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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

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.

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

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.

Brad Gerstner 在 All-In Liquidity Summit 上拿出一组数据：二级市场成交量是 2021 峰值的两倍，secondaries 现在正与 IPO 和并购并列，成为早期投资者退出的第三条路。Gavin Baker（Atreides Management CIO）和 Kelly Rodriques（Forge Global CEO）围绕这一结构性转变展开讨论——公司为何长期保持私有、SPV 的合法性、Forge-Schwab 合作如何把 46 million 零售投资者引入这个市场，以及 VC 主动卖出的利益冲突与估值泡沫风险。最后三位各点出一个值得买二级的私有公司名字。 ## [00:00] Brad Gerstner, Gavin Baker, and Kelly Rodriques join the Besties! 这是一段介绍片段，用预告式引言串联三位嘉宾登场：Jason Calacanis 宣布"Everybody wants access to these private markets"，随后 Kelly Rodriques 报告 19 家私有 AI 公司平均增长 300%，Gavin Baker 抛出"The ROI on AI has empirically, factually, unambiguously been positive"，最后 Chamath 问是否有 Brad 的 slides 启动正式讨论。 > *"The ROI on AI has empirically, factually, unambiguously been positive."* ## [00:47] Secondary Markets are Booming & Competing with IPOs Brad Gerstner 展示三张图：VC 流入远超流出（五年持续净流入），二级市场成交量双倍于 2021 高点，以及溢价/折价的反转——过去 secondaries 以 80 折成交，现在已升至面值 106%。关键结论：secondaries 现在与 IPO、并购三足鼎立，成为企业员工和早期投资人实现流动性的主要渠道之一。他把 Anduril、Anthropic、SpaceX 这类超大型私有公司称为"quasi-public companies"——每天都在买卖，只是不在交易所。 > *"Secondaries are now competing with IPOs and acquisitions as the principal way that these guys are exiting."* ## [03:10] Why Companies are Staying Private So Long? Gavin Baker 认为公司长期私有其实没有好理由，但 Zuckerberg 自己讲的反例最有说服力：Facebook 当年差点押注 HTML5 放弃原生 App，Chamath 亲历了内部辩论（他主张做手机，Brett Taylor 力推 HTML5，Zuck 先选了 Brett，之后花三年纠错）。Gavin 的核心论点是，私有公司 CEO 被所有投资人捧成"most special flower"——没人敢给真实负面反馈，因为一旦说了实话就失去后续参与资格；而公开市场投资者可以随时买卖，反而更直言不讳。Jason 把这种现象概括为"The sycophantic nature of private markets is real." Brad 的 October 2022 公开信"Time to Get Fit"被 Gavin 反复提及，认为这种公开施压正是公有公司才能产生的外部纠错机制。 > *"When you're the CEO of a private company, you are the most special flower to all of your investors."* ## [09:22] SPVs, the Forge-Schwab Deal, Democratizing Private Market Access Chamath 抛出一个尖锐问题：Anthropic 和 OpenAI 都在要求解散 SPV，为什么 SPV 还有存在理由？Kelly Rodriques 给出 Forge 的立场：SpaceX 从 2018 年起就主动批准了有许可的 SPV，并且公开表示欢迎"broad-based distribution at the IPO price"——Schwab 后来被列为 IPO 承销商之一，就是这段关系的延续。 Forge-Schwab 合作的核心数字：Forge 原有 3 million 投资人，Schwab 带来另外 46 million，合并后可以把私有公司股权打包成 interval fund（500 美元起投，无需 accredited investor 资格），让普通零售投资者合规参与。Kelly 明确区分了 interval fund 和 closed-end fund：后者价格往往与标的净值脱钩，靠 FOMO 定价，风险显著高于前者。 > *"What Schwab represents is 46 million investors and 12 trillion. This will change capital access and the way that you distribute your shares moving from private to public."* ## [13:28] Secondary Markets as Exit Liquidity for VCs Brad 坦承 Altimeter 正在主动卖出——VC5/6/7/8 的 LP 要求 DPI，公司愿意在高价格时卖 30% 仓位。这引出了整集最核心的利益冲突讨论：VC 向零售卖出，算不算在用散户做出口流动性？Chamath 进一步追问，二级卖出会不会破坏和创始人的关系，Brad 承认每次都要和 founder 沟通，他们从不喜欢，但这是对 LP 的受托义务。 Gavin Baker 指出一个结构性分化正在形成：没有 Anthropic/OpenAI/SpaceX 敞口的 VC，DPI 会从 top quintile 跌落，正在用 Neolabs 之类的"call option"赌注填报告；有敞口的 VC 则更为保守。他同时预告，当这些公司上市并过了锁定期，Fidelity、Baillie Gifford、Capital Research 等 long-only 基金（每家最多 3%-15% 投私有资产，目前多数已接近上限）将释放"hundreds of billions of dollars of new late-stage demand"。 Jason 点出这条第三路如何改变早期投资逻辑：种子投到 $10-20M 估值，到了 $500M 就和创始人同步卖出，把资本循环到下一个早期标的，创始人也接受这种安排——六七年前行不通，现在顺理成章。 > *"We're in this because we want this to be durable democratization for a long time. We want to build trust among those who feel left out and left behind in capitalism."* ## [27:00] The Private Market Bubble? Chamath 直接戳穿 Kelly 用"extraordinary"描述当前估值的措辞："extraordinary is a coded word for bubble." Kelly 的建议是零售投资者应该买更早期、非 CNBC 每天讨论的标的——比如 SpaceX 2018 年 $30B 估值进场的人现在相当满意。Brad 和 Gavin 对比了 1999-2000 与现在的区别：CMGI 零收入股价从 $2 涨到 $2000 然后归零；而 Anthropic、OpenAI、SpaceX 是"extraordinarily real businesses"。 但 Brad 也警告：14 只 ETF 计划在 SpaceX IPO 当天推出 1.75x 杠杆 SpaceX 产品，这是明显的过热信号。他对 CNBC 上推销高溢价私有产品的人表示担忧，认为零售投资者需要足够的持仓时间才能扛过回调。 > *"There are 14 ETFs launching on the day of the SpaceX IPO that are levered ETFs into SpaceX at like whatever 1.75 trillion."* ## [32:03] Hottest Secondary Companies Right Now Chamath 出的题目规则：不能选 top 10 最知名私有公司，从数十亿到数千亿范围内各选一个目前未持有、但愿意在二级市场买入的公司。 **Brad Gerstner** 选 **Sierra**（Brett Taylor 创办），定位是 agent-native Salesforce——销售、营销、客服全部 AI agent 原生重建，看多理由是 Meta/Google/SpaceX 可能收购来加速 agentic 路径；风险是 OpenAI/Anthropic 直接进场替代。**Chamath** 选 **Revolut**，被 Thomas Leant 在峰会后台现场说服。Neo-bank 用现代技术栈重写银行底层，欧洲数千万用户，正在进入美国市场。**Gavin Baker** 选 AI 数据中心网络基础设施公司 **Arya** 和 **Drivets**（押注推理分解与异构芯片编排的新网络层），另外还有 **Vast**（空间站，搭 SpaceX 降低发射成本的逻辑）和 **Zipline**（无人机配送，在非洲做了七年真实数据积累后进入美国市场，已将非洲部分国家孕产死亡率降低 90-95%）。**Kelly Rodriques** 选 **Neuro Robotics**（德国，AI 驱动物流机器人，已有 $100M 营收，估值尚未进入硅谷主流视野）。 > *"The ROI on AI has empirically, factually, unambiguously been positive. Investing is the search for truth."* ## Entities - **Brad Gerstner** (Person): Altimeter Capital 创始人兼 CEO，Invest America 计划发起人，本场 moderator - **Gavin Baker** (Person): Atreides Management 管理合伙人兼 CIO，SpaceX/Anduril 早期投资人，前 Fidelity 基金经理 - **Kelly Rodriques** (Person): Forge Global CEO，私有市场二级交易平台创始人 - **Jason Calacanis** (Person): LAUNCH 创始人，All-In 主持人之一，早期天使投资人 - **Chamath Palihapitiya** (Person): Social Capital CEO，All-In 主持人之一，前 Facebook VP - **Forge Global** (Organization): 私有公司股权二级交易平台，与 Schwab 达成分销合作 - **Charles Schwab** (Organization): 传统券商，通过 Forge 合作为 46 million 用户提供私有股权产品入口 - **Sierra** (Organization): Brett Taylor 创办的 agent-native 企业软件公司，Brad Gerstner 标注的收购候选 - **Revolut** (Organization): 欧洲 neo-bank，正扩张美国市场，Chamath 峰会后转变看法的目标 - **Zipline** (Organization): 无人机配送公司，非洲医疗配送起家，已进入美国市场 - **Interval Fund** (Concept): 允许非认证投资者以 $500 起投参与私有股权的基金结构，区别于 closed-end fund - **DPI** (Concept): Distributions to Paid-In，VC LP 最关心的资本返还指标，长期私有化导致 DPI 压力积聚 - **SPV** (Concept): Special Purpose Vehicle，单资产投资载体，Anthropic/OpenAI 正要求解散的二级市场结构 - **Invest America** (Concept): Brad Gerstner 推动的政策项目，目标是让普通美国人参与私有股权市场

