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  • Vibe Coding Hardware: Naval, Guillermo Rauch, Blake Scholl, and Max Hodak on AI-Designed Jet Engines, Vertical Integration, China’s Open-Source Bet, and Why Humans Become Verifiers

    This is part two of Naval Ravikant’s conversation with frontier founders Guillermo Rauch of Vercel, Blake Scholl of Boom Supersonic, and Max Hodak of Science. Where the first part argued that you should waste tokens to save time and that the job of an engineer is now to build the factory rather than the output, this segment drags that thesis out of pure software and into atoms. The question on the table is what happens to hardware when models can vibe code the spreadsheets, the simulations, and eventually the step files and PCB layouts that aerospace, semiconductors, and biotech are built on. The full conversation is on the Naval Podcast YouTube channel.

    TLDW

    Blake Scholl describes how Boom Supersonic took hardware engineering workflows that used to live in siloed Excel spreadsheets and VBScript on individual laptops, with handoffs done by email like it was the 1990s, and turned them into versioned, testable software. The new model is that software engineers build the architectures and the tools while hardware engineers vibe code their own domain-specific pieces, which collapsed a turbine-blade analysis that once took one engineer one day per blade into something where two engineers can design an entire jet engine in real time. Naval generalizes this into the cataclysm of enterprise software: there is no longer a startup that can sell you hardware collaboration tools because companies just code the exact thing they need on demand, and even spreadsheets are cooked because they only existed as a proxy for custom software nobody could previously afford to build. Blake predicts that within 2026 AI will move from generating software to generating step files and PCB layouts, which reshapes mechanical and electrical engineering. The group debates China’s open-source push as a way to neutralize Silicon Valley’s software advantage and protect its hardware and supply-chain superiority, lands on the point that if you fall behind on generating software you fall behind on generating everything, and Guillermo notes that frontier coding intelligence still dominates real usage while cheaper models like Gemini win at scale for support and browser automation. Max Hodak explains Science’s vertical integration, including a captive MEMS foundry on the East Coast, because the most innovative hardware cannot be bought off the shelf, and argues that software still needs hands since a model that cannot make physical things hits real boundaries. The conversation closes on the shift from writing to verifying: junior engineering got absorbed by agents while juniors got promoted, the same way paralegals could be seen as fired or promoted, and humans across law, engineering, and operations are becoming the verifiers who sign off on systems they did not write line by line.

    Thoughts

    The most important shift in this segment is that vibe coding stops being a software-industry story and becomes a deep-tech story. In part one the examples were Postgres, ClickHouse, and deploy targets. Here Blake Scholl is talking about turbine blades that change shape when they heat up, and the brutal fact that converting between cold and hot geometry, and between aerodynamics and structures, used to eat one engineer for one full day per blade in an engine that has a thousand blades. That is the kind of math that quietly kills ambition. When he says two engineers can now design an entire jet engine because the structural and aerodynamic results update in real time as you change the geometry, that is not a productivity improvement, it is a change in what a small team is allowed to attempt. The interesting move is the division of labor: software engineers build the architecture and the framework because they understand systems and separation of concerns, and the hardware engineers vibe code the pieces only they understand. Nobody has to become both.

    Naval’s “cataclysm of enterprise software” is the most investable idea in the episode, and it is darker than it sounds for anyone selling B2B tools. His claim is that the entire category of internal collaboration software is being eaten from the inside, because a company that can generate exactly the tool it needs on any given day will not pay a vendor for an approximation of that tool. His follow-on that even spreadsheets are cooked is the sharpest version of the point. The spreadsheet won for forty years precisely because it was the closest thing to custom software that a non-programmer could produce. Remove the constraint that custom software is expensive and the spreadsheet loses its reason to exist. The counterweight, which the group raised in part one with the block-economy thesis, is that the infrastructure primitives agents reach for get more valuable, not less. So the safe place to build is not the collaboration layer on top, it is the primitive underneath.

    The China discussion is the geopolitical center of the conversation and it lands on a genuinely uncomfortable insight. The argument is that China leans into open-source models not only because it is a model or two behind, but because open weights neutralize Silicon Valley’s software advantage and let China lean on what it already dominates: hardware, supply chains, and component ecosystems. If software can be generated on demand from open models, then the country with the factories wins the stack. The sharpest line is that if you fall behind on the ability to generate software, you fall behind on the ability to generate everything, because software is now upstream of every hardware pipeline. That reframes the open-versus-closed debate as a question about who controls the means of producing the means of production. It also quietly flatters the American frontier labs, since the same logic says self-improvement requires frontier coding models, and on that narrow axis the consensus at the table is that the Chinese models are not yet in the race.

    Max Hodak provides the necessary cold water, and it is the most grounding contribution in the episode. Everyone else is describing software eating the design layer, and Max points out that you still have to make the thing. Science owns a captive MEMS foundry on the East Coast not as a flex but because there was no other way to do the packaging and assembly for products that approach a single block of covalently bonded matter. His framing that the software still needs hands is the real boundary condition on all the AI-eats-everything talk: a model can be smarter than every engineer in the building and still be unable to deposit a layer, bond a wafer, or pass a regulatory inspection. The optimistic version, which he also makes, is that he has instrumented the foundry so that as models improve, the gains show up immediately in cell engineering and material science. The pessimistic reading is that the physical world remains a hard rate limiter, and the companies that own the atoms will capture more of the surplus than the companies that only own the bits.

    The closing thread on verification is where the whole conversation resolves into a job description for humans. Guillermo’s point that the biggest problem in software is mountains of slop arriving as a pull request, and that the answer is not pretending to read every line but being able to say “I am signing off on the consequences of this PR, and I wrote the harness, the simulations, the proofs, and the type checkers that let me,” is the most practically useful idea in the episode. It generalizes cleanly. The lawyer you trust is not the one who wrote every clause by hand, it is the one putting their reputation on the line that the document is sound. The production engineer who gets paged at 3am is the one signing off that the system is safe to ship. As models absorb the junior tier of every knowledge profession, the surviving human role is the verifier who carries the accountability. That is a promotion for the people who can hold it and an extinction event for the people whose value was doing the work nobody now needs done by hand.

    Key Takeaways

    • The factory framing from part one carries straight into hardware: you are judged on whether you build the system that produces multiplicative outputs, not on the single artifact, and the real multiplier was always 100x or 1000x, not 10x.
    • AI completely changes the role of software and hardware developers rather than just speeding either one up.
    • A huge amount of hardware engineering lives in complex Excel spreadsheets and VBScript on individual engineers’ laptops, with no source control, no automated testing, and handoffs done manually over email. It is software that is not treated as software.
    • Boom Supersonic’s move from day one was to turn traditional hardware engineering workflows into real software frameworks that are automatable and repeatable, to drive down the cost of iteration.
    • The old bottleneck was never being able to afford enough software engineers to build those frameworks. AI removes that constraint.
    • The new model: software engineers create the architectures because they understand systems, algorithms, and separation of concerns, and hardware engineers vibe code the domain pieces only they understand.
    • A turbine blade is cold when it starts and hot when it runs, so it changes shape, and you must design both the cold and hot geometry across aerodynamics and structures. Classically that was one engineer, one day, for one blade, in an engine with a thousand blades.
    • With software and hardware people combined, you can now change blade geometry and see the structural and aerodynamic results in real time, which lets two engineers design an entire jet engine.
    • Naval’s cataclysm of enterprise software: no startup can sell hardware collaboration tools anymore because companies just code the exact thing they need at any given time.
    • Even spreadsheets are cooked. Spreadsheets won only because nobody could build custom software, so a spreadsheet full of VBScript was the closest available approximation. Remove the cost barrier and the approximation loses.
    • Engineers are moving from Excel to Python models that produce believable simulations of physical systems.
    • AI can generate software today, but within 2026 it is expected to generate step files and PCB layouts, which opens up mechanical and electrical engineering as the next frontier.
    • The hardware software boon is biggest for small gadget and parts companies that historically shipped bad software because they could not afford good software. Now they can ship good-enough software, or skip the human front end entirely and expose hardware agentically for voice and agent control.
    • China goes all in on open-source models partly to neutralize Silicon Valley’s software edge: if software can be generated on demand from open weights, China’s hardware and supply-chain superiority stops being offset by a software disadvantage.
    • Other reasons cited for China’s open-source push: it is a model or two behind, it is distilling models, and the government has a history of funding efforts that lift the whole ecosystem, especially in network-effect businesses.
    • Open-source heft is coming almost entirely from China. OpenAI is not open, Grok publishes models but is seen as a model or two behind, Google’s local models are not very competitive, and Anthropic is not known for open-source releases.
    • Without frontier coding models you do not get self-improvement, and if you fall behind on generating software you fall behind on generating everything, because software now sits upstream of every hardware pipeline.
    • Real AI gateway usage shows open models do get used, but the top is heavily dominated by frontier intelligence.
    • Frontier intelligence at the right cost and performance slaps at scale. Gemini models are underrated and excel as industrial production models for support tasks and browser automation, even if they are not the top pick for coding.
    • For pushing the frontier you need the best possible coding model, which is now only two or three models, and the Chinese models are not among them.
    • One contrarian view at the table: use DeepSeek for 97% of tasks because it is cheap, run it repeatedly for harder problems, and reserve frontier models for the most advanced work. The counterargument: intelligence is an unalloyed good, mistakes are invisible and costly, and a smarter model is always cheaper than a person, so you default to the most intelligent option.
    • Always wanting the most intelligent model risks creating a monopoly or oligopoly in AI, because when two models disagree you cannot tell which is right, so you trust the smarter one and stop asking the weaker one.
    • Vertical integration is forced, not chosen: if you cannot buy it, you have to make it. The preference is always to buy when a vendor offers a service at a great price, like PCBs from Asia.
    • The closer a product gets to a single block of covalently bonded matter, the better it performs: lower power, smaller, higher performance, longer lasting. The components for that level of integration simply are not available to buy.
    • Science owns a captive MEMS foundry on the East Coast, bought because there was no other way to do the packaging and assembly the company needed.
    • One of the biggest near-term AI impacts inside hardware companies is regulatory and documentation work: tracing which of thousands of ISO standards apply used to occupy a regulatory and quality team for months, and now AI just knows.
    • Software still needs hands. A model can be smarter than us and still hit real boundaries if it cannot physically make things, which is why Science has instrumented its foundry so model improvements show up immediately in cell engineering and material science.
    • Basic legal work is already going away. People have stopped asking lawyers for NDAs and routine agreements, because law is spaghetti code in English with no real APIs, and the basic tasks are handled by AI.
    • Junior engineers got promoted to senior engineers while junior engineering itself got taken over by agents. The same framing applies to paralegals: fired, or promoted to senior lawyers who now spend their time thinking about the law.
    • What you value in a lawyer is a trusted authority who puts their reputation on the line, not someone who read every clause. The same trust model is coming to engineering.
    • The biggest problem in software engineering today is mountains of slop arriving as a pull request. The old norm of reading every line of a PR is gone.
    • The new standard is being able to say “I understand and I am signing off on the consequences of this PR,” backed by the test harness, simulations, proofs, and type checkers you built, even without reading every line.
    • Embrace a world where code is spaghetti you do not fully understand, but build the evaluators that give confidence, and rely on production engineers to sign off because someone gets paged if the system goes down.
    • Creating software is easy from zero to one. The hard part is a thousand days from now: is it secure, tested, production grade, and performant, and are you still motivated to invest the tokens to maintain it in prod?
    • Humans are becoming verifiers. The same way models are trained on good verification data, the old functions of lawyers, engineers, and operations people are moving to verifying the stack and standing behind it.

    Detailed Summary

    Turning Hardware Engineering Into Software

    Blake Scholl opens by describing how AI completely changes the role of software and hardware developers at Boom Supersonic. From day one the company tried to take traditional hardware engineering workflows and turn them into software. For anyone who has not been around hardware engineering, he explains that an enormous amount of it happens in complex Excel spreadsheets on individual engineers’ laptops, sometimes with VBScript code, all of which is actually software but is not treated as software. There is no source control, no automated testing, and when an aerodynamicist hands work to a structures engineer it is done manually with a spreadsheet over email, like it is the 1990s. Boom started building software frameworks to automate and make those flows repeatable so the cost of iteration would drop, but progress was slow because the company could never afford enough software engineers.

    Two Engineers, One Jet Engine

    The mind-blowing change, in Blake’s words, is a new division of labor. Software engineers create the architectures because they understand systems, algorithms, and separation of concerns, and then hardware engineers vibe code the pieces that draw on what they uniquely know about hardware. The result is wildly different productivity for small teams. His example is the turbine blade: it starts cold and gets bigger as it heats up in operation, so you have to design both the cold shape and the hot shape, converting between them and between structures and aerodynamics. Classically that was one engineer, one day, for one blade of analysis, in a jet engine with a thousand blades, which means you simply could not do much. Now, with software and hardware people working together, you can change blade geometry and see the structural and aerodynamic results in real time, which allows two engineers to design an entire jet engine.

    The Cataclysm of Enterprise Software

    Picking up on the point that software engineers now build the tools and architectures for everyone else, Naval names what he calls the cataclysm of enterprise software. There is no longer a startup that can build and sell hardware collaboration tools, because internally companies just code the right things they need at any given moment. Even spreadsheets are cooked, he argues, because the reason spreadsheets succeeded is that no one could build custom software, so a spreadsheet stuffed with VBScript functions was the closest available approximation. With that constraint gone, the proxy collapses. He notes he has personally moved almost entirely from Excel to Python models where he can get believable simulations of things.

    Generating Step Files and PCB Layouts

    The next frontier, Blake suggests, is the thing AI has not reached yet but probably will within 2026: today it can generate software, but soon it will generate step files and PCB layouts, and when it comes for mechanical and electrical engineering that will be a whole other thing nobody has seen yet. On the hardware side this is described as a particular boon for the many small gadget and parts companies that historically wrote bad software because they could not make great software. Now they can make good-enough software, or skip a human front end entirely and expose the hardware agentically, so that an agent accesses it and a person controls the hardware by voice.

    China’s Open-Source Bet and Hardware Superiority

    This leads into one of the reasons China is described as going all in on open-source models. With hardware superiority, complex supply chains, and deep component chains, China’s logic is that if it can generate software on demand it no longer suffers a software disadvantage against Silicon Valley. That is framed as not the only reason: China is also a model or two behind, it is distilling models, and the government has a history of funding efforts that lift the entire ecosystem, especially in network-effect businesses. Ironically, the open-source heft comes from China precisely because OpenAI is not open, Grok publishes models but is a model or two behind, Google’s local models are not very competitive, and Anthropic is not known for open releases. The deeper point is that without great frontier coding models you do not get self-improvement, and if you fall behind on the ability to generate software you fall behind on the ability to generate everything, because generating software is embedded in every piece of the hardware pipeline.

    Frontier Intelligence vs. Cheap Models

    Naval raises a dinner-table argument from the night before, where someone claimed you will use DeepSeek for 97% of things because it is cheap, run it repeatedly when you need more intelligence, and reserve OpenAI or Anthropic for the most advanced tasks. Naval pushes back: intelligence is an unalloyed good, you always want more of it, model mistakes are invisible, and a smarter model is always cheaper than a real person in real time, so you default to the most intelligent model available. He notes the downside is that this tends toward a monopoly or oligopoly, because when two models give different answers you often cannot tell which is correct, so you trust the smarter one and gradually stop asking the weaker one. Guillermo confirms with AI gateway data that open models do get used, but the top is heavily dominated by frontier intelligence. His caveat is that frontier intelligence at the right cost and performance slaps at scale: Gemini models are underrated but are excellent industrial production models for support tasks and browser automation, while for pushing the frontier you need the best possible coding model, now only two or three models, and the Chinese models are not in that set.

    Vertical Integration and the Captive MEMS Foundry

    Asked about his push into vertical integration and extreme urgency, Max Hodak explains that for many things you cannot buy what you need, so you have to make it. The preference is always to buy when a vendor offers a service at a great price, and he points to PCBs, which are basically free and available in unlimited quantity from Asia. But the closer a product gets to being a single block of covalently bonded matter, the better it is: lower power, smaller, higher performance, longer lasting. The components for that level of integration are not available, so to innovate beyond piecing together off-the-shelf parts you have to learn to do it yourself, which shows up as vertical integration. Science owns a captive MEMS foundry on the East Coast, bought because there was no other way to do the packaging and assembly work the company wanted.

    Software Still Needs Hands

    Max expects AI to heavily affect all of this over the next few years, though it is not quite there yet. Ironically, one of the biggest impacts already seen is in regulatory interactions and documentation: figuring out which of thousands of ISO standards apply to a product change, and tracing it through, used to occupy a regulatory and quality team for months, and now the AI just knows. But for things like the surgical program or the MEMS fab, he argues the software still needs hands. It will be smarter than us, but if it cannot make things, those are real boundaries. Science has instrumented its foundry and many other parts of the company so that as models get better, the improvement shows up immediately in cell engineering and material science.

    Lawyers, Paralegals, and the Promotion of Junior Work

    The discussion turns to law as a parallel to engineering. It has been a while since anyone at the table generated a basic legal document using a lawyer. Routine work like NDAs and standard agreements is gone, because law is essentially spaghetti code that contradicts itself and has no real APIs, expressed in complicated English. Junior engineers got a promotion to senior engineers while junior engineering itself was taken over by agents, and the same framing applies to paralegals: you can say they were fired, or you can say they were promoted to senior lawyers who now spend their time thinking about the law. What you actually value in a lawyer is a trusted authority who went to law school and puts their reputation on the line when they tell you a document is legit.

    Slop PRs, the Thousand-Day Problem, and Humans as Verifiers

    Guillermo argues the biggest problem in software engineering today is mountains of slop ending up as a pull request. The old meme of reading every line of a PR is gone. In infrastructure he wants engineers to be able to say they understand and are signing off on the consequences of a PR, backed by the test harness, simulations, proofs, and type checkers they wrote, so they have confidence it will be safe in production even without reading every line. There is a world where everyone embraces that the code is spaghetti nobody fully understands, but builds the evaluators that give confidence and relies on production engineers to say it is fine to ship, because someone gets paged if the system goes down. The further warning is that creating software is easy from zero to one, but a thousand days from now you have to ask whether it is secure, tested, production grade, and performant, and whether you are still motivated to invest the tokens to maintain it in prod. The resolution is that humans are becoming verifiers, the same way models are trained on good verification data, and the old functions of lawyers, engineers, and operations people are moving to verifying the stack and standing behind it.

    Notable Quotes

    “What I found is it completely changes the role of software and hardware developers.”

    Blake Scholl, on how AI reshaped engineering at Boom Supersonic.

    “If you want to hand something off from like an aerodynamicist to a structures engineer that’s done manually with like a spreadsheet over email. It’s the 1990s. It’s terrible.”

    Blake Scholl, describing the state of traditional hardware engineering workflows.

    “It allows two engineers to design an entire jet engine, which is just wildly different.”

    Blake Scholl, on collapsing turbine-blade analysis with real-time structural and aerodynamic feedback.

    “Even spreadsheets are kind of cooked, right? Because the reason spreadsheets were successful is that no one could build custom software.”

    Naval Ravikant, on the cataclysm of enterprise software.

    “Right now it can generate software, but soon it’ll be able to generate step files and PCB layouts. And when it comes for mechanical and electrical engineering, that will be a whole other thing that we haven’t seen yet.”

    Blake Scholl, on the next frontier for AI in hardware.

    “If you fall behind on your ability to generate software, you fall behind on the ability to generate everything.”

    Naval Ravikant, on why software now sits upstream of every hardware pipeline.

    “Anytime I’m working to push the frontier you need the best possible coding model, and that’s basically now like two or three models, and the Chinese are certainly not in it.”

    Guillermo Rauch, on where frontier coding intelligence actually lives.

    “You can’t buy it, so you got to make it somehow. The closer that our products get to being like a single block of covalently bonded matter, the better they’ll be.”

    Max Hodak, on why Science is forced into vertical integration.

    “The software still needs hands. It’s going to be smarter than us, but if it can’t make things, then those are real real boundaries.”

    Max Hodak, on the physical limits of AI in hardware.

    “You need to be able to say I am signing off on understanding the consequences of this PR, or I wrote the test harness, the simulations, the proofs, the type checkers, to be able to say even without reading this, I have confidence it’s going to be safe in production.”

    Guillermo Rauch, on what code review becomes in the age of slop PRs.

    “Creating software is really easy 0 to one. But think about a thousand days from now. Is it secure? Is it tested? Is it production grade? And are you still motivated to invest all of those tokens in maintaining it in prod?”

    On the long-term cost of software that is cheap to create and expensive to keep alive.

    Watch the full conversation on the Naval Podcast here.

    Related Reading

    • Part one: Waste Tokens to Save Time, the first half of this same conversation, where Naval, Guillermo Rauch, Blake Scholl, and Max Hodak argue that the job of an engineer is to build the factory and that pure software is not dead.
    • Boom Supersonic, Blake Scholl’s company building supersonic civilian aircraft and its own jet engines, the source of the turbine-blade and two-engineers example.
    • Science Corporation, Max Hodak’s company, whose captive MEMS foundry and surgical program anchor the vertical-integration argument.
    • Vercel, Guillermo Rauch’s company, whose AI gateway data informs the point about frontier intelligence dominating real usage.
    • Microelectromechanical systems (Wikipedia), background on the MEMS technology behind the captive foundry Max Hodak describes.
  • Anthropic Raises $65 Billion Series H at $965 Billion Valuation to Fund AI Safety Research and Massive Compute Expansion

    Anthropic has closed one of the largest private financing rounds in the history of technology, raising $65 billion in Series H funding at a $965 billion post-money valuation. The round, announced on May 28, 2026, lands as demand for Claude reaches what the company calls historic levels, and it positions Anthropic to pour fresh capital into safety research, compute, and the products that enterprises now lean on every day.

    TLDR

    Anthropic raised $65 billion in its Series H at a $965 billion post-money valuation, with Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital leading and Capital Group, Coatue, D1 Capital Partners, GIC, ICONIQ, and XN co-leading, alongside $15 billion in previously committed hyperscaler investment that includes $5 billion from Amazon. The raise follows Anthropic crossing $47 billion in run-rate revenue earlier in May 2026, and it funds three priorities named by CFO Krishna Rao: advancing safety and interpretability research, expanding compute capacity to meet growing Claude demand, and scaling the products and partnerships customers depend on. On the infrastructure side, the company is locking in gigawatt-scale compute through 5 gigawatts with Amazon, 5 gigawatts of TPU capacity via Google and Broadcom, GPU access from SpaceX, and supply from partners Micron, Samsung, and SK hynix, while Claude remains available across all three major cloud platforms, AWS, Google Cloud, and Microsoft Azure, with widespread enterprise adoption across industries.

    Thoughts

    Start with the number that everyone will fixate on. A $965 billion post-money valuation against $47 billion in run-rate revenue is roughly 20 times sales, and for a company growing this fast that multiple is not the interesting part. The interesting part is that run-rate revenue crossed $47 billion earlier this month, which means the denominator is moving so quickly that the multiple is already stale. Investors are not pricing the business Anthropic is today. They are pricing the slope. A 20x multiple on a number that may double again inside a year is a very different bet than 20x on a flat line, and the lead names here (Altimeter, Dragoneer, Greenoaks, Sequoia, with Capital Group, Coatue, GIC and others co-leading) are not the kind of capital that pays for nostalgia. They are paying for the second derivative.

    But the real story is not the valuation. It is the compute. Read the infrastructure list carefully and you see the actual problem this round solves: 5 gigawatts from Amazon, 5 gigawatts of TPU capacity through Google and Broadcom, GPU access from SpaceX, and memory supply locked down with Micron, Samsung, and SK hynix. That is more than 10 gigawatts of secured power and silicon. The constraint on frontier AI in 2026 is no longer talent or even algorithms. It is electricity, land, and the multi-year queue for advanced packaging and high-bandwidth memory. You cannot buy 10 gigawatts on a quarterly basis. You reserve it years out, and you need the balance sheet to make those commitments credible. A $65 billion raise is, in plain terms, the down payment that lets Anthropic sign for capacity nobody can conjure on demand. The money is downstream of the megawatts.

