reSee.it - Tweets Saved By @jdmarkman

Posted - June 27, 2025 at 3:15 PM

Saved - June 28, 2025 at 12:35 PM

reSee.it AI Summary

In 1997, Apple faced imminent bankruptcy, but Bill Gates, its biggest competitor, stepped in to save the company. With Apple’s stock plummeting and cash reserves dwindling, Gates invested $150 million in non-voting shares, allowing Apple to stabilize and regain developer confidence. This partnership, though controversial among Apple fans, ultimately benefited both companies, leading to the launch of iconic products like the iMac. The situation illustrates how opportunities can arise during crises, emphasizing the importance of recognizing value when others see only despair.

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In 1997, Apple was about to go bankrupt. But ONE man saved them from disaster. It was not Steve Jobs or Steve Wozniak. It was their BIGGEST competitor and worst enemy. Here's why Bill Gates saved his biggest rival from death:🧵

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Picture this: 1997. Apple's stock had hit around $13 per share. Cash reserves were critically low. Wall Street analysts openly discussed the company's potential collapse. The company that popularized the personal computer was in trouble...

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In August 1997, Steve Jobs addressed Macworld Expo with Apple on the verge of bankruptcy, losing $1 billion and reportedly 90 days from failure. Despite the company's dire situation, Jobs appeared determined, not defeated. The audience anticipated a revolutionary product announcement that could rescue Apple.

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Speaker 0: It's August 1997. The tech world holds its breath. Steve Jobs takes the stage at Macworld Expo. Apple, the company he co founded, is on life support. They're bleeding $1,000,000,000 and are ninety days from bankruptcy. But the look in Jobs' eyes isn't defeat. It's actually determination. The audience leans forward expecting a miracle, a groundbreaking new product to save Apple from the brink of failure.

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Meanwhile, Microsoft dominated the market. Gates controlled the vast majority of desktop operating systems. His Windows empire was highly profitable while Apple struggled. Gates was seen as ruthless, Jobs was the returning founder trying to save his company...

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Apple declined in the late 1980s and early 1990s due to the loss of its original vision, the absence of Jobs and Wozniak, and the rise of Microsoft. Microsoft's growth, marked by the debut of the desktop GUI in Windows 1.0 in 1985 and the release of Windows 95 in 1995, positioned them as the top dog. This, combined with the failure of poorly made Apple products like the Macintosh Portable and Apple Newton, caused Apple to decline rapidly, necessitating change.

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Speaker 0: With the original vision of the company lost, Apple started steadily declining. Now, of course, not having jobs and waz wasn't the only reason Apple was declining between the late eighties and early nineties. In fact, a big reason Apple was falling behind was because of Microsoft. Whereas the eighties was a big decade for Apple's computer department, the nineties was a huge growth period for Microsoft. With the desktop GUI making its debut in Windows one point o, which was released in 1985, to the release of Windows '95, released in 1995. Microsoft was the top dog. Because of this fact, Apple not having jobs and wants and due to the failure of many poorly made Apple products, such as the Macintosh Portable and Apple Newton, Apple was declining rapidly. Something had to change because their trajectory was only predicting them to fall more and faster than ever before.

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But Apple's potential failure would create problems for Gates. Without meaningful competition, Microsoft faced antitrust scrutiny. Regulators were investigating the company's business practices. Keeping competition alive served Microsoft's interests.

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Microsoft faced a Department of Justice investigation regarding potential monopolistic practices. The Department of Justice was asking uncomfortable questions. Bill Gates used an investment to counter the monopoly claims. He suggested that a company acting as a monopoly would not make such an investment. The investment was used to portray Microsoft as benevolent rather than a ruthless corporation stifling competition.

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Speaker 0: Microsoft was under investigation by the Department of Justice. They were asking some uncomfortable questions about whether Microsoft was a monopoly. The investment made Microsoft look like the good guy instead of a ruthless corporation trying to crush its competition. So Bill Gates could point to the investment and say, see, would a monopoly do this?

@jdmarkman - Jon Markman 🛸

August 6, 1997. Macworld Boston. Steve Jobs took the stage before thousands of Apple fans. What happened next surprised the tech world.

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Jobs announced Microsoft would invest $150 million in Apple. The crowd went silent. Then Bill Gates appeared on a giant screen above Jobs' head. The audience erupted in boos.

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At Macworld Expo, Steve Jobs announced a partnership with Microsoft, stunning the audience. Internet Explorer would become the default browser on Mac. Microsoft Office would be available for Apple computers for the next five years. Microsoft would invest $150 million in Apple.

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Speaker 0: Jobs takes the stage at Macworld Expo. He calls in Bill Gates and does the unthinkable. Jobs announces a partnership with Microsoft? In a shocking twist, Jobs makes three key announcements. One, Internet Explorer was going to become the default browser on Mac. Two, Microsoft Office would be available for Apple computers for the next five years. Three, and here's the crazy part. Microsoft was going to invest a $150,000,000 in Apple. The crowd was stunned.

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Gates appeared via satellite feed while Jobs stood below him. It was a dramatic scene. Many in attendance viewed Microsoft as the enemy. This was an iconic moment in tech history.

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Jobs tried to calm the hostile crowd: "We have to let go of the idea that for Apple to win, Microsoft has to lose." Gates responded: "We're making this investment because we believe in innovation." The reaction was intense...

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Many Mac users felt conflicted about the partnership. One attendee told reporters about concerns over Microsoft's influence. Online discussions were heated and emotional. But the deal provided Apple with the support they needed.

