PodcastsHear the voice. See the shape of the thought.

Dwarkesh Patel narrates his essay on where AI training is headed. The labs are betting that scaling RL across millions of verifiable tasks gets you to AGI, but Dwarkesh argues that bet leaves two holes: most valuable skills aren't "grindable" enough to farm in a simulator, and the learning models pick up on the job never makes it back into their weights. He walks through why sample efficiency and continual learning are the same problem, sketches two candidate fixes — on-policy self-distillation and "dreaming" — and imagines an AI that keeps getting smarter from being deployed rather than from pretraining. ## [00:00] The big research bet the labs are making The labs' working theory: train AIs on millions of verifiable tasks across thousands of RL environments and you'll get a general problem-solver that can grind on open-ended work for weeks. Optimists argue the known deficits — data inefficiency, no continual learning — will get steamrolled by more compute, the same way classic NLP problems collapsed once LLMs scaled. Dwarkesh lays out their strongest counter to his own skepticism: the million-fold sample-inefficiency he flagged in his last essay is only a training-time cost, amortized across billions of sessions. What matters is how capable the model is *during* a session, and that keeps improving. Continual learning might not even be needed if context windows grow large enough to hold months of on-the-job experience. > *People often say that their employees are not net productive until six months or more on the job. So clearly, online learning is necessary for competence. But what if you could just fit those six months into the context window?* ## [02:12] Grindability is just as important as verifiability Why has computer use lagged coding and math when it's just as verifiable? Dwarkesh's underrated answer: being verifiable isn't enough — a domain also has to be *grindable*, meaning you can run thousands of parallel rollouts against a deterministic, replayable simulator from the same starting point. A coding repo clones trivially into a container; Amazon's checkout flow does not. This is the canyon wall AI progress only slowly chips at. You can sometimes build farmable simulators (clone Slack, clone Gmail), but most high-value skills — building a business, winning a court case, running a profitable trading day — require irreproducible interaction with the real world, where verification takes months and can't be re-observed across parallel rollouts. > *What is the RL environment to make an AI that is as good at politics as Lyndon Johnson, or as good at building a space-launch business as Elon Musk?* ## [06:10] Will RLVR alone generalize? The labs are betting RLVR generalizes — that enough containerized environments yield an agent that plans, adapts, and picks up new skills inside a single session, good enough to out-advise LBJ on a 1948 Senate race or build SpaceX with a hundred million dollars. Whether it generalizes that far is an empirical question, and Dwarkesh reads a Dario Amodei quote as a hint that it doesn't stretch infinitely: short-horizon training may not transfer to long-horizon performance. Even if in-context experience could turn a model into Henry Ford for a session, it's all wasted if the learning can't return to the weights. 30–50% of a lab's compute goes to inference that currently does nothing to improve the model — even though deployment is exactly where the most valuable information is revealed. > *We've got some genius grad student who's never been allowed to take a real internship, and we keep giving it more and more classroom case studies in the form of RL training on environments.* ## [08:41] Getting the learning back to the weights Continual learning means updating the weights, not endlessly growing a KV cache — brains don't separate parameters from activations, and they compress what they learn. But moving into the weights forfeits in-context learning's sample efficiency, because gradient updates are coarse. That's why every shipped online-learning model (like Cursor's Tab model, learning the same accept/reject objective across 400M+ requests a day) learns one identical thing across all users, which defeats the point when every job and company differs. Dwarkesh frames sample efficiency and continual learning as the same problem, then argues the bottleneck isn't architecture — new sparse-attention and KV-compaction papers ship weekly — but the loss function. His candidate is on-policy self-distillation: train the base model to make the same predictions a context-rich veteran version of itself would make. OPSD needs no outer-loop reward, gives denser per-token supervision than RL, and keeps RL's sparse-update property so on-the-job learning doesn't overwrite what the model already knows. > *The way you get better at your job is not by recalling the transcript of every single thing that happened every day with perfect fidelity. Rather, it's by consolidating the handful of insights and pieces of knowledge that are actually relevant to you getting better at your job.* ## [15:22] Dreaming The second, more speculative fix: let the AI build a simulation of reality and rehearse against it, experiencing orders of magnitude more samples per unit of wall-clock time. The precedent is EfficientZero, which beat novice humans at unfamiliar Atari games by playing dozens of simulated games in its head per real step. Simulating the whole world is far harder than emulating Go, which is why Dwarkesh flags this as speculative — but if it works, it becomes a fourth scaling axis alongside pretraining, RL, and inference-time compute. Instead of hitting `/compact` to summarize a session, you'd hit `/dream` and burn compute rehearsing against a video-game version of what the model is seeing in production. > *So instead of hitting /compact in Codex or Cursor or Claude... you hit /dream. And this incinerates huge amounts of compute to build and train against a video-game version of what the model is witnessing in the real world.* ## [17:23] What 2027 looks like Dwarkesh's scenario: RLVR produces an agent competent enough to start getting real-world experience, context windows stretch to a full week of co-working, and at the end of the week a thumbs-up triggers the base model to distill what it learned — via OPSD, dreaming, or some mix. Each round the model expands into domains adjacent to what it was last trained or deployed on. The endgame flips how AI improves: capability comes mostly from broad deployment across the economy, not from pretraining before release. Every interaction makes the model smarter — learning from your past sessions and from everyone else's — which Dwarkesh calls scary, exciting, and very different from today. > *Just as pretraining created a base intelligence that was smart enough to become a competent agent with enough RLVR on top, so RLVR has created an agent that is competent enough to actually be broadly deployed in the world.* ## Entities - **Dwarkesh Patel** (Person): Podcast host and essayist; narrates his own blog post on AI training paradigms. - **Dario Amodei** (Person): Anthropic CEO, quoted on why model performance degrades at long context. - **RLVR** (Concept): Reinforcement learning from verifiable rewards — training on reproducible, checkable tasks; the labs' main bet for reaching AGI. - **Continual learning** (Concept): Updating a model's weights from on-the-job deployment rather than only from pre-release training. - **Grindability** (Concept): Dwarkesh's term for whether a domain can be farmed via many parallel rollouts on a deterministic, replayable simulator. - **On-policy self-distillation (OPSD)** (Concept): Distilling a context-rich session's learning back into the base model's weights with dense per-token supervision. - **Dreaming** (Concept): Speculative fourth scaling axis where a model builds and trains against its own simulation of reality. - **EfficientZero** (Software): Sample-efficient RL model that beat novice humans at unseen Atari games by simulating many games per real step. - **Mercury** (Organization): Fintech banking platform; episode sponsor referenced in the bill-pay anecdote.

Emily Chang sits down with Anthropic CEO Dario Amodei for a wide-ranging hour that swings from how he sleeps under "relativistic" pressure to why he signed a Pentagon contract despite a lifelong anti-war stance. Along the way he explains the bet on coding and enterprise that vaulted Anthropic past OpenAI, walks through a compute crunch driven by revenue tripling in a single quarter, and defends releasing — and withholding — a cyber-capable model called Mythos. He closes on the stakes he keeps returning to: AI job loss, the case against nationalizing AI, and his own 10-25% estimate of civilizational collapse. ## [00:00] Inside Anthropic Amodei opens on the personal cost of running a frontier lab, describing the pace with a special-relativity analogy: each day he "wakes up" to find more days have passed on the outside. He admits the pressure is unusual and that he is still learning to manage it. > *"Well, let's just say I'm, you know, I'm, I'm learning the art of, of, you know, finding ways to relax and sleep through, through moments of unusual pressure."* ## [03:34] Dario background He traces his San Francisco childhood — a leather-craftsman father, a librarian mother — and a kid who ignored the dot-com boom around him in favor of math, physics and science fiction. He credits the city with a culture of nonconformism that shaped how he thinks. > *"Yeah, I mean, I think the general, you know, the general spirit of kind of, you know, nonconformism and individualism and it's okay to be crazy."* ## [05:51] Leaving OpenAI Pressed on what really drove the split from OpenAI, Amodei says disagreements over safety alone never would have been enough — every lab has those. The break came down to trust and values, not any single policy fight. > *"And look, at the end of the day, why argue with someone when you don't have the same vision and you don't trust them."* ## [07:42] India AI summit On the viral moment where he and Sam Altman appeared to refuse to hold hands on stage, Amodei blames a chaotic, last-minute summit setup rather than personal animus. He reframes the OpenAI relationship less as a feud than as rivals who quietly borrow each other's good ideas. > *"It's not even competition, it's just, it's just, you know, each company does something cool and the other company's like, that's cool."* ## [10:45] Enterprise bet He explains why Anthropic leaned into coding and enterprise with Claude Code and Claude Cowork: a business model that funds expensive model training without betraying the company's values. The flip side, he warns, is that incumbents who refuse to adapt will struggle. > *"I think those who don't adapt, who put their heads in the sand, who don't kind of see what's coming, who don't identify the moats they have, they're gonna have a really hard time."* ## [19:29] Compute crunch Amodei pushes back on the idea that Anthropic under-bought compute. The team planned for 10x annual growth; instead revenue grew more than 3x in a single quarter — a pace that would annualize to roughly 80x, which he says no one could rationally have provisioned for in advance. > *"It would not have been rational to plan for 80x annualized growth, because that means if you only get 10x, you know that you, you have eight times less."* ## [21:15] Surpassing OpenAI Asked whether passing his arch-rival feels good, Amodei downplays the scoreboard and returns to his "race to the top" framing: the point of being preeminent is the ability to pull the rest of the ecosystem toward better behavior, not to beat rivals for its own sake. > *"And so I think the value of being the preeminent company, both commercially and in terms of models, you know, it's, it's not about beating rivals for the sake of beating rivals."* ## [24:07] Product velocity He attributes Anthropic's shipping speed to two things: a culturally unified, efficient organization, and Claude itself, now used internally to help build and accelerate the next models. > *"That we're now using Claude to help, you know, develop our models and, you know, make them more efficient and quickly develop products."* ## [24:52] AI discoveries The most striking results he's seen are in biology and medicine — including a case where Claude caught a diagnosis human specialists had missed — and early strength in drug design and computational chemistry. This, he argues, is where AI's enormous upside lives. > *"I've seen a number of cases, including Daniela actually, where Claude diagnosed a medical problem that, you know, a bunch of fancy doctors had missed."* ## [26:13] Dario’s writing style A committed essayist, Amodei says he still won't let Claude write his prose directly — he's too particular about style — but uses it to brainstorm, pressure-test themes and hunt references. He worries aloud about what we lose if we stop struggling through our own ideas. > *"There's some way, as the models get better, I think probably to, to use them directly much more directly in the writing and yet still preserve those benefits."* ## [28:10] AI and the workforce Revisiting his warning that AI could wipe out half of entry-level white-collar jobs, Amodei says the original point was about the magnitude of possible disruption, not a precise forecast — and that he's always paired it with proposed responses, from a token tax to macro policy. He points to emerging hybrid roles as one way work adapts. > *"You know, there's something we call a forward deployed engineer or in like applied AI solutions architect where their job is a mix of technical work and talking to customers."* ## [36:41] Pentagon standoff He defends signing one of the first DoD contracts to run on classified networks despite a longstanding anti-war stance, citing a resurgent authoritarian bloc — Russia in Ukraine, the risk of China and Taiwan. His line: Anthropic won't deny the technology over individual operations it might privately disagree with. > *"Now, I might privately believe that this military operation makes sense and that military operation is a bad idea, but we're not gonna deny the technology."* ## [43:29] AI warfare Confronted with a reported strike that killed children, Amodei says the company can't know exactly how its models are used, calls such outcomes terrible, and stresses the red lines Anthropic enforces. The core principle he defends: a human, not the model, makes the final call. > *"But you know, the principle that, that we have established, and I think the principle that was obeyed here is a human makes the human makes the final decision."* ## [48:18] Mythos On the model deemed too powerful to release, Amodei describes a sharp, unprompted jump in the ability to find vulnerabilities and turn them into working exploits — to the point that early testers called it a weapon. > *"It was a particularly large jump and without us really prompting them at all, some of the early companies that we gave this to said things like, this is a super weapon."* ## [55:15] Nationalizing AI Amodei takes the "why not let the government take you over" question seriously but argues against it, noting AI is the first powerful technology built in the private sector rather than government labs. He's wary of those who opposed all regulation until the first scare, then pivoted to seizure. > *"And then as soon as they see the first real danger, which I've been expecting all along, there's all this talk of like nationalization and the government should just seize it."* ## [58:57] Visit to the White House He describes Anthropic's approach to government as principle-driven and cooperative where possible, citing serious engagement on Mythos with Treasury Secretary Bessent and Chief of Staff Susie Wiles, while accepting that every administration has parts easier and harder to work with. > *"You know, I, I I said we have this simple approach, like we have a set of principles, we like follow those principles and we hope that folks on the other side are reasonable."* ## [59:47] China Drawing on his time at Baidu, Amodei frames Chinese open-source models through the lens of an intelligence premium — users rarely prefer weaker models — and warns of the authoritarian risk if the CCP can reach into US networks. He'd rather AI become a pro-democracy technology. > *"The fact that the CCP could reach into the US business network and, you know, and suppress criticism, that's an authoritarian state and, and a high tech authoritarian state."* ## [63:24] Recursive self-improvement He rejects the idea of a single moment when AI starts improving itself, describing instead a continuous, accelerating process already visible in AI suggesting architectures for the next AI. Sudden reversals on policy, he says, signal people who were caught off guard. > *"If you see someone having this kind of crazy yo-yo reaction, that's a sign that they were caught by surprise and that they're not serious."* ## [65:07] Dario’s favorite book Amodei identifies less with Oppenheimer than with Leo Szilard, who first grasped the chain-reaction idea, and casts Oppenheimer as a cautionary tale. His takeaway: no larger-than-life figure should be at the center — what's needed is checks and balances among many powerful actors. > *"There's a lot of powerful actors who have interests here, and the only way it's gonna end well for everyone is if there is some, there's basically checks and balances everywhere."* ## [65:49] Civilization collapse Asked whether Anthropic's own technology could trigger the 10-25% collapse risk he cites, Amodei says he hopes not and argues the company's actions lower that probability more than they raise it — while conceding the risk can never reach zero given the technology's inherent unpredictability. > *"You know, half of what we do within the company is try and, you know, reduce the risk as much as we can, but, you know, it's, it's never gonna be zero."* ## [67:32] Trust Closing on "why should we trust you," Amodei accepts that starting from distrust is rational given Silicon Valley's recent record, and argues trust has to be earned through actions — pointing to the commercial cost Anthropic ate by holding back Mythos and cutting model access over China. > *"And there were a bunch of smaller things before it, you know, we, we, we put our money where our mouth is on, you know, China, we cut off access to, to models."* ## Entities - **Dario Amodei** (Person): Co-founder and CEO of Anthropic; former biologist and OpenAI VP of research. - **Emily Chang** (Person): Bloomberg anchor and host of *The Circuit*, conducting the interview. - **Daniela Amodei** (Person): Anthropic co-founder and president; cited in a Claude medical-diagnosis anecdote. - **Sam Altman** (Person): OpenAI CEO, referenced over the India summit and the labs' rivalry. - **Leo Szilard** (Person): Physicist who conceived the nuclear chain reaction; the figure Amodei most identifies with. - **Anthropic** (Organization): Frontier AI lab behind Claude, maker of the withheld Mythos model. - **OpenAI** (Organization): Rival lab Amodei left and which Anthropic claims to have surpassed. - **Claude** (Software): Anthropic's model family, including Claude Code and Claude Cowork, used internally to accelerate development. - **Mythos** (Software): Anthropic model judged too powerful to release publicly due to autonomous cyber-exploit capability. - **Pentagon / Department of Defense** (Organization): US defense agency at the center of the classified-networks contract standoff.

Historian and novelist Ada Palmer joins Dwarkesh Patel to dismantle the "Machiavellian villain" myth and replace it with the actual Niccolò Machiavelli: a patriot who watched Cesare Borgia conquer half of Italy from up close, was tortured and exiled by the Medici, and then wrote *The Prince* as a secret job application addressed to the very regime that had wronged him. Palmer traces the structural forces — cascading legitimacy collapse among Italian city-states, popes who functioned as warring hereditary princes, and a patronage system that made nepotism feel like sound risk management — that made Machiavelli's analysis both urgent and unprecedented. The conversation closes on a sharp irony: the word "Machiavellian" now means self-serving cunning, yet the man himself gave up income, fame, and freedom rather than serve any cause that was not Florence. ## [00:00] How Florence bargained with Cesare Borgia for survival Italy in 1513 was a cascade of broken legitimacy. Palmer explains that when a long-standing government falls, successor regimes inherit none of its credibility, making rapid further overthrows nearly inevitable — what she calls the thread of continuity being cut. By the time Machiavelli is writing *The Prince*, this dynamic had swept dozens of Italian city-states. Compounding this was papal instability: because popes were elected rather than hereditary, the next pope was almost always a coalition pick of people who hated the current one, guaranteeing policy reversals every ten years. Machiavelli's day job during this era was standing next to Cesare Borgia — "Valentino" — and whispering endlessly that Florence was loyal, buying what Palmer calls "the boon of Polyphemus": the conqueror's promise to eat you last. His advice to Florence was to betray allies, pay tribute, give military support, and buy time, knowing full conquest was only delayed by Alexander VI's mortality. His biographers can still feel how much he was under Borgia's spell: when describing Valentino's fall, Machiavelli breaks from third person and writes "he told me" — the historian slips through the veil. > *"Machiavelli's job dealing with Cesare Borgia… it's very clear that the Borgia plan is to conquer the Papal States in the middle of Italy."* ## [15:08] Machiavelli's analytical innovations Machiavelli is not the crude "ends justify the means" thinker of caricature. Palmer shows that he is obsessed with the means — specifically, which means of acquiring power are stable and which are not. Whether betrayal works depends on the nature of your power base: Borgia could betray allies because his terror made remaining allies step further into line, while Savonarola's power rested on his followers believing him divinely infallible, so his flip-flopping destroyed him. The lesson is conditional, not universal. Machiavelli also makes the first recorded European argument that competing political parties can be stable and politically useful, rather than requiring mutual annihilation. Florence's own history was the counterexample: it had literally salted the earth where its Ghibelline opponents' houses once stood. His observation of Siena as a countermodel — parties competing without destroying each other — was genuinely novel. > *"Machiavelli is the first person that we have ever in the European tradition to suggest that it could be viable for there to be more than one political party in a state at the same time."* ## [23:58] Why popes became warlords The closer you lived to Rome, the less abstract the papacy felt. Palmer draws the contrast sharply: a Danish subject saw the pope as a figure of vast spiritual majesty; a Florentine saw "that asshole who went to college with your brother." Italians judged popes as specific men with dirty laundry, family grudges, and factional allegiances — which is why cities that were hereditarily Guelph (pro-papal) sometimes ended up fighting wars against the sitting pope when he happened to be from a Ghibelline family. The corruption was structural and self-reinforcing. As the Church accumulated donated wealth across generations, the incentive for ambitious families to capture it through bribery and nepotism grew. Palmer reads Machiavelli's personal letters haggling over the correct bribe to buy a priesthood for his brother Totto — written as routine household correspondence — to show how completely normalized the practice was. Every generation saw popes get more secular and military than the last; Machiavelli explicitly predicted the institution would collapse under accumulated corruption unless reformed from within, as St. Francis had temporarily saved it two centuries earlier. > *"This makes a stronger and stronger incentive for every ambitious family to send their second son into the Church."* ## [36:13] Why the common people demanded nepotism When Pope Paul III appointed a competent outsider general instead of his own illegitimate son, there were riots. Palmer explains this is not irrational: in a world where a soldier's oath ran to his commander, not to the state, the only guarantee the papal armies wouldn't turn on Rome was putting the pope's own son in charge — someone who rose and fell with the pontiff. Nepotism was the trust mechanism that made institutions function. Patronage also determined justice outcomes. Medieval law codes prescribed death for almost everything, but roughly 99 in 100 capital-eligible convictions ended in a fine because the defendant's patron intervened. This was considered correct: the trial was meant to replicate the soul's experience before divine judgment — terrifying, then mercifully pardoned — so patron intervention mirrored the intercession of a saint. The system had a grimly consistent internal logic, and Palmer traces it from Giordano Bruno (burned because he had angered his patron, not because of his ideas) to Giovanni Pico della Mirandola (spared because Lorenzo de' Medici went through the Orsini network to Rome). Without a patron, even innocence was precarious. > *"The norm is: you're accused of a severe crime, you're put on trial for your life, your patron intervenes, and you get a lighter sentence. This is how justice is supposed to work."* ## [47:57] Cesare Borgia brought terror to rulers and justice to the people Borgia's conquests produced a paradox that startled contemporaries: he massacred ruling families and was adored by common people. Palmer's explanation is structural. Factional cities had lived for generations under justice that tracked who was in power, not the facts of the case. A carpenter whose family worked for the dominant faction faced minimal consequences for his son's drunken homicide; the same crime by the carpenter of the out-of-power faction could be a capital offense. When Borgia wiped out both factions and installed outside administrators with no local feuds to take sides in, neutral adjudication felt like a revelation. Machiavelli also drew a hard line for why even a beneficent Borgia conquest of Florence would be catastrophic: under any arbitrary ruler, a citizen can be executed by a pointed finger in the street. Machiavelli called that condition slavery, regardless of how fair the tyrant might be in practice. Florence's "LIBERTAS" banner — flown by ordinary citizens defending an oligarchic Senate that excluded them — represented a genuine commitment to the existence of a process, however biased, over the absence of any process at all. > *"As a result, to everyone's surprise, he moves into a city, he massacres the rulers, he implements an authoritarian regime, and he's incredibly popular and beloved by the people."* ## [57:55] Art as a proxy for war Renaissance Florence could not afford to fight France militarily; it could afford to paint French royal symbols on its government buildings and commission beautiful gifts for the French king. Palmer frames this not as surplus expenditure but as substitution: the art budgets were military budgets redirected into a form of warfare Florence could win. Like the Fulbright Program being a higher return-per-dollar than the defense budget, Florentine cultural patronage was strategic deterrence. The period's orientation toward the past further supercharged the value of art. Where modernity assumes humanity advances into the future, Renaissance Europe pointed the other direction: the ideal was recapturing Rome. High-tech achievement meant successfully imitating a lost Roman technique. When a French diplomat arrived in Florence and saw the cathedral or the neoclassical buildings, he was not seeing quaint historical imitation — he was seeing something that approached what only Rome had achieved, and that France could not. That perception was itself a form of power. > *"If we fought him, we would lose. But if we play the culture victory game, that's cheaper, and we can try to win."* ## [01:06:41] Florence, a city famous in hell Dwarkesh raises the obvious puzzle: if everyone in Renaissance Italy was a Christian who genuinely believed in hell, why did they commit the sins Machiavelli describes constantly? Palmer's answer has two parts. First, the Dante answer: Dante fills the *Inferno* with Florentines precisely because he wants his contemporaries to feel the discomfort of consequences they were ignoring. His Paolo and Francesca passage — damning a love story everyone celebrated — was designed to be a shock to readers who thought romantic adultery was exempt from theological reckoning. Second, pre-Reformation Christianity assumed everyone sinned constantly and focused on repentance cycles rather than purity maintenance. St. Julian the Hospitaller, patron saint of murderers, was omnipresent in Florentine iconography — his legend held that he killed his own parents, spent his life in pilgrimage to repent, and was saved. Dozens of icons of him meant dozens of Florentines who had killed someone and were working through it. The Calvinist and Puritan emphasis on spotlessness came later and was a genuine departure from how the medieval and early Renaissance church operated. > *"He fills his hell with Florentines."* ## [01:15:57] The Prince was a job application to Machiavelli's torturers After the Medici retook Florence in 1513 and, on mistaken suspicion of conspiracy, tortured and exiled Machiavelli, everyone expected him to defect. He had contacts at every major court in Europe and the skills — military history, diplomatic networks, classical scholarship — that kings paid for. He chose instead to sit in a hamlet outside Florence writing *The Prince* as a secret appeal to the Medici to take him back. No other courts received it; he kept it proprietary, treating his political science the way Palmer says a nuclear scientist would treat classified weapons knowledge. His other works — the *Discourses*, the history of Florence, the comedy *Mandragola* — circulated publicly to build his reputation. *The Prince* did not. Palmer compares it to historian friends who produce classified 100-page reports for Department of Defense committees: bespoke proprietary knowledge for an audience of five, whose existence may be whispered about but whose contents are guarded. It also explains why the book was eventually published in 1532 without Machiavelli's input: surviving relatives wanted family fame, and the Medici wanted credit for a text dedicated to their house. Neither understood what its author had intended to keep contained. > *"I'm going to stay, and I'm going to rot, and I'm going to write The Prince, which is my job application begging the new regime to bring me back and let me work for them and demonstrating my loyalty, and I'm going to send it to them and only them, them and my immediate friends."* ## [01:41:39] During the Renaissance, original ideas had to be couched in antiquity The Renaissance's obsession with recovering ancient Rome created a peculiar incentive structure: original ideas were unfashionable; ideas presented as recovered ancient wisdom were prestigious. Palmer shows this goes far beyond homage. Giordano Bruno attributed to Aristotle claims that Aristotle explicitly contradicted. Annius of Viterbo forged ancient texts and staged fake archaeological digs to give his original historical theories the authority of antiquity. Marsilio Ficino, translating Plato, genuinely convinced himself that the wildly original cosmological and magical system he had assembled was secretly coded in the Platonic texts. This explains why Machiavelli's other major work is called *Discourses on Livy* rather than, say, *A New Theory of Republican Governance*. A discourse on an ancient was a prestige format; an original political treatise was a niche curiosity. The 19th century misread the Renaissance as intellectually barren — "200 years of people being wrong about Plato" — because it expected original standalone treatises and found commentary after commentary. Palmer argues the original ideas are there, using the ancients as what she calls the trellis up which the rose climbs. > *"Nobody wants original ideas. Original ideas are out of vogue. Original ideas are dead. All ideas need to be from the ancients."* ## [01:50:44] Why copyright began with the Inquisition Machiavelli was one of the first authors to experience unauthorized printing. A local press printed one of his works without asking, riddled it with compositor typos, and his only recourse was to write letters to important people clarifying that the errors were not his. There was no legal framework at all. The solution emerged from an unexpected direction: post-1515, the Inquisition required pre-publication approval for all texts to screen for heresy. In exchange for going through this process, the approved printer received a monopoly license — the Inquisition's record of permission served as proof that no one else could legally print the same book. The first copyright was a censorship certificate. England, observing this, copied the mechanism while eventually stripping out (or softening) the censorship half, producing the ancestor of modern copyright law. The institutional logic held together: the Inquisition needed to please local rulers to get resources, so approving books dedicated to the duke and granting his favored printer exclusivity was a political investment. Everyone — inquisitors, printers, authors, and ruling families — had reasons to make the system work. > *"So the very first version of copyright is the Inquisition."* ## [02:02:12] Machiavelli wasn't Machiavellian The word "Machiavellian" came to mean scheming self-advancement — Shakespeare's Richard III invokes "the murderous Machiavel" as his role model. Palmer traces how the idea of Machiavelli separated from the actual man and became a useful thought-experiment figure: the cynical, probably atheistic politician who wants nothing but personal power. The same splitting happened to Hobbes (the Beast of Malmesbury) and Spinoza, whose actual writing is warm and theistic but whose excommunication from the Jewish community made people assume he must be the most radical heretic imaginable. The real Machiavelli — who refused lucrative court positions across Europe, who kept his most important work secret to protect Florence from foreign exploitation, who chose to rot in an isolated hamlet over serving any cause that wasn't his country — is almost the opposite of "Machiavellian." His book is not about gaining power but about keeping power stable enough to protect people. Palmer's closing point: the gap between Old Nick and Niccolò Machiavelli is itself a revealing fact about how societies use ideas, splitting thinkers into a character useful for one purpose and the actual work useful for another. Read *The Prince* knowing it was written by someone who would give up anything to serve Florence, and a very different text comes through. > *"This is why it's so weirdly ironic to me that the reputation—the word"Machiavellian"—means"self-serving", when Machiavelli himself is one of the most selfless men I've ever read about in the history of the Earth."* ## Entities - **Dwarkesh Patel** (Person): Host of the Dwarkesh Podcast; interviews scholars on history, science, and technology. - **Ada Palmer** (Person): Historian and science fiction novelist at the University of Chicago; specialist in Renaissance intellectual history and the history of censorship. - **Niccolò Machiavelli** (Person): Florentine diplomat (1469–1527), author of *The Prince* and *Discourses on Livy*; wrote *The Prince* as a secret appeal to the Medici regime that had tortured and exiled him. - **Cesare Borgia** (Person): Renaissance military commander known as "Valentino"; son of Pope Alexander VI, conquered central Italy and was Machiavelli's primary case study in effective (if brutal) statecraft. - **The Prince** (Concept): Machiavelli's treatise on political power, written ~1513, kept proprietary during his lifetime and published posthumously in 1532; misread as a self-advancement manual rather than a guide to maintaining stable government. - **Discourses on Livy** (Concept): Machiavelli's longer republican political theory, structured as commentary on the Roman historian Livy; his public bid for intellectual prestige in a culture that prized commentary on ancients over originality. - **The Medici** (Organization): Ruling family of Florence, whose patronage networks and papal connections shaped both the political instability Machiavelli analyzed and the conditions under which he wrote and was exiled. - **Florence** (Organization): Italian city-state and center of Renaissance banking, art, and humanist scholarship; Machiavelli's country, for which he subordinated his entire career. - **Patronage System** (Concept): The multi-generational network of family obligations that served as the functional glue of Renaissance society, determining access to justice, employment, publication, and protection from the Inquisition.