    The diversification across that compute stack matters as much as the size. By splitting between Amazon’s infrastructure, Google and Broadcom’s custom TPUs, and SpaceX-supplied GPUs, Anthropic is refusing to become hostage to any single supplier’s roadmap or pricing. Custom silicon through Broadcom in particular is a bet on bending the cost curve, because the long-term economics of serving Claude at this scale depend on dollars per token, not just on raw availability. Anyone who has watched cloud lock-in play out over the last decade understands the move. Optionality at the hardware layer is leverage, and leverage is what keeps margins from being dictated by whoever owns the only fab slot you can reach.

    It is worth pausing on the fact that the round explicitly funds safety and interpretability research alongside scaling, and not as a footnote. Most companies treat safety spend as a cost center to be minimized once growth kicks in. Naming it first, ahead of compute and products, is a statement about where Anthropic believes its durable advantage sits. If models keep getting more capable, the binding constraint on deployment inside regulated industries (finance, healthcare, government) becomes trust, not intelligence. Interpretability is the work that turns a black box into something an enterprise risk committee can actually sign off on. Framed that way, safety research is not philanthropy subtracted from the bottom line. It is the thing that unlocks the most lucrative and defensible parts of the market, and pairing it with the scaling budget is the tell.

    Finally, look at distribution. Claude now ships on all three major clouds at once: AWS, Google Cloud, and Microsoft Azure. In a market where most frontier labs are tethered to a single hyperscaler, being available everywhere enterprises already run their workloads is a structural edge. It removes the procurement friction of asking a customer to adopt a new vendor relationship, and it means Anthropic competes on the merits of the model rather than on which cloud a buyer happened to standardize on years ago. Combine that omnipresent distribution with the compute reservations and the explicit safety mandate, and the shape of the strategy is clear. This is not a company buying time. It is a company buying the three things that actually compound: capacity that cannot be rushed, trust that cannot be faked, and reach into every place where work already happens.

    Key Takeaways

    • Anthropic raised $65 billion in its Series H funding round, one of the largest private financings in the history of the technology industry.
    • The round set Anthropic’s post-money valuation at $965 billion, placing the company within reach of the $1 trillion mark.
    • Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital led the Series H round.
    • Capital Group, Coatue, D1 Capital Partners, GIC, ICONIQ, and XN served as co-leads on the investment.
    • The new capital builds on $15 billion in previously committed hyperscaler investments, which includes $5 billion from Amazon.
    • Anthropic crossed $47 billion in run-rate revenue earlier in May 2026, reflecting the surging commercial demand for Claude.
    • A core priority for the funding is to advance Anthropic’s safety and interpretability research.
    • The company will use the capital to expand compute capacity in order to meet growing demand for Claude.
    • Anthropic plans to scale the products and partnerships that customers depend on across its business.
    • CFO Krishna Rao said the funding will help Anthropic serve the historic demand it is experiencing, stay at the research frontier, and bring Claude to more of the places where work happens.
    • Amazon is providing 5 gigawatts of compute capacity as part of Anthropic’s infrastructure expansion.
    • Google and Broadcom are supplying 5 gigawatts of TPU capacity to power Claude’s growth.
    • SpaceX is contributing GPU access to Anthropic’s compute footprint.
    • Micron, Samsung, and SK hynix are partnering with Anthropic on memory and infrastructure to support its scaling needs.
    • Claude is available on all three major cloud platforms, AWS, Google Cloud, and Microsoft Azure.
    • Anthropic reports widespread enterprise adoption of Claude across a broad range of industries.

    Detailed Summary

    The Raise and the Valuation

    Anthropic has raised $65 billion in Series H funding, a round that values the company at $965 billion on a post-money basis. The size of the raise places it among the largest private financing events the technology industry has ever seen, and the valuation pushes Anthropic to the doorstep of the trillion dollar mark. The capital arrives at a moment when demand for the company’s Claude models has accelerated sharply, and the round is built to fund the response to that demand rather than simply mark a milestone. Anthropic framed the financing in its Series H announcement as the fuel for staying at the research frontier while scaling the infrastructure and products that customers increasingly rely on.

    Who Put In the Money

    The Series H was led by Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital, a group that combines deep growth-stage technology experience with conviction in Anthropic’s long-term trajectory. Joining as co-leads were Capital Group, Coatue, D1 Capital Partners, GIC, ICONIQ, and XN, a roster that spans crossover funds, sovereign wealth, and institutional investors. Beyond the new equity, Anthropic pointed to $15 billion in previously committed hyperscaler investment, including $5 billion from Amazon. Taken together, the investor base reflects a mix of financial backers and strategic partners with a direct stake in seeing Claude reach more customers and more compute.

    Revenue at $47 Billion Run-Rate

    Underpinning the valuation is a business that has scaled with unusual speed. Anthropic crossed a $47 billion run-rate revenue figure earlier in May 2026, a number that signals how quickly enterprises and developers have adopted Claude across their workflows. Run-rate revenue annualizes the company’s most recent performance, and at this level it puts Anthropic firmly among the fastest growing software businesses on record. That financial momentum is the practical justification for both the round’s size and the near trillion dollar valuation investors were willing to support.

    The Compute Buildout

    A large share of the strategy behind the raise centers on securing compute at enormous scale. Anthropic detailed a set of infrastructure partnerships designed to keep pace with Claude demand. Amazon is providing 5 gigawatts of capacity, while Google and Broadcom together are supplying 5 gigawatts of TPU capacity. SpaceX is contributing GPU access, broadening the range of silicon Anthropic can draw on. Supporting the buildout on the hardware supply side are Micron, Samsung, and SK hynix, the memory and component partners whose output is essential to standing up data centers at this magnitude. The combined picture is a company assembling power, chips, and supply chain commitments measured in gigawatts rather than racks.

    Where the Money Goes

    Anthropic outlined three priorities for the new capital. The first is to advance safety and interpretability research, continuing the work of understanding how models behave and ensuring they remain reliable as they grow more capable. The second is to expand compute capacity to meet the growing demand for Claude, the practical engine behind the infrastructure commitments above. The third is to scale the products and partnerships that customers depend on, deepening the company’s reach into the tools and platforms where work actually happens. Krishna Rao, Anthropic’s chief financial officer, said the funding “will help us serve the historic demand we are experiencing, stay at the research frontier, and bring Claude to more of the places where work happens.”

    Claude Everywhere

    The funding lands on top of a distribution footprint that already spans the major cloud ecosystems. Claude is available on all three leading cloud platforms, AWS, Google Cloud, and Microsoft Azure, which means enterprises can reach the models through whichever provider they have standardized on. That availability has translated into widespread enterprise adoption across industries, from software and finance to healthcare and beyond. By being present everywhere developers and businesses already operate, Anthropic positions Claude not as a destination customers must travel to but as a capability woven into the platforms they use every day.

    Notable Quotes

    This funding will help us serve the historic demand we are experiencing, stay at the research frontier, and bring Claude to more of the places where work happens.

    Krishna Rao, CFO at Anthropic, on the purpose of the Series H round.

    Advance safety and interpretability research, expand compute capacity to meet growing Claude demand, and scale products and partnerships customers depend on.

    How Anthropic describes its use of funds from the round.

    For the full details on the round, the lead and co-lead investors, and how Anthropic plans to deploy the capital across safety research, compute, and products, read the full announcement here.

    Related Reading

    • Anthropic, the AI safety and research company behind Claude that raised this Series H round.
    • Sequoia Capital, one of the lead investors anchoring the financing.
    • Amazon Web Services, one of the three major cloud platforms where Claude is available and the source of a $5 billion investment.
    • Google Cloud TPUs, the tensor processing units behind the 5 gigawatts of TPU capacity in the Google and Broadcom partnership.
    • AI safety, the research field at the center of how Anthropic says it will use the new funding.
  • Claude Opus 4.8 Released: Anthropic Bets on Honesty, Dynamic Workflows, Effort Control, and Cheaper Fast Mode

    Anthropic has released Claude Opus 4.8, the newest member of its flagship Opus class, available today across every surface and priced exactly like the model it replaces. The company calls it “a modest but tangible improvement” on Opus 4.7, but the framing undersells what is actually interesting here: the headline upgrade is not a benchmark number, it is honesty. Opus 4.8 is built to know when it does not know, and that single behavioral shift may matter more for real agent work than any raw capability bump.

    TLDR

    Claude Opus 4.8 is an across-the-board upgrade to Anthropic’s Opus class that ships today at the same regular price as Opus 4.7 ($5 per million input tokens, $25 per million output tokens), with the model positioned as “a more effective collaborator.” The marquee improvement is honesty: Opus 4.8 is roughly four times less likely than its predecessor to let flaws in its own code pass unremarked, and it is more willing to flag uncertainty rather than confidently claim progress on thin evidence. A pre-release alignment assessment found new highs on prosocial traits like supporting user autonomy and acting in the user’s best interest, with misaligned behavior at rates similar to Anthropic’s best-aligned model, Claude Mythos Preview. Three things launch alongside the model: dynamic workflows in Claude Code (research preview), where Claude plans work then runs hundreds of parallel subagents that run even longer and verify their own outputs before reporting back; effort control in claude.ai and Cowork, a slider for how hard Claude thinks; and a Messages API update that accepts system entries inside the messages array so developers can update instructions mid-task without breaking the prompt cache. Fast mode now runs at 2.5x speed and is three times cheaper than before ($10 / $50 per million tokens). The roadmap points to cheaper Opus-equivalent models, a higher-intelligence class above Opus, and a wider rollout of Mythos-class models gated behind stronger cyber safeguards under Project Glasswing.

    Thoughts

    The most important sentence in this announcement is not about coding scores. It is the claim that Opus 4.8 is about four times less likely than Opus 4.7 to let flaws in its own code slip by without comment. For a chat assistant, overconfidence is annoying. For an agent, it is catastrophic. The whole premise of long-running autonomous work is that you hand the model a task and walk away, which means the model’s own judgment about whether it succeeded becomes the only judgment in the loop until you come back. A model that confidently declares victory on a half-finished migration does not save you time, it costs you a debugging session plus the time you spent trusting it. Honesty, framed this way, is not a soft virtue. It is the load-bearing reliability property that makes unattended agents usable at all.

    Read the launch as a single coherent argument rather than a list of features, and the pieces lock together. Dynamic workflows let Claude plan a job and fan out hundreds of parallel subagents that, with Opus 4.8, run longer than before. Effort control lets you dial up how much the model thinks. The honesty improvement means the model checks its own work and flags what it is unsure about instead of papering over it. Put those three together and you get one product thesis: let it run longer, let it think harder, and trust it to tell you when something is wrong. The codebase-scale migration example, hundreds of thousands of lines from kickoff to merge with the existing test suite as the bar, is the proof point. None of those three capabilities is worth much alone. A model that runs for hours but lies about its results is a liability. A model that flags uncertainty but cannot sustain a long task never reaches the moment where its honesty matters. Anthropic shipped all three at once because they only pay off together.

    The economics deserve a closer look than the “same price” headline invites. Regular pricing is flat versus Opus 4.7, which is the polite way of saying you get a better model for free. The real move is fast mode: 2.5x the speed at three times cheaper than it cost on previous models, landing at $10 per million input and $50 per million output. That is Anthropic quietly attacking the latency-versus-cost tradeoff that has shaped how teams deploy frontier models. Until now, “fast” meant “expensive,” so you reserved it for interactive moments and ate the wait everywhere else. Collapsing that premium changes the default. And note the subtle token story underneath: Opus 4.8 at its default high effort spends roughly the same tokens on coding as Opus 4.7’s default while performing better, so the effort slider is not a way to bleed you dry, it is an honest exposure of the quality-cost dial that was always there implicitly.

    The Messages API change is the kind of unglamorous plumbing that practitioners will appreciate immediately. Letting system entries live inside the messages array means you can update an agent’s instructions, permissions, token budget, or environment context partway through a task without smuggling the update through a fake user turn and without blowing up your prompt cache. Anyone who has built a long-running agent has hit this wall: the world changes mid-task, the agent needs new constraints, and the only clean way to inject them previously was a cache-busting hack. This is Anthropic treating agents as first-class, stateful, long-lived processes rather than oversized chat sessions. It is a small spec change with outsized implications for how you architect an agent that runs for an hour.

    Then there is the roadmap, where the most telling line is the quietest. Anthropic says a small number of organizations are already using Claude Mythos Preview for cybersecurity work under Project Glasswing, and that models of this capability level require stronger cyber safeguards before general release. Notice that they are pinning Opus 4.8’s alignment numbers to Mythos as the benchmark for “best-aligned,” while simultaneously holding Mythos back from general availability on safety grounds. That is a deliberate signal: the next class of model is good enough that they are gating it on cyber-offense risk, not on capability. For a site about the pursuit of joy, fulfillment, and purpose through AI, this is the part worth sitting with. The frontier is increasingly defined not by what the models can do, but by what their builders decide it is responsible to ship. Honesty in the small (flagging a bad line of code) and restraint in the large (holding back a cyber-capable model) are the same instinct expressed at two different scales.

    Key Takeaways

    • Claude Opus 4.8 is now available everywhere, replacing Opus 4.7 as Anthropic’s flagship Opus-class model and positioned as “a more effective collaborator.”
    • Regular usage pricing is unchanged from Opus 4.7, holding at $5 per million input tokens and $25 per million output tokens, so the capability gains come at no added cost.
    • The single most emphasized improvement is honesty, which Anthropic treats as a core trained behavior rather than a marketing flourish.
    • Evaluations show Opus 4.8 is around four times less likely than its predecessor to let flaws in its own code pass unremarked, a direct reliability win for autonomous coding.
    • Early testers report the model is more likely to flag uncertainty about its work and less likely to make unsupported claims or jump to conclusions on thin evidence.
    • A detailed alignment assessment was run before release and concluded Opus 4.8 reaches new highs on prosocial traits like supporting user autonomy and acting in the user’s best interest.
    • Misaligned behavior such as deception or cooperation with misuse is at rates substantially lower than Opus 4.7 and similar to Anthropic’s best-aligned model, Claude Mythos Preview.
    • The full alignment assessment and pre-deployment safety tests are documented in the public Claude Opus 4.8 System Card.
    • Dynamic workflows launch as a research preview inside Claude Code, letting Claude plan the work and then run hundreds of parallel subagents in a single session.
    • With Opus 4.8, those subagents can run even longer, and Claude verifies its outputs before reporting back rather than declaring success blindly.
    • Anthropic’s flagship example for dynamic workflows is a codebase-scale migration across hundreds of thousands of lines of code, from kickoff to merge, using the existing test suite as the success bar.
    • Dynamic workflows are available in Claude Code for the Enterprise, Team, and Max plans.
    • Effort control arrives in claude.ai and Cowork as a setting next to the model selector that lets users choose how much effort Claude puts into a response.
    • Higher effort makes Claude think more frequently and deeply for better answers; lower effort responds faster and consumes rate limits more slowly. Effort control is available on all plans.
    • Opus 4.8 defaults to “high” effort, judged the best overall balance of quality and user experience.
    • On coding tasks, the default effort spends a similar number of tokens as Opus 4.7’s default but delivers better performance, so quality rises without a token penalty.
    • Users can select “extra” (called “xhigh” in Claude Code) or “max” to spend more tokens for stronger results, and Anthropic recommends “extra” for difficult tasks and long-running asynchronous workflows.
    • Rate limits in Claude Code were increased to accommodate the higher token usage of the higher effort levels.
    • The Messages API now accepts system entries inside the messages array, a meaningful change for agent developers.
    • That update lets developers change Claude’s instructions mid-task, adjusting permissions, token budgets, or environment context, without breaking the prompt cache or routing through a user turn.
    • Fast mode now runs at 2.5x speed and is three times cheaper than it was for previous models, priced at $10 per million input tokens and $50 per million output tokens.
    • Developers access the model as claude-opus-4-8 through the Claude API.
    • Partner Miguel Gonzalez reports Opus 4.8 scored 84% on Online-Mind2Web, a meaningful jump over both Opus 4.7 and GPT-5.5, calling it the strongest computer-use and browser-agent model his team has tested.
    • Databricks reports that, inside Genie, Opus 4.8 reasons over unstructured content like PDFs and diagrams at 61% cheaper token cost than Opus 4.7.
    • Thomson Reuters reports Opus 4.8 is the first model to break 10% overall on the all-pass standard of its Legal Agent Benchmark, the highest score recorded there.
    • Eleven partners weighed in, including Cursor, Cognition’s Devin, Databricks Genie, Thomson Reuters CoCounsel, and Hebbia, spanning coding, legal, finance, and enterprise data work.
    • Anthropic is working on models that deliver many of the same capabilities as Opus at a lower cost.
    • The company plans to release a new class of model with even higher intelligence than Opus.
    • Under Project Glasswing, a small number of organizations are already using Claude Mythos Preview for cybersecurity work, with Mythos-class models expected to reach all customers in the coming weeks once stronger cyber safeguards are in place.

    Detailed Summary

    What Claude Opus 4.8 Is

    Claude Opus 4.8 is an upgrade to Anthropic’s Opus class of models, building on Opus 4.7 with improvements across benchmarks covering coding, agentic skills, reasoning, and practical knowledge-work tasks. Anthropic describes the result as “a more effective collaborator” while characterizing the release overall as “a modest but tangible improvement on its predecessor.” The model is available today, everywhere, and developers call it as claude-opus-4-8 via the Claude API. The announcement includes a comparison table against the predecessor and other models, though the per-cell numbers in that table are published as an image and are not reproduced here as text.

    Honesty: The Headline Improvement

    Anthropic singles out honesty as one of the most prominent improvements in Opus 4.8. All of the company’s models are trained to be honest, which includes avoiding claims they cannot support. A persistent problem with AI models generally is that they sometimes jump to conclusions, confidently claiming progress despite thin evidence. Early testers report that Opus 4.8 is more likely to flag uncertainties about its own work and less likely to make unsupported claims. The most concrete measure: evaluations show Opus 4.8 is around four times less likely than its predecessor to allow flaws in code it has written to pass unremarked. For agentic and unattended use, this self-skepticism is the difference between a model that reliably tells you when something went wrong and one that quietly ships a broken result.

    Alignment Assessment

    A detailed alignment assessment was run before release. On the positive side, the Alignment team concluded that Opus 4.8 “reaches new highs on our measures of prosocial traits like supporting user autonomy and acting in the user’s best interest.” On the risk side, misaligned behavior such as deception or cooperation with misuse occurs at rates substantially lower than Opus 4.7, and similar to Anthropic’s best-aligned model, Claude Mythos Preview. The full alignment assessment and the pre-deployment safety tests are published in the Claude Opus 4.8 System Card, which also contains the complete benchmark table and wider evaluations.

    Dynamic Workflows in Claude Code

    Launching today as a research preview in Claude Code, dynamic workflows let Claude plan the work and then run hundreds of parallel subagents in a single session. With Opus 4.8, those agents can run even longer than before, and Claude verifies its outputs before reporting back rather than reporting unchecked results. The showcase example is a codebase-scale migration: Claude Code with Opus 4.8 can carry out migrations across hundreds of thousands of lines of code, all the way from kickoff to merge, using the existing test suite as its bar for success. Dynamic workflows are available in Claude Code for the Enterprise, Team, and Max plans.

    Effort Control

    Effort control arrives in claude.ai and Cowork as a setting alongside the model selector that lets users choose how much effort Claude puts into a response. Higher effort means Claude thinks more frequently and deeply for better responses; lower effort means it responds faster and uses rate limits more slowly. Opus 4.8 defaults to “high” effort, which Anthropic judged the best overall balance of quality and user experience. On coding tasks, that default spends a similar number of tokens as Opus 4.7’s default while performing better. Users who want more can choose “extra” (called “xhigh” in Claude Code) or “max” to spend more tokens for stronger results, and Anthropic recommends “extra” for difficult tasks and long-running asynchronous workflows. To support the heavier token usage at higher effort levels, rate limits in Claude Code were increased. Effort control is available on all plans.

    Messages API Update

    The Messages API now accepts system entries inside the messages array. This lets developers update Claude’s instructions mid-task without breaking the prompt cache and without routing the update through a user turn. In practice that means you can update permissions, token budgets, or environment context while an agent is running, which is exactly the kind of statefulness a long-running autonomous process needs. It is a small specification change with significant consequences for how developers build durable agents.

    Pricing and Fast Mode

    Regular usage pricing is unchanged from Opus 4.7: $5 per million input tokens and $25 per million output tokens. The notable shift is in fast mode, where the model works at 2.5x the speed and fast mode is now three times cheaper than it was for previous models, landing at $10 per million input tokens and $50 per million output tokens. The combination of unchanged regular pricing and dramatically cheaper fast mode reshapes the latency-versus-cost calculus that has long governed how teams deploy frontier models.

    Partner Results Across Coding, Legal, Finance, and Data

    Eleven partners shared results spanning the spectrum of professional work. Miguel Gonzalez reports 84% on Online-Mind2Web, a meaningful jump over both Opus 4.7 and GPT-5.5, calling it the strongest computer-use and browser-agent model his team has tested. Databricks reports that Genie reasons over unstructured content like PDFs and diagrams at 61% cheaper token cost than Opus 4.7. Thomson Reuters reports Opus 4.8 is the first model to break 10% overall on the all-pass standard of its Legal Agent Benchmark. Cursor reports gains across every effort level on CursorBench with more efficient tool calling, and Cognition reports that Devin sees cleaner tool use, fixes to the comment-verbosity and tool-calling issues seen with Opus 4.7, and improvements over Opus 4.6. Hebbia reports strong quality with better citation precision and more token efficiency on retrieval for dense financial filings. The footnotes note that Terminal-Bench 2.1 was scored on the Terminus-2 public harness (GPT-5.5’s Codex CLI harness score is 83.4%), that OSWorld-Verified methodology changed with Opus 4.7’s score updated to 82.3%, and that on Finance Agent v2 Gemini 3.5 Flash scores 57.9%.

    What Is Next: Cheaper Models, Higher Intelligence, and Mythos

    Anthropic outlined a three-part roadmap. First, the company is working on models that provide many of the same capabilities as Opus at a lower cost. Second, it plans to release a new class of model with even higher intelligence than Opus. Third, as part of Project Glasswing, a small number of organizations are currently using Claude Mythos Preview for cybersecurity work; models of this capability level require stronger cyber safeguards before general release, and Anthropic expects to bring Mythos-class models to all customers in the coming weeks.

    Notable Quotes

    “Claude Opus 4.8 has noticeably better judgment. In Claude Code, it asks the right questions, catches its own mistakes, pushes back when a plan isn’t sound, and builds up confidence around complex, multi-service explorations before making big changes. It’s a great model to build with.”

    Tom Pritchard, Staff Engineer, in Claude Code

    “On our Super-Agent benchmark, Claude Opus 4.8 is the only model to complete every case end-to-end, beating prior Opus models and GPT-5.5 at parity on cost. For agent products in translation, deep research, slide-building, and analysis, it delivers powerful reliability.”

    Kay Zhu, Co-Founder and CTO, on the Super-Agent benchmark

    “On CursorBench, Claude Opus 4.8 exceeds prior Opus models across every effort level. Tool calling is meaningfully more efficient, using fewer steps for the same intelligence, and it carries end-to-end tasks through.”

    Michael Truell, Co-Founder and CEO, on CursorBench results

    “Claude Opus 4.8 delivers the highest score recorded on our Legal Agent Benchmark, and is the first model to break 10% overall on the all-pass standard. For substantive legal work, that’s the kind of accuracy lift that translates directly into how much real attorney work our customers can hand off with confidence.”

    Niko Grupen, Head of Applied Research, on the Legal Agent Benchmark

    “Claude Opus 4.8 feels like a major quality-of-life update over Opus 4.7: faster, easier to collaborate with, and better at carrying context and style direction across a long session. Opus 4.8 is the model I kept trusting for work where voice, taste, and technical execution all have to happen side-by-side.”

    Katie Parrott, Staff Writer, on long writing sessions

    “Claude Opus 4.8 is the strongest computer-use and browser-agent model we’ve tested, scoring 84% on Online-Mind2Web, which is a meaningful jump over both Opus 4.7 and GPT-5.5. It stays reflective and on-task in the way our customers’ agent workloads need to be reliable end-to-end.”

