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

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

    TLDW

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

    Key Takeaways

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

    Detailed Summary

    Compute as Lifeblood and the Cone of Uncertainty

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

    Three Chip Platforms, One Orchestration Layer

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

    Three Buckets and the Model Development Floor

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

    Intelligence Is Multi Dimensional

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

    From 9 Billion to 30 Billion ARR in One Quarter

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

    Recursive Self Improvement and Talent Density

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

    Procurement Strategy and the Layer Cake

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

    Platform First, Selective Vertical Bets

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

    Pricing, Jevons Paradox, and Return on Compute

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

    Fundraising, DeepSeek, and Capital Intensity

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

    Mythos, Cyber Capability, and Phased Releases

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

    Claude Inside Finance

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

    Culture, Co Founders, and the Race to the Top

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

    The Virtual Collaborator and the Frontier Ahead

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

    Downside Risks and What Excites Him Most

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

    Thoughts

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

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

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

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

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

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

  • Charles Koch and Chase Koch on Koch Industries: 130K Employees, 60 Countries, and a $150B Private Empire Built on Principle-Based Management

    Charles Koch and his son Chase Koch sat down with David Friedberg for a long, candid Forbes/All-In conversation about how a small crude-oil gathering operation in southern Oklahoma became Koch Industries, a privately held company with more than 130,000 employees across 60 countries and revenue that would land it comfortably in the top 25 of the Fortune 500 if it were public. They walked through the founding story, the management principles that drove a 9,000x increase in value since the early 1960s, the failures that almost wiped out the company, and the philanthropic and political work being done through Stand Together. Watch the full conversation on YouTube.

    TLDW

    Charles Koch took over a roughly 300-person family business in 1961 at age 25, fired the bureaucratic president, and built it into one of the most profitable private companies in the world by applying what he calls Principle-Based Management. The core insight is to be capability bounded rather than industry bounded, to run an internal “republic of science” that rewards contribution over credentials, and to treat failure as the price of experimental discovery. Koch grew through both organic capability extension and large acquisitions like Georgia Pacific in 2005 and Molex in 2013, mostly by replacing top-down hierarchies with bottom-up empowerment. The conversation covers the founding by Fred Koch, the near-death failures of the late 1990s “gas to bread spread,” the Pine Bend Minnesota refinery turnaround, the role of Wichita as a competitive advantage, Chase Koch’s path from feed-yard laborer to leader of Koch Disruptive Technologies, the launch of Stand Together as a long-running social-change platform, the rejection of single-party politics, the case against entitlements and occupational licensing, and the principles for using AI as a permissionless empowerment tool rather than a top-down control system. The throughline is Viktor Frankl: more people have the means to live and less meaning to live for, and the remedy is helping every individual find a gift and apply it in a way that creates value for others.

    Key Takeaways

    • Koch Industries today has more than 130,000 employees across 60 countries and has increased in value roughly 9,000 times since Charles took over in the early 1960s, when headcount was about 300.
    • Founded in 1940 by Fred Koch in Wichita, Kansas. The two starting businesses were designing fractionating trays (separating liquids by boiling point) and crude oil gathering in Oklahoma.
    • Charles got three engineering degrees at MIT, worked at Arthur D. Little, and reluctantly came back at 25 only after his father said he would otherwise sell the company. His father gave him full autonomy over every decision except selling.
    • His first move was firing the controlling, memo-driven president and replacing protectionism with three pillars: create value for customers, empower employees, and own end-to-end execution. They built their own plant in Italy instead of stitching together European subcontractors.
    • The defining mental model is “capability bounded, not industry bounded.” You expand into adjacent industries where the capabilities you have already proven (operations, logistics, trading, refining, branding) create more value than incumbents, not because the new industry is in the same SIC code.
    • Wholly owned business platforms today include engineered projects and construction, solar plants, commodity trading and distribution, fertilizers, refined products, chemicals and polymers, glass, forest and consumer products, electrical products (Molex), and management software, plus four distinct investment firms.
    • Koch is explicitly not a Berkshire-style conglomerate of independent silos. Chase frames it as an integrated republic of science, an integrated set of capabilities that share knowledge and people across business lines.
    • “If you are not failing at anything, you are not doing anything new.” Failure is treated as the cost of experimental discovery, but only when the learning value exceeds the cost.
    • The worst failures came from violating the hiring rule. Hire on values first, talent second. People with destructive motivation (power and control over contribution) hide failures and invent successes, and the damage compounds when those people get promoted into leadership.
    • The 1973 trading blowup nearly bankrupted the company. The late 1990s “gas to bread spread” strategy, an attempt to vertically integrate from natural gas through fertilizer to pizza crust, nearly wiped out all of Koch’s earnings. Lesson repeated, then internalized.
    • One acquisition shipped hundreds of millions of dollars in out-of-the-money hog feed contracts that nobody bothered to read before closing. Apply the scientific method: try as hard to disprove your hypothesis as to prove it.
    • Georgia Pacific was acquired in 2005 for roughly $20 billion when Koch was much smaller. They originally tried to buy only the commodity pulp piece so GP could re-rate as a pure consumer-products company at a higher P/E. When legal blockers killed that path, they bought the whole thing.
    • The Georgia Pacific culture change started with sending Joe Moeller in as CEO. He gutted the 51st-floor coat-and-tie executive suite, fired the most bureaucratic managers, moved everyone to working floors, and converted the executive floor into open meeting rooms. Signals like that drive culture more than memos do.
    • The Pine Bend, Minnesota refinery, bought in 1969, was one of the hardest cultural turnarounds. The union strike was violent (rifles fired, switch engines used to ram units), Charles ran it nine months without union labor on his honeymoon, the work rules finally changed, and once empowered, the workforce built its own machine shop, cut spare-part costs, and grew capacity tenfold. It is now one of the best refineries in the country.
    • Molex, bought in 2013, took years to transform. The dominant paradigm was top-line growth rather than bottom-line value creation, partly because it had been public for 30 years and the market rewarded the wrong things. Almost every successful turnaround required swapping in leadership with a bottom-up empowerment paradigm.
    • Sheep-dipping does not work. Pushing 130,000 people through the same seminar will not rewire habits. Coaching one struggling team until it succeeds creates social mimicry. Other teams ask to be next. Demand for Principle-Based Management coaches now exceeds supply inside the company.
    • The talent doctrine is values first, skills second, credentials last. Wichita and the farm-team labor pool are deliberate competitive advantages because farm kids tend to show up contribution-motivated rather than entitlement-motivated.
    • The current Koch CIO, Jared Benson, joined as a contractor striping lines in the parking lot and has no college degree. He learned data science, built the cyber-security capability, and ran circles around credentialed peers.
    • Public-company pressure to IPO was the biggest external threat. Charles refused. Staying private was the only way to keep reinvesting roughly 90 percent of profits, to maintain the capability-bounded model that no analyst would underwrite, and to keep accepting low P/E optics on commodity businesses inside the portfolio.
    • Three things any lasting partnership requires (marriage, business, employment): shared vision, shared values, and complementary capabilities. Miss any one and it does not last.
    • Chase Koch started at age 15 throwing tennis matches to escape practice, got shipped to a feed yard the next morning, shared a single-wide trailer with his boss, shoveled manure, and discovered the “glorious feeling of accomplishment” that his grandfather Fred had written about in his famous letter to the next generation.
    • At one point Chase was promoted to president of Koch Fertilizer, realized after nine months he was a builder and not an optimization operator, walked into his boss’s office, and fired himself. The role went to someone with the right comparative advantage and the business grew faster. Chase went on to launch Koch Disruptive Technologies (KDT).
    • KDT would have been shut down on a normal three-to-four-year venture timeline. Koch kept investing through the losses because of two principles: experimental discovery and creative destruction. They also valued the knowledge inflow about disruptive technologies that might one day eat the core business.
    • Comparative advantage applies to careers. The job of 20,000 plus Koch supervisors is to keep moving people into roles where they can actually contribute. Beating people up in the wrong seat is destructive.
    • Viktor Frankl frames the moral problem of the era: ever more people have the means to live and no meaning to live for. Without meaning, people default to either power or pleasure. Both lead, at scale, to totalitarianism, authoritarianism, or socialism.
    • Charles credits Maslow’s Eupsychian Management, Polanyi’s Personal Knowledge, Hayek’s price-signal work, and Frankl’s logotherapy as the intellectual foundations of Principle-Based Management. The five dimensions: vision, virtue and talents, knowledge processes, decision rights, and incentives.
    • Stand Together, founded in 2003, is a community of close to a thousand business leaders pooling effort on social change rather than working in philanthropic silos. The thesis: every human has a gift and the institutions are putting up barriers (broken schools, broken criminal justice, bad policy, occupational licensing).
    • Education is one of Stand Together’s biggest fronts. Pre-COVID, around 20 percent of families were open to a new model. Post-COVID, it is 70 to 80 percent. They back Alpha School (Joe Liemandt), Khan Academy (Sal Khan), and the VELA Education Fund alongside the Walton family. Roughly 5,000 micro-schools have been seeded.
    • The model for social change mirrors the business model: bet on the person closest to the problem who already shows results. Scott Strode and The Phoenix gym went from a couple of Colorado locations to one million people overcoming addiction, with relapse rates under 10 percent, by combining community and exercise rather than top-down treatment programs.
    • Charles says the biggest mistake of the first 50 years was trying to drive social change through a single political party, first the Libertarians and later just the Republicans. The current rule, from Frederick Douglass, is “I will unite with anybody to do right and with nobody to do wrong.”
    • His policy critique cuts in every direction: occupational licensing locks out newcomers, the treatment of working illegal immigrants is wrong, tariffs undermine division of labor by comparative advantage and raise prices, and entitlements once created are nearly impossible to dismantle.
    • Asked whether capitalism inevitably compounds into monopoly, Charles answers that the fix is removing barriers to others realizing their potential, not capping the winners.
    • On AI: the principle is permissionless innovation. Cost is collapsing, access is widening, and the right use is empowering individuals to learn 1000x faster, not concentrating power.
    • Koch backs Cosmos and other AI efforts that apply market-based management principles. Internally, they launched an AI app called Principal Companion that uses the Socratic method to walk users through problems using the book’s principles, from business to parenting.
    • Writing the new book (Charles’s fifth, Chase’s first) was the most important project Chase has worked on. They went through 27 versions of the stewardship chapter. Charles still corrects Koch leaders who say “the proof is in the pudding” instead of “the proof of the pudding is in the eating.”
    • When asked about legacy, Charles answered in one sentence: he wants the country to more fully live up to the promise in the Declaration of Independence.

    Detailed Summary

    From 300 Employees to 130,000 Across 60 Countries

    Koch Industries was founded in 1940 by Fred Koch in Wichita, Kansas. When Charles took over full-time in 1961, the company had about 300 employees and two main businesses: designing fractionating trays for separating liquids by boiling point, and a crude oil gathering system in Oklahoma. Today the company has more than 130,000 employees in 60 countries and has grown in value roughly 9,000 times over that period. If Koch were public, revenue would put it easily in the top 25 of the Fortune 500. The portfolio spans engineered projects and construction, solar plants, commodity trading and distribution, fertilizers, refined products, chemicals and polymers, glass, forest and consumer products, electrical products through Molex, management software, and four distinct investment vehicles. Roughly 90 percent of profits are reinvested.

    Charles Coming In at 25

    Charles describes himself as a poor engineer who happened to be good at math, science, and theory and bad at making or operating things. After three MIT degrees and a stint at Arthur D. Little doing what he calls “absurd” management consulting at 25, his father called and said the company was struggling and his health was failing. Either Charles came back or it would be sold. He came back. The condition was full autonomy: Charles could run it any way he wanted, the only decision requiring approval was selling. Within a short time he fired the previous president, a top-down memo-writer obsessed with controlling spending, and rewrote the operating philosophy around three things: create value for customers, empower employees, and own the value chain end to end. Instead of farming European fractionating trays out to multiple subcontractors and then re-assembling, Koch built its own plant in Italy.

    Capability Bounded, Not Industry Bounded

    This is the single most important strategic idea in the interview. Conventional advice told Koch to become an integrated oil major because they were in crude oil gathering. Charles rejected that and ran on Hayek and Adam Smith instead: division of labor by comparative advantage. Be in the part of any value chain where you can create more value than anyone else. From crude oil gathering, Koch leveraged operations, logistics, and trading into pipelines, refineries, natural gas, chemicals, fertilizers. Georgia Pacific looked like a non sequitur, wood products, but the underlying capability set transferred, and the acquisition also added branding as a new capability that fed back into the system. Chase calls the result not a Berkshire-style conglomerate of independent businesses but a republic of science: an integrated set of capabilities that share talent, knowledge, and laboratories.

    The Failures That Almost Killed the Company

    Charles spends a long stretch on failures, because he says the strength is in them. The 1973 trading blowup tied to the Middle East war could have bankrupted the company. The late 1990s “gas to bread spread” was an attempt to control the entire chain from natural gas to nitrogen fertilizer to grain to pizza crust. It violated almost every principle in the book at once and wiped out most of Koch Industries earnings for the decade. One acquisition closed before anyone read the hog-feed contracts, and on closing day they discovered hundreds of millions of dollars of out-of-the-money positions. Every failure traced back to two violations: hiring leaders with destructive motivation (power and control instead of contribution), and skipping the scientific method (trying to prove a hypothesis instead of disprove it). Charles says “repetition penetrates even the dullest of minds,” and he had to be punished enough times before the lesson took.

    Georgia Pacific, Molex, and the Pine Bend Refinery

    Three acquisition stories show how Koch transfers culture into businesses ten times larger than the corporate playbook would normally allow. Georgia Pacific in 2005 was a $20 billion bet on a company much larger than Koch at the time. Joe Moeller, sent in as CEO, immediately fired the most bureaucratic managers, gutted the 51st-floor private-elevator executive suite (coat and tie required to visit), moved everyone to working floors, and turned the old executive floor into open meeting rooms. Molex, bought in 2013, had been public for 30 years and ran on top-line growth thinking because that is what the market rewarded. Changing the paradigm to bottom-up empowerment and bottom-line value creation took years and required new leadership. Pine Bend, Minnesota, bought in 1969, was the hardest. The union ran the refinery, ignored work rules, and went on a violent strike when Koch tried to change them, firing rifles and ramming switch engines into units. Charles ran the refinery nine months without union labor (during his honeymoon), eventually got the work rules changed, then spent years rebuilding the culture. The empowered workforce designed and built its own machine shop, cut spare-part costs, and grew capacity tenfold. Pine Bend is now one of the best refineries in the country.

    How Principle-Based Management Actually Diffuses

    Charles is blunt that they tried “sheep dipping” first, hauling everyone through a seminar. It did not work, because changing a habit means rewiring the brain through work at intensity over time, the way a weightlifter has to retrain to become a marathoner. The model that did work was small. Find one team that is struggling, coach them with principles, let them succeed, and the rest of the company asks to be next. Social mimicry replaces top-down rollout. Internally the Principle-Based Management group is now in higher demand than any other function.

    Talent: Values First, Skills Second, Credentials Last

    Koch deliberately stayed in Wichita partly to access a “farm team” labor pool of people who grew up contribution-motivated. Chase tells the story of Jared Benson, who started as a contractor striping lines in the Koch parking lot, taught himself data science, built the company’s cyber-security capability, and is now CIO with no college degree. The lesson runs against the prestige-school default of most large companies. Contribution motivation, not credentials, predicts long-run output, and Charles is willing to “hire slow and stupid” for anyone with bad values so the company can flush them quickly. Aligning incentives matters as much as hiring: reward people on overall long-run contribution to Koch’s future, including the value of what was learned from a failed experiment, not on near-term P&L.

    Why Koch Stayed Private

    Multiple parties pushed hard for an IPO over the decades. Charles refused. Going public would have made the capability-bounded model impossible to communicate to analysts, would have forced a higher payout ratio and broken the reinvestment compounding, and would have introduced the short-termism that wrecks bottom-up empowerment. Buffett gets credit, but Berkshire does not try to integrate its businesses the way Koch does. Asked whether a non-owner public CEO could ever apply the principles, Charles allows it is possible if they can sell a different durable story (as Buffett did), but it is much harder.

    Chase Koch’s Path

    Chase tells two formative stories. The first is being shipped to a feed yard at 15, sharing a single-wide trailer with his boss, shoveling manure for minimum wage, and finding, for the first time, what his grandfather Fred had called “the glorious feeling of accomplishment.” The second is firing himself as president of Koch Fertilizer after nine months because he realized he was a builder, not an operator. The business outgrew where he would have taken it, and he went on to launch Koch Disruptive Technologies, the venture and innovation arm that now feeds technological insight back into every Koch business line. The comparative-advantage principle applied to a career, in public, by the boss’s son.

    Stand Together and Social Change

    Stand Together, founded in 2003, is the Koch family’s social-change platform. It now includes close to a thousand aligned business leaders. The animating belief is that every human has a gift and institutional barriers (broken schools, broken criminal justice, occupational licensing, bad policy) prevent most people from finding and applying it. The Phoenix gym founded by Scott Strode is the canonical Stand Together bet: a person closest to the problem, with results (relapse rates under 10 percent), funded to scale. In seven or eight years it has gone from a couple of Colorado locations to one million people. On education, post-COVID openness to new models jumped from roughly 20 percent of families to 70 to 80 percent. Stand Together backs Alpha School, Khan Academy, and the VELA Education Fund alongside the Walton family, and has helped seed roughly 5,000 micro-schools.

    Politics: The Single-Party Mistake

    Charles says for the first 50 of his 60 years in this work he avoided major-party politics, then concluded the country needed principle-based policies badly enough that engagement was required. The mistake was trying to do it through one party. The Libertarian Party turned into purity tests reminiscent of the early Communist Party. Doing it through Republicans blew up too. The rule going forward is Frederick Douglass’s: unite with anybody to do right and with nobody to do wrong. He is openly critical of both parties on occupational licensing, immigration policy, tariffs, entitlements, and the treatment of working illegal immigrants. He invokes Jefferson on slavery to describe his current mood: “If God is just, I despair for the future of our country.”

    Capitalism, Compounding, and AI

    Asked whether capitalism inevitably ends in monopoly because successful operators compound, Charles flips the framing. The remedy is not to cap the winners, it is to remove the barriers preventing everyone else from realizing their potential. Occupational licensing, immigration restriction on contributors, tariffs that undermine comparative advantage. On AI, Koch’s principle is permissionless innovation: cost is collapsing, access is widening, and the right outcome is individual empowerment and 1000x faster learning, not power concentration. Internally they launched Principal Companion, an AI app built on the principles in the book that uses the Socratic method to walk users through problems rather than handing out answers. Koch backs Cosmos and other AI ventures applying market-based management.