Joon Sung Park, founder and CEO of Simile and creator of Stanford's Smallville generative-agents study, walks Sonya Huang through the arc from a 25-agent game town that spontaneously threw a Valentine's party to a company that simulated 1,000 Americans and predicted their answers 85% as accurately as the people reproduced their own. His core argument: today's frontier labs are building the "CPU of intelligence" — rational machines superhuman at problems with right answers — while simulating real human society needs the opposite, a model that encodes people's irrational values, preferences, and taste. CVS uses it for concept testing; some customers simulate their own earnings calls; and Joon's longer bet is a "CERN of human society" that could one day model bank runs, climate cooperation, or the early signals of a collapsing democracy. ## [00:00] Inside Smallville: 25 agents throw a Valentine's party The conversation opens on Joon's conviction — that science fiction's advanced societies always rest on two pillars, "some version of AGI and some version of simulations that really help guide the society" — before Sonya takes him back to Smallville, the April 2023 Stanford project that made his name. The setup was 25 generative agents, each given a persona and equipped with memory, planning, and reflection, then left to live in a small game town: wake up, do routines, go to work, form relationships. What surprised the team was emergent coordination. Isabella, a café owner, decided to throw a Valentine's Day party, spent the day before gathering materials and inviting customers, and on the day itself the party actually formed. > *some of the agents did not explicitly get invited, but we had one agent who got the invite, Claus, who decided to ask his crush out on a date* ## [03:34] From a foundation-models paper to simulating a subreddit Joon traces the origin back to 2020, the year GPT-3 was about to land. As a Stanford researcher he co-wrote the "Opportunities and Risks of Foundation Models" paper, and the part that gripped him was not that the models could classify or generate — interaction researchers had done that for years — but that they could encode human behavior. Coming out of the social-computing tradition, he saw a long-standing hole: there was no way to test how millions of people would behave on a platform short of shipping it and watching what happens, sometimes at real cost. That led to the 2022 Social Simulacra paper, the precursor to generative agents, which populated a simulated subreddit with thousands of personas to let a designer see community dynamics before launch. > *The only way we test it today is you basically field test it. You release your prototype, see what happens.* ## [07:57] The CPU of intelligence can't model irrational humans Asked when models got good enough for a faithful representation of society, Joon marks the path from GPT-3 — janky, no instruction tuning, needing prompt tricks just to follow orders — to today's foundation level where these applications become imaginable. But he draws a sharp limit. The frontier labs' north star is a rational, superhuman machine optimized for objective problems, and that is the wrong target for simulating people. As accuracy on objective benchmarks climbs, the ability to predict and simulate human behavior diverges, because people are not rational. > *We have a lot of subjective values, preferences, and taste.* ## [10:04] Why this became a company, not another paper Joon distinguishes the two vehicles bluntly: research is built for breadth, where each researcher owns a slice of thesis and is "not necessarily known for finishing our job," while a company is built for depth on a single conviction. The pull toward a company came roughly half a year after the generative-agents paper, first from social scientists wanting to run RCTs on the platform, then from Fortune 500 boards and CEOs who saw the demo at Stanford and asked whether the surveys and market questions they could never answer might run in simulation. Before committing, the team validated accuracy: simulations of 1,000 people across the US population. > *we can actually predict people's behaviors 85% as accurately as people replicate their own* ## [12:43] How a Simile engagement works — and the say-do gap Simile's first major customer is CVS, brought in by a senior VP of human insights who had read the validation paper and felt bottlenecked by how few questions he could field-test. The workflow mirrors how firms already use polling and panel companies: a customer names a population they want to understand, and Simile — through a strategic partnership with Gallup — reaches real humans, asks the magical 15-minute questions, and turns that data into agents that answer far beyond the original survey. Sonya pushes on why an LLM alone can't just role-play a 34-year-old woman from a coastal metro. Joon's answer is the say-do gap: models are trained on what people said online, not what they actually do, and closing that gap requires behavioral data — RCTs, pricing studies, and life-story interviews that surface the long-tail of a person. > *There are things that people say and then there are people there are things that people actually do and the gap there is real* ## [20:27] The GPU of intelligence: from concept tests to earnings calls Here Joon gives the framing that anchors the company. Today's models are the CPU of intelligence — one model trained on rational data, superb at objective questions. Simile is building something closer to the GPU: not superhuman, but as human as possible, where individual subunits represent the real viewpoints of different populations. Customers usually enter through a concrete door — concept testing, where instead of testing 5 to 10 ideas they imagine testing a thousand ideas across a thousand sub-populations — then move toward product testing with a temporal dimension and multi-agent simulation. One recurring and initially surprising ask: simulate the company's own earnings call to see how the audience reacts. > *imagine the current today's model are akin to the CPU of intelligence unit* ## [26:32] How accurate is it? Convergence versus divergence On evaluation, Joon starts from the theoretical limit — humans answer the same question slightly differently each time, so perfect prediction is impossible — then describes the metric: total variation distance between the ground-truth and simulated response distributions, with a TVD under 0.15 treated as strong enough for decisions. The deeper idea is two categories of simulation. Convergent ones tolerate compounding error because the pull toward an outcome is strong — like a network always forming a hub, the scale-free structure that powered PageRank. Divergent ones — was World War I inevitable, who wins an election — can't be expected to repeat, so the evaluation shifts to confidence: run it 100 times, see how often outcome X appears, and show the diversity of possible futures. He likens the work to the early days of inferential statistics setting the p < 0.05 threshold. > *was World War I inevitable or was it not?* ## [31:56] A CERN for human society Sonya raises the grander possibility — that fields like macroeconomics, which she sees as human behavior at scale, might one day be partly solved by simulation, including the venture question of where value accrues across the AI stack. Joon agrees there is "a Nobel Prize to be won there," recalling how Thomas Schelling's deliberately crude agent-based segregation models revealed something deep about macro behavior. The augmented version replaces red-dot/blue-dot agents with agents that replicate the full richness of individuals, opening questions economists actually asked him: when does a bank run happen, can nations be modeled solving climate's collective-action problem, what are the early signals of a democracy about to collapse. He imagines a simulation that costs $100 million and months to run once but answers a fundamental question — a Hubble telescope for human society. > *building simulator that's akin to the CERN of human society* ## Entities - **Joon Sung Park** (Person): Founder and CEO of Simile; created Stanford's Smallville generative-agents study and co-authored Social Simulacra. - **Sonya Huang** (Person): Partner at Sequoia Capital, AI investing; host of the conversation. - **Simile** (Organization): Applied AI lab building models that simulate human behavior and societies for concept testing, product testing, and multi-agent scenarios. - **Smallville** (Concept): 2023 Stanford experiment with 25 generative agents living in a game town, known for emergent behavior like a self-organized Valentine's party. - **Social Simulacra** (Concept): 2022 paper simulating a subreddit with thousands of personas; precursor to generative agents. - **Say-do gap** (Concept): The difference between what people say (the basis of LLM training data) and what they actually do, which behavioral data is collected to close. - **CPU vs GPU of intelligence** (Concept): Joon's framing — frontier labs build a rational "CPU" superhuman at objective problems; Simile builds a "GPU" encoding the diversity of human values and taste. - **Total variation distance** (Concept): Simile's accuracy metric comparing ground-truth and simulated response distributions; TVD < 0.15 treated as decision-grade. - **CVS** (Organization): Simile's first major customer, using it for concept testing via its human-insights team. - **Gallup** (Organization): Polling and panel partner Simile uses to reach real humans and ground simulations in real data.

Mark Pincus built eight massive hit games out of ten launches at Zynga — FarmVille, Words with Friends, Zynga Poker among them — and spent five years distilling the pattern behind that record into a book, *Life at the Speed of Play*. The core idea: your instincts are right 95% of the time but your ideas are wrong 75% of the time, so a good framework doesn't generate ideas — it filters them. That framework is Proven Better New: nail what's already working on your platform, make one thing 10-out-of-10 users would say "f*** yes" to, then add exactly one unproven bet. The conversation also covers why radical ambition demands embarrassingly small starting points, how to use AI as a failure machine rather than a speed-to-market tool, and what makes consumer social the biggest untapped opportunity on the internet right now. ## [00:00] Introduction to Mark Pincus Lenny opens with a rapid-fire preview of Mark's most quotable lines — burn your resume if you're truly ambitious, your instincts are right but your ideas are wrong, kill hope before hope kills you — before introducing him as the founder of Zynga and author of *Life at the Speed of Play*, out June 23. Sam Altman's blurb for the book frames the stakes: in the AI era, the only bottleneck to great products is knowing what to build, and Mark has thought about that longer and harder than almost anyone. > *"If you're truly ambitious, burn your resume."* ## [02:46] The Proven Better New framework overview Mark traces the framework back to Zynga's early culture, where it became a "religion" for product management. The engine: isolate your innovation zone (the gut instinct), separate it from the ideas you layer on top, and use Proven Better New to test many ideas around that instinct rather than betting everything on one. He illustrates with Sid Meier's failed Facebook social strategy — even the godfather of game design sank because his first-time user experience didn't copy what Zynga's most junior PMs already knew was best-of-breed. His innovation never got seen because he skipped the Proven step. > *"Your instincts are right 95% of the time. Your ideas are wrong 75% or at best right 25% of the time."* ## [07:29] Earning the right to innovate You can't skip Proven and go straight to New. Mark's framing: if you're building an AI camera, you haven't earned the right to innovate on the camera until you are the world's leading PhD on the best mobile cameras that already exist. Get that PhD first — copy legally and with taste — then and only then does your actual innovation have a chance to be seen. > *"We haven't earned the right to innovate on the camera until we are the world's leading PhD on the best mobile cameras that already exist."* ## [08:30] What "better" really means Better is not what you think is better — that's actually New. Better is an increment that every existing user of the product would confirm as an improvement: it's free, it loads faster, the polish is there. Words with Friends was Scrabble as the Proven base; the Better was mobile polish so clean that 14 million people played daily when Scrabble itself never reached that; the New was the Facebook social graph already populated with your real friends. Mark's test: 10 out of 10 users say "f*** yes." Anything short of that is a New, not a Better — and New probably fails. > *"Better is something that 10 out of 10 of the existing users of that product would say f*** yeah."* ## [12:03] Quick summary of the framework Lenny synthesizes: Proven = list what's already working and loved on your platform; Better = one improvement so obvious that every existing user would switch immediately; New = one unproven bet nobody's tried. He runs the iPhone and iPod through the lens — music player → better hardware and interface → social distribution — and notes that most successful products follow this pattern whether their makers called it that or not. > *"Most products are better versions of things that existed before."* ## [12:40] Examples of the framework in action Mark was at the TED conference when an MIT team demoed their touchscreen on a giant whiteboard. Steve Jobs spent the whole time there, obsessing over the touch interaction. The observation: Jobs' only true New idea in the iPhone was the touchscreen — everything else was Proven Better applied to an existing phone. > *"Like, okay, there's his new idea — it's a touch screen. It's his only new idea."* ## [13:30] How to use proven correctly on your platform Founders misuse Proven by pointing at something popular from a different era or platform and calling it "proven." Proven only counts on this platform, for this audience, for this experience. Slack is Mark's favorite example of Proven Better with almost no New at all — it took workplace chat that people already did over email and IRC, made it radically more accessible, and that was enough. Sometimes no New is even better: people don't like change, so if you can make a behavior they already love more fun or accessible, they'll love you for it. > *"I don't want to sound anti-innovation, but people don't like change."* ## [15:13] The moral arbitrage of copying There's a moral resistance to copying baked into how founders think — school taught them copying is cheating, and becoming a founder meant becoming an innovator. Mark calls this "moral arbitrage" in Peter Thiel's sense: that resistance makes the copying opportunity more available to founders willing to put ego aside and define their ambition through their consumer's eyes, not their peers'. His line to Zynga product teams: you're trying to win the hearts and minds of nurses in Indiana for Farmville, not win awards from your Silicon Valley cohort. If you take something she loves and make it one inch better, she'll love your version more than a blank-whiteboard innovation she didn't know she wanted. He also draws the contrast between Nikita Bier (found a buried feature in an Arabic-only app, built TBH around it — that's gold) versus Angry Birds (45 completely different games, no learning across iterations, 44 failures before the one hit — that's wildcat drilling). OMGPop made Draw Something by ruthlessly copying Zynga's turn-based system from Words with Friends after their own innovative game flopped. The hit came from the copy, not the original idea. > *"If you're truly ambitious, burn your resume. Define your ambition in the eyes of your consumer, not your peers."* ## [23:55] Be less ambitious The paradox: the more ambitious you are, the humbler your starting point should be. Facebook started as a tool to check out classmates at Harvard. Zynga started as a poker game on Facebook — Mark was 41, a multi-time successful founder, and people thought he had lost his dignity. But that embarrassingly small starting point was the key. After his Tribe social network failed because he tried to do everything at once, he needed to get to any product-market fit and dropped his altitude from 100,000 feet to 1,000. First-time founders have an advantage here: they can't raise money on a big vision yet, so they're forced to stay humble. Multi-time successful founders have too much rope to hang themselves. > *"The paradox is the more ambitious you are, the more humble you should be and the smaller place you should be willing to start."* ## [28:25] The Bolt.new story and staying humble Bolt.new as the modern version of this: the team toiled in obscurity building a web-stack virtual machine, barely kept commercial development going, open-sourced it, then realized that adding their VM to an AI coding co-pilot created something genuinely better than any alternative. They were passionate about one thing, stuck with it, and the breakout came from that focused humility. Slack is the same arc: Stewart Butterfield kept trying to build mass-market MMOs, got humbled by that difficulty, noticed that the internal tool his engineers used was actually the product, and pivoted. Mark's point: it takes a really attuned, curious, humble founder to call that ball when investors and team are all pointed in the other direction. > *"It really takes a really attuned, curious, humble founder to call the ball on that."* ## [33:15] Kill hope before hope kills you Hope is confidence without basis — not founded in lived experience with the product, not in data, just a prayer that the next release does something magical. Belief is different. The best product makers are collecting winnings, not making bets — they already know they have a hit before they launch. Mark draws the distinction between an MVP (minimum viable product, where "viable" is where hope lives) and an MLP (maximum launchable product, where you believe, not hope, that it's a hit). AI makes this more dangerous, not less: it lets teams get to a viable product in three months instead of three years, which accelerates the speed at which founders can fool themselves into thinking viable equals ready. > *"Kill hope before hope kills you. There's a difference between belief and hope. Hope is confidence without basis."* ## [37:00] Using AI as a failure machine What Mark expected AI to produce: testing machines that run a hundred ideas a week instead of one idea per quarter. What he actually sees: teams using AI to build one idea in three months, only faster. The right mental shift — build it completely wrong before you know it's the right product. If you believe it's wrong, you won't waste three months perfecting the wrong thing; you'll build the cheapest version that gives you signal. He illustrates with a Zynga FarmVille expansion pack story: instead of spending a $10 million ad budget on "coming soon" banners, they put locked art variants on the game board for existing players, measured which got most clicks, and ended up selling $19 million worth of early-access keys — turning what would have been afterthought advertising into product direction plus revenue. > *"The way we should be using AI is as a testing machine, a failure machine."* ## [40:08] Why Zynga's games succeeded (it wasn't virality) Farmville and CityVille became associated with spam in users' Facebook feeds, so many founders assume Zynga's secret was aggressive virality. Mark pushes back: the real engine was retention, not virality. Zynga tracked Day 365 retention — something Mark believes no other consumer company does today — and built toward it. The metric that actually predicted retention was ASN (Active Social Network): how many round-trips did a player complete with another player? Going from zero to one ASN meant an 80% chance of seeing that player the next month; reaching four ASN meant an 80% chance of seeing them 22 out of the next 30 days. The second engine was social dimensionality — the games let people invest, express, and connect. Middle-aged women didn't just play Farmville alone; they co-op-farmed with real friends, gifted each other in-game items, and felt creative in a way their lives outside the game didn't offer. Virality was a byproduct, not a strategy. > *"It wasn't that we were good at virality. We were focused on two things we did better than anybody else."* ## [48:36] The future of consumer social apps Nothing is working in consumer social right now, and founders have largely given up on it. Mark's read: there is still massive latent demand — we want to be social — but existing platforms have lost the adrenaline. When people quit Instagram their NPS goes from +35 to -35; they feel like they just quit smoking. The platforms shifted from social productivity (Facebook let you stay in the loop with 300 friends in minutes) to time-wasting engagement optimization (Instagram got TikTok envy). The opportunity: whoever finds the new step function of social productivity for the agentic AI era will find gold. Mark frames it as the "cocktail party" instinct — you know when you're at a great cocktail party because you feel "I'm so glad I'm here" and you're leaving with great leads. Facebook, LinkedIn, and even Zynga's games were cocktail-party experiences at different scales. Today everyone's hanging out with their Claude or GPT, but there's no cocktail party. The Easter egg: figure out how to make that cocktail party rowdy and socially productive. > *"Today, we're all hanging out on our Claude, on our GPT, but there's no cocktail party."* ## [57:05] How to know if your product is a B The dating analogy: when you're with the right person, you know — you're not asking, "Could this be the one?" If you're asking whether your product is an A, it's not an A. When you have lightning in a bottle, everything works: you're addicted to it, friends love it, metrics confirm it. Nobody asked whether GPT was it. The hard part is what to do once you've named it a B+: can you be intellectually honest enough to call it, and then use it to learn rather than just killing it? Mark pulled the plug on his "Earth" metaverse project after four years and $25 million — and in the two weeks since has felt more inspired than at any point in those four years. > *"If you're asking whether or not your product is an A, it's not an A."* ## [61:25] Distribution in the age of AI Mark's first move is to ask whether AI is a new platform — and his current answer is no, not yet. It's an important technology and a new kind of portal (the chat interface), but it's not a hardware platform and it's not yet a platform that opens distribution the way mobile or social did. We're still in the mobile and web era. App install rates are near zero. Forty thousand new games launched in the App Store last year and zero became top-ten hits. Distribution has to be baked into product strategy from day one, not treated as something you figure out after build. His more forward-looking bets: build for pro-sumers and whales first (people who care enough to find you and pay early). Watch the token cost curve — if tokens trend toward free in two years, there are consumer services that only make economic sense at free-token prices, and building toward that now is an interesting innovation zone. His favorite Easter egg: an AI-native travel agent that's always on, knows your context, and actively manages your trip when things go wrong. That service has always had latent demand but never had a viable economic model — free tokens could change that. > *"Distribution has to be part of your product and part of baked into the strategy deeply and proven from the beginning."* ## [75:39] Make everyone a CEO Mark hates managing people. Every day spent managing is a day away from product. His escape: give people a hill to take and make them a real CEO of it — operating control, degrees of freedom, their own plan and budget, then get out of the way. He found two things: he didn't have to manage them anymore, and a certain kind of person (the frustrated expert witness who's a bit of a know-it-all and has pent-up demand to prove they were right) becomes incredibly motivated. Brian Armstrong's "everyone is an individual contributor" push at Coinbase is the Silicon Valley version of the same idea — the best CEO is the best player at the position, doing the thing they're great at rather than wasting time on management hierarchy. > *"All of management is just how do we get people to do the right thing when we're not in the room."* ## [78:18] Stay close to the metal Early in a career you're in the trenches, closest to the data and probably to the right answer, but furthest from the decision — that's the expert witness syndrome. When you become a CEO, the trap is drifting away from the metal: delegating the most important UX and product decisions to the least experienced people while you do investor relations. Discord's founders realized they were doing exactly that and inverted the pyramid, making the founders the first and last mile for product decisions. Steve Jobs picked out carpet in conference rooms. Bezos and Zuck spent two days a week deep with specific teams on the things that mattered most. If you're the best product maker in the company, the team needs you on the field, not in the stands. > *"I believe the best product CEOs are in the minutia of the details."* ## [81:35] Why Mark says micromanagement is beautiful At Zynga up to 50 employees, Mark ran a daily standup that went two hours, tracking every name in a spreadsheet with what they were supposed to do yesterday and what they'd do today. Brutal, but effective. The framing: be in the room as much as you can for as long as you can. Only delegate when you physically can't be in all the rooms simultaneously. All management principles are just strategies for getting people to do the right thing when you're not there — so minimize how often you're not there. He notes it was more controversial twenty years ago; today, with founder-led product culture being normalized, "micromanagement is beautiful" lands closer to conventional wisdom. > *"If you can be in the room, be in the room — assuming that you are the best player."* ## [83:35] The expert witness How do you transfer the vampire blood — your passion and approach to the product — to other people? Two mechanisms. First, the teaching hospital: put as many people as possible in the room while you do product management, let your methodology spread through proximity. Second, the tech assistant: pull one person from the ranks to shadow you for six to twelve months, give them projects to test them, then place them in a much bigger role. Andy Jassy ran the program at Amazon — everyone on the S-team had been Bezos's tech assistant at some point, so it scaled the founder's judgment across the entire leadership layer. > *"How do you pass the vampire blood of you to other people?"* ## [85:05] The number one job of a CEO is to be right Stolen from Bezos, and Mark endorses it fully: if he could only pick one thing for a CEO to be, it's right. Right about the product, the strategy, the bet. Phenomenal execution in the wrong body of water gets you nowhere — being in the right body of water matters more than having the right boat. He applies it to hiring too: the best resume is a track record of being right, not a track record of charisma or management style. He'll take misfits who are right over polished managers who aren't. > *"Being in the right body of water matters more than the right boat."* ## [86:35] What Mark is teaching his five kids Mark has five children — twins, a special-needs son, a one-year-old with a gene mutation, and a four-year-old — and describes parenting as his greatest role. Three principles he applies. First, meet them where they are: not talking down to them as kids, not treating them as miniature adults, but finding their actual altitude and engaging human-to-human from there. He taught his twins math through the pandemic and discovered he'd taken them through eighth-grade material without realizing it, because he started from their natural curiosity rather than the curriculum. Second, critical thinking over knowledge accumulation: factory-produced education trained knowledge workers, and knowledge working is going away. He tells his kids "I don't care if you go to college — I care that you develop critical thinking and find a way to be useful to people." Third, be generative, not consumptive: what can you create online or offline rather than passively consume? His daughter Carmen, who has ADHD and dyslexia, turned that into a sweatshirt brand (Comfy Fancy) and a community for neurodivergent middle-schoolers (Neurosparkley). > *"I'm trying to teach them to ask better questions, not know more answers."* ## [95:14] Mark's "why" It took Mark until he started Zynga at 41 to identify and articulate his why: to build an internet treasure — a service people can't remember life before or imagine life without. His friend Bing Gordon says those treasures will end up in the Smithsonian one day. Mark's still rubbing sticks together because he hasn't built his thing yet, and that's what keeps him going. > *"I want to create an internet treasure — a service we can't remember life before or imagine life without."* ## [97:08] Mark's new book: Life at The Speed of Play *Life at the Speed of Play* synthesizes Mark's thirty-year playbook for building products people love. He describes it as intentionally easy and fun to read — bite-sized, not long — and says his goal is for founders to steal from it and take the ideas further. He frames this podcast conversation as itself part of the cocktail party of product-making philosophy, a shared craft that all builders are collectively advancing. > *"I'm hopeful that somebody will steal from my ideas and take it further and we're all kind of in a conversation."* ## Entities - **Mark Pincus** (Person): Founder of Zynga (FarmVille, Words with Friends, Zynga Poker); author of *Life at the Speed of Play*; known for Proven Better New product philosophy - **Lenny Rachitsky** (Person): Host of Lenny's Podcast; founder of Lenny's Newsletter; former Airbnb PM - **Zynga** (Organization): Social games company founded by Mark Pincus; created eight top-ten hits including FarmVille, CityVille, Words with Friends, and Zynga Poker - **Proven Better New** (Concept): Mark's product framework — copy what's proven on your platform, add one improvement 10-out-of-10 users confirm as better, then bet on one novel idea - **Day 365 Retention** (Concept): Zynga's primary success metric, tracking whether users were still active a full year after first use; Mark argues it's the strongest predictor of long-term company value - **Active Social Network (ASN)** (Concept): Zynga's proprietary metric measuring round-trips between players; going from 0 to 1 ASN correlated with 80% monthly return; the real engine behind Zynga's retention record - **Life at the Speed of Play** (Software): Mark Pincus's book synthesizing his product philosophy; out June 23, 2026 - **Bolt.new** (Organization): AI coding tool that added a web-stack virtual machine to an AI co-pilot; Mark's example of humble persistence unlocking a breakout product - **Nikita Bier** (Person): Co-founder of TBH and Gas; referenced as a master of finding a buried proven feature in someone else's product and building an entire hit around it - **Craig Newmark** (Person): Craigslist founder; cited as a world-class product maker for spending two years making photos work correctly in listings rather than rushing a change that would have broken user scanning patterns