At the All-In Liquidity Summit, moderator Brad Gerstner (Altimeter Capital) puts Cerebras CEO Andrew Feldman and Planet Labs CEO Will Marshall on the couch alongside Jason Calacanis and Chamath Palihapitiya to examine two converging waves—AI silicon and space infrastructure—through the lens of companies that just went public or are about to. Feldman walks through why Cerebras built a wafer-scale chip the size of a dinner plate instead of chasing Nvidia on the GPU form factor, and what 15–18x inference speed means for user behavior. Marshall explains why shrinking satellite hardware and collapsing launch costs are putting orbital data centers within a few years of becoming economically rational. The panel closes with a direct argument to LPs in the room: history shows more money is made holding shares post-IPO than distributing at lockup expiry. ## [00:00] CEOs Andrew Feldman (Cerebras) and Will Marshall (Planet Labs) join the Besties! This opening segment is a promo reel spliced from the panel itself: clips of Jason Calacanis hyping Cerebras as "the AI IPO of the year," Will Marshall declaring that "space and AI are really a match made in heaven," and Brad Gerstner arguing that the current technology wave "will be incredibly beneficial for America." The three speakers then walk onstage to take their seats at the All-In Liquidity Summit. Jason Calacanis shares a backstory: Sacks called him three days out, told him "POTUS needs the world's greatest moderator," and he showed up at Davos to find his badge printed alongside Donald Trump's name. The room erupts. With the ice broken, Chamath frames what follows—two newly public companies sitting at the front of the AI silicon and space data trends. > *"Space and AI are really a match made in heaven. They're getting married. Just like Google figured out how to index the internet and make it searchable, we are indexing the earth and making it searchable."* — Will Marshall ## [02:05] Both CEOs on going public: Impact on employees, customers, and business operations Chamath opens by asking what it actually felt like—Cerebras three weeks out, Planet Labs a year and a half in. Feldman is deliberately deflating: "I think it's really difficult to overestimate the amount of garbage that's involved in going public." The 130-person Zoom calls, the commas moving in documents, the morning after when your engineering backlog hasn't moved and your vendor relationships are unchanged. What did change, Feldman says, was the moment he flew long-tenure employees and their families to the NYSE floor. Engineers showed up in ties he didn't know they owned. One employee's Chinese immigrant father surveyed the scene and said, "I thought it would have happened faster." The celebration was real—then everyone turned back to work. Will Marshall takes the other angle: Planet came public via SPAC in 2021 at $2 billion with almost no fanfare. What the IPO did do, even then, was provide permanence: Planet works with governments that are "fully dependent on us giving them information. They don't want you to just disappear." A public company signals you'll be around for the contract's full term. Four years later the stock is at $50, a 10x move almost entirely in the public markets. Brad presses on the customer-mix question; Jason asks bluntly what percentage of revenue is military. Marshall gives a measured answer—security is a growing fraction, geopolitical demand is real, but Planet also serves farmers, energy companies, NASA, and civil governments. Miniaturization of satellites (hardware that once cost a billion dollars and weighed 20 tons now costs a few kilograms) combined with 4–5x lower launch costs is what unlocked the entire category. > *"Not a damn thing changes in the important parts of your business. If your relationships with your vendors are bad, they're still bad. If they're good, they're still good."* — Andrew Feldman ## [13:18] Timelines for datacenters in space Chamath reframes the macro: "We are rebuilding the data processing infrastructure that has existed on the earth—in the sky." He asks Marshall to explain orbital data centers and whether they're real, then asks Feldman to describe where silicon is heading. Marshall lays out the economics. A study Planet did with Google eight or nine years ago found the crossover point: when launch costs drop to $200–$300 per kilogram, putting compute in orbit becomes simply cheaper than ground. Right now it's just over $1,000/kg, down 10x over the last decade. On current Starship trajectory, Marshall puts the crossover at two to three years. The power math is the engine: a solar panel in a sun-synchronous dawn-dusk orbit collects power 24/7 with no intermittency, no batteries, no gas backup—five times more energy per panel than on the ground. "The infrastructure for compute in space is literally just solar panels and chips and RF signals up and down." Planet has already launched Nvidia GPUs into space and is launching Google TPUs on an early test. Marshall's call: within 10 years, most compute will be in orbit—"trillions, will be bigger than any of the other space businesses today." Feldman pushes back, productively: inter-chip cluster communication in space is still unsolved, and self-driving showed how "the last 10% can be a decade's worth of work." His view is the same destination, a slightly longer timeline, and a prerequisite: "The fundamental driver to even experiment is to get launch costs down. Then you can start doing experiments and getting it wrong and fixing it." > *"When launch costs come down to about $200 to $300 a kilogram, it would be cheaper—just simply cheaper—to put the data centers in space."* — Will Marshall ## [19:28] Cerebras business breakdown, AI's impact on the silicon market Chamath sets up the history lesson: explain the company, explain the bets, explain Cerebras vs. Nvidia vs. AMD. Feldman's answer starts with the structural shift AI enabled—for most of computing history, machines were bad at images and language. "We could store them and that's about it." Starting around 2015–2016, AI opened those doors, simultaneously expanding the problem space and driving demand for a new generation of silicon. Cerebras made two bets in 2015. First: dedicated silicon would win. Second: it couldn't look like a GPU. "If you build a GPU, the odds that you're better than Nvidia are approximately zero. They have eaten all the low-hanging fruit." The architectural insight was that moving data from memory to compute is the core bottleneck in AI inference. Cerebras built a chip the size of a dinner plate—wafer-scale, while most chips are postage-stamp-sized—and placed memory right next to compute using a vastly faster memory type. The result: 15–18x faster than a GPU on inference. Feldman frames the market with a thought experiment: "How big is the market for slow search today? Zero. How big is the market for dialup? Zero. You will not wait for AI. We have to deliver it to you in real time." > *"If you want to be 20 times better than somebody, your architecture can't look like them. They have enjoyed and eaten all the low-hanging fruit."