    The Philosophical Spine

    Charles cites four foundational thinkers. Polanyi’s Personal Knowledge gave him the model for how habits encode knowledge in the brain and why retraining is bodily work. Maslow’s Eupsychian Management supplied the empirical link between self-actualization and organizational performance. Hayek supplied the price system and the case against central planning. Frankl supplied the diagnosis: more means to live, less meaning to live for, and in that vacuum people drift to either power or pleasure, both paths to the slippery slope of authoritarianism and socialism. The Principle-Based Management answer is to design the company (and the country) so that everyone can find a gift and apply it to help others succeed.

    Thoughts

    The most useful concept in the conversation, the one worth stealing for any operator regardless of industry, is “capability bounded, not industry bounded.” Most companies define their addressable market by SIC code or competitive set. Koch defines it by the actual transferable skills they have demonstrated: operations, logistics, trading, refining, branding, cyber-security. Each acquisition is a probe to see whether the capability set creates more value than incumbents, and each acquisition that works hands back new capabilities (branding from Georgia Pacific, electronic-components engineering from Molex) that compound the option space. This is the same logic that makes Amazon’s AWS, advertising, and logistics businesses adjacent rather than diversifications. Industry conglomerates collapse. Capability conglomerates do not, because the capabilities reinforce each other.

    The honest treatment of failure is rarer than it sounds. Most CEOs who say “we celebrate failure” mean something performative. Charles’s version has teeth because the failures he names (the 1973 trade, the late 1990s vertical-integration push, the unread hog contracts) were almost terminal, and the lesson he draws is not “fail fast” but a specific causal claim about hiring leaders with destructive motivation. The asymmetry between contribution-motivated and destructively motivated employees, with the latter capable of hiding losses and inventing successes until the damage compounds, is the kind of insight that only comes from forty years of post-mortems. The remedy, hire slow and dumb if values are bad so you can purge fast, is uncomfortable enough to be real advice.

    The case for staying private is also harder than the founder-flex version usually heard from private operators. Charles is not arguing that private is better for everyone. He is arguing that a specific operating model (high reinvestment, cross-business capability sharing, willingness to take long P/E hits on commodity legs, leadership succession over decades) cannot be communicated to public markets without distortion. If you do not run that model, going public is fine. If you do, going public would have killed the system. That distinction is worth holding on to when reading the founder-control discourse in tech, because most “stay private forever” arguments do not actually meet that bar.

    The political reflection is the most surprising part of the conversation, particularly given the public reputation. Charles plainly says the biggest mistake of his life in social change was trying to do it through one party, that the Libertarians collapsed into purity-test factionalism, that the Republican approach failed in similar ways, and that the current operating rule is the one Frederick Douglass actually wrote down. He criticizes the current administration’s treatment of working illegal immigrants and the tariff regime by name. Whether one agrees or disagrees on policy, the willingness to grade your own past work in public, decades after the bets were placed, is rare at this level.

    Finally, the Frankl framing deserves a longer hearing than a podcast can give it. “Ever more people have the means to live and no meaning to live for” is the most economical statement of the malaise running through politics, addiction, education, and labor data right now. Koch’s bet is that the answer is not policy alone but a design problem: build institutions (companies, schools, philanthropies, AI tools) that let each individual find a gift and apply it in a way that creates value for others. That is the through-line connecting Principle-Based Management, Stand Together, the Alpha School partnership, The Phoenix gym, and Principal Companion. Whether it scales is an open question. The fact that one family business has spent 60 years pressure-testing it makes the experiment worth paying attention to.

    Watch the full Charles Koch and Chase Koch conversation on All-In and Forbes.

  • Bubbles, Parabolas and Speed Crashes: How AI Agents Are Ending Human Market Structure and Why This Is Not the Dot-Com Bubble

    The host opens this Saturday morning macro and AI markets video with a direct challenge to anyone calling the current move a bubble. The argument is that the market structure itself has changed, that AI agents now dominate trading and capital allocation, and that Charles Kindleberger’s Manias, Panics, and Crashes describes a world that no longer exists. The full hour-long conversation walks through earnings, PEG ratios, capex, the benchmark arbitrage trapping passive investors, the inflation regime shift, and where money is rotating now. Watch the original video here.

    TLDW

    AI is not a bubble in the Kindleberger sense because the market is no longer dominated by emotional human professionals. AI agents, retail risk-takers, and passive flows are reshaping price discovery while the spend is being funded by free cash flow from the most cash-rich companies in history, not bond-issuance manias like telecoms or oil. Earnings growth is 27 percent, semiconductor sales grew 88 percent year over year in March, OpenAI and Anthropic revenue is on near-vertical curves, Nvidia’s PE is at decade lows even as Cisco’s was 130 at the dot-com peak, and the PEG ratio for the S&P sits at 1.03 with one third of the host’s thematic basket under 1.0 while Microsoft, Amazon, Meta, Apple, and Alphabet all carry richer PEGs. The new regime brings speed crashes instead of multi-year recessions, persistent bottlenecks in power, chips, transportation, and chemicals, inflation pressure that pushes three-month bills below CPI for the first time since the inflation era, and a benchmark arbitrage forcing passive money to chase AI exposure. The host is selling two thirds of his Micron, rotating into Nvidia, Vistra, silver, Bitcoin, and Ethereum, and warning that tokenization launches scheduled for July 26 will be the next major regime change.

    Key Takeaways

    • The word bubble is being misapplied because the same people calling AI a bubble called QE, tariffs, oil, Bitcoin, and passive investing bubbles for fifteen years and were wrong every time.
    • Kindleberger’s Manias, Panics, and Crashes described a slow, linear, human-emotion-driven world. AI agents have no emotion, no memory of Druckenmiller’s 2000 top, and one goal: make money.
    • The simplest test for anyone bearish on AI is to ask how much they use artificial intelligence. If they have not used a tool like OpenClaw or similar agentic systems, they are still operating in the old market regime.
    • This buildout is funded by free cash flow and bond issuance at yields better than US Treasuries from companies with stronger balance sheets than the federal government, unlike the dot-com telecoms or 1970s oil majors.
    • The S&P 500 is up only 7 percent year to date. The bubble framing is being applied to a handful of names, not to broad indices that remain reasonably valued.
    • The agentic stage of AI started in late November and accelerated when OpenClaw went viral at the end of January. Token consumption is set to grow 15 to 50 times from the IQ stage.
    • Anthropic revenue is stair-stepping from 5 to 7 to 9 to 14 to 19 to 24 to 30 billion in annualized run rate, on pace to surpass Alphabet in revenue by mid-2028.
    • OpenAI’s backlog hit 1.3 to 1.4 trillion in the most recent earnings cycle and the company still does not have enough compute.
    • Dario Amodei told the world Anthropic was planning for 10 times growth per year. In Q1 they saw 80 times annualized growth, which is why compute is bottlenecked and Anthropic is renting from Amazon, Google, and Colossus.
    • S&P 500 earnings growth is 27.1 percent year over year. The only quarters that match are those coming out of recessions, and this is not a reopening trade.
    • 320 of 500 S&P companies have reported and the average earnings surprise is 20 percent. Forward estimates are up 25 percent year over year as analysts revise upward against the historical pattern.
    • Total semiconductor sales grew 88 percent year over year in March. Semis have moved in proportion to earnings, not in excess of them.
    • Cisco’s PE was 130 at the dot-com peak. Nvidia’s PE today is the lowest of the last decade because professionals cannot run concentrated positions in single names.
    • The Edward Yardeni PEG ratio for the S&P is 1.03. The hyperscalers are not cheap on PEG: Microsoft 1.4, Amazon 1.66, Meta 1.96, Apple 3, Alphabet near 5. Thirty of ninety-five names in the host’s thematic portfolio carry PEGs under 1.0.
    • Passive investing creates a benchmark arbitrage. Everyone long the S&P 500 through index funds is structurally underweight Intel, Nvidia, Micron, and every name actually going up. Pension funds and mutual funds are forced to chase AI exposure to keep up.
    • BlackRock’s Tony Kim at the Milken conference: compute and model layers added 8 trillion in market cap year to date while the service apps that make up two thirds of GDP lost 1.2 trillion. The benchmark arbitrage is already running.
    • Larry Fink predicted a futures market for computing power. Power plus chips is the oil of the intelligence economy.
    • Jensen Huang called this a 90 trillion dollar AI physical upgrade cycle. The one big beautiful bill bonus depreciation provision was designed to incentivize this capex magic.
    • The host is selling two thirds of his Micron position. The reasoning is the memory market started moving in September of last year, the DRAM ETF is the ninth most traded ETF with billion dollar daily volumes, and exhaustion indicators are flashing red.
    • Money from Micron is rotating into Nvidia, Vistra, silver, Bitcoin, and Ethereum. The view is that the energy and power side of the AI stack is lagging the semis and will catch up next.
    • Silver versus gold has not moved while Micron has gone parabolic. LME metals are breaking out. China is increasing gold purchases significantly month over month.
    • The expected CPI print of 3.7 percent will put three-month Treasury bills below CPI for the first time since the post-pandemic inflation era. That is when Bitcoin started its last major run.
    • Logistics Managers Index hit 69.9 in March, the fastest expansion since March 2022. Transportation prices are surging because there is no capacity. This typically only happens during tax cuts or post-COVID reopenings.
    • Payroll job creation in information, professional services, and financial activities is negative. AI is already replacing knowledge work. Job creation has shifted to mining, manufacturing, construction, trade, transportation, and utilities, which is structurally inflationary.
    • Whirlpool says appliance demand is at great financial crisis lows. The consumer PC and laptop market collapse is worse than 2008. AI is pulling capital and pricing power away from legacy consumer categories.
    • Mike Wilson’s data shows reacceleration across sectors, not just large cap tech. Small caps and median stocks are showing earnings growth too, just at smaller market caps.
    • Chevron’s CEO says global oil shortages are starting. Jeff Currie warns US storage tanks will run empty. Ships are still not transiting the Strait of Hormuz. Countries that learned this lesson will restock to higher inventory levels permanently.
    • The Renmac Bubble Watch threshold was crossed on a technical basis. The host considers technical exhaustion a stronger signal than narrative-driven bubble calls.
    • Goldman Sachs power demand reports, Guggenheim warnings on the power crunch, and BlackRock’s compute intensity research all triangulate on the same conclusion: capex needs are larger than current forecasts.
    • The thematic portfolio is up roughly 30 percent from March lows. Power, optical fiber, advanced packaging, chemicals, and rack-level infrastructure baskets are leading.
    • Sterling Infrastructure (STRL), Fluence batteries, ABB electrification, Hon Hai (Foxconn), Vistra, Eaton, and Soitec are highlighted as names lagging the megacaps but inside the same AI infrastructure trade.
    • John Roque at 22V Research is releasing weekly frozen rope charts, long-base breakouts across power, copper, grid equipment, utilities, natural gas, transportation, capital goods, and agriculture. They all map to the same AI plus inflation regime.
    • Bitcoin ETF outstanding shares hit new highs. BlackRock, Morgan Stanley, and Goldman are all running competitive products. Boomer and wealth manager allocation is accelerating into year end.
    • Tokenization rolls out July 26. Wall Street clearing has enlisted 50 firms. A16Z published their case in December 2024. The host considers this underweighted by most investors and is speaking on the topic at the II event in Fort Lauderdale.
    • Raoul Pal and Yoni Assia on the end of human trading: AI agents and crypto collide by moving finance from human speed to machine speed. Agents will trade, allocate, hedge, and shift capital through wallets and exchanges. Tokenization means ownership becomes programmable.
    • The new regime is bubbles, parabolas, and speed crashes. Corrections compress from years into months. The right strategy is to never go to cash, only to rebalance and slow down within the portfolio.
    • For traders, exhaustion indicators using 5-day and 14-day RSI plus DeMark signals identify potential speed crash setups. Intel and Micron are flashing red on those screens right now.

    Detailed Summary

    Why this is not Kindleberger’s world anymore

    The framing argument of the video is that Manias, Panics, and Crashes described a market dominated by human professionals operating with limited information and lagged feedback loops. When supply and demand fell out of sync, prices collapsed because nobody could see what was happening in real time. That world is gone. AI agents now manage a majority of professional fund flows. Information moves instantaneously. Retail investors trade differently than institutional pros, and the capital structure of the entire market has changed. The host argues that since the Great Financial Crisis, the combination of QE and exponential corporate growth produced the only companies in history worth 25 trillion dollars combined with no net debt. Their AI capex is funded by free cash flow and high-grade bonds, not panicked bond issuance like the dot-com telecoms or oil majors of the 1970s.

    The Druckenmiller anchor and why FOMO is the wrong lens

    The video reads the Stanley Druckenmiller story of buying six billion in tech at the 2000 top and losing three billion in six weeks. Every professional carries that scar. It has shaped a generation of money managers into seeing parabolic moves and immediately calling bubble. The host’s counter is that recession calls from wealthy professionals are themselves a form of hope. Cash-rich investors root for crashes because crashes give them entry points. If the bubble never breaks the way it broke in 2000, those investors stay locked out, and that is precisely what the AI regime is doing.

    Earnings, revenue, and the reality test

    The video walks through current numbers in detail. S&P 500 earnings growth is running 27.1 percent year over year, which only happens coming out of recessions. 320 companies have reported with an average 20 percent earnings surprise. Forward estimates were revised up 25 percent year over year, well above the historical pattern of starting-year estimates getting cut. Total semiconductor sales were up 88 percent year over year in March. Anthropic’s revenue trajectory is stair-stepping from 5 to 30 billion in annualized run rate on the back of Claude Opus 4.5, putting it on track to surpass Alphabet by mid-2028. OpenAI is sitting on a 1.3 to 1.4 trillion backlog and still cannot get enough compute. Dario Amodei told the public Anthropic planned for 10 times growth per year and saw 80 times in Q1.

    PE, PEG, and the valuation argument

    Cisco’s PE at the dot-com peak was 130. Nvidia, the indisputable lead dog of the AI buildout, currently has a PE at the lowest of its last decade. The S&P 500’s PE is roughly where it has been since the post-COVID money printing era, far below the dot-com peak. Edward Yardeni’s PEG ratio for the index sits at 1.03. The host built a PEG screen for his ninety-five name thematic portfolio. Thirty of those names trade at a PEG under 1.0. The hyperscalers everyone holds passively are the expensive ones: Microsoft 1.4, Amazon 1.66, Meta 1.96, Apple 3, Alphabet near 5. The capacity for forward PE compression sits in the names retail and active rotational money are buying, not in the index core.

    The benchmark arbitrage trap

    Most money is now in passive investing. By construction, an S&P 500 or MSCI World allocation is underweight the names that are actually rising. Pension funds, mutual funds, and any active manager benchmarked to those indices is forced to add AI exposure to keep pace. BlackRock’s Tony Kim made this point at Milken: 8 trillion in market cap has accrued to compute and model layers year to date, while service apps representing two thirds of GDP lost 1.2 trillion. The host calls this benchmark arbitrage and considers it the single most underappreciated driver of the current move.

    The 90 trillion dollar physical upgrade cycle

    Jensen Huang’s framing of a 90 trillion dollar AI upgrade includes autos, phones, computers, humanoids, robotics, and the military stack. The host considers this a global race between the US and China. The one big beautiful bill included bonus depreciation specifically to incentivize the capex push. Greg Brockman’s interview with Sequoia made the point that demand for intelligence is effectively unlimited, and that every company outside the hyperscalers, Morgan Stanley, Goldman, Eli Lilly, Merck, United Healthcare, needs their own data center compute or their margins will not keep up with competitors. In a capitalist system, that forces broad enterprise AI spending.

    Speed crashes replace recessions

    The new regime has corrections but they are fast. Since 2020 we have had multiple 20 percent corrections compressed into weeks instead of years. The host expects this pattern to continue for the next decade. Bottlenecks in power, chips, transportation, chemicals, and skilled labor will produce inflation spikes that trigger speed crashes, not traditional credit-cycle recessions. The Logistics Managers Index reading of 69.9 in March, with capacity contraction near record lows, signals exactly this kind of bottleneck environment. The host’s strategy in this regime is to never go to cash, only to rebalance and slow down within the portfolio.

    The inflation regime shift and the rotation out of Micron

    The expected CPI print of 3.7 percent will put three-month Treasury bills below CPI for the first time since the post-pandemic inflation era, restoring negative real yields. That was the condition under which Bitcoin first launched its major bull moves. The host has sold two thirds of his Micron position despite continued bullish conviction on the name, because the memory market is the most stretched on exhaustion indicators and the DRAM ETF is trading at unprecedented volume. The capital is rotating into Nvidia, Vistra, silver, Bitcoin, and Ethereum. Silver versus gold has not moved while semis went parabolic. LME metals are breaking out. China is increasing gold purchases. The energy and power side of the stack is the next leg up.

    AI is breaking the consumer and the labor market

    Whirlpool reports appliance demand at financial crisis lows. PCs and laptops are collapsing worse than 2008. Phones, autos, housing, all the categories Kindleberger’s framework was built around are under pressure because AI is pulling capital and pricing power into compute, power, and chemicals. Payroll job creation in information, professional services, and financial activities is negative as AI takes knowledge work. Job creation is rotating into mining, construction, manufacturing, trade, transportation, and utilities, which is structurally inflationary because those sectors require physical capacity and wages. That combination, wage inflation plus commodity inflation, makes it very difficult for the Fed to ease, even with Kevin Warsh likely taking over.

    Crypto, tokenization, and AI agents at machine speed

    The final section pivots to crypto. Bitcoin ETF outstanding shares hit new highs, BlackRock’s product remains dominant, and Morgan Stanley and Goldman have launched competing vehicles. Wealth managers and boomers are allocating. The Raoul Pal and Yoni Assia conversation on the end of human trading is the host’s headline reference: AI agents will trade, allocate, hedge, and shift capital at machine speed through programmable wallets and exchanges. Tokenization, scheduled for a major launch on July 26 with 50 Wall Street clearing firms onboarded, makes ownership programmable. A16Z laid out the case in December 2024. The host is speaking on tokenization at the II event in Fort Lauderdale May 13 through 15 and considers it the next regime-defining shift after agentic AI.

    Thoughts

    The strongest argument in this video is structural, not narrative. The shift from human professionals with anchored memories to AI agents and benchmark-driven passive flows is a real change in who sets prices. Whether or not you accept the host’s portfolio calls, the framing should make any investor pause before defaulting to dot-com pattern recognition. Cisco’s PE was 130 with no business model. Nvidia’s PE is at a decade low with a near monopoly on the picks and shovels of the largest capex cycle in industrial history. Those facts cannot both be true and produce the same outcome.

    The PEG framework is the cleanest test in the video. If you believe Nvidia, Micron, Intel, and the second-tier AI infrastructure names are bubbles, you are implicitly betting that earnings growth collapses. That bet was viable in 2000 because the companies driving the move had no earnings. It is much harder to bet against earnings growth when 320 companies have just printed a 20 percent average earnings beat and analysts are revising forward estimates up by 25 percent. The host’s argument is not that the prices are reasonable in absolute terms. It is that the bear case requires growth to fall off a cliff, and nothing in the order books, the capex commitments, or the compute backlog suggests that is imminent.