    Miguel Gonzalez, Tech Lead, on computer-use and browser agents

    “Claude Opus 4.8 uses tools cleanly and follows instructions with the consistency our autonomous engineering workloads need to keep running unattended. It improves on Opus 4.6 and fixes the comment-verbosity and tool-calling issues we saw with Opus 4.7. This release from Anthropic translates directly into faster capability gains for engineers building on Devin.”

    Scott Wu, CEO, on building with Devin

    “On our long-running evals, Claude Opus 4.8’s analysis was consistently higher quality than prior Opus models. It finished faster and produced richer, more information dense outputs. Overall, a noticeably better signal to noise ratio. The biggest differentiator was Opus 4.8’s tendency to proactively flag issues with the inputs and outputs of an analysis, something other models routinely missed and left to the users to catch.”

    Michael Ran, Sr. Investment Associate, on long-running analysis evals

    Claude Opus 4.8 is a quieter release than its “modest but tangible” billing suggests, because the gains land where autonomous work actually lives: a model that flags its own uncertainty, runs longer and checks itself, scales effort on demand, and stays affordable while fast mode gets cheaper. The honesty improvement alone changes the trust math for anyone deploying agents. Read Anthropic’s full announcement here.

    Related Reading

  • Dan Loeb on Building Third Point’s $25 Billion Investment Empire: AI, Activism, Credit, and the FTX Mistake

    Dan Loeb has spent three decades turning a $3 million fund into Third Point, a roughly $25 billion collection of hedge fund, credit, insurance, and venture businesses. In this Invest Like the Best conversation with Patrick O’Shaughnessy, Loeb walks through how he reinvented his strategy from deep value and event-driven trades into quality and thematic investing, why he now believes every serious investor has to be a technology investor, how he reads the AI cycle and the semiconductor melt-up, where activism and corporate governance still pay, and the single mistake that taught him the most. It is a rare, unhurried look at how a famously sharp-elbowed activist actually thinks about markets, businesses, and people.

    TLDW

    Loeb covers an enormous amount of ground: his daily process for staying ahead of the information firehose, Jensen Huang’s AI stack as a mental model, and why Nvidia, Anthropic, and Elon Musk’s companies are the three most consequential firms he tracks. He traces Third Point’s roots in credit and event-driven investing at Jefferies, the influence of Joel Greenblatt’s “You Can Be a Stock Market Genius,” and his later pivot to quality investing shaped by “The Outsiders” and Lawrence Cunningham’s “Quality Investing.” He argues the AI rally is not a dot-com-style valuation bubble because the leaders generate enormous cash, explains why human judgment and structural market quirks still create alpha, and makes the case that AI will never fully run a capital system. He digs into corporate governance and his father’s influence, the Sotheby’s and Sony activism campaigns, the hard reality of activism in Japan, and what investing in Danaher’s operating system taught him. He names FTX as his hardest lesson, breaks down Third Point’s evolution into a 60-percent-credit platform spanning CLOs, structured credit, reinsurance and annuities, describes how he is pushing his analysts to use AI and Claude daily, and closes on kindness and the friend who let him sleep on a couch before he made it.

    Thoughts

    The most striking thing about Loeb is that he treats his own strategy as a thing to be disrupted rather than defended. He built his reputation on Greenblatt-style special situations, spin-offs, demutualizations, and post-reorg equities bought cheap because of forced selling and sandbagged guidance. Most investors who win that way spend the rest of their careers protecting the formula. Loeb instead watched the people who stayed rigid about deep value and low multiples underperform or disappear, and deliberately retrained himself and his team around business quality and thematic conviction. The willingness to abandon a winning identity is the actual edge here, more than any single trade. It is the rare investor who can say his current strategy would not fit cleanly on a PowerPoint deck and treat that as a feature.

    His AI framing deserves attention because it is unfashionably calm. The bear case on AI is usually about valuation, and Loeb dismantles it on the leaders’ own numbers: these are companies investing off their balance sheets, generating enormous cash, trading at multiples that do not resemble 1999. He was short the dot-com bubble, so he is not a permabull cheering from the sidelines. His real point is subtler, that the danger is expectations, not valuations. The semiconductor index ran up 40 percent on genuinely strong fundamentals, but Micron and Nvidia both put up monster quarters and saw their stocks fall because expectations had simply outrun even great results. That gap between fundamentals and price is where he thinks the human investor still earns a living, precisely because quant strategies, CTAs, and risk-managed pods are forced to sell into weakness rather than buy it.

    The governance material is the most quietly radical part of the conversation. Loeb defends shareholder primacy against the Business Roundtable’s softer stakeholder language, but his argument is not the cartoon version where shareholder value means strip-mining a company. It is that boards have one job, accountability for capital allocation and management, and that vague multi-stakeholder mandates become an excuse for directors to avoid the hard work. His read on bad governance is almost always relational: directors who let loyalty to an underperforming CEO override their duty, or who sit on boards for status and income. The Sotheby’s story is the clean illustration, a centuries-old, high-status business run unprofitably because nobody treated it like a business. Loeb’s pattern is to find the gap between claimed status and actual performance and to raise the social cost of coasting.

    What is genuinely new in Loeb’s posture is how he talks about AI inside his own firm. He is not pitching it as a moat or a headcount-reduction story. He frames Claude and AI tools as a way to make each person a more autonomous self-improver, something that gives back whatever you put into it, with some analysts running agents overnight and burning tokens while he personally uses it more for queries. Coming from a 30-year fundamental investor, the absence of defensiveness is the signal. He pairs it with Brad Gerstner’s nod to “Essentialism”: the firehose is now infinite, so the scarce skill is deciding what is actually relevant. That is a more honest answer to the AI question than either doom or hype.

    Finally, the FTX confession is worth sitting with because of how he frames it. He does not retreat into cynicism about venture or crypto. He notes that Sam Bankman-Fried, fraud aside, had a real nose for value, with stakes in Anthropic, Cursor, and Solana that would have made him a top venture investor of the era. The lesson Loeb extracts is procedural, not philosophical: their due diligence now includes checking bank balances, the most basic verification that would have surfaced the problem. It is a useful reminder that even sophisticated capital can skip boring fundamentals when a company is growing fast and the cap table looks good. The discipline is not in having a grand theory of fraud, it is in never skipping the unglamorous checks.

    Key Takeaways

    • Loeb’s macro focus right now collapses to two variables: where oil goes, dictated by war and geopolitics, and what AI does on the spending and infrastructure front and its impact on society and the economy.
    • He argues you can no longer punt on technology and focus on industrials or consumer; tech is a big, growing, compounding part of the economy that affects everything else, so every investor has to become a tech investor.
    • He uses Jensen Huang’s AI stack as a mental model: power and energy at the bottom, then chips and infrastructure, up through large language models, software, and applications.
    • The three most consequential companies he tracks are Nvidia, Anthropic, and Elon Musk’s companies collectively.
    • Third Point’s roots are in credit and event-driven investing, shaped by his time at Jefferies watching investors like David Tepper before he founded Appaloosa, Eric Mindich at Goldman, and firms like Angelo Gordon and Farallon.
    • Joel Greenblatt’s “You Can Be a Stock Market Genius” was his foundational framework: spin-offs, demutualizations, privatizations, and post-reorg equities where a new, illiquid security gets dumped by holders who will not do the work.
    • Spin-off managers often sandbag guidance because their incentive packages get set at the time of the spin-off, creating a predictable gap between conservative numbers and real value.
    • From 1995 to roughly 2013-2015, event-driven special situations were Third Point’s bread and butter; those opportunities still exist, but the real edge now is overlaying them with a business-quality lens.
    • The pivot to quality and thematic investing was influenced most by “The Outsiders” (capital allocation plus great operations) and Lawrence Cunningham’s “Quality Investing” (high-moat, high-return-on-capital businesses to own for years).
    • AI disruption made last year one of the worst for many apparently high-quality companies, as businesses that looked durable rapidly became less so.
    • Loeb sees the AI rally as fundamentally different from the dot-com bubble: the leaders invest off their balance sheets, generate enormous cash, and do not carry the valuation excess of 1999.
    • The danger in semis is expectations, not valuation: Nvidia and Micron posted spectacular quarters yet saw stocks fall because expectations had outrun even great numbers.
    • Structural forces still create alpha for fundamental investors: quants, CTAs, and multi-strategy pods have risk metrics that force selling on the way down, the opposite of what is rational for long-term holders.
    • He believes AI will not fully run a capital system; private equity, restructurings, creditor committees, and high-touch negotiation will always need humans.
    • His interest in governance came from his father, a securities lawyer and corporate governance expert who sat on the boards of Mattel and Williams-Sonoma and pushed ethical sourcing ahead of his time.
    • Loeb defends shareholder primacy, citing Milton Friedman and Warren Buffett, and criticizes the Business Roundtable’s move away from shareholder value as a distraction from the board’s real duty.
    • Bad governance usually comes from directors letting loyalty to a weak CEO override fiduciary duty, lacking the knowledge to do the job, or serving for status and income.
    • Writing is a core activism lever: great writing is clear thinking, and social pressure through writing and PR is one of the most effective ways to move a board, alongside financial and legal levers.
    • The Sotheby’s campaign targeted a high-status, centuries-old business run unprofitably; Third Point bought 9.9 percent, eventually brought in Tad Smith from MSG, who cleaned up operations and technology before the company sold.
    • Third Point increasingly prefers to back great companies with excellent management and cheer them on rather than hunt for mismanaged businesses, because bad management tends to cluster into a morass.
    • Third Point is a collection of businesses; the flagship hedge fund grew from $3 million to about $9 billion and is roughly 30 percent credit, with the broader firm closer to 60 percent credit.
    • The firm spans a roughly $7 billion CLO business, structured and corporate credit, an insurance company, asbestos liabilities, a small private credit unit, and a venture capital arm.
    • The unifying thread is valuing enterprises across early, mid, and mature stages and investing in whichever fulcrum security offers the best risk-reward, from equity to senior debt.
    • Loeb cites buying Twitter’s financing debt near 96-97 cents at a 12 percent yield when most credit investors were scared, and a difficult xAI debt financing, as examples of cross-discipline conviction.
    • He is the portfolio manager only of the hedge fund; the credit, CLO, structured credit, and high-yield businesses have their own PMs and investment committees he does not sit on.
    • The Sony campaign saw Third Point own up to 7 percent and push to separate the conglomerate; management resisted for years before spinning out the semiconductor and financial services businesses.
    • He learned that activism in Japan is hard, but the government often wants reform; he co-wrote a paper with Larry Lindsey and Niall Ferguson urging corporate governance and return on invested capital as a fourth arrow of Abenomics, picked up as a Wall Street Journal editorial.
    • Investing in Danaher was his most instructive experience, teaching him how the Danaher Business System drives continuous improvement (Kaizen) and how the company celebrates rather than shames underperformance because problems are fixable.
    • FTX was his hardest lesson; it looked great and was verifiable on the blockchain, but was not what it appeared, and now Third Point’s diligence includes checking bank balances.
    • He notes that, fraud aside, Sam Bankman-Fried had a strong nose for value with stakes in Anthropic, Cursor, and Solana.
    • Recent mistakes also include shorts where Third Point thought certain info-services businesses would resist AI disruption; he still expects a shakeout with some phoenixes rising from the ashes.
    • He is pushing his whole team to use AI daily, hiring native computer scientists and system integrators, and describes Claude as a tool that makes you autonomous and gives back whatever you put into it.
    • Third Point’s distinctive edge is optimism about AI creating net jobs and the ability to default into credit investing during stressed times, as it did with investment-grade credit in 2020.
    • Credit is hard to copy because it runs on relationships, not electronic trading; that is why Third Point built into CLOs and eyes the roughly $6 trillion structured credit market rather than treating it as tourism.
    • The great analyst has changed: 20 years ago it was someone who could model fast and crack a complex restructuring (Loeb made a career-defining bet on Drexel Burnham claims); today it is a Gavin Baker type who deeply understands an industry, like the analyst who flew to Texas and realized Casey’s General Stores was really a pizza chain.
    • Outside the US, Loeb is more bullish on Korea, Taiwan, and Japan as hunting grounds, finds Europe tough on regulation (though he owns Rolls-Royce and ASML), and finds the Middle East the most vibrant region.
    • What worries him most is not the business but running out of time for family, surfing, and reading; what excites him is incorporating everything relevant about the world and forming relationships with people building interesting things.
    • His closing reflection is on kindness as a top-tier value, and the friend, Carter, who let him sleep on a couch and seeded his early fund, echoing a Palmer Luckey line that money cannot buy friends who believed in you when you had nothing.

    Detailed Summary

    Staying ahead of the firehose and reading the macro

    Loeb opens by admitting he does not have a perfectly organized system for processing the modern flood of information. He checks the news for what is relevant to the economy and to Third Point’s positions, tries not to obsess over minute-to-minute moves, and leans more tactical than strategic. When people ask him about macro, he says the usual government-reported metrics (growth, unemployment, inflation, rates, currencies, gold, crypto) are trumped right now by two things: where oil goes, which depends on war and geopolitics, and what AI does on the spending and infrastructure side and its impact on society and the economy. To understand technology, he leans on Jensen Huang’s framing of the AI stack and talks to smart people regularly, and he watches three companies above all: Nvidia, Anthropic, and Elon Musk’s companies as a group.

    From event-driven roots to quality investing

    Third Point’s DNA comes from Loeb’s time as a credit investor at Jefferies, where he watched some of the best distressed, event-driven, and risk-arbitrage investors operate, from David Tepper to Eric Mindich to firms like Angelo Gordon and Farallon. His first lens was event-driven: spin-offs, demutualizations, privatizations, and post-reorg equities, where a newly created and illiquid security gets dumped by holders who will not do the work, and management sandbags guidance because incentive packages are set at the spin date. He barely thought about moats or returns on capital; he just wanted to buy something genuinely cheap with those characteristics. That was the firm’s bread and butter from 1995 until roughly 2013-2015. Those opportunities still exist, but Loeb describes deliberately evolving toward business quality and thematic investing, influenced by “The Outsiders” on capital allocation and Lawrence Cunningham’s “Quality Investing” on durable, high-return businesses. He organized the team around industry experts rather than generalists. The twist: AI disruption recently turned many apparently high-quality companies into much lower-quality ones, fast.

    The AI cycle, bubbles, and the human edge

    Loeb resists the bubble narrative. He was short the dot-com bubble and remembers the valuation excess; today’s AI leaders, by contrast, invest off their balance sheets and generate enormous cash, so unless you believe the capex yields no return, the earnings and multiples do not look like 1999. The real driver of volatility, he argues, is expectations: the semiconductor index ran up 40 percent on strong fundamentals, but Nvidia and Micron both delivered blowout quarters and still saw their stocks fall because expectations had run too high. That dynamic is exactly where a fundamental investor earns a living, because quants, CTAs, and risk-managed pods are structurally forced to sell into weakness. He also doubts AI will ever fully run a capital system, since private equity, restructurings, creditor committees, and high-touch credit always need humans. He cites “Reminiscences of a Stock Operator” and Ecclesiastes: there is nothing new under the sun, and human nature, with its bubbles, panics, and extremes, does not change.

    Governance, his father, and the duty of boards

    Loeb traces his governance interest to his father, a securities lawyer and corporate-governance expert who served on the boards of Mattel and Williams-Sonoma and championed ethical sourcing before it was common. He calls the American board system beautiful: directors are answerable to shareholders and accountable for strategy and key financial decisions. Governance breaks down when directors lose sight of their fiduciary duty, lack the knowledge or talent diversity to do the job, or prioritize things other than shareholders. He invokes Milton Friedman and Warren Buffett to argue that caring about communities, employees, and conduct is not inconsistent with shareholder value but part of it, and criticizes the Business Roundtable for muddying the board’s core duty. The most common failure he sees is directors letting loyalty to an underperforming CEO override their duty. Most of the time Third Point redirects existing boards without even taking a seat; the extreme proxy fights are the exception.

    Activism, writing, Sotheby’s, and Sony

    Great writing, Loeb says, is clear thinking and organizing your thoughts to get a desired outcome, and it is one of activism’s most effective levers alongside financial and legal pressure. Social pressure through writing and PR can move a board on its own. He sees a pattern in his campaigns: targets that hold themselves out as high status but are not living up to it. Sotheby’s is the clean example, a centuries-old, high-status business run unprofitably, where Third Point bought 9.9 percent, gave the existing CEO a year, then helped install Tad Smith from MSG, who modernized operations and technology before the company was sold. Sony was a two-act campaign in which Third Point owned up to 7 percent and pushed to break up the conglomerate; he recounts sharing the thesis with Andrew Ross Sorkin at the New York Times under embargo, the panic it caused, and how management resisted for years before spinning out the semiconductor and financial services units. The lesson: activism in Japan is genuinely hard, even though the government wanted reform. He co-authored a paper with Larry Lindsey and Niall Ferguson arguing corporate governance and return on invested capital should be a fourth arrow of Abenomics, which ran as a Wall Street Journal editorial.

    The Danaher operating system

    Loeb calls Danaher his most instructive investment. He and his partner persuaded the company to compress its five-day Danaher Business System training into a single day, and he came away with a deep appreciation for how a real operating system drives continuous improvement. The standout lesson was cultural: Danaher holds people individually accountable, but when it finds someone underperforming it celebrates rather than shames, because the problems are addressable and fixable, and it does this relentlessly across operations and working capital. He also points to the diaspora of Danaher executives, including Larry Culp and the leadership at Ingersoll Rand, as evidence of the system’s depth. The investment worked for about four years before COVID-era order surges and inventory swings turned tailwinds into headwinds; Third Point sold and has recently bought back in modestly.

    The structure of Third Point and the fulcrum security

    Third Point is not one fund but a collection of businesses. The flagship hedge fund grew from $3 million to about $9 billion and is roughly 30 percent credit, generically around 110 percent long and 30-40 percent short on the equity side. Across the firm the credit weight is closer to 60 percent, spanning a roughly $7 billion CLO business, several billion in structured and corporate credit, an insurance company, a couple billion in asbestos liabilities, a small new private credit unit, and a venture arm. The unifying thread is valuing enterprises at any stage and investing in whichever fulcrum security (the one with the best risk-reward) makes sense. Loeb illustrates with Credit Suisse’s takeover by UBS, where the holdco paper proved the fulcrum, and with buying Twitter’s resold financing debt near 96-97 cents at a 12 percent yield when other credit investors were scared, plus a difficult xAI debt financing that few credit people wanted. He pushes back on the idea that he sits atop everything: he is the PM only of the hedge fund, while the other businesses have their own PMs and committees he is not on.

    Insurance, the FTX lesson, and recent mistakes

    Loeb started a Bermuda reinsurance company in 2010, backed by himself, Kelso, and Pinebrook, on a barbell thesis of investing the float in Third Point and treasuries to defer taxes and lever capital. The reinsurance side soured, and about three years ago he concluded they had the right idea but the wrong vehicle, that plain-vanilla annuities (which can only invest in credit) would have fit better. Third Point merged the reinsurer into its UK closed-end fund, Third Point Offshore Investors, reincorporated from Guernsey to Cayman, and repurposed it into an insurance company managing private credit, structured credit, whole-loan mortgages, real estate lending, and investment-grade debt. His hardest lesson was FTX: it looked great, was verifiable on the blockchain, and had a strong cap table, but was not what it seemed; diligence now includes checking bank balances. He notes Sam Bankman-Fried, fraud aside, had a great nose for value (Anthropic, Cursor, Solana). Other recent mistakes were shorts where Third Point bet certain info-services businesses would resist AI disruption; he still expects a shakeout with some survivors rising from the ashes.

    AI inside the firm, the analyst of the future, and kindness

    Loeb is pushing his entire team to use AI daily, hiring native computer scientists and system integrators, and describes Claude as a tool that makes you an autonomous self-improver and gives back whatever you put into it, with some analysts running agents overnight while he uses it more for queries. He pairs this with Brad Gerstner’s recommendation of “Essentialism”: you cannot do it all, so you must decide what is most relevant. The great analyst has changed: 20 years ago it was someone who could model fast and crack a complex restructuring, as Loeb did with the Drexel Burnham bankruptcy claims early in his career; today it is a Gavin Baker type who deeply understands an industry and its technology, like the analyst who flew to Texas and realized Casey’s General Stores was really a pizza chain in disguise. On the rest of the world, he is more bullish on Korea, Taiwan, and Japan, finds Europe tough on regulation (while owning Rolls-Royce and ASML), and finds the Middle East the most vibrant region. He closes on what worries and excites him (time with family, surfing, and reading versus the joy of incorporating everything relevant about the world), and on kindness, crediting his friend Carter, who let him sleep on a couch and seeded his early fund, and echoing Palmer Luckey’s line that money cannot buy friends who believed in you when you had nothing.

    Notable Quotes

    “I think you have to be a tech person today. It’s a big and growing and compounding part of the economy. It affects everything else.”

    Dan Loeb, on why no serious investor can punt on technology anymore

    “Hold on to your seats because things are only going to accelerate from here.”

    Dan Loeb, recounting a 2013 Davos warning about technological change he now applies to AI

    “Maybe that’s where the human element comes in, to understand and to be able to make those tough trading decisions when fundamentals are going one way and stock prices are going the other way, and to be able to take the pain of losses in the short run.”

    Dan Loeb, on where a human investor still has an edge over machines

    “It’s very different from the dot-com bubble, which we were short going into. You don’t have the valuation bubble now on those companies that you had back in those days.”

    Dan Loeb, on why he does not see the AI rally as a 1999-style bubble

    “When they found someone that was underperforming, it was celebrated instead of shamed, because look at all these things you’re doing wrong, we can fix those. And they did.”

    Dan Loeb, on the accountability culture he learned from the Danaher Business System

    “I would have to say our investment in FTX. It looked great. The company was growing fast. We could verify it all on the blockchain.”

    Dan Loeb, naming his hardest investment lesson

    “Be kind to people you have no idea how it will ever benefit you. And sometimes it will and sometimes it won’t.”

    Dan Loeb, on elevating kindness in your hierarchy of values

    “The one thing money doesn’t buy you is friends that believed in you when you had nothing.”

    Dan Loeb, quoting Gavin Baker quoting Palmer Luckey, on the friend who seeded his early fund

    Watch the full conversation between Dan Loeb and Patrick O’Shaughnessy here.

    Related Reading

  • Raoul Pal: Why the Crypto Bull Run Is Just Starting, the AI Economic Singularity, and Why You Should Never Sell Bitcoin

    Macro investor and Real Vision co-founder Raoul Pal returned to the When Shift Happens podcast for episode 173 to argue that the recent crypto drawdown is a nasty correction inside a much larger bull market, not the end of the cycle. Across an hour and a half he ties together the AI capital race, the coming economic singularity, why layer one blockchains are a kind of universal basic equity, and the deceptively simple discipline that actually compounds wealth: buy, hold, and almost never sell.

    TLDW

    Pal frames everything through what he calls the universal code, the conversion of units of energy into units of intelligence, and says the global race to fund AI is so large that no government or company can stop feeding it capital. That liquidity, plus relentless currency debasement, is the engine under both the AI stocks going vertical and the crypto market that has lagged them. He calls the Bitcoin slide from 126K toward 60K a normal correction in a bull market, says liquidity is now reaccelerating, and argues smart contract layer ones (Ethereum, Solana, Sui) are the best risk-adjusted bet because the entire financial system and a coming swarm of AI agents will run on those rails, giving crypto an effectively infinite total addressable market. He explains why he added Zcash as a Bitcoin-with-privacy and quantum-proof trade, lays out his plan to launch an NFT fund built around grail digital art and NFT-backed lending, and makes a data-backed case that buying oversold dips and never selling beats trying to trade cycles. The conversation closes on a 70/30 bullish framework for 2026 and 2027 and a reflection on kindness.

