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  • Mark Zuckerberg, Priscilla Chan, and Alex Rives on CZI Biohub, Open-Source AI, and Building World Models of Biology to Cure All Disease

    Mark Zuckerberg, Priscilla Chan, and AI researcher Alex Rives sat down with the No Priors podcast to explain why CZI Biohub became the primary focus of their philanthropy, why they committed $500 million to a virtual biology initiative, and why they are giving the resulting AI models away as open source instead of building a company. The conversation moves from a goal that Nobel laureates once laughed at, curing, preventing, and managing all disease by the end of the century, to a concrete technical strategy: build world models of biology layer by layer, from proteins to cells to whole systems, and put them in every scientist’s hands.

    TLDW

    This is the clearest public articulation yet of how the Chan Zuckerberg Initiative thinks about AI and biology. The throughline starts a decade ago when Zuckerberg and Chan asked scientists how to cure all disease and learned the real bottleneck was tooling, siloed labs, and unshared knowledge, not a lack of ambition. That insight produced the Human Cell Atlas, the CELLxGENE annotation tool, and a corpus of single-cell transcriptomics that large language models could finally make sense of. Now Biohub couples a frontier AI lab with frontier wet-lab biology under one roof across San Francisco, New York, and Chicago, organized around the virtual biology initiative and the long-term goal of a virtual cell. Alex Rives, the AI researcher behind the ESM protein language models, walks through their newly released ESM-based world model of protein biology: trained on billions of protein sequences, it predicts atomic-resolution structures blazingly fast, folded over 1.1 billion proteins, designs novel proteins and single-chain antibodies as an emergent property, and found nanomolar binders in a single 96-well plate. The discussion covers mechanistic interpretability as a way to extract genuinely new biological knowledge, personalized medicine driven by understanding the chain from gene variant to protein to disease, predicting off-target toxicity before human trials, rare-disease patient organizing, the baby KJ CRISPR case, biosafety tradeoffs of open source, talent and why frontier biology plus frontier AI is a recruiting moat, and what success looks like five years out.

    Thoughts

    The most important claim in this conversation is also the easiest to miss because it is delivered casually: protein design is an emergent property of a model that was never asked to design proteins. Rives is explicit that they did not build a model for antibodies and did not build a model to bind a particular target. They built a model that understands proteins, trained on raw sequence with a next-token objective, and protein design, structure prediction, and antibody generation fell out of it. That is the language-model bet transplanted into biology, and the fact that it produced nanomolar binders, the threshold for actual therapeutic activity, in a single 96-well plate rather than a high-throughput screen of millions is the kind of result that quietly resets what a small team can attempt. If that generalizes, the binding curve for “design a molecule” bends the same way the cost curve for “write working code” did.

    What makes the strategy coherent, rather than just a well-funded AI lab, is the insistence that the wet lab and the AI lab are a single effort. Most of biology’s useful data does not exist on the internet the way human language does. You cannot pay a factory to produce it. Someone has to invent the cellular engineering in New York, the inflammation-sensing devices in Chicago, the translucent-zebrafish imaging, and that is the actual product of frontier biology: new instruments that generate data nobody has ever seen, which in turn make new classes of models possible. This is the part venture-backed competitors will struggle to replicate, because it requires patience measured in 10 to 15 year horizons and a willingness to spend on data generation that has no business model attached. Zuckerberg is almost dismissive about it, noting they could probably run it as a business but that not having to think about monetization is strategically simplifying. The nonprofit structure is not charity window-dressing here. It is what lets them release the models as an open discovery engine and harness the entire academic and biotech field rather than competing with it.

    The mechanistic interpretability thread deserves more attention than it will get. Interpretability has mostly been a safety and alignment story for language models, a way to peer inside the black box and check that the representations match our understanding of the world. Rives flips it: the protein models have been trained on both known and unknown biology, billions of sequences including proteins we understand nothing about, and they are building representations that connect the unknown proteins to the known ones through an underlying structural grammar. The promise is that interpretability becomes a discovery tool, not just an audit tool. You open the box and find biology the field has not characterized yet, the mechanism of action for a treatment, a system in the body nobody mapped. That is a fundamentally more optimistic use of the same toolkit, and it is the part of the launch Sarah Guo and Elad Gil both flag as the most interesting.

    Chan’s framing of personalized medicine is worth sitting with because it reframes the entire goal away from “cure disease X.” She wants to treat the individual as an individual: understand this person’s genetics, their risk profile, the mechanistic chain from a specific gene variant through a protein to a disease process, and then design a drug bespoke to them. The current reality she describes, sitting in PubMed reading a paper’s supplement asking “am I represented in this cohort,” guessing whether a drug that kind of impacts a pathway that is probably implicated might do something, is a brutal and accurate picture of how non-standard cases are actually handled today. The vision is generalizable tools delivering personalized answers, which is the same put-the-tool-in-the-individual’s-hands philosophy Zuckerberg applies to open-source AI and, by his own analogy, to social media. Whether you find that analogy reassuring or not, the consistency of the worldview is real: they genuinely do not believe in a central super-intelligence solving science, and the whole architecture follows from that.

    The honest gap they name is the clinic. Chan is candid that the science will start moving fast but that translating to patients requires changing how clinical research itself works, and that part is still shaping up. The most interesting near-term lever is not a virtual FDA trial but the recruitment and economics flip for rare disease: patient groups self-organizing registries, biobanks, and natural-history studies, compressing timelines from decades to a handful of years, paired with models that lower the cost of generating a candidate. The baby KJ case, a custom CRISPR therapeutic to edit a single mutation, delivered to liver cells specifically because that target was deliverable, is the proof of concept for why disease selection and delivery creativity matter as much as the molecule. The molecule is becoming the cheap part. The rest of the chain is where the next decade of work actually sits.