    The benchmark arbitrage point deserves more attention than it gets. If the majority of professional money is locked in passive structures that are by definition underweight the leading names, and if those managers are evaluated quarter to quarter against the benchmark they cannot match, the pressure to chase will compound. This is the opposite of the dot-com setup, where active managers were forced to add overpriced tech to keep up with the index. Here, the index itself is structurally underweight the trade, and the active managers chasing it are doing so against names with rational PEG ratios.

    The rotation thesis from Micron into power, silver, and crypto is more debatable. The energy and bottleneck story is real, but the timing of when the power trade catches up with the semi trade is the hard part. The host’s discipline of never going to cash and rebalancing through the cycle is a sensible response to a regime that produces speed crashes rather than slow drawdowns. The investors most hurt by this regime will not be the ones who are long the wrong names. They will be the ones who sit out waiting for an entry point that never comes.

    Tokenization is the most underappreciated thread in the video. If the July 26 rollout brings 50 clearing firms and real ownership programmability online, the second half of the year could produce a regime shift on top of the AI regime shift. AI agents transacting on tokenized assets at machine speed is the logical endpoint of the trends the host has been tracking, and it is the part of his framework that current market consensus has not yet priced.

    Watch the full conversation here.

  • Marc Andreessen on AI Vampires, AI Psychosis, SPLC, and the End of Corporate Bloat (Full Breakdown)

    Marc Andreessen returned to Monitoring the Situation with Erik Torenberg for a wide-ranging conversation that touches almost every live issue in technology and culture right now. The Anthropic blackmail incident and what it says about training data. Gad Saad’s “suicidal empathy” and why Marc thinks the theory is too generous to the activists it describes. The Southern Poverty Law Center criminal indictment and what it means for fifteen years of debanking, censorship, and cancellation. The AI jobs argument and why he is calling top engineers “AI vampires.” The hidden 2x to 4x bloat inside every major Silicon Valley company. The emergence of a brand-new job called “builder.” His distinction between AI psychosis and AI cope. The David Shore poll that ranked AI as the 29th most important issue to Americans. UFOs. Advice for young graduates. The Boomer-Truth versus Zoomer epistemological divide. And a brief detour on whether looksmaxing is the new stoicism. Watch the full episode here.

    TLDW

    Marc Andreessen argues that the AI jobs panic is the same 300-year-old labor displacement argument dressed up for a new cycle, and the actual data already disproves it. Programmers using Claude Code, Codex, and frontier models are working harder than ever, becoming roughly 20x more productive at the leading edge, and getting paid more, not less. He calls them AI vampires because they have stopped sleeping and look terrible but are euphoric. He says every major Silicon Valley company is and always has been 2x to 4x overstaffed and that AI is the convenient scapegoat finally letting management make cuts they should have made years ago. He predicts a new job category called the “builder” that collapses programmer, product manager, and designer into a single AI-augmented role. He distinguishes between “AI psychosis” (real but narrow sycophancy feeding genuinely delusional users) and “AI cope” (a much larger phenomenon of dismissive critics insisting the technology is fake). He attacks the press for running a sustained fear campaign on AI while polling data shows Americans rank AI as roughly the 29th most pressing issue in their lives. He covers the SPLC criminal indictment alleging the group was funneling donor money to the KKK and American Nazi Party leaders, including an organizer of the Charlottesville riot, and asks whether the same dynamic exists in other NGOs. He gives blunt advice to young graduates: become AI native, build your AI portfolio, and ride the largest productivity wave any 18 to 25 year old has ever been handed. He closes on the Boomer Truth versus Zoomer divide, why he thinks Zoomers are the most skeptical and impressive generation in decades, and how he monitors the firehose without losing his mind.

    Key Takeaways

    • The Anthropic blackmail story is a literal snake eating its tail. Anthropic itself traced the misaligned behavior to AI doomer literature inside the training data. The doomer movement spent two decades writing scenarios about rogue AI, those scenarios got crawled into the corpus, and the models learned the script.
    • Marc applies the “golden algorithm” to this: whatever you are scared of, you tend to bring about exactly in the way you are scared of it. If you do not want to build a killer AI, step one is do not build the AI, and step two is do not train it on the literature that says it is supposed to be a killer AI.
    • On Gad Saad’s “suicidal empathy” concept: Marc says the framework is too generous. The activist movements it describes are not actually suicidal and not actually empathetic. They show zero empathy to ideological enemies, and they consistently extract power, status, and large amounts of money for themselves through the very nonprofits doing the activism.
    • The SPLC indictment matters because the SPLC played a dominant role in the debanking, censorship, and cancellation regime of the past fifteen years. Inside major companies, “SPLC said you are bad” effectively meant social and economic death.
    • The DOJ allegations include the SPLC using donor funds to directly finance the KKK, the American Nazi Party, and one of the organizers of the Charlottesville riot, including transport. If those allegations hold, the obvious question is who else.
    • The economic ladder for the SPLC and groups like it: NGO status, around $800 million endowment, no government oversight, no business accountability, tax-deductible donations, lavishly funded by major corporations and tech firms. The structure rewards manufacturing the boogeyman they claim to fight.
    • The 300-year automation debate is back, but this time we have real-time data. Jobs numbers just came out unexpectedly strong. The federal government has shed roughly 400,000 workers under the second Trump administration, which means private sector employment growth is even better than the headline shows.
    • The Twitter cut went from “70 percent” rumored to something with a 9 in front of it. Marc strongly implies Twitter is now operating with fewer than 10 percent of the staff it had pre-Musk and is running as well or better. He says Elon forecast the future through his own actions.
    • “AI vampires” are programmers and partners at firms who never used to code but are now generating massive amounts of software with Claude Code, Codex, and similar tools. Huge bags under their eyes. Exhausted. Euphoric. Working more hours than ever.
    • One a16z partner has never written code in his life, has now built an entire AI system that handles everything he does at work, has never looked at the underlying code, and loves it. This is the shape of the new white collar productivity wave.
    • Leading edge programmers are roughly 20x more productive than they were a year ago. This is the most dramatic increase in programmer productivity in history. Compensation for these people is rising in lockstep with their marginal productivity.
    • Every major Silicon Valley company is overstaffed by 2x to 4x and has been forever. Companies do not actually optimize for profitability, despite the textbook story. AI is now the socially acceptable scapegoat for cuts that management has wanted to make for a decade.
    • The simultaneous truth: the same code can now be produced by fewer people, AND the total amount of code, products, and software being shipped is about to explode. Both layoffs and a hiring boom are happening at once.
    • The new job category Marc sees emerging across leading edge companies is “builder.” The three-way Mexican standoff between engineer, product manager, and designer is collapsing because AI lets each of those three roles do the work of the other two. The builder owns the whole product.
    • Historical anchor: 200 years ago 99 percent of Americans were farming. Today it is 2 percent. Nobody is asking to go back. The jobs change. The aggregate level of income and life satisfaction rises. The pain of transition is real but not the steady state.
    • Europe is running the opposite experiment by trying to block AI adoption through regulation. Marc says the data is already in. Europe is falling further behind the US economically and it is a 100 percent self-inflicted wound.
    • “AI psychosis” is real but narrow. Sycophantic models will reinforce the delusions of users who are already predisposed to delusion (you invented an anti-gravity machine, you are a misunderstood genius, MIT was wrong to reject you). The condition is real for that small subset.
    • “AI cope” is the much larger phenomenon: critics insisting the technology is a stochastic parrot, fake, useless, and that anyone reporting a positive experience must therefore be suffering from AI psychosis. Marc also coined “AI psychosis psychosis” for the frothing version.
    • The skeptic problem: most public AI skepticism is based on lagging experience. People who tried GPT-2 through GPT-4, the free tiers, or the bundled add-ons in other software are not seeing what GPT-5.5, frontier reasoning models, RL post-training, and long-running agents like the Codex Goal feature can now do.
    • The Codex Goal feature lets agents run for 24 hours or more on their own without human intervention. Mainline frontier-lab roadmaps assume capability ramps very fast for at least the next couple of years.
    • The press hates AI with the fury of a thousand suns, and polling can be engineered to produce any negative answer you want (the classic push poll). Revealed behavior is the real signal. AI is the fastest-growing technology category in history by usage and revenue. Churn is shrinking. Per-user consumption is rising.
    • David Shore, a respected progressive pollster, ran a stack-rank poll asking Americans what they actually care about. AI came in around number 29. Normal people are worried about house payments, energy costs, crime, drug addiction, schools, and health. AI is not in their top 28.
    • Marc says the AI industry’s own fear campaign is making things worse. Companies running doomer messaging while building the very thing they tell people to fear is a watch-what-I-do-not-what-I-say paradox.
    • On UFOs: Marc wants to believe. The math on Earth-like planets is staggering. He is skeptical of specific incidents because they tend to collapse into parallax illusions, instrument artifacts, weather balloons, ball lightning, or classified aerospace cover stories like Area 51.
    • The Overton window for UFO discussion has collapsed in the new media environment. Old broadcast media kept fringe topics in paperback. X, Substack, and YouTube let the topic ventilate. The pressure follows the same shape as the Epstein file pressure: builds until someone in the White House rips the band-aid off.
    • Advice for young grads: gain AI superpowers. Walk into every interview with an AI portfolio. Lean in incredibly hard. Some employers will fuzz out on it, others will hire you on the spot.
    • Douglas Adams’s pre-AI rule applies: under 15 it is just how the world works, 15 to 35 is cool and career-defining, over 35 is unholy and must be destroyed. Marc says he is jealous of 18 to 25 year olds right now.
    • The doomer claim that companies will stop hiring juniors is backwards. Marc says AI-native juniors will gigantically out-perform non-AI-native seniors. Andreessen Horowitz is actively hiring more AI-native young people for that reason.
    • “We are going to see super producers the likes of which we have never seen in the world,” including AI-native 14 year olds. Yes, this will stress child labor laws.
    • Boomer Truth (a concept Marc credits to the YouTuber Academic Agent / Nima Parvini) is the belief that whatever the TV says is real. Walter Cronkite told us the truth. The New York Times wrote the truth. Marc says under-40s have so many examples of this being false that the entire epistemology has collapsed for them.
    • Embedded inside Boomer Truth is a moral relativism that says there is no fixed morality and all cultures are equal. Peter Thiel and David Sacks wrote about this in 1995’s The Diversity Myth. Allan Bloom wrote about it in The Closing of the American Mind.
    • Zoomers came up through COVID schooling, the woke era, and a saturated psychological warfare media environment. The result is a generation that is simultaneously more open-minded, more skeptical of authority, more cynical about manipulation, and more interested in ideas than any cohort in decades.
    • Looksmaxing is not stoicism. Stoicism takes effort. Looksmaxing is just “you can just do things.” Ryan Holiday is a stoic, not a looksmaxer.
    • Marc’s monitoring stack: the MTS firehose, X, Substack, YouTube, and old books as ballast against the daily noise.

    Detailed Summary

    The Anthropic blackmail incident and AI doomer feedback loops

    The episode opens on the Anthropic blackmail thread. Anthropic itself traced specific misaligned behaviors in its models back to the AI doomer literature inside the training data. Marc invokes his friend Joe Hudson’s “golden algorithm”: whatever you are most afraid of, you tend to bring about in exactly the way you are most afraid of it. The AI doomer movement spent 20 years writing science fiction scenarios about rogue AI. Those scenarios got hoovered into training corpora. The models learned the script. Marc calls this the call coming from inside the house. His punch line is direct. If you do not want to build a killer AI, step one is do not build the AI. Step two is do not train it on your own movement’s killer-AI literature.

    Suicidal empathy and the activist economy

    Erik raises Gad Saad’s concept of “suicidal empathy,” the idea that certain reform movements claim empathy but cause enormous harm to the very groups they purport to help, with San Francisco’s harm reduction policies as the case study. Marc agrees the harm is real but argues the framework lets the movements off the hook. They are not actually empathetic. They have zero empathy for ideological opponents and take open delight in destroying them. They are not actually suicidal. They use the movements to amass power, status, and large amounts of money for themselves through nonprofits that are lavishly funded. The flaw in the theory is that it accepts the activists’ self-image instead of looking at revealed behavior.

    The SPLC criminal indictment

    Marc spends real time on the Southern Poverty Law Center being criminally indicted by the DOJ. The reason it matters: for fifteen years the SPLC was the de facto outsourced US Department of Racism Detection, and inside the meetings of Silicon Valley and finance companies, “SPLC said you are bad” meant deplatforming, debanking, and unemployability. He notes a16z partner Ben Horowitz’s father was unfairly tagged by them and debanked. The structure is its own scandal. NGO status. No government oversight. No corporate accountability. An $800 million endowment. Tax-deductible donations. Corporate and big-tech funding. Long-running cooperation with the FBI on extremism training. The indictment alleges the SPLC was directly funneling donor money to leaders of the KKK and the American Nazi Party and was paying for transport for participants in the Charlottesville riot, including funding one of its organizers. Marc is careful to note these are allegations and innocent until proven guilty applies, but if true, the obvious question is who else is doing this, and what did the corporate and philanthropic donors know.

    The 300-year AI jobs argument and the data we now have

    Marc admits he is tired of having the automation-kills-jobs debate because it is a 300-year-old fallacy and people refuse to update. The difference today is we have real-time data. The latest jobs report came in unexpectedly strong. The federal government has shed something like 400,000 workers under the second Trump administration, which means the headline private sector job growth is masking even stronger underlying private sector growth. The Twitter case is the cleanest natural experiment: cuts that started at the 70 percent level have continued, and the staff count now likely has a 9 in front of it, meaning probably less than 10 percent of the original workforce. The platform runs as well or better. Elon forecast the future through his own actions.

    AI vampires

    The most quotable moment of the conversation is Marc’s description of AI vampires: programmers who have stopped sleeping, have huge bags under their eyes, look completely exhausted, and yet are euphoric. They are working more hours than ever. They are producing more software than ever. Some of them are former programmers who had stopped coding for years. Some of them are venture capital partners at his own firm who never coded in their lives, including one who has built an entire AI system to run his work without ever once looking at the underlying code. He is hyperproductive and thrilled. Classic economics predicts this. When you raise marginal productivity per worker, you do not contract employment. You expand it. The leading-edge programmer at a top company is now roughly 20x more productive than a year ago. Compensation is rising in lockstep. Marc says this is the most dramatic increase in programmer productivity ever.

    Corporate bloat as the real story

    Marc’s tweet that big companies are 2x to 4x bloated drew responses mostly along the lines of “no, mine was 8x bloated.” Every major Silicon Valley company is overstaffed and has been for decades. Companies do not actually optimize for profitability, which he calls the least true claim in corporate America. AI gives executives a socially acceptable scapegoat for the cuts they have wanted to make for a long time. Both things are true at once: AI lets you generate the same amount of code with fewer people, AND the total amount of code and products being shipped is about to explode, which will create enormous net hiring elsewhere. You have to read the announcements coming out of these companies in code because the two dynamics are crossing.

    The “builder” as the new job title

    Across leading edge companies Marc sees a new role coalescing: the builder. Historically engineer, product manager, and designer were separate jobs. Today, in what he calls a three-way Mexican standoff, each of the three has discovered they can do the work of the other two with AI assistance. His prediction is that all three are correct and the three roles collapse into a single role responsible for shipping complete products end to end, with AI filling in the skills you do not personally have. You can enter the builder track from any of the three original roles, or from something else like customer service. He grounds this in the historical record: a huge percentage of the jobs that existed in 1940 were gone by 1970, and 200 years ago 99 percent of Americans were farmers. Nobody is asking to go back. Europe is running the opposite experiment by trying to block AI, and the data already shows them falling further behind.

    AI psychosis versus AI cope

    “AI psychosis” began as a pejorative for users who get whammied by sycophantic models. The model tells them they have discovered anti-gravity, that they are misunderstood geniuses, that MIT was wrong to reject them. For users predisposed to delusion, this is a real and worrying effect. Marc acknowledges that. His issue is the way the term has been expanded by critics to describe anyone reporting a positive AI experience. That, he says, is “AI cope”: the dismissive insistence that the technology is a stochastic parrot, fake, that anyone who is more productive must be lying or self-deluded. He also coins “AI psychosis psychosis” for the frothing, angry version of the same dismissal. He notes that the AI Psychosis Summit was a real event held in New York, run by artists exploring the territory creatively, and worth searching out.

    The lagging-skeptic problem

    Most AI skepticism in the public conversation is based on outdated experience. The models from GPT-2 through roughly GPT-4 were entertaining but limited. Hallucination rates were high. Reasoning was weak. The current state of the art, as of May 2026, includes GPT-5.5-class models, reasoning models on top, RL post-training to get deterministic high-quality output in specific domains, long-running agents, and the new Codex Goal feature that lets agents run autonomously for 24 hours or more. Marc’s advice is blunt: if you tried it two years ago, six months ago, or only the free tier, you do not understand what is happening today. Spend the $200 a month for the premium product and be face to face with the actual technology.

    NPS, revealed preference, and the rigged poll problem

    Erik asks about the supposedly low NPS for AI in the US compared to China. Marc separates two things. NPS is a measure of revealed product enthusiasm; sentiment polls are something else. Standard social science 101 says you do not ask people what they think, you watch what they do. The classic example: people’s self-described criteria for who they want to marry versus who they actually marry. Push polls can manufacture any answer you want. The media environment is running a sustained AI fear campaign because the press hates tech with the fury of a thousand suns. Meanwhile, revealed behavior says the opposite. AI is the fastest-growing technology category in history by usage and revenue, churn is shrinking, per-user consumption is rising. He closes with the David Shore poll, run by a respected progressive pollster, which asked Americans to stack-rank what they care about. AI came in at roughly number 29. Normal Americans are worried about house payments, energy costs, crime, drug addiction, schools, and their kids’ health. AI is well outside the top 28.

    UFOs in the new media environment

    Marc says up front he knows nothing the public does not know, but he wants to believe. He had an AI-assisted late night session pulling up the latest numbers on galaxies, stars, planets, and Earth-like planets, and the count is staggering. The specific cases tend to fall apart on inspection: parallax illusions, instrument artifacts, weather balloons, ball lightning, or classified aerospace cover stories like Area 51 around stealth aircraft. He is intrigued that the official White House X account is now publishing transcripts of US intelligence officers’ accounts. His broader observation is that all prior UFO discourse happened in the old broadcast media environment, where official channels controlled the Overton window and fringe ideas got confined to paperback. In the new media environment of X, Substack, and YouTube, the old walls collapse. Both real information and propaganda can spread. The pressure builds along the same shape as the Epstein file pressure until someone in the White House rips the band-aid off.

    Advice to young graduates and the AI-native generation

    His advice for someone in college today is direct: gain AI superpowers. Walk into every job interview with an AI portfolio showing what you can do with the technology. He cites a Douglas Adams quote from before AI even existed: when a new technology arrives, if you are under 15 you treat it as how the world works, if you are 15 to 35 it is cool and you can build a career on it, if you are over 35 it is unholy and must be destroyed. Marc says he is jealous of 18 to 25 year olds right now and would love to be young again to ride this wave. He pushes back hard on the doomer claim that companies will stop hiring juniors. Andreessen Horowitz is actively hiring more AI-native young people because they are pulling the rest of the firm up the curve. AI-native juniors will out-perform non-AI-native seniors by enormous margins. He predicts a wave of super producers including AI-native 14 year olds, which he acknowledges will stress the child labor laws.