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$150 million in non-voting shares meant Gates got no control over Apple. Microsoft committed to developing Office for Mac for five years. Internet Explorer became the default Mac browser. Patent disputes between the companies were settled.

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After averting a money crisis, Steve Jobs cut 75% of Apple's product line, resulting in thousands of layoffs. He made deals with various companies and launched the Apple Store website. The company ended the year with over $300,000,000 in profit. The following May, Apple launched the iMac, a device that rivaled the Macintosh in success and changed the computer industry. It sold 800,000 units in its first six months. It is claimed that Apple would not exist today if it weren't for Bill Gates.

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Speaker 0: With the money crisis averted for the moment, Steve started making a lot of changes. In the coming months, Steve cut 75% of the Apple lineup at the time, which led to thousands of people being laid off. He made deals with many companies and even launched the Apple Store website. The company ended that year making over $300,000,000. But Jobs wasn't finished yet. In May of the next year, Apple unleashed a new product that rivaled the original Macintosh in terms of success. It was a new groundbreaking device that would change the computer industry forever. It was called the iMac. It was a commercial success, selling 800,000 units in the first six months. And if it weren't for Bill Gates, Apple would not exist today.

@jdmarkman - Jon Markman 🛸

Why did Gates help his competitor? Multiple reasons: 1. Maintaining competition helped with regulatory concerns. 2. A healthy ecosystem benefited the entire industry. 3. Office for Mac was a profitable business line.

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The investment helped stabilize Apple. The company's situation improved. Developer confidence increased. Jobs gained time to focus on new product development. The iMac launched successfully and helped revitalize the brand.

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Microsoft eventually sold its Apple shares. The investment proved successful for both companies. Apple went on to create revolutionary products like the iPod and iPhone. Both companies became technology giants.

Video Transcript AI Summary

Microsoft avoided being broken up in their monopoly case, but Bill Gates sold all his Apple shares in 2003, a decision he likely regrets. Had he kept them, they would be worth $50 to $100 billion today. Apple emerged as the real winner, with Steve Jobs orchestrating a major comeback through products like the iMac, iPod, and iPhone.

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Speaker 0: Microsoft walked away from their monopoly case with just a slap on the wrist. Just when the government threatened to break Microsoft up, Gates managed to keep the band together. But here's a twist. In 02/2003, Gates did something he probably regrets. He sold all of his Apple shares. If he'd held on to them, those shares would be worth 50 to a $100,000,000,000 today. So who's the real winner in all of this? Believe it or not, it's Apple. Jobs staged one of the most epic comebacks in corporate history, the iMac, the iPod, the iPhone,

@jdmarkman - Jon Markman 🛸

The lesson for investors is valuable. When sentiment is extremely negative, opportunities arise. Apple faced multiple significant downturns over the years. Each presented challenges but also potential for recovery.

@jdmarkman - Jon Markman 🛸

Extreme pessimism can create investment opportunities. While others saw only problems, Bill Gates saw value. He provided support when it was most needed. Today, both companies are among the world's most valuable. Their temporary alliance helped preserve competition in tech.

@jdmarkman - Jon Markman 🛸

Sometimes cooperation with competitors serves mutual interests. The future often rewards those who act decisively during crises. And this story reveals something crucial about building wealth...

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The biggest opportunities hide during maximum fear. While everyone panicked about Apple's collapse, Gates saw strategic value others missed. This wasn't luck. It was disciplined contrarian thinking. But here's what most investors get wrong...

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Most investors wait for "confirmation" before buying. By then, the best opportunities are gone. The real money is made when:

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• Headlines scream disaster • Analysts predict doom • Everyone else is selling We've spent years studying these exact patterns...

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Not just reading about them, but actively identifying companies trading at maximum fear. Our research has uncovered that 97% of stocks are traps. But the remaining 3%? They create generational wealth. This is why we built our investment research service specifically for self-reliant investors.

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We help 7-figure portfolios avoid the value traps and identify the rare gems trading at deep discounts. Because in a world where most stocks disappoint, knowing which ones won't matters the most. So, if you want to know which stocks aren't going to die in this market reset...

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We help serious investors spot the next Apple before everyone else catches on. Get our FREE Friday Email to join 11,487 investors getting ahead of the market: https://landing.markmancapital.net/friday-email

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Thanks for reading! I'm Jon Markman: • Founder & President of Markman Capital • 2x Pulitzer Prize Winning Journalist & Investor • Obsessed with turning market trends into action Follow @jdmarkman for more insights. Repost for your network! 🔄

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Video/Image Credits: - StormTech: https://www.youtube.com/watch?v=kKYPtFjmjJc - Hiten Shah: https://www.youtube.com/watch?v=3NDkTQq2z4c&t=47s - Flickr: Steve Jobs and Bill Gates

@jdmarkman — Jon Markman 🛸

Posted - June 9, 2025 at 3:31 PM

Saved - June 10, 2025 at 7:44 AM

reSee.it AI Summary

America is lagging in the AI race, despite having top engineers and technology. The critical factor is energy; our aging power grid can't support the massive electricity demands of AI. Major companies are investing heavily in data centers, but this requires a new energy infrastructure. China is rapidly expanding its renewable energy capacity, leaving the U.S. behind. The real investment opportunities lie in companies like Quanta Services, which are essential for building this infrastructure. I aim to help investors identify these opportunities before they become mainstream.

