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- XAI is two and a half years old and has achieved rapid progress across multiple domains, outperforming many competitors who are five to twenty years older and have larger teams. The company claims to be number one in voice, image and video generation, and to be leading in forecasting with Grok 4.20. Grok is integrated into apps like Imagine and Grokipedia, with Grokipedia positioned to become Encyclopedia Galactica—much more comprehensive and accurate than Wikipedia, including video and image data not present on Wikipedia. - XAI has achieved a 100,000-hour GPU training cluster and is about to reach 1,000,000 GPU-equivalent hours in training. The company emphasizes velocity and acceleration as the key drivers of leadership in technology. - The company outlines a four-area organizational structure: Grok Main and Voice (the main Grok model), a coding-focused model (Grok Code), an image and video model (Imagine), MacroHard (digital emulation of entire companies), and the infrastructure layers. - Grok Main and Voice will be merged into one team. In September 2024, OpenAI released a voice product, but XAI states it started later and, in six months, developed an in-house model surpassing OpenAI, with Grok in over 2,000,000 Teslas and a Grok voice agent API. The aim is to move beyond question answering toward building and deploying broader capabilities, such as handling legal questions, generating slide decks, or solving puzzles. - Product vision stresses that Grok Main’s intent is genuinely useful across engineering, law, and medicine, aiming to be valuable in a wide range of areas necessary to understand the universe and make things useful. - MacroHard is described as the effort to digitally emulate entire companies, enabling end-to-end digital output and the emulation of human workers across various functions (rocket design, AI chips, physics, customer service, etc.). MacroHard is presented as potentially the most important project, with the Roof of the training cluster bearing the MacroHard name. The team emphasizes that most valuable companies produce digital output and that MacroHard could replicate the outputs of companies like Apple, Nvidia, Microsoft, and Google, among others, across multiple domains. - Imagine focuses on imaging and video generation; six months into the project, Imagine released v1 and topped leaderboards across several metrics. The team highlights rapid iteration with multiple product updates daily and model updates every other week. Users are generating close to 50,000,000 videos per day and 6,000,000,000 images in the last 30 days, claiming this surpasses other providers combined. The goal is to turn anything you can imagine into reality. - Hakan discusses longer-form video capabilities, predicting end-of-year capabilities for generating 10 to 20-minute videos in one shot, with real-time rendering and interaction in imagined worlds. The expectation is that most AI compute will be real-time video understanding and generation, with XAI leading in this trajectory and continuing to improve Grok code toward state-of-the-art performance within two to three months. - MacroHard details: the team envisions building a fully capable digital human emulator to perform any computer-based task, including using advanced tools in engineering and medicine, like rocket engines designed by AI. The project is framed as a response to the remaining gap between AI and human capability in this domain, making it a high-priority area for recruitment of top talent. - XChat and X Money are described as major products in development. XChat is planned as a standalone standalone messaging app with full features (encrypted messaging, audio and video calls, screen sharing, etc.), with no advertising or hooks in Grok Chat. X Money is currently in closed beta within the company, moving toward external beta and then worldwide, intended to be the central hub for all monetary transactions, including mortgages, business loans, lines of credit, stock ownership, and crypto. - The presentation also emphasizes the synergy between XAI and SpaceX, noting that SpaceX has acquired xAI and that orbital AI data centers are being pursued to dramatically increase available AI training compute. FCC filings indicate plans to launch a million AI satellites for training and inference, with annual launches potentially reaching 200–300 gigawatts per year, and longer-term goals including moon-based factories, satellites, and a mass driver to launch AI satellites into orbit. The mass driver on the moon is described as a path to exponentially greater compute, potentially reaching gigawatts or terawatts per year, with the broader ambition of enabling a self-sustaining lunar city and interplanetary expansion. - The overall message stresses extraordinary progress, a relentless push toward greater compute and capability, and aggressive growth in user adoption and product scope. The company frames its trajectory as a fundamental shift toward real-time, scalable AI that can transform work, communication, and the management of digital assets across the globe and beyond Earth.

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.