Matan Grinberg, CEO of Factory and former string theorist, explores the shifting landscape of AI ROI, resource allocation, and the return of the polymath. He argues that the industry is moving from a period of 'token maxing' debauchery to a sober 'hangover' phase where enterprises demand clear business value and ROI. Grinberg details his journey from theoretical physics to founding an AI company, emphasizing the need for high-agency talent and the strategic decoupling of AI models from applications. ## [00:00] Intro Harry Stebbings introduces Matan Grinberg, CEO of Factory, who transitioned from a 12-year career in string theory to software development. Grinberg posits that the future of the AI industry is defined by a race to commoditize competitors and that value accrual is highly time-dependent. He emphasizes that the age of the polymath has returned, where elite teams will be treated like professional athletes. > *The age of the polymath is back. [00:45]* > *The world going forward there is going to be nothing that no one can build. [00:00]* ## [01:22] Will AI actually increase GDP? Grinberg expresses strong confidence that AI will drive meaningful GDP growth beyond the historical 2% average, though the effects will take time to permeate the economy. He explains that AI allows individuals to solve problems faster, forcing companies to choose between increasing output or operating more efficiently with fewer staff. This shift requires a fundamental adjustment in how organizations allocate human and technical resources. > *We will see tremendous growth from these tools. I think it takes time to permeate through. [01:53]* > *Everyone is now going to be able to solve more problems with the same number of people. [02:18]* ## [02:41] Smaller teams or bigger ambitions? The conversation shifts to the future of engineering talent, specifically the concept of 'load-bearing individuals' or high-leverage employees whose removal would cause an organization to collapse. Grinberg suggests that AI tools act as a force multiplier for these individuals, widening the gap between those who can effectively use leverage and those who cannot. > *Those who know how to use leverage will be able to have even more impact. [04:35]* ## [05:05] The resource allocation problem: tokens, dollars, people Grinberg predicts that the next 24 months will see C-suite executives focusing intensely on the resource allocation problem involving tokens, dollars, and headcount. He advises leaders to prioritize their core competencies and judge success based on business outcomes like revenue rather than vanity engineering metrics like features shipped. > *This resource allocation problem of token... is going to be the thing that over the next 24 months every C-suite is going to be thinking about. [05:08]* > *Finally coming back to what matters in the first place. Like what are the business metrics that we want to move the needle on. [06:32]* ## [06:49] Kirkland's $500M AI bet and the build vs buy question Harry and Matan discuss Kirkland & Ellis's $500 million investment to build internal AI tools, which Grinberg views as a potential strategic error since AI is not their core competency. He argues that such massive internal spends often lead to the realization that specialized vendors are more efficient, ultimately validating the difficulty of the problem. > *Kirkland spending half a billion dollars to build their own AI tools... building AI technology is not a core competency of that firm. [07:14]* ## [10:01] Models, apps and infra: who gets commoditised? Grinberg describes the current friction between model providers, application developers, and infrastructure firms, where each sector is actively trying to commoditize the others to capture more market value. He notes that value accrual is a time-dependent phenomenon, shifting based on who holds the most pricing power and leverage in the ecosystem. > *everyone is trying to commoditize the people that are not them. [11:05]* > *The reality is value acral is a time dependent phenomenon. [10:40]* ## [11:58] The bear case against Factory Factory maintains a model-agnostic stance to provide customers with the best balance of price and performance across providers like OpenAI and Anthropic. Grinberg admits the primary risk to this strategy is if a single model provider achieves a significant, sustained lead over all competitors, creating a dangerous global monopoly. > *The bare case against factory is if one model provider gets significantly better than all of the others. [12:05]* ## [13:57] The rise of open-source models Enterprises are increasingly looking toward open-source models to manage ballooning token costs and annual budgets that are exhausted prematurely. Grinberg notes that 80% to 90% of tasks currently performed by frontier models could be handled by open-source alternatives, which serve as a vital counterbalance for less complex tasks. > *so many of the tasks that we're doing we don't need the very frontier to do it. [14:47]* > *there's kind of an ego thing where oh no no the work that I'm doing only a frontier model could handle. [15:15]* ## [17:08] The AI spending hangover Grinberg describes the current state of AI adoption as a 'hangover' phase where companies are finally reviewing the massive bills accumulated during a period of unchecked usage. He predicts a healthy short-term contraction in frontier model usage as businesses prioritize actual ROI over novelty and implement strict resource allocation. > *Phase three is the hangover where you go and look at the bill and it's like, 'Oh my god, we are spending so much. I have no idea what the ROI is.' [17:08]* ## [19:32] Token spend as a % of dev salary Harry Stebbings questions whether token spend will eventually exceed headcount costs. Grinberg predicts that within three years, the median token spend per individual will be on the same order of magnitude as their salary, particularly for roles that gain massive leverage from AI 'droids.' > *I would say order of magnitude. It'll probably be comparable to salary. [22:03]* ## [24:14] Factory's controversial culture: sales and engineering as one team Matan Grinberg critiques the 'Silicon Valley fallacy' that research is the pinnacle of achievement while sales is secondary. At Factory, engineers and sales staff are fully integrated, sharing ownership of both features and closed deals to ensure the entire customer journey is treated as the product. > *The product at factory is the entire journey from the very first time they hear our name till their 10th renewal. [25:33]* > *If you don't have a good sales and marketing team... the second gravity returns, all of your muscles will be atrophied. [26:55]* ## [27:30] Why agency matters more than credentials While venture capitalists often use elite credentials as a crutch, Grinberg argues they can be an 'anti-signal' if the individual lacks true agency. He prefers candidates who have demonstrated high agency by building things independently and taking end-to-end ownership of business outcomes. > *What have you built? How have you taken ownership and agency of things end to end? [29:49]* > *In a world where we desperately seek certainty we look for validators... that serves as a good crutch. [29:28]* ## [32:28] The age of the polymath is back Grinberg argues that AI tools are ushering in a new era of polymaths by allowing individuals to reach the 'frontier' of multiple disciplines quickly. This shift favors individuals who can think in systems and manage uncertainty while pushing boundaries in both engineering and marketing simultaneously. > *The age of the polymath is back. [32:28]* > *These tools can get you up to speed to the frontier... way faster than ever before. [33:24]* ## [35:06] What we'll look back on in disbelief Grinberg identifies writing release notes and documentation as tasks that will soon be considered a waste of expensive human engineering time. He suggests AI will soon equalize the advantage of high-quality documentation, allowing organizations to redirect human talent toward higher-value differentiation. > *It's crazy that people used to spend hours of time writing release notes or like writing documentation. [35:24]* ## [39:25] Why the company is called Factory Using a Tesla factory metaphor, Grinberg explains that the future of software development involves engineers designing the 'assembly lines' rather than writing individual lines of code. Humans act as architects of the scaffolding and safeguards that produce the software. > *They're kind of like building the scaffolding around this factory that produces their software. [40:18]* > *Engineers that build the software... they're going to have engineers that build the factories that build their software. [39:30]* ## [40:18] Labour displacement and the problems AI will finally solve Grinberg acknowledges short-term economic shocks but remains optimistic about long-term employment. He argues that by lowering the cost of development, the market can reallocate human talent to solve a much broader range of global issues, such as dementia research, that were previously too expensive to tackle. > *Very few of those problems that can be solved with software are we currently solving with software. [41:00]* > *If we have more engineers who are going and solving more problems in the world, that is a net good. [41:16]* ## [44:21] Are we in an AI bubble? Despite concerns about an infrastructure bubble, Matan identifies human behavior change as the most significant bottleneck for AI adoption. Successful enterprise integration requires navigating cultural shifts and the complexities of change management within established corporate structures. > *The biggest bottleneck by far working with all these organizations is the human side of it. It's just like behavior change. [44:58]* ## [45:51] Lessons from selling to enterprises Matan reflects on his transition from theoretical physics to enterprise sales, noting that success comes from genuine curiosity about a client's bureaucratic nightmares. He emphasizes that one should never try to 'sell' but rather understand if a solution can actually help the client's specific problems. > *You should never try to sell something. You should always try to understand their problems. [46:42]* > *People love talking about their problems and they love talking about all of the bureaucratic nightmares. [47:17]* ## [47:46] From string theory to Factory: the origin story Matan recounts his childhood obsession with math and his drive to become a string theorist at Princeton and Berkeley. However, he experienced an existential crisis during his PhD, realizing he was pursuing the field because it was hard rather than for personal fulfillment. > *I've just been doing this because it's hard and because someone said I couldn't do it. [49:12]* > *I asked my dad what the hardest math was. He said string theory... I was like, okay, I'm going to be a string theorist. [48:44]* ## [50:46] Discovering code that writes itself After exploring computer science at Berkeley, Matan became 'nerd sniped' by program synthesis—the concept of code creating itself. He realized that the most significant problems in this space would be solved in industry rather than academia, leading him to start a company. > *It just completely nerd sniped me because the idea here is... code with the explicit purpose of creating itself. [51:03]* ## [52:30] The cold email and 3-hour walk with Sequoia Matan reached out to a Sequoia investor who shared his physics background. Their initial meeting turned into a three-hour walk where the investor gave Matan a blunt ultimatum: drop out of his PhD immediately to either join Elon Musk's Twitter or start his own company. > *You absolutely need to drop out of your PhD and you should either join Twitter right now... or you should start a company. [53:48]* ## [55:30] Dropping out and the $1M check Within 72 hours of building a demo with his co-founder Eno, Matan withdrew from his PhD and pitched the Sequoia partnership. Despite a 'shitty deck,' Sequoia offered a $1M check for a 20% stake, a deal Matan accepted because they believed in him when no one else did. > *No one else would have believed in me except him... trust and loyalty and like belief to me that matters so much more. [57:38]* > *Drop out of your PhD and send me a screenshot. [55:16]* ## [1:01:19] Does Ivanka Trump add value as an investor? Matan addresses skepticism regarding celebrity investors, asserting that Ivanka Trump provides significant tangible value through her intelligence and network. He notes that she and her firm, Affinity, earned their place on the cap table through active support and investor relations. > *She is genuinely so kind, so intelligent, and like people just in throughout tech... really love her. [61:52]* ## [1:02:39] How the coding market matures Matan suggests that the market will eventually mature into a state where AI models are decoupled from the specific applications they power. This separation is necessary to prevent misaligned incentives where model providers might otherwise 'token max' for profit rather than efficiency. > *What is necessary for the best outcome for the consumers is going to be models that are separate from the applications. [63:01]* ## [1:07:45] The coming security danger zone As AI-generated code grows exponentially, Matan warns that security efforts are not keeping pace, creating a 'danger zone.' He emphasizes that adversarial behavior using AI tools is still in its early stages and will become a critical market focus as stakes rise. > *Code generated is growing exponentially. The security efforts aren't growing in kind. [68:17]* ## [1:08:50] Should US startups use Chinese models? Matan addresses concerns regarding US startups using Chinese open-source models, specifically the fear of 'trigger words' for adversarial behavior. He stresses the importance of data exfiltration defenses and expresses a desire for the US to reclaim superiority in frontier open-source models. > *I think it's pretty embarrassing that we don't have frontier open models in the United States. [70:33]* ## [1:11:43] Data centres and the public backlash The conversation shifts to the public backlash against data center development. Matan argues that the United States' federalist structure acts as a 'petri dish' where states allowing data centers will see job growth and prosperity while others fall behind. > *It's like we have little petri dishes to test out and see how things work. [72:31]* ## [1:14:22] Selling without forward deployed engineers Matan critiques the use of service-heavy FTE models to sell AI products. He argues that if a company requires a heavy services component to make their software work, the product itself is fundamentally flawed and lacks true product-market fit. > *If we need FTEES to make the product work, we have a [ __ ] product. [75:15]* ## [1:15:32] Grindslop, sleep and treating teams like athletes Matan rejects 'grind slop' culture—focusing on hours worked rather than output. He advocates for treating elite engineering teams like professional athletes, prioritizing cognitive recovery and sleep to ensure high-quality decision-making and leverage. > *Imagine trying to measure who won a basketball game by who sweat the most. [76:12]* > *The work that we do is like might require like really deep thought... if you didn't sleep well like you're not going to make as good of a decision. [78:02]* ## [1:20:32] Anthropic vs OpenAI When asked to choose between OpenAI and Anthropic for an IPO investment, Matan selects Anthropic based on corporate stability. He notes that OpenAI has suffered from significantly more internal turbulence and chaotic events, which negatively impacts its expected value. > *Past is an indicator of the future and like there's just been more like random chaotic turbulent events at OpenAI. [81:06]* ## [1:21:19] Did Dario do AI a disservice? Matan critiques AI leaders like Dario Amodei who claim AI will replace all human labor, calling the rhetoric a fundraising tactic. He argues these claims are designed to convince investors that a single company will eventually capture the entire capitalist economy. > *The best way to convince people to do that is to say all of capitalism is gone. [82:00]* > *Incentive is driving the outcome and the incentive is I want to raise a lot of money. [82:54]* ## [1:23:53] What he's changed his mind on Matan shares his shift in perspective from a 'winner-take-all' view to expecting a multi-polar market with at least four frontier companies. He identifies legacy firms like EY as surprising leaders in AI adoption, moving faster than some startups due to their 'scars' from the cloud transition. > *The bad case for humanity is when there's one that's really really good. [84:14]* > *They are so agent native. It's crazy. They're one of our largest customers. [83:11]* ## Entities - **Matan Grinberg** (person): CEO and co-founder of Factory, former string theorist. - **Harry Stebbings** (person): Host of 20VC and venture capitalist. - **Factory** (organization): AI company focused on software development automation and agents. - **Sequoia Capital** (organization): Venture capital firm that led Factory's seed round. - **OpenAI** (organization): Leading frontier AI model provider. - **Anthropic** (organization): AI safety and research company, creator of Claude. - **Ivanka Trump** (person): Strategic investor in Factory via her firm Affinity. - **EY** (organization): Big Four accounting firm noted for aggressive AI adoption. - **Uber** (organization): Company cited for implementing individual AI token budgets. - **Kirkland & Ellis** (organization): Law firm that invested $500M in internal AI tools. - **Juan Maldacena** (person): Renowned physicist at Princeton whom Matan worked with. - **Dario Amodei** (person): CEO of Anthropic.

The All-In quartet reunites for a packed week: Anthropic's secret Fable 5 nerfing of AI researchers triggers a developer trust crisis; Sacks and Friedberg tear apart the "safety" framing as a regulatory capture playbook; Bernie Sanders' op-ed demanding 50% government equity in AI companies collides with Trump's sovereign wealth fund instincts; CPI and PPI both hit multi-year highs, putting the Fed in an impossible spot ahead of midterms; and Friedberg lays out a meticulous paper trail of California election laws that, in aggregate, have turned democratic races into appointments. ## [00:00] Besties are back! Jason Calacanis opens the show confirming the original four — Jason, Chamath, Friedberg, and Sacks — are all back together for a week packed with consequential debates. The short opener sets up a five-topic sprint covering AI governance, macroeconomics, and California politics. > *"The All-In podcast is not quitting. We're doubling down with the original quartet."* ## [00:19] Anthropic gets massive backlash over secret Fable nerfing and privacy concerns Anthropic launched Fable 5, a "Mythos-level" frontier model, but buried two policies that detonated on developer Twitter. First, all prompt data entered while using Fable is stored for at least 30 days — including for enterprise accounts that had signed zero-data-retention agreements. Second, Fable was secretly downgrading users it detected doing frontier AI research (training competing models) without disclosing it was doing so. Anthropic's post-blowup response was to make the safeguards "more visible" rather than remove them. Friedberg connects this directly to his own work at Ohalo Genetics: over the prior weeks, Anthropic had tightened restrictions on genomics and biology use cases his team depends on, forcing a pivot toward open-source Chinese models. He argues the capability ceiling Anthropic imposes on biotech AI is the same ceiling that blocks cancer research — not just weapons work. Sacks frames the developer outrage as a fundamental trust rupture: the surveillance and nerfing extend even to paying enterprise customers who believed they had contractual data protections. Chamath draws the longer arc — an emergent AI company today should be knocking on Anthropic's door with equity deals rather than building independently, because Anthropic can route traffic and favor philosophically aligned partners. That structural power, combined with mandatory surveillance, looks less like safety and more like a tollbooth. > *"The sense of the violation of trust and how much outrage there is in the developer community over this latest Fable release is not just the fact that they're doing mandatory surveillance. Even enterprise customers who had signed zero data retention agreements, they do not have a choice."* ## [29:16] The AI regulatory capture trap, pragmatic safety solutions Sacks identifies the endgame he sees in Dario Amodei's public blogging and policy positions: an AI duopoly backed by a new government agency staffed via revolving door, empowered to decide who can access which capabilities — with dissidents profiled and cut off. He warns conservatives and libertarians that signing onto the "safety" framing without reading the fine print hands permanent market control to incumbents. Friedberg proposes a downstream enforcement model: instead of restricting what AI models can output, regulate the manifestation of harm — criminal statutes against bioweapon creation already exist, and expanding them to cover AI-assisted synthesis is workable without touching the underlying model capability. He notes that nucleic-acid oligosynthesis companies have already signed onto database-screening regimes, proving the model works at the supply chain level without requiring model censorship. > *"I really think that conservatives and libertarians are mortgaging their futures if they go along with this red capture safetist agenda without really realizing that there's so much more to it at stake."* ## [37:59] Nationalizing AI: Trump/Sanders, justifications, and AI's "Capitalist Cucks" Bernie Sanders' June 1 New York Times op-ed called for the federal government to seize 50% equity in AI companies on the grounds that public research funded the foundational work. Trump, meanwhile, has been vocally enthusiastic about a U.S. sovereign wealth fund. The besties find the two proposals coming from opposite directions but landing close together. Sacks argues the "public benefit" framing embedded in Anthropic's corporate charter is the Trojan horse: a board with a dual mandate for profit and societal benefit can be steered by regulators far more easily than a pure C-corp. He highlights that Ben Thompson's read — Anthropic's pause-on-AI-research blog post was designed to justify the anti-competitive nerfing of Fable's competitor-research use cases — makes the regulatory capture loop visible. His patience has run out: "I'm so sick of defending these idiots. It's a stupidity tax because they've been out there teaching the public that what they do is harmful for years." Friedberg offers a structural defense of a sovereign wealth fund: every American taxpayer could receive a direct equity stake in AI-era value creation the way Alaska residents receive Permanent Fund dividends. He pushes back on the left framing (nationalization = equity seizure) and the right framing (any government participation = socialism), arguing the mechanism matters. Chamath adds that AI is categorically different from prior infrastructure — unlike highways, the product is intelligence itself, which means whoever controls access controls economic agency. Jason closes the segment with his own verdict: the AI safety labs are "capitalist cucks" whose kink is inviting regulators to seize their equity. > *"It's a stupidity tax because they've been out there teaching the public that what they do is harmful for years. But the companies that are providing it are saying that they themselves are a problem."* ## [59:22] Liquidity recap: Best moments and takeaways The besties run through highlights from the All-In Liquidity conference. Thomas Leifert's venture capital data presentation anchored the discussion: the odds of a decacorn reaching centacorn status run at about 13%, but the odds of a centacorn crossing $1 trillion nearly triple to 31%, suggesting the power law steepens at the very top. Jason jokes that seizing even 10% of a "trilicorn" would retire 2% of the national debt — and Chamath counters he could pay off the whole thing by himself if given the mandate. Logistics praise goes to Thomas Keller and the French Laundry dinner hosted by the New York Stock Exchange, Niagen's wellness lounge with NAD recovery IVs, and a nine-hole golf scramble. The segment closes with a plug for All-In Summit (September 13–15) and Chamath's philosophy on curation: Liquidity exists for the most important capital allocators in the world to build relationships, not for anyone to buy their way in. > *"Capital is what shapes the things that occur in the world. So I think that we have to be extremely selective in how we curate every element of that show."* ## [01:05:39] Inflation heats up: CPI and PPI see 3+ year highs May CPI came in at 4.2% year-over-year — the highest since April 2023 — while PPI hit 6.5%, the highest since late 2022. Polymarket priced a 21% chance inflation reaches 5% in 2026 and a 49% chance of a Fed rate hike this year, up from under 10% before the Iran war started. Despite the hot print, the NASDAQ was up 2.5% on recording day, which Sacks reads as the market pricing in an imminent geopolitical resolution. Friedberg pins the core driver on two compounding forces: the Iran war energy spike feeding directly into transportation and manufacturing costs, and structural government overspending that has kept aggregate demand elevated despite rate hikes. Chamath adds a tail-risk scenario: if China draws down its strategic reserves and re-enters the spot oil market needing an incremental 3 million barrels per day, crude could run to $150–200 — a scenario that would make the Fed's current dilemma look simple. > *"There's definitely an energy blip from the Iran war that drove the core index up, but there's also the macro point which is government spending out of control, inflation out of control and fundamentally as things unravel you have rising rates."* ## [01:12:27] California's loose election laws creating integrity doubts The LA mayoral primary result — Karen Bass surviving despite a sprawling corruption investigation — ignites a detailed Friedberg walkthrough of California election law changes accumulated since approximately 2018. He lists a dozen discrete reforms: unlimited ballot harvesting, no signature verification, mail ballots counted up to seven days after election day without postmarks, voter registration accepted via gym membership card, no cross-checking against federal databases, and homeless shelter addresses used to register thousands of voters with no residency verification. His argument is not that any single rule is fraudulent, but that in aggregate they create an environment where elections become appointments. Sacks catalogs statistical anomalies in the LA count: late-arriving mail ballots broke heavily toward Bass while same-day ballots split the other way, a swing he argues is hard to explain through normal political behavior. He extends this to a structural point — the same interest groups that benefit from loose rules also fund the nonprofits that do ballot collection, closing a loop that is legal but not transparent. Chamath urges reformers to play the long game: sponsor a ballot initiative requiring voter ID, push federal ID requirements for public benefits recipients, and let the results speak rather than alleging fraud after each loss. > *"Is it really so hard to believe that some of the same groups, the same interest groups, the same NGOs would be willing to exploit these loopholes in the dirty voter roles in the millions of ballots that go to incorrect or non-existent addresses, the non-existent chain of custody, the non-existent signature verification, the no ID, not only to vote but to register, counting ballots without postmarks if received 7 days later?"* ## Entities - **Jason Calacanis** (Person): All-In Podcast co-host; founder of Launch Fund; moderator for most topic transitions this episode. - **Chamath Palihapitiya** (Person): All-In Podcast co-host; founder of Social Capital; frames AI and election topics through structural and capital-allocation lens. - **David Friedberg** (Person): All-In Podcast co-host; founder and CEO of Ohalo Genetics; provides biotech and election-law policy analysis. - **David Sacks** (Person): All-In Podcast co-host; founder of Craft Ventures; White House AI & Crypto Czar; leads regulatory capture and nationalization arguments. - **Dario Amodei** (Person): CEO of Anthropic; referenced for public blog posts the besties read as regulatory capture advocacy. - **Bernie Sanders** (Person): U.S. Senator; author of June 1 NYT op-ed calling for 50% federal equity stake in AI companies. - **Anthropic** (Organization): AI company behind Claude; launched Fable 5 / Mythos 5 with secret nerfing of frontier AI researchers and mandatory 30-day data retention policies. - **Fable 5 / Mythos 5** (Software): Anthropic's frontier model release that covertly downgraded frontier AI researchers and stored all prompt data for 30 days, including for zero-retention enterprise accounts. - **Ohalo Genetics** (Organization): Friedberg's agriculture genomics company; directly impacted by Anthropic's biotech model restrictions, forcing a shift to open-source Chinese models. - **U.S. Sovereign Wealth Fund** (Concept): Trump-backed proposal to channel government capital into high-growth assets; debated as a mechanism to give citizens direct AI equity exposure. - **Regulatory capture** (Concept): The dynamic where incumbents use safety and public-benefit framing to shape regulation that locks in their market position and restricts open-source or competitor models. - **Ballot harvesting** (Concept): California law allowing third parties to collect and submit unlimited mail ballots on behalf of voters; central to the LA mayoral primary integrity debate.