@jdmarkman - Jon Markman 🛸

America just lost the AI race in the most embarrassing way possible. We have the best chips and SMARTEST engineers in the world. But China beat us with something so basic, it's humiliating. Here's how Chinese AI is destroying America (& it's not what you think):🧵

@jdmarkman - Jon Markman 🛸

First, some perspective. AI doesn't just need computer chips and data centers. It needs massive amounts of electricity. More than any computing era in history. And America's aging power grid simply can't handle what's coming.

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One ChatGPT query uses 10 times more energy than a Google search, equivalent to running a 5-watt LED for an hour. Creating an AI image consumes the same energy as charging a smartphone. Data centers built for AI are experiencing soaring emissions. In 2019, training one large language model was estimated to produce as much CO2 as five gas-powered cars over their entire lifespan. The aging power grid is struggling to support the energy demands of AI.

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Speaker 0: One ChatGPT query takes nearly 10 times as much energy as a typical Google search and as much energy as keeping a five watt LED bulb on for an hour. Generating an AI image can use as much power as charging your smartphone. Hyperscalers building data centers to accommodate AI have seen emissions skyrocket. And this problem isn't new. Estimates way back in 2019 found training one large language model produced as much CO2 as the entire lifetime of five gas powered cars. And even if we can generate enough power, our aging grid is increasingly unable to handle the load.

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Microsoft just committed $80 billion in capital expenditures for 2025. Over half is going to US data centers for AI workloads. META raised its 2025 infrastructure forecast to $40 billion. Google's targeting $75 billion. The pattern is unmistakable...

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Cloud providers are investing heavily in data centers to support AI. Microsoft, Meta, Google, and Amazon collectively spent $125 billion on data centers in 2024. These data centers require increasing power to train and operate AI models. Data center power demand is projected to rise by 15-20% annually through 2030 in the US due to the AI boom. The average data center, around 100 megawatts, consumes the equivalent energy of 100,000 US households.

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Speaker 0: Cloud providers have been racing to build data centers that can handle them. Microsoft, Meta, Google and Amazon spent a combined $125,000,000,000 on data centers in 2024. And those data centers need more and more power to train and run AI models. The AI frenzy has data center power demand rising 15 to 20% every year through 2030 in The US. An average data center, which is about 100 megawatts, consumes as much energy as 100,000 US households.

@jdmarkman - Jon Markman 🛸

Data center spending hit $282 billion in 2024, up 34% year-over-year. Total capex is on pace to exceed $0.5 TRILLION in 2025. But here's what most investors are overlooking: All this computing power requires an entirely new energy backbone.

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Jensen Huang, Nvidia's CEO, frames this perfectly: America dominated past industrial revolutions by using energy to produce steel faster than anyone else. AI's the same: building & deploying energy infrastructure faster than others Why?

Video Transcript AI Summary

The winners of the last industrial revolution weren't those who invented it, but those who applied it, like the United States with steel and energy. The US wasn't afraid and just took it and ran with it. The infrastructural layer involves applying the technology, not fearing it, engaging with it, and reskilling the workforce to apply it, as well as encouraging adoption. Each layer has its own challenges and opportunities, and the game differs in each one.

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Speaker 0: Ultimately, the winners of the last industrial revolution wasn't the come wasn't the country that invented it. It was the country that applied it. And The United States applied applied steel, applied energy faster than any country. Everybody else was worried about things like labor and, you know, horses being replaced by cars and, you know, those kind of matters. But The United States just we just took it and ran with it. And so the the infrastructural layer above that is about the application of the technology. It's about not being afraid of it, wanting to engage it, reskilling reskilling our workforce so that we're able to apply it, encouraging people to adopt it. And so when you when you look at the the, when you look at AI through the lens that I just described, the framework that I just described, each one of the layers has its own, if you will, challenges and opportunities, and the game's a little different in each one.

@jdmarkman - Jon Markman 🛸

Because that's where the biggest invesetment opportunities are. Companies building transmission lines, substations, & renewable energy projects see explosive demand. Quanta Services is the premier player in this space. And they're raising full-year guidance. Why?

Video Transcript AI Summary

The speaker emphasizes the need for collaboration between the energy industry and regulators to establish frameworks for grid modernization investments, driven by growing energy demand and technological advancements. The industry is facing pressure to accelerate changes, requiring new models for cost allocation, including potential contributions from the technology sector. The speaker notes that implementing these changes within existing rate structures requires open discussion and collaboration, as adapting to this evolving landscape is a challenge for everyone involved. No single entity can manage this transition independently; collective effort is essential.

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Speaker 0: With all this growth in energy demand and the need for grid modernization, how do you see the industry working with regulators to set the right framework for these big investments? You've gotta collaborate here. I think it's more so now than ever. When you think about technology and the pressure they're putting on the states to go faster, to hurry, I think that is something that the industry is not used to and it's coming at you really fast. And so the industry is really thinking through this with the regulators now and take or pay type models, models that are different than ever before on who pays. And I think technology is willing to pay. All those things are good. We just have to get it in rates and you have to really discuss it. I mean, the hardest thing in someone's life is to change. And this change is evolving all across for us, for everyone involved in this transition. So as that change happens, I think the regulators have to be involved. We have to collaborate more and discuss it because no one can do this by themselves. It has to be everybody involved.

@jdmarkman - Jon Markman 🛸

AI is driving a crazy demand for electricity. Quanta's CEO highlighted that "transmission will be very robust here." Their expertise in building & upgrading energy infrastructure is unmatched. And their pipeline of projects keeps growing. But there's more to this story:

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The speaker's company is building infrastructure for both technology and renewable energy industries, playing a central role in a complex landscape. Technology customers demand immediate and clean power, while utilities consider affordability for ratepayers and state regulations. The company facilitates discussions between these stakeholders, aiming to deliver projects on time and within budget for all clients. The company builds about 25% of renewable power generation in North America. This unique position allows them to listen to all parties and contribute to solutions in an exciting time for the business.