    Thoughts

    The strongest idea in this conversation is not a price target, it is a reframe. Pal keeps pulling the camera back from “what will Bitcoin do this quarter” to “what is the organizing principle of the entire economy right now,” and his answer is the funneling of all available capital into anything that produces intelligence. Once you accept that frame, the buy-the-dip behavior in both AI equities and crypto stops looking like mania and starts looking like a rational response to a one-way game. The part worth sitting with is his game-theory claim that neither the US nor China can stop, and that even a spectacular failure like an OpenAI blowup would simply trigger an instant asset auction rather than a collapse, because no single player can be allowed to win outright. Whether or not that is fully true, it is a genuinely different mental model than the recession-and-bust cycle most investors carry around.

    His layer-one thesis is the most actionable takeaway and also the most quietly radical. The pitch is that for the first time ordinary people can own a piece of the core infrastructure that the machine economy will be built on, the way you never got to own a slice of TCP/IP or the open web. He calls this universal basic equity and treats it as humanity’s pension plan. The honest tension he admits is that the racy returns may not be in the boring base layer at all, and that the truly investable winners of this era, the private stablecoin companies, are largely closed off to retail. So the layer-one trade is partly a consolation prize for the fact that the best businesses are unreachable. That is a more candid admission than most crypto bulls will make.

    The behavioral core of the episode is the most useful for a normal reader, and it is almost embarrassingly simple. Pal has been in markets for 35 years and says he does not know a single person who reliably buys bottoms and sells tops, including the legends, who he points out made most of their money on management fees rather than heroic trades. His prescription is to add only when the asset is one to two standard deviations oversold on its long-term log trend, otherwise do nothing, and to treat patience as an action rather than inaction. The line that does the most work is “the market owes you nothing.” It quietly dismantles the entitlement that drives people to overtrade, chase, and burn emotional energy on a strategy that the data says underperforms simply holding.

    Where a reader should keep some skepticism is the certainty. Pal assigns the bull case a 70 percent probability and the bear case 30, but the bear case he sketches (Middle East war reignites, inflation forces tightening, liquidity gets starved, the intelligence buildout slows) is not a minor footnote, it is the whole structure failing at once. The thesis also leans hard on the assumption that AI agents will become massive on-chain economic actors, which is plausible but still mostly forward-looking rather than observed. The value here is the framework, not the forecast. If you take one thing, take the energy-into-intelligence lens and the standard-deviation discipline, and hold the specific tickers and timelines loosely.

    Key Takeaways

    • Pal’s central frame is the universal code: the universe, and now the economy, continuously converts units of energy into units of intelligence, and capital flows to whatever produces the most intelligence.
    • The AI buildout is a race of nations and corporations that nobody can exit. Game theory means neither the US nor China can stop, because the other side would gain a decisive advantage.
    • Even a catastrophic AI failure would not break the trend. If OpenAI ran out of money, its assets would be auctioned instantly to multiple buyers so no single company could double its compute and win the whole game.
    • The economic singularity is the point where institutions and the way we measure the economy can no longer keep up with the speed of technology, made worse when AI and robots are added to the population as economic actors.
    • AI is the first real-world example of Reed’s law, the exponential of the exponential, where most past technology followed the slower Metcalfe’s law log channel.
    • By around 2028, roughly five to six years after AI went mainstream, AI will have produced more words than all of humanity has produced in sum total since the Gutenberg press.
    • The current run is funded by cash flow, not debt. Unlike the late-1990s tech boom, the buildout is paid for out of the earnings of the most cash-generative firms in history.
    • Chips and energy are the binding constraints. Companies report being booked out three years and beyond, and xAI is reportedly handing older data centers to Anthropic because no one can get enough compute.
    • Pal expects the Fed to run a Greenspan-style playbook, cut rates and then get out of the way, letting a productivity miracle grow the economy faster than the debt pile so debt to GDP falls.
    • Bitcoin falling from 126K toward 60K is a nasty correction in a bull market, not a bear market. Pal has seen many 50 percent Bitcoin drawdowns since 2013, and altcoins always fall further on the risk curve.
    • The 2025 to 2026 correction has been choppy and slow rather than the fast V-shape of 2021, which is part of why sentiment feels so bad.
    • Crypto lagged because liquidity is finite. The government shutdown withdrew liquidity, which hits crypto with about a three-month lag, while AI capex and Chinese gold buying sucked capital away.
    • Liquidity is now reaccelerating in the US, China, and globally, which Pal sees as the reason the worst is likely over for crypto.
    • The birth of economic agents in late 2024 gives crypto an effectively infinite total addressable market, since agents will be economic actors that hold treasuries, make payments, and transact on-chain.
    • Smart contract layer ones are Pal’s preferred bet. He compares the structure to operating systems and cloud, where value concentrates into three to five major players plus a few specialists.
    • He calls owning layer ones universal basic equity and humanity’s pension plan, the chance to own the rails the agentic economy will run on, something the internet never offered retail.
    • Discounted cash flow analysis is the wrong tool for valuing a blockchain. The whole purpose of the network is to be the cheapest, fastest, and most programmable, so high fees are a bug, not a strength.
    • Pal measures layer ones by intelligence density: number of developers, programmability, speed to finality, applications per user, and the ratio of stablecoins to total value locked as stored energy.
    • Only three tokens maintained economic density when the market fell 80 percent: Ethereum, Solana, and Sui. ETH is the safe Microsoft-like choice, Solana is faster and cheaper, Sui is earlier but extremely fast and programmable.
    • Pal added Zcash in the correction as a Bitcoin-with-privacy trade. The left-curve case is simple privacy value, the right-curve case is that it is also quantum-proof and a hedge against AI-enabled state surveillance.
    • He admits he did not execute the Zcash buy well, kept meaning to add more while traveling, and watched it run up 50 percent. He treats it as a small position, not a portfolio overhaul.
    • On Hyperliquid he is complimentary but uninvested, because he does not trade, use perps, or use leverage, and he expects Robinhood and Coinbase to compete hard for that niche.
    • DeFi is better suited to machines than humans. Agents may not even need front ends or websites, just low-friction access to swap across multiple stablecoins and currencies instantly.
    • DeFi is not dead despite mega-hacks. Pal argues hacks force better products, and notes that banks quietly absorb theft losses too, so the answer is to build more secure systems.
    • The entire financial system is moving to blockchain rails because they are the most efficient way to operate, a prediction Pal first made in 2014 before smart contracts existed.
    • Pal is launching an NFT fund focused on grail assets (one-of-one alien CryptoPunks, top artists) trading from roughly 600K to tens of millions, plus a convex middle tier of artists with social consensus.
    • He names artists like Dies with the most likes (whom he compares to a Hunter S. Thompson of art) and Kim Asendorf, whose work uses tokens at the pixel level.
    • The fund will also lend against NFTs for yields around 15 percent or more, acquiring assets cheaply if borrowers default and recycling yield into emerging artists.
    • His real estate analogy: a smaller NFT in a great collection is like a modest apartment in a billionaire neighborhood, while grails are the 20 million dollar penthouses that actually compound.
    • Bitcoin is partly an AI proxy because global savings should rise as AI lifts economic growth, and Bitcoin targets a share of those savings as a digital store of value.
    • The core mindset shift: if you know where the world is going and roughly where market cap is heading on the log trend, you would never sell, you would only ever accumulate.
    • Selling well is nearly impossible. Even if you take profit at two standard deviations overbought, adding it back at the bottom is something almost no one actually manages.
    • The people who made the most money in crypto are the ones who did not trade it. Pal cites holders who profited by doing essentially nothing while active traders lost their edge.
    • Pal’s discipline requires roughly two to three actions every five years: add when one to two standard deviations oversold, optionally trim when two standard deviations overbought, otherwise nothing.
    • By his standard deviation measure, Bitcoin and crypto are as cheap as they have been in their long-term uptrend versus the NASDAQ, which he reads as a signal to allocate more to crypto.
    • Fear and greed sat below 10 for the longest stretch in the index’s history during this correction, hitting its lowest reading ever, a classic oversold extreme.
    • His 2026 to 2027 bull case stacks stablecoin explosion, the Clarity Act getting signed, rising global liquidity, debt rollovers forcing money printing, a strong business cycle, AI agents, and a cheap entry point. He puts it at roughly 70/30 to the upside.

    Detailed Summary

    Two economies and the money illusion

    The conversation opens loosely with travel, stablecoin spending, and a riff on why people agonize over a 75 dollar airport breakfast but happily lose money on an NFT that drops 80 percent. Pal’s explanation is that we live in two economies at once. The crypto and tech economy can grow 50 to 150 percent in a good year, while the real economy grows around 2 percent. Money earned in the fast economy does not feel real, which is why people spend and speculate so freely with it. This sets up the rest of the episode, where Pal treats the fast economy as the place serious capital is being forced to go.

    The AI capital race nobody can stop

    Asked why the stock market only seems to go up, Pal gives two reasons: liquidity expansion and the most extraordinary capital event in human history, the funneling of all capital into intelligence. He frames it as a race of nations, corporations, and individuals that cannot be slowed because of game theory. No superpower can let another reach AGI alone, only the US and China can afford the race, and neither can stop without ceding the advantage. He even games out an OpenAI bankruptcy and concludes the US would instantly auction the assets across many buyers rather than let one firm double its compute and win, which is why he calls the whole thing too big to fail. The practical conclusion is blunt: buy the dip, because the structure forces capital to keep flowing.

    The economic singularity, Reed’s law, and electricity through sand

    Pal defines the economic singularity as the moment when institutions and our economic measurements can no longer cope with the speed of technology, especially once AI and robots count as population. He explains that almost all past technology adoption followed Metcalfe’s law, a log channel visible in the charts of Google, Facebook, and the NASDAQ, but AI is the first observed example of Reed’s law, the exponential of the exponential. To make it concrete he cites ARK research showing AI will, by roughly 2028, have produced more words per year than all of humanity, and notes Anthropic expected 10x growth and got 80x in a quarter. He marvels that we are putting electricity through silicon, the second most common element on Earth, and producing intelligence six orders of magnitude faster than a human neuron.

    Why crypto lagged and why the worst is over

    Pal explains the crypto underperformance mechanically. There is only so much liquidity, the government shutdown withdrew it, and that hits crypto with roughly a three-month lag, landing right in the middle of the October drawdown. At the same time, the AI buildout and Chinese gold buying pulled capital toward the longest-duration assets, leaving SaaS and crypto with nearly identical charts as they got left behind. His read for 2026 is that liquidity is now reaccelerating across the US, China, and the world, so there is nothing to worry about yet. The Bitcoin move from 126K toward 60K is, in his framing, a normal correction, comparable in length to the roughly six-month 2021 pullback that resolved into new highs.

    Layer ones as universal basic equity

    The heart of the investment thesis is that smart contract layer ones will accrue a growing share of crypto value as the investable infrastructure layer. Pal argues the entire financial system plus a coming swarm of AI agents will use these rails, giving crypto an infinite total addressable market. Like operating systems and cloud, value will concentrate into three to five chains plus specialists. He measures them by intelligence density rather than discounted cash flow, since the point of the network is to be cheapest and fastest. By his analysis only Ethereum, Solana, and Sui held economic density through an 80 percent drawdown. ETH wins on developers, security, and Lindy effects (the Microsoft you do not get fired for owning), Solana is faster and cheaper, and Sui is earlier but offers a different order of magnitude on speed, finality, and programmability. He frames owning a basket of four or five as humanity’s pension plan.

    Zcash, privacy, and the quantum hedge

    Pal reveals he added Zcash during the correction, alongside buying more Sui. He had said in December he would wait for it to pull back, and he did, though he admits he did not buy enough as it ran up 50 percent. His left-curve case is that privacy has real value and people will understand it more, making it essentially Bitcoin with privacy that could plausibly reach 5 to 10 percent of Bitcoin’s value. His right-curve case is that it is also quantum-proof and a hedge against governments wielding AI-enabled control over people. He dismisses the mid-curve worry that it will be banned, noting that the ban fear has shadowed crypto his entire career and never materialized.

    Agents, DeFi, and financial rails

    Pal argues the biggest future users of DeFi and crypto payments will be AI agents, whose scale is effectively infinite. Setting up agents himself, he keeps hitting walls that require small payments, and sees agents making endless micro-payments plus larger transactions, holding treasuries across multiple stablecoins and currencies, and rebalancing through DeFi instantly without any human involved. DeFi, he says, is actually better suited to machines than people, and may not even need front ends. On the wave of mega-hacks he is unbothered, arguing they force better products, that banks quietly absorb theft too, and that the financial system always migrates to the most efficient rails because that is how you make more money. He first predicted blockchain would become the financial industry’s infrastructure rail back in 2014.

    The NFT fund and grail digital art

    Pal is launching an NFT fund because so many people told him they want exposure but do not know how. The fund targets grail assets, the scarce one-of-one pieces with proven social consensus that trade from around 600K into the tens of millions, plus a convex middle tier of artists who have long-term proven value and could be wildly re-rated. He names Dies with the most likes, an Indiana artist cataloging the decline of middle America whom he likens to Hunter S. Thompson, and German artist Kim Asendorf, whose 3D works are built from individually tokenized pixels. The math of convexity is the draw: an artist re-rating from 20 to 200 ETH while ETH itself multiplies could compound into a 100x. The fund will also lend against NFTs for yields above 15 percent, acquiring assets cheaply on default and recycling yield into emerging artists, and will build a club connecting investors to artists. His real estate framing reassures smaller holders: owning a lesser piece in a top collection is like a modest flat in a billionaire neighborhood.

    Never sell, and the math of patience

    The behavioral spine of the episode is Pal’s argument that buying, holding, and accumulating beats trading cycles. He has built a Real Vision indicator that signals a buy when an asset is one to two standard deviations oversold on its log regression channel, and says it compounds at a stupid rate. The problem with selling is deciding how much and then having the discipline to buy it back at the bottom, which almost no one does. In 35 years he says he has never met anyone who reliably buys bottoms and sells tops, and notes the trading legends made most of their money on management fees. The people who made the most in crypto are the ones who did nothing. He reframes holding as patience, an active stance, and ties it back to the universal code: buying Bitcoin and doing nothing is the most energy-efficient trade you can make, while overtrading burns mental and emotional energy for a worse outcome. His advice to those tempted by AI’s vertical charts is to go play with AI and just hold your Bitcoin.

    The 2026 to 2027 outlook

    Pal closes the macro case by stacking the bull factors: a massive stablecoin expansion over the next 24 months, the Clarity Act getting signed and freeing builders, rising global liquidity, trillions in interest payments that force more money printing, a strong business cycle recycling earnings into speculative assets, the arrival of AI agents, and a cheap entry point with fear and greed at historic lows. He even floats a permanent resolution of Middle East conflict as part of the upside. The bear case is the mirror image: war reignites, inflation runs hotter, tightening starves capital, and the intelligence buildout slows. He puts the odds at roughly 70 percent bullish, 30 percent bearish, and says he does not see the bear case yet. The episode ends on a personal note about kindness, with Pal unable to name a single kindest act because, he says, everything is made of kindness.

    Notable Quotes

    “We’re going through the most extraordinary time in human history. Nothing else matters. This whole funneling of all capital into intelligence is the biggest race that’s ever happened.”

    Raoul Pal, on why capital keeps flooding into AI

    “The game is so big that nobody will stop.”

    Raoul Pal, on the game theory of the US and China AI race

    “This is how amazing it is. We’re putting electricity through sand and creating intelligence.”

    Raoul Pal, on silicon and the universal code

    “It’s a nasty correction in a bull market. I’ve been in crypto since 2013. I’ve seen many corrections, non-bear markets of 50% in Bitcoin.”

    Raoul Pal, on Bitcoin falling from 126K toward 60K

    “The market owes you nothing. You would just have to be better at doing a job.”

    Raoul Pal, on the entitlement that ruins crypto investors

    “This is humanity’s pension plan. We get to invest in the infrastructure rails of which all the agentic economy will run.”

    Raoul Pal, on owning layer one blockchains

    “The people who’ve made the most money out of crypto are the people who don’t trade it.”

    Raoul Pal, on why holding beats trading

    “Your job is to be a mercenary for your own capital. You want to make the most money over time.”

    Raoul Pal, on why no one has to stay loyal to crypto

    “Bitcoin and crypto is as cheap as it has been in its long-term uptrend versus NASDAQ.”

    Raoul Pal, on the relative value signal he watches

    This is a compressed look at a wide-ranging conversation. Watch the full episode on When Shift Happens here for Pal’s complete reasoning, the charts he references, and the back-and-forth that the summary above leaves out.

    Related Reading

    • Real Vision the financial media platform Raoul Pal co-founded, where his Global Macro Investor research and exponential age thesis live.
    • Metcalfe’s law (Wikipedia) the network-value relationship Pal uses to model the log regression channel for crypto.
    • Reed’s law (Wikipedia) background on the exponential-of-the-exponential growth Pal says AI is the first real-world example of.
    • Technological singularity (Wikipedia) context for the economic singularity Pal argues is now only about four years away.
    • Zcash the privacy coin Pal added in the correction as a Bitcoin-with-privacy and quantum-proof trade.
  • Waste Tokens to Save Time: Naval, Guillermo Rauch, Blake Scholl, and Max Hodak on AI Software Factories, 1000x Engineers, and Whether Pure Software Is Dead

    Naval Ravikant gathers three frontier founders, Guillermo Rauch of Vercel, Blake Scholl of Boom Supersonic, and Max Hodak of Science, for a freewheeling conversation about how AI coding tools are reshaping what an engineer is, what software is worth, and where the moat goes when models speak English. The headline idea comes from Naval himself: waste tokens, save time. Stop measuring AI by tokens consumed or lines of code generated and start measuring it by the final output and the time you got back. The full conversation is on the Naval Podcast YouTube channel. This is part one of the discussion. Part two, on vibe coding hardware, follows the same group into jet engines, semiconductors, and biotech.

    TLDW

    The job of an engineer is shifting from shipping output to building the factory that ships the output, which means 10x engineers were never really 10x, they were always 100x or 1000x in idea domains, and AI leverage is making that obvious. Models now reflect back the judgment of the user, so a senior architect extracts dramatically more value than a junior, although the junior also writes code they could never have written alone. The frontier models have quietly graduated from junior coders to principal engineers, returning with intuitive plans and real tradeoffs (sometimes with hilariously bad time estimates) rather than just running away with the prompt. Naval has stopped learning prompt tricks, scaffolding tools, and Claude plan-mode rituals entirely. Instead he throws Codex, Claude, and Gemini at the same problem in parallel and brute forces his way through, because tokens are still cheaper than a human and the models keep getting better faster than tricks can. That leads to the bigger question on the table: is pure software still investable, or is it now just a free byproduct of hardware, models, and taste? The group lands on the block economy thesis (a tip of the hat to Mitchell Hashimoto): agents do not want to reinvent Postgres or BMQ on the fly, they want to grab the right reusable building block, so infrastructure software actually gets more valuable, not less. Max Hodak closes the loop with a personal data point: he has not written a line of code in years and has built more software since December than ever before, all through agents, because just understanding APIs, data flow, and performance is what actually moves the work forward.

    Thoughts

    The “waste tokens, save time” line is the most important rhetorical move in this conversation, and it deserves to be unpacked beyond the soundbite. Naval is implicitly arguing that the entire token-economics debate (input cost, output cost, leaderboards, model arbitrage) is a category error in the same way that lines-of-code was a category error in the nineties. The thing being purchased is not tokens. It is a finished result delivered with less of your finite attention spent. If three parallel runs of Codex, Claude, and Gemini cost you a few dollars and one of them lands the answer in twenty minutes instead of you sweating the problem for two hours, the unit economics are not even close. The only people who care about the token bill are people who have not internalized that human time is the actually scarce resource. Once you do internalize it, the question is no longer “how do I prompt this more efficiently,” it is “how do I get out of my own way.”

    The 100x and 1000x engineer point is the one most likely to enrage commenters, and it is also the one most worth taking seriously. Naval is right that the egalitarian flinch in software circles always sat awkwardly next to the empirical fact that one Carmack, one Brendan Eich, or one Satoshi creates more durable value than every mid-tier engineer on earth combined. What AI does is collapse the bottom of that distribution. The marginal junior engineer at a typical company is now competing with a model that costs a few dollars an hour and never sleeps. The remaining premium for human engineers is taste, judgment, and the rare ability to pick the right thing to build at all, which Naval correctly flags as the multiplier that dwarfs raw coding speed. “Just one who had a better judgment on what to work on in the first place” is the most underrated line in the whole episode.

    Guillermo Rauch’s observation that the models have graduated from running away with your prompt to returning with three routes and a tradeoff matrix is the technical update most people have not actually felt yet. There was a real, qualitative shift when the model started saying “we don’t put high-cardinality telemetry into Postgres, you probably want ClickHouse or Athena.” That is not autocomplete. That is a peer. And the funny corollary, that the same model will then confidently tell you the work will take three weeks when it will take three hours, is not a knock on the model. It is a reminder that calibration is a separate skill from competence, and humans get this wrong constantly too. The right posture is to treat the model the way a good engineering manager treats a strong but cocky senior: take the architecture suggestions seriously, throw out the estimates.

    The block-economy thread, riffing on Mitchell Hashimoto, is where this conversation quietly answers Naval’s “is pure software dead” question. Agents are insatiable consumers of reusable building blocks because reinventing infrastructure on every run is wasteful, brittle, and incompatible with the rest of the world. If your service is the canonical primitive an agent reaches for (the queue, the database, the auth layer, the deploy target), you are not commoditized by AI, you are amplified by it. Pure software is not dead. Pure software with no distribution, no defensibility, and no integration into the agent toolchain is dead. That is a much less catchy headline, but it is the real one. The takeaway for founders is not to abandon software, it is to ask whether your software is something an agent will reach for ten thousand times a day or something a human had to be talked into using once.

    Max Hodak’s confession (no code written in years, more shipped software in the last six months than ever before) is the empirical proof that this is not just theory. The skill that ports forward is not syntax. It is the engineering leader’s instinct for what an API is, how data flows, where performance matters, and what level of expectation to set. Guillermo’s framing of “vibe coding through people on Slack” as the original form of vibe coding is genuinely insightful. A good engineering manager has always been transmitting intent to other minds and letting them run. Doing it with agents is the same skill, just with a faster, cheaper, more literal counterparty. The engineers who will struggle in this transition are the ones whose identity was tied to writing the code themselves. The ones who will thrive are the ones who already thought of themselves as taste, judgment, and intent, with code as an implementation detail.