Everything that moves will be autonomous. And every machine, every company that builds machines will have two factories. There's the machine factory, for example cars, and then there's the AI factory to create the AI for the cars. And so maybe you're a machine factory to build human or robots. You need an AI factory to build a brain for the human or robot. Right. And so every company in the future, in fact, the future of industry is really two factories. Tesla already has two factories. Right? Elon has a giant AI factory. He was very early in recognizing that he needs to have an AI factory to sustain the cars that he has. Now he's got AI

This is the alchemy of intelligence. This newly manufactured intelligence will spawn a new chapter of unprecedented productivity and development, and that will serve to improve human quality of life. The IDC estimates that AI will generate $20,000,000,000,000 in economic impact by 2030. So even if you can earn a small slice of that, that hundreds of billions of dollars of investment will earn an amazing return. For each dollar invested into, business related AI, it's expected to generate $4.60. As my friend Jensen would say, the more you buy, the more you save. Or in this case, the more you buy, the more you make. And we can grow the pie together and usher in a new era of AI driven

Patrick Sarval is introduced as an author and expert on conspiracies, system architecture, geopolitics, and software systems. Ab Gieterink asks who Patrick Sarval is and what his expertise entails. Sarval describes himself as an IT architect, often a freelance contractor working with various control and cybernetics-oriented systems, with earlier experience including a Bitcoin startup in 2011, photography work for events, and involvement in topics around conspiracy thinking. He notes his books, including Complotcatalogus and Spiegelpaleis, and mentions Seprouter and Niburu in relation to conspiratorial topics. Gieterink references a prior interview about Complotcatalogus and another of Sarval’s books, and sets the stage to discuss Palantir, surveillance, and the internet. The conversation then shifts to explaining Palantir and its significance. Sarval emphasizes Palantir as a key element in a broader trend rather than focusing solely on the company itself. He uses science-fiction analogies to describe how data processing and artificial intelligence are evolving. In particular, he introduces the concept of a “brein” (brain) or “legion” that integrates disparate data streams, builds an ontology, and enables predictive analytics and tactical decision-making. Palantir is described as the intelligence brain that aggregates data from multiple sources to produce meaningful insights. Sarval explains that a rudimentary prototype of such a system operates under the name Lavender in Gaza, where metadata from sources like Meta (Facebook, WhatsApp, Instagram), cell towers, satellites, and other sensors are fed into Palantir. The system performs threat analysis, ranks threats from high to low, and then a military operator—still human—must approve the action, with about 20–25 seconds to decide whether to fire a weapon. The claim is that Palantir-like software functions as the brain behind this process, orchestrating data integration, ontology creation, data fusion, digital twins, profiling, predictions, and tactical dissemination. The discussion covers how Palantir integrates data from medical records, parking fines, phone data, WhatsApp contacts, and more, then applies an overarching data model and digital twin to simulate and project outcomes. This enables targeted marketing alongside military uses, illustrating the broad reach of the platform. Sarval notes there are two divisions within Palantir: Gotum (military) and Foundry (business models), which he mentions to illustrate the dual-use nature of the technology. He warns that the system is designed to close feedback loops, allowing it to learn and refine its outputs over time, similar to how a thermostat adjusts heating based on sensor inputs. A central concern is the risk to the rule of law and human agency. The discussion highlights the potential erosion of the presumption of innocence and due process when decisions increasingly rely on predictive models and AI. The panel considers the possibility that in a high-stress battlefield scenario, soldiers or commanders might defer to the Palantir-presented “world view,” making it harder to refuse an order. There is also concern about the shift toward autonomous weapons and the removal of human oversight in critical decisions, raising fears about the ethics and accountability of such systems. The conversation moves to the political and ideological backdrop surrounding Palantir’s leadership. Peter Thiel, Elon Musk, and a close circle with ties to PayPal and other tech-industry figures are discussed. Sarval characterizes Palantir’s leadership as ideologically defined, with statements about Zionism and a political worldview influencing how the technology is developed and deployed. The dialogue touches on perceived connections to broader geopolitical influence, including the role of influence campaigns, media shaping, and the involvement of powerful networks in technology development and national security. As the discussion progresses, the speakers explore the implications of advanced AI and the “new generative AI” era. They consider the nature of AI and the potential for it to act not just as a data processor but as a decision-maker with emergent properties that challenge human control. The concept of pre-crime—predicting and acting on potential future threats before they materialize—is discussed as a troubling possibility, especially when a machine’s probability-based judgments guide life-and-death actions. Towards the end, the conversation contemplates what a fully dominated surveillance state might look like, including cognitive warfare and personalized influence through media, ads, and social networks. The dialogue returns to questions about how far Palantir and similar systems have penetrated international security programs, with speculation about Gaza, NATO adoption, and commercial uses beyond military applications. The speakers acknowledge the possibility of multiple trajectories and emphasize the need for checks and balances, transparency, and critical reflection on the power such systems confer upon a relatively small group of technologists and influencers. They conclude with a nod to the transformative and potentially dystopian future of AI-enabled surveillance and decision-making, cautioning against unbridled expansion and urging vigilance.