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Speaker 0: A customer of our customers and the demand that we see from them is probably the largest user of electricity or will be soon. Certainly that demand is putting pressure on our clients We're also building infrastructure for both sides, including the renewable industry. When you think about what the technology customers want, they want the renewable power generation. We build about 25% it in North America. For us, we play a lot of different roles. And I think that nexus where we're in the middle of all of them listening and it's difficult because people are, you know, technology wants it now. They want it clean, and then you have a utility also that has rate payers, and then they're thinking about affordability, they have states that they're talking to. So it's just a unique place that we're not sitting in front of any of them or a regulator or anything like that, but we also facilitate those discussions as well as try to make sure that we're doing something on time, on budget for all sets of clients. It's just a unique role that we're playing an exciting time in the business.

@jdmarkman - Jon Markman 🛸

While America struggles with its aging grid, China installed over 373 GW of new renewable energy capacity in 2024 alone. They're expected to reach 1,878 GW by year's end. That's enough to power 302,700,000 homes every year. The global energy race is accelerating...

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Solar panels were invented in America in 1954, but China has been better able to capitalize on the technology. China commercialized solar panels at a large scale and now controls over 80% of the global solar panel supply chain. The United States manufactures virtually none of the required components for solar panel production. The US is prioritizing building up its supply chain from scratch to compete with China. The US has less than half of China's solar capacity, and nearly four out of five solar panels installed in the US are from Chinese companies. China dominates the entire global supply chain and has spent almost 10 times as much on solar manufacturing than the US and the EU combined. Of the world's top 10 largest solar manufacturers, seven are Chinese, and only one is American.

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Speaker 0: The solar panels were invented in America in 1954, and yet it's been China that's been better able to capitalize on the technology. Speaker 1: China was one of the countries to realize the potential of solar panels. Panels. China actually took it and really commercialized it at a large scale. Speaker 0: Now China controls over 80% of the global solar panel supply chain, while The United States manufactures virtually none of the required components for solar panel production. So to effectively compete with China in solar panel manufacturing, The US is prioritizing building up its supply chain, basically from scratch. Today, The US has less than half of the solar capacity that China does. And of the solar panels that are installed in The US, nearly four out of five of them are from Chinese companies. Speaker 1: China has such a head start when it comes to solar panel manufacturing is because they dominate the entire global supply chain. Speaker 0: That's because in recent years, China has spent almost 10 times as much on solar manufacturing than The US and The EU combined. And of the world's top 10 largest solar manufacturers, seven are Chinese, only one is American.

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Companies building the electrical infrastructure for AI will grow for years. This secular trend won't reverse. It might pause or consolidate temporarily. But like all fundamental technological shifts, it will keep going up. This is an opportunity to make money...

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The market's distracted by short-term noise & misses these long-term trends. Tariffs and trade wars capture headlines. But they don't change the underlying reality: To compete in the AI race, USA must upgrade its electrical infrastructure. Here's why they have to:

Video Transcript AI Summary

Floating point numbers are being produced at high volume and have value because they represent artificial intelligence. These numbers can be reformulated into various outputs like languages, proteins, chemicals, graphics, images, videos, and robotic movements. In the previous industrial revolution, water was converted into steam and then electrons. Now, electrons are input, and floating point numbers are the output. Similar to the last industrial revolution where the value of electricity was not immediately understood, the significance of these floating point numbers is emerging.

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Speaker 0: You've heard me say an industrial revolution. We're producing floating point numbers at high volume for the time in history, and the floating point numbers have value. The reason why they have value is because it's intelligence. It's artificial intelligence. You can take these floating point numbers, you reformulate it in such a way that it turns into English, French, proteins, chemicals, graphics, images, videos, robotic articulation, steering wheel articulation. Back in the last industrial revolution, water comes into a machine, you light the water on fire, turn it into steam, and then it turns into electrons. Atoms come in, electrons go out. In this new industrial revolution, electrons come in, floating point numbers come out. Just like the last industrial revolution, nobody understood why this electricity is so valuable.

@jdmarkman - Jon Markman 🛸

I've been tracking these patterns for 30+ years. In 1997, I wrote about how the internet would transform business. In 2008, I found key players in mobile computing before smartphones. Now I'm seeing the same opportunity in energy infrastructure and AI...

@jdmarkman - Jon Markman 🛸

Looking at companies like Quanta Services at $322.52, trading at 27.7x forward earnings might seem expensive. But as the premier business in the sector with a huge backlog & bright prospects, there's substantial runway ahead. This is just the start...

Video Transcript AI Summary

The company doesn't need growth; it had a record backlog last quarter at $35 billion. They beat expectations and raised guidance. $4 trillion will be spent on electrification and grid upgrades between now and 2050, and the company is a beneficiary of that spending. 70% of their customers are utilities, which must participate in the grid upgrade cycle. The speaker adds to their position anytime the stock is down significantly.

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Speaker 0: Company does not need it to grow. They had a record backlog last quarter at 35,000,000,000. They beat. They raised. We're gonna spend $4,000,000,000,000 on electrification and the grid upgrades between now and 2050, and they are absolute beneficiary of that. And 70% of their customers are utilities, which have to help in that upgrade cycle of the grid. So anytime it's down a lot, I'm adding to it.