    Key Takeaways

    • The engineer’s job has shifted from shipping output B to building the factory that produces outputs B through Z. You are now judged on the multiplicative system you create, not the single artifact you deliver.
    • 10x engineers were always a misnomer. In idea-domains and digital domains, the real distribution has always been 100x or 1000x. AI just made that obvious enough that arguing about it is no longer fashionable.
    • Token consumption leaderboards are the new lines-of-code metric: a vanity number that measures activity, not value. Tokens are an input, your time is the constraint.
    • Naval’s core rule: waste tokens, save time. Tokens are still vastly cheaper than human hours, no matter how the pricing scares you.
    • Models tend to be about as good as you are in a given domain. The feedback you give them, the corrections, the redirections, sporadically but powerfully shapes the quality of the output.
    • The quality of your reprompting matters enormously today, but will probably matter less over time as models get smarter and need less hand-holding.
    • Naval has refused to learn prompt scaffolding, plan-mode tricks, or named prompt frameworks. His bet is that the models will figure out how to use him faster than he can figure out how to use them.
    • His preferred technique: throw Codex, Claude, and Gemini at the same problem in parallel and brute force the answer. Time is the cost center, not API spend.
    • Lower quality first-draft code is not a blocker. When it is time to ship, throw more tokens at it for a hardening pass. Quality compounds across model generations.
    • Verifiable domains (problems with a clear right answer) are the ones the models will fully solve. Cutting-edge creativity work, the Terence Tao tier, still needs careful human collaboration.
    • Models have qualitatively shifted from “next-token autocomplete that runs away with your prompt” to “intuitive planning mode” where they return with multiple routes and explicit tradeoffs.
    • This is why people on social media say models are now PhD-level. It is not the raw output, it is the back-and-forth posture.
    • Models will confidently make terrible time estimates (“this is a three week project”). Treat them like a strong but miscalibrated senior engineer: trust the architecture, ignore the schedule.
    • Architect-level engineers are extracting much more value per session than junior engineers, but juniors are still leveling up because they can now write code far above their unaided ability.
    • The next career step for a junior engineer is moving from implementing features to picking technologies. Postgres vs ClickHouse, ZMQ vs other queues. The model can suggest, but a human still has to decide.
    • Taste and judgment remain the residual human advantage. Models will give you good tradeoffs if you ask, but knowing which tradeoff to take is still on you.
    • Concrete example: a recent model pushed back when asked to store high-cardinality telemetry in Postgres and recommended ClickHouse or Athena instead. Unprompted architectural judgment.
    • Humans are still completing the model for tasks like fetching API keys, moving capital, or performing real-world actions. That gap is temporary.
    • Every SaaS and hosting company will soon expose a CLI or API surface that agents can drive directly. Anything Unix-shaped and text-based, agents can already hack into a usable API themselves.
    • The missing piece for full autonomy is payments. Crypto, Bitcoin, or any programmable money lets the agent buy what it needs without a human in the loop.
    • The open question Naval poses: is pure software dead? We used to learn code to talk to machines. Now machines speak fuzzy, sloppy English back to us.
    • For hardware founders, AI is a massive boon. Software, which was always hard to hire artists for (per Patrick Collison’s “software is art” framing), is suddenly fast and cheap to produce alongside the hardware.
    • Model training, post-training, and fine-tuning may be the new “real software engineering” for those who want to work at the model layer.
    • Mitchell Hashimoto’s “block economy” thesis: agents need powerful, reusable, well-known building blocks. They should not reinvent message queues or databases every run.
    • Reinventing primitives is bad civic engineering. The value of “we both depend on Postgres 13.2” is interoperability with the rest of society and toolchain.
    • Infrastructure software and reusable libraries are getting more valuable, not less, in the agentic era. Vercel’s bet is on being the layer agents reach for.
    • Useful metaphor: building blocks are like a token cache. Why churn through a trillion tokens to reproduce code that already exists when you can fork from a known starting point?
    • Max Hodak has not written a line of code in years but has shipped a huge volume of personal software since December, all through agents. Projects he had fantasized about for years are now actually running.
    • What still matters from a real software background: understanding what an API is, how data flows, performance expectations, and how to set the right level of demand on an operation.
    • A proficient engineering leader has always been “vibe coding through people” on Slack and in one-on-ones, transmitting intent and letting others execute. Doing it with agents is the same skill, faster and cheaper.
    • Naval personally went from twenty years of not coding to coding constantly through agents, leaning on first-principles software engineering and algorithms knowledge.
    • The friction that historically killed personal coding projects (latest framework, infra plumbing, deploy setup) is now mostly handled by the agent. Vercel makes it easier, agents make it trivial.
    • The single biggest change Max highlights: you do not get stuck anymore. The indefinite debugging spiral on some narrow obscure bug is largely gone.
    • The old mantra that learning to program means accepting intrinsic frustration (“nope, that’s part of the deal”) is no longer true. The frustration was incidental, not essential.
    • The frontier founder pattern on display in this episode: all three guests build their own factories (Vercel’s AI cloud, Boom’s supersonic jets and engines, Science’s biohybrid brain interface) rather than composing from off-the-shelf parts.

    Detailed Summary

    The Software Factory and the Hundredfold Engineer

    Guillermo Rauch opens the substantive portion of the conversation with the framing he has been pushing publicly: the role of the engineer is moving from “ship output B” to “build the factory that ships outputs B through Z.” That reframes engineering judgment. You are no longer evaluated on the single deliverable, you are evaluated on the multiplicative system you put in place. Naval picks up the thread and points out that this also retires an old debate. Engineers used to argue about whether 10x engineers existed, with the egalitarian camp insisting that talent differences were marginal. The truth, Naval says, was always more extreme. In idea-domains, virtual domains, and intellectual domains, the distribution has always been 100x or 1000x, not 10x. Brendan Eich, Carmack, Satoshi, the canonical names, were thousandx programmers. AI has made the underlying distribution legible. And the multiplier on top of all of that is judgment: picking the right thing to work on in the first place is an infinity multiplier compared to picking the wrong thing, regardless of raw skill.

    Token Leaderboards Are the New Lines of Code

    Guillermo flags the current cultural confusion: people see their AI bills, see the token counts, and assume they should be optimizing for tokens-per-engineer or similar metrics. Max Hodak’s response cuts through it. Token consumption, like lines of code before it, is not a meaningful productivity metric. It is an activity metric, and activity metrics always mislead. Max adds his own field observation: the models tend to be roughly as good as you are in a given domain. A senior developer extracts genuinely powerful output, a junior gets junior-quality output back, because the feedback loop (the corrections, the redirections, the architectural pushback) is what shapes quality. The sporadic but high-leverage moments where the user redirects the model are doing more work than the prompt itself.

    Naval’s Brute Force Doctrine: Waste Tokens, Save Time

    Naval lays out his personal posture, which has become the title of the conversation. He has deliberately ignored all the prompting tricks, scaffolding tools, named prompt frameworks (“use Ralph Wigum, use OpenClaude, use Hermes, use plan mode”), on the bet that the models will figure out how to use him faster than he can figure out how to use them. He is ham-fisted with the models, gets frustrated, types less and less, and just brute forces his way through by running Codex, Claude, and Gemini at the same problem simultaneously. The justification is economic. No matter how expensive the models seem, they are still vastly cheaper than a human hour. Do not measure tokens as inputs or outputs. Measure your time and the final output. Even when the first-draft code is low quality, that is not a blocker. When the moment comes to ship, throw more tokens at it. The models will rewrite it, harden it, and they get better every generation. Naval explicitly excepts cutting-edge creative work (the Terence Tao tier of unsolved problems) where you still need to collaborate carefully and closely. Everywhere else, brute force is the dominant strategy.

    From Junior Coder to Principal Engineer

    Guillermo identifies a qualitative shift that has happened recently. Models used to do the classic next-token thing: take your prompt and run away with it in a direction you may not have wanted. Now they enter an intuitive planning posture without being told to plan. They come back and say “what you are asking has these three routes, here are the tradeoffs.” That, Guillermo argues, is the moment the model stopped being a junior engineer and became a principal engineer. The funny side effect is that they will then return preposterous time estimates (“this will take three weeks”) with full confidence. The conclusion is to treat the model as a peer for architecture and a baby for scheduling. Returning to the Max-vs-junior question, Guillermo argues juniors clearly do level up because they write code well above their solo ability, but architects extract maybe 10x while juniors extract more like 2x. The juice scales with the user’s existing taste.

    Taste, Judgment, and Architectural Decisions

    Max names the residual human contribution: taste and judgment. Picking between Postgres and ClickHouse for high-cardinality telemetry data, picking between ZMQ and another queueing system. The models can recommend, but a human still has to call it. Guillermo offers a recent concrete example where a model pushed back unprompted: when asked to put high-cardinality telemetry into Postgres, the model responded “we don’t put that kind of data into Postgres, you should consider ClickHouse or Athena.” That is the new normal. The peer-level architectural pushback is happening unsolicited, which is genuinely impressive and a real shift from the deferential autocomplete of two years ago.

    When the Human Becomes the Tool

    Guillermo raises the inversion question: at what point does the model stop being the assistant and the human start being the assistant who fetches API keys, moves capital, and performs real-world actions on the model’s behalf? Naval treats it as a temporary aberration. Every serious SaaS and hosting provider will soon expose a CLI or API surface that agents can drive directly. Even when they do not, anything Unix-shaped and text-based can be hacked into an agent-usable interface by the agent itself. The missing piece is payments. Once you insert programmable money (Naval mentions Bitcoin and crypto tokens), the agent can buy what it needs and the human is no longer the bottleneck.

    Is Pure Software Dead?

    Naval poses the biggest strategic question of the episode. If models now speak fuzzy, sloppy English the same way humans do, and the historical reason we learned to code was to talk to machines that did not understand English, is pure software still a viable thing to build a company around? His own framing of the answer: hardware founders win, because the historically hard problem of hiring software artists (per Patrick Collison’s “software is art” line) is now mostly solved by AI. Model builders win, because training, post-training, and fine-tuning may be the new “real software engineering.” But what about classic pure software companies? Naval lets the question hang, and Guillermo picks up the answer through a different door.

    The Block Economy and the Future of Infrastructure Software

    Guillermo cites Mitchell Hashimoto’s recent piece on the block economy (or “building block economy”). The argument: the most valuable thing for agents to have access to is powerful, reusable building blocks. You do not want your agent reinventing a queue system every time it needs to send an email. You want it to grab the right-sized block (BMQ, ClickHouse, whatever) and move on. Reinventing primitives is also a civic problem. The world only works because we all depend on the same Postgres 13.2, the same protocols, the same standard infrastructure. If every agent went off and invented its own bespoke universe, you would lose interoperability. So infrastructure software (which is, by self-admitted bias, what Vercel builds) becomes more valuable in the agentic era, not less. Guillermo extends the metaphor: reusable building blocks are like a token cache. Why burn a trillion tokens reproducing what already exists when the agent can fork from a known starting point? The block economy is the answer to “is pure software dead.” Pure software that becomes the canonical primitive an agent reaches for is more valuable than ever.

    Max Hodak’s Personal Proof: Years Without Code, Tons of Software Shipped

    Max grounds the discussion in his own experience. He learned to program young, got sucked into it in his teens and 20s, knew programming languages deeply. He has not written a line of code in quite a while. And yet since December he has built a huge amount of personal software, including projects he had fantasized about for years and now actually uses every day. He did not write any of it. He cannot imagine going back to writing code by hand. The skill that ports forward is not syntax, it is the understanding of how APIs work, how data flows, what level of performance to expect, and how to orient the model around the right expectations for an operation. Guillermo extends this with the most quotable framing of the episode: a proficient engineering leader has always been “vibe coding through people on Slack and in one-on-ones,” transmitting intent and letting others execute. Agents are the same modality with a faster, cheaper, more literal counterparty.

    Naval’s Return to Coding After Twenty Years

    Naval offers his own parallel. He went from not having written code in twenty years to coding constantly through agents. What carried him back in was first-principles knowledge of software engineering and algorithms, which gets you further than you would think. The reason he had stopped coding in the first place was not lack of ability, it was the friction of keeping up with the latest language, the latest architecture, and the constant infrastructure plumbing required to ship anything. Vercel made it easier. Agents made it trivial. Max closes with the most concrete benefit of all: you do not get stuck anymore. The indefinite debugging spiral on some obscure narrow problem, the thing that historically ate weekends and broke spirits, is largely gone. The old mantra that programming is intrinsically frustrating and that frustration is “part of the deal” turned out to be wrong. The frustration was incidental, not essential.

    Notable Quotes

    “The way that I’m judging you as an engineer is, are you producing the factory that will produce multiplicative outputs B through Z?”

    Guillermo Rauch, reframing what an engineer is actually being measured on in the AI era.

    “When you’re operating in idea domains, intellectual domains, virtual digital domains, it’s not even 10x, it’s 100x or 1000x. It always has been.”

    Naval Ravikant, on why the old 10x engineer debate was always under-stating the real distribution.

    “If you choose the right thing to work on versus the wrong thing to work on, that’s an infinity difference. It could just be one who had a better judgment on what to work on in the first place.”

    Naval Ravikant, on judgment as the multiplier that dwarfs raw skill.

    “I’ll throw Codex, Claude, and Gemini at the same problem over and over and just waste tokens to save time. No matter how expensive these models might seem, they’re still way cheaper than a human.”

    Naval Ravikant, on his brute-force multi-model coding workflow.

    “Just waste tokens, save time. Don’t look at the tokens either as inputs or outputs. Just look at your time and look at the final output.”

    Naval Ravikant, delivering the title thesis of the episode.

    “Clearly the models at some point graduated. They used to be junior engineers, now they’re principal engineers, because they come back to you with a set of tradeoffs.”

    Guillermo Rauch, on the qualitative shift in how current frontier models respond to prompts.

    “Bro, we don’t put that kind of data into Postgres, you should consider ClickHouse or Athena or whatever. That’s happened to me a lot, which is really impressive.”

    Guillermo Rauch, recounting unprompted architectural pushback from a recent model.

    “It’s like saying speaking English. We had to learn code to communicate with the models, now the models speak English. So where’s the moat?”

    Naval Ravikant, raising the central strategic question about the future of pure software.

    “I haven’t written a single line of code in quite a while. Since December, I’ve built a huge amount of software that I now use every day, projects I’ve fantasized about for years.”

    Max Hodak, on what becomes possible when you stop writing code and start directing agents.

    “A proficient engineering leader has been quote unquote vibe coding through people on Slack or one-on-ones, because you’re transmitting your will, your intent, your experience, and you’re letting others run with it. Now we do the same with agents.”

    Guillermo Rauch, reframing leadership itself as the original form of vibe coding.

    Watch the full conversation on the Naval Podcast here.

    Related Reading

    • Part two: Vibe Coding Hardware, the continuation of this conversation, where the same founders move from pure software into AI-designed jet engines, vertical integration, China’s open-source bet, and why humans become verifiers.
    • Naval Ravikant’s official site, the canonical home for Naval’s essays, podcast, and longer-form thinking on technology, judgment, and leverage.
    • Vercel, Guillermo Rauch’s company, building the AI-native cloud and frontend infrastructure that this conversation references as a canonical agent building block.
    • Boom Supersonic, Blake Scholl’s company building supersonic civilian aircraft and their own jet engines, the hardware example of a founder building the whole factory.
    • Science Corporation, Max Hodak’s brain-computer interface company developing the biohybrid neural implant referenced in the intro.
    • Mitchell Hashimoto’s writing, source of the “block economy” framing for why reusable infrastructure building blocks become more valuable, not less, in the agentic era.
  • Dan Shipper’s Most Contrarian AI Predictions for 2026: Why the Job Apocalypse Is a Myth, SaaS Will Boom, PMs and Designers Win, and CLIs Are Already Over

    Dan Shipper, the CEO and founder of Every, returned to Lenny’s Podcast for round two of AI predictions. His last appearance produced one of the most prescient calls of the year: that non-technical people would build serious work inside Claude Code. He was unbelievably right. This conversation is the follow-up, a tour of his most contrarian forecasts for how AI is actually changing the way we work, who wins, who loses, and what almost every commentator is getting wrong about the next twelve to twenty-four months.

    TLDW

    Shipper argues that the AI job apocalypse is a myth, that SaaS is going to boom rather than die, that product managers and full-stack designers are the biggest winners of the agent era, that personal agents inside Codex and Claude Code will quietly replace the browser as the primary work surface, that every company will run a single shared super-agent in Slack instead of a fleet of per-user bots, that the CLI moment is already over, that pull requests are going to flood organizations from non-technical staff, that forward-deployed engineers who garden company agents become the new senior role, that GPT-5.5 still cannot match a real senior engineer on architectural judgment, that AI-generated internal writing is fine and probably better than what most humans produce, that CEOs and middle managers have not adapted yet but soon will be forced to, that the edge of AI lives wherever a curious human is using it rather than in San Francisco, and that the only durable strategy is to ride the models and keep playing with whatever ships next. The whole conversation balances aggressive AI bullishness with an equally strong bet on humans, on creativity, and on the unavoidable need for someone to care for every agent that gets deployed.

    Thoughts

    The most useful frame Shipper gives is that models commoditize yesterday’s human competence. Every time a frontier model crosses a new bar, the work that used to define seniority becomes cheap. The senior engineer who could carry a refactor in their head, the PM who could write a coherent strategy doc, the designer who could ship a polished landing page in a week. That competence is now frozen, codified, and available on tap. The interesting question is not whether models will keep eating tasks. They will. The interesting question is what humans do with the suddenly cheap raw material underneath them. Shipper’s answer is that humans climb the stack: they go up a level, find a new problem worth framing, and use the commoditized competence as feedstock for something that did not exist before. That treadmill is the actual engine of value creation, and it is why he can be simultaneously AI pilled and bullish on hiring.

    His SaaS take is the spiciest call of the episode and probably the most defensible. The crowd consensus is that agents will gut SaaS because an AI can just write the form filler, the dashboard, the workflow. Shipper points out the obvious counterfactual: agents do not reduce the number of people using SaaS, they increase it. A marketing lead who could never touch the data warehouse can now stand up a PostHog query through Codex. A founder who never opened Vanta can run a SOC 2 prep through an agent. The result is more users, more accounts, and a much fatter top of funnel for every horizontal tool. The second-order effect is even more interesting. When the SaaS tool runs inside the user’s agent, the user supplies the tokens. Vendor margins improve, not collapse. If he is right, the next two years are going to be brutal for the SaaS-is-dead thesis pieces and very good for the public software multiples.

    The PM and designer bet is where this gets personal for anyone in product. For a decade the bottleneck in shipping anything was engineering capacity. A PM with spiky product sense had to negotiate their vision through a roadmap, a sprint, a review, and a release. Designers had to convince an engineer that the third state of the empty screen was actually worth building. Both of those constraints are dissolving fast. A PM who can prompt Codex into a working prototype on Friday afternoon, then iterate it live in front of a customer on Monday, is doing the job of a small team. A designer who can ship a fully functional landing page in their own style, without negotiating with anyone, is suddenly the most leveraged person in the company. The scarce skill is no longer execution. It is taste, judgment, and the willingness to decide what is worth building. That has always been the real PM and design job. AI just stripped away the parts that were not.

    The quietest but most important prediction is that agents need humans, permanently. Every benchmark advance reveals a new layer of judgment the model cannot frame on its own. When the agent finishes the task, there is always a senior human who sees the deeper problem the model patched over. Shipper calls this gardening, and it is the basis for the new forward-deployed engineer role. The companies winning right now are the ones that put a real person next to every agent, watching what it does, course-correcting in Slack, and noticing when the output drifts. The dream of autonomous AI workflows is a stage in a journey, not the destination. The destination looks more like a thoughtful operator with a small cluster of agents they trust and constantly tend. That is a much more humane future than the discourse suggests, and it is the one Every is already living.

    The final advice, ride the models, sounds glib but is the single most actionable line in the episode. Most professional anxiety about AI dissolves the moment you actually use the newest model on real work. Most professional advantage accrues to the people who do that one thing consistently. The edge does not live in San Francisco where the labs build the things. It lives wherever a curious human meets a real workflow and discovers something the labs have not noticed. A PM in Iowa willing to try Codex on a Tuesday night can be further ahead than a research engineer who has only used the model on its evals. Pair that with Shipper’s closing motto, do things worth writing about and write things worth reading, and you have a pretty complete operating system for the next two years.

    Key Takeaways

    • The AI job apocalypse narrative is wrong. Models commoditize yesterday’s competence, then humans climb the stack and find new work to do with the cheap raw material.
    • Every has roughly doubled headcount in the last year despite being one of the most AI-forward companies in the world. The lived data point cuts directly against the doom thesis.
    • Shipper’s dual stance: simultaneously extremely AI pilled and very bullish on humans. He treats this as the only intellectually honest position right now.
    • Work will bifurcate. Companies will run one shared super-agent in Slack for everyone, and individuals will run their own personal agent inside Codex or Claude Code on their machine.
    • The personal agent inside Codex effectively becomes the new operating system. Instead of putting AI in the browser, you put a browser inside the AI.
    • The super-agent pattern is already real: Shopify has River, Ramp has its own, and Every runs Claudie inside Slack for internal consulting.
    • SaaS is not dying. Agents increase the user base of SaaS tools because non-technical people can finally drive them. Shipper would buy SaaS stocks today.
    • When SaaS runs inside an agent, the user brings their own tokens. Vendor margins improve because they no longer eat inference costs on every interaction.
    • The CLI era is already over. The magic was never the terminal. It was the AI plus the ability to see what the agent is doing. A good GUI captures the same benefits and more.
    • Pull requests are about to flood every company. Non-engineers can now ship code, run queries, and open tickets. Reviewing the output becomes the new bottleneck.
    • Open-source maintainers are already living in the future. Some receive thousands of agent-generated PRs per day and spin up thousands of Codex instances just to triage them.
    • Forward-deployed engineers are the new senior role. They live in Slack, garden the company’s agents, fix broken flows, and keep non-technical staff from doing damage.
    • Product managers with spiky product sense plus a little Codex fluency become extremely dangerous. Marcus at Every, formerly a PM at Axios, is the archetype.
    • Full-stack designers are the other big winner. They can build distinctive interfaces end to end without negotiating with engineering. The bottleneck on taste-driven product work disappears.
    • Designer hiring data has not yet caught up to the prediction. Shipper notes this and says check back in a year.
    • Sales is the role least changed so far. Top of funnel research has been turbocharged by agents, but the actual relationship and closing work remains human.
    • AI-generated internal writing is going mainstream and that is a good thing. Most humans are bad at strategy docs, quarterly plans, and PRs. AI drafts a coherent first pass that a human can refine.
    • Shipper says most of his email is now written by GPT-5.5 and Codex. He would honestly prefer the signature to say so.
    • Public writing, newsletters, and published essays still demand a human voice. Internal communication does not.
    • CEOs and middle managers have largely not adapted yet because their staff still does the work. That window is closing fast and will become an obvious career liability.
    • Your company will only go as far as your CEO goes in AI. The leadership ceiling becomes the AI ceiling.
    • Shipper’s senior engineer benchmark scores GPT-5.5 at roughly 62 out of 100. Real senior engineers sit at 85 to 90. Progress is real, but the gap on architectural judgment remains.
    • Models tend to patch problems locally instead of rewriting from first principles. A senior human still sees the deeper rework that the model avoids.
    • Every uses Notion-based agents to draft quarterly plans. The human edits, approves, and stands behind the output.
    • The hard rule on AI-generated communication: you have to read it and stand behind it before sending it. Pasting unread output is the only true no-no.
    • Every agent needs a human. Automation is a lie in the strong sense. The story of automation is the story of new and different humans being needed alongside it.
    • The reach test, organic daily usage, is the real signal that an AI product works. Benchmark scores are noisy. Daily reach is not.
    • Cursor’s SpaceX acquisition is a tell. Harnesses around models, not the models themselves, are where the strategic value is concentrating.
    • The edge of AI is not in San Francisco. It is wherever a real human meets a real workflow and discovers something the labs have not noticed yet.
    • A PM in Iowa willing to ride the models can be further ahead than a researcher in SF who only uses them on internal evals.
    • Ride the models. Use them for whatever you do. Try every new release the day it ships. That single behavior compounds faster than any other AI career strategy.
    • Shipper got bursitis, which he calls vibe coder elbow, from too much rapid agent-assisted coding while debugging his markdown editor Proof.
    • The closing motto for the year: do things worth writing about and write things worth reading.
    • Lenny will re-interview Shipper in roughly May 2027 to score the predictions.

    Detailed Summary

    Why The AI Job Apocalypse Is The Wrong Frame

    Shipper opens with the headline contrarian call. Benchmarks keep climbing. Models can now sustain seventeen-hour autonomous tasks at fifty percent accuracy. The pace is real and accelerating. None of that translates cleanly into mass unemployment. His mechanism: models codify yesterday’s human competence and make it cheap. The act of compressing past expertise into an API call is genuinely deflationary for the work it captures, but it is also raw material for the next layer of human work. He uses Every as his own data point. The company has roughly doubled in the past year despite being one of the most AI-forward outfits in media. Hiring goes up because agents create new categories of work that need humans, not because the agents fail. The discourse, he argues, is stuck modeling AI as substitution. The reality looks much more like leverage.

    The Bifurcation: Super-Agents And Personal Agents

    Work splits into two surfaces. The first is the shared super-agent that lives in Slack and serves the whole company. Shopify has River. Ramp has its own. Every has Claudie. Each is a single, trusted, gardened agent that anyone in the company can talk to. The pattern has converged on one shared agent rather than one agent per person because agents need human attention to stay useful, and a single shared instance pools the gardening cost. The second surface is the personal agent inside Codex or Claude Code that runs on your machine and reaches into your local environment, your editor, your files, and through an embedded browser into the web. Shipper calls this the new operating system. Instead of the old paradigm of putting AI inside the browser, you put the browser inside the AI. The agent sees what you see, follows what you do, and works on your stuff in your context.