Speaker 0 says that the richest people in the world have recently started telling people they need to produce more energy, which they find “a little weird” because the same group has spent at least the past fifteen years—since Al Gore became famous—telling people the opposite. Speaker 0 claims they said energy is not the source of life or the base of civilization, but instead the cause of humanity’s downfall: the destruction of the earth and the main reason for climate change. Speaker 0 further states that CO2 is the reason it is getting warmer and that this warming happens because climate cycles are part of nature, including the example that glaciers existed and now do not. Speaker 0 says this group previously taught that burning fossil fuels was not only bad for the environment but a sin, and that society should be organized around being “carbon conscious” because they “love the earth.” Speaker 0 then claims that the same people, including Larry Fink of BlackRock, have since said they are going to take a pause on concern about global warming and that society needs more electricity. Speaker 0 states that most electricity on Earth is produced by boiling water to move turbines, and that a small portion uses radioactive material in nuclear reactors, while most generation is from coal, then natural gas, and some oil. Speaker 0 characterizes this as essentially industrial-age technology: refining and cleaning, but fundamentally the same process of burning fuel to boil water and generate power. Speaker 0 says these figures who previously framed that technology as inefficient and morally wrong are now calling for a massive expansion of it. Speaker 0 links this shift to AI, describing artificial intelligence as a dramatic, quantum increase in processing power that enables computers to reason and mimic human thinking, replacing a lot of human labor. Speaker 0 states that AI is incredibly demanding of power and will require far more electricity than most people understood. Speaker 0 concludes that society will need to put on hold—and invert—its concerns about global warming in order to build AI.

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.

Artificial intelligence is projected to generate $4 trillion in annual productivity by the end of the decade, providing significant economic competitiveness for companies and nations. This has led to widespread excitement.