@jdmarkman - Jon Markman 🛸

That's why the smart money isn't just investing in AI chips and software. They're looking at the entire ecosystem, including the critical power infrastructure that makes it all possible. Because even the most powerful AI is useless without electricity. Here's why that matters:

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Demand for powerful servers in data centers is at an all-time high due to the Internet's need for cloud computing. The cloud is not somewhere else, but is a physical presence. Data centers are essential for streaming, social media, photo storage, and especially for training and running chatbots like ChatGPT, Gemini, and Copilot, which require significant data. The generative AI race is causing data centers to be built rapidly, increasing the demand for power to run and cool them. If the power problem is not addressed, the strain could limit the potential of this technology.

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Speaker 0: Demand has never been higher for these racks and racks of powerful servers feeding the Internet's insatiable appetite for computing in the cloud. Speaker 1: The reality is that the cloud is not up there somewhere. It's right here. We are in it. You're in the middle of the cloud as we speak. Speaker 0: And data centers like this can't ever stop. Streaming, social media, photo storage, and more recently and requiring much more data, training and running chatbots like OpenAI's ChatGPT, Google's Gemini, and Microsoft's Copilot. Speaker 1: And you can feel the heat coming off of these. Speaker 0: Thanks to the generative AI race, data centers like this are springing up as quickly as companies like Vantage can build them. And that means demand for power to run them and cool them is through the roof too. Speaker 1: If we don't start thinking about this power problem differently now, we're never gonna see the strain we have or the potential we have of this amazing technology because that can truly

@jdmarkman - Jon Markman 🛸

After decades of analyzing markets, I believe: The biggest opportunities hide in plain sight. While everyone fights over the latest AI stocks, smart investors quietly position themselves in companies building AI's backbone. Companies that'll grow whoever wins the AI race...

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This is exactly what I've built my career on. Seeing what others miss by looking beyond the headlines. Let me explain how that can help you:

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The market consistently underprices these secular shifts until they become obvious. By then, the biggest gains have already been made. That's precisely why I built a solution:

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I started my newsletter to help self-reliant investors identify these opportunities before Wall Street catches on. To find the 2% of stocks that create 50% of market wealth. So...

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If you want to get ahead of Wall Street on the AI power infrastructure revolution? Join 11,427+ investors who receive my weekly insights on the stocks positioned to benefit from this trend. Subscribe to our FREE Friday email here: https://landing.markmancapital.net/friday-emailAmerica.

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Video credits: CNBC: https://www.youtube.com/watch?v=MJQIQJYxey4&t=3s CNBC: https://www.youtube.com/watch?v=JIjJtyRjiOI&t=174s Synergy report group: https://www.srgresearch.com/articles/2024-was-a-boon-year-for-sales-of-data-center-gear-thanks-to-generative-ai The Hill & Valley Forum: https://www.youtube.com/watch?v=nkhrEnuZi20&t=707s The Wall Street Journal: https://www.youtube.com/watch?v=JlhfC7Vg_Xo&t=1s CNBC Television: https://www.youtube.com/watch?v=Vwm4oFKS0yQ New York Stock Exchange: https://www.youtube.com/watch?v=EreAqIYLgt4&t=97s Stripe: https://www.youtube.com/shorts/MeJ7MZphfhQ

2024 Was a Boon Year for Sales of Data Center Gear - Thanks to Generative AI | Synergy Research Group Synergy Interactive Analysis srgresearch.com

@jdmarkman - Jon Markman 🛸

@jdmarkman — Jon Markman 🛸

Posted - May 26, 2025 at 2:14 PM

Saved - May 26, 2025 at 8:50 PM

reSee.it AI Summary

Jensen Huang's recent keynote revealed groundbreaking advancements in AI, including the powerful Blackwell chip, which processes more data than the entire internet. He described a shift from traditional manufacturing to "AI factories" producing digital tokens, highlighting the emergence of "agentic AI" as a solution to labor shortages. This transformation is set to create a trillion-dollar AI infrastructure industry. Jensen's vision includes humanoid robots trained in simulation and significant investments in AI-driven manufacturing. Understanding these changes is crucial for investors seeking to capitalize on this unprecedented shift.

@jdmarkman - Jon Markman 🛸

Jensen Huang just gave a WILD speech: - How one AI computer can process more data than the entire internet - How NVIDIA plans to kill 50 MILLION jobs with AI agents - Why Biden failed (& why Trump's doing a better job) But here's the most terrifying moment no one understood:🧵

@jdmarkman - Jon Markman 🛸

People missed the most important part of Jensen's Computex keynote. It wasn't about GeForce cards or gaming performance. It was about a fundamental shift in how our economy works. Here's what he revealed that people should have paid close attention to...

Video Transcript AI Summary

The next ten years will focus on the application science of AI, applying it to fields like digital biology, climate technology, agriculture, fisheries, robotics, transportation, teaching, and podcasting. A key area of interest is physical AI, including humanoid robots, self-driving cars, smart buildings, autonomous warehouses, and lawnmowers. A significant leap in robot capabilities is anticipated due to changes in how they are trained. Previously, robots were trained in the real world, risking damage, or with limited data from sources like humans in motion capture suits.