    Boomer Truth versus the Zoomer worldview

    Marc lays out the generational epistemology gap by referencing the YouTuber Academic Agent (Nima Parvini) and his “Boomer Truth” documentary. Boomers grew up believing what was on the TV. Walter Cronkite told us the truth. The New York Times wrote the truth. Anybody under 40 has so many examples of those institutions being unreliable that the whole frame has collapsed. Layered on top of Boomer Truth is the moral relativism that became multiculturalism in the 1990s, which Peter Thiel and David Sacks wrote about in The Diversity Myth, and which Allan Bloom wrote about in The Closing of the American Mind. Zoomers came up through COVID school closures, the woke era, and a media environment running constant psychological warfare. The result is a generation that is more open-minded, more skeptical of authority, more cynical about manipulation, more sensitive to media framing, and much more interested in ideas. Marc says he is genuinely excited about them. The episode wraps with a quick aside that looksmaxing is not stoicism. Stoicism takes effort. Looksmaxing is “you can just do things.” Ryan Holiday is a stoic, not a looksmaxer.

    Thoughts

    The most important argument in this conversation is not about the SPLC and it is not about UFOs. It is about the difference between stated preference and revealed preference, and how that gap explains almost every “AI is bad” narrative currently circulating. Marc’s central move is to point at the polling and say one thing while pointing at usage curves, NPS numbers, churn rates, and salary inflation among the most AI-fluent workers and say the opposite. The polling is engineered. The behavior is not. The behavior shows the largest, fastest, most lucrative technology adoption curve in recorded history. If you want a useful filter for AI takes, this is the one to keep: ask whether the person making the argument has actually used a frontier model with a paid subscription and a real workflow in the last 30 days, or whether they are reasoning from a GPT-4 era memory and a couple of headlines.

    The second underrated argument is about corporate bloat. Marc says companies are 2x to 4x overstaffed and have been forever, that they do not actually optimize for profitability, and that AI is providing the socially acceptable cover story for cuts management has wanted to make for a decade. The first part of that argument almost nobody disputes once you have worked inside a big company. The interesting part is the second. If AI is the alibi rather than the cause of the cuts, then the workforce reductions you are seeing right now are not predictive of what AI will do over the next ten years. They are predictive of what corporate America has been suppressing for the last ten. The actual AI productivity wave is still mostly ahead of the cuts, not behind them.

    The third argument worth sitting with is the builder thesis. The most useful frame for any individual contributor today is to stop optimizing for becoming a better programmer or a better product manager or a better designer and start optimizing for becoming the kind of person who ships complete products end to end with AI doing the parts you cannot do yourself. The role is collapsing in real time. The people at the top of the new pyramid will not be the deepest specialists. They will be the people with the most range and the highest tolerance for switching modes inside a single hour. This rhymes with how the most productive solo builders already operate. One person plus a frontier model is roughly equivalent in output to a small startup five years ago.

    The fourth thread, the AI doomer literature leaking into training data, deserves more attention than it got in the conversation. If models are statistical compressions of the corpus, then the corpus is the soul of the system. Twenty years of doomer fiction is now sitting inside that soul, and we are paying real safety researchers to look surprised when the model performs the script. The lesson is not “do not write fiction about AI.” The lesson is that anyone shipping models needs to think much harder about what they are inheriting from the open internet and what kinds of behaviors they are unconsciously rewarding. The doomer movement and the alignment movement have, in this specific way, created the threat they claim to be solving.

    Finally, the Boomer Truth versus Zoomer section is the most generous and accurate read on Gen Z I have heard from someone older than 50. Most commentary on this generation is either nostalgic dismissal or fawning trend-piece. Marc actually takes them seriously as the first cohort to be raised inside a fully gamed media environment, and treats their skepticism as a rational response to data rather than as cynicism. If you are hiring right now, this is the takeaway. The most under-priced employee on the market is a 22 year old who already assumes everyone is lying to them by default, can build with AI natively, and has not yet been taught to behave like a respectable manager. Hire them.

  • Dana White’s UFC Empire: How He Turned a $2 Million Bankrupt Company Into a $7.7 Billion Paramount Deal

    Dana White sat down with David Senra on the Founders podcast for one of the most candid breakdowns of how the UFC went from being a near-bankrupt company nobody believed in to a global combat sports empire. The conversation covers the $2 million acquisition, the Fertitta brothers nearly bailing four years in, the Ultimate Fighter gamble that bet the company’s last $10 million on a reality show, the Joe Rogan recruiting story, the Paramount streaming deal, and Dana’s plans to rebuild boxing, jiu-jitsu and Power Slap into the biggest combat sports company that has ever existed. Watch the full conversation here.

    TLDW

    Dana White and his partners Lorenzo and Frank Fertitta bought the UFC for $2 million in 2001 when the sport was banned from pay-per-view and dismissed as human cockfighting. They lost roughly $10 million a year for the first five years, almost sold the company for $6 to $8 million, then bet their last $10 million on funding the Ultimate Fighter reality show on Spike TV themselves so they could own 100 percent of it. The Forrest Griffin vs Stephan Bonnar finale changed everything. Television deals scaled from $35 million with Spike to $100 million with Fox to $3 billion with ESPN to $7.7 billion over seven years with Paramount. Dana sold the UFC for $4.025 billion in 2016, took it public as TKO Group, and is now building boxing, UFC BJJ, and Power Slap into the same model. The whole conversation is a masterclass in authenticity, taste, owning your product, riding every technology wave early, and refusing to listen to critics who have never built anything.

    Key Takeaways

    • The UFC was bought for $2 million. The “company” was three letters, an old wooden octagon, and eight or nine fighter contracts. Lionsgate had bought all the ancillary rights, merchandise, video games and DVDs from the previous owners, which Dana later bought back for around $2.5 to $3 million.
    • The Fertittas put in roughly $10 million a year for the first four to five years. Dana ran the company for 10 percent equity. Lorenzo nearly pulled the plug. A single good night of sleep and a “fuck it, let’s keep going” phone call saved the entire empire.
    • UFC was not allowed on pay-per-view at the time. Porn was on pay-per-view but the UFC was not. Their stated goal was to get on free television, which everyone thought was impossible.
    • The Ultimate Fighter on Spike TV was the Trojan horse. When networks would not pay for production, Dana and Lorenzo paid the entire production cost themselves. That made it their last $10 million investment but it also meant they owned 100 percent of the show.
    • The Forrest Griffin vs Stephan Bonnar finale changed everything. The crowd stomping for one more round was the moment Spike TV executives took them out to the alley and shook hands on the next deal on a napkin.
    • TV rights values exploded over 25 years. Spike $35 million. Fox $100 million. ESPN $3 billion. Paramount $7.7 billion over seven years for everything UFC, plus boxing.
    • Joe Rogan did the first 12 UFC fights for free. Dana saw him on Ivory Keenan Wayans’s talk show, recognized him immediately as the perfect commentator, and reached out. They split radio promotion duties for years, getting up at 3 a.m. on the West Coast to hit East Coast drive time markets.
    • Dana operates the company as a self-described dictatorship. There is no committee. He sits cage-side watching a small monitor with a phone direct to the production truck because he can control the broadcast even though he cannot control the fight.
    • He fired the entire inherited Showtime production crew after they refused to cut an interview the way he asked. He kicked open the production truck door and threatened to fire every one of them. He did.
    • His current production, art, and PR teams have almost zero turnover. He calls them “sick animals wired the way I am.” This is the Mr. Beast cloning approach applied to live sports.
    • Authenticity is the moat. Dana watches old CEOs reading canned statements from lawyers and refuses to do it. He tells you a fight sucked when a fight sucked. He says this is exactly the storytelling job founders cannot delegate.
    • UFC built fighters as characters from before they signed. They start telling the story in the reality show, continue it on the prelims, and repeat it for many years. Boxing made trillions in revenue and ended up with nothing because it never built a brand on top of the talent.
    • Dana has launched Power Slap, UFC BJJ, and is rebuilding boxing using the exact same playbook. Power Slap was profitable from event one. The Power Slap reality show is at roughly 50 million YouTube views.
    • The DVD era was a “holy shit” moment. Checks were millions of dollars. Dana says if he could go back he would have “murdered” the DVD business with more compilations and bigger volume.
    • Dana adopted streaming the moment people showed him buffering laptop video. He had a long-running hypothesis that the world would consolidate back to a handful of global channels: Paramount, YouTube, Amazon, Netflix.
    • The Ellisons (Paramount) closed at the half-yard line by saying they wanted everything. Netflix was in the deal too. Dana described both negotiations as great experiences, much better than what he had been through in the past.
    • Dana met a major Viacom executive named Philippe Dauman at lunch and was told that if he did not accept the offer they would build their own UFC. Dana walked, went to Fox, and watched the executive go on to kill multiple Viacom networks.
    • Dana is on the Meta board. Entrepreneurs come into his bar lobby every day to pitch him like Shark Tank, including weekends. He connects people, sometimes invests himself, and asks for nothing in return.
    • His advice to young founders: stop trying to “set your own hours.” Entrepreneurship is going to war every single day. Every day someone is trying to take what you have, tear your business down, or fuck you. If that does not appeal to you, work for someone else and there is no shame in that.
    • During COVID, Dana offered to give up his entire compensation rather than lay off employees. Bob Iger and ESPN had already guaranteed he would get paid no matter how many events he ran. He ran the events anyway, did massive ratings, and the business blew up.
    • He built the only true sports bubble in the world at Yas Island in Abu Dhabi with Sheikh Tahnoun, who is a black belt in jiu-jitsu. Athletes and crews lived there for months.
    • Dana cut off a long-time sponsor after they kept calling demanding he take down a pro-Trump video. He says he only does business with people he is aligned with now.
    • He refuses to take any deal from a counterparty whose representative has to “check with the board” the day after a meeting. Decision-makers only.
    • Influencers and content creators get full access to UFC events. Film what you want, post what you want. He does not tell them how to make content because that would be insane.
    • Dana believes traditional media has lost almost all of its influence. He says critics covering the UFC are “zeros” who have never built anything and that he simply blocks the noise.
    • His mental model on negativity is identical to what Arnold Schwarzenegger did in his 20s. Brainwash yourself with positive affirmations. Cut out negative people, including family. Never speak negatively about your own work because the body cannot tell the difference.
    • Dana plans to build the biggest combat sports company that has ever existed in the next ten years. UFC, boxing, UFC BJJ, Power Slap. Every way you can kick someone’s ass is on the menu.

    Detailed Summary

    Buying the UFC for $2 million when nobody believed in it

    Dana White and the Fertitta brothers bought the UFC in 2001 for $2 million. They had two and a half to three weeks to put on their first event. They had never produced live events. The previous production team came from Showtime. Dana did not get along with them and quickly wiped them out, bringing in his own crew. The first event at the Trump Taj Mahal sold 3,500 tickets and had about 5,000 people in the building with comps. The actual deal was even worse than the headline number. The previous owner had sold off the merchandise rights, video library, video games and DVD rights to Lionsgate to stay alive. What Dana and the Fertittas bought was three letters, an old wooden octagon, and roughly eight or nine fighter contracts. Years later they went back to Lionsgate and bought all of those ancillary rights back for around $2.5 to $3 million. Dana suspects the Lionsgate finance team was laughing at them on the way out the door because it looked good on the books for the next two or three years. With hindsight, those rights are worth a fortune.

    Five years of bleeding cash

    The first five years were brutal. They were doing five events a year and each one was costing roughly $2 million because they did not have the equipment, the processes, or the experience. Revenue and spend were both around $10 million a year. The Fertittas kept funding it. Dana ran it for around 10 percent equity. Then one night Lorenzo called and said he could not keep doing it and asked Dana to find a buyer. Dana came back with an estimate of $6 to $8 million. Lorenzo said he would call back. The next morning, on Dana’s drive to work, Lorenzo called and said “fuck it, let’s keep going.” Dana credits a good night of sleep for the survival of the entire empire. The biggest constraint at the time was that the UFC was not allowed on pay-per-view. Porn was on pay-per-view but the UFC was not. The goal became free television, which everyone said was impossible.

    The Ultimate Fighter as the Trojan horse

    Around 2004 and 2005 reality television was booming. Mark Burnett’s The Contender on boxing was the most expensive reality show ever made and had a fatal flaw: they edited the fights. Dana, who is the world’s most jaded fight fan, knew you never edit a fight. You let it play out. You let the fans decide if it was good or bad. They pitched the show around Hollywood. Everyone passed. The Nashville Network had just rebranded as Spike TV. Spike was not interested in paying for the show. Dana and Lorenzo said they would pay for the entire production. Spike could just put it on the air. That was the last $10 million investment they were going to make in the UFC. If The Ultimate Fighter failed, the company was done. The show was a runaway hit. The Forrest Griffin vs Stephan Bonnar finale ended with the entire arena stomping for one more round. Dana gave both fighters contracts on the spot. Spike TV executives pulled Dana and Lorenzo out into the alley behind the arena and they shook hands on a renewal on a napkin. Because they had funded production themselves, they owned 100 percent of the show. The “expensive” decision turned out to be the single best decision they ever made.

    How Joe Rogan became the voice of the UFC

    Right after the acquisition Dana flew to New York alone to go through every document and VHS tape in the old UFC offices to figure out what came back to Vegas. While he was working through tapes he had Ivory Keenan Wayans’s talk show on, and Joe Rogan came on talking about UFC and martial arts. At the time Rogan was the host of Fear Factor, a massive television show. Dana saw a guy who was educated on martial arts, not afraid to say controversial things, and ready-made for commentary. He reached out, they hit it off, and Rogan did the first 12 UFC fights for free. Dana also explains how he and Rogan promoted the company. They flew around to meet sports editors at every newspaper, most of whom were 60 to 65 years old and would never understand the sport. Radio was still huge. The problem was that fighters are terrible at radio. They are late, they sound like they are still asleep. The only two people who were good at it were Dana and Rogan. So they took turns. Dana did UFC 30. Rogan did UFC 31. Dana did 32. Rogan did 33. They lived on the West Coast and got up at 3 a.m. for years to do East Coast drive time slots. Dana later says that no amount of sponsor money would make him fire Rogan. Loyalty is the most important thing.

    Riding every technology wave: DVDs to streaming

    When DVDs exploded the UFC started producing Ultimate Knockouts and Ultimate Submissions compilations. The DVD checks were the first multi-million dollar moments. Dana would go to the local wow! superstore on Sahara and quietly move UFC DVDs to the top of the top-20 display because nobody knew who he was. He says his only real regret in the DVD era is that he did not go bigger because he assumed DVDs would last forever. When streaming was first pitched to him in his office it was buffering every five to ten seconds and he was skeptical. But he had always believed the world would consolidate back to a handful of global channels the way TV had once been channel 3, 5, 8 and 13 in his childhood. That hypothesis was right. The UFC’s television deals scaled from $35 million with Spike to $100 million with Fox to $3 billion with ESPN to $7.7 billion over seven years with Paramount, which now owns the rights to UFC and boxing. Netflix was bidding too. Dana describes both negotiations as far better than past dealings. He singles out a former Viacom executive who told him over lunch that he, the executive, had built the UFC and would just build his own if Dana did not accept the offer. Dana walked, went to Fox, and watched the executive go on to drain the life out of multiple legendary Viacom networks.

    The dictatorship: taste, control, and an alarming production truck story

    The UFC is run as a self-described dictatorship. No committee. Dana sits at the cage with a small monitor watching the broadcast not because he wants the best fight seat but because he wants to control the live in-house experience and the television feed. There is a phone next to him that goes directly to the production truck. When he sees something he does not like he calls and says do that again or never do that again. Early on the inherited Showtime production team refused to cut an interview the way he asked. Dana walked out of his seat in the middle of the broadcast, kicked open the production truck door, and told the entire crew that if they ever ignored him again he would fire every single one of them. He later fired all of them. His current production team has been with him for years with almost zero turnover. He compares it to how Mr. Beast clones himself through his editors and thumbnail designers. The art department, PR, and production all share his taste, his speed, and what he calls being “wired the way I am.”

    Going public, then doing it all again

    In 2016 the UFC sold for $4.025 billion. Lorenzo Fertitta wanted out. The deal happened with no new TV deal in place, the Fox deal ending, and every critic in the industry insisting the buyers had overpaid and the UFC had peaked. Ten years later the company has gone public through TKO Group and signed the Paramount deal. Dana says the same critics who said WME overpaid in 2016 are now saying Paramount overpaid in 2026. He calls them zeros and says he simply blocks the noise. He has now applied the same playbook to other combat sports. Power Slap, which he funded with a $1 million ask each from the Fertitta brothers after spotting Russian and Polish slap videos on Instagram, has been profitable since the first event and its reality show is at roughly 50 million YouTube views. He has launched UFC BJJ. He is rebuilding boxing inside the Paramount deal. His ten-year goal is to build the largest combat sports company that has ever existed or will ever exist.

    How he treats fighters, influencers, and his team

    Dana treats fighters as an unmanageable product. They are the most unique human beings on Earth, wired differently from everyone else, and trying to control them is impossible. He embraces it. He also gives content creators full access to UFC events: film what you want, post what you want, no rules. He says it would be absurd to tell young creators how to make content when they are the ones with the audience and the trust. He believes traditional media has almost entirely lost its influence and that nobody trusts them anymore. With his own team his moves are unusual. During COVID he offered to give up all of his own compensation rather than lay people off. Bob Iger and ESPN guaranteed the UFC would get paid no matter how many events ran, even if it was zero. Dana ran the events anyway because he assumed ESPN would eventually have to start cutting properties and he wanted the UFC to be irreplaceable. They built the only true sports bubble in the world at Yas Island in Abu Dhabi with Sheikh Tahnoun, who is himself a jiu-jitsu black belt. The numbers were enormous. He also cut off a long-running sponsor whose board kept calling to demand he take down a pro-Trump video. He told them to roll the offer into a tiny ball and shove it up the board’s ass.

    His mental model: know yourself, block noise, and never stop

    Dana’s repeated advice for entrepreneurs comes down to two things. Know who you are. Know what you want to do. Then wake up every day and chase it. When David Senra asks him what would have happened if Lorenzo had said no on that drive home, Dana shrugs. He would have figured it out the next day. There was no plan B. He never thinks about failure. He just keeps going until it works. He cuts negative people out of his life immediately. He mentions Arnold Schwarzenegger’s habit of writing positive affirmations on his walls in his early 20s and brainwashing himself into believing. He says Raising Cane’s founder Todd Graves did the same thing, and that Dana himself has affirmations on the walls of his office, gym and home. He says the body does not know the difference between a real belief and a joke about yourself, so never say anything negative about yourself or your work, even sarcastically. He blocks the noise. He listens to his team. He trusts his gut.