* — Andrew Feldman ## [24:45] How Founder/CEOs think about liquidity on the road to going public Brad turns explicitly to the LPs in the room. He walks through Planet's investor history—early backers included Capricorn, Peter Thiel's Founders Fund, and Yuri Milner's DST. Planet went public at $2 billion via SPAC in 2021. Four years later, 90% of the value was still ahead of them. Most investors held, including Google (still the largest shareholder, hasn't sold a share) and Capricorn (held until very recently). The counter-lesson for LPs: demanding shares at lockup expiry can mean giving up the bulk of the return. Altimeter ran into this themselves, distributing shares at $3–4 billion on a company that went to $50 billion eighteen months later. For Cerebras, Brad describes a structural innovation Altimeter and the banks built: a "dribble lockup" that releases shares over six months against performance hurdles rather than in a single lockup expiry event—a structure SpaceX is expected to replicate. Feldman makes the empirical case: every study shows more money in percentage and in absolute dollars is made after IPO than before, because public markets let you put far more capital to work at scale. Brad notes the macro shift: a decade of "stay private forever" pressure is reversing; portfolio companies are now asking to go public at $1–3 billion. Chamath closes with the operational argument—public market scrutiny sharpens execution, "iron sharpens iron." Marshall ends on vision: LLMs trained on internet text are "blind to the real world." Feed them real-time planetary imagery and "they can answer real world problems"—what he calls "large earth models" or "planetary intelligence." > *"Historically more money is made after IPO than before. Every single study shows there is more money to be made both in percentage and in absolute."* — Andrew Feldman ## Entities - **Brad Gerstner** (Person): Founder and CEO of Altimeter Capital; moderator of the All-In Liquidity Summit IPO Panel; early Cerebras board member. - **Andrew Feldman** (Person): Co-founder and CEO of Cerebras Systems; architect of the wafer-scale CS-3 chip; company IPO'd at $185/share in 2026. - **Will Marshall** (Person): Co-founder and CEO of Planet Labs; pioneered the miniaturized satellite fleet; Planet went public via SPAC in 2021 at $2B. - **Chamath Palihapitiya** (Person): Founder/CEO of Social Capital; All-In bestie; co-moderates the panel with Brad. - **Jason Calacanis** (Person): Launch founder; All-In bestie; moderates the opening segment. - **Cerebras Systems** (Organization): AI hardware company building wafer-scale chips; 15–18x faster than GPUs on inference; IPO'd 2026 at $185/share, opened at $320. - **Planet Labs** (Organization): Earth-observation company operating ~200 satellites delivering daily full-earth imagery; went public 2021, stock 10x'd in public markets. - **Altimeter Capital** (Organization): Tech-focused growth equity fund; early Cerebras investor and board member; designed the "dribble lockup" structure. - **Wafer-scale chip** (Concept): Cerebras' architectural bet—a chip the size of a dinner plate with on-chip SRAM co-located with compute, eliminating the memory bottleneck that limits GPU inference speed. - **Space data centers** (Concept): Orbital compute infrastructure powered by 24/7 solar panels in sun-synchronous orbits; crossover economics vs. ground data centers projected at ~$200–300/kg launch cost, 2–3 years out on current Starship trajectory. - **Dribble lockup** (Concept): Post-IPO lockup innovation releasing shares incrementally over 6 months against performance hurdles, rather than all at once; designed by Altimeter and banks for Cerebras; expected in SpaceX's eventual IPO structure. - **Planetary intelligence** (Concept): Will Marshall's framing for AI models grounded in real-time satellite earth-observation data, enabling answers to real-world physical questions that text-trained LLMs cannot address.

Dan Loeb, CEO y CIO de Third Point, se une a los besties del All-In para recorrer su evolución: desde troll anónimo en los foros de valores de los años noventa hasta gestor de un fondo de cobertura multi-estrategia de 30.000 millones de dólares. Argumenta que las ventas en corto —dormidas durante años— vuelven a ser imprescindibles, que la alfabetización en IA es hoy un requisito para cualquier inversor serio, y que el papel del ser humano en la gestión de carteras es insustituible precisamente porque ningún agente puede replicarlo. La conversación concluye con el relato de Loeb sobre cómo ayudó a conseguir el indulto presidencial de Ross Ulbricht, enmarcándolo en un compromiso más amplio con la reforma de la justicia penal y la equidad educativa. ## [00:00] ¡Dan Loeb se une a los Besties! Este segmento de apertura es un avance rápido de los momentos más destacados de la entrevista: fragmentos que anticipan las frases más afiladas de Loeb antes de que comience la conversación propiamente dicha. Loeb declara que las ventas en corto han vuelto y son "absolutamente críticas", mientras los presentadores responden con comentarios sobre los mercados de selección de valores y de crédito. También aparecen aquí su reflexión sobre la vergüenza y el humor como herramienta activista temprana de Third Point, y su frase lacónica: "El activismo sin contienda por delegación es como el catolicismo sin el infierno." > *"El arte perdido de las ventas en corto ha vuelto y es absolutamente crítico."* ## [00:34] Trayectoria inversora: de los foros de internet y las provocaciones a Wall Street a un fondo de cobertura multimillonario Loeb reconstruye la prehistoria de la cultura inversora en línea. Antes de que existiera Reddit, él publicaba en Yahoo Finance y Silicon Investor bajo un seudónimo, apuntando a lo que describe como "empresas increíblemente fraudulentas" a finales de los años noventa: las descubría, hostigaba a la dirección y en ocasiones ganaba. Se define a sí mismo no como "OG" sino como "OT" —el troll original— aunque lo enmarca menos como malicia y más como un joven inversor desahogándose en un salvaje oeste sin vigilancia. La historia de Act Trade ilustra la época: un defraudador reincidente que empaquetaba cuentas por cobrar de refrigeradores como una tecnología propia llamada TADS y cotizaba a un múltiplo absurdo sobre el valor en libros. > *"Cuando éramos pequeños, nuestra principal herramienta era la vergüenza y el humor."