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  • Krishna Rao on Anthropic Going From 9 Billion to 30 Billion ARR in One Quarter and the Compute Strategy Powering Claude

    Krishna Rao, Chief Financial Officer of Anthropic, sat down with Patrick O’Shaughnessy on Invest Like the Best for one of the most detailed public looks yet at the operating engine behind Claude. He covers how Anthropic compounded from $9 billion of run rate revenue at the start of the year to north of $30 billion by the end of Q1, why he spends 30 to 40 percent of his time on compute, the playbook for buying gigawatts of AI infrastructure across Trainium, TPU, and GPU platforms, how Anthropic prices its models, why returns to frontier intelligence keep climbing, and what the Mythos release tells us about the cyber capabilities of the next generation of Claude.

    TLDW

    Anthropic is running the most compute fungible frontier lab in the world, with active deployments across AWS Trainium, Google TPU, and Nvidia GPU, and an internal orchestration layer that lets a chip serve inference in the morning and run reinforcement learning the same evening. Krishna Rao explains the cone of uncertainty that governs gigawatt scale compute procurement, the floor Anthropic refuses to drop below on model development compute, the Jevons paradox unlock from cutting Opus pricing, the 500 percent annualized net dollar retention from enterprise customers, the layer cake of long term deals with Google, Broadcom, Amazon, and the recent xAI Colossus tie up in Memphis, the phased release of the Mythos model in response to spiking cyber capabilities, the internal use of Claude Code to produce statutory financial statements and run a Monthly Financial Review skill, and why the team believes scaling laws are alive and well. The interview also covers fundraising history through Series D and Series E, the $75 billion already raised plus another $50 billion coming, talent density beating talent mass during the Meta poaching wave, and Rao’s belief that biotech and drug discovery represent the most exciting frontier for AI.