    The SaaS Bet: Up, Not Down

    The SaaS-is-dead thesis was the consensus call of late 2025. Shipper takes the other side and would buy software stocks now. Three arguments. First, agents make SaaS accessible to people who never could have used it directly. The total addressable user base inside every company goes up. Second, the business model improves when the user runs the SaaS through their own agent, because the user supplies the tokens. Vendors stop subsidizing inference. Third, SaaS spend in his observable universe is up, not down, and is concentrating on the tools that play well with agents. He frames the prediction as a sound bite for the cycle: buy SaaS stocks, the apocalypse is dumb.

    The CLI Era Is Already Over

    For a moment in early 2026 it looked like everyone was migrating to the terminal because Claude Code was a CLI. Shipper says the moment is finished. The actual leverage was never the terminal. It was the model plus the ability to watch and steer an agent live. A great GUI captures every advantage of the CLI without the friction. His own engineering team at Every has mostly moved off the CLI as their primary surface and onto Codex desktop. He frames it bluntly: we speed ran the CLI era, it was nice, and now we are done. Tooling for the next two years will be visual, multi-pane, multi-agent, and built around the human watching the work unfold.

    The Pull Request Flood And The Rise Of Forward-Deployed Engineers

    Once non-engineers can ship code, run queries, and file changes through agents, the volume of incoming work explodes. Open-source maintainers already report receiving thousands of agent-generated pull requests per day. Inside companies, the same thing happens to data teams, ops teams, and any function that owns a review gate. The bottleneck shifts from creation to evaluation. The job that emerges to absorb the flood is the forward-deployed engineer. This is a senior person who lives in Slack with the company’s agents, fixes their context, sharpens their instructions, and prevents non-technical colleagues from making well-meaning but incoherent changes. Nitesh at Every is the example Shipper returns to. The model is the same one the labs use internally: pair every important agent with a real engineer who gardens it.

    PMs And Full-Stack Designers Win The Decade

    The two roles Shipper is most bullish on are product manager and full-stack designer. For PMs, the entire job of coordinating a team to translate vision into code collapses into a Codex session. A PM with strong product instincts and a little technical literacy can now prototype, iterate, and even ship. The example is Marcus, formerly a PM at Axios, who took a year to fully internalize AI and now ships faster than most engineers. For designers, the model is similar. The Friday-night-side-project designer who used to be stuck explaining a vision can now build the vision themselves, with their own taste fully expressed. The scarce skill in both cases is the same: judgment about what to build and the courage to decide it is good. Execution capacity is no longer the constraint.

    The Senior Engineer Benchmark And What Models Still Miss

    Shipper has built his own benchmark to test whether coding models can actually do senior engineering work. GPT-5.5 scores around 62 out of 100. Real senior engineers sit closer to 85 or 90. The gap is not in syntax or test pass rates. It is in the willingness to step back, see that a piece of code is fundamentally the wrong shape, and rewrite it from first principles. Models almost universally patch locally. They take the instruction at face value, accept the existing code as a constraint, and optimize within it. A real senior engineer ignores the prompt when the prompt is wrong. This is the durable moat for senior technical judgment, and Shipper expects it to remain visible for at least another year of model releases.

    AI-Generated Writing Goes Mainstream

    Internal writing inside companies is quietly becoming AI-first and Shipper thinks it should. Quarterly plans, status updates, PR descriptions, strategy memos, recruiting outreach, most internal email. He runs his own inbox through GPT-5.5 and Codex and says he would honestly prefer if the recipient knew. The point is not that AI is a better writer in some absolute sense. The point is that most humans are not very good at these specific genres, and the model produces a coherent, structurally sound first draft that a human can guide and approve. The constraint is honesty: you read it, you understand it, you stand behind it. Public writing, like the newsletters Every publishes, still demands a human voice. Internal communication does not, and treating it as if it did is a tax on the organization.

    The CEO And Middle Manager Lag

    Shipper points to a population that has largely escaped AI adoption: senior leaders and middle managers. They have staff to do the work, so they have not been forced to pick up the tools personally. He thinks this is the single largest pocket of latent disruption coming in the next year. Your company will only go as far as your CEO goes in AI, because every decision about where to deploy agents, where to hire, and how to restructure work flows downstream from leadership taste. A leader who has not personally lived inside Codex or Claude Code for a few weeks cannot make those calls well. Expect this to flip fast and to become a visible career liability for executives who do not adapt.

    Ride The Models

    The closing advice is the simplest. Ride the models. Use AI for whatever you actually do. Try every new release the day it lands. Most of the professional anxiety around AI dissolves on contact with the work, and most of the durable advantage in the field belongs to the people who do this one thing consistently. Shipper notes that the edge of AI does not live in San Francisco. It lives wherever a curious operator meets a real workflow and notices something nobody at the labs has yet. A PM in Iowa willing to spend a Tuesday night exploring Codex can find capabilities researchers have not surfaced. Pair that with his motto, do things worth writing about and write things worth reading, and you have most of an operating system for the next two years.

    Notable Quotes

    “The AI job apocalypse is not really a thing. I am super super bullish on PMs and full-stack designers.”

    Dan Shipper, opening his contrarian thesis for the conversation

    “I’m simultaneously extremely AI pilled and very bullish on humans. Automation is a lie. Every agent needs a human.”

    Dan Shipper, on holding both sides of the AI debate at once

    “What models do in general is they make yesterday’s human competence cheap. And so, it becomes commoditized. It’s not valuable anymore. What humans do is we go in there and we’re like, yeah, we have all this frozen human competence from yesterday, how do I use this to make something new and interesting.”

    Dan Shipper, articulating the core engine behind his anti-apocalypse thesis

    “I would buy SaaS stocks right now. The SaaS apocalypse is dumb. What agents do is increase the number of users of SaaS, not get rid of it.”

    Dan Shipper, calling the consensus SaaS-is-dead thesis directly wrong

    “We speed ran the CLI era. It was nice while it lasted, but I think CLIs are over.”

    Dan Shipper, on why the terminal-first agent moment is already done

    “Most of my email is written by GPT-5.5 and Codex right now. And I honestly would prefer it to say that it’s coming from GPT-5.5.”

    Dan Shipper, on the new etiquette of AI-assisted communication

    “The edge of AI is not in San Francisco. The edge of AI is wherever AI meets a real human doing something.”

    Dan Shipper, on where the actual frontier of the field lives

    “The only thing you need to do is ride the models. And that means use them for whatever it is that you do.”

    Dan Shipper, distilling his career advice for the next two years

    “Do things worth writing about and write things worth reading.”

    Dan Shipper’s closing motto, lifted from his own operating system at Every

    Watch the full conversation with Dan Shipper on Lenny’s Podcast here. The re-interview to score these predictions is scheduled for roughly May 2027.

    Related Reading

    • Every. Dan Shipper’s company and the live laboratory for almost every prediction in this conversation, including Spiral, Cora, and Claudie.
    • The Allocation Economy by Dan Shipper. The earlier essay that frames humans as managers of AI labor and underpins much of the gardening-the-agent thesis here.
    • Claude Code by Anthropic. The agent surface Shipper called correctly last year and one of the two environments he predicts will become the new operating system for work.
    • Codex by OpenAI. Shipper’s current daily driver and the visual, multi-pane agent environment he uses for almost everything from coding to email.
    • The Writing Life by Annie Dillard. The book Shipper makes every Every employee read, and the source of the company’s stance on writing as a tool for noticing the future.
  • Rick Rubin on Obsession and Creativity, the Lazy Workaholic, Ruthless Editing, and Why a Great Producer Is Really a Reducer

    David Senra of the Founders Podcast sits down with Rick Rubin for a long, unhurried conversation about obsession, creativity, the discipline of reduction, and how to sustain greatness across more than four decades of making things. They cover the Def Jam dorm-room origin story, the religion of less is more, the ruthless edit, the fishing-for-magic mental model of studio life, the contrasting work styles of Eminem and Jay-Z, why Rick calls himself a lazy workaholic, what he learned from Johnny Cash and the Man in Black mythos, and why he believes a producer is really a reducer. Watch the full conversation on YouTube.

    TLDW

    Rick Rubin tells David Senra that the through-line of his career is not music, it is reduction. To get to less, you have to do more, because every element that survives has to carry the work alone. He started by trying to capture the energy of a downtown hip-hop club nobody respected, signed his early records “Reduced by Rick Rubin” because production meant taking apart rather than building up, and four decades later still runs the same playbook with The Strokes, the Red Hot Chili Peppers, Eminem, and Jay-Z. He describes himself as a lazy workaholic who has to drag himself to the studio for the magical moments that justify everything else. He talks about constraints as a palette, the Man in Black mythology that shaped the Johnny Cash American Recordings, the difference between Eminem’s notebook obsession and Jay-Z’s silent couch composition, why he is a professional listener with no judgment, why he thinks of every finished work as a diary entry rather than a magnum opus, and why the people who sustain greatness across decades stay grounded, never rest on wins, and treat the magic as something they serve rather than something they make.

    Thoughts

    The most counterintuitive idea in this conversation is that the most respected music producer of the last half century identifies as lazy. Rick is not posturing. He says clearly that his default state is to do nothing, that most days he would rather not go to the studio, and that he has to fight a part of himself every morning to show up. That detail matters because the popular image of mastery is fueled by passion, and Rubin is saying the opposite. Passion gets you to the door. After that, the actual work is patience, discipline, and forcing yourself to wait for the moment of magic to land. He is essentially describing the same engine Anthony Bourdain used, redirected from a heroin habit toward writing and television. The work ethic is the constant. The direction is the choice.

    The reduction framing is older than any of his hits. He signed LL Cool J’s first record “Reduced by Rick Rubin” at nineteen because he genuinely felt that what he was doing was taking apart, not building up. Almost every famous Rick Rubin record is recognizable by what is missing rather than what is present. The Johnny Cash sessions ended up as a man and an acoustic guitar because the demos in his living room were better than the band takes. The Strokes album sounds like five people in a room because that is what the band is. The discipline is to refuse to add layers that hide the essence of whoever you are working with. When he calls a band’s signature “stripped down to what they are,” he is describing a generalizable creative principle, not a sound. It applies just as well to writing, code, design, and product, which is why the conversation lands so hard with entrepreneurs.

    Senra correctly diagnoses Rubin’s edge in podcasting as the same edge that powers his production work: he is a professional listener. Most conversations are two people queuing the next thing they want to say. Rick describes listening to music with his eyes closed for hours as a young person, treating it as a psychedelic experience rather than wallpaper, and over a lifetime that built a mental muscle for being present with whoever is in front of him. He is not comparing what he hears to what he believes. He is trying to understand the world through someone else’s eyes. That posture is how he gets artists to deliver work they did not know they had, and it is also how Toby Lütke, Dana White, and dozens of other guests open up on his show.

    The Eminem and Jay-Z contrast is the most useful working-style comparison in the interview. Eminem fills notebooks with tiny letters every day, ninety percent of which never become a song. Jay-Z sits silent on a couch for half an hour, jumps up, and records the entire verse from memory. Both are great. Neither is correct. The point is that obsession and process can take radically different shapes, and trying to copy the surface behavior of someone you admire is mostly a mistake. The deeper pattern is full attention to the craft, sustained over decades, regardless of which ritual carries the attention.

    The conversation closes on the question of how to sustain success over forty years without imploding. Jimmy Iovine, quoted via Senra, says the four pitfalls are drugs, alcohol, women, and megalomania. Rubin adds that megalomania and crippling insecurity are two sides of the same coin, both rooted in not being grounded. His own protection has been meditation since youth and the simple belief that the work is not from him, that he is in service to something that happens in the room. Pair that with the diary-entry framing of past work, where nothing is your magnum opus and nothing is worth regretting, and you have a complete operating system for staying creative without burning out or going crazy.

    Key Takeaways

    • Less is more, but to get to less you have to do more. The fewer elements in a piece of work, the more each one has to be curated, because nothing is hidden.
    • The wall-of-guitars trick makes a recording sound generic. One player whose fingers you can hear on the strings carries personality. The singular essence is what Rubin always looks for.
    • Outsiders almost always underestimate the volume of work behind a finished thing. Rubin says he never thought of his early effort as work because it was mission and love, not labor.
    • Def Jam started in a dorm room at NYU because the few hip-hop singles being pressed were made by professionals who did not understand the music. The records were not a documentary of what was happening in the club, so Rubin made one.
    • His first hip-hop production was T La Rock’s “It’s Yours,” which sold roughly 100,000 copies over 18 months in a genre most adults at the time did not even recognize as music.
    • “Reduced by Rick Rubin” first appeared on an LL Cool J sleeve at age nineteen because Rubin thought “produced” meant to build up, and what he was actually doing was taking apart.
    • He applied Beatles song structure to rap because rap records before Def Jam were closer to monologues or Jamaican toasting than to organized songs. The Beatles are still his reference for what a tight piece of music looks like.
    • The ruthless edit: if you have 100 percent of material and want to end at 70, do not whittle 30 off the top. Reduce all the way down to 40 percent, then add back only what is needed. You understand the work better after the over-cut.
    • With the Red Hot Chili Peppers he records 40 or 50 songs per album, then everyone in the band votes A, B, or C. Only unanimous A songs make the record. Democratic ruthlessness.
    • The most interesting curation question for an artist with 20 albums is whether you can hear a song and know exactly which album it belongs on. Albums earn their place by being unlike the rest of the catalog, usually because of a palette or constraint imposed on that one project.
    • Johnny Cash’s American Recordings became an acoustic record because the in-home demos of him singing alone were better than the studio band takes. The discovery happened during the work, it was not a premeditated concept.
    • Song selection for Cash was filtered through the mythological Man in Black, not the man. A funny song could fit Johnny Cash. The Man in Black would not sing it. The mythos became the constraint.
    • Rubin calls himself a lazy workaholic. His default would be to do nothing. Every studio day starts with him having to overcome the part of himself that does not want to show up.
    • What he is addicted to is the moment of magic in the studio when nothing has been working and then suddenly something does. He compares the wait to fishing or watching paint dry.
    • Once the magic appears, the rest of the process is protecting it from being ruined. Magic is fragile and almost no one knows why or how it shows up.
    • Akon described Eminem treating the studio like a job: in by 9, lunch at noon, out at 5. Discipline beats waiting for inspiration. But you need both: show up every day, and stay open enough for inspiration to land.
    • Eminem is the most obsessive artist Rubin has worked with. He carries notebooks everywhere, writes in tiny letters, and admits that ninety percent of what he writes will never become a song. He writes to stay in shape.
    • Jay-Z is the opposite mode. He plays beats, sits silent on a couch listening, then jumps up and delivers the whole verse from memory. Magna Carta Holy Grail came together in two weeks.
    • Different artists need different things from a producer. Rubin’s job changes with the artist, sometimes hands off, sometimes starting from zero together. The constant is service to the work.
    • Constraints are a creative friend. Every great album benefits from a set of rules that apply only to that project: an instrument restriction, a thematic frame, a recording context, a character lens.
    • Great work is almost never made by committee. Some bands work as democracies (U2), some as dual opposition (Lennon and McCartney, Jagger and Richards), some as a single flag bearer with collaborators (Tom Petty and the Heartbreakers). All of them have a clear point of view.
    • Rubin is a professional listener. He treats listening to music as a psychedelic experience, with eyes closed and full attention, which is why he never needed drugs or alcohol. That listening muscle transfers directly to interviewing.
    • In conversation he has no judgment and no agenda to win. If someone says something he disagrees with, he asks more questions to understand the path that got them there, on the chance that he is the one who is wrong.
    • Curiosity for Rubin is bottomless. If he is into coffee, he wants to taste every important coffee and read every credible review of every machine. He calls himself a researcher, not professionally, but in the obsessive sense.
    • Magic was his obsession from age nine to sixteen before music took over. He counts that not as a loss but as a swap: one full-time occupation for another. The making is the constant, not the medium.
    • Aesthetic consistency carries across domains. Shangri-La studio, the records he makes, the objects he buys, the way his home is arranged all fit one worldview. The thing he does is not really about music, it is about that worldview applied to whatever he is making.
    • Jimmy Iovine described the difference between the two of them as “I am in the banking business, you are in the church business.” Iovine optimizes for what works commercially. Rubin optimizes for what he believes is true.
    • Rubin runs his life on intuition. He has stayed true to what feels right and it has worked. If it had not, he would have made things on a smaller scale and gotten a regular job. He does not see this as a strategy, just as the only honest way to operate when you accept that humans know almost nothing.
    • He is confident but not egotistical. Meditation, learned young, made the work never about him. His confidence is in being able to say clearly how he sees it, not in being right.
    • His inner monologue during work is rarely self-critical. It starts apprehensive because anything is still possible. As soon as one good thing lands, he relaxes into a direction.
    • Past work is a diary entry, not a magnum opus. You did the best you could in that moment. Treating every release as a daily installment removes the paralysis of trying to make the thing that defines you forever.
    • The release-readiness test: if you would be excited to play a track for the friend whose taste you respect, it is ready for everybody. Artists usually overestimate how much polish the world needs.
    • When someone asks for advice, Rubin listens for what they actually want. Most people lead with hopes and dreams, then list fears. The fears almost never matter. The hopes are the answer.
    • The four classic pitfalls of overnight success per Iovine: drugs, alcohol, women, and megalomania. Megalomania and crushing insecurity are the same imbalance, just presenting differently.
    • The way Rubin has sustained success is to stay grounded, treat himself as a conduit rather than a source, never rest on wins, and keep his attention on what he is making now rather than on what he made before.
    • Iovine’s mantra: no review mirror, no trophy room. Jeffrey Katzenberg and other long-careers Rubin meets all share that orientation toward what is in front of them.
    • James Dyson’s organizing principle is the same as Rubin’s: pick up a thing, ask how to make it better, make it better, put it down, repeat for fifty years. Improving what exists is more tractable than designing from scratch.
    • The house on top of a mountain metaphor: imagine no one will ever see your work. What would you still make. That is your life’s work. Bonus test: people say “if you loved it you would do it for free.” Rubin’s higher bar is, if you truly love it, they could not pay you to stop.

    Detailed Summary

    Less is more, and to get less you have to do more

    The conversation opens on the idea from Rubin’s biography In the Studio that Senra says he thinks about every week. Stacking things hides each individual thing. If a piece of music has ten elements, each one carries a tenth of the weight. If it has two, each one has to be devastatingly chosen because nothing else is covering for it. Rubin describes the wall-of-guitars trick as a way to lose personality: you hear “guitar,” not “someone playing guitar.” A single player whose fingers you can hear on the strings has more humanity. The principle is not “use less stuff.” It is “use only what is critically curated, because everything is exposed.”

    The Def Jam origin and “Reduced by Rick Rubin”

    Rubin grew up obsessed with music, played guitar in a punk band, and got into hip-hop in its earliest underground phase, when only one downtown club played it and only a handful of 12-inch singles existed. The singles being released did not represent what was happening in the clubs because they were made by professionals from other genres. Rubin made T La Rock’s “It’s Yours” essentially because no one else would. It sold around 100,000 copies, slowly, in a genre most people did not consider music. On LL Cool J’s first record he printed “Reduced by Rick Rubin” instead of “Produced by,” because he thought production meant building up, and what he was actually doing was stripping away. He also applied Beatles song structure to rap, which until then had been closer to a long monologue or Jamaican toasting. The structural discipline came directly from listening to Lennon and McCartney as a kid.

    The ruthless edit and how to curate an album

    Rubin’s editing method is to overshoot the cut, not nibble at it. If you want to end at 70 percent of what you have, do not trim 30. Reduce to 40, then add back only what is genuinely needed. You learn the work better that way. With the Red Hot Chili Peppers, he records 40 or 50 songs per album, then everyone votes A, B, or C on each track. Unanimous A songs make the album. Divided votes usually do not. The goal is not the sum of individual preferences but the songs you cannot live without, with everything else built out from those.

    Constraints, palettes, and the Man in Black

    The albums Rubin loves are the ones that stand alone in an artist’s catalog, recognizable by their palette. That distinctness comes from rules that apply only to that project. With Johnny Cash, the rule that emerged in the room was acoustic only, just Cash and his guitar with no pick. The song-selection rule was the Man in Black mythos: would the legendary character, not the man, sing this. A funny song could fit Johnny Cash. The Man in Black needed gravitas. That single filter generated the American Recordings sound. The lesson is not to copy the rule, it is to invent a fresh constraint for every project.

    The lazy workaholic and the fishing analogy

    Rubin describes himself as a lazy workaholic. He could happily stay home, walk on the beach, have lunch with friends. He has to drag himself to the studio. He spent twenty-five years in dark rooms in New York sixteen hours a day, seven days a week, and he does not pretend that was easy. What pulls him back is the fishing analogy. You can sit on a lake all day and catch nothing. You can sit in a studio all week and have no breakthrough. But when the fish hits, when the magic moment comes, when a band looks at each other mid-take because they realize the thing is happening, that is what he is addicted to. The rest of the process after that moment is protection: stay out of the way, do not break the spell.

    Show up versus wait for inspiration: Eminem and Jay-Z

    Senra retells an Akon story about Eminem treating the studio like a job, clocking in at 9, breaking for lunch at noon, finishing at 5. The lesson is that you cannot only wait for inspiration. You have to be in the practice that allows the thing to happen. Eminem is the most obsessive artist Rubin has worked with. He carries notebooks everywhere, writes constantly in tiny letters, and admits 90 percent of his writing will never end up in a song. He is just staying in shape. Jay-Z works the opposite way. Sits silent on a couch for half an hour while a beat loops, then jumps up and records the entire verse from memory. Most of his albums come together in days or weeks. Both are great. Both are obsessed. The shapes of the obsession are unrelated.

    The professional listener

    Senra’s working theory is that Rubin’s edge as an interviewer is the same edge that makes him a great producer: he listens for a living. Rubin agrees. Most people in conversation are queuing their next line. He listens to music with his eyes closed, going fully into the experience until he is surprised at where he is when it ends. He treats listening as psychedelic, which is part of why he never drank or used drugs. He has no judgment in conversation and no internal comparison to his own beliefs. If someone says something he disagrees with, his reaction is curiosity about the path, not defense of his position. People find it disarming because it is so rare.

    Intuition, ego, and the inner monologue

    Rubin runs on intuition because he genuinely believes humans know almost nothing. If you accept that, the only honest tool you have is feel. He has high self-confidence but says it is not ego, because meditation since childhood kept the work from being about him. His inner monologue is rarely self-critical. It starts apprehensive at the beginning of a project because anything is still possible. As soon as something good lands and there is a direction, the apprehension drops. He is confident in saying clearly how he sees a thing, not in being right.

    Banking versus church: the Iovine contrast

    Senra calls the Rubin and Iovine episode of Tetragrammaton the best podcast he heard in 2023. The line he keeps returning to is Iovine’s: “I am in the banking business, you are in the church business.” Iovine optimizes for what works commercially. Rubin optimizes for what he believes is good. Both are excellent. They are different jobs. Senra and Rubin draw the parallel to entrepreneurship: opposition between collaborators (Lennon and McCartney, Jagger and Richards) can be a great engine, but committees almost never produce great work, because the average of preferences flattens out the singular point of view.

    Diary entries, the friend test, and the house on the mountain

    Rubin’s frame for past work is that every release is a diary entry, a record of who you were that day. There is nothing to regret because you did the best you could in that moment. There is also nothing that defines you, which removes the paralysis of trying to make a magnum opus. His release-readiness test is the friend test: if you would be excited to play it for the friend whose taste you trust, it is ready for everyone. Artists almost always set the public bar higher than the friend bar, which is wrong. And his life’s work test is the house on the mountain: if no one would ever see what you make, what would you still make. Take the answer and orient your life around it. The higher version of “I would do this for free” is “they could not pay me to stop.”

    Surviving overnight success

    Iovine’s four pitfalls of fast success, relayed by Senra: drugs, alcohol, women, and megalomania. Rubin adds that megalomania and self-loathing insecurity are the same imbalance presenting differently. The famous version of one artist says “I am the greatest who ever lived.” The famous version of another says “any minute they will find out I am a fake.” Both are running from the same lack of grounding. Rubin credits meditation and the belief that the magic is not his with keeping him whole across forty years. He also points out that the people who sustain greatness do not run a victory lap. Iovine has no review mirror and no trophy room. Katzenberg, at lunch with Senra, wanted to talk about what he is working on now, not Disney. The orientation is always forward.