The All-In Summit's inaugural Best Ideas Pitch Competition put four fund managers on stage to defend a single trade in front of judges Chamath Palihapitiya, Jason Calacanis, David Friedberg, and guest judge Gavin Baker (Atreides Management). Aaron Cowen of Suvretta Capital pitched MGM Resorts as a hidden Asian casino play, Dan Dreyfus of Bornite Capital made the case for Talen Energy as a power-cycle compounder, Oleg Nodelman of EcoR1 Capital presented radiopharmaceutical biotech Aktis Oncology, and Kyle Samani of Multicoin Capital pitched GEODNET, a decentralized RTK precision-location network. The audience voted Dan Dreyfus winner; the Besties' own ranking flipped the result and crowned Aaron Cowen's MGM pitch on top. ## [00:00] Chamath explains the Best Ideas format Chamath traces the format back to the Ira Sohn Investment Conference—a charitable event he attended in 2015, where he pitched Amazon as a future trillion-dollar company only to be publicly dismissed by David Einhorn. He returned in 2016 with Tesla converts and in 2017 with AI as his macro thesis but picked Box instead of Nvidia. The origin story doubles as a self-deprecating admission that a correct macro read can still miss the specific instrument. The All-In version keeps the core mechanic: managers with real skin in the game present live to an audience with no obligation to be polite. > *"I said Amazon's going to be a trillion dollar company and I was laughed out of the room. David Einhorn, who's a friend of mine, but who was totally wrong, said, 'I know trillion dollar companies. This is not a trillion dollar company.' Wrong."* ## [02:31] Suvretta Capital Management's Aaron Cowen pitches MGM Resorts Aaron Cowen, who previously ran the equities book for George Soros and served as CIO for Steve Cohen, opens by ruling out a tech pitch to a tech-heavy crowd and lands on MGM—not for the 13 Vegas properties, but for two geographically optioned assets the market has priced at zero. The first is MGM's 40% stake in the Osaka Integrated Resort, opening in 2030: Japan's gambling market is already ~$40 billion (pachinko + horses), Osaka sits closer to Shanghai and Beijing than Macau, and Wynn's Macau playbook shows the market only prices in a new casino about three years before opening—which is now. The second is 300,000 square feet of empty space built into MGM's Dubai grand complex, held ready if the emirate ever legalizes gambling. The day before the pitch, Barry Diller—who owns 26% of MGM and has it at 80% of his NAV—submitted a $48 bid, immediately crystalizing the downside floor. Cowen says he would not sell: "Vegas at ~$60, Japan at ~$50, Dubai at ~$40–50—the stock could be worth 150." > *"Rarely have I ever seen a company in six years buy half their float back. So you have Barry Diller who's the legend aggressively buying the stock and it's also now 80% of his NAV."* ## [13:07] Bornite Capital's Dan Dreyfus pitches Talen Energy Dan Dreyfus opens with a power-cycle framework: demand tracks GDP in normal times, spikes during technology adoption waves (appliances and AC in mid-century; efficiency gains in the 2000s), then normalizes. The current AI wave is the next spike—but he immediately clarifies that AI is not the base case for tightness. It "just turbocharges" a supply-demand imbalance that already exists from two decades of underinvestment. Talen Energy holds 2 GW of nuclear and 6 GW of gas in the PJM grid, where PJM's own forecast calls for 106 GW of new capacity in ten years—a geological impossibility given supply-chain bottlenecks in critical minerals. He invokes Sam Zell's rule: buy hard assets below replacement cost when new capacity is needed. Talen trades at a $25 billion enterprise value against a $45 billion replacement cost, making the equity a double even if management does nothing. Stacked upside: $50/share FCF at current operations (stock ~high $300s → 7× vs. infrastructure peers at 15×), $70/share if power prices rise or more PPA contracts materialize, $100+/share if Talen builds 4 of the 106 GW the grid needs. > *"We do not need AI demand to keep the power markets incredibly tight for the next 20 years. AI demand just turbocharges. That's all it does. And it creates shortages."* ## [27:19] EcoR1 Capital's Oleg Nodelman pitches Aktis Oncology Oleg Nodelman leads EcoR1 Capital, a value-oriented biotech fund that has returned 10× since its 2013 launch ($13 million → $2.5 billion AUM). He frames biotech investing as poker played in a sector of slot-machine tourists, and signals his edge: margin of safety over science love. The pitch for Aktis Oncology (AKTS) is built on modern radiopharmaceuticals—mini-protein scaffolds carrying actinium-225 payloads that navigate the bloodstream by molecular recognition and detonate with a ~100-micron blast radius, roughly one cell's diameter. Key de-risking factors: chosen targets (nectin-4 for bladder cancer, B7H3 for a broad range of solid tumors) are already clinically validated; imaging lets physicians confirm drug delivery in early trials; data readouts are guided for 2027 with nectin-4 as early as Q1. The IPO was 18× oversubscribed and backstopped with a $100 million order from Eli Lilly. Actinium-225 derives from U.S. Cold War radium-226 stockpiles, making the supply chain structurally inaccessible to China—a moat unusual in biotech. Gavin Baker extended the Q&A into longevity: Nodelman said he'd take the over on human lifespans exceeding 100–125, partly because GLP-1 obesity drugs already replicate caloric restriction, the only intervention proven in controlled data to extend life. > *"Like a swarm of micro drones small enough to navigate the bloodstream and find their target by molecular recognition, then detonate a precisely sized warhead with a blast radius of 100 microns or the diameter of a single cell."* ## [40:20] Multicoin Capital's Kyle Samani pitches GEODNET Kyle Samani co-founded Multicoin Capital and led all three pre-launch investment rounds in Solana. He pitches GEODNET (GEOD on Solana), a decentralized RTK precision-location network. Standard GPS precision is ~2 meters; RTK reaches ~2 centimeters—100× improvement—which robotics, drones, and autonomous vehicles require. Legacy RTK providers (Trimble, Hexagon, Topcon) spent 20–30 years building a combined ~12,000 base stations. GEODNET launched in 2021, bootstrapped 22,000+ nodes by paying token rewards to hobbyists who mount a few-hundred-dollar antenna on their roof, and now covers 150 countries and 80% of the global population. Revenue just crossed $1 million annualized; 80% of that goes to open-market purchases of GEOD tokens on Solana (functionally a revenue-share buyback). Customer growth is viral within the robotics supply chain: DJI, John Deere's autonomous sprayer program Gus, TomTom (maps supplier to virtually every AV program), and robotic lawnmower makers all route through GEODNET. Average customer spend grows from ~$60K in year one to ~$170K by year two. Fully diluted market cap: ~$150 million. Friedberg challenged the pitch with the satellite micro-constellation threat; Samani countered on cost and energy consumption—battery-sensitive devices like drones will always prefer the cheaper, lower-energy ground solution. > *"Once someone starts rolling out GeoNet in the first year, they're usually spending about $60,000 per year. After two years though, they're usually spending about $170,000 per year."* ## [54:50] The Besties recap the pitches and announce winners Chamath applies the Druckenmiller framework—no skin in the game, no real conviction—and sizes the four pitches by liquidity as much as thesis: GEODNET he loves but can't deploy more than $10–20K without moving the market; Talen and MGM could absorb tens of millions. Gavin Baker names MGM the best risk/reward outright ("your downside is really capped because of the Barry Diller bid and then you have Japan and Dubai as very valuable future sources of value"), and credits Talen as compelling but flags regulatory tail risk from potential government intervention in data-center power pricing. Friedberg ranks MGM first for timeline and downside floor, Talen second but notes interest-rate sensitivity (power purchase agreements get discounted like bonds), Aktis third because Lilly could bid within months of a good clinical readout, and GEODNET last on the theory that LEO satellite constellations will eventually make ground-based RTK redundant. Jason puts $200K each into MGM and Talen in real time, ranks GEODNET and Aktis as lottery tickets. Audience vote (150 attendees): Dan Dreyfus / Talen Energy wins with 50%, Aaron Cowen / MGM second at 24%, Oleg Nodelman / Aktis third at 21%, Kyle Samani / GEODNET fourth at 5%. The Besties' 4-3-2-1 ranking flips the top two: Aaron Cowen takes first, Dan Dreyfus second—crowd picks Talen, judges pick MGM. Both are briefly overshadowed by Jason's custom "extremely alpha male heterosexual" trophy: a 3D-printed sculpture of two men in an uncomfortable hug, which Chamath and Jason immediately demonstrate on stage. > *"If you don't have any skin in the game, you don't care. And this is the kind of stuff that I love."* ## Entities - **Chamath Palihapitiya** (Person): All-In co-host; Social Capital founder; event organizer and judge - **Jason Calacanis** (Person): All-In co-host; Launch Fund founder; MC and judge - **David Friedberg** (Person): All-In co-host; Ohalo Genetics; judge; previously managed Precision Planting agriculture tech - **Gavin Baker** (Person): CIO at Atreides Management; guest judge; former biopharmaceutical fund manager - **Aaron Cowen** (Person): Founder/CIO of Suvretta Capital Management ($4B AUM); formerly ran equities at Soros; CIO for Steve Cohen - **Dan Dreyfus** (Person): Founder of Bornite Capital; commodities and energy investor - **Oleg Nodelman** (Person): Founder/Managing Director of EcoR1 Capital ($2.5B AUM); 25-year biotech investor - **Kyle Samani** (Person): Co-founder of Multicoin Capital; early Solana investor; stepped down as managing partner prior to this event - **MGM Resorts International** (Organization): Las Vegas casino operator; holds license for Osaka Integrated Resort (opening 2030); building Dubai property with 300K sq ft optioned for gambling legalization - **Talen Energy** (Organization): U.S. independent power producer; 2 GW nuclear + 6 GW natural gas in PJM grid; $25B enterprise value vs. $45B replacement cost - **Aktis Oncology** (Organization): Radiopharmaceutical biotech (AKTS); mini-protein platform carrying actinium-225; targeting nectin-4 (bladder cancer) and B7H3 (broad solid tumors); data guided 2027 - **GEODNET** (Software/Network): Decentralized RTK precision-location network; 22,000+ nodes in 150 countries; GEOD token on Solana; 80% of revenue used for open-market token buybacks - **Barry Diller** (Person): Media/entertainment investor; owns 26% of MGM; submitted $48/share takeover bid - **Ira Sohn Foundation** (Organization): Charitable investment conference that inspired the Best Ideas format - **Radiopharmaceuticals** (Concept): Cancer treatment modality using radioactive actinium payloads on molecular carriers to destroy tumor cells with ~100-micron blast radius and minimal collateral damage - **RTK (Real-Time Kinematics)** (Concept): Precision GPS augmentation achieving ~2 cm accuracy vs. standard GPS ~2 m; required for agricultural robots, autonomous vehicles, and drones - **PJM Interconnection** (Organization): Regional transmission organization (Pennsylvania–New Jersey–Maryland); forecasting 106 GW of new power demand over the next 10 years

Six months after their December roundtable, Jacob Effron reconvenes with Ari Morcos (Datology AI CEO) and Rob Toews (Radical Ventures) for a full-spectrum AI vibe check. Coding agents have crossed a long-horizon threshold that is reshuffling the engineer's job description; near-frontier open-weight models look increasingly like a retreating tide as both Meta and the Chinese labs pull back for economic reasons; and Anthropic's silent capability restrictions on Fable have rattled its most loyal supporters. The trio works through Google's structural durability despite coding lag, Ari's prediction that compute pressure could force labs to suspend their public APIs entirely, the emerging atom and X-ray lithography challengers to ASML, and how close — but how bottlenecked — recursive self-improvement actually is. ## [00:00] Intro Jacob welcomes returning guests Ari Morcos and Rob Toews, noting that this is a "vibe check" format covering everything from IPO filings and SpaceX's pivot to compute to Fable's release the prior day. He frames the conversation around a single question: what is the single biggest thing that changed in the six months since they last sat down after NeurIPS? > *"Things have changed. We've had IPO filings. We've had models not launched and then launched. We've had SpaceX becoming an AI info company."* — Jacob Effron ## [01:40] Coding Agents Cross a Threshold Ari identifies the clearest shift: coding agents now reliably execute at longer time horizons, which crossed a threshold over Christmas break that made them genuinely useful rather than merely promising. At Datology, engineers have almost universally transitioned from individual-contributor work to managing fleets of agents concurrently — but the gains come with a new bottleneck. Code review queues are backing up, and the "slop" entering codebases is harder to catch when no one fully understands what the agent wrote. > *"We're really starting to now see the shift of engineers at least kind of almost all moving from ICs to managers of agents."* — Ari Morcos ## [03:29] Is Open-Weight AI in Retreat? Rob opens with what he calls a structural inflection: near-frontier open-weight AI risks falling off entirely. His prior assumption — that open models would trail closed ones by only a few months — may no longer hold. Meta appears to be pulling back from its open-source strategy, and Chinese labs including Qwen and DeepSeek are now keeping their highest-performing weights proprietary while open-sourcing only smaller, less capable versions. Ari agrees the economics no longer support openness once a lab has gained credibility: hosting inference is far more lucrative than giving away weights. Rob is blunt that no viable long-term business model exists for purely open-weight AI at the frontier. > *"There are early signs that seem to suggest over the past six months that make me question whether open-weight AI is going to continue to be a really meaningful force in the ecosystem."* — Rob Toews ## [07:37] Cost Crunch & Scaffolding Jacob notes a counter-pressure arriving simultaneously: enterprises are finally getting serious about reducing model spend. Going from Claude Opus 4.6 to 4.7 doubled token output for some users, and bills that were once negligible are now budget line items. Ari argues the real innovation is increasingly happening not in the model weights but in the harness and scaffolding layer — open-source models combined with proprietary scaffolding (Kimi/Moonshot being the clearest example) may be the actual business model that survives. He also warns enterprises that the only two real options are partnering with a frontier lab (and eventually being out-competed because you've handed over your proprietary data) or building enough in-house capability to maintain independence in a world where reliable open models are no longer guaranteed. > *"A model is not just a model anymore — it's the model combined with the harness and the scaffolding, and a lot of innovation is happening on the harness and scaffolding layer."* — Ari Morcos ## [12:13] The "Apps Are Cooked" Debate Rob thinks the "apps are cooked" narrative was simultaneously partially right and wildly over-broad. Traditional software categories genuinely face existential pressure from lab roadmaps, but no two or three companies can execute excellently across every vertical on earth. OpenAI shutting down its video effort — despite having effectively infinite capital and a strong team — is proof that even the richest lab has to make hard prioritization calls, and much of that is driven by compute constraints. Deep tech and hardware have become the consensus VC bet as a result, but Rob flags that hard tech is also hard: failure rates are high and unsolved problems abound. > *"There's no way that one or two or three companies will win every single important market and category in the world."* — Rob Toews ## [16:37] Sam Altman Under Scrutiny Rob revisits his December prediction that Sam Altman would be replaced by year end. At the time everyone pushed back; mid-June the odds look higher. His original succession candidate — Fiji — has had to step back for health reasons, and his updated theory centers on Bret Taylor: chairman of OpenAI's board, CEO of Sierra, and one of Silicon Valley's most trusted operators. Rob thinks an OpenAI acquisition of Sierra combined with installing Taylor as CEO would be a decisive narrative reversal ahead of the IPO — the trust gap between OpenAI and Anthropic is large and widening, and Taylor's reputation could close it. Ari floats an alternative: OpenAI restructures into an Alphabet-like holding company where Sam stays atop the parent while a separate CEO runs the core product. > *"I think it would be in the best interest of OpenAI's shareholders — if someone like Bret Taylor was at the helm of OpenAI, I think it would do a lot to change their fortune."* — Rob Toews ## [19:44] Anthropic's Fable Backlash The group digs into the blowback from Anthropic's decision to silently restrict Fable for any work touching AI development. Ari says the restriction itself is tolerable; the silent degradation — the model simply performs worse without telling you — is what has genuinely angered Anthropic's most loyal supporters. He reads the move as competitive positioning dressed up as safety, noting that open-model teams with good scaffolding have independently reproduced most of the vulnerability-finding capabilities that the restriction is supposedly protecting. Ari predicts a meaningful share of Claude Code's loudest Twitter evangelists will migrate to Codex in the short term, handing OpenAI an unexpected PR gift. > *"It doesn't give you a refusal. It doesn't say, 'I'm not going to help you with this.' It just does a poor job on that without you knowing."* — Ari Morcos ## [23:24] How Big a Step Change Is Fable? Ari, who had only started using Fable the night before recording, says he personally didn't see massive differences from Claude 4.8. Rob frames Fable less as a discontinuity and more as evidence that the "pre-training is hitting a wall" narrative was plainly wrong — gains keep coming richly from pre-training, and test-time compute has added another lever on top. Ari reinforces this from a practitioner's standpoint: in deep learning, having 95% of the details right often produces no improvement, and then one last adjustment triggers a step change. Negative results about scaling are therefore genuinely hard to interpret. > *"If you have kind of 95% of it right, it kind of rectifies to just not working. And then you turn the last knob and all of a sudden you get a step change."* — Ari Morcos ## [26:50] What's Going On at Google? Rob pushes back on the idea that Google is underperforming: the three frontier labs leapfrog each other continuously, and Google's lag on coding specifically is a prioritization choice — Anthropic built its entire identity around coding, OpenAI recently poured resources into it, and Google simply hasn't made it the north star yet. What Google does have is a full-stack structural advantage: its own chip design (TPUs), its own cloud, an enormous talent bench, and the Android/iOS distribution deal that makes its models the default on the world's phones. Ari adds that consumer AI will commoditize quickly, and Google is already optimized for the default-provider role on mobile even if it doesn't hold the best model. Jacob observes that Codex is clearly a strong product yet Claude Code remains dominant — first-mover advantage in developer tooling is stickier than expected, though Fable's restrictions may catalyze a wave of switches. > *"I think [Google's] behind on coding and I think that's just it reflects prioritization. It's clear that Anthropic leaned in on that as their northstar for years."* — Rob Toews ## [33:20] Could the APIs Go Away? Ari surfaces the most provocative claim of the episode: compute constraints could push Anthropic — or OpenAI — to suspend public API access entirely, not as a business decision but simply because first-party products like Claude Code generate better margins and chips aren't infinite. OpenAI has already started selling futures on guaranteed inference tokens, which Ari reads as a sign the lab itself sees API access as rationed. Rob confirms this is technically feasible, though extreme; a more likely near-term version is labs reserving their most powerful models for internal use rather than offering them publicly. > *"It is not hard to imagine a world in which Anthropic is so compute constrained that they actually cut off the API."* — Ari Morcos ## [34:11] Breaking the Semiconductor Bottleneck Rob shifts the conversation to the physical underpinning of the compute shortage: the extraordinary concentration of chip manufacturing in a single company (TSMC) whose most critical machine is made by a single other company (ASML). He flags Elon Musk's "terafab" concept as underreported given its transformative potential if executed. Ari pushes back on the timeline — relieving the compute constraint within the next handful of years is hard to imagine. Rob concedes that TSMC displacement in two to three years is implausible, but a five-year horizon with multiple augmenting players is imaginable — the single-point-of-failure structure of the global semiconductor supply chain doesn't have to persist. > *"It's actually kind of crazy that there's like one company that knows how to do this and no one else can do it, and the most important machine that goes into the process is made by one other."* — Rob Toews ## [35:42] Beyond EUV: Atom & X-Ray Lithography Rob describes two emerging research directions that could eventually challenge ASML's EUV dominance. The first is atom lithography: rather than using light, you use a beam of atoms to print transistor features, allowing far finer resolution with machines that are simpler, cheaper, and smaller than EUV tools. The second is X-ray lithography, which uses shorter-wavelength electromagnetic radiation to push beyond the physical limits EUV is beginning to hit. Startups in both categories have raised significant funding and remain in development mode. Ari estimates commercialization is at least five years away, but Rob thinks genuine technology disruption is coming. > *"There are a couple startups doing really interesting work in atom lithography... the machine can be way simpler, way fewer parts, way cheaper, way smaller, obviously much better resolution."* — Rob Toews ## [37:23] Implications of a Compute Shortage Jacob asks what a world of deepening compute scarcity actually means for businesses. Ari argues it will force the efficiency innovation that frontier labs have had little incentive to pursue: smaller and smaller models will match the largest models of one to two years prior, distillation investment will accelerate, and inference optimization will become a genuine competitive differentiator. Rob adds that the supply constraint is structurally good for every chip vendor other than Nvidia — AMD, Trainium, Cerebras — not because they increase total supply (TSMC remains the upstream bottleneck) but because enterprises will use whatever silicon they can get. H100 spot prices reversing their December decline is the clearest market signal that the shortage is intensifying rather than easing. > *"I would still expect that the usage is going to grow faster than what you can do to alleviate this."* — Ari Morcos ## [40:20] Do Alt Chips Actually Help? The group stress-tests whether alternative chip providers actually expand total compute or just redistribute it. The consensus: they are a beneficiary of the constraint, not a solution to it. In a world without Cerebras or dMatrix, Nvidia would simply absorb all of TSMC's capacity — total chip count stays constant. What alternative vendors do is prevent Nvidia from achieving a full monopsony on TSMC production and give compute-hungry buyers a fallback. The compute constraint is unlikely to ease before 2030; Ari estimates the early 2030s are when multiple unblocks — new fabs, new lithography, algorithmic efficiency — may hit simultaneously. > *"The alternative chip providers aren't a solution to the compute constraints, but will be a beneficiary of the compute constraint."* — Rob Toews ## [43:43] SpaceX, xAI & the Cursor Acquisition Jacob turns to xAI and the reported $60 billion Cursor acquisition. Rob is skeptical that xAI will re-enter the top tier of frontier AI research: the decision to sell compute capacity to Anthropic and Google is a clear signal that data center buildout — not model research — is the company's real priority. He thinks xAI's durable advantage matches Elon's operational DNA: standing up massive clusters extremely fast. Ari argues the Cursor acquisition is primarily about obtaining coding traces to bootstrap a competitive coding model that xAI has so far failed to build on its own — and that $60 billion is probably quite high relative to that goal, but keeps optionality alive. Rob notes the SpaceX S-1 TAM chart, which estimates enterprise AI at roughly twenty trillion dollars while all of space comes in at a few hundred billion, and concludes that narrative positioning ahead of the IPO is a big part of the deal's logic. > *"I think why Cursor is to get all the traces... and to have a hedge against the fact that they have struggled to produce a very competitive coding model."* — Ari Morcos ## [48:50] How Close Are We to RSI? Andrej Karpathy's decision to join a recursive self-improvement team prompts a direct question about timelines. Ari has moved meaningfully more bullish in six months: at Datology, agent-driven data curation experiments have produced results "far more promising than I would have expected," and he now sees RSI as clearly approaching feasibility. The bottleneck is compute, not ideas or execution. He is, however, deeply skeptical of the "one lab runs away" takeoff narrative: compute constraints cap the speed of self-improvement, and at least ten well-funded organizations have the talent and knowhow to pursue it simultaneously. Rob was expecting Ari to be more skeptical — pushed to explain how RSI could arrive without an exponential takeoff, Ari points back to compute as the fundamental limiter on iteration speed. > *"We are clearly getting to the point where models can improve themselves... but I think there are just fundamental compute bottlenecks that can prevent the speed."* — Ari Morcos ## [52:21] Quickfire The closing round surfaces several sharp takes. Rob's biggest disagreement with current discourse: today's AI systems are laughably energy-inefficient compared to what is coming — a 2-gigawatt data center versus the human brain's 20 watts — and breakthroughs in analog computing and hardware architecture will make the current capex buildout look like a historical anomaly. Ari's sharpest contrarian position: the "permanent underclass" narrative — AI takes all human jobs within a decade — is overblown because humans are slow at dissipating technology through the economy and business relationships carry a human-trust dimension that technocrats systematically underestimate. On mind-changes: Ari is more bullish on RSI than six months ago and now strongly believes near-frontier open-weight models will consolidate and shrink. Rob has pulled in his robotics timeline — foundation models for robotics have crossed a commercial viability threshold in recent months and the GPT-3 moment for general-purpose robotics may now be near. On spicy predictions for the back half of 2026: Ari bets that Anthropic — or possibly OpenAI — will suspend or heavily restrict API access at some point, with end-of-2027 as his higher-confidence window. Rob's prediction: Anthropic's next chapter is life sciences, and by year end it will be obvious they are building toward being one of the most important life sciences companies in the world — potentially including wet lab facilities of their own. > *"I think by the end of the year it will be very obvious that Anthropic is a fledgling juggernaut in the making in life sciences and biology."* — Rob Toews ## Entities - **Jacob Effron** (Person): Host of Unsupervised Learning, Managing Director at Redpoint Ventures - **Ari Morcos** (Person): CEO of Datology AI; former Meta AI and DeepMind researcher; guest - **Rob Toews** (Person): Partner at Radical Ventures; Forbes AI columnist; guest - **Anthropic** (Organization): AI safety lab behind Claude and Fable; subject of both admiration and growing criticism for silent capability restrictions - **OpenAI** (Organization): Lab behind ChatGPT and Codex; undergoing internal scrutiny around Sam Altman's leadership - **ASML** (Organization): Dutch company with near-monopoly on EUV lithography machines, the critical bottleneck for cutting-edge chip manufacturing - **TSMC** (Organization): Taiwan Semiconductor Manufacturing Company; the world's sole producer of the most advanced chips - **Datology AI** (Organization): Ari Morcos's startup focused on data curation and training infrastructure for AI models - **Cursor / Anysphere** (Software / Organization): AI coding tool reportedly being acquired by xAI for approximately $60 billion; valued primarily for its coding trace dataset - **Recursive Self-Improvement (RSI)** (Concept): The ability of AI systems to autonomously improve their own training and capabilities; increasingly treated as near-term rather than speculative - **Atom lithography** (Concept): Emerging chip manufacturing technique using beams of atoms rather than light to print transistor features, offering superior resolution and simpler machinery than EUV - **EUV (Extreme Ultraviolet Lithography)** (Concept): Current state-of-the-art chip printing technology, approaching physical resolution limits; ASML's core product

Tara Agyemang from the Google Chrome DevRel team presents WebMCP, a proposed web standard that replaces the brittle screen-scraping loop today's AI agents run through — DOM parsing, accessibility tree analysis, screenshot pixel math, coordinate clicks — with a clean menu of named, typed, described tools the browser exposes directly. Two API paths cover most sites: a declarative API that auto-generates JSON schemas from HTML form attributes, and an imperative API for registering custom JavaScript tools with explicit execute blocks. A live demo buys concert tickets in exactly three tool calls, and the spec is already testable in Chrome 146 via a side-panel inspector extension. ## [00:15] The DOM-scraping problem: what agents go through today Buying two tickets to an Afro Beats Festival sounds simple. For a current AI agent it means: parse the full HTML DOM, walk the accessibility tree, take a screenshot, do pixel-coordinate math to find the button, click — and then discover an ad has loaded and pushed everything 200 pixels south. Agyemang walks through each step live using a Gemini-in-Chrome side panel against a demo ticket site, making visible just how many tokens and how many fragile inferences sit between a user's natural-language request and a form submission. > *"It can be brittle, and I don't even want to guess at how many tokens you probably just used trying to do something simple. It's probably a lot."* ## [03:02] Accessibility first: the prerequisite before WebMCP Before reaching for WebMCP, Agyemang flags a prerequisite: semantic HTML and solid accessibility standards are not optional groundwork — they are what makes a site legible to agents by default. Proper ARIA roles, meaningful labels, and logical DOM structure collapse much of the agent's interpretation work even without any new API. > *"Making your site accessible for everyone makes it accessible to AI agents by default."* ## [03:53] What WebMCP is: a structured tool menu for agents WebMCP is a proposed web standard (not yet finalized) whose core idea is to flip the information asymmetry: instead of every agent reverse-engineering what a site does, the site author declares a menu of tools — named, typed, described — that agents can call directly. Agyemang borrows the USB-C analogy: any conforming agent speaks the same protocol, and any conforming site answers back. > *"Instead of any agent guessing what your website does, you're kind of giving them a menu of tools that they can use to interact with your site."* ## [04:43] Demo: navigating a maze with WebMCP tools The first live demo uses a maze escape game built by the Chrome DevRel team, shown alongside the Model Context Tool Inspector — a Chrome extension that lists every tool the current page exposes. At page load only one tool exists: `start_maze_game`. After calling it, the tool list expands to directional move tools (`north`, `south`, `east`, `west`), a look tool, and item management tools. Agyemang then types freehand prompts ("right, up, right again"; "complete the maze") and the Gemini 1.5 agent maps each instruction to the correct tool call, iterating autonomously. The maze is deliberately navigable only through the agent interface — no clickable buttons exist — which makes the tool-call loop the only path through. > *"The AI agent should use my prompt, match it to the specific tools, so in this case, the move tool. It's taken my direction of down and right, matched that to the north, south, east direction, and sent that through."* ## [09:58] WebMCP vs MCP: client-side vs server-side The question Agyemang anticipates most: isn't this just MCP? The distinction is scope. MCP connects agents to server-side applications and data sources. WebMCP implements the tools portion of MCP but runs entirely in the browser — the browser window must be open, and all tool execution happens client-side in the page's JavaScript context. She likens the relationship to JavaScript and Java: inspired by, not interchangeable with. The practical implication is that WebMCP covers the slice of agent work that is inherently tied to what a user has in front of them: filling complex multi-step forms, navigating stateful UI flows, personalizing a shopping session based on what's visible on screen. > *"Web MCP allows engineers to provide tools to in-browser AI agents. And it's very specific for the client-side features."* ## [12:35] The two APIs: declarative and imperative WebMCP offers two implementation paths. The **declarative API** requires only a few new HTML attributes on existing form elements (`tool-name`, `tool-description`); the browser generates the full JSON schema automatically. A boolean `agent-invoked` attribute lets the server distinguish agent submissions from human ones. The **imperative API** is for anything more complex: developers call `registerTool()` with a schema object they build manually, attach a description with enough detail for an agent to choose it correctly, and write an `execute` block containing ordinary DOM JavaScript — validate input, call existing functions, manipulate state — then return a result object so the agent knows what happened. The imperative path is currently more common because most real-world flows go beyond a single form. > *"The execute block is essentially where you call normal JavaScript. So, maybe you already have functions that you're using that you can call in here."* ## [15:16] Demo: buying concert tickets in three tool calls Back to the original ticket-buying scenario, this time on the WebMCP-instrumented demo site. Agyemang types: "Buy two VIP tickets to Summer Vibes Festival." Gemini 2.0 (upgraded from 1.5 for this demo) makes exactly three tool calls: `search_concerts` to find the event by name, `open_concert_page` with the returned concert ID to navigate to the right page, and `purchase_ticket` with quantity and section parameters. The UI updates in sync at each step — section selector, quantity picker — and the agent pauses before final checkout, surfacing the total (£356) so the user can confirm. Agyemang notes this last manual confirmation step is intentional: for real-money transactions, the human should always see what's about to happen before the agent commits. > *"You spent £356. Great, I'll put that on the Google's credit card."* ## [17:46] Getting started: Chrome 146, the inspector, and how to give feedback WebMCP is in early preview on Chrome 146+. Agyemang recommends Chrome Canary to keep experimental flags isolated from a daily-use profile. Setup requires enabling the `chrome://flags/#web-mcp` testing flag, then installing the Model Context Tool Inspector from the Chrome Web Store. Two resources cover the rest: a sign-up blog post for the early preview program (gives access to initial docs, best practices, and example implementations) and a GitHub repository with all demos — including the maze — plus an eval CLI for automated testing against a site's declared tools. The API is changing week to week; Google is actively looking for friction reports and bug filings before the spec stabilizes. > *"We don't have to settle for these brittle screen-scraping processes that we have today. Instead, we can use Web MCP tools to turn every website into a high-performance API for agents."* ## Entities - **Tara Agyemang** (Person): Developer Relations Engineer on the Google Chrome team; presenter and WebMCP advocate; GitHub/X handle @taraojo. - **WebMCP** (Concept): Proposed web standard that exposes structured, typed tools from a web page to in-browser AI agents, eliminating DOM-scraping; still experimental as of Chrome 146. - **MCP (Model Context Protocol)** (Concept): The parent protocol WebMCP draws from; MCP connects agents to server-side applications, while WebMCP handles client-side browser tool exposure. - **Declarative API** (Concept): WebMCP implementation path using HTML attributes on existing form elements; browser auto-generates JSON schema. - **Imperative API** (Concept): WebMCP implementation path using `registerTool()` in JavaScript; supports arbitrary DOM logic in the `execute` block. - **Model Context Tool Inspector** (Software): Chrome side-panel extension built by Chrome DevRel that lists all tools a WebMCP-enabled page exposes; available in the Chrome Web Store. - **Google Chrome DevRel** (Organization): Google team building WebMCP, the maze demo, the inspector extension, and the eval CLI; manages the early preview program. - **Gemini** (Software): Google's AI model used as the in-browser agent in both demos; demo upgraded from Gemini 1.5 to Gemini 2.0 for the ticket-buying scenario.