    Key Takeaways

    • Anthropic entered the year with about $9 billion of run rate revenue and ended the first quarter with north of $30 billion of run rate revenue, a more than 3x leap driven by model intelligence gains and the products built around them.
    • Compute is described as the lifeblood of the company, the canvas everything else is built on, and the most consequential class of decisions Rao makes. Buy too much and you go bankrupt. Buy too little and you cannot serve customers or stay at the frontier.
    • Rao spends 30 to 40 percent of his time on compute, even today, and the leadership team meets repeatedly on both procurement and ongoing compute allocation.
    • Anthropic is the only frontier language lab actively using all three major chip platforms in production: AWS Trainium, Google TPU, and Nvidia GPU. It is also the only major model available on all three clouds.
    • Flexibility is the central design principle. Anthropic builds flexibility into the deals themselves, into the orchestration layer that maps workloads to chips, and into compilers built from the chip level up.
    • The cone of uncertainty frames procurement. Small differences in weekly or monthly growth compound into wildly different two year outcomes, so the team plans across a range of scenarios rather than a single point estimate, and ranges toward the upper end while protecting downside.
    • Compute allocation across the company sits in three buckets: model development and research, internal employee acceleration, and external customer serving. A non negotiable floor protects model development even when customer demand is tight.
    • Anthropic estimates that if it cut off internal employee use of its own models, the freed compute could serve billions of dollars of additional revenue. It chooses not to, because internal use compounds into better future models.
    • Intelligence is multi dimensional, not a single IQ score. Anthropic measures real world capability through customer feedback, long horizon task performance, tool use, computer use, and speed at agentic tasks, not just leaderboard benchmarks that have largely saturated.
    • Each Opus generation, 4 to 4.5 to 4.6 to 4.7, delivers both capability improvements and an efficiency multiplier on token processing. New models often serve customers at a fraction of the prior cost while doing more.
    • Reinforcement learning is described as inference inside a sandbox with a reward function, so model efficiency gains directly improve internal RL throughput. The flywheel is tightly coupled.
    • Over 90 percent of code at Anthropic is now written by Claude Code, and a large share of Claude Code itself is written by Claude Code.
    • Anthropic shipped roughly 30 distinct product and feature releases in January and the pace has accelerated since.
    • Scaling laws, in Anthropic’s internal data, are alive and well. The team holds itself to a skeptical scientific standard and still does not see them slowing down.
    • Anthropic recently signed a 5 gigawatt deal with Google and Broadcom for TPUs starting in 2027, plus an Amazon Trainium agreement for up to 5 gigawatts, totaling more than $100 billion in commitments. A significant portion lands this year and next year.
    • A new partnership for capacity at the xAI Colossus facility in Memphis was announced just before the interview, aimed at expanding consumer and prosumer capacity.
    • Pricing has been remarkably stable across Haiku, Sonnet, and Opus. The biggest deliberate change was lowering Opus pricing, which produced a textbook Jevons paradox: consumption rose far faster than the price drop, and the new Opus 4.6 and 4.7 slot in at the same price point.
    • Mythos is the first model Anthropic chose to release in a phased way because of a sharp spike in cyber capability. In an open source codebase where a prior model found 22 security vulnerabilities, Mythos found roughly 250.
    • The Mythos release framework focuses on defensive use first, expands access over time, and is presented as a template for future capability spikes.
    • Anthropic now sells to 9 of the Fortune 10 and reports net dollar retention above 500 percent on an annualized basis. These are not pilots. Rao describes signing two double digit million dollar commitments during a 20 minute Uber ride to the studio.
    • The platform strategy is mostly horizontal. Anthropic will go vertical with offerings like Claude for Financial Services, Claude for Life Sciences, and Claude Security where it can demonstrate the model’s capabilities, but expects most application value to accrue to customers building on top.
    • Investors raised over $75 billion in equity since Rao joined, with another $50 billion in commitments tied to the Amazon and Google deals. Capital intensity is real, but the raises fund the upper end of the cone of uncertainty more than they fund current losses.
    • The Series E close coincided with the day the DeepSeek news broke, forcing investors to reassess their AI thesis in real time. Anthropic closed the round anyway.
    • Inside finance, Claude now produces statutory financial statements for every Anthropic legal entity, with a human checker. A library of more than 70 finance specific skills underpins workflows.
    • A custom Monthly Financial Review skill produces a 90 to 95 percent ready monthly close report, so leadership discussion shifts from reconciling numbers to debating implications.
    • An internal real time analytics platform called Anthrop Stats compresses weekly insight cycles from hours to about 30 minutes.
    • The biggest token user inside Anthropic’s finance team is the head of tax, focused on tax policy engines and workflow automation. The most senior people, not the youngest, are leading internal adoption.
    • Talent density beats talent mass. When Meta and others ran aggressive offer waves, Anthropic lost two people while peer labs lost dozens.
    • All seven Anthropic co founders remain at the company, as does most of the first 20 to 30 employees, which Rao credits to a collaborative, transparent, debate friendly culture and a real culture interview that can veto otherwise top tier candidates.
    • Dario Amodei holds an open all hands every two weeks, writes a short prepared document, and takes unscripted questions from anyone at the company.
    • AI safety investments in interpretability and alignment have a commercial side effect. Looking inside the model helps Anthropic build better models, and enterprises selling sensitive workloads want to trust the lab they hand customer data to.
    • Anthropic explicitly identifies as America first in its approach to model development, and engages closely with the US administration on capability releases such as Mythos.
    • The longer term product vision is the virtual collaborator: an agent with organizational context, access to the company’s tools, persistent memory, and the ability to work on ideas, not just tasks, over long horizons.
    • CoWork, Anthropic’s extension of the Claude Code paradigm into general knowledge work, is being adopted faster than Claude Code itself when indexed to the same point in its launch curve.
    • Anthropic’s product teams ship daily, with a fleet of agents working across the company on specific tasks. Everyone effectively becomes a manager of agents.
    • The dominant downside risks to Anthropic’s high end forecast are slower customer diffusion of model capability into real workflows, scaling laws flattening unexpectedly, and Anthropic losing its position at the frontier.
    • Rao is most excited about biotech and healthcare outcomes, especially the prospect that AI could push drug discovery and lab throughput up 10x or 100x, turning currently incurable diagnoses into treatable ones within a patient’s lifetime.

    Detailed Summary

    Compute as Lifeblood and the Cone of Uncertainty

    Rao opens with the claim that compute is the most important resource at Anthropic, and the most consequential decision class in the company. You cannot buy a gigawatt of compute next week. You have to anticipate demand a year or two in advance, and the cost of being wrong in either direction is high. Buy too much and the unit economics collapse. Buy too little and you cannot serve customers or stay at the frontier, which are described as the same failure mode. To navigate this, the team uses a cone of uncertainty rather than point estimates. Small differences in weekly growth compound into vastly different two year outcomes, and Anthropic tries to position itself toward the upper end of that cone while preserving optionality. Rao notes he has had to consciously break a lifetime of linear thinking and force himself into exponential models.

    Three Chip Platforms, One Orchestration Layer

    Anthropic uses Amazon’s Trainium, Google’s TPUs, and Nvidia’s GPUs fungibly. That was not free. Adopting TPUs at scale started around the third TPU generation, when outside observers thought it was a strange choice. Anthropic invested years into compilers and orchestration so workloads can flow across chips by generation and by job type. The team works deeply with Annapurna Labs at AWS to influence Trainium roadmaps because Anthropic stresses these chips harder than almost anyone. The result is what Rao believes is the most efficient utilization of compute across any frontier lab, with a dollar of compute going further inside Anthropic than anywhere else.

