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  • Jensen Huang at Stanford CS153 Frontier Systems on Co-Design, Agentic Computing, Vera Rubin, Open Models, and the Million-X Decade That Reshaped AI Infrastructure

    https://www.youtube.com/watch?v=tsQB0n0YV3k

    NVIDIA CEO Jensen Huang returned to Stanford for the CS153 Frontier Systems class (the room nicknamed itself “AI Coachella”) to lay out, in raw form, how he thinks about the computer being reinvented for the first time in over sixty years. Across roughly seventy minutes of student questions he walks through the codesign philosophy that gave NVIDIA a million-x decade, the architectural through-line from Hopper to Grace Blackwell to Vera Rubin to Feynman, the case for open source foundation models, the realities of tokens per watt and MFU, energy demand running a thousand times higher, the China and export-control debate, and his own biggest strategic mistakes. Watch the full conversation on YouTube.

    TLDW

    Huang argues every layer of computing has changed: the programming model, the system architecture, the deployment pattern, the economics. Co-design across CPUs, GPUs, networking, storage, switches and compilers gave NVIDIA roughly a million-x speed-up over ten years versus the ten-x Moore’s Law era, and that headroom is what let researchers say “just train on the whole internet.” Hopper was built for pre-training, Grace Blackwell NVLink72 for inference and reasoning (50x over Hopper in two years), Vera Rubin is built for agents that load long memory, call tools and need a low-latency single-threaded CPU bolted directly to the GPU, and Feynman extends that to swarms of agents that spawn sub-agents. Open weights matter because safety, sovereignty (230-plus languages no one else will fund) and domain models for biology, autonomy, robotics and climate need a foundation that NVIDIA is willing to seed. Compute is not really the scarce resource (Huang says place the order and the chips ship), the broken thing is institutional budgeting that can’t put a billion dollars into a shared university supercomputer. Energy demand is heading a thousand times higher and this is finally the moment market forces alone will fund sustainable generation. On geopolitics he rejects the GPUs-as-atomic-bombs framing and warns America will end up like its telecom industry if it cedes two thirds of the world. On career he advises seeking suffering on purpose. On strategy he says observe, reason from first principles, build a mental model, work backwards, minimize opportunity cost, maximize optionality.

    Key Takeaways

    • The computing model has been substantially unchanged since the IBM System 360, sixty-plus years ago. Huang’s first computer architecture book was the System 360 manual. AI is the first true reinvention.
    • Old computing was pre-recorded retrieval. New computing is generated, contextually aware and continuous. Cloud was on-demand. Agentic systems run continuously.
    • Codesign is NVIDIA’s central thesis. Inherited from the Hennessy and Patterson RISC era at Stanford, extended across CPUs, GPUs, networking, switches, storage, compilers and frameworks all optimized together.
    • The result of full-stack codesign: roughly 1,000,000x faster compute over ten years, versus a generous 10x to 100x for Moore’s Law in the same period. Dennard scaling effectively ended a decade ago.
    • That million-x speed-up is what unlocked “train on all of the internet” as a realistic AI strategy.
    • After GPT, Huang says it was obvious thinking was next. Reasoning is just generating tokens consumed internally, then using tools is generating tokens consumed externally. Agentic systems followed predictably.
    • Education needs AI baked into the curriculum, not just taught as a subject. Pre-recorded textbooks cannot keep pace with knowledge being generated in real time.
    • Huang says he cannot learn anymore without AI. He has the AI read the paper, then read every related paper, then become a dedicated researcher he can interrogate.
    • Mead and Conway and the first-principles methodology of semiconductor design are still worth learning even though most of the scaling tricks have been exhausted.
    • NVIDIA itself is one of the largest consumers of Anthropic and OpenAI tokens in the world. One hundred percent of NVIDIA engineers are now agentically supported. Huang recommends Claude and similar tools by name and says open-source downloads will not match the integrated product harness.
    • NVIDIA still invests heavily in open foundation models because language and intelligence represent the codification of human knowledge. Five pillars: Nemotron (language), BioNeMo (biology), Alphamayo (autonomous vehicles), Groot (humanoid robotics) and a climate science model (mesoscale multiphysics).
    • Sovereign language models matter. Roughly 230 world languages will never be a top priority for a commercial frontier lab. Nemotron is near-frontier and fully fine-tunable so any country can adapt it.
    • Safety and security require open weights. You cannot defend against or audit a black box. Transparent systems let researchers interrogate models and let defenders deploy swarms.
    • The future of cyber defense is not bigger-model-versus-bigger-model. It is trillions of cheap fast small models like Nemotron Nano surrounding the threat.
    • Domain models fuse language priors with world models. Alphamayo learned to drive safely on a few million miles instead of billions because it can reason like a human about the road.
    • MFU (Model Flops Utilization) is a misleading metric. Huang says he wants low MFU, because that means he over-provisioned every resource and never gets pinned by Amdahl’s law during a spike.
    • The xAI Memphis cluster running at 11 percent MFU is not necessarily a failure mode. In disaggregated prefill plus decode inference you can deliver very high tokens per watt with very low MFU.
    • The right metric is performance, ultimately tokens per watt as a proxy for intelligence per watt, and even that needs adjustment because not all tokens are equal. Coding tokens are worth more than other tokens.
    • Hopper was designed for pre-training. NVIDIA chose to build multi-billion-dollar systems when the largest existing scientific supercomputer cost $350 million, with no proven customer base. It worked.
    • Grace Blackwell NVLink72 was designed for inference, especially the high-memory-bandwidth decode phase. It is the world’s first rack-scale computer and delivered a 50x speed-up over Hopper in two years, against an expected 2x from Moore’s Law.
    • Vera Rubin is designed for agents. Long-term memory wired into storage and into the GPU fabric, working memory, heavy tool use, and Vera, a CPU optimized for low-latency multi-core single-threaded code so a multi-billion-dollar GPU system does not stall waiting on a slow tool call.
    • Feynman is being shaped for swarms of agents with sub-agents and sub-sub-agents, a recursive software topology that demands a new compute pattern.
    • Tokens per watt improved 50x in one generation. Compounding energy efficiency is the lever NVIDIA controls directly.
    • Total compute energy demand is heading roughly a thousand times higher than today, possibly two orders of magnitude beyond that. Huang says he would not be surprised if the estimate is low.
    • For the first time in history, market forces alone are enough to fund solar, nuclear and grid upgrades. Government subsidies are no longer required to make sustainable energy investment rational.
    • Copper interconnect is becoming a bottleneck. Photonics is moving from optional to structural inside racks and across them.
    • Comparing NVIDIA GPUs to atomic bombs, Huang says, is a stupid analogy. A billion people use NVIDIA GPUs. He advocates them to his family. He does not advocate atomic bombs to anyone.
    • If the United States cedes two thirds of the global market to competitors on policy grounds, the American technology industry will end up like American telecommunications, which was policied out of existence.
    • Huang directly rejects AI doom-by-singularity narratives. It is not true that we have no idea how these systems work. It is not true that the technology becomes infinitely powerful in a nanosecond. He calls the rhetoric irresponsible and harmful to the field students are about to enter.
    • On Stanford specifically: if the university president places an order, NVIDIA will deliver the chips. The bottleneck is that no university department has a billion-dollar compute budget because budgeting is fragmented across grants. Stanford’s $40 billion endowment is more than enough to fix that.
    • “It’s Stanford’s fault” is meant as empowerment. If something is your fault, you can solve it.
    • Career advice: do not optimize purely for passion. Most people do not yet know what they love. Pick the job in front of you and do it as well as possible. Even as CEO, Huang says, 90 percent of the work is hard and he suffers through it.
    • Suffering on purpose builds the muscle of resilience. When the company, the team or the family needs you to be tough, that muscle has to already exist.
    • NVIDIA’s first generation of products was technically wrong in nearly every dimension: curved surfaces instead of triangles, no Z-buffer, forward instead of inverse texture mapping, no floating point. The strategic recovery, not the technology, taught Huang the lessons that have lasted decades.
    • The biggest clean strategic mistake Huang names is the move into mobile chips (Tegra). It grew to a billion dollars then went to zero when Qualcomm’s modem dominance shut NVIDIA out of the 3G to 4G transition. The recovery into automotive and robotics (the Thor chip is the great great great grandson of that mobile lineage) was real, but Huang refuses to rationalize the original choice.
    • Forecasting framework: observe, reason from first principles, ask “so what” and “what next” until you have a mental model of the future, place your company inside that model, then work backwards while minimizing opportunity cost and maximizing optionality.
    • Best part of the CEO job: living at the intersection of vision, strategy and execution surrounded by people capable enough to make ambitious visions real. Worst part: the responsibility for everyone who joined the spaceship, especially in the near-death moments NVIDIA had four or five times early on.
    • Underrated insider note: Huang’s first apple pie with cheese, first hot fudge sandwich and first milkshake all happened at Denny’s. The Superbird, the fried chicken and a custom Superbird-style ham and cheese with tomato and mustard are his order.