    Key Takeaways

    • CZI Biohub is now the primary philanthropic focus of the Chan Zuckerberg Initiative, a shift the team formalized in the past year.
    • They committed $500 million to the virtual biology initiative, the unifying theme across the Biohubs.
    • The original goal, set roughly 10 years ago, was to cure, prevent, and manage all disease by the end of the century. Zuckerberg now thinks “end of the century” is too conservative.
    • Nobel Prize winning scientists initially laughed at the all-disease ambition. When pressed for why it was impossible, the real answers were silos, locked-up unpublished information, and the inability to build shared tools.
    • The recurring example: a postdoc builds a great tool, it lives on their computer, they graduate, and the tool is gone. Shared, durable tooling was the missing layer.
    • CZI is explicit that they are not the ones who will cure diseases. Their role is building tools that accelerate the entire scientific field so the field collectively cures them.
    • The first request for application was single-cell sequencing, funding methods so scientists could share how to do it.
    • That work led to funding the Human Cell Atlas, now one of the largest databases of single-cell transcriptomics.
    • They built CELLxGENE, a simple annotation tool, around which a community formed and contributed data CZI had nothing to do with creating. It is now a corpus underpinning many transcriptomic models.
    • Critics called the data gathering “stamp collecting.” The arrival of large language models, which can make sense of large amounts of data, answered that critique.
    • The ambition is to move biology from a discovery-based science to an engineering-based science, systematically understanding how living cells work and why things go wrong.
    • Biohub couples a frontier AI lab with a frontier biology effort. Unlike language models, biology lacks abundant internet-scale data, so new science is required to generate the data the models need.
    • The Biohubs are specialized: New York focuses on cellular engineering, Chicago builds devices to measure things like inflammation, plus imaging work and translucent-zebrafish development studies.
    • Alex Rives, who built the ESM protein language models and founded EvolutionaryScale after working at Meta FAIR, now leads the AI effort. The team raised venture capital before joining CZI’s nonprofit structure.
    • The strategy is hierarchical: model proteins first, then cells, then whole systems, because you cannot understand cells without understanding protein interactions.
    • They collect data strategically to bridge across the hierarchy, for example spatial transcriptomics showing where RNA localizes within a cell, and sensors that observe cell-to-cell communication.
    • The newly released ESM-based model is a world model of protein biology, trained on billions of protein sequences, predicting atomic-resolution structure extremely fast at a Pareto-optimal frontier of speed and accuracy.
    • They folded over 1.1 billion proteins and predicted their structures, identifying connecting features through mechanistic interpretability.
    • The model hits state of the art on structure prediction benchmarks, especially protein-protein and protein-antibody interactions, which are critical for therapeutic design.
    • Protein and antibody design are emergent properties. They designed a model to understand proteins, not to bind any specific target, and design capability fell out of it.
    • In one experiment, they selected from hundreds of thousands of digital trajectories, synthesized 96 proteins in a single well plate, and found nanomolar binders, the threshold for therapeutic activity.
    • Results were validated with the Biohub’s cryo-EM microscopes and structural biology center, confirming function and atomic-resolution binding interfaces.
    • Mechanistic interpretability is reframed as a discovery tool: open the black box to find biology nobody has characterized, not just to audit the model.
    • Chan’s vision of personalized medicine: understand a person’s genetics, the mechanistic chain from gene variant to protein to disease, then design a bespoke drug and intervene.
    • A comprehensive model of how cells work could predict off-target effects, like a receptor on kidney cells causing renal toxicity, before human trials.
    • They study systems rather than individual diseases. Inflammation is a major Chicago focus because it connects to many diseases.
    • A typical drug trial runs about 15 years and $1.5 billion. Only roughly $50 million is the molecule and preclinical work. The other $1.45 billion is drug development, much of it gated on regulation, recruitment, and failures from toxicity or absorption.
    • The baby KJ case at CHOP delivered a custom CRISPR therapeutic to edit a single mutation, chosen carefully because his liver cells were a deliverable target.
    • CZI’s “Rare As One” program supports rare-disease patient groups self-organizing registries, biobanks, and even their own clinical trials, compressing gene-therapy timelines from decades to 3 to 5 years.
    • Letting people opt in to frontier trials, while preserving historical vetting for the general population, is named as a key shift that could accelerate biology.
    • The open-source philosophy mirrors Zuckerberg’s broader ethos: empower individuals with tools rather than centralizing power in a few institutions or a single super-intelligence.
    • Biosafety is acknowledged as a real consideration that open-source biology will need to balance and handle carefully.
    • On talent: AI researchers could join any frontier lab, but no other organization pairs frontier biology with frontier AI, which is the recruiting moat.
    • You do not need a huge team. Zuckerberg argues real AI progress can come from a strong group of a dozen or a couple dozen people.
    • Researchers have been connecting the released model to agentic systems to automate the entire protein design process.
    • The next big challenge is the virtual cell: a system that models the proteomic, genetic, and transcriptomic layers and connects them to phenotype, generalizing to interventions it was never trained on.
    • Like every lab, Biohub is compute and data constrained, constantly deciding whether to double down on proteins or push further into cellular work.
    • Five-year success: a hierarchical set of world models of biology and doing the highest-quality, uniquely contributive work in the world, a setup the team believes no other organization has.
    • The biggest update of the past year: formalizing Biohub as the philanthropy’s core, and flipping leadership from biologists interested in technology to an AI researcher with a biology background.
    • Zuckerberg’s read on the broader industry: the exponential curve is on track and still accelerating, which validates making a very big long-term investment.