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Speaker 0: The next ten years is going to be the application science of AI. The fundamental science versus the application science. And so the the applied research, the application side of AI now becomes, how can I apply AI to digital biology? How can I apply AI to climate technology? How can I apply AI to agriculture, to fishery, to robotics, to transportation, optimizing logistics? How can I apply AI to, you know, teaching? How do I apply AI to, you know, podcasting? Right? And so Speaker 1: I'd love to choose a couple of those to help people see how this fundamental change in computing that we've been talking about is actually gonna change their experience of their lives, how they're actually gonna use technology that is based on everything we just talked about. One of the things that I've now heard you talk a lot about and I'm have a particular interest in is physical AI, or in other words, robots. My friends. Meaning humanoid robots, but also robots like self driving cars and smart buildings or autonomous warehouses or autonomous lawnmowers or more. From what I understand, we might be about to see a huge leap in what all of these robots are capable of, because we're changing how we train them. Up until recently, you've either had to train your robot in the real world, where it could get damaged or wear down, or you could get data from fairly limited sources like humans in motion capture suits.

@jdmarkman - Jon Markman 🛸

First, Jensen unveiled Blackwell – NVIDIA's newest AI chip system that's so powerful it defies comprehension. A single Blackwell rack moves more data than the ENTIRE INTERNET at peak traffic. The entire internet moves about 900 terabits per second. One Blackwell spine? 1,040 terabits.

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Every GPU can communicate with every other GPU simultaneously using SerDes, which is driven to its maximum limit. This necessitates placing everything in a single, liquid-cooled 20-kilowatt rack. The GPUs are disaggregated across an entire rack, effectively creating one motherboard. This disaggregation results in incredible GPU performance and memory capacity. These setups are not merely data centers but AI factories, such as the xAI Colossus factory and Stargate, which spans 4,000,000 square feet and consumes one gigawatt. A one-gigawatt factory costs approximately $60 to $80 billion, with the computing systems accounting for $4 to $5 billion of that cost. The Blackwell B200 superchip undergoes stress testing at KYEC, involving baking, molding, curing, and being pushed to its limits in 125-degree Celsius ovens for several hours.

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Speaker 0: Every single GPU can talk to every other GPU at exactly the same time. And because there's a limit to how far you can drive SerDes, this is as far as any SerDes has ever driven From chip this goes chip to the switch, out to the spine, to any other any other any other switch, any other chip. All electrical. And so that limit caused us to put everything in one rack. And that one rack is a 20 kilowatts, which is the reason why everything has to be liquid cooled. We now have the ability to disaggregate the GPUs out of one motherboard, essentially, across an entire rack. And so that entire rack is one motherboard. That's the miracle. Completely disaggregated, and now the GPU performance is incredible. The amount of memory is incredible. But really, in the end, we're not building data centers, we're building AI factories. And this is the xAI Colossus factory. This is Stargate. 4,000,000 square feet. 4,000,000 square feet. One gigawatt. And so just think about this factory here. This one gigawatt factory, this one gigawatt factory is probably going to be about 60 to $80,000,000,000. Out of that 60 to $80,000,000,000, the electronics, the computing part of it, these systems are $4,050,000,000,000 dollars of it. I made you a movie. Take a look. Blackwell is an engineering marvel. Then the assembly is baked, molded, and cured, creating the Blackwell b 200 superchip. At KYEC, each Blackwell is stress tested in ovens at 125 degrees Celsius and pushed to its limits for several hours.

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That's like going from dial-up to fiber in one generation. But the most shocking part is what these systems are being used for. Jensen didn't call them "data centers" anymore. He called them "AI factories" that produce "tokens" instead of products. This represents a complete shift in our economy:

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This infrastructure, like the Internet and electricity, requires factories, but these are unlike data centers of the past, which are part of a trillion-dollar industry providing information and storage. While originating from the same industry, these new factories will be completely separate from the world's data centers. These AI data centers are better described as AI factories. Applying energy to them produces something valuable: tokens.

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Speaker 0: This infrastructure, just like the Internet, just like electricity, needs factories. And these factories are essentially what we build today. They're not data centers of the past. A $1,000,000,000,000 industry providing information and storage, supporting all of our ERP systems and our employees. It's That's a data center. A data center of the past. This is similar in the sense that it came from the same industry. It came from all of us. But it's going to emerge as something completely different. Completely separated from the world's data center. And these AI data centers, if you will, are improperly described. They are in fact AI factories. You apply energy to it and it produces something incredibly valuable. And these things are called tokens.

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"Companies are starting to talk about how many tokens they produced last quarter and how many tokens they produced last month." "Very soon we will be talking about how many tokens we produce every hour just as every single factory does." The implications of this are staggering...

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Companies are now reporting token production on a quarterly and monthly basis. Soon, token production will be tracked hourly, similar to factory output. The world has fundamentally changed. In 1993, the speaker estimated NVIDIA's business opportunity to be $300 million.

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Speaker 0: To the point where companies are starting to talk about how many tokens they produced last quarter and how many tokens they produced last month. Very soon, we'll be talking about how many tokens we produce every hour, just as every single factory does. And so the world has fundamentally changed. We went from a company on the day that we started our company. I was trying to figure out how big our opportunity was in 1993. And I came to the conclusion NVIDIA's business opportunity was enormous. $300,000,000. We're going to be rich.

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Remember when cloud computing transformed everything? This is 100x bigger. Jensen revealed that what used to be a $300 million chip business is now becoming an "AI infrastructure industry that will be measured in trillions of dollars." But there's something more alarming he mentioned:

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The speaker discusses building AI factories to run companies, describing it as more significant than buying a TV or bicycle. They state that the world is building trillions of dollars worth of AI infrastructure over the next several years, characterizing this as a new industrial revolution. The speaker compares AI factories to historical innovations like the steam engine and railroads, but asserts that AI factories are much bigger due to the current scale of the world economy. They claim that with a $120 trillion global GDP, AI factories will underpin a substantial portion of it, suggesting that trillions of dollars in AI factories supporting a hundred trillion dollars of the world's GDP is a sensible proposition.