    Detailed Summary

    Computing reinvented from the ground up

    Huang frames the moment as the first true rewrite of the computer in sixty-plus years. From the IBM System 360 forward, the mental model of writing code, running code, taking a computer to market and reasoning about applications stayed roughly constant. AI changes the programming model itself. Software is no longer a compiled binary running deterministically on a CPU. It is a neural network running on a GPU producing generated, contextual, real-time output. That cascades into how companies are organized, what tools developers use, what the network and storage stack look like, and what an application is even allowed to do. Robo-taxis, he notes, are an application no one would have attempted before deep learning unlocked perception.

    Codesign and the million-x decade

    Codesign is the philosophical center of the talk. Huang traces it to the RISC work of John Hennessy at Stanford, where simpler instruction sets won by being co-designed with the compiler rather than maximally optimized in isolation. NVIDIA extends the principle across every layer simultaneously: GPU architecture, CPU architecture, NVLink and NVSwitch fabrics, photonic interconnects, networking silicon, storage paths, CUDA libraries, frameworks and ultimately the model design. The numbers Huang gives are arresting. Moore’s Law in its prime delivered roughly 100x per decade. By the time Dennard scaling broke, real-world gains had compressed to roughly 10x. NVIDIA’s codesigned stack delivered between 100,000x and 1,000,000x over the same ten-year window. That non-linear speed-up is, in Huang’s telling, the precondition for modern AI: it is what allowed researchers to stop curating training sets and just feed the entire internet to the model.

    Education has to fuse first principles with AI tools

    Asked how curriculum should evolve, Huang argues AI must be integrated into the learning process, not just taught about. He recalls Hennessy writing his textbook by hand a chapter a week while Huang was a student, and says pre-recorded textbooks cannot keep up with the rate at which AI generates new knowledge. He describes his own learning workflow: hand the paper to an AI, then have it read the entire surrounding literature, then treat the AI as a dedicated researcher who can be interrogated. At the same time he defends the classics. Mead and Conway are still the foundation. Most modern semiconductor scaling tricks have been exhausted, but knowing where the field came from sharpens judgment when designing what comes next.

    Open source and the five domain pillars

    Huang gives one of the most detailed public accounts of why NVIDIA invests so heavily in open foundation models even while being a top customer of closed labs. He recommends Claude and OpenAI by name for production coding work, and says 100 percent of NVIDIA engineers are now agentically supported. The open-weights case rests on three legs. First, language is the codification of intelligence, and there are at least 230 languages that no commercial lab will ever prioritize. Nemotron is built near frontier and released so any country or community can fine-tune it. Second, the same representation-learning approach has to be replicated in domains where the data is not internet text, so NVIDIA seeded BioNeMo for biology, Alphamayo for autonomy, Groot for humanoid robotics and a climate model for mesoscale multiphysics. The economics of those fields would never produce a foundation model on their own. Third, safety and security require transparency. A black box cannot be defended or audited, and the future of cyber defense is not bigger-model-versus-bigger-model but swarms of cheap fast small models like Nemotron Nano surrounding the threat.

    MFU is the wrong metric, tokens per watt is closer

    A student raises the leaked memo that the xAI Memphis cluster is running at 11 percent Model Flops Utilization. Huang flips the framing. He says he would rather be at low MFU all the time, because that means he over-provisioned flops, memory bandwidth, memory capacity and network capacity. Bottlenecks shift constantly, so over-provisioning across every dimension is what lets the system absorb a spike without getting pinned by Amdahl’s law. In disaggregated inference, where prefill and decode are physically separated and decode is bandwidth-bound rather than flop-bound, NVLink72 can deliver extremely high tokens per watt while reporting very low MFU. Huang argues the right framing is performance, and ultimately tokens per watt as a rough proxy for intelligence per watt, adjusted for the fact that not all tokens are equal. A coding token is worth more than a generic token.

    Hopper, Grace Blackwell NVLink72, Vera Rubin, Feynman

    Huang gives the clearest public framing of NVIDIA’s roadmap as a sequence of architectural answers to evolving compute patterns. Hopper was built for pre-training, at a moment when NVIDIA chose to build multi-billion-dollar machines while the largest scientific supercomputer in the world cost $350 million and the marketplace for such systems was, on paper, zero. Grace Blackwell NVLink72 was the answer to inference and reasoning: a rack-scale computer that ganged 72 GPUs together because decode needs aggregate memory bandwidth far beyond a single chip. The generation-over-generation speed-up was 50x in two years, twenty-five times what Moore’s Law would have delivered. Vera Rubin is being built explicitly for agents. Agents load long-term memory from storage that has to be wired directly into the GPU fabric, they use working memory, they call tools that run on a CPU, and they wait. So the CPU has to be Vera, optimized for low-latency single-threaded code, because the multi-billion-dollar GPU system cannot afford to idle waiting on a slow tool call. Feynman extends the pattern to swarms of agents with sub-agents and sub-sub-agents, a recursive software topology that will demand its own compute pattern.

    Energy demand and the grid

    Huang’s energy projection is one of the most aggressive numbers in the talk. NVIDIA can compound tokens per watt by 50x per generation through codesign, but the total compute demand is heading roughly a thousand times higher, and Huang says he would not be surprised if the real figure is one or two orders of magnitude beyond that. The reason is structural: future computing is generative and continuous, not pre-recorded and on-demand. The good news, he argues, is that this is the best moment in the history of humanity to invest in sustainable generation. Market forces alone are now sufficient to fund solar, nuclear and grid upgrades. Government subsidies are no longer required to make the math work.

    Adversarial countries, export controls and the telecom warning

    This is the segment where Huang is visibly fired up. He attacks the GPUs-as-atomic-bombs framing on its face. NVIDIA GPUs power medical imaging, video games and soy sauce delivery. A billion people use them. He advocates them to his family. The analogy collapses at the first comparison. He attacks the second framing, that American companies should not compete abroad because they will lose anyway, as a self-fulfilling defeat. Competition makes the company better. The third framing, that depriving the rest of the world of general-purpose computing benefits the United States, also fails on first principles: it benefits one or two American companies at the cost of an entire industry. The cautionary parallel is telecommunications. The United States once had a leading position in telecom fundamental technology and policied itself out of it. Huang’s worry, voiced explicitly to a room of CS students, is that they will graduate into a shell of a computer industry if the same path is repeated.

    AI doom and rational optimism

    In the same arc Huang rejects the science-fiction framing of AI as a singularity that arrives suddenly on a Wednesday at 7pm and ends civilization. He calls those claims irresponsible, says they are not true, and points out that the people advancing them are believed by audiences who then make policy on that basis. It is not true that no one understands how these systems work. It is not true that intelligence becomes infinitely powerful instantaneously. It is not true that there is no defense. His framing, which the host echoes as “rational optimism,” is that the goal is to create a future where people care about computers because the technology students are learning is worth mastering.

    Stanford’s compute problem is Stanford’s fault

    A student presses on the scarcity of compute for independent researchers, startups and universities inside the United States. Huang’s answer is sharp: there is no shortage. Place the order and the chips will arrive. The actual broken thing is institutional. University grants are fragmented across departments. No researcher can raise enough on a single grant to fund a billion-dollar shared cluster, and no one shares. He compares it to showing up at the grocery store demanding a billion dollars of tomatoes today. The solution is planning, aggregation and a campus-scale supercomputer, the way Stanford once built the linear accelerator. The endowment is $40 billion. Pulling a billion off it, contracting cloud capacity and giving every student and researcher AI supercomputer access is, in Huang’s view, obviously doable. When he says “it is Stanford’s fault” the host laughs, but Huang clarifies: if it is your fault you have the power to fix it.