Former Tesla AI director Andre Karpathy discusses software in the era of AI, emphasizing how software is changing at a fundamental level and what this means for students entering the industry. Key framework: three generations of software - Software 1.0: the code that programs computers. - Software 2.0: neural networks, where you tune data sets and run optimizers to create model parameters; the weights program the neural nets rather than hand-written code. - Software 3.0: prompts as programs that program large language models (LLMs); prompts are written in English, effectively a new programming language. - He notes that a growing amount of GitHub-like activity in software 2.0 blends English with code, and that the ecosystem around LLMs resembles a newer GitHub-like space (e.g., Hugging Face, Model Atlas). An example: tuning a LoRa on Flux’s image generator creates a “git commit” in this space. Evolving software stacks in practice - At Tesla Autopilot, the stack evolved from heavy C++ (software 1.0) to neural nets handling image processing and sensor fusion, with many 1.0 components being migrated to 2.0. The neural network grew in capability and size, and the 1.0 code was deleted as functionality migrated to 2.0. - We now have three distinct programming paradigms: 1.0 coding, 2.0 weights, and 3.0 prompts. Fluent capability in all three is valuable because tasks may be best solved with code, trained networks, or prompts. LLMs as a new computer and ecosystem view - Andrew Ng’s “AI is the new electricity” is cited to frame LLMs as utility-like (CapEx for training, OpEx for API serving, metered usage, low latency, high uptime) and also as fabs-like (large CapEx, rapid tech-tree growth), though software nature means more malleability. - LLMs are compared to operating systems: CPU-like core, memory in context windows, and orchestration of compute/memory for problem solving. App downloads can be run across various LLM platforms similarly to cross-OS apps. - The diffusion pattern of LLMs is inverted compared to many technologies: governments and corporations often lag behind consumer adoption, with AI topics sometimes used for everyday tasks like “boiling an egg” rather than high-level strategic aims. Practical implications for developers and students - Build fluently across paradigms: code in 1.0, tune 2.0 models, and design 3.0 prompts; decide when to code, train, or prompt depending on task. - Partially autonomous apps: exemplified by Cursor and Perplexity. - Cursor: traditional interface plus LLM integration, with under-the-hood embeddings, diffs, and multi-LLM orchestration; GUI support for auditing changes; autonomy slider lets users control how much the AI acts vs. what humans verify. - Perplexity: similar features, with sources cited and ability to scale autonomy from quick search to deep research. - Autonomy slider concept: users can limit or increase AI autonomy depending on task complexity; the AI handles context management and multi-call orchestration, while humans verify for correctness and security. - Education and “keeping AI on the leash”: emphasize concrete prompts, better verification, and development of structured education pipelines with auditable AI-generated content. Opportunities and caveats in AI-assisted workflows - Education and governance: separate roles for AI-generated courses and AI-assisted delivery to students, ensuring syllabus adherence and auditability. - Documentation and access for LLMs: docs should be machine-readable (e.g., markdown), and wording should be actionable (avoid “click” commands; provide equivalent API calls like curl) to facilitate LLM interactions. - Tools to ingest data for LLMs: services that convert GitHub repos into ingestible formats (e.g., git ingest, DeepWiki) to create ready-to-query knowledge bases. - Agents vs. augmentation: early emphasis on augmentation (Iron Man-like suits) rather than fully autonomous systems; the autonomy slider enables gradual handover from human supervision to more autonomous tasks while maintaining safety and auditability. - The future of “native” programming: vibe coding and byte coding illustrate how language-based programming lowers barriers, enabling broad participation in software creation; the takeaway is that natural-language interfaces can act as a gateway to software development, even for non-experts. Closing synthesis - We’re at an era where enormous code rewriting is needed, and LLMs function as utilities, fabs, and operating systems, though still early—like the 1960s of OS development. - The next decade will likely feature a spectrum of partially autonomous products with specialized GUIs and rapid verification loops, guided by an autonomy slider and careful human oversight. - Karpathy envisions an ongoing collaboration with AI: building partial autonomy products, evolving tooling, and experimenting with how the industry and education adapt to this new programming reality. He invites readers to participate in shaping this future.

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.

Digital models have the advantage of running the same neural network with the same weights on different hardware. This allows each piece of hardware to analyze different parts of the internet and suggest changes to its weights to absorb information. These changes can then be averaged across all hardware because they all use the same weights. Humans can't do this because our brains are analog and different. Knowledge transfer requires actions and trust to change connection strengths, which is inefficient, transferring only a few hundred bits of information per sentence, communicating at a few bits per second.

- 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.

Jensen Huang (NVIDIA) discusses how the amount of compute—and the energy required for that compute—is likely to increase dramatically, moving from “a hundred times” to “a thousand times” compared with current levels. He frames future computing as two simultaneous shifts: it will be intelligent and contextually aware with generative outputs, and it will be continuous rather than based on prerecorded retrieval that is initiated only when prompted. The discussion contrasts concerns about today’s AI being “backward looking” and copying previous work, potentially leading to feedback loops where people rely on AI and become stagnant without new regenerative creativity. Jensen Huang’s described future addresses this by arguing that software will not remain static code stored on a hard drive; instead, people will ask AI to write software in real time as needed (for example, generating a Photoshop clone to edit an image or generating an original movie tailored to a preference). Creating such continuous generative experiences is said to require a tremendous amount of energy—“a thousand times more” than today’s levels. Speakers note that existing energy sources cannot easily support this scale. The conversation states that it cannot be done on hydrocarbons, not even on nuclear due to long build-out time, and not on solar because current energy sources are insufficient. It also emphasizes efficiency: having the ability to use vastly more energy does not mean it should be used, and continuous regeneration is not always the more efficient approach. Speaker 0 then argues for limiting market cap and having these groups invest themselves without government backing or government liability protection, suggesting a free-market approach rather than government-directed competition framed as an arms race. Speaker 2 responds that pursuit of “superintelligence” requires centralized power and therefore cannot be decentralized. The conversation claims this centralized effort is being directed toward a quest for superintelligence connected to world domination and competition, particularly framed as an attempt to “beat China,” and concludes that once superintelligence is achieved, humanity’s fate would be in question.

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.