    Three Buckets and the Model Development Floor

    Compute gets allocated across model development, internal acceleration of employees, and customer serving. The conversations are collaborative rather than zero sum, but there is a hard floor on model development that the company refuses to cross even if it makes customer demand harder to serve in the short term. The thesis is simple. The returns to frontier intelligence are extremely high, especially in enterprise, so cutting model investment to chase near term revenue is a bad trade. Internal employee use is also explicitly protected. Rao notes that diverting that internal usage to external customers would unlock billions of additional revenue today, but the compounding benefit of accelerating researchers and engineers outweighs that.

    Intelligence Is Multi Dimensional

    Rao pushes back hard on the IQ framing of model progress. Benchmarks saturate quickly, and the real signal comes from how customers actually use the models. Anthropic looks at long horizon task completion, tool use, computer use, and time to result on agentic tasks. Two equally capable agents who differ only in speed produce dramatically different value, because the faster one compounds into more attempts and more outcomes. Frontier model leaps are also fuel efficient. The sedan to sports car analogy breaks down because each Opus generation, 4 to 4.5 to 4.6 to 4.7, delivers a step up in capability and a multiplier on per token efficiency.

    From 9 Billion to 30 Billion ARR in One Quarter

    The headline number for the quarter is a leap from about $9 billion of run rate revenue to over $30 billion, accomplished without onboarding a corresponding step up in compute, because new compute lands on ramps locked in 12 months prior. Rao attributes the leap to model capability gains, products that surface that intelligence in usable form factors, and an enterprise customer base that pulls more workloads onto Claude as each generation unlocks new use cases. Coding started the wave with Sonnet 3.5 and 3.6, and the same pattern is now playing out elsewhere in the economy.

    Recursive Self Improvement and Talent Density

    Over 90 percent of Anthropic’s code is now written by Claude Code, including most of Claude Code itself. Rao describes this as a structural reason to keep allocating internal compute to employees even when external demand is hungry. Recursive self improvement is not happening through models that need no humans. It is happening through researchers who set direction and use frontier models to compress months of work into days. Talent density beats talent mass. When Meta and other labs went after Anthropic researchers with very large packages, Anthropic lost two people while peer labs lost dozens.

    Procurement Strategy and the Layer Cake

    Compute lands as a layer cake. Last month Anthropic signed a 5 gigawatt TPU deal with Google and Broadcom starting in 2027, alongside an Amazon Trainium agreement for up to 5 gigawatts. The total is north of $100 billion in commitments. A new tie up with xAI’s Colossus facility in Memphis was announced just before the interview, intended for nearer term capacity to support consumer and prosumer growth. Anthropic evaluates near term and long term compute deals against the same set of variables: price, duration, location, chip type, and how efficiently the team can run it. The relationships are deeper than procurement. The hyperscalers are also distribution channels for the model.

    Platform First, Selective Vertical Bets

    Rao describes Anthropic as a platform first business, with most expected value accruing to customers building on the platform. The team will only go vertical when it can either demonstrate capabilities that are skating to where the puck is going, like Claude Code did before the models could fully support it, or when it wants to set a template for an industry vertical, as with Claude for Financial Services, Claude for Life Sciences, and Claude Security. He acknowledges that surprise capability jumps make customers anxious about the platform competing with them, and frames Anthropic’s mitigation as deeper partnerships, early access programs, and an emphasis on accelerating customer building rather than disintermediating it.

    Pricing, Jevons Paradox, and Return on Compute

    Pricing across Haiku, Sonnet, and Opus has been stable. The notable exception is Opus, which Anthropic deliberately repriced lower when launching Opus 4.5 because Opus class problems were being squeezed into Sonnet workloads. Efficiency gains made it possible to serve Opus profitably at the new level. The consumption response was a classic Jevons paradox, with usage rising far more than the price reduction would have predicted, and Opus 4.6 then slotted in at the same price with a capability bump. Margins are not framed as a per token markup. Compute is fungible across model development, internal acceleration, and customer serving, so Anthropic measures return on the entire compute envelope rather than software style variable cost per call.

    Fundraising, DeepSeek, and Capital Intensity

    Rao joined while Anthropic was closing its Series D, mid frontier model launch and during the FTX share liquidation. Investors initially questioned whether Anthropic needed a frontier model, whether AI safety and a real business could coexist, and why the sales team was so small. The Series E closed the same day the DeepSeek news broke, with markets violently re pricing AI in real time. Since Rao joined, Anthropic has raised over $75 billion, with another $50 billion tied to the Amazon and Google compute deals. The reason for the size of the raises is the cone of uncertainty, not current losses. Returns on compute today are described as robust.

    Mythos, Cyber Capability, and Phased Releases

    The Mythos release marks the first time Anthropic shipped a model under a deliberately phased rollout because of a specific capability spike. Cyber is the dimension that spiked. Where a prior model found 22 vulnerabilities in an open source codebase, Mythos found roughly 250. The defensive applications, automatically patching massive codebases, are genuinely valuable, but the offensive risk is real enough that Anthropic chose to release to a smaller group first and expand access over time. Rao positions this as a template for future capability spikes, not a permanent restriction. He also describes the relationship with the US administration as cooperative, including the Department of War interaction, with Anthropic supporting a regulatory framework that does not strangle innovation but takes responsibility seriously.