    Career, suffering and resilience

    Asked how a CS student should spend the next few years, Huang pushes back on the standard “follow your passion” advice. Most people do not know what they love yet, because no one knows what they do not know. The bar of demanding joy from every working day is too high. Whatever the job is, do it as well as you can. Even as CEO of NVIDIA he says he genuinely loves about 10 percent of his work. The other 90 percent is hard and he suffers through it. He recommends suffering on purpose, because resilience is a muscle that only builds under load, and when the company, the team or the family needs that muscle, it has to already exist. Earlier in his life that meant cleaning toilets and busing tables at Denny’s. He does it today running a multi-trillion-dollar company.

    The biggest mistakes

    Huang separates technical mistakes from strategic mistakes. NVIDIA’s first generation of products was technically wrong in almost every way: curved surfaces instead of triangles, no Z-buffer, forward instead of inverse texture mapping, no floating point inside. The company wasted two and a half years. But the strategic genius of the recovery, the reading of the market, the conservation of resources and the reapplication of talent, is what taught him strategy. The clean strategic mistake he names is mobile. NVIDIA’s Tegra line grew to a billion dollars of revenue and then collapsed to zero when Qualcomm’s modem dominance locked NVIDIA out of the 3G to 4G transition. Huang explicitly refuses the comforting rationalization that the Tegra effort fed the Thor automotive chip (“Thor is the great great great grandson”). The original decision, he says, was a waste of time. The lesson is to think one or two clicks further about whether a market is structurally winnable before committing the company.

    Forecasting under fog of war

    The final substantive exchange is on forecasting. Huang’s method has four steps. Observe what is actually happening (AlexNet crushing two decades of computer vision research in one shot, GPT producing reasoning by token generation). Reason from first principles about why it works. Ask “so what” and “what next” recursively until a mental model of the future emerges. Place the company inside that future and work backwards. Crucially, expect to be partly wrong. Some outcomes will absolutely happen, some will likely happen, some might happen, and the strategy has to be robust across that distribution. The real cost of any strategic choice is the opportunity cost of the alternatives you did not take, so the discipline is to minimize that cost and maximize optionality while letting the journey itself pay for the journey.

    Thoughts

    The most useful thing in this conversation is the explicit architectural mapping of compute patterns to chip generations. Hopper for pre-training. Grace Blackwell NVLink72 for inference, because decode is bandwidth-bound and a single chip cannot supply it. Vera Rubin for agents, because tool calls stall multi-billion-dollar GPU systems and so the CPU has to be optimized for low-latency single-threaded code. Feynman for swarms. That sequence is not marketing. It is a falsifiable thesis about where the bottleneck moves next, and every other infrastructure company should be measuring themselves against it. If Huang is right that swarms of sub-agents are the next dominant pattern, then the design pressure shifts from raw flops to fabric topology, memory hierarchy and storage-to-GPU latency. That has implications for everyone downstream, including the hyperscalers building competing accelerators.

    The MFU section is the most intellectually generous moment in the talk. The instinct in the AI ops community has been to chase MFU as if it were a virtue. Huang argues, persuasively, that low MFU is consistent with high tokens per watt in a disaggregated inference setup, and that bottlenecks rotate fast enough that over-provisioning every resource is the rational design. That reframing matters because it changes what “scarce” means. Compute is not scarce in the way the discourse treats it. What is scarce is a coherent system designed end-to-end. The xAI 11 percent number, in that frame, is not embarrassing. It is the natural reading of a workload that is mostly decode.

    The Stanford segment is the part most likely to be quoted out of context. “It’s Stanford’s fault” is a deliberately provocative line, but the underlying claim is correct and load-bearing. Compute is not gated by NVIDIA refusing to ship chips. It is gated by the fact that fragmented grant funding cannot aggregate into the billion-dollar order that NVIDIA can fulfill. The implication is that universities and national labs need a structural change in how they pool capital for compute, and that the current model of every researcher buying a handful of cards is genuinely obsolete. Huang’s nudge about pulling a billion off the endowment is concrete enough to be acted on, and other major research universities should read this segment as a direct prompt.

    The geopolitical segment is the highest-stakes one. The telecommunications comparison is correct as a historical pattern, and Huang is one of the very few executives in a position to deliver that warning credibly. The unresolved tension is that the argument applies symmetrically. If American AI dominance is built by selling globally, that includes selling into adversarial states, and the policy question is where the line falls. Huang does not answer that question. He attacks the framing that lets the question be answered badly. That is a meaningful contribution to the discourse even if it does not resolve the underlying tradeoff.

    The career advice section is the part the social-media clips will mishandle. “Seek suffering” reads as macho when extracted. In context it is a specific operational claim about how resilience compounds, and it is paired with the Tegra story where Huang himself paid the price of not thinking one more click ahead. That kind of self-implication is rare in CEO talks, and it is the reason the talk is worth listening to in full rather than only reading the recap.

    Watch the full Stanford CS153 Frontier Systems conversation with Jensen Huang here.

  • 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 Nvidia’s Supply Chain Moat, TPU Competition, China Export Controls, and Why Nvidia Will Not Become a Cloud (Dwarkesh Podcast Summary)

    TLDW (Too Long, Didn’t Watch)

    Jensen Huang sat down with Dwarkesh Patel for over 90 minutes covering Nvidia’s supply chain dominance, the TPU threat, why Nvidia will not become a hyperscaler, whether the US should sell AI chips to China, and why Nvidia does not pursue multiple chip architectures at once. Jensen framed Nvidia’s entire business as transforming “electrons into tokens” and argued that Nvidia’s real moat is not any single technology but the full stack ecosystem it has built over two decades. He was blunt about his regret over not investing in Anthropic and OpenAI earlier, passionate about keeping the American tech stack dominant worldwide, and dismissive of the idea that China’s chip industry can be meaningfully contained through export controls.

    Key Takeaways

    1. Nvidia’s moat is the ecosystem, not the chip. Jensen repeatedly emphasized that Nvidia’s competitive advantage comes from CUDA, its massive installed base, its deep partnerships across the entire supply chain, and the fact that it operates in every cloud. The moat is not a single product but an interlocking system that took 20+ years to build.

    2. Supply chain bottlenecks are temporary, energy bottlenecks are not. Jensen argued that CoWoS packaging, HBM memory, EUV capacity, and logic fabrication bottlenecks can all be resolved in two to three years with the right demand signal. The real constraint on AI scaling is energy policy, which takes far longer to fix.

    3. TPUs and ASICs are not an existential threat to Nvidia. Jensen was emphatic that no competitor has demonstrated better price-performance or performance-per-watt than Nvidia, and challenged TPU and Trainium to prove otherwise on public benchmarks like InferenceMAX and MLPerf. He described Anthropic as a “unique instance, not a trend” for TPU adoption.

    4. Jensen regrets not investing in Anthropic and OpenAI earlier. He admitted he did not deeply internalize how much capital AI labs needed and that traditional VC funding was not sufficient for companies at that scale. He described this as a clear miss, though he said Nvidia was not in a position to make multi-billion dollar investments at the time.

    5. Nvidia will not become a hyperscaler. Jensen’s philosophy is “do as much as needed, as little as possible.” Building cloud infrastructure is something other companies can do, so Nvidia supports neoclouds like CoreWeave, Nebius, and Nscale instead of competing with them. Nvidia invests in ecosystem partners rather than vertically integrating into cloud services.

    6. Jensen is strongly against US chip export controls on China. This was the longest and most heated segment of the interview. Jensen argued that China already has abundant compute, energy, and AI researchers, and that export controls have accelerated China’s domestic chip industry while causing the US to concede the world’s second-largest technology market. He compared the situation to how US telecom policy allowed Huawei to dominate global telecommunications.

    7. AI will cause software tool usage to skyrocket, not collapse. Jensen pushed back on the narrative that AI will commoditize software companies. He argued that agents will use existing tools at massive scale, causing the number of instances of products like Excel, Synopsys Design Compiler, and other enterprise tools to grow exponentially.

    8. Nvidia does not pick winners among AI labs. Jensen explained that Nvidia invests across multiple foundation model companies simultaneously and refuses to favor any single one. He cited his own company’s unlikely survival story as the reason for this humility: Nvidia’s original graphics architecture was “precisely wrong” and would have been counted out by anyone picking winners.