Speaker 0: I think what a lot of people aren't really familiar with is the bioengineering aspect of this, and we only need to look to this recently published headline from the Daily Mail, which was resurfaced, declassified CIA files that revealed a chilling blueprint to manipulate Americans' minds through covert drugging with vaccines. And it's not just vaccines that was in that blueprint. It's also the food, the water supply, pretty much altering our state of mind and our biology through all of these methods. And this is going back all the way to the fifties. One can only imagine how far they've come now, but you've been digging into this, and you have a bit of an idea as to how far they've come. To us about your latest research. Speaker 1: So you're absolutely right. And this has been, you know, a slow progression. Nothing is just being, you know, introduced new. I mean, it the technology has advanced, but it's been going on for decades decades, hundreds of years. And when you think about pharmaceuticals, the the apparatus of pharmaceuticals, they are all they it is medicinal chemistry, which is synthetic materials, synthetic biology, engineered bacteria, yeasts, molds, and all of those things like you just said. We have we are being assaulted with these these materials, which are now considered devices, you know, with the manipulated EMF and frequencies. And all of those are to exactly what you just said, weaken the system. And really this pro this slow progression of a we're in the midst of a forced evolution to become providers of a synthetic material, hybrid synthetic material. So we'll continue to produce as we do because the humanity's biological systems are by design meant to thrive and recycle and and repurpose themselves, but to survive. And so we accept these synthetic materials, and we and our body slowly begin to make accommodations to those mutations, natural mutations, but also so much of these so much of the synthetic material is coded to go in and trigger a mutation or to forcibly cause a mutation. So we literally are walking around. I mean, all of us, and it goes from the tiny little mushroom that's growing in the woods to, you know, aquatic life to every single biological electrical system, the nervous system, you know, is based on frequency. It's based on electricity. And so that is that's what's being attacked is the nervous system and the immune systems of every living being. Speaker 0: Now you're talking about some very important things here, Lisa. You've sent me this article from Medium titled the synthetic nervous system, a blueprint for physical AI. And in this article, it talks about how for the past decade, AI has lived primarily in a box, but now, our, you know, our interaction with AI has been linguistic and digital. We've cracked the code apparently, completely on generative AI, unlocking the ability to, listen to this, manipulate symbols, pixels, and code at scale, but we're now entering a far more complex epoch, the era of physical AI. And they are talking about the transition from AI that thinks to AI that acts. So they're saying the intelligence behind humanoid robots. They also give, you know, autonomous systems and things of this nature. My concern is that their plan stated goal is that they want humans to integrate with AI. This is something that even Elon Musk itself has said we need to do in order to stay relevant. And your research shows that they're already in the process of doing that. Talk to us a little bit about that. Speaker 1: Yes. And probably have. We and and, you know, I think that life as we know it will fairly stay the same because what the integration is through, and you've heard of this, is the digital twin. You know, assigning each of us a representative in the AI ecosystem, ecosystem, which which is is a a digital twin. But that digital twin is able to function and, perform because it is it is based off of your data, your biological data, your, that they are going in and removing and stealing through the infiltrators and facilitators that is vaccines, bioengineered foods, bioengineered bacteria. The, you know, the pharmaceutical industry is the perfect setup, and it's only one of one setup that goes in, and now these are all synthetic material devices. They work off of Wi Fi. They're software platforms, and they are all digital. And they are being monitored by the Department of Energy, HHS, MITRE now, these private companies and private oligarch, you know, tech companies that all have access to our free our our inner, you know, biological data DNA and and everything. And so that the AI platform, in order for it to succeed and for its longevity, there has to be a cohesive connection between humanity because we are the fuel that is going to feed that AI ecosystem. And it cannot it it's not gonna be one or the other. It has to work cohesively, and and they have to be joined. And how the the joining of those literally is through an infiltration system, which is primarily vaccines and engineered pathogens.

The speaker reframes computers as AI factories, which produce tokens, numbers. These AI factories should be used for three fundamental things, with the first being to train the next frontier model so you can build the best AI and get to market first. The goal is to train it as fast as possible. Regarding performance, Rubin is described as a 4x leap compared to Blackwell, meaning the fourfold improvement could be achieved in one month instead of four months.

And I I think that that AI, in my case, is creating jobs. It causes us to be able to create things that other people would customers would like to buy. It drives more growth. It drives more jobs. The other thing that that to remember is that AI is the greatest technology equalizer of all time.