    Claude Inside Finance

    Anthropic’s finance team is one of the strongest internal case studies. Statutory financial statements for every legal entity are produced by Claude, with a human reviewer. A skill library of more than 70 finance specific skills underpins a Monthly Financial Review skill that drafts the monthly close at 90 to 95 percent ready, so leadership meetings shift from explaining the numbers to discussing what to do about them. An internal analytics platform called Anthrop Stats compresses weekly insight cycles from hours to 30 minutes. The biggest internal token user in finance is the head of tax, building policy engines, which Rao highlights as evidence that adoption is driven by the most senior people, not just younger engineers.

    Culture, Co Founders, and the Race to the Top

    Seven co founders should not, on paper, work as a leadership group. Rao argues it works because the culture was set early around collaboration, intellectual honesty, transparency, and humility. The culture interview is a real veto, not a checkbox. Dario Amodei runs an all hands every two weeks with a short written piece followed by unscripted questions, and decisions, once made, get clean alignment rather than residual politics. Anthropic frames its approach as a race to the top, where being a model for how to build the technology responsibly is itself a recruiting and retention advantage.

    The Virtual Collaborator and the Frontier Ahead

    The product vision Rao describes is the virtual collaborator. Not just a smarter chatbot, but an agent with organizational context, access to the company’s tools, memory, and the ability to work on ideas over long horizons. Coding was the first domain to feel this, but CoWork, Anthropic’s extension of the Claude Code pattern into general knowledge work, is being adopted faster than Claude Code was at the same age. Product development inside Anthropic already looks different. Teams ship daily, with fleets of agents working across the company, and individual humans increasingly act as managers of those fleets.

    Downside Risks and What Excites Him Most

    The three risks Rao names if asked to do a premortem on a softer year are slower customer diffusion of model capability into real workflows, scaling laws unexpectedly flattening, and Anthropic losing its frontier position to competitors. None of these are observed today, but he is unwilling to claim them with certainty. On the upside, he is most excited about biotech and healthcare. Lab throughput rising 10x or 100x, paired with AI assisted clinical workflows, could turn currently incurable diagnoses into treatable ones within a patient’s lifetime. That is the outcome he wants the technology to chase.

    Thoughts

    The most consequential structural point in this interview is the framing of compute as a single fungible resource pool measured by return on the entire envelope, not as a variable cost per inference call. That accounting shift, if you accept it, breaks most of the bear cases about AI lab unit economics. The bear argument almost always assumes that a token served to a customer is the only thing the chip did that day. Rao’s version is that the same fleet trains models in the morning, runs reinforcement learning at lunch, serves customers in the afternoon, and accelerates internal engineers in the evening. If even half of that is real, the right comparison is total compute spend versus total enterprise value created by the platform, and on that ratio Anthropic looks structurally strong rather than weak.

    The Jevons paradox on Opus pricing is the most actionable insight for anyone running an AI product. Most teams default to either chasing premium pricing on the newest model or undercutting to chase volume. Anthropic did something more disciplined: it left Sonnet and Haiku alone, dropped Opus when efficiency gains made it serveable, and watched aggregate usage rise faster than the price cut. The lesson is that frontier model pricing is not really a price problem. It is a capability access problem, and elasticity around the right tier is much higher than the standard SaaS playbook implies.

    The Mythos cyber jump deserves more attention than it has gotten. Going from 22 to 250 vulnerabilities found in the same codebase is the kind of capability discontinuity that genuinely changes the regulatory calculus. Anthropic is signaling that it can identify these discontinuities ahead of release and choose a deployment shape that respects them. Whether peer labs adopt similar discipline is the open question. Anthropic’s race to the top framing assumes they will be forced to. The competitive market may say otherwise.

    The hiring data point is the most underrated investor signal. Two departures while peer labs lost dozens, during the most aggressive talent war in tech history, is not a culture poster. It is a structural advantage that compounds every time another lab tries to buy its way to the frontier. Money can be matched. Conviction in the mission, transparent leadership, and a culture interview that can veto otherwise stellar candidates cannot. If you believe scaling laws hold, talent retention at this density is one of the few moats that actually scales with capital.

    Finally, the most interesting personal admission is that Krishna Rao, a finance leader trained at Blackstone and Cedar, is openly telling investors that linear thinking is the failure mode he had to break out of. The companies that pattern match this moment to prior technology waves are mispricing it, in both directions. The cone of uncertainty Anthropic uses internally is the right metaphor for everyone else too. If you are forecasting AI as if it is cloud in 2010, you are almost certainly wrong, and the magnitude of the error is much larger than it would be in any prior era.