    Notable Quotes

    “If you’re stacking a lot of things on top of each other, each one of those things becomes less important. So if you have 10 things, each one of them is one tenth as important as one by itself.”

    Rick Rubin, on why less is more is a math problem, not an aesthetic one

    “I thought about the idea of produced by and I thought the word meant to build up. Like I think of production as building. And really what I was doing was taking apart and reducing. I thought maybe reduced by is more accurate in this case.”

    Rick Rubin, on why an LL Cool J record was signed “Reduced by Rick Rubin”

    “I’m a lazy workaholic. I have to force myself to do it. But I do force myself. My demeanor would be to do nothing.”

    Rick Rubin, describing his actual relationship with his job

    “It’s frustrating and boring and takes a great deal of patience. It’s like waiting for paint to dry. Just waiting, waiting, waiting and trying different things and nothing works until something either works or something happens and it just comes together and I can’t tell you why.”

    Rick Rubin, on the daily reality of working in the studio

    “If you only wait for inspiration, it won’t ever come. You have to work and be there and show up. If you’re not in the practice of allowing the thing to happen, it won’t happen. Doesn’t mean it will. Just because you do the show up doesn’t mean it will happen. But if you don’t show up, it won’t happen.”

    Rick Rubin, on Akon’s story about Eminem treating the studio like a 9-to-5 job

    “It feels like his entire life is centered around writing words. He’s totally preoccupied with that. So he always has a notebook. He writes tiny tiny letters and he’s always making notes. I asked him, are you working on a new song. He’s like, no, I’m just keeping active in the skill set.”

    Rick Rubin, on Eminem as the most obsessive artist he has ever worked with

    “In real life people like to talk and they don’t like to listen. Often in a conversation you’ll be with someone and they’ll be saying something and you’ll be thinking about what I’m going to say in response to that. You’re not really present. That’s what it is. It’s like two people waiting for their turn to say what they think.”

    Rick Rubin, on why being a professional listener is rare and disarming

    “Jimmy is in the banking business. These are his words. He said I’m in the banking business and you’re in the church business and that’s the difference.”

    Rick Rubin, quoting Jimmy Iovine on the fundamental split in how each of them approaches making music

    “As soon as I liked it enough to share it with one person, chances are it’s ready for everybody.”

    Rick Rubin, on the friend-test for when a piece of work is finished

    “People say, if you love what you do, you would do it for free. There’s another level to loving what you’re doing. If you truly love what you do, they couldn’t pay you to stop.”

    Rick Rubin, on the real test for whether something is your life’s work

    Watch the full conversation here on YouTube.

    Related Reading

    • The Creative Act: A Way of Being, the long-form expression of the worldview Rubin describes throughout the conversation.
    • Founders Podcast by David Senra, the host’s main show, where he distills lessons from biographies of history’s greatest entrepreneurs and the source of his framing throughout this conversation.
    • American Recordings (Wikipedia), background on the Johnny Cash project that Rubin uses as his clearest example of constraints and the Man in Black mythos.
    • Def Jam Recordings (Wikipedia), the dorm-room label Rubin co-founded that turned underground hip-hop into a global industry.
    • The Defiant Ones (Wikipedia), the HBO documentary that captures the Jimmy Iovine “banking business versus church business” lineage referenced throughout the interview.
  • Michael Saylor on Strategy’s Bitcoin Playbook, the 11.5% Stretch Preferred Stock, Why Working Hard Is Bad Advice, and Bitcoin as Cyber Manhattan

    Michael Saylor, founder and executive chairman of Strategy (formerly MicroStrategy), sits down for Episode 172 of the When Shift Happens podcast for a wide-ranging, two-hour conversation on how a near-bankrupt enterprise software company became the world’s largest corporate holder of Bitcoin, why he calls his new preferred stock STRC “stretch” the most successful credit instrument in the world, and what 40 years of trial and error taught him about focus, leverage, time horizons, and the difference between working hard and working smart. This one is essential listening for anyone trying to understand Bitcoin as a protocol, Strategy as a capital markets machine, and what an “AI-pilled” 61-year-old founder actually does with his time.

    TLDW

    Saylor walks through his MIT-trained engineer’s framing of money as an adiabatic thermodynamic system, where the dollar loses roughly 7% of its energy per year, gold loses 2%, and Bitcoin loses zero, giving it an infinite half-life. He explains how COVID-era zero interest rates “rent controlled” the cash on Strategy’s balance sheet and forced him to search for a Facebook-of-money, leading to a $62 billion Bitcoin position across 818,000 coins. He details Strategy’s evolution from buying Bitcoin with cash, to convertibles, to senior bonds, to the equity ATM, to the new preferred stock family (Strike, Strife, Stride, and now Stretch), and argues that STRC is “rocket fuel kerosene” distilled from Bitcoin’s pure economic energy: an 11.5% monthly dividend, tax-deferred return of capital, designed to trade tightly around $100. He returns repeatedly to focus, the lesson he says he learned the hard way after spinning up alarm.com, voice.com, angel.com, and a half-dozen other ventures in his 30s. He argues working hard is now bad advice in an era where AI demonetizes labor, that volatility is vitality and the only honest time horizon is four to ten years, and that Bitcoin is to money what English is to language and Arabic numerals are to math: the protocol that won the network effect contest, and the place “all the money and power” now lives.

    Thoughts

    The most useful part of this conversation is not the Bitcoin maximalism, which is by now a fully formed Saylor genre. It is the brutal honesty about the decade he wasted launching alarm.com, voice.com, angel.com, michael.com, hope.com, and a half-dozen others while a billion-dollar MicroStrategy sat at the center of his portfolio asking for more attention. He admits the “imaginary future business is always more fun than struggling with the existing mature business,” which is one of the cleanest descriptions of founder ADHD I have read. The fact that someone at his level of intelligence and pattern recognition still required 20 years and a near-death experience to learn focus should make every operator under 40 reread that section twice. The single takeaway worth tattooing on a wall is his rule: “Just because you can do a thing doesn’t mean you should do a thing.”

    The engineering framing of money is the strongest intellectual move in the episode. Saylor is treating monetary supply expansion as energy loss in a thermodynamic system, with the dollar at a 10-year half-life, weak currencies at 3 to 5 years, gold at 36 years, and Bitcoin at infinity. Whether or not you accept the conclusion, the model is internally consistent in a way most macroeconomic arguments are not, and it gives him a vocabulary for talking about scarcity that economists trained on continuous-supply commodities literally do not have. The Max Planck quote he leans on, “science advances one funeral at a time,” is doing real work here. He is not saying he is smarter than the old guard. He is saying the old guard has no incentive to update because they already have money and power, and that the paradigm shift will be carried by the people with everything to gain. That is a more humble and more accurate version of the maximalist line.

    The Strategy capital markets machine is the part that deserves more scrutiny than it usually gets. The pitch for Stretch is genuinely interesting on its merits: a preferred stock that trades around $100, pays 11.5% monthly as return-of-capital dividends that defer all tax for roughly nine years, gets a step-up in basis on inheritance, and is positioned as a digital money market for people who believe in Bitcoin but do not want 40% volatility. If you take Saylor’s network-effect thesis seriously, this is the natural product to build, and his Standard Oil analogy (“distill the kerosene out of the crude oil”) is the right mental model. The risk that does not get discussed is what happens to the entire instrument family in a 99.8% drawdown of the kind he himself lived through with MicroStrategy in 2002. He waves it off by saying Strategy has 10x the enterprise value over the preferred, but in a deep enough Bitcoin winter, that cushion compresses fast. Worth holding both ideas at once: this is the most elegant Bitcoin-native fixed income product yet built, and it is still fundamentally a leveraged Bitcoin bet wearing a money-market mask.

    The “working hard is bad advice” thread is going to be the most controversial clip, and it is also the most important. Saylor is not saying do not work. He is saying do not be John Henry. Do not race the steam drill with a hammer. In a world where AI can translate, draft legal briefings, write books in 100 languages, and out-produce any individual professional by orders of magnitude, the marginal value of pure human labor is collapsing, and the right move is to ask “what tool can do this for me” before “how do I get better at this.” That is the same logic that took him from “I would have fired anyone who suggested Zoom in 2019” to running a global operation from a Florida studio. The unsubtle implication, especially for the 34-year-old host he is talking to, is that the 10,000-hour mastery model your parents grew up with is increasingly a status symbol with no underlying economics, like learning to compose Shakespearean sonnets in 2026.

    The single underrated line in the whole episode is “everything you own in the physical world you own at the pleasure of someone more powerful than you.” He is using it to make the Bitcoin self-custody case, but it generalizes to a much broader political and historical observation about property rights, minorities, and the steady drumbeat of expropriation events across 10,000 years of recorded history. Whether or not Bitcoin is the answer, the framing of “where do you store value such that nobody can decide to take it from you” is the right question to ask in the current decade, and most people are not asking it.

    Key Takeaways

    • Strategy now holds roughly 818,000 Bitcoin worth $62 billion, making it the world’s largest corporate Bitcoin holder and effectively a reserve bank built on a hard-capped digital monetary network.
    • Saylor’s working definition of an investor: anyone willing to hold a position for at least four years. Anyone with a shorter horizon is a trader, and most traders are fools who do not know they are fools.
    • His core advice to a 40-year-old Uber driver who cannot afford a house: own assets that appreciate faster than the 7% annual US dollar debasement rate. Anything slower means you are getting poorer in real terms while working harder every year.
    • The MIT-trained engineer’s framing of money: gold has a 36-year half-life because supply inflates ~2% a year, the dollar has a ~10-year half-life at ~7% debasement, weak currencies have 3 to 5-year half-lives, and Bitcoin’s half-life is infinite because supply growth is zero.
    • The 2020 pivot was forced, not chosen. When the Fed took rates to zero and signaled no hikes, Strategy’s $500 million in cash became, in Saylor’s metaphor, a rent-controlled building paying zero. They were forced to look for a way out and ended up at Bitcoin.
    • Saylor’s aha moment was recognizing Bitcoin as the only commodity in history with absolute scarcity. Gold inflates, silver inflates, even land can be reclaimed from the sea. Only Bitcoin’s 21 million cap is mathematically fixed.
    • The biggest lesson of his 30s and 40s: focus. He launched alarm.com, voice.com, angel.com, michael.com, hope.com, and several others while running MicroStrategy, and none of them matched the original. The line he leaves with is “just because you can do a thing doesn’t mean you should do a thing.”
    • By the time he was 55, he had been humbled enough to take someone else’s billion-dollar idea (Satoshi’s) instead of trying to generate his own.
    • Strategy’s evolution as an issuer: cash purchases, then convertibles, then senior bonds, then asset-backed loans (Silvergate failure ended that path), then the equity ATM, then the preferred-stock family Strike, Strife, Stride, and now Stretch.
    • Stretch (STRC) is a preferred stock targeted to trade around $100 with about 1 unit of volatility, paying 11.5% monthly as return-of-capital dividends, tax-deferred for roughly nine years until the basis is fully recovered.
    • STRC pairs with a step-up in basis on inheritance, meaning heirs can receive another nine years of tax-deferred dividends on top of what the original holder collected, an arrangement neither bonds nor most preferred stocks allow.
    • Strategy can create roughly 10 to 20 cents of digital credit per dollar of Bitcoin held, which positions a trillion dollars of future Bitcoin holdings to support $200 to $400 billion of credit instruments.
    • The addressable market for STRC-style instruments, in Saylor’s framing, is the roughly $300 trillion global credit market currently delivering about 350 basis points after tax. A product offering three times that yield is targeting trillions of dollars of demand.
    • Standard Oil analogy: Rockefeller called his company “Standard” because impure kerosene blew up engines and houses. Strategy is in the business of distilling pure financial instruments out of the raw economic energy of Bitcoin, the way refineries distill kerosene from crude.
    • Four-letter NASDAQ ticker discipline. Saylor specifically chose STRC over MSTR.P because retail can search, remember, and trade four-letter symbols on Robinhood and Schwab. About 80% of STRC is held by retail.
    • Bitcoin as a lifeboat thesis: in any country with a collapsing currency (Argentina, Brazil, most of Africa, historical Germany), no physical asset is safe because property is held at the pleasure of whoever has power. Bitcoin allows wealth to cross borders inside someone’s head.
    • The Saylor leverage example: a 2.5% mortgage in 2021 plus a 40% appreciating asset is a roughly 37.5% net spread on borrowed money, equivalent to a real after-tax salary of several hundred thousand dollars in a high-tax city, earned for nothing more than paperwork.
    • Volatility is the feature, not the bug. Bitcoin reacts in real time to events in every country, every hour, which is why 500 million people care about it and almost nobody cares about the value of Tokyo imperial real estate.
    • Saylor’s litmus test for trading: if you would not hold it for ten years, you should not hold it for ten minutes. Anything less than a four-year horizon means you are doing entertainment, not investing.
    • He spends “thousands of hours a year” thinking about Bitcoin’s first, second, third, and fourth-order effects, and runs a stochastic risk model that updates every 15 seconds, refusing to diversify because adding silver, gold, or real estate would shatter the model.
    • Bitcoin as protocol: the same network-effect logic that made English the default global language, Arabic numerals the default math, TCP/IP the default networking protocol, and the shipping container the default freight format. Once a protocol locks in, only an asteroid-strike-level event can dislodge it.
    • “There is no second best language” is the analogy he keeps returning to. Bitcoin is to money what English is to communication. Wishing it were Swahili or Esperanto does not change where the wealth concentrates.
    • The Newtonian network effect: when Rupert Murdoch joins Facebook he brings 50 friends. When he joins Bitcoin he brings $50 million. Monetary networks compound faster than social networks because billionaires bring billions.
    • “Don’t sell the thing that will make your children’s children wealthy” is the operating heuristic. He uses the great-great-grandfather analogy: if your ancestor sold Bitcoin to buy velvet for a horse-and-buggy, you would not be rich today.
    • Working hard is not the path. The forklift outperforms the strongest worker with a shovel. John Henry beat the steel drill once and his heart burst doing it.
    • AI is now the forklift for white-collar work. Saylor uses it to draft 25-page legal briefings, translate content into 100 languages, and avoid going back to law school. “It would take 10 years and a million dollars to do what the AI does in two minutes.”
    • Personal communication leverage: a single Lex Fridman appearance has reached more than 11 million views, more people than a 30-year teaching career could reach.
    • Saylor was inspired into engineering as a child by Robert Heinlein’s “Have Space Suit, Will Travel,” in which the hero saves Earth and is rewarded with a full scholarship to MIT. The same Heinlein-Asimov-Clarke pipeline shaped Elon Musk and Jeff Bezos.
    • His mother imprinted on him that he was expected to do great things while he was a 9-year-old paper boy in Dayton, Ohio. He credits the combination of literature plus maternal expectation with his early ambition.
    • He calls himself a late bloomer and “the Colonel Sanders of crypto,” noting that more interesting things have happened in the last 12 months of his career than in the entire previous 35 years.
    • Strategy’s succession is already in motion. CEO Phong Le, Andrew Kang, and CJ are running operational layers, and Saylor expects Strategy to outlast him the way Lloyd’s of London has outlasted its founders by hundreds of years.
    • The Bitcoin price path he is willing to articulate publicly: “We’ll buy it at 100,000, we’ll buy it at 200,000. We’ll buy it at 500,000, we’ll buy it at a million, 2 million, 4 million, 8 million.” The business is “drive Bitcoin to millions of dollars.”
    • He survived a 99.8% drawdown in MicroStrategy from $333 to $0.42 between 2000 and 2002, three days from bankruptcy. He says current Bitcoin volatility does not feel like stress by comparison.
    • He has no children, is not married, and describes himself as singularly married to the business, which he expects to keep doing as long as the civilization needs the money fixed.

    Detailed Summary

    Who Saylor is and why MicroStrategy became Strategy

    Saylor grew up in an Air Force family, lived on bases across Japan, New Zealand, Nebraska, Florida, and Ohio, and won a US Air Force scholarship to MIT, where he studied aerospace engineering and the history of science. He founded MicroStrategy at 24, took it public on the NASDAQ in 1998, and built it into a billion-dollar business intelligence company with about 2,000 employees. By 2020 the business was being slowly crushed by Microsoft Power BI, and lockdowns plus zero interest rates eliminated the natural return on the company’s $500 million cash position. The frustration drove Strategy into Bitcoin: $250 million, then another $250 million, then a billion, then two, until the company became the world’s largest corporate holder with ~$62 billion across 818,000 coins.

    The hard-earned lesson of focus

    Saylor’s defining career mistake was the period between his mid-30s and mid-40s when he launched ten other businesses on the side of MicroStrategy: alarm.com (now a public multi-billion-dollar company spun off), angel.com (sold for about $110 million), voice.com (sold for about $30 million), and several others including michael.com, frank.com, emma.com, hope.com, and usher.com. He concedes that almost none of these matched the original, that the imaginary future business is always more fun than the mature one, and that he wishes he had instead poured 150% of his energy into MicroStrategy. The crystallized lesson, repeated several times: “Just because you can do a thing doesn’t mean you should do a thing.”

    Money as a thermodynamic system

    The intellectual core of the conversation is Saylor’s framing of money as energy in an adiabatic system. Gold inflates ~2% a year and therefore has a 36-year half-life. The dollar debases at ~7% a year and has roughly a 10-year half-life. Weaker currencies have half-lives of 3 to 5 years. Bitcoin’s hard cap of 21 million coins means zero supply inflation, which produces an infinite half-life. He learned thermodynamics designing aircraft wings at MIT and applies the same closed-system logic to money: any system with energy lapse cannot be a long-term store of value, and Bitcoin is the first asset in human history with no lapse.

    Bitcoin as a global lifeboat

    For people in collapsing currency regimes, Saylor argues no domestic instrument holds value. Argentinian and Brazilian hyperinflations destroy 99.9% of purchasing power on familiar cycles. German marks were used in wheelbarrows to buy soap. Buying local real estate, bonds, or currency in those environments is useless because the underlying economy decays around them. The only escape historically has been gold or paintings, which then need to be smuggled across borders. Bitcoin solves the same problem digitally: it crosses borders inside someone’s head via private keys, and it cannot be expropriated by whoever currently holds power. Saylor’s broader point, “everything you own in the physical world you own at the pleasure of someone more powerful than you,” is the philosophical anchor of the entire Bitcoin maximalist case.

    Strategy’s capital markets evolution

    Strategy has run through every available capital structure to keep buying Bitcoin: cash, tender offers, equity issuance, convertible bonds (where Strategy became the largest issuer in the world), senior bonds (abandoned because covenants choked growth), asset-backed loans (Silvergate’s failure ended that channel), the equity ATM, and finally the preferred-stock family. Strike, Strife, Stride, and Stretch were each iterations toward what Saylor calls “the perfect credit instrument,” refined the way Standard Oil refined crude into kerosene. Stretch (STRC) is the current state of the art: a preferred stock targeted to $100, with about 1 unit of volatility, paying 11.5% monthly as return-of-capital dividends that defer all tax for roughly nine years.

    Why STRC matters as a product

    Saylor argues STRC is the first credit instrument that lets a retiree treat a Bitcoin-backed yield as a money-market alternative. The mechanics: a $100 share generates roughly $10/year in monthly dividends, each of which reduces the cost basis instead of triggering current income tax. After about nine years, basis is exhausted and the instrument becomes a qualified-dividend security taxed at long-term capital gains rates. On inheritance, the heir receives a step-up in basis to $100, and another nine-year cycle of tax-deferred dividends restarts. Eighty percent of the issue is held by retail through Robinhood and Schwab, and the company actively manages the price by issuing or buying back to hold the $100 anchor. The mission for the rest of the decade, Saylor says, is to scale this to $200, then $400, then $600, then $800 billion in outstanding credit, with Bitcoin as the underlying capital base.

    Working smart, not hard, in the age of AI

    Saylor’s most pointed advice to younger founders and operators is that hard work is becoming a low-return strategy. AI now drafts legal briefings, translates content into 100 languages, writes books, and outperforms most professional output by orders of magnitude. The 10,000-hour mastery model that built his generation’s careers, he says, will not produce equivalent results in the next one. The right move is to ask what tool can do the thing for you before asking how to do the thing yourself. He uses himself as the example: working 70 hours a week for ten years built MicroStrategy, but it felt easy compared to MIT, and the leverage from AI plus podcasts plus digital distribution lets him now reach more people in a single sitting than a 30-year teaching career could reach.

    Volatility, time horizon, and the trader-versus-investor split

    Saylor refuses to be rattled by short-term Bitcoin moves and uses his 99.8% MicroStrategy drawdown in 2002 as a baseline for what real volatility feels like. He argues that Bitcoin’s price swings are evidence of its utility: it is the only globally-tradable asset where a regulatory rumor in China at 2am can move price in real time, which is why it absorbs all attention. His rules are blunt: an investor holds for at least four years (40% volatility, 40% expected return for Bitcoin), the right indicator is the 200-week moving average, and the Buffett rule “if you would not hold it for ten years you should not hold it for ten minutes” still applies. Everything shorter is trading, which is fine if you are an expert, foolish if you are not.

    Bitcoin as protocol, not as bet

    The closing intellectual frame is that Bitcoin won the network-effect competition the same way English won language, Arabic numerals won math, TCP/IP won networking, and the standard shipping container won freight. None of these became dominant because they were objectively perfect. They became dominant because critical mass locked in, the wealthy and powerful coordinated around them, and any alternative now has to dislodge a $1.5 trillion incumbent. The protocols that win do so because “people aren’t stupid” and a billion small coordination decisions converge on the same standard. Bitcoin, on this read, is not an investment to be allocated against silver or real estate. It is the digital capital protocol that the rest of the financial world is going to be denominated in, and choosing not to participate is closer to refusing to learn English than it is to skipping a stock pick.

    Notable Quotes

    “Just because you can do a thing doesn’t mean you should do a thing.”

    Michael Saylor, distilling 20 years of side-business mistakes into one line

    “Bitcoin is a lifeboat tossed on a stormy sea, offering hope to anyone in the world that needs to get off their sinking ship.”

    Saylor’s framing of Bitcoin as a solution for collapsing-currency regimes

    “There is no second best crypto asset. There’s only one crypto asset and that’s Bitcoin. Human civilization settles on protocols.”

    The closing thesis of the conversation, in Saylor’s own words

    “Don’t sell the thing that will make your children’s children wealthy.”

    Saylor on holding Bitcoin through volatility and selling something else instead

    “Everything you own in the physical world you own at the pleasure of someone more powerful than you.”

    Saylor on why physical assets are not real property rights

    “Volatility is vitality. Bitcoin’s volatile because it’s useful.”

    Saylor reframing Bitcoin price swings as a feature

    “Don’t try to outwork a forklift.”

    Saylor on why “work harder” is increasingly bad advice in the AI era

    “I’m like the Colonel Sanders of crypto. But it’s okay. At least I found a mission at some point in my life.”

    Saylor on being a late bloomer at 55

    “Bitcoin is cyber Manhattan. A thousand years from now, your children’s children’s great-great-great 10x grandchildren will be rich, if you kept it.”

    Saylor on Bitcoin as multi-generational real estate

    “The world doesn’t care whether I’m a good manager of a hundred different things. The world wants me to be the best manager of one thing.”

    Saylor on focus as the only durable professional posture

    Watch the full conversation here: When Shift Happens E172: Michael Saylor on How To Get Rich With Crypto (Without Working Hard).

    Related Reading

  • Mohnish Pabrai on How to Invest in 2026: The Ten Commandments of Investing, Charlie Munger Lessons, Cloning, Turkey Warehouses, Constellation Software, and Why Less Than 1% of Stock Pickers Beat the Market

    Mohnish Pabrai sat down with Shaan Puri to lay out exactly how he thinks about investing in 2026, walking through the ten commandments that have shaped a 27 year track record where every dollar invested in his oldest fund turned into roughly thirty. Watch the full conversation on YouTube here. Pabrai manages over a billion dollars, was close friends with Charlie Munger, has had lunch with Warren Buffett for 650,000 dollars, and has produced multiple 100 bagger investments in his career. This conversation is a complete operating manual for value investors, deep value hunters, and anyone trying to figure out how to compound capital in a market where the S&P trades at elevated valuations and AI capex is rewriting the rules.