* ## [03:15] Los primeros años de Third Point: mentores y turbulencias del mercado Loeb traza su formación inversora formal desde una etapa adolescente archivando libros en una sucursal de Paine Weber —donde sospecha que se infringieron ciertas leyes de valores— hasta Warburg Pincus, una firma de arbitraje de riesgo y, finalmente, el escritorio de deuda distressed en Jefferies. Rechaza la narrativa convencional del mentor: su aprendizaje más profundo provino de su propia generación y de observar a los clientes que cubría, en especial a David Tepper, descomponiendo sus procesos de pensamiento. El primer Third Point se construyó sobre inversión event-driven —adquisiciones, escisiones, quiebras, demutualizaciones— donde los directivos que subestimaban el desempeño durante los períodos de fijación de opciones creaban alfa sistemático para los co-inversores que entendían la opacidad y los catalizadores. Cita a Jesse Livermore: "No hay nada nuevo bajo el sol." > *"Pude observar su proceso de pensamiento y era como una corporación china que copiaba, aplicaba ingeniería inversa, lo absorbía todo y creaba mi base de conocimiento y mi propio sistema operativo."* ## [08:47] Cambio de estrategia: de event-driven a calidad e IA Third Point es hoy una plataforma multi-estrategia: el fondo long/short insignia, un negocio de CLO, crédito privado, préstamo directo y una compañía de seguros que despliega el tramo investment-grade del libro. Chamath pregunta cómo será el rol de Dan Loeb dentro de diez años a medida que proliferen los agentes; la respuesta de Loeb es que la red humana, la capacidad de mirar a alguien a los ojos, nunca podrá ser replicada por la IA. En cuanto a la inversión, ha pasado de los valores baratos con catalizador hacia negocios de calidad duradera con ventajas competitivas genuinas, admitiendo que los inversores anteriormente se engañaban sobre las ventajas competitivas de IBM, AOL y Yahoo. El filtro clave ahora es la adaptabilidad directiva: un equipo que ha demostrado mantenerse por delante de la disrupción importa más que cualquier ventaja de producto actual, y Loeb reconoce que tras treinta años la evaluación sigue siendo reconocimiento de patrones, no una rúbrica cuantificable. > *"Podías ser tecnológicamente analfabeto o simplemente decir que no lo haces —y hasta la CGF creo que podías ser más o menos económicamente analfabeto y ganar mucho dinero. Ahora no querría ser ninguna de las dos cosas."* ## [16:01] El arte de vender en corto y una operación con constructoras Loeb rechaza las ventas en corto basadas puramente en valoración —demasiados cortos "de valoración torpe" son exprimidos por las hordas de Reddit o el momentum meme. Su enfoque preferido es estructural: buscar sectores con exceso de inventario post-COVID, inflación de costes que los márgenes no pueden absorber y pasivos ocultos en el balance. Las constructoras encajaban en esa tesis: se presentaban como asset-light al estilo de NVR mientras mantenían opciones de terreno masivas y efectivamente comprometidas, y los compradores ya no podían afrontar los precios de la era pandémica en el entorno de financiación actual. El grupo pasa luego a la pregunta perenne de cuándo distribuir posiciones privadas: Loeb vendió Palantir en los 20 ("un error enorme"), se perdió la mayor parte de la subida de Enphase tras liderar la ronda B de Upstart y vendió Enphase por debajo de un dólar cuando al final habría generado 4.000 millones. Sobre Nvidia es inequívoco: los pods long/short lo usan como corto estructuralmente "seguro" igual que antes hacían con Google y Amazon, y espera que rompa al alza. > *"Nvidia parece un corto seguro. Por cierto, Google era un corto seguro. Amazon era un corto seguro. Esto simplemente ocurre y a veces languideceran en una valoración para luego romper."* ## [22:15] Reforma de la justicia penal y el indulto de Ross Ulbricht El marco filantrópico de Loeb parte de la desigualdad de ingresos —concretamente, del fracaso a la hora de dotar a los niños vulnerables de herramientas intelectuales— lo que le llevó del trabajo en el consejo de escuelas concertadas en Success Academy a la reforma de la justicia penal. Identifica tres categorías que merecen la lucha: los condenados injustamente, los auténticamente rehabilitados y quienes cumplen condenas desproporcionadas. Ulbricht encajaba en la tercera: condenado a doble cadena perpetua más 40 años por gestionar Silk Road, el mercado temprano de criptomonedas donde se vendían drogas, pero nunca procesado por los cargos de asesinato por encargo que el gobierno planteó posteriormente. Loeb contactó con Charlie Kirk, quien llevó el caso al presidente Trump; el último día del primer mandato de Trump, el Departamento de Justicia amenazó con represalias si Trump conmutaba la condena, por lo que se retiró. Cuatro años después, con la continua defensa de Kirk y del Consejero de la Casa Blanca David Warrington —abogado de Ulbricht durante una década— llegó el indulto completo. Loeb sigue trabajando en casos individuales a través de una organización llamada Olive. > *"No hay recurso a través del sistema para sacar de la cárcel a alguien con cadena perpetua. Esto solo funcionará con un indulto presidencial."* ## Entidades - **Dan Loeb** (Persona): CEO y CIO de Third Point; inversor activista; fundó Third Point a mediados de los años noventa; troll original en Yahoo Finance y Silicon Investor. - **Third Point** (Organización): Fondo de cobertura multi-estrategia; ~30.000 M$ AUM; gestiona long/short equity, CLO, crédito privado, préstamo directo y una compañía de seguros. - **Chamath Palihapitiya** (Persona): Presentador; CEO de Social Capital; plantea preguntas sobre la disrupción de la IA, la durabilidad de las ventajas competitivas y el papel de los humanos frente a los agentes. - **Jason Calacanis** (Persona): Presentador; fundador de LAUNCH; ancla el debate sobre las decisiones de distribución. - **David Sacks** (Persona): Presentador; fundador de Craft Ventures; responsable de IA y Criptomonedas de la Casa Blanca; debate sobre mantener o distribuir posiciones de capital riesgo. - **David Friedberg** (Persona): Presentador; CEO de The Production Board; indaga si la evaluación de la calidad directiva puede cuantificarse. - **Ross Ulbricht** (Persona): Fundador de Silk Road; condenado a doble cadena perpetua más 40 años; indultado por el presidente Trump en 2025 tras un esfuerzo colectivo en el que Loeb participó. - **Silk Road** (Organización): Primer mercado darknet basado en criptomonedas; central en el proceso contra Ulbricht. - **Nvidia** (Organización): Empresa de chips que Loeb considera infravalorada en un horizonte de 2 a 3 años de beneficios; citada como el nuevo corto estructuralmente "seguro", igual que lo fueron Google y Amazon. - **Event-Driven Investing** (Concepto): Estrategia temprana de Loeb —adquisiciones, escisiones, quiebras, demutualizaciones— que aprovechaba los desalineamientos de incentivos directivos y las dislocaciones estructurales. - **Activist Investing** (Concepto): Adquisición de participaciones accionariales para presionar cambios en el gobierno corporativo; enfoque distintivo de Third Point, ahora combinado con long/short centrado en la calidad.