    Watch the full conversation with Krishna Rao on Invest Like the Best here.

  • Jensen Huang on Lex Fridman: NVIDIA’s CEO Reveals His Vision for the AI Revolution, Scaling Laws, and Why Intelligence Is Now a Commodity

    A deep breakdown of Lex Fridman Podcast #494 featuring Jensen Huang, CEO of NVIDIA, covering extreme co-design, the four AI scaling laws, CUDA’s origin story, the future of programming, AGI timelines, and what it takes to lead the world’s most valuable company.

    TLDW (Too Long, Didn’t Watch)

    Jensen Huang sat down with Lex Fridman for a sprawling two-and-a-half-hour conversation covering the full arc of NVIDIA’s evolution from a GPU gaming company to the engine of the AI revolution. Jensen explains how NVIDIA now thinks in terms of rack-scale and pod-scale computing rather than individual chips, breaks down his four AI scaling laws (pre-training, post-training, test time, and agentic), and reveals the near-existential bet the company made putting CUDA on GeForce. He shares his views on China’s tech ecosystem, his deep respect for TSMC, why he turned down the chance to become TSMC’s CEO, how Elon Musk’s systems engineering approach built Colossus in record time, and why he believes AGI already exists. He also discusses why the future of programming is really about “specification,” why intelligence is being commoditized while humanity is the true superpower, and how he manages the enormous pressure of leading a company that nations and economies depend on. His core message: do not let the democratization of intelligence cause you anxiety. Instead, let it inspire you.

    Key Takeaways

    1. NVIDIA No Longer Thinks in Chips. It Thinks in AI Factories.

    Jensen’s mental model of what NVIDIA builds has fundamentally changed. He no longer picks up a chip to represent a new product generation. Instead, his mental model is a gigawatt-scale AI factory with power generation, cooling systems, and thousands of engineers bringing it online. The unit of computing at NVIDIA has evolved from GPU to computer to cluster to AI factory. His next mental “click” is planetary-scale computing.

    2. Extreme Co-Design Is NVIDIA’s Secret Weapon

    The reason NVIDIA dominates is not just better GPUs. It is the extreme co-design of the entire stack: GPU, CPU, memory, networking, switching, power, cooling, storage, software, algorithms, and applications. Jensen explains that when you distribute workloads across tens of thousands of computers and want them to go a million times faster (not just 10,000 times), every single component becomes a bottleneck. This is a restatement of Amdahl’s Law at scale. NVIDIA’s organizational structure directly reflects this co-design philosophy. Jensen has 60+ direct reports, holds no one-on-ones, and runs every meeting as a collective problem-solving session where specialists across all domains are present and contribute.

    3. The Four AI Scaling Laws Are a Flywheel

    Jensen outlined four distinct scaling laws that form a continuous loop:

    Pre-training scaling: Larger models plus more data equals smarter AI. The industry panicked when people said data was running out, but synthetic data generation has removed that ceiling. Data is now limited by compute, not by human generation.

    Post-training scaling: Fine-tuning, reinforcement learning from human feedback, and curated data continue to scale AI capabilities beyond what pre-training alone achieves.

    Test-time scaling: Inference is not “easy” as many predicted. It is thinking, reasoning, planning, and search. It is far more compute-intensive than memorization and pattern matching. This is why inference chips cannot be commoditized the way many predicted.

    Agentic scaling: A single AI agent can spawn sub-agents, creating teams. This is like scaling a company by hiring more employees rather than trying to make one person faster. The experiences generated by agents feed back into pre-training, creating a flywheel.

    4. The CUDA Bet Nearly Killed NVIDIA

    Putting CUDA on GeForce was one of the most consequential technology decisions in modern history. It increased GPU costs by roughly 50%, which crushed the company’s gross margins at a time when NVIDIA was a 35% gross margin business. The company’s market cap dropped from around $7-8 billion to approximately $1.5 billion. But Jensen understood that install base defines a computing architecture, not elegance. He pointed to x86 as proof: a less-than-elegant architecture that defeated beautifully designed RISC alternatives because of its massive install base. CUDA on GeForce put a supercomputer in the hands of every researcher, every scientist, every student. It took a decade to recover, but that install base became the foundation of the deep learning revolution.

    5. NVIDIA’s Moat Is Trust, Velocity, and Install Base

    Jensen was direct about NVIDIA’s competitive advantage. The CUDA install base is the number one asset. Developers target CUDA first because it reaches hundreds of millions of computers, is in every cloud, every OEM, every country, every industry. NVIDIA ships a new architecture roughly every year. No company in history has built systems of this complexity at this cadence. And the trust that NVIDIA will maintain, improve, and optimize CUDA indefinitely is something developers can count on. If someone created “GUDA” or “TUDA” tomorrow, it would not matter. The install base, velocity of execution, ecosystem breadth, and earned trust create a compounding advantage that is nearly impossible to replicate.