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Speaker 0: Well, this is building an industry and building business, and we're trying to build business every year, as you know. And it's not it's not like buying a TV or, you know, buying a bicycle. This is something that you're building these AI factories to run your company. You're building these AI factories to run other companies' companies. And so they're gonna want your AI factories right now. The reason why I lay out our our road map, for several years is because the world is building AI infrastructure, trillions of dollars of it over the next several years. Can't comprehend trillions. Well, you know, we're. This is the the new new, industrial revolution. And inside underpinning an industrial revolution are new factories, and we are the factories of the future. But but but we're talking about the we think about fault and the steam engine. We think about railroads. These are yours is so much bigger than all of those. Well, the world is bigger now than it was back then. Right? There's a hundred and $20,000,000,000,000 industry, GDP around the world, and now this new factory called AI factories is the underpinning for all of it. And so the idea that that, a hundred trillion dollars of the world's GDP will be underpinned by trillions of dollars of AI factories It's very sensible. With

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Jensen announced something called "agentic AI" – digital workers that can: • Reason about problems • Break down tasks step by step • Use tools to solve problems • Collaborate with other AIs These aren't just chatbots. They're digital employees. And now the terrifying part:

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Agentic AIs, or AI agents, are essentially digital robots because they perceive, understand, and plan. The goal is to build physical robots, but learning to be a robot must first occur productively in a virtual world. This virtual world must obey the laws of physics. Most physics engines lack the ability to deal with rigid and soft body simulation with fidelity. Therefore, a partnership with Google DeepMind and Disney Research was formed to build Newton, the world's most advanced physics engine. Newton will be open-sourced in July. It is GPU accelerated, differentiable, high fidelity, and super real time.

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Speaker 0: Agentic AIs, AI agents, a lot of different ways to say it, agents are essentially digital robots. Re re reason for that is because a robot perceives, understands, and plans. And that's essentially what agents do. But we would like to build also physical robots. And these physical robots, first, it starts with the ability to learn to be a robot. The ability to learn to be a robot can't be done in the physical world productively. You have to create a virtual world where the robot can learn how to be a good robot. That robe, that virtual world has to obey the laws of physics. Most physics engines don't have the ability to, with fidelity, deal with rigid and soft body simulation. And so we partnered with deep with with DeepMind, Google DeepMind, and Disney Research to build Newton. The world's most advanced physics engine. It's going to be open sourced in July. It's incredible what it can do. It's completely GPU accelerated. It's differentiable, so you could learn from experience. It is incredibly high fidelity. It's super real time.

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"The world has a shortage of labor. We have a shortage of workers by 2030 by about 30 to 50 million." Jensen sees these AI agents as the solution – digital workers that will fill labor gaps. "100% of NVIDIA software engineers now have digital agents working with them." But this creates a new problem:

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Being surrounded by "superhuman" experts doesn't make one feel unnecessary; instead, it empowers confidence to tackle ambitious goals. Similarly, super AIs will empower people, making them feel confident. Using tools like Chat GPT increases feelings of empowerment and the ability to learn. AI reduces barriers to understanding almost any field, acting as a personal tutor available at all times. Everyone should acquire an AI tutor to teach them anything, including programming, writing, analysis, thinking, and reasoning, to feel more empowered.

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Speaker 0: I can tell you exactly what that feels like. I'm surrounded by superhuman people, superintelligence from my perspective because they're the best in the world at what they do, and they do what they do way better than I can do it. And and I'm surrounded by thousands of them. And yet what it it never one day caused me to to think all of a sudden I'm no longer necessary. It actually empowers me and gives me the confidence to go tackle more and more ambitious things. And so so suppose suppose now everybody is surrounded by these super AIs that are very good at specific things or good at some of the things. What would that make you feel? Well, it's gonna empower you. It's gonna make you feel confident. And and I I'm pretty sure you probably use chat GPT and AI, and I feel I feel more empowered today, more confident to learn something today. The knowledge of almost any particular field, the barriers to that understanding it has been reduced. I have a personal tutor with me all of the time. And and so I I think that that feeling should be universal. And and I if if there's one thing that I would encourage everybody to do is to go get yourself an AI tutor right away. And that AI tutor could, of course, just teach you things, anything you like, help you program, help you write, help you analyze, help you think, help you reason. You know, all of those things, is gonna really make you feel empowered.

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As these AI agents take over more jobs, IT departments won't just manage humans. They'll manage armies of digital workers. Jensen predicts IT becoming "the HR of digital workers" – evaluating, improving, and managing AI agents that work inside companies. But there's an even bigger revolution happening:

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Jensen showed off humanoid robots trained entirely in simulation using AI. The robots learned physical tasks just by watching humans demonstrate them once. Then AI amplified that data to create thousands of variations so the robots could generalize their skills. This technology is already being deployed:

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The biggest challenge in AI is data strategy, especially in robotics. Human demonstration, similar to coaching, teaches robots tasks via teleoperations, which the robot can then generalize. However, teaching robots many skills requires numerous teleoperation experts. To address this, AI is used to amplify human demonstration systems, expanding the data collected during human demonstrations to train AI models. Breakthroughs in mechatronics, physical AI, and embedded computing have ushered in the age of generalist robotics, crucial due to worldwide industrial growth being limited by labor shortages. A major challenge for robot makers is the lack of large-scale real and synthetic data to train models.

