just, you know, the other leaders as well, but especially me, to articulate what the company is about, why it's doing what it's doing, what its strategy is, what its values are, what its mission is, and what it stands for. When you get to 2,500 people, you can't do that person by person. You have to write or you have to speak to the whole company. This is why I get up in front of the whole company every two weeks and speak for an hour. It's actually I mean, I wouldn't say I write essays internally. I do two things. One, I write this thing called the DVQ, Dario Vision Quest. I wasn't the one who named it that. That's the name it it received, and it's one of these names that I kind of I tried to fight it because it made it sound like I was going off and smoking peyote or something, but the name just stuck. So I get up in front of the company. Every two weeks. I have a three or four page document, and I just talk through three or four different topics about what's going on internally, models we're producing, the products, the outside industry, the world as a whole as it relates to AI and geopolitically in general, just some mix of that. And I just go through very, very honestly. I just go through and I just say, This is what I'm thinking and this is what anthropic leadership is thinking. And then I answer questions. That direct connection, I think, has a lot of value that is hard to achieve when you're passing things down the chain, six levels deep. And Large fraction of the company comes to attend, either in person or virtually. It really means that you can communicate a lot. Then the other thing I do is I just I have a channel in Slack where I just write a bunch of things and comment a lot. Often that's in response to just things I'm seeing at the company or questions people ask or we do internal surveys and there are things people are concerned about and so I'll write them up. And I'm like, I'm very honest about these things. I just say them very directly, and the point is to get a reputation of telling the company the truth about what's happening, to call things what they are, to acknowledge problems, to avoid the sort of corpo speak, the kind of defensive communication that often is necessary in public because, you know, the world is very large and full of people who are, you know, interpreting things in bad faith. But, you know, if you have a company of people who you trust and we try to hire people that we trust, then then, you know, you can you can you can, you know, you can you can really just be entirely unfiltered. I think that's an enormous strength of the company. It makes it a better place to work. It makes people more of the sum of their parts and increases likelihood that we accomplish the mission because everyone is on the same page about the mission. Everyone is debating and discussing how best to accomplish the mission. Speaker 0: Well, in lieu of an external Dario vision quest, we have this interview. This interview is a little like that. This has been fun, Dario. Thanks for doing it. Speaker 1: Yeah. Thank you, Dorkesh. Speaker 0: Hey, everybody. I hope you enjoyed that episode. If you did, the most helpful thing you can do is just share it with other people who you think might enjoy it. It's also helpful if you leave a rating or a comment on whatever platform you're listening on. If you're interested in sponsoring the podcast, you can reach out at bwarkesh.com/advertise. Otherwise, I'll see you at the next one.
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