    Detailed Summary

    From “cure all disease” to a tooling problem

    The origin story is a decade old. Zuckerberg and Chan wanted to build an organization that could cure, prevent, and manage all disease by the end of the century, and a series of meetings with famous, Nobel Prize winning scientists produced laughter rather than encouragement. Instead of retreating, they kept asking why it was impossible. The answers, once scientists relented, were not about biology being too hard. They were about how science is organized: researchers work in silos, published information gets locked up for long periods, and there is no good way to build and share durable tools. The image that stuck was a postdoc building an excellent tool that lives on a single computer and vanishes when that person graduates. The bottleneck was infrastructure and shared knowledge, and that is where CZI decided it could contribute.

    The path from single-cell sequencing to a world model

    The original Biohub model brought engineers and scientists together across universities for long-term tool development, and it worked. CZI’s first request for application targeted single-cell sequencing, funding the methods so scientists could share how to read the RNA transcribed in individual cells. That seeded the Human Cell Atlas, now one of the largest single-cell transcriptomics databases. When annotation became a bottleneck, CZI built CELLxGENE, a simple annotation tool, and a community formed around it and contributed data CZI never funded. Critics dismissed it as stamp collecting, gathering bits of data without extracting wisdom. Then large language models arrived and demonstrated they could make sense of exactly that kind of large-scale data, and Chan describes the delight of realizing the missing engine had appeared.

    Frontier AI married to frontier biology

    The unifying theme is the virtual biology initiative, and the structural insight is that the AI effort and the wet-lab effort are a single integrated organization, not two collaborating ones. Biology lacks the internet-scale data that language models enjoy. You cannot buy the data from a factory. So Biohub invents the science that generates it: cellular engineering in New York to record what happens inside the body, devices in Chicago to measure inflammation, imaging to visualize the previously invisible, and translucent zebrafish to watch development unfold across cells as the brain forms. Each new instrument creates a new dataset, which enables a new class of model. Rives, who built the ESM models and founded EvolutionaryScale before joining, frames this as the start of a new era of science, where systems that predict the next token can learn world models of biology from the data, provided you build at the right scale with the right people.

    Building biology hierarchically

    The team is deliberate that each layer of biology is qualitatively different and must be built up in order. You cannot jump to cells without understanding protein interactions, and you cannot model the immune system without first understanding cells. So the approach starts with the building blocks, the proteins, and ladders upward. The advantage of a single integrated effort is the ability to gather data that connects the hierarchy: spatial transcriptomics that show where RNA localizes inside a cell, sensors that capture cell-to-cell communication, developmental imaging in zebrafish. That connective tissue is what lets the modeling generalize across levels. The interviewer, a former wet-lab biologist with a PhD, notes that the reductionist and systems camps of biology historically never worked together deeply, and that bridging them is one of the genuinely novel things about the effort.

    The ESM-based protein world model

    The launch at the center of the conversation, roughly a week old at recording, is an open system for scientific discovery in protein biology: a language-model-based world model trained on billions of protein sequences. It learns emergent representations of protein biology and predicts atomic-resolution structure at blazing speed, sitting on a Pareto-optimal frontier of speed and accuracy. They folded over 1.1 billion proteins and used mechanistic interpretability to identify features connecting them. It reaches state of the art across structure-prediction benchmarks, with particular strength on protein-protein and protein-antibody interactions that matter for therapeutics. The headline result: they used the model to design proteins and single-chain antibodies digitally, selected from hundreds of thousands of trajectories, synthesized just 96 in a single well plate, and found nanomolar binders, replacing high-throughput screens of millions of antibodies. Validation came from the Biohub’s cryo-EM structural biology center, confirming both function and the atomic-resolution binding interfaces.

    Interpretability as discovery, and personalized medicine

    Rives reframes mechanistic interpretability, usually aimed at language models, as a way to extract new biological knowledge. The protein models are trained on both known and unknown biology and develop representations that connect uncharacterized proteins to understood ones through an underlying structural grammar. Opening that black box could reveal systems in the body or mechanisms of action for treatments that the field has never mapped. Chan connects this to a personalized-medicine vision: understand an individual’s genetics and the mechanistic chain from gene variant to protein to disease, then design a bespoke intervention. She contrasts it with today’s reality of reading PubMed supplements and guessing whether you are represented in a study cohort. For some diseases, simply knowing which gene variants cause disease is already empowering. For others, the chain is understood and the missing piece is the ability to change a protein’s function, which is where designed proteins could actually cure.

    Drug development, off-target effects, and rare disease

    The interviewers press on translation, noting a typical trial runs 15 years and $1.5 billion, with only about $50 million in the molecule and preclinical work and the rest in development gated on regulation, recruitment, toxicity, and absorption failures. Chan’s hope is that comprehensive cell models predict off-target effects, like an unanticipated receptor on kidney cells causing renal toxicity, before human trials. They study systems such as inflammation and the immune system rather than chasing individual diseases. The baby KJ case at CHOP, a custom CRISPR therapeutic editing a single mutation delivered to liver cells, illustrates how careful disease and delivery selection unlocks first applications. The “Rare As One” program shows rare-disease patient groups self-organizing registries, biobanks, and trials, compressing timelines from decades to a few years, and the molecule becoming cheap flips the economics of the long tail of niche diseases.

    Open source, talent, and the five-year view

    Zuckerberg ties the open-source posture to a consistent worldview: empower individuals with tools rather than centralizing intelligence in a few institutions. He does not believe in a single super-intelligence solving all of science, and sees decentralization, the same instinct behind giving people a voice, as how progress is historically made, with biosafety as a real tradeoff to manage. On talent, the pitch is that frontier biology attached to frontier AI is work you cannot do anywhere else, and that meaningful progress needs only a dozen or two dozen strong people, not thousands. Researchers are already wiring the model into agentic systems to automate design. The next frontier is the virtual cell, modeling proteomic, genetic, and transcriptomic layers and connecting them to phenotype with enough generality to answer untrained questions. Five years out, success is a hierarchical set of world models and doing uniquely high-quality work, with Chan adding that the teams are now “arms linked,” directed and interlocked rather than merely moving in the same direction.