Thomas Laffont, de Coatue Management, hizo su debut en podcasts en All-In para presentar un diagnóstico basado en datos de la economía unicornio de IA: por qué la cosecha de IA de 2024 podría eclipsar todas las anteriores, cómo el valor de SpaceX se multiplica con cada lanzamiento, y por qué $4 billones en OPV de IA están a punto de llegar a los mercados públicos en una ventana que los inversores no han visto antes. Los Besties sondaron el problema de concentración por ley de potencia, el futuro del capital riesgo en un mundo donde el capital corre hacia tres nombres, y qué hace una avalancha de liquidez de esa magnitud al ecosistema del Valle del Silicio. ## [00:00] ¡Thomas Laffont de Coatue se une a los Besties! Laffont explica por qué eligió All-In para su debut en podcasts: rechazó todas las demás plataformas hasta encontrar esta. Sacks presenta Coatue como uno de los hedge funds más exitosos de las últimas dos décadas, con $55 mil millones bajo gestión. Laffont resume en una sola línea la ventaja diferencial de Coatue antes de adentrarse en su presentación. > *"Estamos en el negocio de las ideas. Y cuando tienes una idea verdaderamente revolucionaria, puede llegar a ser muy grande."* ## [00:30] Los mercados públicos vuelven mientras la IA domina la «Economía Unicornio» Laffont repasa los datos propietarios de Coatue sobre la economía unicornio. El índice ha subido un 70% en promedio desde septiembre de 2024, siguiendo de cerca al NASDAQ; la cuota de IA en la captación de fondos sigue creciendo año a año, pero la composición ha dado un giro: se crean muchos menos unicornios nuevos, y cada uno capta 5 veces más capital que en 2021. La cosecha de 2021 es el caso de advertencia: 479 empresas creadas, y solo el 20% había salido o levantado una nueva ronda 20 trimestres después, frente al 80% de salud de la era pre-ZIRP con solo 73 empresas. La pregunta abierta es a cuál de las dos cosechas se parecerá la nueva generación de IA de 2024. En cuanto a las salidas, 2026 muestra buena tendencia, aunque aún no recupera los máximos de 2021. Introduce la idea de un índice privado de las «magníficas 8» — SpaceX, Stripe, Anthropic, Databricks, Revolut, ByteDance, Anduril — que representa casi $4 billones en valor y ha superado con creces al Mag 7 tradicional en rentabilidad. > *"Me sentiría bastante cómodo con este índice durante la próxima década o más, si pudiera tenerlo."* ## [05:15] La explosión de OPV de IA de $4T SpaceX está a semanas de salir a bolsa; Anthropic presentó su S1 de forma confidencial el día de la grabación. Sumar solo SpaceX, OpenAI y Anthropic al registro de salidas generaría más liquidez que las OPV de los diez años anteriores juntos, convirtiendo el ecosistema de consumidor de caja en generador de caja casi de la noche a la mañana. Laffont traza la trayectoria de ingresos de OpenAI y Anthropic desde enero de 2025: en pocos meses superaron a Workday, luego a ServiceNow, Adobe, Salesforce, y ahora son mayores que Google Cloud y Azure, con proyecciones que sugieren que Anthropic sola podría superar a AWS a finales de año y a todo Microsoft en 2028. Señala que los grandes proveedores de nube no solo observan la disrupción: la están financiando, con compromisos de capital de las mayores empresas del mundo que son «verdaderamente sin precedentes». > *"En parte se debe a que las tasas de crecimiento de OpenAI y Anthropic son como nada que hayamos visto antes."* ## [07:48] El argumento por SpaceX: monopolio de lanzamientos y Starlink Laffont presenta el marco CODE interno de Coatue para explicar por qué la valoración por lanzamiento de SpaceX ha aumentado a medida que crecía la cadencia de lanzamientos, algo contraintuitivo para un negocio de volumen. La respuesta: la calidad del modelo de negocio de SpaceX se multiplica con la escala. La fase uno es puramente un negocio de lanzamientos — ingresos irregulares por contratos gubernamentales. La fase dos añade una constelación (Starlink), convirtiendo los lanzamientos en ingresos recurrentes por suscriptores. La fase tres introduce múltiples constelaciones y una plataforma, donde corporaciones y ejércitos buscan su propia capacidad orbital. Más allá está la opcionalidad en centros de datos espaciales, la Luna y Marte. > *"La calidad del modelo de negocio de SpaceX aumenta cuantos más lanzamientos realizas."* ## [10:38] La paradoja del 10x: por qué estamos viendo un escalado sin precedentes Los datos sobre retornos de 10× en distintas etapas empresariales son llamativos: los unicornios tienen un 8% de probabilidad de convertirse en decacornios; los decacornios tienen un 13% de llegar a $100 mil millones; pero los centacornios ($100 mil millones+) tienen un 31% de probabilidad de un 10×. La escala multiplica los retornos, no los diluye. Tres empresas públicas cruzaron de $500 mil millones a $1 billón en un solo año; dos lo hicieron en semanas. Laffont usa Cerebras — una empresa de la cartera de Coatue en cuyo consejo participó — como contraejemplo: años de oscuridad sin nuevo capital, trabajando en arquitectura de chips, hasta que un gran contrato con OpenAI quintuplicó el valor de la empresa casi de la noche a la mañana. Los semiconductores como sector han superado a todos los índices desde el All-In Summit 2024. Sobre el debate de los escépticos ante el revenue: Coatue estima el ecosistema total de IA en $140 mil millones hoy, $300 mil millones este año, con otra duplicación en 2027, impulsado por tres pilares: suscripciones de consumidor, herramientas de productividad de código para empresas y la nube, y publicidad habilitada por IA (actualmente al 25% de penetración en Meta y Google, con previsión de alcanzar el 100%). > *"Anthropic en particular está escalando como ninguna otra empresa que hayamos visto."* ## [15:33] Segmentación de mercados de IA e impacto futuro El segmento publicitario es el que más analistas pasan por alto: si los anuncios servidos por IA pasan del 25% al 100% de penetración solo en Meta y Google, eso representa $150 mil millones en valor incremental. Las herramientas de código para empresas (Claude Code, Codex) añaden otro pilar. En toda la economía, la disrupción es simultánea: telecomunicaciones (Starlink haciendo obsoletas las llamadas perdidas), computación (los centros de datos remodelando la red eléctrica de Pensilvania), automoción (Ferrari luchando con el giro hacia el vehículo eléctrico autónomo) y consumo (los GLP-1 reestructurando el consumo de alimentos y alcohol). La tesis central de Laffont: la nueva economía unicornio es estructuralmente más sana, los ganadores se multiplican más rápido que nunca, y el coste de quedar fuera de un ganador es por tanto mayor que nunca, y eso sin superinteligencia todavía. > *"La disrupción está afectando a cada parte de la economía global. Y por cierto, ni siquiera tenemos superinteligencia aún."