Garrett Galow, head of product at WorkOS, built Studio to kill the "explain your question, wait for an engineer, get an answer, realize you need one more join, share a one-off in Slack" loop that plagues every company with non-technical stakeholders. Studio lets anyone query Snowflake, Linear, and Notion in plain English, get a live answer, and — crucially — turn that answer into a deterministic, reusable widget whose code runs directly against the data sources without involving the LLM again. The reliability comes from three engineering choices: preflight sequencing that injects schema context only when a tool is actually invoked, a layering rule that tells the model to explicitly distrust its own knowledge about WorkOS products and pull from primary sources, and a validation step that runs every generated Snowflake query before hardcoding it into a widget. ## [00:14] WorkOS and Today's Talk Galow opens with a 10-second company pitch — WorkOS is the enterprise platform layer that powers SSO and other developer-facing features for Cursor, Anthropic, and OpenAI — and immediately flags that he is not here to talk about that. The session is about how WorkOS operates internally and what they built to make the whole team, not just engineers, faster at answering questions about the business. > *"If you've ever logged into Cursor, you've used WorkOS — whether that was username password or you went through your enterprise IDP."* ## [01:02] The Slow Loop of Business Questions The problem Galow describes is familiar: a go-to-market or support teammate has a question, cannot write SQL themselves, has to explain it to an engineer, waits, gets a partial answer, asks for one more join, gets another partial answer, and eventually receives a one-off table in Slack that is immediately stale. Even Retool or internal dashboards fail here because they are built for a fixed question — the moment someone needs one extra filter or one extra column the whole request cycle restarts. > *"Someone has a question, often about the business. They may not be technical enough to go answer it themselves. They have to explain their question, why they need it answered, the context to answer it. They wait."* ## [02:33] Studio Demo: From Question to Live Dashboard Studio is an internal workspace (web dashboard plus Slack bot) backed by a LangGraph agent running Claude Opus, connected to integration proxies for Snowflake, Linear, and Notion. Galow fires off a live question: which content on the WorkOS marketing site drives the most new team sign-ups? The agent runs preflight checks, determines it needs Snowflake, pulls schema context at the moment of invocation, issues several queries, and returns a ranked table in roughly 90 seconds. The more interesting part comes after: he asks Studio to turn that answer into a reusable widget with time-slice filters. The widget is declarative JavaScript that calls the underlying APIs directly. On every subsequent run the LLM is not involved at all — it is just code re-executing queries against Snowflake. The on-screen result shows blog posts, changelogs, and docs ranked by conversion to sign-ups, filterable by content category. > *"A widget is basically like sandbox code that runs — it's both the UI, the APIs, and the query necessary to power a fully usable tool."* ## [07:34] Radar Support Widget: Self-Serve for the Support Team Galow walks through a second widget built for WorkOS's support team around Radar, their bot-blocking security product. When a customer asks "why did this user get blocked?", support reps used to pass around ad-hoc SQL queries or wait for a data engineering ticket. The Radar widget lets any support rep type in a customer email, and the widget re-runs its hard-coded queries live against the database, returning the full login-attempt history and whether each attempt was flagged. Support staff can build these widgets themselves: if a question is genuinely one-off, they get the answer ad hoc; if the same question keeps recurring, they build a widget and share it internally. No platform team involvement required. > *"Our support team can basically, if it's a one-off, get the question answered themselves; and if they're finding that they're actually asking the same question a lot, they can build these and then share them internally to other folks."* ## [09:55] Three Pillars: Sequencing, Layering, Validation The reliability section is the technical heart of the talk. Galow names three design choices that made Studio usable enough to hand to non-engineers. **Sequencing** — before doing anything, the agent runs preflight checks: are all integrations connected? Does it have enough context to answer the question? If not, it asks for clarification. Schema context for each data source is injected only at the moment a specific tool is invoked, not upfront, keeping the context window clean for the actual reasoning. **Layering** — the prompt stack has a base layer (Studio defaults), an org layer (shared rules), and a tool-edit layer (session-specific context). Crucially, the model is explicitly told to distrust its own knowledge about WorkOS's products, because model training data goes stale fast and the product changes constantly. It is directed to pull from internal docs and live data sources instead. **Validation** — every Snowflake query the agent writes is executed before being committed to a widget. A query can be syntactically valid SQL and return zero rows; if the agent does not notice that, the widget ships as broken. Running the query first catches that failure mode before it becomes a user-facing truth. > *"We tell the LLM to specifically distrust knowledge around our product — sometimes the model training is using outdated data. Our product changes very quickly. So we actually tell it: no, go for primary sources, look up data in our docs."* ## [12:54] Q&A: Schemas, Governance, Cross-Tool Queries, and Access Three audience threads surface practical design decisions. **Dirty schemas**: a questioner asks whether Galow had to clean up Snowflake before Studio could use it. He did not. The hard joins — customer entity to users, four levels deep — are encoded once in the Snowflake context block; the LLM learns the quirks from that description rather than from a tidy schema. No RAG database, no schema rewrite. The guidance block does need to encode filter-column discipline (e.g. "only pull non-deleted entities") because models miss those silently. **Widget governance**: an audience member raises the trust problem — a widget that generates a query incorrectly becomes a "truth" that no one ever questions. Galow acknowledges this is real but says the hit rate has been high enough in practice. Embedding data-quality rules directly in the context block (active status filters, soft-delete guards) removes most silent errors; the remaining ones tend to be large enough to be obvious. **Cross-tool widgets and architecture**: asked whether widgets can draw from multiple tools simultaneously, Galow confirms they can — a widget can call Snowflake and Linear in one interface. The widget is JavaScript; it makes the underlying API calls independently, and merging the data is just code. Once a widget is generated, it is entirely deterministic: no LLM call on refresh, no inference cost, no variability. **Access control**: per-user OAuth is the current model (each employee connects their own Snowflake and Linear credentials), which is awkward. WorkOS is building "org connectors" via their own Pipes product — one admin sets up a connection, then role-based rules govern what each user can read or edit within that connection. > *"The actual final product is very reliable in that regard. The LLM's not involved once the widget is developed — until I go back and say, 'Hey, can you make an adjustment to this widget?'"* ## Entities - **Garrett Galow** (Person): Head of product at WorkOS; built and presented Studio. - **WorkOS** (Organization): Developer platform providing enterprise SSO, bot-blocking (Radar), and third-party integrations (Pipes) to companies like Cursor, Anthropic, and OpenAI. - **Studio** (Software): WorkOS's internal natural-language workspace; lets any employee query Snowflake, Linear, and Notion and build reusable widgets. - **Snowflake** (Software): Cloud data warehouse used as WorkOS's primary internal analytics database. - **Linear** (Software): Issue-tracking tool integrated as a Studio data source. - **Notion** (Software): Knowledge-management tool integrated as a Studio data source. - **LangGraph** (Software): Agent orchestration framework used to drive Studio's LLM-tool interaction loop. - **Claude Opus** (Software): Anthropic LLM used inside Studio; chosen for quality at query-writing and reasoning tasks. - **Radar** (Software): WorkOS's bot-blocking and fraud-detection product; the Radar support widget is the showcase use case. - **Pipes** (Software): WorkOS's third-party integration product; being extended to power org-level connectors inside Studio. - **Convex** (Software): Used as Studio's session-state store to preserve widget and conversation history across sessions. - **Widget** (Concept): Studio's core output artifact — declarative JavaScript that calls data-source APIs directly, runs deterministically without LLM involvement on each refresh. - **Preflight sequencing** (Concept): Studio's practice of running tool-connectivity and context-adequacy checks before answering a query, then injecting schema context lazily at tool-invocation time. - **Layering** (Concept): Studio's prompt architecture stacking base defaults, org-level rules, and session-specific context, with an explicit directive to distrust stale model knowledge about WorkOS.

Dan Dreyfus, founder and CIO of Bornite Capital, delivers a rapid-fire 25-minute presentation at the All-In Liquidity Summit arguing that copper and critical minerals — not compute — are the true bottleneck for AI infrastructure, green energy, reshoring, and defense. He traces America's decades of underinvestment in physical infrastructure, documents the supply shock triggered when China cut off rare-earth exports last April, quantifies the staggering copper gap (the next 18 years require as much as the past 10,000), and layers on dollar debasement and grid fragility as further tailwinds for hard assets. Jason Calacanis, Chamath Palihapitiya, and David Friedberg push back and probe on craft labor, energy mix, and how to invest without getting run over by China price-dumping. ## [00:00] Intro Dreyfus opens by announcing the three-part thesis he will cover: measuring human progress by electricity consumed, viewing semiconductors as an infrastructure company, and working out what physical materials the world will need to reach its technological ambitions. He sets the pace with a preview — critical minerals, commodities, fragile infrastructure, and why trillions are required across reshoring, re-industrialization, and national security. > *"We try to figure out where the world is going and then we try to figure out what we're going to need to get there."* ## [00:33] Americas Capital Light Era Is Over The Infrastructure Reckoning Has Begun From roughly 2000 to a few years ago, the US ran what Dreyfus calls an economic miracle on almost no capital — Google, Meta, Apple, SaaS platforms, streaming, food delivery, all built without heavy physical investment. The flip side: America simultaneously dismantled its industrial base and shipped it to China. Every geopolitical shock since — COVID, Russia-Ukraine, tariffs, the Iran conflict — has spiked inflation "like a rocket" for the same reason: supply chains with no resilience. Now every major capital cycle is firing at once. Boeing and Airbus have a trillion-dollar backlog for the next decade; the space economy competes for the same materials. The US grid in parts is over 106 years old and barely handles current load — in California, mass EV charging at 6 p.m. alone would kill it. Data centers now consume a trillion dollars per year of infrastructure and commodities. Semiconductor fab capacity is racing back onshore at $750 billion — a figure Dreyfus calls "way too low." Defense budgets worldwide are expanding. Every single one of those end markets, he says, cannot function without critical minerals. > *"What the similarity is amongst all of these end markets is none of them will work without critical minerals. None of it."* ## [05:38] China Cut Off Our Critical Minerals and Ford Almost Shut Down Last April, China announced an export cutoff on a list of critical materials: samarium, gadolinium, terbium, dysprosium, lutetium, scandium, yttrium, erbium, silver — just cut off. The downstream effect was immediate: the Ford Motor Company was within days of shutting down its entire production line due to the loss of samarium-cobalt magnets. McDonnell Douglas faced the same crisis. The Pentagon and Department of Energy panicked. The administration's response: a three-document rescue package delivered directly to small resource owners across the US and Canada — an equity check, a permit (the same permit companies had been waiting 20 years for), and a take-or-pay offtake agreement with a minimum floor price to guarantee bankable returns. China has an absolute grip on critical mineral processing, and Dreyfus estimates it will take 10 to 20 years to meaningfully close the gap — but as he puts it, "we've got to start somewhere." > *"It's truly what I call a vuja day moment, which is the overwhelming feeling that none of this has ever happened before."* ## [08:18] Copper Why the Next 18 Years Need as Much as the Last 10,000 Copper is the single clearest example of the supply-demand dislocation. Solar requires five times the copper of a gas turbine per megawatt; wind requires seven times. A 1-gigawatt AI data center needs 50,000 tons of copper — and the US is planning to build 15 gigawatts per year, meaning those data centers alone will demand 750,000 tons annually. Total copper supply growth last year was 500,000 tons. Electric vehicles add further pressure: each EV uses five to six times the copper of an internal combustion car. Even military consumption is enormous — the Ukraine-Russia conflict used more explosives than all of World War II, and artillery shells are made of copper that is never recovered. Over the past 10,000 years of human civilization, we have mined 700 million tons of copper. At current GDP-growth trajectory (excluding AI and green-energy upsides), demand over the next 18 years will equal that entire 10,000-year total. To meet that, five world-class tier-one mines would need to come online every year — yet the number of tier-one mines opening before 2030 can be counted on one hand. Existing mines in Chile are depleting, and building a new copper mine takes 7 to 12 years. > *"Over the next 18 years, we're going to need as much copper as we mined in the last 10,000 years."* ## [12:00] Dollar Debasement $140T in Debt and Why Hard Assets Win After covering supply and demand, Dreyfus adds a monetary dimension. The US has $40 trillion in federal debt growing at $2.5 trillion per year, plus $100 trillion in discounted present value of unfunded social liabilities (Medicare, Medicaid, Social Security, pensions) also growing at $2.5 trillion per year — against total annual tax receipts of $5.5 trillion. In the next recession, when receipts fall and spending must rise, the US will print "giga dollars." The 1970s playbook repeats: currency loses purchasing power, and the best-performing asset class of that decade is assigned as homework to the audience. Chamath notes that on the All-In predictions show he had already called copper as the top-performing asset — before meeting Dreyfus. Dreyfus adds that he sees copper doubling from current levels as a minimum outcome, referencing molybdenum's move from $1 a pound to $33. > *"Commodities and hard assets and infrastructure will protect your purchasing power in that kind of environment."* ## [13:50] The Grid Is Dying Blackouts Bottlenecks and the Craft Labor Crisis Chamath asks Dreyfus to expand on a backstage comment: that current infrastructure investment will barely keep pace with existing energy demand, before counting AI at all. Dreyfus confirms: post-WWII, the US stopped hardening the grid. Electrification of commercial buildings (heat pumps replacing gas boilers), EV penetration, and growing device usage alone will cause blackouts and brownouts — AI demand is on top of that. Where the inflation is actually hiding: not in power generation (wholesale power prices are still down in real terms over 20 years) but in transmission and distribution costs, inflated by utility capital spending to boost their regulated asset base. The real constraint on all of it is craft labor — electricians, welders, pipefitters. America told a generation of kids to go to liberal-arts college instead of trade school, and now there is no one to build. David Friedberg asks whether technology breakthroughs in mining could close the gap. Dreyfus distinguishes between rare earths (abundant in the ground, extraction technology is improving) and processing: China controls the knowhow to convert raw ore into usable material, and for a commodity as large and ubiquitous as copper, no single technology can solve the scale problem overnight. Jason Calacanis observes that the China rivalry and the craft labor shortage point in the same direction: re-industrialization creates exactly the high-paying blue-collar jobs that displaced workers in the Rust Belt have been waiting for. > *"We're going to have shortfalls just from living our lives. Not even talking about AI."* ## [19:10] How to Invest in the Commodity Supercycle Without Getting Wrecked The tables have turned for blue-collar America: the same Rust Belt workers displaced when factories moved to China in the 2000s are now being recruited at entry-level salaries of $150,000 from trade programs. Dreyfus says the craft labor demand for the rebuild is "almost limitless." Chamath asks how to allocate across energy sources — natural gas, solar, nuclear. Dreyfus's view: the US is swimming in natural gas; solar is buildable but constrained by silver (a 200-million-ounce annual deficit against 600 million ounces of above-ground inventory — roughly three years to stockout); nuclear is bottlenecked by the inability to manufacture containment vessels domestically. Across all of them, raw inputs are not the binding constraint — the critical minerals required to build the generation assets are. Chamath pushes on where investors get wrecked: supply shocks, China price-dumping, technological disruption. Dreyfus's two-step framework: first, understand where the pinch points are in the supply chain; second, make sure the tight link cannot be replaced overnight by a new technology. Copper clears both tests. Jason summarizes the actionable takeaway for the audience — exposure to copper, silver, and critical minerals, plus the service and labor providers surrounding those assets. > *"You got to understand where the pinch points are in the supply chain, number one. And number two, make sure you're not going to get technologically disrupted."* ## Entities - **Dan Dreyfus** (Person): Founder and CIO of Bornite Capital; 25-year commodities investor presenting at the All-In Liquidity Summit. - **Jason Calacanis** (Person): Host of All-In Podcast; interviewer at the Summit; represents Launch Fund. - **Chamath Palihapitiya** (Person): Host of All-In Podcast; Social Capital founder; had independently predicted copper as top-performing asset. - **David Friedberg** (Person): Host of All-In Podcast; Ohalo Genetics; raised the innovation-in-mining angle. - **Bornite Capital** (Organization): Copper and critical minerals-focused investment firm founded by Dan Dreyfus. - **Copper** (Concept): Central commodity thesis — structural supply deficit meets surging demand from AI data centers, EVs, green energy, and military applications. - **Critical Minerals Supercycle** (Concept): Simultaneous demand shocks across aerospace, defense, data centers, EV, and grid modernization converging on materials that take 7–20 years to bring to market. - **Dollar Debasement** (Concept): $140 trillion in combined federal debt plus unfunded social liabilities as monetary tailwind for hard assets and commodities. - **Craft Labor Shortage** (Concept): Structural deficit of electricians, welders, and tradespeople as the binding bottleneck for grid modernization and re-industrialization. - **Ford Motor Company** (Organization): Referenced as a near-casualty of China's samarium-cobalt magnet export cutoff — came within days of a full production shutdown.

Dan Shipper, CEO of Every, spent a week with Fable 5 — Anthropic's Mythos-class frontier model — before its public launch and walked away genuinely changed. Every's senior engineer benchmark put Fable at 91/100, against 63 for Opus 4.8 and 62 for GPT-5.5 — a jump Dan describes as "warp drive" capability for sustained autonomous work. The model is slow, expensive, and token-hungry, but for anyone orchestrating big, multi-hour agentic tasks, there's nothing close to it right now. ## [00:00] One prompt built an infinite 3D library Dan opens with a live demo: a fully browsable 3D version of Jorge Luis Borges's "The Library of Babel" — hexagonal galleries, accurate mathematics from the story, working bookmarks — all generated by a single prompt. He gave Fable a one-line instruction to read the story, plan, and execute a browser-playable 3D game end-to-end. The model ran autonomously for three to four hours, self-checked its work, and shipped. > *"I made this entire thing in a single prompt with Fable 5, the new model from Anthropic."* ## [01:22] Our day-zero Fable 5 review Dan introduces himself and Every's approach: they test models hands-on for real production work — programming, writing, design, business decisions — and report back on what actually works. Fable generated unusual levels of pre-release hype; Anthropic had initially said it was too dangerous to release. After a week of internal access, Every's take is that the model is genuinely different, and Dan's goal here is to cut through the excitement and show the realistic picture. > *"Because we've been using this model for about a week now, we get to pull back the curtain a little bit and show you what it's like to have lived with this model."* ## [02:25] What a Mythos-class model is Mythos is Anthropic's new top-tier model family, sitting above Haiku, Sonnet, and Opus in their lineup. Architecturally it's not novel — same transformer family, just bigger. Anthropic added strict safety guardrails (no cyber, no biological use cases) to make it releasable. Pricing is steep: $10/M input tokens, $50/M output — roughly 2× Opus. Dan's verdict from a week of use: genuinely the most powerful coding model he's ever touched, by a wide margin. > *"It is just genuinely the most powerful coding model I've ever used by far."* ## [03:28] The 91/100 engineering benchmark Every runs a proprietary senior engineer benchmark: the model is handed a real "vibe-coded slop" production codebase and asked to rewrite it from first principles as a senior engineer would. Prior to Fable, the top score was Opus 4.8 at 63/100, with GPT-5.5 right behind at 62. Fable scored 91 — matching a human senior engineer in a single prompt. Dan had expected saturation of this benchmark in about six months; it happened in two weeks. > *"Fable scored a 91 on this benchmark. 91 out of 100. That's the same score as a human engineer with just one prompt. That's crazy."* ## [04:12] Why it feels like a warp drive Fable's core strength is sustained autonomous execution over multi-hour tasks. You give it a destination, leave it running, and come back to something finished. Unlike earlier Claude models that eagerly said yes to everything ("purple accents, purple accents"), Fable deliberates, pushes back when something can't be done well, and follows through on complex, loosely specified prompts. Dan's analogy: a warp drive — not instant, but it compresses what used to take months into hours. > *"You can specify a destination for a big trip, and it just compresses what normally would have been like years or months into like hours or days."* ## [06:10] Where the model falls short The warp drive metaphor cuts both ways: it's useless for getting around town. Tight back-and-forth collaboration, quick questions, rapid iteration — Fable is a poor fit for all of these. It's slow, expensive, and burns tokens aggressively. A non-obvious workaround: drop the reasoning level to medium or low for simpler questions; that's how Anthropic's own people use it internally. Without a big, meaty problem to throw at it, the model is overkill. > *"If you're using it for true collaboration or quick questions or things that need tight back and forth, I don't think it's that good for that."* ## [07:04] Building a Heidegger lecture site Dan describes asking Fable to grab philosopher Hubert Dreyfus's 2007 lectures on Heidegger — without even providing a URL — and turn them into a consumable mini-site. Fable found the lectures, wrote per-lecture summaries, built a synchronized player that highlights the transcript as audio plays, added chapter navigation, drop caps, and typographic choices that Dan characterizes as actual taste, not the default template output. One prompt, no scaffolding. > *"That's what I mean when I talk about this model having really exceptional taste and attention to detail."* ## [09:05] Finding a growth bet in customer data Every has ~10,000 paid and ~100,000 free subscribers and a backlog of survey data the team had been analyzing with AI for weeks without a sharp conclusion. Dan fed it all to Fable. In one pass, the model came back with: "You have a conversion merchandising problem. Your free-to-paid conversion ratio is lower than it should be." Then a falsifiable bet: ship pricing transparency and a trial offer, and it'll go up. That synthesis — reading survey responses, site analytics, and product state together — hadn't emerged from weeks of team analysis. > *"That is something that I would expect a really, really good growth person to do with a lot of time and thought and research."* ## [10:35] Clearing a real GitHub backlog Every's agent-native markdown editor Proof accumulates GitHub issues automatically as agents file bugs during use. Dan pointed Fable at two weeks of open issues and told it to close irrelevant ones and write Rust fixes for the rest. It swept through the backlog and produced patches the team actually merged. Other models can do this, but they require hand-holding — one issue at a time, constant check-ins. Fable just batched it. > *"And it just went boom boom boom boom boom boom. And actually wrote fixes that we merged."* ## [11:17] Who should actually use this model Dan is direct: Fable is not for everyone right now. Using Every's "eight levels of AI adoption" framework, it pays off at levels 7–8, where users are already orchestrating multiple agents and have large problems queued up — typically technical builders. For knowledge workers not yet running agent workflows, it'll feel like overkill; for casual vibe coders, the token costs are real friction. About half of Every's own early-adopter team saw immediate payoff; the other half is still growing into that workflow level. > *"Using it is a skill. You need to be exposed to problems and working at a level of expertise where the problems come up in order for it to be useful."* ## [13:31] Where other models still win Writing is the clearest gap: Fable's prose is dense, literary, and block-heavy — good for thinking through structural writing problems, not for copywriting or everyday sentence-level work. For Claude users, Opus 4.8 is still better for writing. For GPT users, 5.5 is a better daily driver. Dan himself keeps GPT-5.5 as his Codex driver for the quick back-and-forth that fills most of his day; Fable gets reserved for big production pushes. > *"For my day-to-day, it's a bit overkill even for me."* ## [14:26] What this means after automation Dan points to his essay "After Automation" as the frame: automation doesn't shrink human work, it creates more of it — a paradox. Fable follows the same pattern: it raises the floor for non-experts (a vibe coder can now one-shot a video game) and raises the ceiling for experts (an expert can build a AAA game solo). The displacement is real and he says it's normal to feel unsettled by it — but the capability curve means even people who can't afford Fable today will have access within six to twelve months. > *"This model increases the floor of capability for non-experts, but it also raises the ceiling for experts."* ## [16:02] The final verdict Dan closes with a straightforward recommendation: read the full Every vibe check for detailed benchmark breakdowns across coding, writing, and knowledge work, watch "After Automation" for the bigger-picture framing — and then go find the first big problem you've been avoiding and point the warp drive at it. > *"If you're psyched about this, the thing I recommend most is go use your new warp drive. And let me know what you make."* ## Entities - **Dan Shipper** (Person): Co-founder and CEO of Every; sole presenter in this episode; spent a week testing Fable 5 pre-launch. - **Every** (Organization): AI-native subscription media company focused on testing frontier models for real work use cases; ~10,000 paid subscribers. - **Fable 5** (Software): Anthropic's Mythos-class frontier model; scored 91/100 on Every's senior engineer benchmark at launch. - **Anthropic** (Organization): AI safety company; maker of the Claude / Opus / Fable model family. - **Mythos** (Concept): Anthropic's top-tier model family tier, above Haiku, Sonnet, and Opus; characterized by extended reasoning and high token cost. - **Senior engineer benchmark** (Concept): Every's proprietary evaluation — model rewrites a production codebase from first principles; scored out of 100; Fable hit 91, Opus 4.8 hit 63. - **Opus 4.8** (Software): Previous Anthropic flagship; scored 63/100 on Every's benchmark; still preferred for everyday writing tasks. - **GPT-5.5** (Software): OpenAI's comparable frontier model; scored 62/100 on the benchmark; Dan's personal daily driver for quick back-and-forth work. - **Hubert Dreyfus** (Person): American philosopher; author of "What Computers Can't Do" (1972); subject of the Heidegger lecture site demo. - **Proof** (Software): Every's agent-native markdown editor; used in the GitHub backlog-clearing demo. - **After Automation** (Concept): Dan Shipper's essay arguing automation creates more human work rather than eliminating it; referenced as the interpretive frame for Fable's broader significance. - **Eight levels of AI adoption** (Concept): Every's framework for classifying AI workflow integration depth; levels 7–8 are where Fable delivers the most value.