    Notable Quotes

    “We didn’t design a model for antibodies. We didn’t design a model to be able to bind one particular target. We just designed a model that could understand proteins.”

    Alex Rives, on protein design emerging from a general model

    “The theory isn’t that we’re going to cure the diseases. We’re not. It’s that we want to help accelerate the pace of progress for the whole scientific field.”

    Mark Zuckerberg, on why CZI builds tools rather than cures

    “My goal is to be able to treat the individual as an individual, understand the mechanisms and be able to intervene.”

    Priscilla Chan, on the vision for personalized medicine

    “It’s not just like there’s some factory somewhere that you can pay to produce the data. You actually need to invent new novel scientific approaches.”

    Mark Zuckerberg, on why frontier biology has to generate its own data

    “If we could design a protein to actually change the physiology, then we can actually cure someone.”

    Priscilla Chan, on the payoff of protein design

    “You open up the black box and you can actually understand the biology that the model is representing.”

    Alex Rives, on mechanistic interpretability as a discovery tool

    “We don’t believe in this like very centralized future where there should be a small number of institutions that basically are advancing all this stuff.”

    Mark Zuckerberg, on the open-source ethos behind Biohub

    “Before we had amazing teams moving generally in the same direction. But now we are arms linked moving together.”

    Priscilla Chan, on how the Biohub teams now operate under Alex Rives

    Watch the full conversation with Mark Zuckerberg, Priscilla Chan, and Alex Rives on the No Priors podcast here.

    Related Reading

    • CZI Biohub Network the official program page for the San Francisco, New York, and Chicago Biohubs discussed throughout.
    • EvolutionaryScale Alex Rives’s lab and the home of the ESM protein language models behind the world model in this conversation.
    • Human Cell Atlas the single-cell transcriptomics effort CZI funded that became foundational to modern cell modeling.
    • AlphaFold (Wikipedia) background on the protein-folding breakthrough referenced as an early proof that structure prediction was tractable at scale.
    • Rare As One CZI’s program supporting patient-led rare-disease research organizations described near the end of the talk.

  • Gavin Baker on Orbital Compute, TSMC, Frontier AI Models, Anthropic’s Vertical Take Off, and the Coming Wafer Shortage

    Gavin Baker, founder and CIO of Atreides Management, returns to Patrick O’Shaughnessy’s Invest Like the Best for his sixth appearance. He calls the current AI moment the most extraordinary moment in the history of capitalism, walks through what Anthropic’s vertical takeoff in revenue actually means, lays out why orbital compute is closer than skeptics believe, dissects the TSMC bottleneck that may be the only thing standing between today’s market and a full-on AI bubble, and rates every hyperscaler on how they have positioned for a world where frontier model providers may stop selling API access altogether.

    TLDW

    Anthropic added eleven billion dollars of ARR in a single month, which is roughly the combined business of Palantir, Snowflake, and Databricks built over a decade. That is the setup. From there Gavin Baker covers the March and April selloff, the contrarian read that a closed Strait of Hormuz was actually bullish for American manufacturing competitiveness, why Anthropic and OpenAI multiples may be misleadingly cheap on an unconstrained run rate basis, why Elon Musk’s discipline on SpaceX valuation created a superpower of permanent access to capital, the practical engineering case for orbital compute as racks in space rather than Pentagon sized space stations, why TSMC’s capacity discipline is the single most important variable in whether the AI cycle becomes a bubble, what Terafab in Texas changes, why the Pareto frontier of AI models has flipped from Google dominance to Anthropic and OpenAI dominance in nine months, the shift from all you can eat AI subscriptions to usage based pricing and what that means for revenue scaling, Richard Sutton’s bitter lesson as the largest risk to the AI trade, why frontier tokens still capture an overwhelming share of economic value, the role of continual learning as the third great open question, why most new chip startups should not try to build a better GPU, why Cerebras did something different and hard, why disaggregated inference may extend GPU useful lives to ten or fifteen years and rescue the private credit industry, why being in the token path is the new venture filter, the new prisoner’s dilemma around releasing frontier models via API, an honest rating of Google, Meta, Amazon, and Microsoft, why personal safety is becoming a real AI era risk, and why he remains an AI optimist maximalist who believes this could be the next Pax Americana.