    9. Nvidia added Groq for premium token economics. Nvidia recently acquired Groq and is folding it into the CUDA ecosystem because the market is now segmenting into different token tiers. Some customers will pay premium prices for faster response times even at lower throughput, creating a new segment of the inference market.

    10. Without AI, Nvidia would still be very large. Jensen was clear that accelerated computing, not AI specifically, is the foundational mission of the company. Molecular dynamics, quantum chemistry, computational lithography, data processing, and physics simulation all benefit from GPU acceleration regardless of deep learning.

    Detailed Summary

    Nvidia’s Real Business: Electrons to Tokens

    Jensen opened the conversation by reframing Nvidia’s entire value proposition. When Dwarkesh suggested that Nvidia is fundamentally a software company that sends a GDS2 file to TSMC for manufacturing, Jensen pushed back hard. He described Nvidia’s job as transforming electrons into tokens, with everything in between representing an “incredible journey” of artistry, engineering, science, and invention. He said the transformation is far from deeply understood and the journey is far from over, making commoditization unlikely.

    Jensen described Nvidia as operating a philosophy of doing “as much as necessary and as little as possible.” Whatever Nvidia does not need to do itself, it partners with someone else and makes it part of the broader ecosystem. This is why Nvidia has what Jensen called probably the largest ecosystem of partners in the industry, spanning the full supply chain upstream and downstream, application developers, model makers, and all five layers of the AI stack.

    On the question of whether AI will commoditize software companies, Jensen offered a contrarian take. He argued that agents are going to use software tools at unprecedented scale, meaning the number of instances of products like Excel, Cadence design tools, and Synopsys compilers will skyrocket. Today the bottleneck is the number of human engineers. Tomorrow, those engineers will be supported by swarms of agents exploring design spaces and using the same tools humans use today. Jensen said the reason this has not happened yet is simply that the agents are not good enough at using tools. That will change.

    The Supply Chain Moat

    Dwarkesh pressed Jensen on Nvidia’s reported $100 billion (and potentially $250 billion) in purchase commitments with foundries, memory manufacturers, and packaging companies. The question was whether Nvidia’s real moat for the next few years is simply locking up scarce upstream components so that no competitor can get the memory and logic they need to build alternative accelerators.

    Jensen confirmed this is a significant advantage but framed it differently. He said Nvidia has made enormous explicit and implicit commitments upstream. The implicit commitments matter just as much: Jensen personally meets with CEOs across the supply chain to explain the scale of the coming AI industry, convince them to invest in capacity, and assure them that Nvidia’s downstream demand is large enough to justify that investment. Nvidia’s GTC conference serves this purpose too, bringing the entire ecosystem together so upstream suppliers can see downstream demand and vice versa.

    Jensen described a process of systematically “prefetching bottlenecks” years in advance. CoWoS advanced packaging was a major bottleneck two years ago, but Nvidia swarmed it with repeated doubling of capacity until TSMC recognized it as mainstream computing technology rather than a specialty product. More recently, Nvidia has invested in the silicon photonics ecosystem through partnerships with Lumentum and Coherent, invented new packaging technologies, licensed patents to keep the supply chain open, and even invested in new testing equipment like double-sided probing.

    When Dwarkesh asked about the ultimate physical bottlenecks, Jensen surprised him. The hardest bottleneck to solve is not CoWoS or HBM or EUV machines. It is plumbers and electricians needed to build data centers. Jensen used this as a launching point to criticize “doomers” who discourage people from pursuing careers in software engineering or radiology, arguing that scaring people out of these professions creates the real bottlenecks.

    On EUV and logic scaling specifically, Jensen was optimistic. He said no supply chain bottleneck lasts longer than two to three years. Once you can build one of something, you can build ten, and once you can build ten, you can build a million. The key is a clear demand signal. If TSMC is convinced of the demand, ASML will produce enough EUV machines. Meanwhile, Nvidia continues to improve computing efficiency by 10x to 50x per generation through architecture, algorithms, and system design.

    The TPU Question

    Dwarkesh pushed hard on whether Google’s TPUs represent a real threat, noting that two of the top three AI models (Claude and Gemini) were trained on TPUs. Jensen drew a sharp distinction between what Nvidia builds and what a TPU is. Nvidia builds accelerated computing, which serves molecular dynamics, quantum chromodynamics, data processing, fluid dynamics, particle physics, and AI. A TPU is a tensor processing unit optimized for matrix multiplies. Nvidia’s market reach is far greater than any TPU or ASIC can possibly have.

    Jensen emphasized programmability as Nvidia’s core architectural advantage. If you want to invent a new attention mechanism, build a hybrid SSM model, fuse diffusion and autoregressive techniques, or disaggregate computation in a novel way, you need a generally programmable architecture. The only way to achieve 10x or 100x performance leaps (versus the roughly 25% per year from Moore’s Law) is to fundamentally change the algorithm, and that requires the flexibility CUDA provides.

    On the specific question of whether hyperscalers with huge engineering teams can simply write their own kernels and bypass CUDA, Jensen acknowledged they do write custom kernels but argued that Nvidia’s engineers still routinely deliver 2x to 3x speedups when they optimize a partner’s stack. He described Nvidia’s GPUs as “F1 racers” that anyone can drive at 100 mph, but extracting peak performance requires deep architectural expertise. Nvidia uses AI itself to generate many of its optimized kernels.

    Jensen was particularly blunt about public benchmarks. He pointed to Dylan Patel’s InferenceMAX benchmark and said neither TPU nor Trainium has been willing to demonstrate their claimed performance advantages on it. He said Nvidia’s performance-per-TCO is the best in the world, “bar none,” and challenged anyone to prove otherwise.

    Regarding Anthropic’s multi-gigawatt deal with Broadcom and Google for TPUs, Jensen called it “a unique instance, not a trend.” He said without Anthropic, there would be essentially no TPU growth and no Trainium growth. He traced this back to his own mistake: when Anthropic and OpenAI needed multi-billion dollar investments from their compute suppliers to get off the ground, Nvidia was not in a position to provide that capital. Google and AWS were, and in return, Anthropic committed to using their compute.

    Nvidia’s Investment Strategy and Regrets

    Jensen was unusually candid about his regret over not investing in foundation model companies earlier. He said he did not deeply internalize how different AI labs were from typical startups. A traditional VC would never put $5 to $10 billion into a single AI lab, but that was exactly what companies like OpenAI and Anthropic needed. By the time Jensen understood this, Nvidia was not in a financial or cultural position to make those kinds of investments.

    Now, Nvidia has invested approximately $30 billion in OpenAI and $10 billion in Anthropic. Jensen said he is delighted to support both and considers their existence essential for the world. But he acknowledged that these investments came at much higher valuations than would have been possible years earlier.

    Jensen explained Nvidia’s broader investment philosophy: support everyone, do not pick winners. He invests in one foundation model company, he invests in all of them. This comes from hard-won humility. When Nvidia started, there were 60 3D graphics companies. Nvidia’s original architecture was “precisely wrong” and the company would have been at the top of most lists to fail. Jensen said he has enough humility from that experience to know that you cannot predict which AI company will ultimately succeed.

    Why Nvidia Will Not Become a Hyperscaler

    Dwarkesh pointed out that Nvidia has the cash to build and operate its own cloud infrastructure, bypassing the middleman ecosystem that converts CapEx into OpEx for AI labs. Jensen rejected this path based on his core operating philosophy.

    If Nvidia did not build its computing platform, NVLink, and the CUDA ecosystem, nobody else would have done it. He is “completely certain” of that. These are things Nvidia must do. But the world has lots of clouds. If Nvidia did not build a cloud, someone else would show up. So the answer is to support the ecosystem instead: invest in CoreWeave, Nscale, Nebius, and others to help them exist and scale, rather than competing with them.

    Jensen was clear that Nvidia is not trying to be in the financing business either. When OpenAI needed a $30 billion investment before its IPO, Nvidia stepped up because OpenAI needed it and Nvidia deeply believed in the company. But these are targeted ecosystem investments, not a strategic pivot into cloud services.