* ## [18:32] Preguntas de los Besties: Ley de potencia en IA, futuro del capital riesgo, de dónde viene el revenue, explosión de liquidez Jason plantea directamente la pregunta del asignador de capital: si los datos del centacornio dicen que la concentración gana, ¿deberían los LP simplemente apostar por los tres mayores nombres privados? La respuesta de Laffont: las valoraciones parecen extremas, pero son negocios reales que generan ingresos reales con múltiplos de beneficios históricamente bajos: «el mercado público es el gran antiséptico». Chamath señala que el verdadero descubrimiento de precios puede tardar seis meses tras la OPV, no el primer día, dada la oleada de flujos de compra pasiva. Chamath pregunta si la aceleración del centacornio es ineficiencia estructural o sesgo de supervivencia. Laffont señala Claude Code como ejemplo A: «Anthropic antes de Claude Code era una empresa completamente distinta a la de después. Un solo evento torció por completo la trayectoria de casi toda esa industria.» La narrativa del modelo como commodity, dice, está «bastante ampliamente desmentida». Sacks extrapola el dato del 31% de centacornio a 10× hacia arriba: ¿cuáles son las probabilidades para una empresa de un billón de dólares? Su intuición: más del 30%, posiblemente mucho más. Friedberg añade el filtro de durabilidad de los beneficios: cada nivel de escala selecciona para la ventaja compuesta, de modo que el filtro se vuelve más fuerte, no más débil, en la cima. La conversación cierra con lo que hace al ecosistema reciclar $3-4 billones de liquidez a través de GPs y LPs. Laffont plantea el riesgo más contraintuitivo: una guerra de precios entre OpenAI y Anthropic, donde el capital abundante permitiría activar una palanca de precios al estilo del ride-sharing. Se compromete a volver a All-In en dos años para valorar qué salió bien y qué no. > *"¿Podríamos ver una guerra de precios entre OpenAI y Anthropic? Si estas empresas tienen tanto capital, ¿alguna va a tirar de la palanca de precios para intentar competir con la otra?"* ## Entidades - **Thomas Laffont** (Persona): Cofundador de Coatue Management ($55 mil millones de AUM); miembro del consejo de Cerebras; presentó la investigación propietaria sobre la economía unicornio en el All-In Summit 2026 - **Chamath Palihapitiya** (Persona): Presentador, CEO de Social Capital; cuestionó la explicación estructural frente al sesgo de supervivencia en la aceleración del centacornio - **Jason Calacanis** (Persona): Presentador, fundador de LAUNCH e inversor ángel; planteó preguntas sobre la asignación de capital y la concentración por ley de potencia - **David Sacks** (Persona): Presentador, fundador de Craft Ventures y Zar de IA y Criptomonedas de la Casa Blanca; extrapoló la probabilidad de centacornio a decacornio - **David Friedberg** (Persona): Presentador, CEO de The Production Board; aplicó el enfoque de durabilidad de beneficios al estilo de Ben Graham a los datos de ley de potencia - **Coatue Management** (Organización): Gestora de fondos de crecimiento y hedge fund; creadora del conjunto de datos de la economía unicornio y del marco CODE para la valoración de SpaceX - **Anthropic** (Organización): Laboratorio de IA; presentó su S1 de forma confidencial el día de la grabación; trayectoria de ingresos de mayor escalado en la historia registrada, con un mes reportado como rentable - **OpenAI** (Organización): Laboratorio de IA; proyectado para superar a AWS a finales de año y a todo Microsoft en 2028; nombrado junto a Anthropic como detonante de la ola de OPV de $4T - **SpaceX** (Organización): Empresa de cohetes y satélites; OPV inminente en el momento de la grabación; analizada mediante el marco CODE de Coatue para el valor compuesto de lanzamientos y la captura del mercado de telecomunicaciones por Starlink - **Cerebras** (Organización): Empresa de chips de IA (cotiza en bolsa); Coatue lideró la Serie B; caso de estudio de capital paciente que sobrevivió períodos de oscuridad antes de que un contrato con OpenAI quintuplicara su valor - **Claude Code** (Software): Asistente de programación de Anthropic citado como el único evento de producto que «torció por completo la trayectoria de casi toda esa industria» - **Starlink** (Organización): Constelación de internet por satélite de SpaceX; proyectada para abordar un mercado de beneficios de telecomunicaciones global de $200-400 mil millones - **Ley de potencia** (Concepto): La concentración creciente de retornos en un número reducido de empresas; los datos de Coatue muestran que las probabilidades de un 10× aumentan con cada nivel de escala: 8% (unicornio), 13% (decacornio), 31% (centacornio) - **Economía Unicornio** (Concepto): El marco de Coatue para seguir el ecosistema de mercado privado de empresas valoradas en más de $1 mil millones, incluyendo la salud de la financiación, la velocidad de salida y el comportamiento de las cosechas a lo largo del tiempo

Bill Ackman 与 All-In Podcast 四位主持人深入对谈，从 20 年投资哲学演变讲到 AI 对现有投资组合的双重冲击，再到"橡皮筋效应"如何指导他在 COVID 崩盘与近期市场低点的公开押注。Ackman 力主持有创始人主导的公司，并详解他正在以 Howard Hughes Corporation 为载体、参照伯克希尔·哈撒韦模式打造下一个复利飞轮。 ## [00:00] Bill Ackman joins the show! 开场由节目音频剪辑拼出 Ackman 的几句核心论断——做空公开表态是"相当严肃的事"，全球最优质企业正以历史最低倍数交易，封闭式基金正在经历"重生"。随后 Jason Calacanis 顺势抛出对 OpenAI CFO Sarah Friar 的问题，将话题过渡到 Ackman 对 OpenAI 领导层的看法，为下一章铺垫。 > *"Interestingly, some of the best businesses in the world are trading at the lowest multiples."* ## [00:30] Evolving investment philosophy: What's changed over 20 years? David Friedberg 请 Ackman 回顾他从激进维权到长期持有的转变轨迹。Ackman 说，变化的核心是对"持久、受保护、不可颠覆的增长"的认识越来越深——规模小时可以靠公开施压敲门；今天他只需要买入 5% 的股份，CEO 就主动致电。他以早期投资 Wendy's International 为例：买入 10% 后 CEO 根本不回电，于是联合 Blackstone 的 Steve Schwarzman 写了一封公开信，6 周后 Tim Hortons 完成拆分，CEO 打来电话道谢时已被解雇。 随着声誉建立，Pershing Square 的介入方式也从"砸门"转向"被邀请入局"。Ackman 强调，好的投资不需要插手——有时候最好的持仓就是"站在边上鼓掌"。但对于需要长期决策的大型上市公司，拥有一个持有大比例股份的股东坐在董事会里，是帮助管理层抵抗季度短视主义的有效机制。 > *"The best investments are ones where you don't need to join the board and do anything."* ## [04:40] AI: Greatest time to build a business, and a major threat to portfolios Chamath 追问 Ackman 如何从外部评估 AI 企业的商业模式质量。Ackman 的立场很直接：Pershing Square 持有微软、Meta、亚马逊——不直接持有 AI 标的，但也已经身处 AI 之中；所有公司不是 AI 投资机会，就是 AI 威胁。 他用 2000 年互联网泡沫做类比：当年人人追芯片、带宽、能源，导致 Procter & Gamble 跌到历史最低估值，因为"那是旧东西"。