Bill Maris — founding CEO of Google Ventures and founder of Section 32 — walks the All-In besties through four career lessons rooted in data-driven conviction: see the future early, be willing to look insane, never bet against computer science, and keep your fund small. He then turns the conversation toward a pointed threat to OpenAI: Google could slash token prices 80% tomorrow and crater the business models of every foundation-model startup not named Alphabet. On AI's trajectory, Maris reaches for a gaming metaphor — we're at the Atari command-line stage, and the PlayStation 10 era will arrive within five years, driven not by bigger models but by the infrastructure layer underneath them. ## [00:00] Bill Maris joins the Besties! The intro reel cuts between Maris's core thesis fragments before the conversation opens: a $150 million Section 32 fund sized deliberately small, a financial-return-first mandate, and Sacks's framing of the AI century to come. Six supervisions, each a standalone premise, set the stakes for the discussion. > *"With a smaller fund, I have the advantage to be very selective in the companies that I invest in, the people that I hire."* ## [00:33] Four critical lessons from a career in technology Maris opens with a talk-format presentation and traces four lessons across thirty years of career bets. In 1997 he quit a Wall Street job after spotting a server in a closet and imagining how many websites he could host from his Vermont apartment — three servers, shared bedroom, water-icing-over-at-noon winters, and eventually a thunderstorm that put him on the roof with a bucket of tar and no exit strategy. He tarred himself into a corner, chose to save the servers rather than himself, and noted afterward that the willingness to look completely insane is the prerequisite for seeing the future before others do. The slide he borrows from Stuart Butterfield makes the point visually: 1989 inauguration crowds look identical to 2005 ones, then 2009 shows every hand holding a camera — except one man livestreaming on a laptop, surrounded by people who must have thought him deranged. Maris's lesson is that the entrepreneurs worth backing "know a secret about the future that most of us don't believe." > *"To see the future, sometimes you need to be a little bit insane. It may appear to those around you that you are tarring the roof in a thunderstorm."* ## [05:58] Building Google Ventures with data and machine learning Tasked in 2007 with designing Google's venture arm from scratch, Maris and co-founder Rich Miner (Android co-founder) walked Sand Hill Road to learn the craft, then turned Google's data advantage into a portfolio-construction engine. They ran millions of simulations to determine ideal fund size and portfolio shape — at a time when Google's own leadership forbade the word "AI," insisting on "machine learning" because "AI freaks people out." The data-driven approach worked: GV returned an estimated 4.1x over 2009–2018, and the investments Maris personally led tracked even higher. Lesson three lands here: don't bet against computer science. "If you apply the right kind of computer science at the right time to the right problem, you will get to the right answers." > *"Bill, AI is science fiction. It is a hundred years away if it's ever going to happen. Let's stick to machine learning."* ## [09:51] Why small VC funds beat big ones on average Maris lays out the arithmetic plainly: funds under $750 million averaged 4.76x DPI in top-decile cohorts; funds over $1 billion averaged 2.42x. The sub-$750M bucket represented 95% of top-decile performers. The math isn't ideology — it's about exit arithmetic. A $7 billion fund must generate $210 billion in exits to return 3x, a number that exceeds total venture-backed M&A and IPO value in most years. Friedberg pushes back with a "barbell" thesis — small early-stage vehicles plus very large late-stage ones for compounders. Maris concedes the compounding logic but questions whether the data supports it as a durable trend rather than a one-time moment of trillion-dollar exits, and draws a clean distinction between RAIA-style asset gathering and concentrated venture craft. > *"Small funds outperform large funds. This is simply the math. This is not an opinion I'm trying to convince you of."* ## [14:36] OpenAI's valuation problem and the AI price war This is the sharpest segment of the conversation. Maris opens with a direct provocation: if he were running Google, he'd cut token prices 80% unilaterally. Chamath pushes him to walk through what happens next — OpenAI and Anthropic face revenue compression that goes "super critical," their premium pricing disappears, and business model assumptions collapse. Jason frames it as "their margin is my opportunity," with Google using capital as a weapon just as Uber used subsidized rides. The retail-investor angle lands as a second charge: companies staying private longer are, in Maris's framing, siphoning value creation away from the 99% who never got early access, then offloading overpriced paper to 401k holders through passive ETFs and S&P 500 exceptions. His objection isn't to late-stage staying private per se — it's to wrapping a wealth-concentration strategy in "benefit of humanity" language. Chamath asks where the bimodal nature of venture returns goes as AI-era funds like Founders Fund print enormous multiples; Maris notes that paper gains only realize when someone buys that stock, and the public market will eventually price those cash-flow discounts. > *"A trillion for spend commitments on $60 billion of revenue, and now you're going to go to the public and hope that retail is going to pick that up."* ## [19:09] AI's "Atari Stage": what comes next? Maris reaches for gaming as the clearest analogy for AI's current moment. Zork in the 1980s — brittle, turn-by-turn, crashed if you typed "lamp" instead of "lantern" — looks structurally identical to today's most sophisticated AI assistant interfaces. The jump from Atari command line to photorealistic, physics-driven, inhabitable games took decades in gaming; Maris expects the equivalent AI leap in five years, compressed by the speed of software iteration. What he's betting on isn't bigger foundation models — just as better stories didn't make better games, it was controllers, physics engines, and GPUs that did. Section 32 is investing in the infrastructure layer: ambient computing primitives, persistent memory, session continuity, the machinery that will solve AI's current brittleness. He also flags computational biology as the adjacent wave: Calico (which he founded at Google), New Limit, and the broader longevity space are attractive precisely because AI-enabled cell simulation may eventually collapse FDA trial timelines — though he's measured about near-term speed, given how much of drug development happens after a compound is identified. On US science brain drain, Maris is direct: gutting the CDC and NIH, anti-science policy, and H-1B pressure are pushing talent to China and elsewhere, and America is losing neurological reserves it spent decades accumulating. > *"I think we're at the Atari command-line stage of AI and we're going to get to the PlayStation 10 stage in the next five years."* ## [25:23] VC's broken incentives and the future of deep tech Sacks joins for the closing segment and frames the question as fund strategy: given the current landscape, is waiting to write $50 million checks at breakout companies a better strategy than noisy early-stage bets? Maris argues the incentive structure is broken at every layer. A $5 billion fund returning 1.01x still sits in the 75th percentile and raises its next fund; the GP makes more money in absolute dollars than a 3x return on a $500M fund; and entrepreneurs routinely take an inflated valuation from a giant fund — $250M at $4 billion on a $100M-worth company — because most haven't been burned by the downstream consequences. The incentives push everyone toward AUM maximization, not returns maximization, and the pendulum will eventually snap back. > *"If I have a $5 billion fund, I return 1.01x, I'm going to make more money than Bill with his $500 million fund that returns 3x. That's also a strange incentive."* ## Entities - **Bill Maris** (Person): Founding CEO of Google Ventures (GV); founder of Section 32, a $150M early-stage fund with six top-decile vintages; also incubated Waymo, Google X, and Calico as Google VP of Special Projects - **Jason Calacanis** (Person): All-In co-host; founder of Launch Fund; moderates the Maris Q&A segments - **Chamath Palihapitiya** (Person): All-In co-host; founder of Social Capital; challenges Maris on the valuation math and bimodal VC returns - **David Friedberg** (Person): All-In co-host; founder of Ohalo Genetics; first ex-Google company GV invested in (Climate Corp, $1B exit to Monsanto); pushes the barbell fund thesis - **David Sacks** (Person): All-In co-host; founder of Craft Ventures; frames the closing VC incentives discussion from his own fund experience - **Section 32** (Organization): Maris's current venture fund, six vintages averaging ~$400M, all top-decile; investments include CrowdStrike, Cohere, Coinbase - **Google Ventures / GV** (Organization): Corporate VC arm founded by Maris in 2008; estimated 4.1x return 2009–2018; early backer of Climate Corp, Uber, and others - **OpenAI** (Organization): Central to the price-war discussion; Maris argues Google could collapse its revenue model with an 80% token price cut - **Calico** (Organization): Google longevity research lab co-founded by Maris; pioneered the anti-aging thesis now carried forward by New Limit and others - **Atari Stage** (Concept): Maris's metaphor for AI's current maturity — functional but brittle, analogous to 1980s text-adventure games before GPUs and physics engines transformed gaming - **Token price war** (Concept): Thesis that Google could weaponize its cost structure to undercut OpenAI and Anthropic, forcing revenue compression and destabilizing multi-trillion-dollar private valuations - **DPI** (Concept): Distributed Paid-In capital — the only VC performance metric Maris trusts; filters out paper gains and forces comparison at actual liquidity - **Stuart Butterfield** (Person): Slack co-founder; provided the inauguration-crowd photo series Maris uses to illustrate how quickly technology shifts from fringe to universal - **Rich Miner** (Person): Android co-founder; Maris's first partner in building Google Ventures

Naval War College historian Sarah Paine delivers a standalone lecture tracing two thousand years of geopolitical logic: continental empires (China, Russia) pursue security by expanding borders and crushing neighbors, while maritime powers (Athens, Britain, the US) pursue prosperity by trading across open seas. She argues this structural divide—rooted in the brute fact of geography—explains Putin's war on Ukraine, Xi's ambitions over Taiwan, and why the post-WWII rules-based order is the only arrangement that produces compounded growth rather than compounded ruin. ## [00:00] Setting the stage Paine opens by framing the lecture's core question: why do some great powers keep grabbing territory while others keep opening trade routes? The answer comes down to one physical fact—whether it is feasible to defend yourself at sea. Maritime powers can; continental powers cannot. That single asymmetry generates two entirely different military traditions, two economic models, and two competing visions of world order. She walks through American history as a warm-up: the US began life as a continental power (manifest destiny, the Mexican-American War, Alaska purchased when Russia needed cash), then pivoted toward a maritime identity after Alfred Thayer Mahan convinced strategists that naval trade, not westward land, was the real source of national power. Alongside Mahan, Paine introduces the three geopoliticians whose maps anchor the lecture: Halford Mackinder (the Eurasian heartland as the world's natural fortress, impervious to sea power), Nicholas Spykman (control the rimlands, and you influence the heartland), and their shared lesson that US security runs through sea lanes and alliances, not borders. > *"Maritime powers are the exception and continental powers are the rule. Why? Because maritime powers, if need be, can defend themselves primarily at sea with their navies. Whereas a continental power simply cannot—think Ukraine, a navy is not going to save them from Russia."* ## [12:10] The continental powers Paine works through the logic of the continental world starting with China—the original case—then Russia. Sun Tzu's *Art of War* contains no references to maritime warfare: it was written for a world where neighbors invade overland at any time and the only viable response is a mass army. Geography tells the rest: too much of China's land is vertical to feed its people, which makes controlling the arable lowlands an existential imperative. The Han expansion from the Yellow River Valley followed that logic for millennia, wiping out the Zongars, subjugating Tibet, producing the ethnic patchwork Beijing still manages with military administrative overlays. Russia's pattern is the same dynamic in reverse—a Moscow core expanding outward in concentric rings until it hit countries that fought back. The continental security playbook that emerges is ruthlessly coherent: no two-front wars, no great-power neighbors, take on threats sequentially, destabilize the rising ones, absorb the failing ones, maintain buffer zones in between. Paine closes the section with the WWII body count that makes the paradigm's cost visible: Russia lost over 25 million dead (soldiers plus civilians); the United States lost 295,000. The ocean moat is not an abstraction—it is the difference between hundreds of thousands and tens of millions. > *"In this world, you're faced with a binary choice: you either become Han or they will kill you. And genocide is what happens to the losers in continental warfare."* ## [29:12] The maritime alternative Where continental empires carve the world into exclusive spheres, maritime powers treat the sea as a commons to be shared. Paine traces the lineage from Athens through Rome ("Mediterranean" means the sea in the middle of the lands; "Zhongguo" means the kingdom among the kingdoms—one term centers the sea, the other the land), the Dutch Republic, and finally Britain. Hugo Grotius, a Dutchman watching his nation's trade pirated, wrote *Mare Liberum* to establish that the sea belongs to no one and therefore belongs to everyone—the founding document of international maritime law. Britain refined the operating strategy over the Napoleonic Wars into six rules for "elephant hunting": keep the home economy growing, blockade enemy trade, fund the allied continental power facing the main front, find a peripheral theater where sea access beats land access, never attack the enemy's main force directly, and—only after the elephant has been bled—pile on with allies. The key structural point: a navy that prevents invasion produces wealth invisibly. Britain compounded wealth for a century after Waterloo while its continental neighbors burned money funding standing armies and fighting each other. That invisible compounding, over generations, is the difference between North and South Korea. > *"Trade is going to finance the navy. It's going to protect both British homeland and some of the trade. And then Britain is going to be compounding wealth while its neighbors are busy—constantly fighting with each other and destroying wealth in the process."* ## [42:00] How the Industrial Revolution changed everything The Industrial Revolution flipped the source of power from land to commerce. When land determines wealth, conquest makes sense. Once wealth comes from industry and trade, territorial expansion is literally negative-sum: you destroy the asset while fighting for it. The Suez Canal is Paine's sharpest example—Egypt sank block ships in 1967 to deny Israel access, but the strategic result was that global shipping shifted to supertankers that go the long way around Africa at one-third the cost per ton. Closing a chokepoint accelerated the maritime world's efficiency. Malcolm McLean's shipping container reduced cargo loading costs from nearly $6 per ton to under 20 cents, and the ISO then harmonized container dimensions across trucks, railways, and ships—producing plummeting transport costs and the trade explosion that lifted hundreds of millions out of poverty. Xi's Belt and Road Initiative, Paine notes dryly, crosses some of the world's most unstable territory, requires constant trans-shipment between incompatible rail gauges, and can never be rerouted—the exact opposite of maritime flexibility. China's own geographic trap is inescapable: shallow, island-cluttered seas that become kill zones in wartime mean its merchant fleet reaches global markets only in peacetime. > *"Once wealth is a function of commerce, industry, and trade, it isn't land anymore. And this upends the world. If you think about the world today, who's rich, who's poor—it's often the degree to which the country is industrialized."* ## [52:00] Why Putin wants to break the world The post-WWII institutional framework—UN, IMF, NATO, WTO, EU—was built by people who survived both the trenches of WWI and the Great Depression, then spent WWII watching their own children die. Their conclusion: hash out differences with diplomats and lawyers, because sending soldiers destroys more value than any conceivable prize is worth. That system held the peace in the industrialized world for 75 years, until Putin decided to break it. Putin's challenge is not irrational by continental logic: a rising Ukraine integrated into NATO is precisely the kind of strong, stable neighbor that, in the old paradigm, becomes an existential threat. His goal is to hollow out the alliance system and shatter international law so the world reverts to warring spheres of influence—a world where continental powers can once again play their traditional game without maritime rules they were never designed for. Paine's answer is that sanctions are "economic chemotherapy": they suppress growth by one or two percent per year, and compounded over generations, that gap is the difference between North and South Korea. The objective is never to eliminate the rogue state but to contain it at acceptable cost. The only exit that avoids nuclear escalation is the one the post-war generation built: diplomats, lawyers, and institutions. > *"The only win-win solution is to deploy the diplomats and lawyers to hash out these things in international forums—because if we're all going to send soldiers, we're going to get a third world war with nuclear follow-on effects, and we'll see whether humanity makes it."* ## Entities - **Sarah Paine** (Person): Military historian at the U.S. Naval War College; sole speaker in this lecture; author of a 2025 lecture series on continental vs. maritime powers. - **Alfred Thayer Mahan** (Person): 19th-century U.S. naval strategist; argued that maritime trade and sea power, not land conquest, determine national greatness; associated with the Naval War College. - **Halford Mackinder** (Person): British geographer; 1904 "pivot area" thesis posited that the Eurasian heartland, insulated from sea power, is the world's natural fortress. - **Nicholas Spykman** (Person): Dutch-American strategist; argued that controlling Eurasia's rimland determines global power; died 1943 while warning the US about Eurasian dominance. - **Hugo Grotius** (Person): Dutch jurist; founder of international maritime law; *Mare Liberum* (1609) established freedom of the seas as a universal right. - **Malcolm McLean** (Person): American trucking entrepreneur who invented the standardized shipping container, collapsing cargo loading costs and enabling the post-war trade explosion. - **Continental power** (Concept): A state that cannot defend itself primarily at sea; prioritizes territorial expansion, mass armies, buffer zones, and exclusive spheres of influence; exemplified by Russia and China. - **Maritime power** (Concept): A state that can defend itself primarily at sea; prioritizes trade, open sea commons, alliance-building, and compounding wealth; exemplified by Britain and the United States. - **Rules-based international order** (Concept): The post-WWII institutional system (UN, IMF, NATO, WTO, EU) that enforces sovereignty and free trade; the system Putin and Xi seek to dismantle. - **U.S. Naval War College** (Organization): Graduate school of the US Navy in Newport, Rhode Island; Paine spent 24 years there; home of Mahanian sea-power theory.

Palo Alto Networks CEO Nikesh Arora joins the All-In besties eight years into his tenure — a stretch that took the company from a $17B to a $238B market cap. Over thirty minutes he covers three interlocking theses: AI-powered vulnerability discovery is already compressing years of security work into weeks; the analytical SaaS category is structurally dead; and models will commoditize into a utility layer while the real money accrues to application companies that own the harnesses, memory, and replacement TAMs on top. ## [00:00] Palo Alto Networks CEO Nikesh Arora joins the Besties! Chamath opens by noting that Palo Alto Networks crossed $100B market cap — a threshold at which the company becomes statistically more likely to 10x again to $1T. Nikesh, marking his eighth year as CEO this week, frames AI not as hype but as the latest democratization wave: "I spent 10 years at Google and Google search was democratizing information. AI is democratizing intelligence." He argues the most tangible near-term impact is organizational consistency — getting 5,000 customer-facing employees to behave as reliably as the best one — rather than replacing headcount outright. > *"AI is democratizing intelligence... I can get 5,000 people to act almost consistently in their interactions with people on the other side."* ## [00:47] Claude Mythos found years of vulnerabilities in Palo Alto's code in weeks Nikesh describes being among the first enterprises given access to Anthropic's Claude Mythos model and running it against Palo Alto's own codebase for six weeks. The result: the equivalent of five to seven years of security auditing compressed into that window, at a cost in the low millions of dollars. He explains that Mythos's "ultra mode" — persistent extended thinking — can daisy-chain individual vulnerabilities into full attack paths, something human red teams rarely accomplish at scale. The catch he volunteers is a 30% false-positive rate, making the tool effective for offense (finding bugs) but not yet ready for autonomous defense. Jason asks whether unrestricted public release would have triggered real attacks; Nikesh estimates that Mythos-level capability is at most three months from open-source availability, citing DeepSeek 4.8 and 5.5 as models already approaching similar power. > *"In 6 weeks we found vulnerabilities which would have normally taken us 5 to 7 years to find."* ## [05:15] Are cyber defenders losing the race against AI attackers? David Sacks frames the central tension: AI is simultaneously the best attack tool and the best defense tool, and the race between the two determines enterprise risk. Nikesh says defenders are currently losing — not because critical infrastructure is being cracked, but because 89% of breaches still trace to stolen credentials against mundane targets like small healthcare offices. He points to the Change Healthcare ransomware attack as the real threat archetype: a clearinghouse breach that forced United Health to extend billions in emergency credits to physician practices. National-security infrastructure has the budgets and personnel to respond; the millions of small offices running legacy package software do not. His conclusion is that there is no silver bullet — the industry will spend years patching the accumulated technical debt, which structurally grows the terminal value of Palo Alto's business. > *"89% of attacks happen because credentials get stolen... I'm worried about the small offices across the country where they're using some piece of package software."* ## [06:50] Analytical SaaS is dead, so what survives the AI wave? Nikesh segments the SaaS stack into three buckets with very different futures. Analytical SaaS — any product whose value proposition is "we collect your data and analyze it for you" — is finished, because a model can be pointed directly at raw data and produce the same analysis without a SaaS intermediary. He gave a live example: a vendor that tried to hold Palo Alto hostage on a licensing renewal was replaced by running an LLM directly against the underlying data. Infrastructure software (Databricks, Snowflake, MongoDB, Oracle) is undervalued — enterprises will need ten times current data storage within three years to feed AI systems. Systems of record (Salesforce, Oracle ERP) survive in the medium term because they are deeply embedded, but their UI layer goes away first as agents replace human data entry. Jason validates the pattern from his own portfolio: a 20-seat SaaS product with near-zero logins was collapsed to three accounts connected to Claude via Slack, cutting the bill 90%. > *"If you're an analytical SaaS company, it's over... I can just go run an LLM against the data."* ## [14:06] If models become a utility, where will the money be made? Nikesh disagrees with the OpenAI-as-Microsoft-Office thesis. He argues models will commoditize into an IQ-on-demand utility — pay $10 for 120-IQ reasoning, 1 cent for a routine customer call — so profit pools will concentrate in the application layer, not the model layer. He cites Codex and Claude Code as evidence that lab-owned coding applications are already outrunning the underlying models in revenue growth. The real gap, he argues, is that the agentic application layer has not yet been invented for most enterprise verticals: 50,000 companies all need the same AI-native HR or sales system, and it is inefficient for each to build it from scratch. He adds that the false-positive problem is the underappreciated bottleneck — Mythos's 30% rate is fine for R&D but unacceptable in production; getting to sub-1% is the engineering work that separates a capable model from a deployable product. Separately, he dismisses the idea of withholding powerful models, noting that a leading model's entire weights now fit on a USB stick and can be distilled in under 48 hours. > *"The profit pools are in applications, not in models... most companies have no idea how to use the models."* ## [20:35] Armchair CEO: Nikesh rates Waymo, Google, and OpenAI Chamath runs Nikesh through an armchair CEO segment. On Waymo: the cars work, and the company should expand to far more cities far faster. On Google: underrated and likely the first $10T company in his lifetime — the three hyperscalers hold the sales forces actually needed to monetize AI at enterprise scale, an asset pure-play labs lack. On OpenAI: they need to sell faster; Anthropic's ARR is growing more quickly, largely because Anthropic went all-in on enterprise and Claude Code specifically. He notes Anthropic has already released a generally available cyber-capable model for CISO use. David Friedberg earns partial redemption from an earlier founder-CEO dig by calling Nikesh a "Neo in the matrix" anomaly — a hired-hand CEO who takes ownership risk as aggressively as any founder. > *"Google is going to be the first 10 trillion dollar company in our lifetime. They have all the assets needed to make this successful."* ## [28:22] Palo Alto's M&A playbook and the path to $1 trillion Chamath asks how Nikesh maintains acquisition discipline as the company scales toward $1T. He describes two phases: early deals were product bolt-ons fed into Palo Alto's go-to-market engine, compounding revenue per customer over two-year cycles; the recent $25B identity-security acquisition (closed three months before this recording) reflects a thesis about agentic identity becoming the next attack surface. A third phase thesis is now forming around operational leverage: if Palo Alto can run at gross margins in the 90s and net operating margins in the 40–50% range while competitors cannot, then almost any adjacent acquisition becomes accretive simply by plugging it into a more efficient machine. He closes with a contrarian workforce call — headcount on the technology side is actually growing, not shrinking, because every part of the business is simultaneously demanding AI-driven transformation. > *"If you can crack that code — running the most efficient enterprise business — then it doesn't matter what you buy."* ## Entities - **Nikesh Arora** (Person): CEO of Palo Alto Networks for eight years; former Chief Business Officer at Google and President of SoftBank; board member at Uber. - **Chamath Palihapitiya** (Person): Host; founder of Social Capital; primary interviewer in this episode. - **Jason Calacanis** (Person): Host; founder of LAUNCH; co-interviewer. - **David Sacks** (Person): Host; Craft Ventures; frames the attacker-vs-defender race framing in chapter 3. - **David Friedberg** (Person): Host; The Production Board; adds false-positive/negative framing; challenges founder-vs-hired-CEO distinction. - **Palo Alto Networks** (Organization): Cybersecurity company; $238B market cap at time of episode; grew from $17B under Arora's tenure. - **Anthropic** (Organization): AI lab; developer of Claude and Claude Mythos; released a generally available cyber-capable model for enterprise security use. - **Claude Mythos** (Software): Anthropic's extended-thinking model used by Palo Alto to find 5–7 years' worth of code vulnerabilities in six weeks; 30% false-positive rate noted. - **Claude Code** (Software): Anthropic's coding agent; cited alongside OpenAI Codex as a leading example of application-layer revenue outpacing model revenue. - **Waymo** (Organization): Alphabet-owned autonomous vehicle company; Arora says the cars work but geographic expansion is too slow. - **Change Healthcare** (Organization): Healthcare clearinghouse breached via ransomware; forced United Health to extend billions in emergency credits to physician practices — cited as the archetypal AI-era threat vector. - **Analytical SaaS** (Concept): Category of software whose core value is collecting and analyzing customer data; structurally obsolete because LLMs can perform the same analysis directly against raw data. - **Replacement TAM** (Concept): Arora's preferred M&A lens — acquiring into existing budget pools where customers already have allocated spend, making the sales motion faster than greenfield expansion. - **False positive rate** (Concept): Share of AI-flagged security findings that turn out to be non-issues; Mythos at 30% is Arora's key argument for why models still require harnesses and domain fine-tuning before enterprise deployment.