    On GPU allocation during shortages, Jensen pushed back on the narrative that Nvidia strategically “fractures” the market by giving allocations to smaller neoclouds. He said the process is straightforward: you forecast demand, you place a purchase order, and it is first in, first out. Nvidia never changes prices based on demand. Jensen said he prefers to be dependable and serve as the foundation of the industry rather than extracting maximum short-term value.

    The China Debate

    The longest and most heated section of the interview was Jensen’s case against US chip export controls on China. This was a genuine debate, with Dwarkesh pushing the national security argument and Jensen pushing back forcefully.

    Jensen’s core argument rested on several pillars. First, China already has abundant compute. They manufacture 60% or more of the world’s mainstream chips, have massive energy infrastructure (including empty data centers with full power), and employ roughly 50% of the world’s AI researchers. The threshold of compute needed to build models like Anthropic’s Mythos has already been reached and exceeded by China’s existing infrastructure.

    Second, export controls have backfired. They accelerated China’s domestic chip industry, forced their AI ecosystem to optimize for internal architectures instead of the American tech stack, and caused the United States to concede the second-largest technology market in the world. Jensen compared this directly to how US telecom policy allowed Huawei to dominate global telecommunications infrastructure.

    Third, Jensen argued that AI is a five-layer stack (energy, chips, computing platform, models, applications) and the US needs to win at every layer. Fixating on one layer (models) at the expense of another layer (chips) is counterproductive. If Chinese open source AI models end up optimized for non-American hardware and that stack gets exported to the global south, the Middle East, Africa, and Southeast Asia, the US will have lost something far more valuable than whatever marginal compute advantage the export controls provided.

    Dwarkesh countered with the Mythos example: Anthropic’s new model found thousands of high-severity zero-day vulnerabilities across every major operating system and browser, including one that had existed in OpenBSD for 27 years. If China had enough compute to train and deploy a model like Mythos at scale before the US could prepare, the cyber-offensive capabilities would be devastating.

    Jensen’s response was direct. Mythos was trained on “fairly mundane capacity” that is already abundantly available in China. The amount of compute is not the bottleneck for that kind of breakthrough. Great computer science is, and China has no shortage of brilliant AI researchers. He pointed to DeepSeek as evidence: most advances in AI come from algorithmic innovation, not raw hardware. If China’s researchers can achieve breakthroughs like DeepSeek with limited hardware, imagine what they could do with more.

    Jensen also argued for dialogue over confrontation. He said it is essential that American and Chinese AI researchers are talking to each other, and that both countries agree on what AI should not be used for. The idea that you can prevent AI risks by cutting off chip sales, when the real advances come from algorithms and computer science, reflects a fundamental misunderstanding of how AI progress works.

    The debate ended without resolution, but Jensen’s final point was sharp: “I’m not talking to somebody who woke up a loser. That loser attitude, that loser premise, makes no sense to me.”

    Why Not Multiple Chip Architectures?

    Near the end of the interview, Dwarkesh asked why Nvidia does not run multiple parallel chip projects with different architectures, like a Cerebras-style wafer-scale design or a Dojo-style huge package, or even one without CUDA.

    Jensen’s answer was simple: “We don’t have a better idea.” Nvidia simulates all of these alternative approaches in its internal simulators and they are provably worse. The company works on exactly the projects it wants to work on. If the workload were to change dramatically (not just the algorithms, but the actual market shape), Nvidia might add other accelerators.

    In fact, Nvidia recently did exactly this by acquiring Groq. The inference market is now segmenting into different tiers. Some customers will pay premium prices for extremely fast response times even if throughput is lower. This creates a new “high ASP token” segment that justifies a different point on the performance curve. But Jensen was clear: if he had more money, he would put it all behind Nvidia’s existing architecture, not diversify into alternatives.

    Nvidia Without AI

    Jensen closed by saying that even if the deep learning revolution had never happened, Nvidia would be “very, very large.” The premise of the company has always been that general-purpose computing cannot scale indefinitely and that domain-specific acceleration is the way forward. Molecular dynamics, seismic processing, image processing, computational lithography, quantum chemistry, and data processing all benefit from GPU acceleration regardless of AI. Jensen said the fundamental promise of accelerated computing has not changed “not even a little bit.”

    Thoughts

    This interview is one of the most revealing Jensen Huang conversations in years, partly because Dwarkesh actually pushes back instead of lobbing softballs. A few things stand out.

    The Anthropic regret is real and significant. Jensen is essentially admitting that Nvidia’s biggest strategic miss of the AI era was not understanding that foundation model companies needed supplier-level capital commitments, not VC funding. The fact that Google and AWS used compute investments to lock in Anthropic’s architecture choices has had downstream consequences that Nvidia is still working to unwind. When Jensen says Anthropic is “a unique instance, not a trend” for TPU adoption, he is simultaneously downplaying the threat and revealing exactly how seriously he takes it.

    The China debate is the highlight. Jensen’s argument is more nuanced than it first appears. He is not saying “sell China everything.” He is saying the current binary approach of near-total restriction has backfired by accelerating China’s domestic chip industry and pushing the Chinese AI ecosystem away from the American tech stack. His comparison to the US telecom industry losing global market share to Huawei is pointed and historically grounded. Whether you agree with his conclusion or not, the framing of AI as a five-layer stack where the US needs to compete at every layer is a useful mental model.

    The “electrons to tokens” framing is Jensen at his best. It is a simple metaphor that captures something genuinely complex about where value is created in the AI supply chain. And his insistence that the transformation is “far from deeply understood” is a subtle way of arguing that Nvidia’s competitive position will be durable because the problem space is not close to being solved.

    The Groq acquisition reveal is interesting for what it signals about the inference market. If Nvidia is creating a separate product tier for premium-priced, low-latency tokens, it suggests the company sees inference economics fragmenting significantly. This aligns with the broader trend of AI becoming an enterprise product where different customers have wildly different willingness to pay based on how they use tokens.

    Finally, Jensen’s refusal to diversify chip architectures is a bold bet. “We simulate it all in our simulator, provably worse” is an incredibly confident statement. History is full of companies that were right until they were not. But Nvidia’s track record of 50x generation-over-generation improvements through co-design across processors, fabric, libraries, and algorithms is hard to argue with. The question is whether the current paradigm of transformer-based models on GPU clusters represents a local or global optimum for AI compute.

  • The Genesis Mission: Inside the “Manhattan Project” for AI-Driven Science

    TL;DR

    On November 24, 2025, President Trump signed an Executive Order launching “The Genesis Mission.” This initiative aims to centralize federal data and high-performance computing under the Department of Energy to create a massive AI platform. Likened to the World War II Manhattan Project, its goal is to accelerate scientific discovery in critical fields like nuclear energy, biotechnology, and advanced manufacturing.

    Key Takeaways

    • The “Manhattan Project” of AI: The Administration frames this as a historic national effort comparable in urgency to the project that built the atomic bomb, aimed now at global technology dominance.
    • Department of Energy Leads: The Secretary of Energy will oversee the mission, leveraging National Labs and supercomputing infrastructure.
    • The “Platform”: A new “American Science and Security Platform” will be built to host AI agents, foundation models, and secure federal datasets.
    • Six Core Challenges: The mission initially focuses on advanced manufacturing, biotechnology, critical materials, nuclear energy, quantum information science, and semiconductors.
    • Data is the Fuel: The order prioritizes unlocking the “world’s largest collection” of federal scientific datasets to train these new AI models.

    Detailed Summary of the Executive Order

    The Executive Order, titled Launching the Genesis Mission, establishes a coordinated national effort to harness Artificial Intelligence for scientific breakthroughs. Here is how the directive breaks down:

    1. Purpose and Ambition

    The order asserts that America is currently in a race for global technology dominance in AI. To win this race, the Administration is launching the “Genesis Mission,” described as a dedicated effort to unleash a new age of AI-accelerated innovation. The explicit goal is to secure energy dominance, strengthen national security, and multiply the return on taxpayer investment in R&D.

    2. The American Science and Security Platform

    The core mechanism of this mission is the creation of the American Science and Security Platform. This infrastructure will provide:

    • Compute: Secure cloud-based AI environments and DOE national lab supercomputers.
    • AI Agents: Autonomous agents designed to test hypotheses, automate research workflows, and explore design spaces.
    • Data: Access to proprietary, federally curated, and open scientific datasets, as well as synthetic data generated by DOE resources.