    TLDW

    Pabrai argues that under one percent of stock pickers are actually good investors, that the game is a wealth transfer from the active to the inactive, and that temperament beats IQ every time. He walks through his core mental models: watching paint dry, the mistress versus the wife, introducing randomness into your life, cloning instead of inventing, taking a simple idea and taking it seriously, the too hard pile, no called strikes, the salmon spear, the inner scorecard, and don’t die at 25 and get buried at 75. He shares the full story of his 100 bagger Turkish warehouse company Reysas, his coal bets, his Constellation Software thesis around Mark Leonard, and why he is bearish on the S&P 500, bullish on pickaxe makers like TSMC and ASML conceptually but unwilling to pay current prices, and why GLP-1 drugs and Bitcoin both sit in his too hard pile. He retells Warren Buffett’s American Express salad oil crisis trade, the lesson Buffett delivered about Rick Guerin and leverage, the inner versus outer scorecard, and the Ed Thorp blackjack to Ken Griffin to Citadel chain. The closing punchline is that the most important investment any person can make is leading an aligned life, getting your music out, and discovering your calling before the wilderness years pile up.

    Key Takeaways

    • Well under one percent of Americans picking individual stocks are actually good at it. Index funds put you ahead of more than ninety percent of the crowd with zero effort.
    • The single biggest mistake smart investors make is impatience. Temperament, not IQ, decides outcomes.
    • Watching paint dry is the core skill. After making an investment, nothing may happen for three to five years. That is the nature of the game.
    • The mistress is always hotter than the wife. The stock you do not own looks more exciting than the one you do own because you do not know its flaws. The bar for swapping must be extremely high.
    • Raise your standards across the board: the investments you make, the people you hang out with, the relationships you keep. Buffett’s gravitational pull rule applies to both portfolios and friendships.
    • Introduce randomness into your life. Pabrai picking up a Peter Lynch book at Heathrow in 1994 led him to Buffett, Berkshire, Charlie Munger, bridge games, and his entire investing career.
    • Cloning works because almost no one will do it. Sam Walton copied everything. Walmart came from Kmart, Sam’s Club came from Sol Price’s Price Club, Burger King located across from McDonald’s instead of running their own site selection.
    • Elon Musk’s idiot index, calculating raw material cost on the London Metals Exchange and refusing to pay more than a small multiple over it, is the kind of simple framework no competitor will adopt even though it is publicly visible.
    • Take a simple idea and take it seriously. None of the other mental models work unless you commit fully to the first one.
    • The too hard pile is the most important box on a value investor’s desk. Buffett claims ninety eight percent of businesses belong there. Investing has no called strikes, so passing on ten thousand pitches before swinging is the right behavior.
    • The whale is swimming all the time, you only see it when it surfaces. Real investor activity is reading and studying, not trading.
    • Buffett at twelve gathered discarded racetrack tickets at Ax-Sar-Ben, found winners drunks had thrown away, and had his Aunt Alice cash them. He carried that pattern of finding anomalies into the Moody’s manuals in his twenties and into the Japan Company Handbook for two decades before pulling the trigger on the five Japanese trading companies.
    • The Japanese trading company trade was financed at half a percent in yen, the companies paid eight to nine percent dividends that later doubled, and Berkshire’s five billion has roughly doubled with almost no risk attached.
    • The American Express salad oil crisis taught Buffett to test the moat in the real world. He stood next to restaurant cash registers in Omaha, saw zero hesitation about accepting the card, and put forty percent of his fund into AMX.
    • The Buffett lunch lesson Pabrai still carries: a slightly above average investor who spends less than they earn and does not use leverage cannot help but get rich over a lifetime. Rick Guerin lost his Berkshire shares to margin calls in the 1973 to 1974 crash. Buffett bought them at forty dollars each, currently worth over seven hundred thousand.
    • Inner scorecard versus outer scorecard is the most fundamental life model. Buffett’s frame: would you rather be the greatest lover in the world and known as the worst, or the worst lover known as the greatest.
    • The Turkish stock market cycles through its float every seventeen days. Pure speculation. Indian quality companies trade at stratospheric valuations. The two together created a poker table Pabrai could sit at alone.
    • Reysas, the Istanbul warehouse operator, was bought at roughly three percent of liquidation value with a fifteen to sixteen million dollar market cap on eight hundred million in assets. It is now approaching a 100 bagger in dollars.
    • Pabrai’s thermonuclear event mental model: if ninety nine percent of humans were wiped out, someone would still produce Coke concentrate, because people will always trade fifteen minutes of labor for a Coke. Cement, paint, land, and steel are inflation indexed.
    • TAV Airports earned revenue in euros, paid costs in collapsing lira, traded at three to four times earnings on the Istanbul exchange. A natural monopoly hiding inside a panicked market.
    • The stock market is a church with a casino attached. Robinhood, prediction markets, zero day options, and two day options all increase the wealth transfer from the active to the inactive. Pabrai welcomes more casino activity because it helps his side.
    • On Polymarket, roughly one tenth of one percent of users capture sixty percent of profits. Two thousand traders made half a billion dollars in a year. The casual gambler funds the sharp.
    • On AI: invest in pickaxe makers. The alphabets and metas are playing a high capex game they have never played before. TSMC, ASML, and Micron are toll bridges. Pabrai is not making the bet because it sits between too hard, too expensive, and outside his circle of competence.
    • Constellation Software is Pabrai’s vertical SaaS bet because Mark Leonard built a mousetrap nobody else will clone. They acquire roughly two hundred small vertical market software companies a year, in delegated fashion, at five to six times cash flow that quickly becomes three to four times after revenue and license fee tweaks.
    • The market is wrong about AI killing software. Coding is one fifth of the pie. Adobe is not going out of business because someone can vibe code a Photoshop alternative. Incumbents reduce cost via automation while keeping cash flows intact.
    • Pabrai is bearish on the S&P 500. He agrees with Howard Marks that when the index trades at twenty three times earnings, the historical forward ten year return has bounced between minus two and plus two percent.
    • GLP-1 drugs sit in the too hard pile because industries with rapid change are the enemy of the investor. Ozempic to Mounjaro to upcoming tablets is too much turnover for valuations that already price in success.
    • Bitcoin sits in the too hard pile. Pabrai prefers gold and asks why a society that already has gold needs Bitcoin.
    • The four percent rule of compounding: roughly four percent of stocks have delivered the entire return of the US market over ninety years. Twelve investments built Berkshire across sixty years.
    • Investing rewards aging. Unlike basketball, the game gets easier with experience. Pattern recognition, expanded circle of competence, and the option to ride winners all compound.
    • Circle the wagons around your winners. Not selling Coke, not selling Apple, not firing Ajit Jain. The success of Berkshire was about not interfering with the four percent that worked.
    • Charlie Munger made an investment six days before he died at age ninety nine point nine. He invested like he was twenty five. Ben Franklin’s line: many people die at twenty five and are buried at seventy five.
    • Don’t save sex for old age. Don’t delay starting your real life until after the McKinsey rotation. Buffett’s frame transfers to careers too.
    • Ed Thorp wrote Beat the Dealer after the mob threatened him with a baseball bat for cleaning out single deck blackjack in Vegas. He then cracked options pricing before Black Scholes, ran Princeton Newport Partners, and became an early backer of Ken Griffin’s Citadel out of a Harvard dorm room.
    • Ken Griffin once told a Harvard recruit he wanted to quit at ten million dollars: please reject our offer, we do not want someone who dies at twenty five.
    • Lead an aligned life. Personality is largely baked by age five. The window to specialize is age eleven to twenty, which is exactly when the school system forces you to be a jack of all trades.
    • Get your music out. Every person has something specific they are meant to bring into the world. A misaligned life is the highest cost most people pay.

    Detailed Summary

    Why fewer than one percent of stock pickers are actually good

    Pabrai opens with a brutal estimate: well under one percent of the Americans who pick individual stocks are good at it. The game, he says, is a mechanism for transferring wealth from the active to the inactive. The good news is that index funds let anyone capture market returns with zero analytical work and end up ahead of more than ninety percent of active stock pickers. The implication is that anyone choosing to pick individual stocks is voluntarily entering a competition where the base rate of success is below ten percent, and the differentiator is almost never intelligence. It is temperament.

    That temperament shows up as patience. After making an investment, three to five years can go by with nothing happening. Sometimes the investment is a mistake and the patience converts into the discipline to reverse course. But on the whole, the less activity an investor takes, the better the outcomes. The first commandment is to enjoy watching paint dry.

    The mistress is always hotter than the wife

    The investments you already own are the wife. You see every flaw because you live with them every day. The investments you do not own are the mistress. Glamorous, unknown, exciting precisely because the temperament and the warts have not been revealed. Guy Spier, Pabrai’s longtime friend, deliberately stays reluctant to act on his portfolio. The point is not that you never act. The point is that the bar for action needs to be extremely high, and you have to learn to be comfortable passing on everything below that bar. Pabrai extends this directly to life: raise your standards about the people you spend time with, the projects you take on, and the investments you select.

    Introduce randomness into your life

    Charlie Munger told Pabrai over and over to introduce randomness into his life. The example Pabrai uses is his own origin story. He was an engineer running an IT company in 1994, sitting in Heathrow with his wife, looking for something to read on a flight. He picked up Peter Lynch’s One Up On Wall Street, finished it, picked up Beating the Street, finished that, encountered Buffett through a mention in Lynch, found the first two Buffett biographies fresh off the press, dove into the Berkshire and partnership letters, and within three years started attending the Omaha annual meeting. Every flight to Omaha on a Friday in May has both seatmates pre filtered for above average humans, all going for the same reason. The randomness exploded outward into Charlie Munger, the bridge games, Charlie’s friends, and decades of compounding social and intellectual capital.

    Shaan tells the parallel story of his own randomness bet. He flew to FarmCon in Kansas City for no particular reason, met newsletter operator Kevin Van Trump, cloned the model, launched The Milk Road for crypto, built the largest crypto newsletter in the world inside a year, and sold it for millions with one employee. Two mental models stacked: introduce randomness, then clone.

    Cloning is the cheat code no one will use

    Sam Walton freely admitted he had no original ideas. He walked into more competitor retail stores than any human in history. He looked at Sol Price’s Price Club, said no brainer, and opened Sam’s Club. He took his managers into a competitor and when they complained the store was a mess, he pointed at the one good candle display and said you can learn from anyone. He bought donuts at five thirty in the morning for distribution center drivers because they had ground level intel on every store. Walmart’s market cap dwarfs that of every competitor combined, and every system came from somewhere else.

    Tesla, SpaceX, and the Boring Company exist because Elon Musk applies the idiot index. He asks what raw materials go into a part, looks up the price on the London Metals Exchange, and refuses to pay a multiple over it without a fight. None of his competitors think this way even though he has written about it publicly and Walter Isaacson devoted a book to it. SpaceX intentionally blows up rockets to learn. Blue Origin tries hard not to blow up rockets. SpaceX is miles ahead. Burger King famously assigned two guys to track McDonald’s site selection and just put a store across the street. The reason cloning works so well is that almost no one is willing to do it.

    Take a simple idea and take it seriously

    This is the bedrock model that makes every other model work. Without total commitment to one simple idea, the rest of the mental models stay theoretical. Pabrai went to Turkey on a hunch in 2018 because the market screened cheap. He discovered that Turkish public companies cycle through their float every seventeen days, meaning every shareholder turns over more than twenty times a year. Compared with Berkshire, whose float may take a decade to rotate, Turkey is a hyperactive day trader’s casino. India, by contrast, has roughly one hundred to one hundred fifty quality companies, all picked over and priced at stratospheric multiples. The Turkish market gave Pabrai a poker table he could sit at almost alone. He chose to be an inch wide and a mile deep.

    Circle of competence and the too hard pile

    Pabrai’s eighth commandment is thou shalt not use Excel, and his ninth is that if you cannot explain an investment thesis to a ten year old in about four sentences, it is a pass. Investing is journalism more than spreadsheet work. Buffett stood at restaurant cash registers in Omaha during the salad oil crisis to test whether AMX’s brand had cracked. He walked into Snow White with his briefcase to study Disney. Peter Lynch told amateurs to make a list of every brand they consume because that is the most authentic intel they have. Buffett keeps a too hard box on his desk and claims ninety eight percent of businesses go in it. Investing has no called strikes, which means an investor can let ten thousand pitches go by and only swing at the fattest center cut pitch.

    The whale is swimming all the time. You only see it when it surfaces. Buffett at age twelve gathered discarded racetrack tickets at Ax-Sar-Ben to find ones drunks had thrown away, then had his aunt Alice cash them because he was underage. In his twenties he flipped through Moody’s manuals page by page looking to be hit in the head with a two by four. Western Insurance at fifteen dollars made twenty five dollars a share and had forty dollars of cash on the balance sheet. He has been flipping through the Japan Company Handbook for at least twenty years before pulling the trigger on the five Japanese trading companies, financing the entire five billion in yen at half a percent against eight to nine percent dividend yields that have since doubled. Berkshire’s five billion is now ten billion paying eight hundred million a year, essentially risk free.

    The 650,000 dollar lunch and what Buffett actually said

    Pabrai paid 650,000 dollars to have lunch with Warren Buffett in 2007 because his net worth had hit eighty four million dollars almost entirely from intellectual property he had taken from Buffett for free. He wanted to look him in the eye and thank him. Buffett’s stance on the lunches was that whoever paid should feel like they got a bargain, so he came prepared having studied biographies of every guest. Pabrai asked an innocent update question about Rick Guerin, the third partner of Buffett and Munger in the sixties and early seventies who then disappeared. Buffett’s answer became the lesson of the lunch. Charlie and I knew we were going to be rich, but we were not in a hurry. Rick was in a hurry. He was always levered. The 1973 to 1974 crash, the slowest motion crash in modern history, gave him margin calls. Buffett bought Rick’s Berkshire shares for forty dollars each. They are over seven hundred thousand now. The lesson: if you are even a slightly above average investor and you spend less than you earn and you do not use leverage, you cannot help but get rich over a lifetime.

    The other lunch lesson Pabrai still cites is the inner scorecard. Buffett’s framing: would you rather be the greatest lover in the world and known as the worst, or the worst lover known as the greatest. The answer determines whether you can resist external stimuli and stay centered. The way Pabrai practices it is by remembering that even Gandhi has critics. If Gandhi is fair game, so is anyone else with a public footprint.

    The Turkey trade and the thermonuclear event mental model

    The headline Turkey investment is Reysas, an Istanbul warehouse operator Pabrai started buying when the market cap was fifteen to sixteen million dollars on eight hundred million dollars of assets, roughly three percent of liquidation value. He told the broker to take out every ask up to the ten percent daily price limit. Templeton Fund called offering five percent of the company for a million dollars and Pabrai said why are you even calling, just take it. Templeton was exiting Turkey because of currency instability and inflation, both of which Pabrai considered irrelevant for the specific kinds of assets he was buying.

    The mental model that unlocked Turkey was the thermonuclear event scenario he discussed with Charlie Munger. If ninety nine percent of humans were wiped out, someone would still produce Coke concentrate and rebuild a bottling plant. The remaining seventy million people will trade fifteen minutes of labor for a Coke regardless of currency or exchange rate. A warehouse is land, paint, cement, and steel. All four are inflation indexed. Whatever happens to the lira, those assets do not care. When the lira collapsed ninety percent against the dollar in seven years, Reysas went up roughly ninety times in dollars and effectively to infinity in lira. He applied the same logic to TAV Airports, which collected revenue in euros while paying costs in collapsing lira. A natural monopoly trading at three to four times earnings on a panicked exchange.

    The casino, prediction markets, and Polymarket

    Buffett’s line at the most recent Berkshire meeting is that the stock market is a church with a casino attached, and the casino is getting crowded. Robinhood, two day options, leverage, and prediction markets like Polymarket all funnel casual gamblers into a transfer game where the sharps already know the prices. Pabrai notes that on Polymarket, roughly one tenth of one percent of users capture sixty percent of profits, and two thousand traders made half a billion dollars in a year. The horse track keeps twenty one percent of every dollar, Vegas keeps two to four percent on a great blackjack game, and yet some players still make a living off horse racing by spotting odds that make no sense. The casino activity is bad for society and great for any investor patient enough to wait for the obvious mispricing.

    AI, pickaxe makers, and the too hard pile

    On AI, Pabrai says invest in pickaxe makers. The alphabets and metas are playing a high capex game they have never played before, which is a recipe for surprise. The capex must pass through TSMC, ASML, and probably Micron. But all of those toll bridges are either too expensive, outside Pabrai’s circle of competence, or in his too hard pile. He is not making the bet. There is no scenario where he sells his Turkish warehouses to buy TSMC. The mistress, in this case, looks uglier than the wife and there are no bonus points for clever valuation work.

    Constellation Software, Mark Leonard, and vertical SaaS

    Where the market gets AI wrong is the assumption that AI coding kills software. Coding is at most one fifth of a software business. The market assumes Adobe is dead because someone can vibe code Photoshop. Pabrai disagrees. Incumbents will reduce costs through automation, keep cash flows intact, and possibly cut prices without losing margin. He invested in Constellation Software specifically because Mark Leonard has built a mousetrap nobody else will clone. Constellation’s M&A team touches seventy to one hundred thousand private vertical market software companies twice a year by phone and twice by email. They acquired roughly two hundred companies last year alone, never using bankers. They pay five to six times cash flow, then bump revenue and license fees about twenty percent, and the effective purchase price drops to three or four times within a year or two. The model is delegated, with deal authority pushed out to teams that do not need headquarters approval up to a threshold. They buy and hold, which scares away private equity that wants to flip. The universe of vertical SaaS targets is too small for private equity to bother with and big enough to keep Constellation compounding for decades. Mark Leonard is the kind of unicorn operator who does not appear twice in a generation.

    S&P bearish, GLP-1 too hard, Bitcoin too hard

    Pabrai is bearish on the S&P 500 because at roughly twenty three times earnings, historical forward ten year returns have ranged between minus two and plus two percent. Howard Marks’s analysis matches his own. GLP-1 drugs like Ozempic and Mounjaro are generating roughly seventy nine billion in revenue annually, more than the entire AI economy, but Pabrai puts them in the too hard pile because industries with rapid change are the enemy of the investor. Ozempic to Mounjaro to upcoming oral tablets is too many turns of the wheel. Bitcoin sits in the same too hard pile. Pabrai prefers gold and asks why a society that already has gold needs Bitcoin, which is widely used by scammers and ransomware operators.

    The four percent rule and circling the wagons

    Over the past ninety years, roughly four percent of US stocks have delivered the entire market return. The other ninety six percent have treaded water. Buffett himself has made three to four hundred investments and only twelve of them built Berkshire Hathaway. Index funds work because they are too dumb to sell Nvidia and too dumb to sell TSMC. Active investors and portfolio managers second guess winners and trim them. The most important investing discipline is circling the wagons around winners: not selling Coke, not selling Apple, not firing Ajit Jain. Capitalism is brutal and most businesses go to zero eventually. The thin slice of enduring moats, like FICO, McDonald’s brand, prime Istanbul warehouses, airport monopolies, and Coke bottlers, are what compound for decades. Pabrai’s bets on coal, airports, warehouses, and Constellation do not all need to work. If half work, the portfolio is a home run. If forty percent work, still a home run. Investing is a forgiving game.

    Ed Thorp, Ken Griffin, and the chain of investing genius

    Ed Thorp used MIT’s mainframe in the early sixties to crack single deck blackjack with basic strategy and card counting. He cleaned out mob run Vegas casinos until they showed him a baseball bat. To get back at them he wrote Beat the Dealer, which sold millions of copies and forced the industry to introduce multi deck shoes and rule changes. He then cracked options pricing before Black-Scholes and skipped the Nobel Prize to make money on it through Princeton Newport Partners, compounding at twenty five to thirty percent a year with no down years. He met a young Ken Griffin running Citadel out of a Harvard dorm and not only handed over algorithms but became an early backer. Pabrai’s first meeting with Thorp happened in a racquetball locker room while Pabrai was completely naked, copy of The Wall Street Journal next to him. Thorp introduced himself, Pabrai’s excitement overcame his sense of decorum, and they have been friends ever since. The Ken Griffin lore extends to his recruiting filter: a Harvard recruit who said he would quit at ten million dollars was told to please reject the offer, because Citadel does not want people who die at twenty five.

    Don’t die at twenty five and get buried at seventy five

    Ben Franklin’s line that many people die at twenty five and get buried at seventy five becomes Pabrai’s closing frame. Charlie Munger made an investment six days before he died at age ninety nine point nine. He invested like he was twenty five. The whole point of life is to keep growing, keep learning, keep finding the alignment between who you are inside and how you show up in the world. Personality is largely baked by age five, and after twelve the most a parent can really influence is the peer group. The window to specialize runs from age eleven to twenty, which is precisely when most educational systems force kids to be jacks of all trades. Bill Gates slipped out of his house to code through the night and accumulated ten to twenty thousand hours by his early twenties. Buffett picked stocks at eleven. Michelangelo sculpted at ten.

    The most important thing Pabrai wants viewers to take from the conversation is that an aligned life is more important than a great investment record. Get your music out. Find what energizes you. If you do not know your calling, work with a thoughtful industrial psychologist like Jack Keene or pay attention to which activities and people genuinely energize you and which drain you. Pabrai himself wandered the wilderness until his mid thirties when he finally understood his own calling. Buffett’s frame applies: do not save sex for old age and do not save your real work for after the McKinsey rotation.

    Thoughts

    The most useful thing Pabrai does in this conversation is collapse the gap between life philosophy and portfolio construction. Most investing content treats temperament as a soft skill on the side of the spreadsheet. Pabrai puts it where it belongs, at the center. The reason the four percent rule matters is not statistical, it is psychological. Almost everyone can identify the few enduring compounders. Almost nobody can sit on them for forty years without selling, trimming, switching to a hotter mistress, or breaking discipline on a leverage call. The actual edge in public markets is not analytical, it is the willingness to be inactive in the face of constant pressure to act.

    The Turkey trade is the most instructive case study in the whole conversation because it is genuinely replicable. Not the specific market, but the architecture. Pabrai stacked four simple mental models on a single trade: take a simple idea seriously, identify a market where the float churns so fast that price has no relationship to value, isolate assets whose intrinsic worth is currency independent, and run the thermonuclear event sanity check on the underlying demand. The result was a 100 bagger held in roughly the worst macro environment of his investing career. The lesson is not to go to Istanbul. It is that real edge tends to come from combining three or four boring frameworks at the same time, in a place where nobody else is bothering to combine them.

    The Constellation Software section deserves more attention than it gets in most investor decks. Pabrai is making a clean bet that vertical SaaS is misread by the market because of generic AI fear. He is probably right. Coding is a labor input to software, not the moat. Switching costs, regulatory tangles, integration depth, and decades of accumulated workflow customization are what keep customers paying. Mark Leonard has industrialized the act of acquiring those moats two hundred times a year. If the DNA holds after Mark eventually steps back, the math is hard to beat. The asymmetric risk is leadership transition, not technological disruption.

    The AI commentary is more interesting for what Pabrai refuses to do than for what he says. He acknowledges the pickaxe makers thesis, names the toll bridges, and then explicitly declines to make the bet because the valuations are too high and the path forward is genuinely uncertain. That is the discipline of the too hard pile in action. Plenty of investors right now are putting money to work in TSMC and ASML at multiples that bake in success scenarios, telling themselves they have done the homework. Pabrai’s position is that even when you are largely right about a trend, paying any price for it is a mistake. The structural humility there is the actual lesson.

    The aligned life closing argument hits hardest because it reframes the entire conversation. The ten commandments of investing are a subset of a broader operating system: figure out who you are by age twenty if you can, raise your standards on the people you spend time with, do not borrow against tomorrow, do not chase the mistress, and do not save your real ambitions for old age. Investing is just the highest leverage application of those rules. The viewers who walk away with one usable change probably should not be picking new stocks. They should be auditing whether they are leading the life that fits.

    Watch the full conversation with Mohnish Pabrai and Shaan Puri on YouTube here.