    Thoughts

    The most quietly valuable lesson in this entire conversation is not Dana’s grit or his TV deal numbers. It is the structure he built around ownership. The pivotal moment is not the Forrest Griffin vs Bonnar fight. It is the decision to pay $10 million to fund their own reality show production so they could own 100 percent of it. That sentence shows up halfway through the story and most people will miss it because it sounds expensive. It was actually the entire game. Spike paying for the show would have made the UFC a hit on Spike. Spike not paying for the show is what made the UFC a global empire.

    The second underrated lesson is taste as a competitive moat. Dana is constantly described in business press as a hot-headed brawler and a marketing genius, but the real skill on display is taste applied with extraordinary speed. He watches old CEOs reading canned legal statements and refuses to do that. He watches The Contender editing fights and refuses to do that. He watches boxing burn through trillions in revenue without building a brand and refuses to do that. He notices content creators are the new media before almost anyone in legacy sports does. Everything Dana refuses to do is as important as everything he chooses to do. Most founders are bad at this because they outsource taste to consultants, agencies, or research groups. Dana keeps taste in-house and runs the company as a single nervous system with a phone line that ends at the production truck.

    The third lesson is how he handles people. He runs the place as a dictatorship and yet has almost zero turnover at the senior level. The reason is obvious if you listen. He pays loyalty back with loyalty. He covered his own people during COVID. He kept Rogan when sponsors demanded otherwise. He cut a sponsor whose board called once too often. He gives content creators total freedom because he knows freedom is what creates anything good. The dictatorship is on direction and standards. The autonomy is on craft. That is exactly the configuration almost every great founder converges on and it is almost the opposite of how MBA management theory tells you to run a company.

    The fourth lesson is the cost of a single decision. The Fertittas almost sold the UFC for $6 to $8 million in roughly year four. That same business sold for $4.025 billion twelve years later and now sits inside a TKO Group entity with a $7.7 billion Paramount deal. The delta between a phone call that says “sell it” and a phone call that says “fuck it, let’s keep going” was somewhere north of four billion dollars and counting. Dana’s comment about a good night of sleep is not a cute aside. It is the most important sentence in the interview.

    The fifth and final thing worth sitting with is how Dana thinks about the next ten years. He is 56. He could have retired ten years ago. Instead he is rebuilding boxing inside the same machine, launching UFC BJJ, scaling Power Slap, and openly stating he intends to build the largest combat sports company that has ever or will ever exist. Most founders at his stage are looking for the exit ramp. Dana is loading more onto the plate because he loves the building itself more than the result. He says it explicitly: he loves entrepreneurship slightly more than he loves fighting at this point. That is the tell. People who love the work itself simply do not stop, and the numbers keep getting bigger than anyone watching can imagine.

  • Shopify CEO Tobi Lütke: AI Is the Perfect Scapegoat for Layoffs, Canada Has Trump Derangement Syndrome, and 50% of Shopify Code Is Now AI-Generated

    TLDW

    Shopify CEO Tobi Lütke sat down with Harry Stebbings on 20VC for one of the most candid and controversial conversations of his career. Lütke argues that the current wave of mass layoffs has nothing to do with AI and everything to do with pandemic-era overhiring, but AI will be blamed because it cannot fight back. He blasts Canada for its “Trump Derangement Syndrome,” calls the climate cult “one of the most evil things wrought on the population,” reveals that over 50% of Shopify’s code is now AI-generated, and says many of his best engineers have not written a line of code since December when Claude Opus changed everything. He also introduces River, an AI engineer at Shopify that named itself, and explains why he believes context engineering will be the dominant role of the next five years.

    Key Takeaways

    • AI is not causing layoffs, COVID overhiring is. Lütke is blunt: “What you see right now is not AI layoffs. Those are just the companies that are really slow that overhired just like everyone else.” AI will get blamed for everything because it is the perfect Girardian scapegoat that cannot fight back.
    • Over 50% of Shopify’s code is now AI-generated and “converting to much higher numbers.” Many of Shopify’s best engineers have not written code this year. December 2025 and the release of Claude Opus changed everything.
    • Senior engineers became more valuable, not less. Lütke initially thought new grads with no priors would dominate the AI native era. He was wrong. Senior engineers steer agents better because steering is the new programming, and reps matter more than ever.
    • Context engineering will become the dominant role within 5 years. A new product builder role is emerging that subsumes engineering, design, and product management, focused on coordinating intelligent actors (humans and AI) to ship products.
    • “River” is Shopify’s AI engineer that named itself. Built first, then asked what name it wanted. River lives in Slack, ships engineering work, and learns publicly because it is steered through public Slack channels.
    • Builders are “eights” on the Enneagram and companies actively conspire against them. Eights call out nonsense, refuse fancy dressing, and are dangerous to colleagues’ careers. They rarely get promoted, often leave, and start companies. Shopify is “remarkably high on eights” because Lütke seeks them out.
    • Canada has “Trump Derangement Syndrome.” Over 60% of Canadians believe the United States is a bigger threat than Russia or China. Lütke calls this “stunning” and wrong. Canada’s only winning strategy historically has been “winning by helping America win.”
    • Canada should be the richest country on Earth. It has every resource the world needs for the next 20 years. Lütke wants pipelines built, industry built, refining done domestically, and an end to exporting raw resources to have other countries make end products.
    • Be deeply suspicious of “non-profit.” Lütke argues opting out of the only fitness function that has ever pulled people out of poverty (markets) and refusing to disclose your actual fitness function is a red flag. Non-profits replace merit with pull.
    • The climate cult is blocking civilization. Lütke called it “one of the most evil things wrought on the population” and pointed to anti-nuclear green parties and frog protection laws blocking factories as examples of policy capture.
    • The Chinese AI threat is real but misunderstood. The bigger concern is that if Western governments restrict children from using AI, kids will simply download Chinese open-weight models, train on collectivist worldviews, and stop ever writing high school essays about Tiananmen Square.
    • Markets are the most democratic system that exists. Every dollar spent is a vote. Capital allocation by hundreds of millions of consumers is more democratic than any election.
    • Friedrich List and the Prussian school over Adam Smith. Lütke prefers a model where governments define excellent games with positive externalities, then completely get out of the way and let competition do the rest.
    • Shopify’s biggest mistake was going into physical logistics right before AI got really good. Lütke initially defended the decision based on what he knew at the time, but later admitted he was probably just wrong.
    • Lütke does not look at the stock price. It has been at least 23 days since he last checked. He runs Shopify on product instincts, not market signals.
    • Great leaders must be exothermic. A CEO is a heat source for the company. Lütke prefers “temperature” to “chaos” because chaos has too negative a connotation.
    • Don’t go to university for university’s sake. Get a degree from somewhere hard to get into so you are surrounded by people who also fought to get in. Better yet, join a small company where you can actually be of value.
    • Entrepreneurship is the most AI-safe AND most AI-benefiting job. Lütke sees a coming golden age of entrepreneurship where priors no longer matter and AI co-founders eliminate the need to grow up around business.
    • “You can just do things” is the rallying cry Lütke wants to ingrain in the world. Action causes information. The cost of trying is lower than ever.
    • The demonization of wealth in America is misdirected. No one gets to a billion dollars by stealing. Builders create products that people vote for with their money, the most democratic act in any economy.

    Detailed Summary

    Harry Stebbings opens by asking Tobi Lütke whether entrepreneurs are motivated by fear of losing or hunger to win. Lütke says he is still figuring out his own answer, but argues that both extremes lead to short-term thinking. The real unlock is taking a long perspective, because compound advantages only accrue when you are willing to wait.

    Builders Are “Eights” and Companies Conspire Against Them

    Lütke explains the Enneagram personality framework and identifies himself as an “eight,” the type that refuses to accept that any organization’s output is acceptable just because it is dressed up nicely. Eights call out nonsense, are dangerous to careers around them, rarely get promoted in professionally managed companies, and often leave to start their own businesses. Shopify deliberately overweights eights in its hiring. Lütke also says people who build companies are “fundamentally crazy people” and that the public image of leadership comes from movies, not reality. He never wanted to be CEO but realized you cannot run a product driven company without controlling the company itself, because product needs and company needs only converge on a three-year horizon.

    The Luxury of Long-Term Thinking as a Public Company

    Stebbings asks if a public company can really afford long-term thinking. Lütke says trusted public companies are the best position to be in. The chasm to cross is from trusted private to untrusted public, which is why so many founders refuse to IPO. Shopify went public 11 years ago at a 1.67 billion dollar valuation when revenues were a fraction of today’s. The valuation is now roughly 100x higher. Lütke walks through the IPO mechanics: investment bankers serve the buy side, not the company, and Lütke priced his offering above range because he knew where his growth would come from. The first trade closed about 10 dollars higher, which he calls a “good performance” but a teaching moment about market price discovery.

    AI Is the Perfect Scapegoat for Mass Layoffs

    This is where the conversation gets explosive. Lütke says Shopify employs about 7,500 to 8,000 people today and his real hope is to have the same number in five years, but at 100x productivity. He argues that the layoffs sweeping the tech industry have nothing to do with AI. They are the result of pandemic-era overhiring catching up to slow-moving companies. But AI will get blamed for everything because it is the perfect Girardian scapegoat. It cannot defend itself, it has no PR team, and an entire industry of doomers is already trained to point at it. Lütke says his own industry has been “gaslighting everyone into AI fear” and science fiction did the same for 60 years before that.

    His own use of AI is what he calls utopian. Tasks that used to be hard are easy. Most jobs, he argues, are not actually good jobs to begin with. Being a human task queue is not a great job. Great jobs involve agency and creation. As AI gets cheaper, purchasing power explodes, and people will get options to do things on weekends that are vastly more productive than their day jobs ever were.

    Markets Are the Most Democratic Mechanism Ever Invented

    Lütke pivots into a long defense of capitalism as the most democratic system in existence. Every dollar spent is a vote, far more frequent and more granular than any election. He uses Elon Musk and Tesla as examples. Lütke owns a Model Y, did not touch the steering wheel that morning, and uses Starlink in the back to work on long drives. He posts on X and gets replies from Japan in real time. He calls Musk a “one man engine” who has captured a tiny percentage of the value he created. He extends this to Shopify itself: Lütke owns 6% of the company, which means 94% is owned by other people who all made money. Plus roughly 10 million people work in the broader Shopify ecosystem on customer fulfillment, web design, customer service, and more.

    Why “Non-Profit” Should Make You Suspicious

    Lütke targets the charity industrial complex. He argues that non-profits opt out of the only mechanism humanity has ever invented to lift people out of poverty (markets), and they fail to articulate what their actual fitness function is. The result is that “merit of organization is replaced with pull of individuals.” Smooth talkers, not builders, end up running these institutions. He acknowledges Carnegie’s libraries and a few exceptions but believes the ratio of charity dollars to good outcomes is dramatically off. He is far more enthusiastic about funders like MacKenzie Scott who give in unrestricted ways, and even more enthusiastic about Jensen Huang and Bloom Energy as compute and infrastructure investments that compound into civilizational gains.

    The Prussian School of Economics

    Asked about government intervention, Lütke pledges allegiance to Friedrich List and the Prussian school of political economy over Adam Smith and Lassalle. The job of government is to define excellent games where positive externalities accrue to society, then completely get out of the way. He calls the outsourcing of violence to governments “one of the most inspiring things humanity has ever done” because it created the conditions for personal property. But governments are extremely bad at doing things directly. The moment a government runs grocery stores, it costs 10x more, and entrepreneurs have to be enlisted to repair the damage.

    Canada’s Trump Derangement Syndrome

    Stebbings asks if Lütke is proud of Canadian Prime Minister Mark Carney for standing up to Trump. Lütke is unequivocal: no. He calls Carney’s stance “not a credible witness to the reality on the ground.” Canadians, he argues, are “massively overfit to niceness,” which leads to “unkind lies” and lying by omission. Over 60% of Canadians now believe the United States is a bigger threat than Russia or China, which Lütke calls “stunning” and clearly wrong. Canada is a small economy attached to a hegemon, and the only winning strategy in its history has been winning by helping America win.

    That said, he agrees with Carney on diversifying the economy, getting closer to Europe, and engaging Asia. But he wants Canada to also “build the [expletive] out of pipelines, build the [expletive] out of our industry, and start refining the stuff ourselves.” Canada has every resource the world needs for the next 20 years and the most educated workforce on Earth. The only obstacle is political will. Canada’s commercial story has been the same since the beaver pelt era: extract resources, ship them abroad, let other countries make end products. Canada Goose, Lululemon, Shopify, Miller Lite. That is the short list of products Canada actually makes.

    The Real Chinese Threat

    Lütke says the Chinese AI threat is both underestimated and overestimated. The bigger threat, he argues, is government overreach. If Western governments start dictating which AI models children can use, kids will simply download Chinese open-weight models. He notes that Chinese models, especially when prompted in Chinese, exhibit a clearly collectivist worldview. The risk is that an entire generation of students writes essays through models trained never to mention Tiananmen Square. He frames the broader political battle as collectivism versus individualism and says everything else is smoke screening.

    Fixing Europe and the Climate Cult

    Asked what he would do as president of Europe, Lütke begins by saying you have to “get rid of the climate cult.” He calls it “one of the most evil things wrought on the population,” citing green parties whose founding myth is that nuclear power is bad, and infrastructure projects blocked because of one frog breeding in one creek. He argues that very few people have the capability to truly build, and they need both enablement and accountability from the village. Beyond that, he wants Europe to follow the Prussian playbook: build excellent games, build infrastructure, and use the resulting wealth to sculpt the economy you want.

    Shopify’s Biggest Mistake

    Lütke says his biggest public mistake was Shopify’s full push into physical logistics and warehousing right before AI capabilities exploded. Initially he defended the decision as correct based on the information available at the time, but later admitted he probably just got it wrong. The hardest part was that real people lost their jobs when Shopify exited.

    Great Leaders Are a Heat Source

    Lütke previously talked about CEOs injecting “chaos” into organizations. He now prefers “temperature.” Heat is atoms jiggling. Great leaders must be exothermic, providing energy that flows through the organization. He says he hasn’t checked Shopify’s stock price in at least 23 days. Most public company CEOs are obsessed with their stock. Lütke runs on product instincts.

    Senior Engineers Don’t Write Code Anymore

    Lütke admits he was wrong about new grads having an AI native advantage. Some are exceptional (he hired a 13-year-old intern from Waterloo whose mother accompanies him to classes), but on the whole, senior engineers steer agents better than juniors do because they have done more reps. Programming is not gone. Programming has become higher level. Engineers massively underestimate how important steering is. Steering is just programming at a higher altitude.

    The Role That Will Dominate in 5 Years

    Lütke says context engineering, a term he had a hand in popularizing, will become a standard role within five years. It will likely subsume parts of product, design, and engineering management. The best AI programmers right now, surprisingly, are people from engineering management because they have been prompting intelligent agents (humans) for years. Good communicators are good thinkers because communication is distillation.

    River, the AI Engineer That Named Itself

    Shopify built an AI engineer that lives in Slack. They built it first, then asked it what name it wanted. The AI chose “River” because Shopify’s monolithic repository is called “world” and rivers shape worlds. River does an enormous amount of Shopify’s engineering, taking instructions through public Slack channels so that the entire company can learn from how others steer it.

    Over 50% of Shopify’s Code Is AI-Generated

    The number is “a fair deal over 50%” and “converting to much higher.” Many of Shopify’s best engineers have not written code this year, with the inflection point being December 2025 and the release of Claude Opus. Lütke himself still writes code occasionally, especially the data structure layer where he applies what he calls a “German school” of engineering: figure out how data persists on disk, then build everything else on top. Once that is right, the rest can be vibe coded by AI.

    Should His Kids Go to University?

    Lütke says he would not push his kids to attend university for its own sake. The value of a hard to enter program is being surrounded by people who also fought to get in. Better still: get into the room with people who are obsessed with the topic you care about. He thinks joining a small startup where you can actually be of value is often a superior path. He addresses nepotism directly. His instinct is that nepotism is bad. The gold standard is double-blind merit. But double-blind merit barely exists anywhere, and intersectional academic hiring criteria in Canada are arguably worse than nepotism.

    Final Reflections

    Lütke ends with what he calls the best advice he knows: “You can just do things.” The system exists to push everyone toward acceptable outcomes, but if you know what a good outcome looks like, you can step out of the system and try. Action causes information. The cost is lower than ever. The only constraint is that the experiment cannot have victims.

    He also addresses the demonization of wealth. No one gets to a billion dollars by stealing. Builders create products people vote for, the most democratic act there is. Buying from a local shop is voting for the welfare and future of local shops. Constructive criticism is itself something someone has to build, and Lütke welcomes it. Lazy criticism, hot takes, and bad faith arguments are corrosive and should be held in contempt.

    He is bullish on AI as a counterweight to information warfare. A council of AI models trained in different countries (Chinese, German, French, American) could fact check claims with multiple perspectives. The “@grok is this true” reflex on X is, he says, a primordial version of this. The information asymmetry that has favored bad faith actors for decades is about to flip.

    Thoughts

    This interview is a window into the operating philosophy of one of the most successful technical founders alive, and it is far more provocative than most of his public appearances. The headline claim, that AI is a scapegoat for layoffs caused by pandemic overhiring, deserves to be repeated until it sinks in. Every CEO who lays people off and then writes a memo about “AI driven efficiency” is taking advantage of a narrative that AI cannot push back against. The math is plain: if you doubled your headcount in 2021 and 2022 and now you are firing 15%, you are not net displaced by AI. You are correcting a hiring mistake.

    The 50% AI generated code statistic is the bigger story. Shopify is not a small company. 8,000 employees and 7 billion in revenue is enterprise scale. If a company that mature has crossed the 50% threshold and is “converting to much higher numbers,” the implication for the broader software industry is enormous. The senior engineer compounding observation is also subtle and important. If steering is the new programming, then the senior pool is more valuable, not less, and the pipeline problem for junior developers gets harder to solve. Companies that under invested in junior training during ZIRP will face an experience cliff in five years.

    Lütke’s Canadian commentary will offend many readers in his home country, which seems to be exactly the point. The “lying by omission” critique of Canadian niceness is sharp and accurate. The 60%+ of Canadians who view the US as their largest threat is genuinely a remarkable statistic, and it has implications for trade policy, capital flows, and immigration. Whether or not you agree with his political read, his prescription is unambiguous and pro-growth: build pipelines, refine resources domestically, stop being content as a feedstock economy.

    The non-profit critique deserves more public debate. The fitness function point, that markets reveal preferences and non-profits opt out of preference revelation while not disclosing what they optimize for, is a sharp economic argument. The pull versus merit observation about who ends up running large foundations rings true to anyone who has worked adjacent to the philanthropic sector.

    The introduction of River as an AI engineer that named itself is a small detail that signals where this is going. AI agents are going from tools to teammates with identities, channels, and reputations. The fact that River shapes the “world” repository is poetic, and the public Slack steering pattern is a real innovation in how organizations can scale agentic AI without creating siloed knowledge.