    3. Timeline and Milestones

    The Secretary of Energy is on a tight schedule to operationalize this vision:

    • 90 Days: Identify all available federal computing and storage resources.
    • 120 Days: Select initial data/model assets and develop a cybersecurity plan for incorporating data from outside the federal government.
    • 270 Days: Demonstrate an “initial operating capability” of the Platform for at least one national challenge.

    4. Targeted Scientific Domains

    The mission is not open-ended; it focuses on specific high-impact areas. Within 60 days, the Secretary must submit a list of at least 20 challenges, spanning priority domains including Biotechnology, Nuclear Fission and Fusion, Quantum Information Science, and Semiconductors.

    5. Public-Private and International Collaboration

    While led by the DOE, the mission explicitly calls for bringing together “brilliant American scientists” from universities and pioneering businesses. The Secretary is tasked with developing standardized frameworks for IP ownership, licensing, and trade-secret protections to encourage private sector participation.

    Analysis and Thoughts

    “The Genesis Mission will… multiply the return on taxpayer investment into research and development.”

    The Data Sovereignty Play
    The most significant aspect of this order is the recognition of federal datasets as a strategic asset. By explicitly mentioning the “world’s largest collection of such datasets” developed over decades, the Administration is leveraging an asset that private companies cannot easily duplicate. This suggests a shift toward “Sovereign AI” where the government doesn’t just regulate AI, but builds the foundational models for science.

    Hardware over Software
    Placing this under the Department of Energy (DOE) rather than the National Science Foundation (NSF) or Commerce is a strategic signal. The DOE owns the National Labs (like Oak Ridge and Lawrence Livermore) and the world’s fastest supercomputers. This indicates the Administration views this as a heavy-infrastructure challenge—requiring massive energy and compute—rather than just a software problem.

    The “Manhattan Project” Framing
    Invoking the Manhattan Project sets an incredibly high bar. That project resulted in a singular, world-changing weapon. The Genesis Mission aims for a broader diffusion of “AI agents” to automate research. The success of this mission will depend heavily on the integration mentioned in Section 2—getting academic, private, and classified federal systems to talk to each other without compromising security.

    The Energy Component
    It is notable that nuclear fission and fusion are highlighted as specific challenges. AI is notoriously energy-hungry. By tasking the DOE with solving energy problems using AI, the mission creates a feedback loop: better AI designs better power plants, which power better AI.

  • Inside Microsoft’s AGI Masterplan: Satya Nadella Reveals the 50-Year Bet That Will Redefine Computing, Capital, and Control

    1) Fairwater 2 is live at unprecedented scale, with Fairwater 4 linking over a 1 Pb AI WAN

    Nadella walks through the new Fairwater 2 site and states Microsoft has targeted a 10x training capacity increase every 18 to 24 months relative to GPT-5’s compute. He also notes Fairwater 4 will connect on a one petabit network, enabling multi-site aggregation for frontier training, data generation, and inference.

    2) Microsoft’s MAI program, a parallel superintelligence effort alongside OpenAI

    Microsoft is standing up its own frontier lab and will “continue to drop” models in the open, with an omni-model on the roadmap and high-profile hires joining Mustafa Suleyman. This is a clear signal that Microsoft intends to compete at the top tier while still leveraging OpenAI models in products.

    3) Clarification on IP: Microsoft says it has full access to the GPT family’s IP

    Nadella says Microsoft has access to all of OpenAI’s model IP (consumer hardware excluded) and shared that the firms co-developed system-level designs for supercomputers. This resolves long-standing ambiguity about who holds rights to GPT-class systems.

    4) New exclusivity boundaries: OpenAI’s API is Azure-exclusive, SaaS can run elsewhere with limited exceptions

    The interview spells out that OpenAI’s platform API must run on Azure. ChatGPT as SaaS can be hosted elsewhere only under specific carve-outs, for example certain US government cases.

    5) Per-agent future for Microsoft’s business model

    Nadella describes a shift where companies provision Windows 365 style computers for autonomous agents. Licensing and provisioning evolve from per-user to per-user plus per-agent, with identity, security, storage, and observability provided as the substrate.

    6) The 2024–2025 capacity “pause” explained

    Nadella confirms Microsoft paused or dropped some leases in the second half of last year to avoid lock-in to a single accelerator generation, keep the fleet fungible across GB200, GB300, and future parts, and balance training with global serving to match monetization.

    7) Concrete scaling cadence disclosure

    The 10x training capacity target every 18 to 24 months is stated on the record while touring Fairwater 2. This implies the next frontier runs will be roughly an order of magnitude above GPT-5 compute.

    8) Multi-model, multi-supplier posture

    Microsoft will keep using OpenAI models in products for years, build MAI models in parallel, and integrate other frontier models where product quality or cost warrants it.

    Why these points matter

    • Industrial scale: Fairwater’s disclosed networking and capacity targets set a new bar for AI factories and imply rapid model scaling.
    • Strategic independence: MAI plus GPT IP access gives Microsoft a dual track that reduces single-partner risk.
    • Ecosystem control: Azure exclusivity for OpenAI’s API consolidates platform power at the infrastructure layer.
    • New revenue primitives: Per-agent provisioning reframes Microsoft’s core metrics and pricing.

    Pull quotes

      “We’ve tried to 10x the training capacity every 18 to 24 months.”

      “The API is Azure-exclusive. The SaaS business can run anywhere, with a few exceptions.”

      “We have access to the GPT family’s IP.”

    TL;DW

    • Microsoft is building a global network of AI super-datacenters (Fairwater 2 and beyond) designed for fast upgrade cycles and cross-region training at petabit scale.
    • Strategy spans three layers: infrastructure, models, and application scaffolding, so Microsoft creates value regardless of which model wins.
    • AI economics shift margins, so Microsoft blends subscriptions with metered consumption and focuses on tokens per dollar per watt.
    • Future includes autonomous agents that get provisioned like users with identity, security, storage, and observability.
    • Trust and sovereignty are central. Microsoft leans into compliant, sovereign cloud footprints to win globally.

    Detailed Summary

    1) Fairwater 2: AI Superfactory

    Microsoft’s Fairwater 2 is presented as the most powerful AI datacenter yet, packing hundreds of thousands of GB200 and GB300 accelerators, tied by a petabit AI WAN and designed to stitch training jobs across buildings and regions. The key lesson: keep the fleet fungible and avoid overbuilding for a single hardware generation as power density and cooling change with each wave like Vera Rubin and Rubin Ultra.

    2) The Three-Layer Strategy

    • Infrastructure: Azure’s hyperscale footprint, tuned for training, data generation, and inference, with strict flexibility across model architectures.
    • Models: Access to OpenAI’s GPT family for seven years plus Microsoft’s own MAI roadmap for text, image, and audio, moving toward an omni-model.
    • Application Scaffolding: Copilots and agent frameworks like GitHub’s Agent HQ and Mission Control that orchestrate many agents on real repos and workflows.

    This layered approach lets Microsoft compete whether the value accrues to models, tooling, or infrastructure.

    3) Business Models and Margins

    AI raises COGS relative to classic SaaS, so pricing blends entitlements with consumption tiers. GitHub Copilot helped catalyze a multibillion market in a year, even as rivals emerged. Microsoft aims to ride a market that is expanding 10x rather than clinging to legacy share. Efficiency focus: tokens per dollar per watt through software optimization as much as hardware.

    4) Copilot, GitHub, and Agent Control Planes

    GitHub becomes the control plane for multi-agent development. Agent HQ and Mission Control aim to let teams launch, steer, and observe multiple agents working in branches, with repo-native primitives for issues, actions, and reviews.

    5) Models vs Scaffolding

    Nadella argues model monopolies are checked by open source and substitution. Durable value sits in the scaffolding layer that brings context, data liquidity, compliance, and deep tool knowledge, exemplified by Excel Agent that understands formulas and artifacts beyond screen pixels.

    6) Rise of Autonomous Agents

    Two worlds emerge: human-in-the-loop Copilots and fully autonomous agents. Microsoft plans to provision agents with computers, identity, security, storage, and observability, evolving end-user software into an infrastructure business for agents as well as people.