    Key Takeaways

    • Anthropic added eleven billion dollars of ARR in one month, more than the combined businesses of Palantir, Snowflake, and Databricks built across a decade. There is no precedent for this in the history of capitalism.
    • The SaaS and cloud revolution created between five and ten trillion dollars of value over twenty years. AI is replaying that compression on a timeline measured in months.
    • The March selloff was a drawdown driven by disagreement with price action, not invalidated thesis. That is the kind of drawdown an investor can lean into.
    • Deep Seek Monday in January 2025 was a similar setup. By the day of the selloff, AWS Asia GPU prices had already doubled, GPU availability had fallen, and it was obvious reasoning models would be vastly more compute hungry at inference. The market priced the opposite.
    • The Strait of Hormuz closing was actually positive for America. US natural gas (the primary input into US electricity, which feeds AI) fell twenty percent on Bloomberg while Asian and European natural gas doubled or tripled. American manufacturing competitiveness improved overnight.
    • The US is now the world’s largest producer and exporter of oil and gas. The economy is dramatically less energy intensive than in the 1970s. The shortage trauma comparison does not hold.
    • Tech as a sector traded as cheaply versus the rest of the market in early April as at any point in the last ten years, into the single most bullish moment for AI fundamentals on record.
    • Anthropic is dramatically more capital efficient than OpenAI, having burned roughly eighty percent less to reach a similar revenue scale. They have very different structural returns on invested capital.
    • Anthropic at roughly nine hundred billion for fifty billion of ARR (growing a thousand percent) is striking. Adjusted for compute constraint, the unconstrained run rate could be one hundred fifty to two hundred billion, putting the implied multiple closer to five times.
    • Claude Opus generates roughly seventy percent fewer tokens for the same question than previously, with token quantity tied to answer quality. Subscribers on flat-fee plans are getting a lobotomized model.
    • Elon Musk’s superpower is twenty years of making investors money. He never pushes valuation. SpaceX compounded low thirty percent per year for a decade because Musk treats fair pricing as a sacred covenant.
    • Capitalism will solve the watts shortage. The current bottleneck has shifted from chips and energy to zoning and political approval. Many capex decisions are paused until after the US midterms.
    • The watts shortage probably begins to alleviate in 2027 and 2028. Orbital compute solves it longer term.
    • Orbital compute is not Pentagon sized data centers in space. It is racks in space. A Blackwell rack is three thousand pounds, eight feet tall, four feet deep, three feet wide. SpaceX has shown a satellite roughly that size.
    • The satellites operate in sun synchronous orbit so solar wings (around five hundred feet per side) always face the sun and the radiator on the dark side always points to deep space.
    • Starlink V3 satellites already run at around twenty kilowatts. A Blackwell rack runs at one hundred kilowatts. SpaceX engineers express genuine confidence they have already solved cooling and radiator design at these scales.
    • Racks in space are connected with lasers traveling through vacuum, the same lasers already on every Starlink. SpaceX operates the world’s largest satellite fleet and, via xAI Colossus, the world’s largest data center on Earth.
    • Inference will move to orbit. Training will stay on Earth for a long time. Terrestrial data centers remain valuable for the rest of an investor’s career.
    • The wafer bottleneck is structural and political. TSMC is essentially Taiwan’s GDP, water, and electricity. The leaders see themselves as inheritors of Morris Chang’s sacred legacy and they do not behave like a Western public company.
    • Jensen Huang has never had a contract with TSMC. The relationship is run on handshakes and the assumption that things will be fair over time.
    • If TSMC did everything Jensen wanted, Nvidia could be selling two to three trillion dollars of GPUs in 2026 and 2027. TSMC’s discipline is the single largest factor preventing a true AI bubble.
    • Historically, foundational technologies always get a bubble. Railroads, canals, the internet. The current AI buildout is overwhelmingly funded out of operating cash flow, GPUs are running at one hundred percent utilization, and that is fundamentally different from the year 2000 fiber overbuild.
    • If one of Intel or Samsung Foundry catches up at the leading node, the other will follow, and TSMC’s discipline collapses. Watch TSMC capacity decisions to predict a bubble.
    • Terafab, the SpaceX and Tesla joint venture to build the world’s largest fab in America, has a partnership with Intel that grants access to fifty years of institutional foundry knowledge. The A teams at ASML, KLA, Lam Research, and Applied Materials will follow Elon’s reputation in hardware engineering.
    • The hiring playbook for Terafab includes building Taiwan Town, Japan Town, and Korea Town next to the fab. Recruit the engineers and import their families, their restaurants, and their staff.
    • Frontier tokens still capture an overwhelming share of all economic value created at the model layer. This is surprising and is one of the three big open questions for AI investing.
    • The Pareto frontier of intelligence versus cost has flipped. Nine months ago Google’s TPU dominated every point on the frontier. Today Anthropic and OpenAI dominate, with Grok 4.3 on the frontier and Gemini 3.1 hanging on.
    • Google’s conservative TPU V8 design (partly an attempt to reduce dependence on Broadcom and Nvidia) is the leading explanation for the loss of per token cost leadership.
    • AI pricing is shifting from all you can eat to usage based, mirroring the cellular and long distance industries. Cellular stopped being a great growth industry when it went all you can eat. AI just made the opposite move.
    • OpenAI and Anthropic together could exceed two hundred billion in ARR this year if compute keeps coming online and frontier token pricing holds.
    • The two hundred fifty dollar a month consumer AI plan is no longer enough to evaluate frontier capability. Enterprise plans with usage based billing are required because rate limits are now severe.
    • The three biggest open questions for AI investors are: violation of the bitter lesson via ASI or human ingenuity, whether frontier tokens keep commanding their premium, and when continual learning arrives.
    • Today’s continual learning is crude reinforcement learning during mid training on verifiable tasks. True continual learning means weights updating dynamically, like a human who learns the first time they touch fire.
    • Trying to build a better GPU is a losing strategy. Jensen will copy any one to three percent share design. Startups should target one percent share, do something different, and make it hard enough that Nvidia cannot fast follow.
    • Disaggregated inference (separating prefill and decode) opens new design canvases. Prefill is memory capacity bound. Decode is memory bandwidth bound. Each can be optimized independently.
    • Cerebras did something different and hard with wafer scale computing. Three generations of chips and real grit to get there.
    • Disaggregation of inference may stretch GPU useful lives to ten or fifteen years, dropping financing costs from low sevens to five or six percent, mathematically lowering the cost of the AI buildout and likely saving the private credit industry from its SaaS loan exposure.
    • Sellers of shortage outperform buyers of shortage. But owning the largest installed base of what is currently in shortage (hyperscaler CPU fleets, for example) is also a strong position.
    • Most of the economic value at the application layer of AI has been destroyed, not created. The exceptions are companies in the token path or in niches small enough that frontier labs ignore them.
    • Coding may be the shortest path to ASI. If you can write code, you can write code that does anything. Cursor, Cognition, and Anthropic correctly focused on it.
    • Jensen could probably get close to the frontier with his own Nemotron family of models whenever he wants. The fact that he chooses not to is a strategic decision about not commoditizing his customers.
    • The new prisoner’s dilemma in AI is whether frontier labs release their best model via API. If everyone agrees not to, Chinese open source falls behind. If anyone defects, the defector pulls ahead on revenue and resources, forcing everyone else to defect.
    • Google still owns the largest compute installed base. Without TPU’s prior cost advantage, this matters more. YouTube data has real value in a world of robotics. GCP is going crazy.
    • Meta deserves credit for becoming AI first internally faster than any other internet giant. Musa, their first MSL model, is impressively close to the Pareto frontier.
    • Amazon is strong because of Trainium and robotics driven retail P&L efficiency. Nova is better than it gets credit for.
    • Microsoft flinched on capex in early 2025 and lost position. Satya Nadella’s current decision to use Microsoft compute for Microsoft products rather than reselling to OpenAI is a courageous and probably correct call, even at the cost of an eight hundred dollar stock price.
    • The hyperscalers most engaged with startups are Amazon and Nvidia by a mile, followed by Google. Broadcom is the favorite ASIC partner. AMD, Microsoft, and Meta have minimal startup engagement and that will cost them as the best teams are now at startups.
    • Personal safety in an AI era requires a family or company safe word that cannot be socially engineered. Deepfake voice and video extortion at the speed of FaceTime is already feasible.
    • Ukraine is winning largely on the back of having the best battlefield AI outside America and Israel. Adversaries are starting to internalize what AI dominance means geopolitically.
    • An optimistic read is that this becomes a new Pax Americana, the way the post 1945 American nuclear monopoly was used to rebuild Germany and Japan rather than dominate.
    • AI cured a friend’s daughter’s rare disease by spinning up a research effort that identified a market drug capable of impacting her condition. That is the upside that keeps Gavin an AI optimist maximalist.