他认为今天 Amazon、Meta、Microsoft 正在经历类似的被遗忘，这恰是买入机会。与此同时，他对 Salesforce 这类 SaaS 公司明确表示担忧——多年来在订阅模式下对客户收取垄断性溢价，一旦 AI 提供替代品，这类公司首当其冲。 > *"This is the greatest era in history to build a business. There's unlimited access to compute, unlimited access to capital."* ## [07:50] Predicting market moves, the "rubber band effect" Chamath 追溯 Ackman 在 COVID 熔断时段上 CNBC 喊话、随后宣布抄底、再到近期公开看涨的一系列高调押注，追问他是什么驱动他在这些时刻如此笃定。 Ackman 解释"橡皮筋效应"：估值就是绑在市场价格上的橡皮筋，拉太高必然回弹，拉太低同样有弹力拉着往上。他 2020 年 3 月去上电视，是为了通过媒体向特朗普总统传递信息——关闭经济 30 天，果断行动，病毒就会过去，之后股票会非常便宜，"我们在买入"。近期他再次看涨，理由相同：高质量公司的估值跌到了极端便宜的位置。 话题延伸到 SpaceX、Anthropic、OpenAI、Palantir 的定价逻辑。Ackman 主张用风险投资框架来看这些后期成长型公司——关键变量是"人、机会、情境、条款"（People, Opportunity, Context, Deal）。SpaceX 前三项都是"one of one"，唯一待解的问题是估值是否合理。他也坦言对 OpenAI 烧钱速度远超收入有顾虑，认为其应尽早向公众清楚说明盈利路径。 > *"Valuation is like a tether on the market. When it gets too high, it's like this rubber band that's stretching. And inevitably, it bounces back."* ## [16:00] Owning founder-led companies David Friedberg 提出一个反常识的观察：在科技领域，创始人主导的公司在规模化阶段表现远优于职业经理人主导的公司——而这和传统 Ben Graham 价值投资框架几乎是矛盾的。 Ackman 全盘认同。标普 500 的 CEO 平均任期大约 4 年，薪酬结构天然偏向短期，没有足够的经济利益捆绑。创始人则不同：这家公司是他的全部，声誉、资产、时间全押在这里，不存在"换个地方重来"的退路。他举 Zuckerberg 收购 Instagram 为例——当时几乎所有人都骂他，但这个决策证明了创始人的长周期视野。 他与 Ben Graham 的分歧也很清晰：Graham 时代没有 EDGAR 系统，大量股票以低于账面净现金的价格交易，清算套利是现实。今天那种机会几乎不存在了，而能够识别"优秀创始人 + 长期复利机器"的投资者会收到完全不同的回报。 > *"You're a founder, this is your entire life. It's your entire reputation. It's not like you're going to go get another job. You've got to make it work."* ## [19:30] Building the next Berkshire Hathaway Ackman 详细拆解了他以 Howard Hughes Corporation 为平台复刻伯克希尔·哈撒韦模式的逻辑。伯克希尔的本质是：用保险浮存金作为低成本甚至零成本的杠杆，把负债端（承保纪律）和资产端（股票复利）同时做好——这件事 Buffett 之后几乎没人复制成功，因为真正擅长投资的人都去了对冲基金，而不是去经营保险公司。 Howard Hughes 是 Pershing Square 当年从 General Growth Properties 破产重组中拆分出来的资产包，持有 Summerlin（拉斯维加斯）、The Woodlands（休斯顿）等多个"袖珍城市"的全部商业和住宅用地。这家公司对华尔街来说一直太长期、太复杂，长期以大折价交易。Ackman 的计划是：不再把所有现金流再投入房地产，而是附加一个保险业务，把保险浮存金交由 Pershing Square 按一贯策略投资——"在 60 美分的价格买 1 美元资产，然后用 50 年复利"，目标是从 40 亿美元市值最终建成万亿级企业。 他也谈到 Twitter 影响力对当代投资者的意义：高股价会自我强化（降低资本成本、提升融资灵活性），Elon Musk 把信徒圈经营成了竞争护城河之一。Pershing Square 则给出三种共同投资路径：Pershing Square 管理公司本身（royalty on compounding）、PSUS（封闭式基金，目前以 18% 折价交易）、Howard Hughes（"如果你相信我们能建成下一个伯克希尔"）。 > *"You want to believe that we can build the next Berkshire Hathaway, you own Howard Hughes."* ## Entities - **Bill Ackman** (Person): Pershing Square Capital Management 创始人兼 CEO，知名维权投资者；本集嘉宾 - **Chamath Palihapitiya** (Person): Social Capital CEO，All-In Podcast 联合主持人 - **Jason Calacanis** (Person): LAUNCH 创始人，天使投资人，All-In Podcast 联合主持人 - **David Sacks** (Person): Craft Ventures 创始人；美国白宫 AI 与加密货币事务主管，All-In Podcast 联合主持人 - **David Friedberg** (Person): The Production Board CEO，All-In Podcast 联合主持人 - **Pershing Square Capital Management** (Organization): Ackman 创立的专注高集中度长期持股的对冲基金，管理规模约 250 亿美元 - **Howard Hughes Corporation** (Organization): 持有多个美国"袖珍城市"地产的上市公司；Ackman 正将其改造为伯克希尔·哈撒韦式复利平台 - **伯克希尔·哈撒韦** (Organization): Warren Buffett 创建的多元化控股公司，以保险浮存金驱动长期股票投资著称；Ackman 明确将其作为 Howard Hughes 的对标模型 - **PSUS** (Organization): Pershing Square USA，封闭式基金，目前以净资产值 18% 折价交易 - **封闭式基金** (Concept): closed-end fund，基金份额固定在交易所上市流通，可能长期以折价或溢价相对净资产值交易 - **橡皮筋效应** (Concept): Ackman 的估值框架——市场价格偏离内在价值越远，回归均值的弹力越大，当估值极端便宜时是最可信的顺势买入信号 - **维权投资者** (Concept): activist investor，通过持有大比例股份、公开施压或进入董事会推动被投公司战略变革 - **OpenAI** (Organization): 大型语言模型领军企业；Ackman 对其烧钱速度远超收入有顾虑 - **SpaceX** (Organization): Elon Musk 的商业航天公司；Ackman 以"人、机会、情境各项均为 one of one"描述其投资逻辑

OpenAI CFO Sarah Friar makes her All-In debut days after the company's $122B fundraise, walking the four hosts through IPO logic, the Anthropic rivalry, a teased Jony Ive device, and how OpenAI is buying compute through the early 2030s. Her thesis: an IPO is a milestone, not a destination; compute is the binding constraint; and OpenAI is buying capacity ahead of revenue on the bet that cost curves keep falling. ## [00:00] OpenAI CFO Sarah Friar joins the show! Jason Calacanis opens by calling OpenAI's March raise the most successful fundraising round in history. Friar sets her frame right away — AI is the biggest productivity era we've seen, and luck is preparation meeting opportunity that you then have to grab. > *You have just completed what I regard as the most successful fundraising round in history.* ## [00:31] How OpenAI thinks about its IPO timeline David Sacks presses on whether there's a first-mover advantage to IPOing early now that SpaceX is public, and asks when OpenAI and Anthropic will actually go. Friar deflects: an IPO is a milestone, not a destination, and the $122B March raise — the largest private round in history, an order of magnitude past Saudi Aramco's ~$30B — exists to buy maximum optionality, not to race anyone to the SEC. Chamath checks whether it's the biggest private raise to date; Jason needles her on whether a later filing means "third place." > *No one remembers who went first, Google or Yahoo, Lyft or Uber.* ## [03:31] OpenAI, Anthropic, Google: The AI arms race Jason Calacanis challenges Friar directly: has Anthropic blown past OpenAI on developers and revenue, and were Sora and too many scattered bets a mistake? Friar rejects the consumer-vs-enterprise binary — revenue is now roughly 50/50 — and leans on scale: 900M weekly ChatGPT users, a single-model compounding advantage, and fastest growth now in Africa, with Azerbaijani and Kazakh among the fastest-growing languages. > *Over 900 million people use Chat GPT weekly and it's become the noun and the verb.* ## [07:43] Navigating the compute crunch and AI bottlenecks, device preview! Chamath Palihapitiya revives a framing Friar coined ~18 months earlier — one gigawatt ≈ $10B/year of revenue — and asks where supply stands now. Friar's answer: compute is scarce, 2026–2027 is effectively locked, and she's already focused on 2030–2032. She details the Michigan (Seline) 1GW build's community deal: paying for its own power, 2,500 union jobs, $1B in taxes, and $45M in Codex education credits. Pushed on the rumored device, she confirms a Jony Ive-designed consumer "substrate" — reveal by year-end, launch early next year — while refusing to say what it is. Friedberg asks if using it felt like holding the first iPhone. > *So first of all, yes, compute is a very scarce resource at the moment.