    7) MAI: Microsoft’s In-House Frontier Effort

    Microsoft is assembling a top-tier lab led by Mustafa Suleyman and veterans from DeepMind and Google. Early MAI models show progress in multimodal arenas. The plan is to combine OpenAI access with independent research and product-optimized models for latency and cost.

    8) Capex and Industrial Transformation

    Capex has surged. Microsoft frames this era as capital intensive and knowledge intensive. Software scheduling, workload placement, and continual throughput improvements are essential to maximize returns on a fleet that upgrades every 18 to 24 months.

    9) The Lease Pause and Flexibility

    Microsoft paused some leases to avoid single-generation lock-in and to prevent over-reliance on a small number of mega-customers. The portfolio favors global diversity, regulatory alignment, balanced training and inference, and location choices that respect sovereignty and latency needs.

    10) Chips and Systems

    Custom silicon like Maia will scale in lockstep with Microsoft’s own models and OpenAI collaboration, while Nvidia remains central. The bar for any new accelerator is total fleet TCO, not just raw performance, and system design is co-evolved with model needs.

    11) Sovereign AI and Trust

    Nations want AI benefits with continuity and control. Microsoft’s approach combines sovereign cloud patterns, data residency, confidential computing, and compliance so countries can adopt leading AI while managing concentration risk. Nadella emphasizes trust in American technology and institutions as a decisive global advantage.

    Key Takeaways

    1. Build for flexibility: Datacenters, pricing, and software are optimized for fast evolution and multi-model support.
    2. Three-layer stack wins: Infrastructure, models, and scaffolding compound each other and hedge against shifts in where value accrues.
    3. Agents are the next platform: Provisioned like users with identity and observability, agents will demand a new kind of enterprise infrastructure.
    4. Efficiency is king: Tokens per dollar per watt drives margins more than any single chip choice.
    5. Trust and sovereignty matter: Compliance and credible guarantees are strategic differentiators in a bipolar world.

  • Composer: Building a Fast Frontier Model with Reinforcement Learning

    Composer represents Cursor’s most ambitious step yet toward a new generation of intelligent, high-speed coding agents. Built through deep reinforcement learning (RL) and large-scale infrastructure, Composer delivers frontier-level results at speeds up to four times faster than comparable models:contentReference[oaicite:0]{index=0}. It isn’t just another large language model; it’s an actively trained software engineering assistant optimized to think, plan, and code with precision — in real time.

    From Cheetah to Composer: The Evolution of Speed

    The origins of Composer go back to an experimental prototype called Cheetah, an agent Cursor developed to study how much faster coding models could get before hitting usability limits. Developers consistently preferred the speed and fluidity of an agent that responded instantly, keeping them “in flow.” Cheetah proved the concept, but it was Composer that matured it — integrating reinforcement learning and mixture-of-experts (MoE) architecture to achieve both speed and intelligence.

    Composer’s training goal was simple but demanding: make the model capable of solving real-world programming challenges in real codebases using actual developer tools. During RL, Composer was given tasks like editing files, running terminal commands, performing semantic searches, or refactoring code. Its objective wasn’t just to get the right answer — it was to work efficiently, using minimal steps, adhering to existing abstractions, and maintaining code quality:contentReference[oaicite:1]{index=1}.

    Training on Real Engineering Environments

    Rather than relying on synthetic datasets or static benchmarks, Cursor trained Composer within a dynamic software environment. Every RL episode simulated an authentic engineering workflow — debugging, writing unit tests, applying linter fixes, and performing large-scale refactors. Over time, Composer developed behaviors that mirror an experienced developer’s workflow. It learned when to open a file, when to search globally, and when to execute a command rather than speculate.

    Cursor’s evaluation framework, Cursor Bench, measures progress by realism rather than abstract metrics. It compiles actual agent requests from engineers and compares Composer’s solutions to human-curated optimal responses. This lets Cursor measure not just correctness, but also how well the model respects a team’s architecture, naming conventions, and software practices — metrics that matter in production environments.

    Reinforcement Learning as a Performance Engine

    Reinforcement learning is at the heart of Composer’s performance. Unlike supervised fine-tuning, which simply mimics examples, RL rewards Composer for producing high-quality, efficient, and contextually relevant work. It actively learns to choose the right tools, minimize unnecessary output, and exploit parallelism across tasks. The model was even rewarded for avoiding unsupported claims — pushing it to generate more verifiable and responsible code suggestions.

    As RL progressed, emergent behaviors appeared. Composer began autonomously running semantic searches to explore codebases, fixing linter errors, and even generating and executing tests to validate its own work. These self-taught habits transformed it from a passive text generator into an active agent capable of iterative reasoning.

    Infrastructure at Scale: Thousands of Sandboxed Agents

    Behind Composer’s intelligence is a massive engineering effort. Training large MoE models efficiently requires significant parallelization and precision management. Cursor’s infrastructure, built with PyTorch and Ray, powers asynchronous RL at scale. Their system supports thousands of simultaneous environments, each a sandboxed virtual workspace where Composer experiments safely with file edits, code execution, and search queries.

    To achieve this scale, the team integrated MXFP8 MoE kernels with expert and hybrid-sharded data parallelism. This setup allows distributed training across thousands of NVIDIA GPUs with minimal communication cost — effectively combining speed, scale, and precision. MXFP8 also enables faster inference without any need for post-training quantization, giving developers real-world performance gains instantly.

    Cursor’s infrastructure can spawn hundreds of thousands of concurrent sandboxed coding environments. This capability, adapted from their Background Agents system, was essential to unify RL experiments with production-grade conditions. It ensures that Composer’s training environment matches the complexity of real-world coding, creating a model genuinely optimized for developer workflows.

    The Cursor Bench and What “Frontier” Means

    Composer’s benchmark performance earned it a place in what Cursor calls the “Fast Frontier” class — models designed for efficient inference while maintaining top-tier quality. This group includes systems like Haiku 4.5 and Gemini Flash 2.5. While GPT-5 and Sonnet 4.5 remain the strongest overall, Composer outperforms nearly every open-weight model, including Qwen Coder and GLM 4.6:contentReference[oaicite:2]{index=2}. In tokens-per-second performance, Composer’s throughput is among the highest ever measured under the standardized Anthropic tokenizer.

    Built by Developers, for Developers

    Composer isn’t just research — it’s in daily use inside Cursor. Engineers rely on it for their own development, using it to edit code, manage large repositories, and explore unfamiliar projects. This internal dogfooding loop means Composer is constantly tested and improved in real production contexts. Its success is measured by one thing: whether it helps developers get more done, faster, and with fewer interruptions.

    Cursor’s goal isn’t to replace developers, but to enhance them — providing an assistant that acts as an extension of their workflow. By combining fast inference, contextual understanding, and reinforcement learning, Composer turns AI from a static completion tool into a real collaborator.

    Wrap Up

    Composer represents a milestone in AI-assisted software engineering. It demonstrates that reinforcement learning, when applied at scale with the right infrastructure and metrics, can produce agents that are not only faster but also more disciplined, efficient, and trustworthy. For developers, it’s a step toward a future where coding feels as seamless and interactive as conversation — powered by an agent that truly understands how to build software.

  • Why Every Nation Needs Its Own AI Strategy: Insights from Jensen Huang & Arthur Mensch

    In a world where artificial intelligence (AI) is reshaping economies, cultures, and security, the stakes for nations have never been higher. In a recent episode of The a16z Podcast, Jensen Huang, CEO of NVIDIA, and Arthur Mensch, co-founder and CEO of Mistral, unpack the urgent need for sovereign AI—national strategies that ensure countries control their digital futures. Drawing from their discussion, this article explores why every nation must prioritize AI, the economic and cultural implications, and practical steps to build a robust strategy.

    The Global Race for Sovereign AI

    The conversation kicks off with a powerful idea: AI isn’t just about computing—it’s about culture, economics, and sovereignty. Huang stresses that no one will prioritize a nation’s unique needs more than the nation itself. “Nobody’s going to care more about the Swedish culture… than Sweden,” he says, highlighting the risk of digital dependence on foreign powers. Mensch echoes this, framing AI as a tool nations must wield to avoid modern digital colonialization—where external entities dictate a country’s technological destiny.