    Detailed Summary

    The most extraordinary moment in the history of capitalism

    Gavin’s framing of the current moment is unusually direct. Anthropic added eleven billion dollars of annual recurring revenue in a single month. The three highest profile SaaS companies of the last decade plus, Palantir, Snowflake, and Databricks, took a decade and tens of thousands of employees collectively to build the combined business that Anthropic added in thirty days. He has been investing through every major tech cycle and says there is no historical analog. Not the dotcom era, not the cloud transition, not mobile. This is its own thing.

    The market response, then, was peculiar. The NASDAQ sold off into the single most bullish moment for AI fundamentals on record. Tech traded at roughly its widest discount versus the rest of the market in a decade. Investors who said they wished they had bought into AI during 2022, during COVID, or during Deep Seek Monday got the same valuation setup again in early April, this time with an even clearer inflection.

    Why the Strait of Hormuz closing was secretly bullish for America

    One reason the macro fear in March may have been mispriced is that the same geopolitical event that drove the selloff was, in practice, a relative benefit to the United States. American natural gas, the input into American electricity, which is the input into American AI training and inference, fell roughly twenty percent. Asian and European natural gas prices doubled or tripled. The US emerged with sharply improved relative manufacturing competitiveness, which is exactly what the current administration cares about.

    The 1970s comparison does not hold. The US economy is dramatically less energy intensive, it is now the world’s largest producer and largest exporter of oil and gas, and there are no shortages, only price moves. That backdrop made it easier for disciplined investors to stay focused on AI fundamentals through the volatility.

    Anthropic and OpenAI valuations on an unconstrained run rate

    Anthropic at roughly nine hundred billion for fifty billion of ARR sounds rich until you adjust for the fact that the company is severely compute constrained. Gavin estimates that, unconstrained, Anthropic might be at one hundred fifty to two hundred billion in run rate revenue, putting the implied multiple closer to five times. He also points out that Claude Opus now generates roughly seventy percent fewer tokens for the same question than it used to. Token quantity correlates with answer quality, and Anthropic is rate limiting and shrinking outputs to ration capacity across its user base.

    Anthropic and OpenAI are also structurally very different. Anthropic has burned around eighty percent less cash than OpenAI to reach a comparable revenue scale. That implies very different long term returns on invested capital, though OpenAI has done a better job locking in compute and Sarah Friar is one of the most exceptional CFOs Gavin has worked with.

    Why neither lab is raising at a three trillion dollar valuation

    The answer Gavin gives is that both labs are deliberately leaving valuation on the table the way Elon has done for two decades. SpaceX compounded at low thirty percent annually for a decade because Elon never pushed price. The result is a permanent superpower of access to capital. Investors trust him because they have made money with him for twenty years. That is a moat that compounds with every round.

    Anthropic could probably raise at a one hundred percent premium to its rumored latest mark. They are choosing not to. In an uncertain world (Ukraine, Russia, Iran, Taiwan), preserving the ability to raise more capital later at fair prices is more valuable than maximizing this round.

    Watts and wafers, the two real constraints

    Capitalism is solving the watts problem. The leading PE infrastructure investors now say zoning and political approval, not chips or energy, are the gating factors. Companies are deferring big capex announcements until after the US midterms. Turbine capacity is being doubled at the manufacturers. Companies like Boom Aerospace are repurposing jet engines for grid use. Watts probably ease meaningfully in 2027 and 2028 and then orbital compute does the rest.

    Wafers are the harder problem because they live in Taiwan, run on handshakes, and depend on a corporate culture that does not respond to public market incentives. TSMC is essentially the GDP, water consumption, and electricity consumption of Taiwan. Its leadership treats the company as the legacy of Morris Chang. The Silicon Shield doctrine is real and internal.