    6. Jensen Believes AGI Is Already Here

    When asked about AGI timelines, Jensen said he believes AGI has been achieved. His reasoning is practical: an agentic system today could plausibly create a web service, achieve virality, and generate a billion dollars in revenue, even if temporarily. This is not meaningfully different from many internet-era companies that did the same thing with technology no more sophisticated than what current AI agents can produce. He does not believe 100,000 agents could build another NVIDIA, but he believes a single agent-driven viral product is within reach right now.

    7. The Future of Programming Is Specification, Not Syntax

    Jensen believes the number of programmers in the world will increase dramatically, not decrease. His reasoning: the definition of coding is expanding to include specification and architectural description in natural language. This expands the population of “coders” from roughly 30 million professional developers to potentially a billion people. Every carpenter, plumber, accountant, and farmer who can describe what they want a computer to build is now a coder. The artistry of the future is knowing where on the spectrum of specification to operate, from highly prescriptive to exploratory and open-ended.

    8. China Is the Fastest Innovating Country in the World

    Jensen gave a nuanced and detailed explanation of why China’s tech ecosystem is so formidable. About 50% of the world’s AI researchers are Chinese. China’s tech industry emerged during the mobile cloud era, so it was built on modern software from the start. The country’s provincial competition creates an insane internal competitive environment. And the cultural norm of knowledge-sharing through school and family networks means China effectively operates as an open-source ecosystem at all times. This is why Chinese companies contribute disproportionately to open source. Their engineers’ brothers, friends, and schoolmates work at competing companies, and sharing knowledge is the cultural default.

    9. The Power Grid Has Enormous Waste That AI Can Exploit

    Jensen proposed a pragmatic solution to the energy problem for AI data centers. Power grids are designed for worst-case conditions with margin, but 99% of the time they run at around 60% of peak capacity. That idle capacity is simply wasted. Jensen wants data centers to negotiate flexible contracts where they absorb excess power most of the time and gracefully degrade during rare peak demand periods. This requires three things: customers accepting that “six nines” uptime may not always be necessary, data centers that can dynamically shift workloads, and utilities that offer tiered power delivery contracts instead of all-or-nothing commitments.

    10. Jensen Turned Down the CEO Role at TSMC

    In 2013, TSMC founder Morris Chang offered Jensen the chance to become CEO of TSMC. Jensen confirmed the story is true and said he was deeply honored. But he had already envisioned what NVIDIA could become and felt it was his sole responsibility to make that vision happen. He sees the relationship with TSMC as one built on three decades of trust, hundreds of billions of dollars in business, and zero formal contracts.

    11. Elon Musk’s Systems Engineering Approach Is Instructive

    Jensen praised Elon Musk’s approach to building the Colossus supercomputer in Memphis in just four months. He highlighted several principles: Elon questions everything relentlessly, strips every process down to the minimum necessary, is physically present at the point of action, and his personal urgency creates urgency in every supplier. Jensen drew a parallel to NVIDIA’s own “speed of light” methodology, where every process is benchmarked against the physical limits of what is possible, not against historical baselines.

    12. Intelligence Is a Commodity. Humanity Is Not.

    Perhaps the most philosophical takeaway from the conversation: Jensen argued that intelligence is a functional, measurable thing that is being commoditized. He surrounded himself with 60 direct reports who are all “superhuman” in their respective domains, more educated and deeper in their specialties than he is. Yet he sits in the middle orchestrating all of them. This proves that intelligence alone does not determine success. Character, compassion, grit, determination, tolerance for embarrassment, and the ability to endure suffering are the real differentiators. Jensen wants the audience to understand that the word we should elevate is not intelligence but humanity.

    Detailed Summary

    From GPU Maker to AI Infrastructure Company

    The conversation opened with Jensen explaining NVIDIA’s evolution from chip-scale to rack-scale to pod-scale design. The Vera Rubin pod, announced at GTC, contains seven chip types, five purpose-built rack types, 40 racks, 1.2 quadrillion transistors, nearly 20,000 NVIDIA dies, over 1,100 Rubin GPUs, 60 exaflops of compute, and 10 petabytes per second of scale bandwidth. And that is just one pod. NVIDIA plans to produce roughly 200 of these pods per week.

    Jensen explained that extreme co-design is necessary because the problems AI must solve no longer fit inside a single computer. When you distribute a workload across 10,000 computers but want a million-fold speedup, everything becomes a bottleneck: computation, networking, switching, memory, power, cooling. This is fundamentally an Amdahl’s Law problem at planetary scale. If computation represents only 50% of the workload, speeding it up infinitely only doubles total throughput. Every layer must be co-optimized simultaneously.