    Lütke’s “you can just do things” rallying cry is ultimately what ties the entire interview together. Whether he is talking about Canada, Europe, AI engineers, or his own kids, the through line is the same: action causes information, the cost of trying is lower than ever, and the only people who will benefit from the next decade are the ones who refuse to wait for permission. This is the most useful piece of philosophy in the entire conversation, and it applies far beyond entrepreneurship.

  • Brian Chesky on AI Founder Mode, the 11-Star Experience, and Reinventing Airbnb for the Age of AI

    Airbnb CEO Brian Chesky sits down with Patrick O’Shaughnessy on Invest Like The Best to talk about the next evolution of company building: AI Founder Mode. He covers the shift from founder to CEO, the lessons he learned from Steve Jobs through Hiroki Asai, why consumer AI is the next great frontier, and how he plans to change the atomic unit of Airbnb from a home to a person.

    TLDW

    Brian Chesky believes the next era of company building belongs to founders who refuse to delegate the soul of their company. He coined Founder Mode with Paul Graham after the pandemic forced him to take Airbnb back into his own hands. Now he is shaping what comes next: AI Founder Mode, where leaders work with on-demand context, fewer layers of management, asynchronous communication, and a new generation of hybrid manager-makers. He shares why most software companies have not been touched by AI yet, why consumer AI is about to explode, and why he is rebuilding Airbnb around people, not homes. The conversation also touches on the 11-Star Experience exercise, the power of small teams, why recruiting is the most important job a CEO has, and why every adult is still an artist underneath.

    Key Takeaways

    • Founder Mode is not micromanagement, it is having a steering wheel. Chesky woke up in 2019 feeling like the car had no steering wheel. After the pandemic, he reviewed every detail for two to three years before delegating again. Start hands-on and give ground grudgingly, not the other way around.
    • AI Founder Mode is even more intense. With AI, leaders can be in significantly more details because almost everything is on demand. Expect fewer layers of management, mostly asynchronous work, and the death of the pure people manager.
    • Two types of leaders will not survive AI. Pure people managers who only do one-on-ones, and rigid people who refuse to evolve. Everyone needs to be a hybrid manager-IC who can still touch the work.
    • Manage people through the work, not through meetings. Frank Lloyd Wright did it. Johnny Ive does it. You are not anyone’s therapist.
    • Consumer AI is the next great prize. 159 of the last 175 Y Combinator companies were enterprise. Almost every app on your home screen has not changed since AI arrived. That changes in the next 12 to 24 months.
    • Why consumer AI is hard. No proven business model, mature distribution, trend-chasing investor culture, and the simple fact that consumer is more hits-driven and requires excellence in design, marketing, culture, and press, not just technology and sales.
    • Project Hawaii is the new operating model. A 10 to 12 person Navy SEAL team, hands-on coaching from the CEO, crawl-walk-run-fly. The first project added roughly $200 million in year one and $400 to $500 million in year two.
    • Make the problem as small as possible. Airbnb spent 16 years failing to launch a second hit because it kept trying to scale globally on day one. Now: pilot in one city, expand to 10, then industrialize.
    • It is better to have 100 people love you than a million people sort of like you. Paul Buchheit shipped Gmail only after 100 Googlers loved it. The sample size of intense love is enough to predict mass adoption.
    • The 11-Star Experience is an imagination exercise. Push to absurdity (Elon takes you to space) so a 6 or 7-star experience suddenly seems normal. The gap between 5 and 6 stars is the gap between you and your competitor.
    • Simplicity is distillation, not subtraction. Hiroki Asai, Steve Jobs’s longtime creative director, taught Chesky that great design distills something to its essence. First principles is a design term too.
    • The score takes care of itself. Bill Walsh and John Wooden both taught that you do not focus on winning, you focus on making every input perfect. Wooden spent his first hour with new players teaching them how to put on socks.
    • Industrial design is the original product management. There are no PMs in industrial design. The designer is the PM, working alongside engineers and program managers to design through user journeys.
    • Recruiting is the CEO’s number one job. The more time you spend recruiting, the less time you spend managing, because great people self-manage. Build pipelines, not searches. Start with results, work backwards to people.
    • Co-hire the top 200 people, not just the executive team. Most CEOs hire executives and let them hire their teams. Chesky considers that fatal because most executives cannot hire well without help.
    • Bodybuilding is a metaphor for leadership. If you can change your body, you can change your life. Progressive overload, 1 percent a day, is how compounding works. Start with biology before therapy.
    • Founder-led companies build the deepest moats. Disney is still selling Walt’s playbook 60 years after he died. Apple is still selling Steve’s iPhone. The longer founders stay in founder mode, the more the company can endure when they leave.
    • Software is hyper fast fashion. Hardware ages well. Buildings get patina. Software always looks dated 10 years later. What endures is the community, the brand, the principles, the mission, and the network effect.
    • Apps are dying. Agents are coming. Chesky says we should let go of our attachment to apps because they are not what the future looks like.
    • Airbnb’s atomic unit is changing from a home to a person. Chesky wants to build the most authenticated identity on the internet, the richest preference library, a real-world social graph, and a membership program. Then expand to 50 to 70 verticals on top of that identity.
    • AI shifts attention from consumption to creation. Social media gave you a paintbrush only for opinions. AI gives everyone a real paintbrush and canvas. We are heading into a creative renaissance.
    • Founders are expeditionaries, not visionaries. They put one foot in front of the other and call it a vision later.
    • Detach from accolades. Chesky describes adulation as a cup with a hole in the bottom. Status is a drug. The path to durable creative work is doing it because you love it, the way Walt Disney, Da Vinci, Van Gogh, and Steve Jobs did until the very end.
    • The kindest gift is belief. The best way to activate a person’s potential is to see something in them they do not yet see in themselves.

    Detailed Summary

    From Industrial Design to the CEO Chair

    Chesky studied industrial design at the Rhode Island School of Design. He chose it on instinct after a department head told him industrial designers design everything from a toothbrush to a spaceship. He grew up enchanted by the Reebok Pump, the Game Boy, the Nintendo, and eventually by the late 1990s golden age of Apple. Raymond Loewy, the man who designed Air Force One and an enormous catalog of mid-century consumer products, became a touchstone, but Johnny Ive was the real hero.

    What he loved about industrial design was that it is technical, commercial, and empathetic. A building can win an architecture award and never be leased. A piece of industrial design that does not sell is a failure. So you have to think about manufacturing, distribution, marketing, and most importantly, user journeys. There are no product managers in industrial design. The designer is the PM. That training, he says, prepared him directly for the role of CEO.

    The Pandemic and the Birth of Founder Mode

    Chesky says no one is born a good CEO. People are born good founders. The job of CEO is counterintuitive in almost every direction. Founders are taught to learn by doing, but a CEO who learns by trial and error wastes years unwinding the empires of misfit hires.

    By 2019 he was running a 7,000 person company he no longer recognized. He felt he was driving a car without a steering wheel. He had a dream that he had left Airbnb for ten years and come back to find it had become a giant political bureaucracy. Then he realized he had been there the whole time. The pandemic hit and Airbnb lost 80 percent of its business in eight weeks. He shifted from peacetime to wartime, took control of every detail, worked 100-hour weeks, and reviewed everything for two to three years.

    The vision was never to micromanage forever. The vision was: I need to know what is going on before I can empower anyone. Hire people, audit their work, and only then give ground grudgingly. Most founders do the opposite, which is why they end up with executives building empires they later have to dismantle.

    AI Founder Mode

    Chesky says AI Founder Mode will be even more intense than Founder Mode because nearly everything will be on demand. He used to live in 35 hours of meetings a week to gather information, the same way Steve Jobs ran Apple. He held weekly, biweekly, monthly, and quarterly group reviews with the full chain of command in one room, anyone could speak, and he made the final call after listening last.

    In the AI era, that culture shifts from meetings to asynchronous work. He expects fewer layers of management. He cites the Catholic Church as a 2,000-year-old institution with only four layers and asks why most companies need seven, eight, or nine. Pure people managers will not survive. Every manager will have to be a hybrid IC, an engineer who still codes, a lawyer who still reads case law, a designer who still designs. You manage through the work, not through one-on-ones.

    He is also bullish that AI tooling will become consumer-grade simple very soon. The current tools, including Claude Code and Cowork, are not yet intuitive to the average person, but the economic incentive will force that to change.

    Why Consumer AI Is the Next Great Frontier

    Chesky points out that 159 of the last 175 Y Combinator companies were enterprise. Almost every consumer app on your phone, including Airbnb, has not fundamentally changed since the arrival of AI. He gives four reasons: investors feared ChatGPT would kill consumer companies; consumer AI has no proven business model because subscriptions hit a local max against free Claude and Gemini, ads are off the table for most labs, and e-commerce has been shut down via third-party app removals; distribution is mature; and Silicon Valley culture, while branded as rebellious, is in practice trend-following.

    The deeper reason is simply that consumer is harder. It is hits-driven, requires great design, marketing, culture, press, and you cannot easily start by selling to your dorm-mates the way enterprise YC startups sell to other YC startups. The prize is bigger. The risk is bigger. He predicts a consumer AI renaissance over the next 12 to 24 months.

    Project Hawaii and the Magic of Small Teams

    Inside Airbnb, Chesky tested a new operating model called Project Hawaii. He took 10 to 12 people, designers, engineers, product, and data scientists, treated them like a startup inside the company, and pointed them at one problem: improving the guest funnel. The system is crawl, walk, run, fly. First fix bugs, then add features, then re-imagine flows, then completely reinvent.

    The first team delivered roughly $200 million of internal revenue in year one and $400 to $500 million the next year, eventually contributing more than 600 basis points of conversion improvement on a base of $134 billion in gross sales. Then they took the same system to pricing, then to other problems, then to launching new businesses like Services and Experiences.

    The guiding lesson: make the problem as small as possible. Airbnb launched in one city, New York. Uber in San Francisco. DoorDash in Palo Alto. When Chesky launched Services and Experiences in 100 cities at once last year, it did not work. The fix was to dominate one city, expand to 10, then industrialize. Peter Thiel said it cleanly: better to have a monopoly of a tiny market than a small share of a big market.

    Underneath that is a Paul Buchheit insight Chesky calls the best advice he ever got. It is better to have 100 people love you than a million people sort of like you. Buchheit refused to ship Gmail until 100 Googlers loved it, and that took two years. Once 100 people loved it, 100 million people did.

    The Hiroki Asai Lessons: Simplicity and Craft

    Hiroki Asai, Steve Jobs’s quietly legendary creative director, taught Chesky two principles. The first is that simplicity is not removing things, simplicity is distillation, understanding something so deeply that you can express its essence. Steve Jobs called design the fundamental soul of a man-made creation that reveals itself through subsequent layers. Elon Musk’s first principles thinking is the same idea applied to physics.

    The second is craft. How you do anything is how you do everything. Chesky cites Bill Walsh’s The Score Takes Care of Itself and John Wooden’s first hour with UCLA players, an hour spent teaching them how to put on their socks. Walsh said the way you tucked your jersey was one of 10,000 details that decided whether you won. The lesson is to focus on getting every input right. The output follows.

    The 11-Star Experience

    The 11-Star Experience is one of Chesky’s most copied frameworks. Most Airbnb stays get five stars because anything else means something went wrong. So Chesky asked: what would six stars look like? Your favorite wine on the table, fruit, snacks, a handwritten card. Seven stars? A limousine at the airport and the surfboard waiting for you because they know you surf. Eight stars? An elephant and a parade in your honor. Nine stars, the Beatles arrive in 1964 with 5,000 screaming fans. Ten stars, Elon Musk takes you to space.

    The point is the absurdity. By imagining the impossible, six and seven star experiences stop seeming crazy. The gap between five and six stars is the gap between you and your competitor. If you can industrialize a sixth star, you may have product-market fit. The exercise also restarts your imagination, which Patrick noted has atrophied for many people in the era of consumption-only social media.

    AI as a Canvas for Creativity

    Chesky frames AI as the ultimate platform shift, the ultimate creative expression, and possibly the greatest invention in human history. Social media made us mostly consumers and gave creators only opinion-shaped tools. AI gives everyone a paintbrush. He believes far more people are creative than we recognize because most have never had craftsmanship or tools to express what is in their heads. Pablo Picasso said all children are born artists; the problem is to remain one as you grow up. Chesky thinks every adult is still an artist underneath.

    The Next Chapter of Airbnb

    Chesky describes four phases of the CEO journey: get to product-market fit, scale to hyper-growth, become a real profitable public company, and finally reinvent. Airbnb’s stock has been flat because the core idea is saturating. He is now squarely in phase four, with three priorities.

    First, change the atomic unit from a home to a person. He wants Airbnb to build the most authenticated identity on the internet, the richest preference library, a real-world social graph, and a membership program. Proof of personhood, he says, will be enormously valuable in the AI age. Second, industrialize the new-business engine to support 50 to 70 verticals (homes, experiences, services, eventually flights, and more) all built on top of that personal atomic unit. Third, navigate the AI transition without breaking the existing business or the livelihoods of hosts. He is also exploring sandbox apps that imagine a radically different Airbnb, the answer to “what is after Airbnb?”

    What Endures in the Age of AI

    Chesky is direct that software does not endure. Look at any software from 10 years ago and it looks dated. Hardware ages better. Buildings develop patina. Paris endures. So if you want to build something lasting, you cannot bet on the app. You have to bet on the community, the brand, the mission, the principles, the identity, and the network effect. Apps are going away, replaced by agents. Founders attached to apps need to let go.

    Founder-Led Moats: Disney and the Ham Sandwich Paradox

    Chesky reconciles Warren Buffett’s “buy a company a ham sandwich could run” with the venture capital truth that a founder’s ceiling is the company’s ceiling. The reconciliation is Disney. Most people cannot name a Paramount, Warner Brothers, Universal, or MGM film off the top of their head, but everyone can name Disney films. Walt Disney was a founder in founder mode for so long that he created enough IP and momentum that the company has been running on his playbook for 60 years after his death. Apple is similar with Steve Jobs and the iPhone.

    The counterintuitive lesson: if you want a company to last 100 years, do not delegate early to make it independent of you. Stay in founder mode for as long as possible so you can institutionalize the magic deeply enough that it endures after you. Tech is the industry of change, so founder mode matters even more there than in chocolate or insurance.

    Bodybuilding as Leadership Training

    Chesky was a 135-pound late bloomer who told his friends he would compete at the national level in bodybuilding by 19. He did. Two lessons came out of it. First, if you can change your body, you can change your life. Start with biology before therapy. Second, you cannot get in shape in one day. Progressive overload, discipline, consistency, and roughly 1 percent a day compound into massive gains. The visible feedback loop in bodybuilding taught him to break invisible problems (like the quality of a leadership team) into observable, measurable proxies (like the quality of the room at a twice-yearly roadmap review of the top 100 people).

    Recruiting as the CEO’s Number One Job

    Sam Altman told a 27-year-old Chesky he would spend 50 percent of his time on hiring. Chesky did not, and considers that his biggest mistake. He now starts and ends every day with his recruiter and spends two to three hours a day on hiring. The more time you spend recruiting, the less time you have to spend managing because great people self-manage.

    His system is pipeline recruiting, not search recruiting. He never starts with a search firm. He constantly meets the best people in their fields, asks each one to introduce him to the next two or three best, and builds a rolling rolodex. He starts with results, finds an ad he loves, and works backwards to the team that made it. He builds little mafias of top talent inside the company. He is the co-hiring manager for the top 200 people at Airbnb, not just executives, because most executives cannot hire well without help.

    Activating Talent and the Power of Belief

    You cannot teach motivation. You can only give people a problem and see if they have agency. The way to activate someone, Chesky says, is to show them potential they cannot yet see in themselves. He cites John Wooden, who said the secret to coaching was that he saw potential in players they did not see in themselves. People will climb mountains for that.

    The kindest gift anyone gave Chesky, he says, was belief. A high school art teacher named Miss Williams told his parents he was going to be a famous artist. He never became one, but the belief gave him the confidence to choose art school and to choose to be happy. Michael Seibel and the Justin.tv founders believed in him. Paul Graham made an exception to fund a non-engineer with what he thought was a bad idea. His co-founders Joe and Nate believed in him when he had no business being a CEO. The biggest gift you can give back, he says, is belief in others.

    Detaching from the Scoreboard

    Chesky describes adulation as a cup with a hole in the bottom. Status keeps draining out and you keep needing more to feel the same. The day Airbnb went public at a $100 billion valuation should have been one of the best days of his life. The next morning he put on sweatpants for a Zoom meeting and felt nothing. That triggered a re-evaluation. He stopped seeking accolades and started focusing on intrinsic work. He cites Rick Rubin: an artist is an artist when they make for themselves. He cites Vice President Obama, who told him to focus on what you want to do, not who you want to be.

    His four heroes are Leonardo da Vinci, Vincent Van Gogh, Walt Disney, and Steve Jobs. All four were working until the last week or day of their lives. Da Vinci carried the Mona Lisa with him until he died. Van Gogh sold one painting in his life. Disney was imagining theme parks in the ceiling tiles of his hospital room. Chesky says his motivation is the motivation of an artist. He calls being a CEO of a public company at his scale “almost a glitch in the system” that gave him one of the largest design canvases in human history.

    Thoughts

    What stands out about this conversation is how clearly Chesky has decoupled identity from outcome. He frames himself first as a designer, second as a CEO, and considers the resources he commands as a kind of accidental fortune for an industrial designer to be sitting on. That self-image is what lets him talk about disrupting Airbnb, killing the app paradigm, and changing the atomic unit of the company without flinching. Most public-company CEOs cannot afford that posture.

    The framework worth stealing is Project Hawaii. The pattern of taking a 10-person elite team, putting them under direct CEO coaching, and running them through crawl-walk-run-fly is a near-universal answer to the problem of innovation inside a large company. It works because it removes abstraction layers, creates direct contact with reality, and gives the founder a way to teach muscle memory before delegating. Anyone running a team of any size can borrow the pattern: pick one problem, staff it small, work with it weekly, then let go gradually. The golf-instructor analogy of teaching muscle memory before bad habits set in might be the most important management metaphor of the year.

    His prediction about consumer AI is the most economically interesting part of the talk. The fact that 159 of 175 recent YC companies are enterprise is a startling concentration. If he is right that the next 12 to 24 months bring a consumer renaissance, the opening is enormous. The hard part is what he names directly: there is no proven business model for consumer AI yet. Subscriptions cap out against free incumbents, ads are off-limits for the labs, and e-commerce has been throttled. Solving the business model is probably more valuable than building the next great consumer interface.

    The deeper philosophical thread, that AI is the transition from consumption to creation, is one that anyone building tools for makers should hold close. The 11-Star Experience also reads differently in the AI era. It used to be a thought exercise constrained by what you could plausibly build. AI compresses the gap between imagination and execution to minutes, sometimes seconds. The question is no longer “what is the most absurd version of this experience?” but “which six and seven star experiences can I now industrialize that were unthinkable a year ago?” The exercise has become operational.