    Orbital compute as racks in space

    The biggest mental update Gavin asks listeners to make is to stop picturing data centers in space as Pentagon sized space stations. A Blackwell rack is three thousand pounds and roughly the size of a refrigerator. SpaceX has shown a concept satellite of about that size. Solar wings extend five hundred feet to each side and the radiator extends hundreds of feet behind, both possible because the orbit is sun synchronous and the orientation is fixed relative to the sun.

    SpaceX engineers Gavin has spoken to at Starbase express genuine confidence that they have solved cooling at these power levels. They have. Starlink V3 satellites already operate at twenty kilowatts. A Blackwell rack is one hundred kilowatts. The same company operates the world’s largest satellite fleet and the world’s largest data center on Earth via xAI Colossus. The racks are connected to each other with lasers traveling through vacuum, technology already deployed in every Starlink. The naysayers, Gavin observes, are armchair skeptics and Larry Ellison’s response (he is out there landing rockets, no one else is) is the right frame.

    Terafab in Texas and the threat to TSMC’s discipline

    Terafab, the SpaceX and Tesla joint venture, intends to be the largest fab in the world. The partnership with Intel grants access to fifty years of foundry institutional knowledge, allowing Terafab to start three to five quarters behind the leading node rather than fifteen years behind. The A teams at the semicap equipment companies (ASML, KLA, Lam Research, Applied Materials) will follow Elon’s reputation in hardware engineering the same way they followed TSMC twenty years ago when Intel stumbled.

    The talent strategy is the part most observers underestimate. Recruit the best engineers globally, then import their families, their restaurants, their staff. Build Taiwan Town, Japan Town, and Korea Town next to the fab. Optimize the human experience for the people whose work matters. Intel and Samsung do not think that way.

    Bubble watch and the year 2000 comparison

    Every foundational technology in modern history has had a bubble. Railroads, canals, the internet. Carlota Perez documented why. Markets correctly identify the importance, diversity of opinion collapses, supply gets ahead of demand, the bubble crashes. The current cycle has two important differences. The buildout is overwhelmingly funded out of operating cash flow, not debt. Every GPU is running at one hundred percent utilization, while at the peak of the fiber bubble ninety nine percent of fiber was unused.

    TSMC discipline is the single largest reason a bubble has not formed. If Jensen could buy everything TSMC could theoretically make, Nvidia could sell two to three trillion dollars of GPUs in 2026 and 2027. At some point that becomes more than the market can absorb. If Intel or Samsung Foundry catches up at the leading node, the other will too. TSMC’s pricing discipline collapses and the bubble starts.

    The Pareto frontier and the loss of Google’s cost advantage

    The most important chart in AI is the Pareto frontier of model intelligence versus per token cost. Nine months ago, Google’s TPU based models dominated every point on it. OpenAI, Anthropic, and xAI sat inside the frontier. Today the frontier is dominated by Anthropic and OpenAI, with Grok 4.3 on the frontier and Gemini 3.1 hanging on by subsidization more than economics. The most likely cause is Google’s conservative TPU V8 design, an attempt to reduce dependence on Broadcom and Nvidia that sacrificed per token economics.

    The bitter lesson, frontier tokens, and continual learning

    Three open questions dominate AI investing. The first is whether Richard Sutton’s bitter lesson (more compute beats human algorithmic cleverness) gets violated by ASI itself optimizing for efficiency. Closer observers of AI are more skeptical of a violation. Gavin thinks ASI’s first move will be to make itself more efficient and more resourced, which is technically a temporary violation.

    The second is whether frontier tokens keep capturing the overwhelming share of economic value at the model layer. Today they do, surprisingly. Gemini 3.1 Pro was mindblowing nine months ago and is intolerable today. The third is when continual learning arrives. Today’s models need a million fire touches to learn what a human learns from one. True continual learning would mean dynamic weight updates in real time and would produce a fast takeoff.

    From all you can eat to usage based AI pricing

    AI is shifting from flat fee plans to usage based pricing. The historical analogy is cellular and long distance. Both stopped being great growth industries when they went all you can eat. AI just made the opposite move. The consequence is that flat fee subscribers, even on premium consumer plans, get a rate limited and token throttled version of the frontier model. Enterprise plans with usage based billing are now required to evaluate true capability. Gavin thinks the combination of new compute coming online and usage based pricing is what gets OpenAI and Anthropic past two hundred billion in combined ARR this year.

    Chip startups, prefill decode disaggregation, and Cerebras

    Trying to build a better GPU is the wrong move. The four scaled players (Nvidia, AMD, Trainium, TPU) have copy capability for any one to three percent share design that looks attractive. The good news for startups is that disaggregated inference (separating prefill and decode) opens a richer design canvas. Prefill is memory capacity bound. Decode is memory bandwidth bound. Each can be optimized independently. Andrew Fox’s analogy is a British naval ship of the eighteenth century. Prefill is loading the cannon. Decode is firing it.

    Cerebras is the model. Wafer scale computing is genuinely different and genuinely hard. It took three generations of chips to get right. Andrew Feldman and his team had the grit to keep going through chip one being a failure. The design has a high ratio of on chip compute and memory relative to shoreline IO, which is why Cerebras is now experimenting with putting an optical wafer on top of the compute wafer to solve scale out.

    GPU useful lives and the rescue of private credit

    One of the strongest claims in the conversation is that disaggregated inference will stretch GPU useful lives to ten or fifteen years. The skeptical narrative (GPUs are obsolete in two years, companies are cooking their depreciation books) is wrong. You can put a Cerebras system or Groq LPU in front of older Hopper or Ampere parts, use them only for prefill, and run them until they physically melt. Private credit, which is in pain from SaaS loans and which underwrote GPU loans on three to four year lives, may be saved by this.