    NVIDIA’s organizational structure is a direct reflection of this co-design philosophy. Jensen has more than 60 direct reports, almost all with deep engineering expertise. He does not do one-on-ones. Every meeting is a collective problem-solving session where the memory expert, the networking expert, the cooling expert, and the power delivery expert are all in the room together, attacking the same problem.

    The Strategic History of CUDA

    Jensen walked through the step-by-step journey from graphics accelerator to computing platform. The company invented a programmable pixel shader, then added IEEE-compatible FP32 to its shaders, then put C on top of that (called Cg), and eventually arrived at CUDA. The critical strategic decision was putting CUDA on GeForce, a consumer product.

    This was nearly an existential move. It increased GPU costs by roughly 50% and consumed all of the company’s gross profit at a time when NVIDIA was a 35% gross margin business. The market cap cratered from around $7-8 billion to approximately $1.5 billion. But Jensen understood a principle that many technologists overlook: install base defines a computing architecture. x86 survived not because it was elegant but because it was everywhere. CUDA on GeForce put a supercomputing capability in the hands of every gamer, every student, every researcher who built their own PC. When the deep learning revolution arrived, CUDA was already the foundation.

    How Jensen Leads and Makes Decisions

    Jensen described a leadership philosophy built on continuous reasoning in public. He does not make announcements in the traditional sense. Instead, he shapes the belief systems of his employees, board, partners, and the broader industry over months and years by reasoning through decisions step by step, using every new piece of external information as a brick in the foundation. By the time he formally announces a strategic direction, the reaction is not surprise but rather, “What took you so long?”

    He applies this same approach to his supply chain. He personally visits CEOs of DRAM companies, packaging companies, and infrastructure providers. He explains the dynamics of the industry, shares his vision of future demand, and helps them reason through why they should make multi-billion-dollar capital investments. Three years ago, he convinced DRAM CEOs that HBM memory would become mainstream for data centers, which sounded ridiculous at the time. Those companies had record years as a result.

    Jensen’s “speed of light” methodology is his framework for decision-making. Every process, every design, every cost is benchmarked against the physical limits of what is theoretically possible. He prefers this to continuous improvement, which he views as incrementalism. He would rather strip a 74-day process back to zero and ask, “If we built this from scratch today, how long would it take?” Often the answer is six days, and the remaining 68 days are filled with accumulated compromises that can be challenged individually.

    AI Scaling Laws and the Future of Compute

    Jensen broke down the four scaling laws in detail. The pre-training scaling law, which depends on model size and data volume, was thought to be hitting a wall when the industry worried about running out of high-quality human-generated data. Jensen argued this concern is misplaced. Synthetic data generation has effectively removed the ceiling, and the constraint is now compute, not data.

    Post-training continues to scale through fine-tuning and reinforcement learning. Test-time scaling was the most counterintuitive for the industry. Many predicted that inference would be “easy” and that inference chips would be small, cheap, and commoditized. Jensen saw this as fundamentally wrong. Inference is thinking: reasoning, planning, search, decomposing novel problems into solvable pieces. Thinking is much harder than reading, and test-time compute is intensely resource-hungry.

    Agentic scaling is the newest frontier. A single AI agent can spawn sub-agents, effectively multiplying intelligence the way a company scales by hiring. The experiences and data generated by agentic systems feed back into pre-training, creating a continuous improvement loop. Jensen described this as the reason NVIDIA designed the Vera Rubin rack architecture differently from the Grace Blackwell architecture. Grace Blackwell was optimized for running large language models. Vera Rubin is designed for agents, which need to access files, use tools, do research, and spin off sub-agents. NVIDIA anticipated this architectural shift two and a half years before tools like OpenClaw arrived.

    China, TSMC, and the Global Supply Chain

    Jensen provided a thoughtful analysis of China’s tech ecosystem. He identified several structural advantages: 50% of the world’s AI researchers are Chinese, the tech industry was born during the mobile cloud era (making it natively modern), provincial competition creates internal Darwinian pressure, and the culture of knowledge-sharing through school and family networks makes China effectively open-source by default.

    On TSMC, Jensen emphasized that the deepest misunderstanding about the company is that its technology is its only advantage. Their manufacturing orchestration system, which dynamically manages the shifting demands of hundreds of companies, is “completely miraculous.” Their culture uniquely balances bleeding-edge technology excellence with world-class customer service. And the trust that Jensen places in TSMC is extraordinary: three decades of partnership, hundreds of billions of dollars in business, and no formal contract.

    Jensen also discussed the AI supply chain more broadly. NVIDIA has roughly 200 suppliers contributing technology to each rack. Jensen personally manages these relationships, flying to supplier sites, explaining industry dynamics, and helping CEOs reason through multi-billion-dollar investment decisions. When asked if supply chain bottlenecks keep him up at night, he said no, because he has already communicated what NVIDIA needs, his partners have told him what they will deliver, and he believes them.