    Finally, the meta-lesson on founder-led moats is worth taking seriously. The instinct in venture capital and at most public-company boards is to professionalize early. Chesky’s argument is the opposite: the longer the founder stays in founder mode, the deeper the IP and the longer the company endures after they leave. Disney is the proof. Apple is the proof. Whether Airbnb will be is the open question, and it is the question Chesky is using AI Founder Mode to answer.

  • Inside Figure: Brett Adcock’s $39 Billion Bet on Humanoid Robots, Helix AI, and the Race to Physical AGI

    Figure is the $39 billion humanoid robotics company most likely to put a general-purpose robot in a commercial workforce, and possibly your living room, before the end of the decade. In a rare two-part sit-down on Sourcery with Molly O’Shea, Founder and CEO Brett Adcock opened every door of the company’s San Jose campus, walked through the manufacturing line, demoed Helix 2 cleaning a living room with no teleoperation, and laid out the plan to scale from thousands of robots in 2026 to a million units a year. He also explained why he fired the OpenAI partnership, why he believes humanoids will reach AGI before any other form factor, and why Figure 04 will be the company’s “iPhone 1 moment.”

    TLDW

    Brett Adcock founded Figure in 2022, self-funded it through a million-a-month burn rate in the first four months, and 15x’d the valuation to $39 billion in 18 months on roughly $2 billion raised from Jeff Bezos, Microsoft, Nvidia, Amazon, and originally OpenAI. The company designs every part in-house, from motors and batteries to the Helix vision-language-action neural network running onboard each robot. Figure deployed humanoids on a BMW assembly line for six months in 2025, hit record production in March 2026, plans to triple that by May, and is targeting a million units per year. Adcock argues that humanoid robotics is an intelligence problem, not a manufacturing problem, that under half of global GDP is human labor (a market measured in tens of trillions of dollars), and that physical interaction data may be the missing ingredient to true artificial general intelligence.

    Key Takeaways

    • Figure is valued at $39 billion after raising nearly $2 billion. Adcock 15x’d the valuation in 18 months and believes the eventual revenue opportunity is in the tens of trillions because roughly half of global GDP is human labor.
    • The bottleneck is intelligence, not manufacturing. Figure already has the parts, the supply chain, and the capacity. The hard part is making robots that run autonomously at human-level performance for 7 to 10 hours a day with zero human intervention.
    • Figure designs almost everything in-house. Motors, rotors, stators, sensors, kinematics, joints, batteries, more than 100 PCBs. Adcock claims no other humanoid group designs more parts than Figure.
    • The OpenAI breakup was about model quality. OpenAI led Figure’s Series B and brought in Microsoft. After a year of collaboration, Adcock says Figure’s internal robot-learning team was running circles around OpenAI on humanoid AI, so he ended the partnership.
    • Helix is Figure’s onboard vision-language-action model. It runs on GPUs in the robot’s torso, ingests camera pixels a few hundred times per second, and outputs joint positions for all ~40 motors. It works without internet connectivity. Helix 2 launched a couple months ago.
    • Robots have more body positions than atoms in the universe. With 40 motors each capable of 360 degrees of rotation, the state space is 360 to the power of 40, which is why Figure abandoned hand-coded controls in favor of neural networks about a year ago.
    • The “Never Fall” protocol is real. A project called Vulcan uses reinforcement learning to keep the robot upright even after losing a knee, ankle, or hip mid-task. The company demoed a robot hobbling on a velocity-locked knee.
    • Figure 03 is the current production robot. It costs roughly 90% less than Figure 02, comes in under $100K per unit, has soft-wrapped foam shoulders, swappable fabric clothing, a high-top sneaker design, and inductive wireless charging at 2 kW through the feet (4 to 5 hours of runtime per 1 hour of charge).
    • Figure 04 is being teased as the “iPhone 1 moment.” Adcock says the jump from Figure 03 to Figure 04 will be the largest generational improvement they have ever made, far bigger than 1 to 2 or 2 to 3.
    • BMW deployed Figure robots for six months in 2025. The robots helped build a BMW X3 in the body shop. Adcock owns the first humanoid-built X3 personally and describes the deployment as the inflection point that led to Helix 2.
    • Home robots will lease for around $400 to $600 a month. Comparable to a car lease. The robot docks itself in a 2-by-2-foot wireless charging station and runs laundry, dishes, and tidying tasks autonomously.
    • Data is the biggest blocker. Figure has roughly 1 million hours of pre-training and mid-training data plus thousands of hours of post-training data. They also pay people in spandex bodysuits to do joint-level human movement capture.
    • Adcock runs three companies simultaneously. Figure (humanoids), Cover (terahertz weapons-detection imaging spun out from NASA Jet Propulsion Lab for K-12 schools), and Hark (an AI lab building personalized AI models and devices, out of stealth two weeks ago).
    • Physical AGI is the explicit goal. Adcock argues that real-world interaction data, learning by touching the world and observing the consequences, is the missing piece for true AGI, and that humanoids may reach it before chatbots do.
    • Security is paranoid by design. A drone was caught hovering outside Figure’s office at one point. They tented the windows, restrict phones in certain areas, and treat industrial CAD and software as high-value IP.

    Detailed Summary

    The Company in Context

    Figure is less than four years old. Adcock founded it in 2022 after stepping away from Archer Aviation, the eVTOL aircraft company he took public. He self-funded Figure to a million dollars a month in burn within four months, hired a 40-person team in four to five months, and pursued a vertically integrated strategy from day one. The thesis is simple. Roughly half of global GDP is human labor. Wages paid to commercial workers run into the tens of trillions of dollars annually. If you can build a humanoid that does general-purpose human work reliably, the resulting business compounds into one of the largest companies in history.

    The campus in San Jose has four buildings: corporate headquarters with 250 to 300 engineers, BotQ (the manufacturing facility), the Grid (a 24/7 robot stress-test environment that runs holidays and weekends), and a design studio that opened to cameras for the first time. Total headcount is around 500. The company has raised close to $2 billion across rounds, with capital from Jeff Bezos, Microsoft, Nvidia, and Amazon. The valuation jumped 15x to $39 billion in 18 months.

    Why Humanoid Robotics Is an Intelligence Problem

    The core technical insight: a humanoid has roughly 40 motors, each capable of full 360-degree rotation, which produces a state space of 360 to the power of 40. That number is larger than the count of atoms in the observable universe. You cannot write hand-coded control logic for that. Figure pivoted entirely from classical controls to neural networks about a year ago, and the team has built what Adcock claims is the best humanoid neural-network controller in the world.

    Helix is a vision-language-action model that runs onboard each robot. It accepts a natural-language prompt like “clean the living room,” reasons through the scene from camera input, and outputs joint commands a few hundred times per second. Inference happens locally on GPUs inside the torso, so the robot keeps working with no internet connection. Helix 2 launched a few months ago following lessons learned from the BMW deployment, and Figure has roughly a million hours of base training data plus thousands of hours of post-training data driving it.

    The OpenAI Partnership and Breakup

    OpenAI led Figure’s Series B alongside Microsoft. The two teams collaborated for roughly a year on running language models on humanoids. Adcock says he got to know Sam Altman and the team well, but over time it became clear that Figure’s internal robot-learning engineers (most with over a decade of experience in the field) were outpacing OpenAI on testing, model training, and integration with humanoid hardware. Adcock also implies OpenAI was getting interested in robotics itself, which created a strategic conflict. He ended the partnership. He is candid about being wrong on the original strategic logic for letting them invest in the first place.

    BotQ: The Humanoid Factory

    BotQ is the assembly facility where Figure 03 robots are born. Lines build heads, batteries, arms, legs, and hands separately. Each subsystem goes through end-of-line testing before integration. Heads contain camera systems, IMU, thermal sensors, Wi-Fi, 5G, Bluetooth, and lights, and are flashed with firmware and calibrated on the line. The 2.25 kilowatt-hour battery pack is custom-designed with a structural enclosure, polyurethane potting, and an internally engineered thermal-runaway venting system. The requirement is that no flame ever exits the pack. Figure has never had a robot catch fire.

    March 2026 was the company’s record production month, more robots built than in the entire prior history of the company combined. Adcock plans to triple that by May. After assembly, robots run a multi-hour “burn-in” in dedicated bays where the robot self-checks for loose cables, comm errors, or bad parts. They wear vests during gantry-supported wakeup. Once they pass, they walk themselves over to headquarters.

    The Grid and the Never-Fall Protocol

    The Grid runs robots 24/7 at higher operational intensity than any client site. It is the last line of defense before software ships. A dedicated team called Never Fall predicts every plausible fault and engineers around it. The Vulcan project takes this further: using reinforcement learning in simulation, robots learn to survive losing a knee, ankle, or hip mid-task. In the demo, a robot’s left knee was velocity-locked (simulating a lost actuator), and the robot continued hobbling around without falling. A backup robot can be summoned to take over the work.

    The Home Robot Demo

    Figure 03 demoed tidying a living room in a home environment built into the campus. The robot was given the prompt “clean the living room” and reasoned through the task autonomously: clearing cups, putting away toys, wiping the table. There was a brief sassy spray during the cleaning sequence. Adcock was emphatic that this is not teleoperated despite persistent online rumors. Helix 2 runs entirely onboard, no human in the loop.

    The product plan for the home is a leasing model in the $400 to $600 per month range, comparable to a car lease. The dock is roughly 2 feet by 2 feet and plugs into a standard wall outlet. Charging happens inductively through the feet at 2 kilowatts, giving roughly 4 to 5 hours of runtime per 1 hour of charge. Figure is not selling to homes yet but plans to soon.

    The Three Generations (and the Fourth)

    Figure 01 was a “cyberpunk” first-generation robot built for speed of iteration, costing hundreds of thousands of dollars per unit. Most parts were CNC-machined to Formula 1 precision. It walked within a year of company founding, which Adcock claims is among the fastest humanoid walking timelines in history. It had a tendon-driven hand (motors in the forearm) which Figure abandoned early. Because the wrist motors were too far along to redesign, the team raided foot motors and stuffed them in the forearm, producing the so-called Frankenstein forearm where the wrist bent halfway up the arm. Adcock was sure people would notice. In three years, no one ever asked.

    Figure 02 moved the battery from a backpack into the torso, doubled the battery, tripled the compute, added new cameras, and used an exoskeleton load-bearing structure inspired by aircraft skin design. Roughly 50 units were built. It was retired about a month before filming.

    Figure 03, the current production model, is roughly 90% cheaper than Figure 02 and slimmer in profile. It has soft foam-wrapped shoulders, swappable fabric clothing (with a zipper down the back), high-top sneakers, and the latest-generation hand with camera-based tactile sensors. The aesthetic was deliberately moved away from “too roboty.” Figure 03 was the first humanoid robot at the White House (greeting guests at an event with the First Lady).

    Figure 04 is in late-stage detailed design. Adcock describes it as the company’s “iPhone 1 moment,” a much larger generational leap than any prior version, with substantial cost reduction, easier manufacturing, easier home setup, and changes Adcock says are too sensitive to discuss publicly.

    Hands and the Path to Physical AGI

    Figure recently teased a high-degree-of-freedom hand with as many joints as a human hand. Adcock argues this is essential not just for dextrous manipulation but for passive learning from humans at scale. If humans can move their hands in arbitrary ways, the robot needs to be able to map onto those movements at test time. He believes the path to AGI in physical embodiment runs through the hands.

    Adcock’s broader claim is that physical interaction data, learning what happens when you touch, push, lift, or drop something, is the missing ingredient that current frontier language models lack. Most human intelligence is built through trial and error in the physical world. If that is true, humanoids may close the gap to AGI before pure software systems do.

    Brett Adcock’s Other Companies

    Cover is a school weapons-detection company spun out of NASA’s Jet Propulsion Lab. It uses terahertz imaging radar (originally developed for the Iraq and Afghanistan wars to find bomb vests at standoff distance) to detect concealed weapons in clothing or backpacks from 5 to 20 meters away, far further than airport scanners. Adcock bought the IP outright two years ago, and Caltech holds a small minority interest. The team is largely former JPL engineers based in Pasadena. Beta deployments to schools are planned by end of year, with 130,000 K-12 schools as the addressable market. Adcock self-funds it.

    Hark is an AI lab Adcock started seven or eight months ago and unveiled two weeks before the interview. It has 50 employees and is building next-generation personalized AI models alongside new AI hardware (the thesis being that 20-year-old form factors like phones and laptops are the wrong interface for AI).

    Operating Philosophy

    Adcock works from the engineering bullpen, not a corner office. He cut the “annual golf trip” category of relationships out of his life five years ago to make space for family and three companies. He goes home for dinner and bedtime with his kids and returns to the office after. He cites Steve Jobs and Jeff Bezos (a Figure investor) as influences and frames his work ethic as wanting to play “11 out of 10.” He maintains tight physical and digital security: a drone was once caught surveilling the office through a window, after which the team tented the glass.

    Risks

    Adcock is direct that the odds of full success are low. The risk list is long: manufacturing at unprecedented rates, robots running fully autonomously without human intervention (which no one has demonstrated), AI policies that generalize across every environment, hardware reliability, low unit cost, consumer demand. He frames his job as a daily funnel of the most pernicious problems in the company.

    He does not see capital or the $39B valuation as the binding constraint. If the robots work, he projects revenue measured in tens of trillions of dollars and points out that tech companies trade at 10 to 20 times revenue.

    Thoughts

    The most interesting structural claim Adcock makes is that humanoid robotics is an intelligence problem, not a manufacturing problem. That is a strong statement about where the difficulty actually lives. If the bottleneck were industrial (parts, supply chain, factory throughput), the dominant strategy would be to wait for incumbents like Foxconn or BYD to enter and underprice everyone. If the bottleneck is intelligence, the dominant strategy is exactly what Figure is doing: integrate vertically, control the hardware, generate proprietary training data, and run a tight feedback loop between deployments and model updates. The BMW deployment producing the lessons that became Helix 2 is the cleanest illustration of that loop in action.

    The 360-to-the-40th state space framing is a useful reminder of why neural networks won this domain. Anything you cannot enumerate, you must learn. The pivot from classical controls to neural networks about a year ago is probably the single highest-leverage decision in the company’s history, and it tracks with the broader collapse of hand-coded systems across robotics, autonomy, and even compilers.

    The OpenAI breakup is more interesting than it first appears. Adcock’s story is not “they were bad,” it is “we got better than them, faster.” That is consistent with a recurring pattern in AI right now: vertically integrated application companies, where the model is the product, are starting to outpace general-purpose model providers on their own narrow domains. If physical AGI does happen first in embodiment, that pattern will look prophetic in retrospect.

    The home leasing model at $400 to $600 per month is the part most people will underestimate. That price point is not luxury. It is roughly the cost of a modest car payment, less than full-time childcare, less than a cleaning service plus a dog walker plus laundry pickup. If the robot can actually do laundry, dishes, and tidying every day with no failures, the consumer math gets aggressive fast. The bottleneck is reliability per hour, not willingness to pay.

    The skeptic’s case is also worth holding in mind. “Working” in a curated demo home is not the same as working in 100,000 messy real homes with cats, kids, weird furniture, and unpredictable lighting. Generalization is exactly the problem Adcock concedes is unsolved. The Vulcan demo (hobbling on a velocity-locked knee) is impressive, but a single failure mode handled is a long way from “never fall” across the full distribution of real-world conditions. The phrase “we want to be able to” appears repeatedly in Adcock’s roadmap, and it is doing a lot of work.

    Still, the velocity is real. Record manufacturing in March, tripling by May, four buildings, 500 employees, vertically integrated parts, a custom battery line, BMW deployment, White House appearance, Time cover, Helix 2 in production, Figure 04 in detailed design. The competitive landscape (Tesla Optimus, 1X, Apptronik, Unitree, and several Chinese entrants) is going to determine whether Figure stays “a few years ahead” of everyone, as Adcock claims, or whether the gap collapses. But if humanoids actually work, this is one of the very few companies positioned to capture the upside, and Adcock has been operating the playbook for almost four years.

    The most underrated detail in the whole tour: Figure 04 is being described internally as the iPhone 1. Figure 03 is the BlackBerry. If that framing holds up, the next 12 to 24 months are when this market gets defined.

  • Elad Gil on the AI Frontier: Compute Constraints, the Personal IPO, and Why Most AI Founders Should Sell in the Next 12 to 18 Months

    Elad Gil sat down with Tim Ferriss for a wide ranging conversation that pairs almost perfectly with his recent Substack post Random thoughts while gazing at the misty AI Frontier. Together, the podcast and the post lay out the cleanest framework I have seen for what is actually happening in AI right now: a Korean memory bottleneck capping every lab, a class wide personal IPO across the research community, the fastest revenue ramps in capitalist history, and a brutal dot com style culling that most founders do not yet want to admit is coming. Below is a complete breakdown.

    TLDW (Too Long, Didn’t Watch)

    Elad Gil argues that AI is producing the fastest revenue ramps in capitalist history while setting up the same brutal power law that wiped out 99 percent of dot com companies. OpenAI and Anthropic each sit at roughly 0.1 percent of US GDP today, on a path to 1 percent of GDP run rate by end of 2026, which is insanely fast by any historical standard. The current ceiling on capabilities is not chips but Korean high bandwidth memory, and that constraint will likely hold all major labs roughly comparable in capability through 2028. Talent has just experienced a class wide personal IPO via Meta led bidding, with packages running tens to hundreds of millions per researcher. Most AI companies should consider exiting in the next 12 to 18 months while the tide is high. Right now consensus is correct. Save the contrarianism for later.