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Speaker 0: Well, the biggest challenge in AI overall is what is your data strategy? And your data strategy has to be that's where a great deal of research and a great deal of technology goes into. In the case of robotics, human demonstration, just like we demonstrate to our children or a coach demonstrates to an athlete, you demonstrate using teleoperations. You demonstrate to the robot how to perform the task. And the robot can generalize from that demonstration. Because AI can generalize and we have technology for generalization. You can generalize from that one demonstration other techniques. Okay? And so, what if, what if you want to teach this robot a whole bunch of skills? How many different teleoperation people do you need? Well, it turns out to be a lot. And so what we decided to do was use AI to amplify the human demonstration systems. And so, this is essentially going from real to real and using an AI to help us expand, amplify the amount of data that was collected during human demonstration to train an AI model. Let's take a look. The age of generalist robotics has arrived with breakthroughs in mechatronics, physical AI, and embedded computing. Just in time, as labor shortages limit worldwide industrial growth. A major challenge for robot makers is the lack of large scale, real, and synthetic data to train models.

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Taiwan's manufacturing giants like TSMC, Foxconn, and Delta are creating complete digital twins of their factories using NVIDIA Omniverse. These digital twins serve as "robot gyms" where AI-powered robots learn to work before ever touching the real world. The stakes? Enormous.

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Electronics companies like Quanta, WeWin, and Gigabyte are using NVIDIA Omniverse to create digital twins for manufacturing processes. TSMC and MedAI generate 3D fab layouts from 2D CAD and develop AI tools on CUOP to optimize piping systems, saving months. Quanta, Wishtron, and Pegatron virtually plan new facilities and production lines to cut costs by reducing downtime. Pegatron simulates solder paste dispensing to reduce defects. Quanta uses Siemens Teamcenter X with Omniverse to analyze multi-step processes. Foxconn, Wistron, and Quanta simulate power and cooling efficiency of data centers using Cadence Reality Digital Twin. Companies use digital twins as "robot gyms" to develop, train, test, and simulate AI-enabled robots, including manipulators, AMRs, humanoids, and vision AI agents. When connected to IoT, each digital twin becomes a real-time interactive dashboard.

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Speaker 0: Electronics, Quanta, WeWin and Gigabyte are developing digital twins on NVIDIA Omniverse for every step of the manufacturing process. TSMC with MedAI generate three d layouts of an entire fab from two d CAD. And develop AI tools on CUOP that can simulate and optimize intricate piping systems across multiple floors, saving months of time. Quanta, Wishtron, and Pegatron plan new facilities and production lines virtually prior to physical construction, saving millions in costs by reducing downtime. Pegatron simulates solder paste dispensing, reducing production defects. Quanta uses Siemens Teamcenter X with Omniverse to analyze and plan multi step processes. Foxconn, Wistron and Quanta simulate power and cooling efficiency of test data centers with Cadence Reality Digital Twin. And to develop physical AI enabled robots, each company uses its digital twin as a robot gym to develop, train, test, and simulate robots. Whether manipulators, AMRs, humanoids, or vision AI agents as they perform their tasks or work together as a diverse fleet. And when connected to the physical twin with IoT, each digital twin becomes a real time interactive dashboard.

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Jensen revealed that $5 trillion of new manufacturing plants are being planned globally over the next three years. All of them will be designed with AI and robots from the start. This isn't science fiction – it's happening right now. But the most stunning announcement was saved for last:

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Jensen unveiled "Nvidia Constellation" – a massive new headquarters being built in Taipei. This signals NVIDIA's commitment to Taiwan as the epicenter of the AI revolution. Why does this matter to you?

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Nvidia is growing, along with its partnerships and the number of engineers in Taiwan. To accommodate this growth beyond the limits of the current office, Nvidia will build a new Taiwan office called NVIDIA Constellation.

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Speaker 0: Nvidia Constellation. NVIDIA Constellation. Well, as you know, we have been growing. And all of our partnerships with you have been growing. The number of engineers we have here in Taiwan have been growing. And so we are growing beyond the limits of our current office. And so I'm going to build them a brand new NVIDIA Taiwan office. And it's called NVIDIA Constellation.

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This isn't just about technology. It's about investment. For 7-figure investors, understanding this shift is critical to generating income now and building legacy wealth. The companies building AI infrastructure will create more wealth than we've seen in generations. But most stocks won't benefit.

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Remember: 97% of stocks are junk. Only 3% create real wealth. The AI revolution won't lift all boats – just the ones with the right technology and positioning. My research shows the biggest winners will be those controlling the physical infrastructure of AI.

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The scale of what's happening is unprecedented. Jensen showed XAI's "Colossus" factory: 4 million square feet, one gigawatt of power, and $60-80 billion in investment. The computing systems alone cost $40-50 billion. These are the new factories of our era.

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Want to learn which companies are positioned to capture this AI infrastructure boom? I've identified several overlooked players that should be on every serious investor's radar. Join our Friday email for my latest analysis: https://landing.markmancapital.net/friday-email

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Video credits: Cleo Abram: https://www.youtube.com/watch?v=7ARBJQn6QkM NVIDIA: https://www.youtube.com/live/TLzna9__DnI CNBCtelevision: https://www.youtube.com/shorts/uAM--r4-t-M DigitalFuture3000: https://www.youtube.com/shorts/sZ60uM9Vzwg