    The Energy Challenge and Space Computing

    On the energy front, Jensen proposed a practical approach to the power problem. Rather than waiting for new power generation, he wants to capture the enormous waste already present in the grid. Power infrastructure is designed for worst-case peak demand, but 99% of the time it runs far below capacity. AI data centers could absorb this excess capacity with flexible contracts that allow graceful degradation during rare peak periods.

    On space computing, NVIDIA already has GPUs in orbit for satellite imaging. Jensen acknowledged the cooling challenge (no conduction or convection in space, only radiation) but sees it as a future frontier worth cultivating. In the meantime, he is focused on the lower-hanging fruit of eliminating waste in the terrestrial power grid.

    On AGI, Jobs, and the Human Future

    Jensen stated directly that he believes AGI has been achieved, at least by the practical definition of an AI system capable of creating a billion-dollar company. He sees it as plausible that an agent could build a viral web service that briefly generates enormous revenue, just as many internet-era companies did with technology no more sophisticated than what current AI agents produce.

    On jobs, Jensen was both compassionate and clear-eyed. He told the story of radiology: computer vision became superhuman around 2019-2020, and the prediction was that radiologists would disappear. Instead, the number of radiologists grew because AI allowed them to study more scans, diagnose better, and serve more patients. The purpose of the job (diagnosing disease) did not change, even though the tools changed completely.

    He applied this principle broadly: the number of software engineers at NVIDIA will grow, not decline, because their purpose is solving problems, not writing lines of code. The number of programmers globally will grow because the definition of coding is expanding to include natural language specification, opening it up to potentially a billion people.

    His advice to anyone worried about their job is straightforward: go use AI now. Become expert in it. Every profession, from carpenter to pharmacist to lawyer, will be elevated by AI tools. The people who learn to use AI will be the ones who get hired, promoted, and empowered.

    Mortality, Succession, and Legacy

    The conversation closed with deeply personal reflections. Jensen said he really does not want to die. He sees the current moment as a “once in a humanity experience.” He does not believe in traditional succession planning. Instead, he believes the best succession strategy is to pass on knowledge continuously, every single day, in every meeting, as fast as possible. His hope is to die on the job, instantaneously, with no long period of suffering.

    He described a vision for a kind of digital continuity: sending a humanoid robot into space, continuously improving it in flight, and eventually uploading the consciousness derived from a lifetime of communications, decisions, and reasoning to catch up with it at the speed of light.

    On the emotional experience of leading NVIDIA, Jensen was candid about hitting psychological low points regularly. His coping mechanism is decomposition: break the problem into pieces, reason about what you can control, tell someone who can help, share the burden, and then deliberately forget what is behind you. He compared this to the mental discipline of great athletes who focus only on the next point.

    His final message was about the relationship between intelligence and humanity. Intelligence, he argued, is functional. It is being commoditized. Humanity, character, compassion, grit, tolerance for embarrassment, and the capacity for suffering are the true superpowers. The word society should elevate is not intelligence but humanity.

    Thoughts

    This is one of the most substantive CEO interviews of 2026. What makes it remarkable is not just the breadth of topics but the depth of reasoning Jensen demonstrates in real time. You can actually watch him think through problems on the spot, which is rare for someone at his level.

    A few things stand out. First, the CUDA origin story is one of the great strategic narratives in tech history. The decision to absorb a 50% cost increase on a consumer product, watching your market cap collapse by 80%, and holding the course for a decade because you understood the power of install base is the kind of conviction that separates generational companies from everyone else.

    Second, Jensen’s framing of the four scaling laws as a flywheel is the clearest articulation anyone has given of why AI compute demand will continue to accelerate. Most people understand pre-training. Fewer understand test-time scaling. Almost nobody is thinking about agentic scaling as a compute multiplier. Jensen has been thinking about it for years and already designed hardware for it before the software ecosystem caught up.

    Third, the discussion on jobs deserves attention. The radiology example is powerful because it is a completed experiment, not a prediction. The profession that was supposed to be eliminated first by AI instead grew. The mechanism is straightforward: when you automate the task, you expand the capacity of the purpose, and demand for the purpose increases. This does not mean there will be no pain or dislocation. Jensen acknowledged that explicitly. But the historical pattern is clear.

    Finally, the philosophical distinction between intelligence and humanity is the kind of framing that could genuinely help people navigate the anxiety of this moment. If you define your value by your intelligence alone, AI commoditization is terrifying. If you define your value by your character, your compassion, your tolerance for suffering, and your willingness to keep going when everything goes wrong, then AI is just the most powerful set of tools you have ever been given.

    Jensen Huang is 62 years old, has been running NVIDIA for 34 years, and shows no signs of slowing down. If anything, his conviction about the future is accelerating alongside his company’s growth.

    Watch the full episode: Lex Fridman Podcast #494 with Jensen Huang