    If GPU financing rates can come down from low sevens to five or six percent, the mathematics of the AI buildout improves materially. That is a structural tailwind that compounds for years.

    The application layer, the token path, and a new prisoner’s dilemma

    Trillions of dollars of value have been destroyed at the application layer, not created. Cursor and Cognition are the rare scaled exceptions, and they got there by focusing on coding very early. As Amjad Masad noted, coding is plausibly the shortest path to ASI because a coding agent can write itself into any new domain. Jamin Ball’s frame is that the new venture filter is whether the company is in the token path. Data Bricks is. Most application layer startups are not.

    Jensen could probably get close to the frontier with Nemotron whenever he wants, and the strategic question of whether to do that is a new prisoner’s dilemma. If every frontier lab agrees not to release best models via API, Chinese open source falls steadily behind. If anyone defects, the defector gains revenue and resources, and everyone else has to defect. The same dynamic exists between TSMC, Intel, and Samsung. If Nvidia or AMD ever truly used an alternative foundry, that foundry would catch up rapidly.

    Rating the hyperscalers

    Google has the largest compute installed base, the YouTube data that matters in a robotics world, and a search business that prints. Their loss of TPU cost leadership is the surprise of the year. If Google IO in five days does not produce a leapfrog model, the Nvidia centric narrative gets even stronger.

    Meta deserves real credit. Zuckerberg made Meta AI first internally faster than any other internet giant, paid up for the talent contracts when no one else would, and shipped Musa as a first model from MSL that is close to the Pareto frontier. Amazon is well positioned on Trainium, robotics in retail, and a Nova model line that is better than it gets credit for. Microsoft flinched on capex in early 2025 and lost position. Satya Nadella’s current decision to use Microsoft compute for Copilot rather than reselling to OpenAI is courageous and probably correct, even at the cost of stock price.

    The most interesting cross hyperscaler metric is startup engagement. Nvidia and Amazon engage deeply with startups. Google is next. Broadcom is the favored ASIC partner. AMD, Microsoft, and Meta have minimal startup engagement, which Gavin believes will cost them as the best teams now sit at startups.

    Personal safety, geopolitics, and the Pax Americana case

    The closing section turns darker. Personal safety in an AI era requires a family or company safe word that cannot be socially engineered. Deepfake voice and video extortion via something that looks exactly like your child calling on FaceTime is already feasible. Political violence against AI leaders is a real concern. Geopolitically, Ukraine is winning largely because it has the best battlefield AI outside America and Israel. How adversaries respond to that asymmetry is the next great variable.

    Gavin’s optimistic frame is the Pax Americana. After 1945 the US had a nuclear monopoly and could have controlled the world. Instead it rebuilt Germany and Japan, both of which became the most reliable American allies for the next eighty years. If AI dominance plays out similarly, this is a generationally positive story rather than a destabilizing one. The personal anecdote that closes the conversation is a friend whose daughter was diagnosed with a rare genetic condition. He spun up agents, identified a drug already on the market that addresses her mutation, and her life is immeasurably different because of AI. That is the upside.

    Thoughts

    The Anthropic eleven billion in a month framing is the kind of stat that resets priors. The right way to interpret it is not as a one off but as a measure of how fast value can compound when the underlying technology improves on a curve steeper than the ability of the rest of the economy to absorb it. The skeptical question is whether that ARR is durable or whether it is heavily tied to a customer base of other AI companies that are themselves on a single venture funded year of runway. The bullish answer is that frontier coding, frontier research, and frontier enterprise tasks are not going to stop being valuable, and Anthropic is the best at all three. Both can be true. The number is still extraordinary.

    The argument that TSMC discipline is the only thing preventing a bubble is the analytically tightest part of the conversation. The implied trade is to watch TSMC capacity additions like a hawk and to be more, not less, cautious if Intel Foundry or Samsung Foundry ever announce real share at the leading node. The Terafab thesis is more speculative but more interesting. If Elon’s talent recruiting playbook works and the Intel partnership gives Terafab a real seat at the table within five years, the geometry of the global semiconductor industry shifts in a way that is bullish for American manufacturing, bullish for power and water infrastructure in Texas, and ambiguous for TSMC itself.

    The Pareto frontier discussion deserves more attention than it usually gets. Pricing leadership in AI is not a vanity metric. It determines who can subsidize free tier usage, who can absorb compute shortages, who can ship cheaper enterprise plans, and ultimately whose model becomes the default for any given workload. Google losing per token leadership in nine months is one of the most under analyzed events in the sector and it explains a lot about why Anthropic and OpenAI are growing the way they are. If Google IO does not produce a leapfrog model, the implied verdict on TPU V8 design choices gets a lot harsher.

    The application layer destruction point is worth sitting with. Founders building on top of frontier models are competing in a world where the model itself moves faster than any moat they can build, where the model lab can absorb their niche if it gets interesting, and where the only protection is either deep token path integration or a niche so small the lab does not bother. That is a much harsher venture environment than the early SaaS era. The compensating opportunity is that one human can now run a hundred agents, so the ceiling on what a small team can build is correspondingly higher. The bet is that productivity per founder rises faster than competitive pressure from the labs. We will find out.

    The orbital compute pitch is the section that will polarize listeners. The naive read is that this is science fiction. The closer read is that every component (sun synchronous orbit, laser interconnect, twenty kilowatt satellite buses, ten thousand satellite manufacturing cadence, full rocket reusability) already exists. The remaining engineering problems are repair, maintenance, and radiator scale, all of which are real but tractable on a five to ten year horizon. The strategic implication is that the political and zoning ceiling on terrestrial data centers becomes less binding if orbital compute is a credible alternative for inference workloads. The investor implication is that being short the watts and cooling complex on a five year horizon is a real trade, not a meme.

    Watch the full conversation here.