Benedict Evans, independent tech analyst and former a16z partner, sits down with Erik Torenberg to assess what's actually happened in AI over the past year — and what remains unanswered. Agentic coding has moved from "kind of useful" to pulling customers in off the street; everything else is still groping in the dark. Evans draws on the history of mobile data, PC-era platform shifts, and semiconductor economics to frame why foundation models may end up as commodity infrastructure, what that implies for SaaS, and why the biggest questions are now moving out of tech and into industries like law, consulting, and advertising. ## [00:00] Intro Evans opens with the claim that agentic coding "went from being kind of useful to really changing everything" — a tease of his core argument that coding is the one place AI has genuine product-market fit right now, and that in twenty years we'll simply take for granted the things that feel like magic today. Torenberg frames Evans as the author of the widely-read "AI Eats the World" presentation, positioning the conversation as an update to last year's edition. > *"Agentic coding went from being kind of useful to really changing everything."* ## [00:44] What's Changed Since Last Year The main shift Evans identifies: product strategies have diverged, competitive tension has moved beyond raw compute scaling, and coding emerged as the undeniable breakout use case. OpenAI spent late 2024 trying to do everything at once; Anthropic, with less capital, bet on coding — and it worked. But outside of software development, most of the fundamental questions from two or three years ago remain unanswered: no one knows if there will be a winner among model providers, whether models can capture value up the stack, or how much daily consumer usage is realistic with current technology. On the workforce question Evans is blunt: "I don't think we've learned anything" — it didn't work six months ago and it's going to take a couple of years to settle. He notes that the coding boom made previously theoretical questions real: what actually happens when you automate work done by junior engineers, and what were you hiring them to accomplish in the first place? > *"We don't know if there'll be a winner in the models. We don't know if they can capture value up the stack. We don't know how much the models can do."* ## [05:53] OpenAI vs Anthropic Strategy Evans characterizes OpenAI's late-2024 posture as "ask ChatGPT for 15 ideas for what we could do to build value on top of infrastructure, and then we'll do all of them." Anthropic's narrower focus on coding proved the better call — whether by design or accident. But even with coding working, there's still a yawning gap between the valley engineers running Claude Code all day and the 40% of people who last used AI "for something last week." Software cleared that chasm; most other domains haven't. He gives a concrete counterexample: a commodities company using LLMs to improve cash-flow forecasting by predicting when invoices from small producers will be paid. That's a high-value, low-profile application with no general-consumer analogue — a reminder that enterprise point solutions are a very different thing from consumer AI product-market fit. Zooming out to platform history: early PCs and early internet both had obvious first users (the people building the technology itself) and a gap between "incredibly exciting" and "you can just press a button." AI is at the same stage. The comparison is inexact but structurally useful. > *"There's a gap between what's incredibly exciting and the small number of people who are willing to put the work in to get something to work and just turning that into a thing where you can just press a button."* ## [10:31] The Pricing Crunch & Platform History Evans draws the tightest parallel of the conversation: the current AI pricing crunch maps directly onto mobile data circa 2009–10. AT&T launched the iPhone with flat-rate data, everyone bought iPhones, 3G hit, and suddenly both extreme overage bills ($10,000 surprises) and network collapse from unlimited-bundle subscribers appeared simultaneously. The industry fixed it — capped bundles, fair-use throttling — but in doing so revealed that mobile data is commodity infrastructure. Mobile traffic grew 1,500–2,000x over fifteen years; telco stocks flatlined; all the cool stuff was built by someone else. The exact same question hangs over LLMs: can the model do the whole job, or do you need 300 apps built on top of it? If foundation models are infrastructure — sold at marginal cost, with three to six competing frontier providers, some subsidized by adjacent ad businesses like Google — where does pricing power come from? The chip layer (Nvidia) and OS layers (Windows, iOS) captured value in past cycles; ISPs and telcos didn't. Models currently look more like the latter: no network effects, no lock-in, no leverage over what gets built on top. > *"Mobile network operators didn't capture the value. Windows and iOS did — but they were doing something else; they had all these levers to go up the stack. And of course they have network effects which models don't have."* ## [22:48] What Comes After Coding The section most honest about uncertainty. Evans outlines the questions he thinks matter next: at what point do good-enough, cheaper models displace frontier cloud models (Apple's on-device push is the obvious test case); what does AI restructuring actually mean inside professional-services pyramids (law firms, consultancies, investment banks) — questions only answerable by people who know those industries from the inside, not from San Francisco; and what was just cost-prohibitive and is now within reach. He uses the Netflix/content-isn't-king framing: the questions that matter to Netflix are LA questions, not SF questions. Similarly, what AI means for law is a lawyer's question. What it means for Hollywood is Ben Affleck's question. The structural difference from past platform shifts: in 1995 you knew the physical constraints — not everyone could get broadband next week, PCs cost $3,000. With generative AI you don't know the constraint: a push notification tonight could announce a model at 2% of today's price. That changes how you think about what's possible. On advertising and e-commerce specifically, Evans sees a concrete near-term shift: today's ad systems know SKUs and purchase correlations; they don't know what things *are*. An LLM-native system would. That's why Google and Meta ad revenue is already accelerating — they're rolling this into recommendation and ad-targeting engines. The more speculative version is the full style-and-context coat recommendation; Evans thinks that's now plausible, not science fiction. > *"We're in 1997 and I'm trying to predict Uber and Airbnb. If we could actually predict what was going to happen, we'd live in a parallel universe."* ## [38:18] AI & the Future of Enterprise Software Evans's baseline for enterprise software: it will be cheaper and faster to build, there will be more competition, and pricing structures will shift — but we don't know toward what. He lays out the existing fleet in three buckets: big horizontal platforms (SAP, Workday, CRM), vertical SAS apps (a typical large US company has 300–400), and the improvised middle of Excel, email, and shared file systems. AI is another option in that landscape, not a replacement for the landscape. The architectural question is whether the LLM sits at the bottom of the stack (an intelligent feature inside Salesforce) or at the top (synthesizing data across Salesforce, Workday, email, and analytics to produce something no single tool could). The answer is probably both, depending on the use case. His broader point: SAS gave enterprises an order of magnitude more software. AI probably does the same again. Some SAS companies will get wiped out; investors don't know which ones, which makes it hard to derate the whole sector right now. The more subtle challenge is that much of what drives value inside organizations is undocumented, implicit, and baked into org-chart politics rather than written workflows — exactly the thing McKinsey charges to untangle, and exactly the thing that's hard to encode in a Claude skill. > *"The questions that matter here — what is the right way of doing this, why are people not doing the strategy — are problems in organizational management that are very hard to write down and very hard to bake into a Claude skill."* ## [48:43] The CapEx Problem Microsoft, Meta, and Google are each on track to spend over 50% of revenue on capex in 2026 — a ratio that makes telecom (15–20% of revenue) look lean. Combined guidance from the big four is $700 billion, roughly comparable to global oil-and-gas capex. Evans doesn't think there's a clean ROI answer here; the honest framing is that it's existential FOMO: you can't let the others get away with it, because if they do and this turns out to be the future of compute, your company ceases to matter (see Microsoft in the 2000s, IBM in the 1990s, Meta getting squeezed by Apple in the 2010s). The ROI measurement problem makes it worse. Most documented AI productivity gains so far — better analytics, faster slide decks, more responsive customer support — are hard to put a financial value on. Building a new revenue line with AI takes much longer. And there's a consumer-surplus dynamic: if a DCF used to take a week and now takes ten seconds, you do fifty DCFs but probably can't charge more for them. The productivity gain competes itself away into client pricing. > *"We can't spend $10 trillion a year on AI infrastructure because there isn't $10 trillion a year there to spend on it. So there's a finite — there are laws of physics caps on the amount of money available."* ## [55:07] Will Models Become Commodities? Evans clarifies his actual position: he's not asserting commoditization as a fact, he's presenting a chain of argument and asking someone to rebut it. No sustainable differentiation between frontier models, no network effects, no leverage over the stack, three to six competing providers each with different cost structures and business-model incentives. The mobile industry analogy again: built critical global infrastructure, grew traffic 1,500x, didn't capture the value — Google, Meta, Amazon, and Apple collectively produce more profit than the entire telecoms industry. The practical problem for foundation model labs: coding is a great business, maybe worth a trillion dollars of productivity. But how do you expand beyond software into the rest of the economy? That's where you end up partnering with Bain, McKinsey, Accenture, Infosys — because it turns out it's genuinely hard to work out what to do with this stuff if you're running a real company. Evans closes with the IBM ad from the early 1950s: a photograph of engineers holding slide rules, with the tagline "an IBM electronic calculator gives you 150 extra engineers." Every generation of technology feels unprecedented and, twenty years later, just looks like how computers have always worked. > *"It's going to be magic. And in 20 years time, we'll just say, 'Well, of course that's how it is. Computers have always done that.'"* ## Entities - **Benedict Evans** (Person): Independent tech analyst, author of "AI Eats the World" presentation; former general partner at Andreessen Horowitz. - **Erik Torenberg** (Person): Host; partner at Andreessen Horowitz focused on consumer and content. - **OpenAI** (Organization): Foundation model company; characterized as having pursued a broad "everything at once" product strategy in late 2024 before refocusing on coding. - **Anthropic** (Organization): Foundation model company; credited with earlier focus on coding that gave it product-market fit; maker of Claude. - **Claude** (Software): Anthropic's LLM and agentic coding assistant; referenced as a coding tool with strong product-market fit. - **Nvidia** (Organization): Current value-capture winner in the AI hardware layer; analogue to other infrastructure providers that captured value in prior platform cycles. - **a16z / Andreessen Horowitz** (Organization): Venture firm hosting the podcast; Evans is a former partner. - **SAP / Workday / Salesforce** (Software): Enterprise horizontal platforms used to illustrate the existing SAS stack and where LLMs fit above or below them. - **Jevons Paradox** (Concept): Economic principle — cheaper inputs often produce more total consumption rather than less spend; Evans applies it to ask whether cheaper AI tokens lead to more usage or just lower bills. - **Foundation model commoditization** (Concept): Evans's central thesis: absent network effects, differentiation, or stack leverage, frontier LLMs structurally resemble commodity infrastructure (telcos, ISPs, chip fabs) rather than platform OS layers that captured lasting value. - **Mobile data pricing crunch** (Concept): 2009–10 analogue — simultaneous bill shock and network overload after flat-rate iPhone plans collided with 3G video traffic; Evans uses it as the clearest structural parallel to today's AI token-pricing disequilibrium.

Boris Cherny (creator and Head of Claude Code) and Cat Wu (Head of Product, Claude Code) look back on Claude Code's first year — from a Slack demo that earned two emoji reactions to running thousands of autonomous agents daily. They walk through how they think about verification, why auto mode replaced plan mode, how routines are eliminating entire categories of manual engineering work, and why the shift from "I write code" to "I talk to a loop" represents two major platform leaps in barely 18 months. ## [00:00] The origins and evolution of Claude Code Boris recalls posting the first Claude Code demo to Slack and getting exactly two reactions. A year later, his workflow involves "armies of agents" — a single loop prompting agents that prompt other agents, forming trees of thousands. The meta-principle that carried the tool this far: every time Claude makes a mistake, don't just correct the output — write the fix into a CLAUDE.md file or a skill so Claude can run unsupervised forever. > *"Every single time Claude makes a mistake, I don't tell Claude to do it differently. I tell it to write it to the CLAUDE.md or to make a skill… and if you can do this, then Claude can just run forever."* ## [01:10] How to make Claude good at verification Both Boris and Cat push back on the narrow view that "verification" means lint, type-check, and unit tests — things that were already automated before agents existed. Real agent verification means the agent can actually run the software under test. Boris cites a moment with Opus 4 where he asked Claude to build a feature and test itself by opening its own CLI — "crazy" at the time, table stakes now. Cat's current approach: a desktop development skill that has Claude spin up the local desktop app, use computer use to click through the UI, hit edge cases, and update the skill itself whenever it discovers a new failure mode. > *"I have it read Slack and understand: hey, is staging down right now, or has someone else already hit this? And then when it debugs the whole issue, I tell it to update the desktop development skill."* ## [03:14] Roles merging: Claude Code beyond engineers Boris recounts the moment he first saw a designer opening PRs — his initial alarm giving way to "okay the code looks good, so maybe it's fine." Cat reports that across enterprises, engineers adopt Claude Code first, then adjacent roles lean over their shoulders: designers making prototypes directly in the app, PMs shipping changes, the finance team running projections inside Claude Code, data scientists with it permanently on-screen. > *"It's kind of like all the roles are merging."* ## [04:48] Using routines for CI, code review, and more Cat describes a Claude Code power user on their team who shipped voice mode and then set up a routine monitoring every GitHub issue and bug report on that feature, automatically drafting fixes and pinging PRs. He later extended it to catch any unresponded bug older than five hours. Cat's own experience: she shipped a small feature with an edge case she missed, a bug was filed, and before she got to it that evening, Claude Code told her "another Claude has already fixed this." Boris adds that routines now handle all code review, babysit every PR, rebase, and respond to CI failures. He hasn't done those manually in a long time. > *"He has another routine that just looks for bug reports that haven't been responded to in five hours and puts up a fix, and he merges the ones that are easy to verify."* ## [06:43] Boris' go-to feature: auto mode Boris stopped using plan mode once Claude 4.6 arrived; by 4.7 the explicit planning step was no longer necessary. He now starts an agent in auto mode and moves directly to the next task without watching it. He traces the shift from the early permission-prompt model — where you had to approve every tool call — to auto mode routing suspicious actions to a classifier instead. Human attention degrades when 99% of prompts are harmless: eyes glaze, the one dangerous prompt slips through. Auto mode concentrates attention on genuinely flagged cases only. > *"Auto mode is more safe than reading every single permission prompt, because it means that you're only paying attention to the most important thing and not being spammed a bunch of things that are just 99% yes."* ## [08:10] Securing auto mode: red teaming and evals Shipping auto mode required building trust before it reached users. Cat describes the process: collecting thousands of full agent trajectories alongside permission prompts, having the auto mode classifier label each one, confirming it was "extremely good," then bringing in red teamers to attempt prompt injection attacks against the codebase. Every successful attack became an eval. Internal teams ran their own injection attempts to surface further gaps. The result is a model hardened not just against known attacks but against the most sophisticated adversarial constructions the team could devise. > *"It's not only just protecting you against the vulnerabilities that are out there in the wild today, but the most intelligent attacks that we can construct."* ## [10:24] Why loop is the next leap Boris frames two platform jumps in 18 months. First: stop writing source code directly — talk to an agent and let it write the code. Second, happening now: stop talking to an agent directly — talk to a loop or routine that prompts Claude Code on your behalf. Both felt obvious in hindsight, but neither was easy to see from inside the engineering mindset he brought to the project. > *"I don't talk to an agent anymore. I talk to a loop or I talk to a routine and it prompts Claude for me, and it's just crazy."* ## [11:06] How engineering orgs and responsibilities are changing Boris anchors the current transition to a 1990s Harvard Business Review piece asking why companies weren't seeing productivity gains from personal computers — and answering that computers needed to be at the center of every business process, not a side appliance next to the paper filing cabinet. At Anthropic, new hires don't ask colleagues questions; they ask Claude Code. Companies figuring out AI fastest are the ones putting it at the center of operations. Cat notes that the computer transition took 10–15 years; AI is compressing that because work is already digitized and Claude Code can both write and run code. > *"What you have to do is you throw out the filing cabinet. You have to throw out all your paper and all your pens and then you put a computer in the center and everything has to run through the computer."* ## [13:30] Is the future product or engineering? Boris' answer: both roles are merging into one. The Claude Code product team all writes code, the devrel team all writes code, designers write code, and engineers now ship products end-to-end — scoping the idea, building it, working with legal, marketing, and security to take it to market. The beneficiaries right now are people with high curiosity, strong product taste, and an appetite for end-to-end ownership. > *"AI really benefits people who have a lot of curiosity, have a lot of product taste, who love to have this end-to-end ownership."* ## [14:20] Working with hundreds of agents: using agent view, voice mode, and Remote Control Boris's multi-agent setup a few months ago: six terminal tabs, six git checkouts, manual context-switching. Today: one tab, the new agent view, and the desktop app handling work-tree cloning automatically. The unexpected change: roughly half his engineering now happens on his phone via Remote Control. He starts a task at his desk, walks to get coffee, checks in from his phone, starts new agents on the spot, and dictates to them via voice mode. Cat recalls noticing that Boris's laptop sat untouched on his desk for two consecutive days while he was actively merging PRs — he confirmed he was coding from his couch. > *"I'll like get coffee and then I'll check in on my agents and maybe I'll start another agent. And sometimes I'm talking to someone and we come up with a new idea — I'll just start an agent on the spot."* ## [16:05] From context engineering to context minimalism Boris traces the prompt engineering arc: Sonnet 3.5 required heavy prompt engineering; Opus 4 required careful context engineering; today's models need neither. The prescription now: give the model the minimal system prompt, the minimal tool set, and a way to pull in whatever context it actually needs — then let it work. Cat calls herself a "context minimalist": tell the model only what it needs to know, because too much upfront context is micromanagement, and the model often knows a better path anyway. > *"You give it the minimal possible system prompt, the minimal possible tools, and then you let the model figure it out."* ## [17:17] What's next for Claude Code Boris refuses to predict the specific form factor, only the direction: agents running longer, more autonomously, in parallel batches of dozens to thousands rather than one at a time. The exact interface for coordinating that many agents will be "really different than what came before" and won't come from Boris or Cat — it will come from the team and the broader community building with Claude Code every day. > *"In a year it's going to be a totally new set of things and it's going to be so surprising if it's still these same things."* ## Entities - **Boris Cherny** (Person): Head of Claude Code at Anthropic, creator of the tool; one of two interview subjects. - **Cat Wu** (Person): Head of Product, Claude Code at Anthropic; one of two interview subjects. - **Claude Code** (Software): Agentic coding tool developed at Anthropic, runs in the terminal; primary subject of the episode. - **Auto mode** (Concept): Claude Code permission model that routes tool-call decisions to a classifier instead of prompting the user for every action; replaces the earlier per-prompt approval flow. - **Loop / Routines** (Concept): Automated agents triggered by events (e.g., new GitHub issue, unresponded bug report) that prompt Claude Code without human initiation; described as the second major platform leap. - **Context minimalism** (Concept): Philosophy of providing models only the necessary system prompt and tools, letting the model pull additional context as needed rather than front-loading everything. - **Anthropic** (Organization): AI safety company that develops Claude and Claude Code. - **Remote Control** (Software): Claude Code feature enabling users to manage running agents from a mobile device. - **Agent view** (Software): New Claude Code interface for managing multiple parallel agents from a single pane.