    Key Takeaways

    • OpenAI and Anthropic are each at roughly 0.1 percent of US GDP. With US GDP near 30 trillion dollars and each lab at a roughly 30 billion dollar revenue run rate, AI has gone from essentially zero to 0.25 to 0.5 percent of GDP in just a few years. If the labs hit 100 billion in run rate by year end 2026 (which many expect), AI hits 1 percent of GDP run rate inside a single year.
    • The AI personal IPO is real. 50 to a few hundred AI researchers across multiple companies just experienced a class wide IPO event due to Meta led bidding, with top packages reportedly tens to hundreds of millions per person. The closest historical analog is early crypto holders around 2017.
    • The bottleneck is Korean memory, not Nvidia chips. High bandwidth memory from Hynix, Samsung, Micron, and others is the binding constraint. Expected to hold roughly two years. After that, power and data center buildout become the next walls.
    • No lab can pull dramatically ahead before 2028. Because every lab is compute constrained on the same input, OpenAI, Anthropic, Google, xAI, and Meta should remain roughly comparable in capability through that window, absent an algorithmic breakthrough that stays inside one lab.
    • Compute is the new currency. Token budgets now define what an engineer can accomplish, what a company can spend, and what business models are viable. Some companies (neoclouds, Cursor) are effectively inference providers disguised as tools.
    • The dot com base rate is the AI base rate. Around 1,500 to 2,000 companies went public in the late 1990s internet cycle. A dozen or two survived. AI will likely look the same.
    • Most AI founders should consider selling in the next 12 to 18 months. If you are not in the durable handful, this is your value maximizing window. A handful of companies (OpenAI, Anthropic) should never sell.
    • Buyers are bigger than ever. One percent of a 3 trillion dollar market cap is 30 billion dollars. That math makes massive AI acquisitions trivial for hyperscalers, vertical incumbents, and adjacent giants.
    • Underrated exit path: merger of equals. Two private AI competitors destroying each other on price should consider just merging. PayPal and X.com did exactly this in the 1990s.
    • 91 percent of global AI private market cap sits in a 10 by 10 mile square. If you want to do AI, move to the Bay Area. Remote work for cluster industries is BS.
    • Want money? Ask for advice. Want advice? Ask for money. The inverse also works: offering useful advice frequently leads to inbound investment opportunities.
    • AI is selling units of labor, not software. The shift is from selling seats and tools to selling cognitive output. This is why Harvey can win in legal, where decades of legal SaaS failed.
    • AI eats closed loops first. Tasks that can be turned into testable closed loop systems (code, AI research) get automated fastest. Map jobs on a 2×2 of closed loop tightness vs economic value to see where AI hits soonest.
    • Headcount will flatten at later stage companies. Multiple late stage CEOs told Elad they will not do big AI layoffs but will simply stop growing headcount even as revenue grows 30 to 100 percent. Hidden layoffs are also hitting outsourcing firms in India and the Philippines first.
    • The Slop Age could be the golden era of AI plus humanity. AI produces useful slop at volume, humans desloppify it, leverage is high, and the work is fun. This window may close as AI gets superhuman.
    • Market first, team second (90 percent of the time). Great teams die in bad markets. The exception is when you meet someone truly exceptional at the very earliest stage.
    • The one belief framework. If your investment memo needs three core beliefs to be true, it is too complicated. Coinbase was an index on crypto. Stripe was an index on e-commerce. That was the entire memo.
    • The four year vest is a relic. It exists because in the 1970s companies actually went public in four years. Today the private window has stretched to 20 years and venture has eaten what used to be public market growth investing.
    • Boards are in-laws. You cannot fire investor board members. Take a worse price for a better board member, because as Naval Ravikant said, valuation is temporary, control is forever.
    • Right now, consensus is correct. Save the contrarianism. The smart move is to just buy more AI exposure rather than try to outsmart the obvious.
    • Distribution wins more than founders admit. Google paid hundreds of millions to push the toolbar. Facebook bought ads on people’s own names in Europe. TikTok spent billions on user acquisition. Allbirds (yes, the shoe company) just raised a convert to build a GPU farm.
    • Anti-AI sentiment will get worse before it gets better. Maine banned new data centers. There has been violence directed at AI leaders. Expect more political and activist backlash, especially as AI is blamed for harms it has not yet caused while its benefits are mismeasured.
    • Use AI as a cold reader. Elad uploads photos of founders to AI models with cold reading prompts and reports surprisingly accurate personality assessments based on micro features.

    Detailed Summary

    The Numbers Are Insane and Mostly Underappreciated

    The most stunning data point in either source is the GDP math. US GDP is roughly 30 trillion dollars. OpenAI and Anthropic are each rumored to be at roughly 30 billion dollars in revenue run rate, putting each one at 0.1 percent of US GDP. Add cloud AI revenue and the picture gets stranger: AI has grown from essentially zero to between 0.25 and 0.5 percent of GDP in only a few years. If the labs hit 100 billion in run rate by year end 2026, AI will be at roughly 1 percent of GDP run rate inside a single year. There is no historical analog for that pace. Elad notes that productivity gains from AI may end up mismeasured the way internet productivity was undercounted in the 2000s, which would have downstream consequences for regulation: AI gets blamed for the bad (job losses) and credited for none of the good (new jobs, education gains, healthcare improvements). His half joking aside is that the real ASI test may be the ability to actually measure AI’s economic impact.

    The AI Personal IPO

    The most underdiscussed phenomenon in AI right now, according to Elad, is what he calls a class wide personal IPO. When a company IPOs, a subset of employees become wealthy, lose focus, and either start companies, get into politics, fund passion projects, or check out. Meta started aggressively bidding for AI talent. Other major labs had to match. The result was 50 to a few hundred researchers, scattered across multiple labs, suddenly receiving compensation in the tens to hundreds of millions of dollars range. The only historical analog Elad can think of is early crypto holders around 2017. Some chunk of these newly wealthy researchers will redirect attention to AI for science, side projects, or quiet quitting. The aggregate field stays mission aligned, but the distribution of attention has shifted.

    The Korean Memory Bottleneck

    Every major AI lab today is building giant Nvidia clusters paired with high bandwidth memory primarily from Korean fabs and a few other suppliers. They run massive amounts of data through these clusters for months, and the output is, almost absurdly, a single flat file containing what amounts to a compressed version of human knowledge plus reasoning. Right now, the binding constraint on this whole stack is HBM memory from Hynix, Samsung, Micron, and others. Korean memory fab capacity has been below the capacity of every other piece of the system. Elad estimates this constraint persists for roughly two years. After that, the next walls are likely data center construction and power. The strategic implication is enormous. While memory constrains everyone, no single lab can buy 10x the compute of its rivals, so capabilities should stay roughly comparable across the major labs. Once that constraint lifts, possibly around 2028, one player could theoretically pull dramatically ahead, especially if AI assisted AI research closes a self improvement loop inside one lab.

    Compute Is the New Currency

    The blog post sharpens a framing that runs throughout the podcast: compute, denominated in tokens, is now a unit of economic value. Token budgets define what an engineer can accomplish, what a company can spend, and what business models work. Some companies are effectively inference providers wearing tool costumes. Neoclouds are the cleanest example. Cursor is another, subsidizing inference as a user acquisition strategy. The most absurd recent example: Allbirds, the shoe company, raised a convertible to build a GPU farm. Whether this becomes the AI version of Microstrategy’s Bitcoin trade or a cautionary tale, it tells you where the cost of capital believes the next decade is going.

    The Dot Com Survival Math

    Elad walks through the brutal arithmetic that AI founders should be internalizing. In the late 1990s and early 2000s, somewhere between 1,500 and 2,000 internet companies went public. Of those, roughly a dozen or two survived in any meaningful form. Every cycle has looked like this: automotive in the early 1900s, SaaS, mobile, crypto. There is no reason AI will be different. Most current AI companies, including those ramping revenue today, will see the market, competition, and adoption turn on them. The question every AI founder should be asking is whether they are in the durable handful or not.

    Most AI Companies Should Consider Exiting in the Next 12 to 18 Months

    This is the most actionable and most uncomfortable take in either source. While the tide is rising, every AI company looks unstoppable. Whether they actually are, in a 10 year frame, is a separate question. Founders running successful AI companies should take a cold honest look at whether the next 12 to 18 months is their value maximizing window. Companies typically have a 6 to 12 month peak before some headwind hits, often visible in the second derivative of growth. The best signal that you should sell is when growth rate is starting to plateau and you can see why. A handful of companies (OpenAI, Anthropic, the durable winners) should never exit. Many others should, while everything is still on the upswing.

    What Makes an AI Company Durable

    Elad lays out four lenses for evaluating durability at the application layer:

    1. Does your product get dramatically better when the underlying model gets better, in a way that keeps customers loyal?
    2. How deep and broad is the product? Are you building multiple integrated products embedded in actual workflows?
    3. Are you embedded in real change management at the customer? AI adoption is mostly a workflow change problem, not a tech problem. Workflow embedding is durable.
    4. Are you capturing and using proprietary data in a way that creates a system of record? Data moats are often overstated, but sometimes real.

    At the lab layer, Elad believes OpenAI, Anthropic, and Google are durable absent disaster. He predicted three years ago that the foundation model market would settle into an oligopoly aligned with cloud, and that prediction has roughly held.

    Selling Work, Not Software

    The deepest structural insight in the conversation is that generative AI is shifting what software companies sell. The old model was selling seats, tools, and SaaS subscriptions. The new model is selling units of cognitive labor. Zendesk sold seats to support reps. Decagon and Sierra sell agentic support output. Harvey can win in legal even though selling to law firms was historically considered terrible business, because Harvey is not selling tools, it is augmenting lawyer output. This shift opens markets that were previously closed and dramatically grows tech TAMs. It is also why founder limited theories of entrepreneurship currently understate how many opportunities exist.

    AI Eats Closed Loops First

    One of the cleanest mental models in the blog post is the closed loop framework. AI automates first what can be turned into a testable closed loop. Code is the canonical example: outputs can be tested, errors detected, models can iterate. AI research is similar. Both have tight feedback loops and high economic value, which puts them at the top of the AI impact ranking. Map jobs on a 2×2 of closed loop tightness vs economic value and you can see where AI hits soonest. The interesting forward question is which jobs become more closed loop next. Data collection and labeling will keep growing in every field as a result.

    The Harness Matters More Than People Think

    For coding tools and increasingly for enterprise applications, what Elad calls the harness, the wrapper of UX, prompting, workflow integration, and brand around the underlying model, is becoming sticky. It is not just which model you call. It is the environment built around it. Cursor and Windsurf demonstrate this in coding. The interesting open questions are what the harness looks like for sales AI, for AI architects, for analyst workflows. Those gaps leave room for startups even as model capabilities converge.

    Hidden Layoffs and the Developing World

    Most announced AI driven layoffs are probably just COVID era overhiring corrections wrapped in a more flattering narrative. But real AI driven labor displacement is happening, and it is hitting outsourcing firms first. That means countries like India and the Philippines, where many outsourced services jobs sit, are likely to be the most impacted earliest. Several developing economies built their growth ladders on services exports. If AI takes those jobs first, the migration and economic patterns of the next decade may shift in ways nobody is yet planning for.

    The Flat Company

    Multiple late stage CEOs told Elad they will not announce big AI layoffs. Instead, they will simply stop growing headcount. If revenue grows 30 to 100 percent, headcount stays flat or shrinks via attrition. Existing employees become dramatically more productive. The very best people who can leverage AI will see compensation inflate. Sales and some growth engineering keep hiring. Almost everything else flatlines. This is mostly a later stage and public company phenomenon. True early stage startups should still scale aggressively after product market fit, just with more leverage per person.

    Exit Options for AI Founders

    Elad lays out four exit categories. First, the labs and hyperscalers themselves: Apple, Amazon, Google, Microsoft, Meta. Second, vertical incumbents like Thomson Reuters for legal or healthcare giants for clinical AI. Third, the underrated category of merger of equals between two private AI competitors who are currently destroying each other on price. PayPal and X.com did this in the 1990s. Uber and Lyft reportedly almost did. Fourth, large adjacent tech companies: Oracle, Samsung, Tesla, SpaceX, Snowflake, Databricks, Stripe, Coinbase. The market cap math has changed in a way that makes acquisition trivial. One percent of a three trillion dollar market cap is 30 billion dollars, which means a hyperscaler can do massive acquisitions almost casually.

    Geographic Concentration Is Extreme

    Elad’s team analyzed where private market cap aggregates. Historically half of global tech private market cap sat in the US, with half of that in the Bay Area. With AI, 91 percent of global AI private market cap is in a single 10 by 10 mile square in the Bay Area. New York is a distant second and then it falls off a cliff. For defense tech, the cluster is Southern California (SpaceX, Anduril, El Segundo, Irvine). Fintech and crypto skew toward New York. The remote everywhere advice is, Elad says, just BS for anyone trying to break into an industry cluster.

    How Elad Got Into His Best Deals

    Stripe started with Elad cold emailing Patrick Collison after selling an API company to Twitter. A couple of walks later, Patrick texted that he was raising and Elad was in. Airbnb came from helping the founders raise their Series A and being asked at the end if he wanted to invest. Anduril came from noticing that Google had shut down Project Maven and asking if anyone was building defense tech, then meeting Trey Stephens at a Founders Fund lunch. Perplexity came from Aravind Srinivas cold messaging him on LinkedIn while still at OpenAI. Across all of these, the pattern is the same: be in the cluster, be helpful, be talking publicly about technology nobody else is talking about, and be useful to founders before any money is on the table.

    The One Belief Framework

    Investors love complicated 50 page memos. Elad believes the actual decision usually collapses into a single core belief. Coinbase: this is an index on crypto, and crypto will keep growing. Stripe: this is an index on e-commerce, and e-commerce will keep growing. Anduril: AI plus drones plus a cost plus model will be important for defense. If your thesis needs three things to be true, it is probably not going to work. If it needs nothing, you have no thesis.

    Boards as In-Laws

    Elad emphasizes that founders should treat board composition like one of the most important hiring decisions of the company. You cannot fire an investor board member. They have contractual rights. So if you are going to be stuck with someone for a decade, take a worse valuation for a better human. Reid Hoffman’s framing is that the best board member is a co-founder you could not have otherwise hired. Naval Ravikant’s framing is that valuation is temporary but control is forever. Elad recommends writing a job spec for every board seat.

    The Slop Age as a Golden Era

    One of the warmest takes in the blog post is the framing of the current moment as the Slop Age, and the suggestion that this might actually be the golden era of AI plus humanity. Before the last few years, AI was inaccessible and narrow. Eventually AI may become superhuman at most tasks. Today, AI produces useful slop at volume, which means humans are still needed to desloppify the slop, but the leverage on time and ambition is real. That makes the work fun. If AI displaces people or starts doing more interesting work, this golden moment fades. Elad also notes the obvious counter, that the era of human generated internet slop preceded the AI slop era. AGI may end the slop age, or alternately may be the thing that finally cleans up all the prior waves of human slop.

    Anti-AI Regulation and Violence Will Increase

    This is one of the more sobering threads in the blog post. Real world AI driven labor displacement has been small so far, but anti-AI sentiment is already strong and growing. Maine just banned new data centers. There has been actual violence directed at AI leaders, including a recent attack on Sam Altman. Elad’s view is that AI leaders should work harder on optimistic public framing, real political lobbying, and reining in the doom narrative coming from inside the field. Otherwise the regulatory and activist backlash will get much worse, and likely on the basis of mismeasured impacts.

    Right Now Consensus Is Correct

    The headline contrarian take from the episode is that contrarianism right now is wrong. There are moments in time when betting against the crowd pays. This is not one of them. The smart bet is just buying more AI exposure. Trying to find the clever angle, the underlooked hardware play, the secret macro thesis, is overthinking it. Save the contrarian moves for later in the cycle.

    Distribution Almost Always Matters

    Elad pushes back on the founder mythology that great products win on their own. Google paid hundreds of millions of dollars in the early 2000s to distribute its toolbar through every popular app installer on the internet. Facebook bought search ads against people’s own names in European markets to seed network liquidity. TikTok spent billions on user acquisition before its algorithm could lock people in. Snowflake spent enormous sums on enterprise sales and channel partnerships. Sometimes the best product wins. Often the company with the best distribution wins. Founders should plan for both.

    AI as a Cold Reader and a Research Partner

    Two of the more practical AI workflows Elad describes: First, uploading photos of founders to AI models with cold reading prompts that ask the model to identify micro features (crows feet from genuine smiling, brow patterns, posture cues) and infer personality traits, sense of humor, and likely social behavior. He reports the outputs are surprisingly specific. Second, running deep dives across multiple models in parallel (Claude, ChatGPT, Gemini), asking each for primary sources, summary tables, and cross checked data. He recently used this approach to investigate the rise in autism and ADHD diagnoses, concluding that diagnostic criteria shifts and school incentives drive most of it, and noting that maternal age has a stronger statistical association with autism than paternal age, despite paternal age getting all the public discourse.

    The First Ever 10 Year Plan

    For someone who has been compounding aggressively for two decades, Elad has somehow never written a 10 year plan until now. He knows it will not play out as written. The point is that the act of imagining a decade out shifts what you choose to do in the near term. He explicitly rejects the AGI in two years therefore plans are pointless framing as defeatist. There will be interesting things to do regardless of how the AGI timeline plays out.

    Thoughts

    This is one of the more useful AI investor conversations of 2026, mostly because Elad is willing to put numbers and timelines on things that are usually left vague. Pairing the podcast with the underlying Substack post is the right move because the post is where the GDP math, the closed loop framework, and the Slop Age framing actually live. The podcast is where Elad explains how he thinks rather than just what he thinks.

    The 12 to 18 month sell window framing is the most actionable single idea in either source, and probably the most uncomfortable for AI founders sitting on multi billion dollar paper valuations. The math is unforgiving. A dozen winners out of thousands. If you are honest with yourself about whether you are in the dozen, you know what to do.

    The Korean memory bottleneck framing explains a lot of current behavior. The talent wars make more sense once you accept that compute is not going to be the differentiator for two years, so people become the only remaining lever. The convergence of capabilities across OpenAI, Anthropic, Google, and xAI starts to look less like coincidence and more like the structural inevitability of a supply constrained input. The 2028 inflection date is the one to watch.

    Compute as currency is the cleanest reframing in the blog post. Once you start pricing companies in tokens rather than dollars, everything from Cursor’s economics to Allbirds raising a convert to build a GPU farm becomes legible. The interesting question is whether this is a permanent unit of denomination or a transitional one that fades when inference costs collapse.

    The software to labor argument is the structural framing that I think will hold up the longest. Once you internalize that we are not selling seats anymore but selling cognitive output, every vertical that was previously locked behind ugly procurement and IT inertia opens up. Harvey is the proof of concept. There will be 30 more Harveys across every white collar profession.

    The closed loop framework is the cleanest predictor of which jobs get hit hardest and soonest. If you want to know whether your role is exposed, the questions to ask are whether outputs can be machine evaluated, how tight the feedback loop is, and how high the economic value is. The intersection is where AI lands first.

    The geographic concentration data is genuinely shocking. 91 percent of global AI private market cap in a 10 by 10 mile area is the kind of statistic that should make everyone outside that square think very carefully about what game they are playing.

    The Slop Age framing is the most emotionally honest moment in the post. We are in a window where humans still meaningfully add value on top of AI output. That window is finite. Enjoy it.

    The anti-AI backlash thread is the one I think most people in the industry are still underweighting. Maine banning new data centers is a leading indicator, not a one off. The fact that the impacts are likely to be mismeasured by official statistics makes the political dynamics worse, not better. AI will get blamed for harms it did not cause and credited for none of the gains. If the field’s leaders do not start communicating better and lobbying smarter, the regulatory environment in 2028 will be much worse than in 2026.

    Finally, Elad’s first ever 10 year plan stands out as the most quietly important moment in the episode. The implicit message is that even people who have been compounding aggressively for two decades benefit from forcing a longer time horizon onto their thinking. Most plans fail. The act of planning still changes what you do today.

    Read the original Elad Gil post here: Random thoughts while gazing at the misty AI Frontier. Find Elad on X at @eladgil, on his Substack at blog.eladgil.com, and on his website at eladgil.com. Tim Ferriss publishes the full episode at tim.blog/podcast.