    AI as a General-Purpose Technology

    Mensch positions AI as a transformative force, comparable to electricity or the internet, with applications spanning agriculture, healthcare, defense, and beyond. Yet Huang cautions against waiting for a universal solution from a single provider. “Intelligence is for everyone,” he asserts, urging nations to tailor AI to their languages, values, and priorities. Mistral’s M-Saaba model, optimized for Arabic, exemplifies this—outperforming larger models by focusing on linguistic and cultural specificity.

    Economic Implications: A Game-Changer for GDP

    The economic stakes are massive. Mensch predicts AI could boost GDP by double digits for countries that invest wisely, warning that laggards will see wealth drain to tech-forward neighbors. Huang draws a parallel to the electricity era: nations that built their own grids prospered, while others became reliant. For leaders, this means securing chips, data centers, and talent to capture AI’s economic potential—a must for both large and small nations.

    Cultural Infrastructure and Digital Workforce

    Huang introduces a compelling metaphor: AI as a “digital workforce” that nations must onboard, train, and guide, much like human employees. This workforce should embody local values and laws, something no outsider can fully replicate. Mensch adds that AI’s ability to produce content—text, images, voice—makes it a social construct, deeply tied to a nation’s identity. Without control, countries risk losing their cultural sovereignty to centralized models reflecting foreign biases.

    Open-Source vs. Closed AI: A Path to Independence

    Both Huang and Mensch advocate for open-source AI as a cornerstone of sovereignty. Mensch explains that models like Mistral Nemo, developed with NVIDIA, empower nations to deploy AI on their own infrastructure, free from closed-system dependency. Open-source also fuels innovation—Mistral’s releases spurred Meta and others to follow suit. Huang highlights its role in niche markets like healthcare and mining, plus its security edge: global scrutiny makes open models safer than opaque alternatives.

    Risks and Challenges of AI Adoption

    Leaders often worry about public backlash—will AI replace jobs? Mensch suggests countering this by upskilling citizens and showcasing practical benefits, like France’s AI-driven unemployment agency connecting workers to opportunities. Huang sees AI as “the greatest equalizer,” noting more people use ChatGPT than code in C++, shrinking the tech divide. Still, both acknowledge the initial hurdle: setting up AI systems is tough, though improving tools make it increasingly manageable.

    Building a National AI Strategy

    Huang and Mensch offer a blueprint for action:

    • Talent: Train a local workforce to customize AI systems.
    • Infrastructure: Secure chips from NVIDIA and software from partners like Mistral.
    • Customization: Adapt open-source models with local data and culture.
    • Vision: Prepare for agentic and physical AI breakthroughs in manufacturing and science.

    Huang predicts the next decade will bring AI that thinks, acts, and understands physics—revolutionizing industries vital to emerging markets, from energy to manufacturing.

    Why It’s Urgent

    The podcast ends with a clarion call: AI is “the most consequential technology of all time,” and nations must act now. Huang urges leaders to engage actively, not just admire from afar, while Mensch emphasizes education and partnerships to safeguard economic and cultural futures. For more, follow Jensen Huang (@nvidia) and Arthur Mensch (@arthurmensch) on X, or visit NVIDIA and Mistral’s websites.

  • How NVIDIA is Revolutionizing Computing with AI: Jensen Huang on AI Infrastructure, Digital Employees, and the Future of Data Centers

    NVIDIA CEO Jensen Huang discusses the company’s role in revolutionizing computing through AI, emphasizing decade-long investments in scalable, interconnected AI infrastructure, breakthroughs in efficiency, and the future of digital and embodied AI as transformative for industries globally.

    NVIDIA is transforming the landscape of computing, driving innovation at every level from data centers to digital employees. In a recent conversation with Jensen Huang, NVIDIA’s CEO, he offered a rare look at the strategic direction and long-term vision that has positioned NVIDIA as a leader in the AI revolution. Through decade-long infrastructure investments, NVIDIA is not just building hardware but creating “AI factories” that promise to impact industries globally.

    Decade-Long Investments in AI Infrastructure

    For NVIDIA, success has come from looking far into the future. Jensen Huang emphasized the company’s commitment to ten-year investments in scalable, efficient AI infrastructure. With an eye on exponential growth, NVIDIA has focused on creating solutions that can continue to meet demand as AI expands in complexity and scope. One of the cornerstones of this approach is NVLink technology, which enables GPUs to function as a unified supercomputer, allowing unprecedented scale for AI applications.

    This vision aligns with Huang’s goal of optimizing data centers for high-performance AI, making NVIDIA’s infrastructure not only capable of tackling today’s AI challenges but prepared for tomorrow’s even larger-scale demands.

    Outpacing Moore’s Law with Full-Stack Integration

    Huang highlighted how NVIDIA aims to surpass the limits of traditional computing, especially Moore’s Law, by focusing on a full-stack integration strategy. This strategy involves designing hardware and software as a cohesive unit, enabling a 240x reduction in AI computation costs while increasing efficiency. With this approach, NVIDIA has managed to achieve performance improvements that far exceed conventional expectations, driving both cost and energy usage down across its AI operations.

    The full-stack approach has enabled NVIDIA to continually upgrade its infrastructure and enhance performance, ensuring that each component of its architecture is optimized and aligned.

    The Evolution of Data Centers: From Storage to AI Factories

    One of NVIDIA’s groundbreaking shifts is the redefinition of data centers from traditional storage units to “AI factories” generating intelligence. Unlike conventional data centers focused on multi-tenant storage, NVIDIA’s new data centers produce “tokens” for AI models at an industrial scale. These tokens are used in applications across industries, from robotics to biotechnology. Huang believes that every industry will benefit from AI-generated intelligence, making this shift in data centers vital to global AI adoption.

    This AI-centric infrastructure is already making waves, as seen with NVIDIA’s 100,000-GPU supercluster built for X.AI. NVIDIA demonstrated its logistical prowess by setting up this supercluster rapidly, paving the way for similar large-scale projects in the future.

    The Role of AI in Science, Engineering, and Digital Employees

    NVIDIA’s infrastructure investments and technological advancements have far-reaching impacts, particularly in science and engineering. Huang shared that AI-driven methods are now integral to NVIDIA’s chip design process, allowing them to explore new design options and optimize faster than human engineers alone could. This innovation is just the beginning, as Huang envisions AI reshaping fields like biotechnology, materials science, and theoretical physics, creating opportunities for breakthroughs at a previously impossible scale.

    Beyond science, Huang foresees AI-driven digital employees as a major component of future workforces. AI employees could assist in roles like marketing, supply chain management, and chip design, allowing human workers to focus on higher-level tasks. This shift to digital labor marks a major milestone for AI and has the potential to redefine productivity and efficiency across industries.

    Embodied AI and Real-World Applications

    Huang believes that embodied AI—AI in physical form—will transform industries such as robotics and autonomous vehicles. Self-driving cars and robots equipped with AI will become more common, thanks to NVIDIA’s advancements in AI infrastructure. By training these AI models on NVIDIA’s systems, industries can integrate intelligent robots and vehicles without needing substantial changes to existing environments.

    This embodied AI will serve as a bridge between digital intelligence and the physical world, enabling a new generation of applications that go beyond the screen to interact directly with people and environments.

    Sustaining Innovation Through Compatibility and Software Longevity

    Huang stressed that compatibility and sustainability are central to NVIDIA’s long-term vision. NVIDIA’s CUDA platform has enabled the company to build a lasting ecosystem, allowing software created on earlier NVIDIA systems to operate seamlessly on newer ones. This commitment to software longevity means companies can rely on NVIDIA’s systems for years, making it a trusted partner for businesses that prioritize innovation without disruption.

    NVIDIA as the “AI Factory” of the Future

    As Huang puts it, NVIDIA has evolved beyond a hardware company and is now an “AI factory”—a company that produces intelligence as a commodity. Huang sees AI as a resource as valuable as energy or raw materials, with applications across nearly every industry. From providing AI-driven insights to enabling new forms of intelligence, NVIDIA’s technology is poised to transform global markets and create value on an industrial scale.

    Jensen Huang’s vision for NVIDIA is not just about staying ahead in the computing industry; it’s about redefining what computing means. NVIDIA’s investments in scalable infrastructure, software longevity, digital employees, and embodied AI represent a shift in how industries will function in the future. As Huang envisions, the company is no longer just producing chips or hardware but enabling an entire ecosystem of AI-driven innovation that will touch every aspect of modern life.