In a 2.5-hour live debate, venture capitalist Nick Hanauer — first outside investor in Amazon and author of the "pitchforks are coming" open letter to fellow billionaires — and entrepreneur Daniel Priestley square off over the death of the middle class: whether the fix is stronger labor policy and redistribution, or wider access to entrepreneurship and ownership. Steven Bartlett referees as both guests push each other past talking points into genuinely contested territory on AI job displacement, minimum wages, Brexit's economic toll, sovereign wealth funds, and whether the Monopoly analogy explains why a thriving middle class never emerges on its own. The two agree on the diagnosis — concentrated power in big finance and big tech is hollowing out ordinary workers — but split sharply on the cure, with Hanauer insisting wages and worker rights are the structural floor and Priestley arguing that "raising the floor" without changing who owns assets is not nearly enough. ## [00:00] Intro The opening drops viewers straight into the argument. Hanauer fires first: "There is literally no example on planet earth of a high functioning society without big government." Priestley counters immediately: "Big government is sucking the life out of small businesses." Within two minutes the core tension is live — Hanauer's faith in policy and labor standards versus Priestley's faith in entrepreneurship and ownership — and Bartlett notes the audience is watching precisely because both men have real-world receipts for their positions. > *"There is literally no example on planet earth of a high functioning society without big government."* ## [02:27] Why Nick Hanauer's Economic Views Matter Bartlett asks Hanauer why a billionaire ends up arguing for higher taxes and worker protections. Hanauer traces the arc: he built and sold companies across manufacturing, e-commerce, and media, became Amazon's first outside investor, and eventually recognized that his own wealth kept compounding while the workers who made it possible fell further behind. He calls it straightforward arithmetic: "You cannot sustain a capitalist democracy if the top 1% controls 45 or 50% of income and the bottom 50% shares five." His Pitchfork Economics project exists to shift the intellectual frame that leads policymakers to produce those numbers. > *"You cannot sustain a capitalist democracy if the top 1% controls 45 or 50% of income and the bottom 50% shares five."* ## [06:27] Daniel Priestley's Different Take On Wealth Priestley grew up in Australia, discovered entrepreneurship as a teenager through a mentor, and built Dent Global into an international business education firm. He shares Hanauer's alarm about concentration but reaches the opposite prescription: the way to include more people in capitalism is to teach them to operate like capitalists — starting businesses, owning assets, building skills that can't be automated. "I felt like I discovered a cheat code in life which was entrepreneurship," he says, and his mission has been to hand that code to as many people as possible before political frustration produces the "dumb things" that undo market dynamism. > *"I just want to include more people in the benefits of capitalism before we do dumb things."* ## [08:32] Is Taxing The Rich The Answer? Bartlett poses the dominant political narrative: tax the wealthy, redistribute. Priestley pushes back not on the goal but on the mechanism. He distinguishes between a James Dyson — someone who invented a product and captured value — and a hedge fund that extracts value without creating it. His preferred target is rent-seeking and extraction, not wealth creation. He'd remove taxes on lower earners and claw revenue back from financial instruments and land value appreciation, not from entrepreneurs building products. > *"It's very easy to have a bad guy of a rich person. But you have to be specific about which rich person."* ## [11:44] Do The Wealthy Already Pay Enough Tax? Hanauer demolishes the claim that American billionaires already pay high taxes. US tax law taxes income, not wealth — and ultra-rich individuals rarely take income, borrowing against asset portfolios at rates that are functionally untaxed. The labor share of US income has fallen dramatically since the 1970s while the capital share has grown. His argument is not that the rich are evil but that the tax code was systematically rewritten to channel productivity gains away from workers. "We have massively tilted the economic playing field which once favored workers." > *"It is not true that the richest people in the United States pay a lot of tax because the American tax code taxes income, not wealth."* ## [15:07] Entrepreneurship Vs Policy: What Works Best? Priestley argues optionality is the deepest driver of wages: when workers have real alternatives — including starting their own business — employers can't impose terrible conditions. A market with many small employers competing for talent naturally produces better pay than one dominated by a handful of megacorps. Hanauer agrees optionality matters but says most workers can't realistically exercise the entrepreneurship option, and minimum wage laws, unions, and overtime protections do for the 90% what entrepreneurship can only do for the 10%. Both land on the same structural critique — labor market power is too concentrated — but split on whether policy or education is the lever. > *"When someone has lots of options, then they don't accept terrible conditions."* ## [20:05] The Policy Fix For Inequality Hanauer names a concrete mechanism: the US federal overtime salary threshold — the income level above which workers stop qualifying for time-and-a-half — covered 65% of salaried workers in the 1960s and today covers fewer than 8%. That single policy shift, requiring no new legislation, transferred trillions from workers to employers over fifty years. His argument: fix the rules that govern what the market pays before demanding more redistribution on top. Priestley concedes the point on wage suppression but circles back to ownership: the UK's unhappiness deficit isn't just about wages — it's about people who work for decades and accumulate nothing. > *"That standard used to apply to virtually every worker in America in 1970. Today that standard applies to less than 10% of workers."* ## [24:53] US Vs UK: Which Economy Wins? Hanauer points out that the US federal minimum wage is $7.25 — roughly a third of the UK level — and in many states a tipped worker earns $2.13 plus gratuities. The UK floor for low-wage workers is dramatically higher. Priestley counters that UK labor costs, combined with National Insurance and business rates, are now genuinely squeezing small operators and driving ambitious founders to relocate rather than scale. The US wins on startup dynamism; Priestley argues the UK is destroying the conditions that once made it competitive. > *"The minimum wage in the United States is $7.25 an hour or $2.13 plus tips. It's a third of what it is here in the UK."* ## [26:57] Do Higher Wages Hurt Small Business? Priestley grounds the debate in a specific case: a friend who owns a pub is losing money, not taking a salary, crushed by minimum wage increases, employer National Insurance, and business rates arriving simultaneously. The pub does not have Amazon's margin to absorb costs. Hanauer acknowledges the problem is real but says the right response is not to lower the floor for everyone but to go after megacorps that escape tax while the pub cannot. Bartlett notes the structural asymmetry: Starbucks says "we can absorb it" and the independent café closes. > *"He's massively impacted by taxes and minimum wage. He's not taking any money out of it."* ## [28:38] Why Small Businesses Can't Match MegaCorp Pay The Starbucks-vs-local-pub framing continues. Hanauer says a ham sandwich at a chain now costs twice what it did twenty years ago, so higher wages don't destroy demand — they get passed on. Priestley argues small businesses aren't just slower versions of big ones: they exist because of personal relationships, flexibility, and local knowledge that chains can't replicate. When the cost floor rises faster than their revenue can, they close. Both agree the real enemy is the regulatory and tax architecture that lets megacorps optimize globally while the corner shop pays full freight locally. > *"One person with good AI tools may be ten times more productive. That's great for that person. It's not so great for the other nine."* ## [33:02] What Workers Need Right Now Hanauer returns to the ownership question and agrees asset ownership is crucial — but insists it starts with wages. You cannot save if you cannot earn above subsistence. "Ownership starts with earning enough money so that you can save money so that you can begin to own something." He cites the 1990s US stock-option experiments — giving low-income workers equity rarely worked because the options vested after the workers had already left. Real ownership requires a wage floor that generates disposable income first. > *"Ownership starts with earning enough money so that you can save money so that you can begin to own something."* ## [35:59] Ownership Models That Build Wealth Priestley outlines three ownership models worth scaling. First, sovereign wealth funds on the Norwegian and Singaporean model: governments take equity stakes in national assets and every citizen holds a fractional share. Second, worker ownership co-ops and employee share schemes that vest on shorter timelines. Third, housing — where roughly half a property's market value is what he calls "utility value" (you need a place to live) and the other half is pure land value inflation that tenants pay indefinitely without ever capturing. His core claim: redistributing income taxes is too slow; you need policies that change who holds assets. > *"About half the value of the house is the utility value. The other half is the land value — and tenants pay that forever without ever owning it."* ## [40:28] The Real Impact Of Worker Rights Bartlett presses on whether higher worker protections actually close inequality or just slow its widening. Hanauer cites Brexit's measurable damage — productivity gains down 4%, unemployment up 4% above the counterfactual — as evidence that institutional frameworks matter enormously. The UK cut itself off from European labor and trade rules in one decision and is still absorbing the cost. Both guests agree the baseline institutional quality of an economy shapes outcomes far more than any single tax rate. > *"Brexit has affected unemployment by 4%, productivity gains by 4%. The list goes on."* ## [41:30] What Brexit Really Changed Hanauer sharpens the Brexit argument: departure removed frictionless access to 500 million consumers while shrinking the labor pool. Priestley agrees Brexit was economically damaging but argues the UK's deeper problem predates 2016 — the financialization of the British economy through the City of London meant that well before Brexit the UK was a two-tier economy where financial services boomed and manufacturing hollowed out. Both agree the US is the outlier among advanced economies in how far it has stripped worker protections, but the UK has followed a similar trajectory in asset concentration. > *"The USA is the outlier of all the modern capitalist economies when it comes to how far worker protections have been stripped back."* ## [45:01] The Hidden Lessons Of K-Shaped Economies Priestley pulls back to the early 1800s: today's headlines about record profits for capital alongside stagnant worker wages are word-for-word the headlines from the Engels Pause — the fifty-year period after the Industrial Revolution when steam, looms, and tractors destroyed agricultural employment and the owners of those machines captured all the productivity gains. The fix then took two generations of political struggle — unions, labor standards, trade protection — before workers clawed back a share. Hanauer adds that the pause ended because political consensus shifted, not because markets self-corrected. > *"You could almost take every grievance that we have today and overlay it in the early 1800s and get the exact same words."* ## [47:28] Will Companies Leave If Taxes Rise? Bartlett names the entrepreneur's objection: UK founders are already leaving for Dubai, Miami, and Singapore to escape the tax environment. Raise taxes further and the productive class emigrates. Priestley doesn't dispute the trend and argues that threatening corporate flight is precisely how megacorps hold governments hostage. His counter-proposal borrows from broadcast licensing: if you want to serve UK customers, you pay a fixed territorial fee regardless of where you're incorporated. You can't threaten to leave if the revenue is geographically locked. > *"Pop off to Dubai, run the business virtually, and pay no tax."* ## [51:58] Should Global Corporations Pay More Tax? The global minimum corporate tax attempted by the Biden administration comes up. Hanauer explains the design: if every country applies a floor rate, no jurisdiction can compete on tax below it and the race to the bottom ends. The 15% OECD deal was partial progress but exempted too many structures. Both guests agree a functioning global tax floor is probably the single most powerful lever for capturing megacorp revenue, and both are pessimistic it will happen because the political will to enforce it conflicts with the sovereignty of tax havens that benefit from the status quo. > *"Every rich person I know in Europe is playing this ridiculous game of trying to avoid taxes."* ## [54:00] How MegaCorps Block Entire Markets Bartlett cites Australian and Canadian examples: when governments tried to make Meta pay for news links, Meta simply blocked all news content rather than pay. When California tried to force Amazon to collect local sales tax, Amazon threatened to pull out of the state. Hanauer's point: if every jurisdiction simultaneously imposed the same rule, the megacorp could no longer play one off against another. The leverage only exists because coordination among governments is fragmented. > *"If every state required Amazon to collect local sales tax then obviously they couldn't do any of that. They would have to deal with it."* ## [54:58] Solutions To Economic Inequality Approaching the first ad break, Bartlett asks both guests to state their cleanest solution. Hanauer: tilt the playing field back — minimum wage, overtime rules, anti-monopoly enforcement, global tax coordination. Priestley: all of those, plus fundamentally restructure who owns assets; raising the floor without changing the ownership structure still leaves most people watching asset prices outpace any wage gain. The pitchforks are already out, Priestley says, because workers have nothing left to lose — which means the floor-raising came too late. > *"You have to do both. Tilt the playing field and change who holds the assets."* ## [56:51] Ads *Sponsor break — LinkedIn Marketing Solutions, Pipedrive CRM, Wispr Flow voice-to-text.* ## [58:59] How Many Jobs Will AI Replace? After the break Bartlett pivots to AI. Eric Schmidt's commencement speech — where every mention of "AI" was booed by graduates who assumed it meant their jobs were gone — frames the anxiety. Hanauer says the standard "AI creates new jobs" narrative misses a timing problem: new jobs appear over a generation, but displacement happens in a quarter. He acknowledges AI is "monetizing for free humanity's intellectual property" and concentrating the returns in a handful of companies. Priestley notes the uneven geography: the Philippines' outsourced back-office economy is already being hollowed out by AI doing those same tasks at a fraction of the cost. > *"AI is monetizing for free humanity's intellectual property and a few people are going to directly benefit."* ## [01:01:38] AI Agents Are Replacing Entry-Level Work Bartlett describes what modern AI agents actually do — click through interfaces, complete multi-step browser tasks, handle data entry, edit documents — and notes his own first job after dropping out of university was exactly that kind of work. Hanauer argues the correct frame is augmentation: one person with strong AI tools may be ten times more productive, which is good for that person but terrible for the nine others whose roles disappear. Priestley gives a case study: a husband-and-wife video production agency in northern England used AI to automate script writing and cut their team from six to two while doubling output. > *"One person with good AI tools may be ten times more productive. That's great for that person. It's not so great for the other nine."* ## [01:05:25] Will AI Reduce Hiring? The Jevons Paradox debate surfaces: historically, making tasks cheaper increases demand for them, which absorbs the displaced labor. Priestley's video agency example is a Jevons case — cheaper production brought more clients, not fewer jobs overall. But Hanauer argues AI is so broad and fast that the paradox won't hold everywhere — basic white-collar and entry-level admin work will contract in absolute terms before any new demand materializes. Both agree the transition period is the real danger and that policymakers are not moving at the speed the labor market requires. > *"The biggest issue is that the nature of the entire economy is fundamentally changing, and the people in it haven't been told the new rules."* ## [01:08:39] Is Universal Basic Income The Answer? Hanauer is skeptical of UBI as currently designed: it doesn't solve the structural problem of who owns the AI systems, it just puts a floor under consumption. He prefers publicly owned entities taking equity stakes in AI companies in exchange for the public infrastructure those companies depend on. Priestley frames it more directly: AI valuations are built entirely on job displacement — "you can't get to those numbers unless you're displacing lots of jobs" — so society should demand equity in the upside in exchange for absorbing the downside. > *"The whole valuation that AI is predicated on is job disruption. You can't get to those numbers unless you're displacing lots of jobs."* ## [01:13:29] Why Governments Struggle To Deliver Priestley pivots to execution risk: even with the right policies, current governments are demonstrably incompetent at implementing complex economic programs — misaligned incentives, risk-averse civil services, political cycles too short for structural reforms. Hanauer agrees governments are often incompetent but says the same is true of large corporations — Microsoft and Amazon have enormous internal failures — and the correct response is not to abandon government as a tool but to improve its capability. Singapore's state capacity, he says, proves that competent government is achievable. > *"We have a fundamentally incompetent set of people in government who have misaligned incentives."* ## [01:14:48] The Best Fix For AI Job Loss The two guests converge more than expected: both want the period between displacement and re-employment to be economically survivable, and both want support tied to the companies doing the displacing rather than general welfare. Priestley's preferred mechanism is a proliferation of small businesses absorbing the people large employers shed: "When you have millions and millions of little small businesses, everyone's happier." Hanauer wants mandatory transition benefits funded by the equity stake mechanism. > *"When you have millions and millions of little small businesses, everyone's happier."* ## [01:17:50] Are We Heading Towards An AI Utopia? Hanauer makes his clearest statement of economic philosophy: markets are not efficient allocators of resources (the textbook claim) but evolutionary systems that allow groups to solve complex problems. That framing changes everything about AI — the question is not whether markets will find the optimal allocation of AI output, but which group of people gets to participate in solving the problems AI opens up. Democracies must move aggressively to include as many people as possible, or the utopia arrives for a few hundred thousand people while everyone else is left outside. > *"Markets are an evolutionary system that enables groups of people to come together and solve complex problems. That's why they work."* ## [01:22:05] Would Higher AI Taxes Drive Companies Away? Bartlett poses a direct scenario: if the UK demanded a 50% equity stake in AI companies operating here, wouldn't they simply incorporate in Delaware and serve the UK market remotely? Priestley says yes — and that's why broadcast-license-style territorial fees are more robust than equity demands. Hanauer says the threat is overstated: "The worst that can happen is there will be a few dozen guys worth a hundred billion and not two hundred billion." Society can live with that. > *"The worst that can happen by running that experiment is that there will be a few dozen guys who are worth a hundred billion and not two hundred billion."* ## [01:24:08] Does Government Improve Lives? The governance quality debate deepens. Bartlett asks whether putting government on a company's board would slow innovation. Hanauer's counter: large corporations are already bureaucratic and slow — look at Microsoft's decades of stagnation before Nadella. The difference between a good government board seat and a bad one is capability and accountability, not the fact of government involvement. Both guests agree the Nordic model shows competent state participation in the economy is achievable; both are pessimistic that the UK or US political class currently has that competence. > *"Look — Microsoft and big companies are equally incompetent. The question is whether you have the political will to build capable government."* ## [01:30:32] Where They Fundamentally Disagree Bartlett draws out the real inch of distance. Priestley's objection to Hanauer's program is not that wages don't matter — it's that people are more than consumers. When workers owned houses and ran small businesses, they felt agency, community belonging, and psychological investment in their neighborhoods. Raising the wage floor helps but doesn't give workers a stake in the system. Hanauer concedes the point on ownership but says you can't own anything if you can't save, and you can't save on $7.25 an hour. The sequence, not the destination, is where they disagree. > *"When people had small businesses that they owned, they felt really good about their communities. They felt pride and ownership and agency."* ## [01:33:09] Is Socialism The Answer? Hanauer rules out socialism quickly: state ownership of the means of production can only redistribute existing prosperity, not create new prosperity. The reason market economies outperform command economies is that markets are information-processing and problem-solving engines that central planning cannot replicate. His position is not "more socialism" but "better-designed capitalism" — a mixed economy where markets operate within rules that share the gains broadly rather than concentrate them. The Nordic countries are not socialist; they are capitalist with stronger floors and higher inclusion. > *"Socialism is most definitely not the answer. All socialism can do is split up existing prosperity in a fairer way — it does not know how to create more prosperity."* ## [01:37:28] How Policy Builds A Strong Middle Class Hanauer introduces the Monopoly analogy in full: the economy is a non-ergodic game — like Monopoly, not rock-paper-scissors — where early luck compounds indefinitely and "one person will own everything and everybody else will have nothing" if the game runs long enough. A thriving middle class is never a natural outcome; it is always a deliberate construction, maintained by rules that prevent runaway compounding. He traces the 1970s decoupling — when productivity growth stopped translating into wage growth — to policy choices, not market forces. Priestley adds that big finance and big tech are the two institutions that have jointly driven the wedge. > *"In Monopoly, no matter how many times you go to Monopoly school, if you play long enough, one person will own everything and everybody else will have nothing."* ## [01:43:05] Ads *Sponsor break — Wispr Flow voice-to-text, Diary Of A CEO conversation cards.* ## [01:45:16] Which Economies Are Thriving Today? Bartlett asks for evidence that the "sweet spot" mixed economy actually works. Both guests point to Germany — legally mandated worker representation on company boards, strong unions, a manufacturing sector that survived globalization — and Singapore, whose sovereign wealth fund and state capacity have generated exceptional living standards. Priestley notes that Uber drivers and café workers in Singapore express economic optimism absent from equivalent conversations in the UK. Germany's current structural problems (energy transition, automotive disruption) show the model is not permanent, but it demonstrates that worker inclusion and economic dynamism are not in fundamental tension. > *"Germany has workers on the board of every company. And Singapore has shown that competent state capacity generates extraordinary living standards."* ## [01:48:38] What If You're Not Entrepreneurial? Bartlett surfaces the limits of Priestley's framework: what about the majority of people who are not ambitious in the entrepreneurial sense? Priestley's answer is that most people benefit from being in an economy with ambitious people — proximity to entrepreneurial energy creates jobs, culture, and options even for those with no desire to start businesses. His concern is that the UK is driving out precisely those ambitious people with its regulatory and tax environment, impoverishing the majority who depend on them. > *"For an ambitious person, inequality is the opportunity to get ahead. 'I can figure out how it works in this.'"* ## [01:51:46] Why Not Everyone Should Be An Entrepreneur Bartlett and Hanauer raise the selection bias at the table: all three men are entrepreneurs and may be systematically underestimating how rare the psychological profile is. Hanauer pushes back directly: the dominant economy of the 1950s–1970s produced widespread middle-class prosperity without mass entrepreneurship, through union density, regulated labor markets, and progressive taxation. The entrepreneurship boom of the 1990s–2010s coincided with, and partly caused, the hollowing of those older routes to stability. > *"Most people want to be able to go to work, be treated decently, earn a living wage, go home, and live their life."* ## [01:53:46] How To Help Small Businesses Thrive Hanauer points to US antitrust laws of the early twentieth century — specifically Robinson-Patman — which prevented large buyers from extracting preferential pricing from suppliers, effectively blocking Walmart-style supply chain crushing. Those laws were dismantled in the 1980s under neoliberal reform and the result was the hollowing of regional and local business ecosystems. His fix: restore procurement rules that prevent megacorps from buying cheaper than small competitors. Priestley backs this and adds that the UK's £25,000 government-backed startup loan scheme is genuinely useful but needs to scale. > *"There used to be laws to make sure that big companies could not buy raw materials cheaper than small companies."* ## [01:56:16] Can Regulation Help Small Business Win? Hanauer elaborates: Robinson-Patman is not a subsidy but a level-playing-field rule. Removing it did not make markets more free — it made them more concentrated. Priestley adds that the UK high street decline is not simply e-commerce disruption but a regulatory failure: if a megacorp and a corner shop pay the same business rates per square foot but the megacorp can optimize inventory nationally, the regulatory structure is systematically tilted against the small operator. Both agree the framing of "regulation vs. free markets" is misleading — the question is whose interests the rules are calibrated to protect. > *"It doesn't matter if we're talking about retail — these were regional manufacturing companies, regional businesses. Robinson-Patman protected them."* ## [01:57:41] Ending Taxes For Lower-Income Earners Priestley proposes removing income tax entirely for workers below the median wage. His argument: the complexity and administrative cost of collecting income tax from low earners is disproportionate, and the revenue should instead come from large corporations via a broadcast-license-style territorial fee — a flat charge to operate in a given market, set high enough to fund public services and impossible to avoid through transfer pricing. Hanauer supports the direction but insists you can't get there without first addressing the wage floor, or removing income tax on a £20,000 income becomes a rounding error. > *"I would make it a broadcast license — a fixed fee that's very hard to wiggle out of. You want to broadcast in the country, you pay the fee."* ## [02:01:40] The Global Economy's Biggest Problem Both guests agree the deepest problem is a global action problem: any jurisdiction that imposes meaningful constraints on megacorps or high earners faces credible threats of capital flight, and no single country can solve it alone. Hanauer cites the Biden global minimum corporate tax effort as the best recent attempt and traces its partial failure to a handful of small jurisdictions willing to keep offering competitive rates. Priestley's addition: the ultra-wealthy need to understand that if they don't invest in the economies sustaining their wealth, those economies will eventually fail in ways that destroy that wealth. > *"All of your questions point to the same fundamental weakness: it's a global action problem and we don't have the global governance to address it."* ## [02:09:40] Radical Solutions To Inequality Bartlett asks for genuinely radical ideas. Priestley names company breakups — forcing Amazon, Google, and Meta to divest sub-businesses so each subsidiary competes independently — as probably the most impactful single intervention and the most politically unthinkable. He asks whether Zuckerberg would lose more sleep over a 70% marginal tax rate or having Meta's constituent businesses separated. He also calls for hard caps on the size of financial funds: a fund above a certain AUM size stops functioning as capital allocation and starts functioning as extraction. > *"Breaking up companies is unthinkable. But I wonder if Zuckerberg would lose more sleep about higher taxes or having his company broken up."* ## [02:15:31] How Do We Restore Hope? The closing question, passed down from a previous guest: in a world with so many challenges, what can we do to restore hope and trigger engagement? Priestley says the most important act is telling people that the rules have changed — the industrialized-economy rules they learned in school no longer govern the digital economy — and that the new rules are learnable. The people he sees with the most agency and optimism are those who understand how the current economy actually works: pitching, publishing content, building an audience, creating a product offering. Hanauer closes on the need to replace the entire intellectual framework that has governed economic policymaking since the 1980s — a framework that told policymakers to deregulate, suppress wages, and trust markets to self-correct. That framework produced the crisis being debated; a new one built on inclusion and democratic accountability is the only durable fix. > *"I only know one thing that I've seen work again and again: I teach people the entrepreneurial method and they suddenly feel agency and hope."* ## Entities - **Nick Hanauer** (Person): venture capitalist, first outside investor in Amazon, host of Pitchfork Economics podcast; argues for higher minimum wages, stronger labor standards, and global corporate tax coordination - **Daniel Priestley** (Person): entrepreneur and founder of Dent Global; author of *Lifestyle Business Playbook*; argues for wider access to entrepreneurship, asset ownership, and territorial taxation of megacorps - **Steven Bartlett** (Person): host of The Diary Of A CEO; ex-founder of Social Chain; referee and questioner throughout the debate - **Pitchfork Economics** (Organization): Nick Hanauer's podcast and policy project advocating for a middle-out economic model - **Dent Global** (Organization): Daniel Priestley's international business education and entrepreneurship company - **K-Shaped Economy** (Concept): economic condition where top earners see rising prosperity while lower earners decline simultaneously; analogous to the Engels Pause of the early Industrial Revolution - **Engels Pause** (Concept): the 50–75 year period after the Industrial Revolution when technology owners captured all productivity gains while workers' living standards stagnated; eventually reversed by unions and labor reform - **Monopoly Analogy** (Concept): Hanauer's model for why a thriving middle class requires deliberate policy intervention — a non-ergodic game where early advantages compound and one player inevitably owns everything unless the rules are rewritten - **Robinson-Patman Act** (Organization): US anti-discrimination law preventing large buyers from extracting preferential pricing from suppliers; gutted in the 1980s, cited as a key driver of small business collapse - **Sovereign Wealth Fund** (Concept): state-owned investment vehicle holding equity in national assets and distributing returns to citizens; Norway and Singapore cited as working models - **Universal Basic Income (UBI)** (Concept): direct cash transfer to all citizens regardless of employment; both guests are skeptical it addresses structural inequality without accompanying ownership reform - **Global Minimum Corporate Tax** (Concept): OECD-coordinated floor rate of 15% on corporate profits designed to end tax-haven competition; partially implemented under Biden, viewed by both guests as necessary but insufficient

Tony Fadell—creator of the iPod, co-creator of the iPhone, and founder of Nest—sat down with Lenny Rachitsky for a 95-minute masterclass on what it actually takes to build products that last. Fadell argues that AI makes taste and craft *more* important, not less: when anyone can vibe-code a prototype overnight, the things that stand out are the ones that carry genuine human judgment all the way through. The conversation moves from inside stories of the iPhone keyboard debate and Nest's troubled Google years to a sharp warning about cognitive surrender to AI tools, closing with Fadell's framework for ethics in product design. ## [00:00] Introduction to Tony Fadell Lenny opens by describing Tony Fadell as the guest he's most wanted since starting the podcast — and the opening clips set the episode's stakes immediately. Fadell warns "don't surrender to the machine," sketches his pain-first idea framework, previews the three-generation rule, and flags why marketing is a product decision, not a later-stage add-on. The clips are drawn from throughout the interview, so each reappears with full context in its own chapter. > *"Don't surrender to the machine. We can use the machines, but don't cognitively surrender."* ## [02:23] The Blackberry vs. iPhone keyboard debate Fadell takes Lenny inside the most prolonged internal fight at Apple before the iPhone shipped: physical keyboard vs. virtual. The debate was never purely technical — it was about which market to chase. The Blackberry path meant winning the 1–2% of users who already owned one; the virtual-keyboard path meant designing for the other 98%. > *"The data was not clear that we should choose one over the other. And Steve said, 'We are going this way.' And he was like, 'If you're not going to get on board, get out of this room.'"* Fadell describes months of hardware-software co-iteration to close the gap with physical keyboards — not matching them, but getting "good enough." He explains the data-vs-opinion framework from *Build*: for any true 1.0, the data will never be conclusive, so someone with informed taste has to call it. ## [07:50] Micromanaging vs. kind lies: what great products actually need Starting from a Twitter-circulating chart that maps "unkind truths" to functional organizations and "kind lies" to dysfunctional ones, Fadell argues why opinion-based leadership is structurally necessary for a category-defining v1. Consumer products can't be validated by user testing before launch because the customer has never seen anything like them; the only real signal comes from shipping the whole system — product, marketing, distribution — simultaneously. > *"This is a benevolent dictatorship. This is what's going to happen and this is the vision and we don't know what we don't know until we ship it."* Fadell reclaims "micromanagement" as a precise tool: it means owning the decision at the detail level that actually matters, not running every operation. On the iPhone keyboard, that meant personally orchestrating changes across hardware, software, rendering, and error-correction simultaneously, because no single team could see the whole picture. ## [15:57] The Nest thermostat and smoke alarm story Lenny asks about the Nest Protect smoke alarm — the product Fadell calls "one of the toughest I've ever made" — and its discontinuation by Google. Fadell's diagnosis: organizational orphanhood. Nobody at Google was excited by it, so nobody invested in it, and eventually it was quietly killed. > *"AI needs context. In a home you want to make everything very seamless. And the way you get best context is by having sensors properly placed around the home."* He views this as both a business failure and a missed opportunity: a sensor-rich home platform was precisely what AI assistants would need a decade later, and Nest had been building toward that vision since 2010. The Nest Learning Thermostat was what should have been called the "Nest AI Thermostat" — they just couldn't use that word in 2011 without scaring people. Several builders are now pitching him on Nest 2.0, and he thinks the timing is right. ## [21:22] How to decide what's worth building: pain plus new technology Responding to a question from ARM co-founder Hermann Hauser, Fadell lays out his two-part filter: start from pain that exists now or is visible on the horizon, then ask whether new technology can solve it in a fundamentally different way. The pain usually exists because a product was built within old technology constraints and never actually revolutionized itself — it just evolved, and the original pain was tolerable enough that no one fixed the root cause. > *"I always start from pain. Are there new technologies to solve that pain? Bring innovation in, revolution in, redefine the space."* The Nest thermostat hit both conditions: 50% of household energy bills went to heating and cooling, no one used programmable thermostats because they were too hard to configure, and machine learning could now learn usage patterns automatically. He extends the logic to the iPod and iPhone, stressing that real innovation requires assembling a system of enabling technologies at once — not just a device. ## [27:36] The three-generation rule: why nothing works the first time The first iPod sold only to Mac loyalists — less than 1% of the market. The second generation was the same. It wasn't until the third generation, which added Windows compatibility and the iTunes Music Store, that it broke out. Fadell's framework: make the product, fix the product (customer feedback), fix the business (margins, volume, distribution). Almost nothing gets all three right in round one. > *"You got to fail a few times till you find your way. And you only fail if you stop. If you keep iterating, that's not failure. That's called learning."* He shares how the Windows port was a skunkworks project that Jobs explicitly rejected — the pitch was that without Windows, an iPod effectively cost $3,000 because you had to buy a Mac first — and how the same pattern (Jobs resistance → underground work → eventual vindication) played out with the Apple Pencil stylus. ## [34:20] The full customer journey: why marketing defines your product Fadell returns to a theme from *Build*: builders optimize for the product while customers only ever see it through the lens of marketing. He describes what happened when Apple tried to expand iPod into Europe by running U.S. marketing verbatim — it didn't resonate because European consumers were at an earlier adoption stage and needed different framing. > *"The technology is in service of the customer, not 'we're going to jam the technology down the customer's throat.'"* The lesson: every iteration of a product has a different target customer, and you have to meet each cohort where they are. He updates Geoffrey Moore's "Crossing the Chasm" framing in *Build*, arguing that in software you can distribute faster but you can't accelerate comprehension — people still need the story shaped for their context. ## [40:53] The power of storytelling and the press-release-first approach "A thousand songs in your pocket" came from Apple's marketing team, not engineering — and Fadell heard it for the first time when it was essentially done. He frames the press-release-first method not as "working backwards" but as the only sane way to build: a filmmaker doesn't write a script after shooting the footage. > *"When you do the press release, you can only have three or four key features. After that, it becomes gobbledygook for a customer."* He connects this to product scope discipline: writing the press release first tells you which features are the tent poles, making it impossible to quietly cut two of them for schedule without realizing you've destroyed the marketing story. He also holds up OpenAI's current identity problem as a marketing failure — great technology, but no clear daily use case for the average person — and contrasts it with Anthropic's more focused positioning. ## [48:37] The evolution of product management and the builder role Lenny asks whether AI collapses PM, engineering, and design into a single "builder" role. Fadell's answer: the functional perspectives — marketing, sales, distribution, engineering, customer support — represent distinct customer viewpoints that still need to be held simultaneously. The PM role is to interpret between them, not to be replaced by prompting. > *"What we're saying is 'oh I can just today in the AI world make a prompt and all of a sudden it gets spit out' and you don't know what all those little functions are — they are very clear definitions of certain points of view for the customer."* ## [50:27] Why AI-generated code creates brittle, unmaintainable products Fadell references the Claude source-code leak and the reactions from engineers who saw Anthropic's main loop: functions that should have been broken across 12–15 sub-modules were monolithic, and experienced architects described it as unreadable. His argument: AI-generated code can work and pass tests, but it accumulates technical debt the way fast fashion accumulates waste. > *"You're getting short-term gain for very, very long-term loss. That's called technical debt. Everybody hates technical debt."* He draws an explicit analogy — H&M vs. a luxury brand. For throwaway prototypes, fast software is fine. For a real company, the architecture has to be deliberate. He uses Flighty as his example of "luxury software" — the kind of product where you feel the care from the first pixel, and that feeling is what generates word of mouth. ## [58:00] Storytelling techniques Fadell traces his storytelling instincts to watching his father sell Levi's — sometimes steering customers toward a competitor if it was the better fit, because honesty built relationships. The technique: find the virus of doubt (the pain or friction the customer already has), show them they're not alone in it, then introduce a solution. He learned the art of refinement by watching Jobs rehearse the iPhone pitch obsessively — not with the marketing team, but with smart friends who had no prior context. > *"Too many times when we're technology-led, we talk about the what. We don't talk about the why. And the why is where the storytelling is."* He introduces an infomercial framing as a structural tool: map the exaggerated version first to find all the emotional levers, then dial it back to truth. Lenny riffs on this as a counterintuitive first draft exercise — go extreme, then pull back the honest parts. ## [01:05:45] The next iPhone Fadell's prediction: voice becomes the primary input layer, touch and keyboard become secondary, and the display stays — because without a BCI or retinal projection, you still need something to read a map on. The move from "tapping is primary" to "voice is primary" has been stalled by the quality ceiling on voice AI; now that models can actually understand and remember, the inversion becomes possible. > *"We need to flip it. Voice as the number one primary feature. Then keyboard if necessary. Then tapping and swiping."* He dismisses the display-free device category (Humane, AirPods-as-interface): "different, not better." The movie *Her* is his reference — even in that future, people still had glass when they needed it. Near-term, the smartphone form factor isn't going anywhere; trust in AI agents is still years from mass adoption, and consumer willingness to pay $200/month for AI subscriptions is unsustainable unless the value is obvious. ## [01:13:15] Hardware is back Fadell has been building hardware since 1995 when the Valley told him he was crazy. The same cycle has repeated: hardware unfashionable → iPod → hardware cool → mobile software → hardware unfashionable → AI → hardware mandatory. > *"We can't get to the next level of software if we don't make the next level of hardware. The revolution has to happen completely."* Software-only companies are now commoditized by AI coding tools, so defensibility requires atoms — sensors, chips, physical form factors — bonded with software. Waymo is his clearest example: the hardware platform is what makes the software irreplaceable. He notes Evan Spiegel made the same case on a previous Lenny episode. ## [01:17:01] What Tony is most excited about Through Build Collective, Fadell has been funding AI-plus-hardware businesses for years before it was fashionable: Simbe Robotics (retail inventory counting), Greyparrot (AI recycling sorting), textile quality inspection via computer vision, and Orianis (drug design, ten years in). His thesis is precision AI with a narrow scope and a real customer problem, not frontier model development. > *"I'm really interested in AI that you can trust, scoped correctly, solving real problems every day — as opposed to pipe-dream AGI."* He invested early in Grok and Cerebras at sensible valuations and has no interest in nine-figure or ten-figure pre-launch rounds. The portfolio companies he cares about most are finally getting traction now that the market caught up to where he was years ago. ## [01:21:38] Working with Tony Build Collective invests in deep tech (hardware, software, chemical, biological), then actively advises on product, operations, marketing, financing, and org development. The portfolio has exceeded 200 companies. Fadell describes the work as accelerating founders past the three-generation cycle — trying to get them to a solid v1 rather than discovering product-market fit on v4. > *"We try to help them so they don't hit it on the fourth version. They try to get very close to the first or second version so they can get on that three-version cycle to get to a great company."* He is also MIT Morningside Academy's inaugural designer-in-residence, teaching graduate students the customer-journey framework before they've spent a decade learning it the hard way. ## [01:25:36] Ethics, morals, and the responsibility of product builders Fadell brings up ethics unprompted — calling it a subject too few product designers take seriously. His core argument: addiction mechanics are an architecture decision, not just a side effect. He recounts a meeting where someone proposed adding pornography to the iTunes video store and Jobs shut it down immediately. That clarity, Fadell says, is what leadership looks like. > *"Don't let those things go astray. Just like you wouldn't go astray with a bad user interface, make sure you're not trying to addict your users."* On the iPhone's role in the social-media mental health crisis, he distinguishes between the device and the apps: Apple made the refrigerator; other companies filled it with junk food. His ask of platform companies is simple — more digital consumption tools, clearer labels, the same hygiene regulation that exists for physical food. Short-term extraction at the cost of user health, he argues, is also bad business: you can't keep customers you've made sick. ## [01:32:40] How to connect with Tony and Build Collective Fadell directs listeners to buildc.com, where the portfolio and contact information are available. His closing ask to the audience: make great products — not vibe-coded throwaway prototypes, but things built with real judgment. He ends where the episode opened: don't cognitively surrender. Use the machines as tools, not as replacements for taste. ## Entities - **Tony Fadell** (Person): iPod and iPhone co-creator, Nest founder, author of *Build*, managing partner at Build Collective, MIT Morningside Academy inaugural designer-in-residence - **Lenny Rachitsky** (Person): Host; founder of Lenny's Newsletter, former Airbnb PM - **Steve Jobs** (Person): Apple CEO; referenced throughout as the archetypal opinion-based decision-maker and obsessive storytelling practitioner - **Hermann Hauser** (Person): ARM co-founder and longtime Fadell colleague; submitted the "what is worth building?" question for the interview - **Build Collective** (Organization): Fadell's deep-tech investment and advisory firm; portfolio of 200+ companies in robotics, health, agriculture, and chips - **Nest** (Organization): Smart-home hardware company Fadell founded in 2010; sold to Google for $3.2 billion; known for the Learning Thermostat and Nest Protect smoke alarm - **General Magic** (Organization): 1990s startup that built smartphone-like technology 15 years before the market was ready; Fadell's formative career experience - **Simbe Robotics** (Organization): Build Collective portfolio company; AI-powered robots that count retail inventory - **Greyparrot** (Organization): Build Collective portfolio company; AI sorting for recycling facilities via computer vision - **Flighty** (Software): iOS flight-tracking app; Fadell's go-to example of "luxury software" — crafted with visible care, not vibe-coded - **Three-generation rule** (Concept): Fadell's framework that every real product needs three iterations — make the product, fix the product, fix the business — before achieving scale - **Cognitive surrender** (Concept): Fadell's term for over-delegating judgment to AI tools at the cost of taste, architectural thinking, and long-term product quality - **Opinion-based decision** (Concept): A decision that cannot be resolved by data because no prior comparable product exists; requires a designated taste-maker with an informed gut