* ## [15:53] OpenAI's economics David Friedberg asks for OpenAI's high-ROC capital-allocation engine — its version of Amazon's warehouse flywheel or Google's search-ads loop. Friar gives a three-layer model: create customer value first, expand gross margin on a steep compute-deflation curve (token cost down ~97% across GPT generations), then deploy capital timed against that cost curve. She makes the counterintuitive case for buying compute ahead of demand, citing $2,000/month agentic seats that once sounded as absurd as $200/month ChatGPT Pro. Friedberg presses on multi-year forecasting; David Sacks asks whether a $100B raise buys two gigawatts or five. Friar walks through OpenAI's shift from a single Azure deal to a multi-cloud, multi-chip stack — Oracle, CoreWeave, AWS, GCP, plus Vera Rubin and a Broadcom chip. > *They're going to look like the great companies of prior eras.* ## [26:08] Push into chips, the cloud Chamath Palihapitiya asks whether, as Nvidia, Google, Microsoft and OpenAI each push into one another's layers — silicon, models, cloud, consumer — the stack eventually merges, and whether convergence makes competition simpler or harder. Friar's answer: everyone is fighting to own the layer closest to the user, and OpenAI's edge is the agentic memory-and-context layer — a model that knows who you are and carries your context — which makes it both more powerful and far stickier for individuals and enterprises. > *So do you think that in 5 years from now the stack is just merged together?* ## [29:32] OpenAI's ad business and strategy Jason Calacanis closes on advertising — two of the three greatest consumer businesses ever built are ad-funded — and asks whether ads are what make AI free for the world. Friar: ads must never bias the model's results, and there will always be an ad-free tier, but ChatGPT's high-intent signal could power a potent ad platform that subsidizes access for those who can't pay. For now, she notes, every token is worth far more on the API than on the consumer side. > *But is ads the solution to making this free for the world?* ## Entities - **Sarah Friar** (Person): OpenAI CFO; former seven-year Nextdoor CEO; the episode's guest - **Jason Calacanis** (Person): All-In host and moderator; LAUNCH founder, angel investor - **Chamath Palihapitiya** (Person): All-In host; Social Capital CEO - **David Sacks** (Person): All-In host; Craft Ventures founder; White House AI & Crypto Czar - **David Friedberg** (Person): All-In host; CEO of The Production Board - **OpenAI** (Organization): AI lab behind ChatGPT; closed a record $122B private raise - **Anthropic** (Organization): rival AI lab; filed a confidential S-1 during the taping - **Compute scarcity** (Concept): OpenAI's binding constraint, framed as a gigawatt-to-revenue ratio and a multi-year buy-ahead bet

Benchmark GP Bill Gurley joins Jason Calacanis, David Sacks, and Chamath Palihapitiya (David Friedberg out this week) for a 95-minute session covering six fronts of the AI debate: Gurley's new theory that Anthropic is not just pursuing regulatory capture but actively "midwifing a deity"; Pope Leo XIV's 235-page AI encyclical and its uncomfortable historical parallel to Leo XIII's 1891 warnings about the industrial revolution; the growing consensus that open-source AI faces a coordinated regulatory crackdown; and the week's sharpest narrative flip — Dario Amodei and Sam Altman both quietly walking back their AI jobs-apocalypse rhetoric while Goldman Sachs CEO David Solomon published a New York Times op-ed declaring the apocalypse overblown. ## [00:00] Bill Gurley joins the show! Bill Gurley, Benchmark general partner and author of *Running Down a Dream*, fills in for David Friedberg and joins live from Chamath's pool house where Jason has been staying. After banter about unauthorized Uber Eats orders on Chamath's house iPad, Jason introduces Gurley as a first-time guest who specifically requested to appear the moment the pod covered the Pope. Gurley plugs his new P3 Institute and a grant program he launched to fund people pivoting toward work they love. He teases a TED talk — rooted in the book's argument that high agency and lifetime learning are the only durable defenses against disruption — which sets the frame for everything that follows. > *"And I told the house manager like, listen, any packages that come in the next 72 hours, right to the pool house, if it says JCAL, right to the pool house."* ## [06:00] Making yourself valuable in the age of AI, first class of "AI Natives" Chamath opens with the question that has been driving the show for 18 months: if you're a young person right now, is AI doom much ado about nothing, or a real career threat? Gurley cites a Gallup poll showing 59% of workers are "quiet quitters" — ambivalent about their jobs and therefore low-agency. His core thesis: the best protection against AI displacement is becoming the most AI-enabled version of yourself in your field. He invokes Mark Cuban's framing — "there are two types of people: those who use AI to learn faster than ever before, and those who use AI to avoid learning altogether." Sacks walks through how the pod's producer Nick built a daily Claude briefing document that not only summarized news but predicted specific topics Sacks would care about based on his prior comments on the show. Sacks had dismissed it as likely AI slop; it was not. Gurley extends the point across every job category: in marketing, legal, accounting, and sales, being the most AI-capable person among your peers makes you "golden," and the early lead compounds. Jason adds that in his own team experiments, the skill separating strong performers from weak ones was systems thinking — could they break a complex problem into context the AI could execute, or did they hand it a task and wait? > *"I think the best way to protect yourself from AI is to be the most AI enabled version of yourself you can be."* ## [17:37] Reacting to Pope Leo's AI encyclical: Who guards the guardians? Pope Leo XIV released *Magnifica Humanitas*, a 235-page, 42,000-word encyclical warning business leaders to safeguard humanity from AI. His central argument: technology is never neutral — it takes on the characteristics of those who build, finance, and control it. Jason reads the core line and notes the Pope presumably does not think highly of Silicon Valley's current roster of builders. Sacks finds himself largely agreeing with the Pope's diagnosis: the biggest risk of AI is centralization of power and its Orwellian misuse by governments. Where he parts ways is on the remedy. Giving government the power to regulate AI development creates its own guardian problem — the American founders' answer to *Quis custodiet ipsos custodes?* was separation of powers, forcing guardians to check each other. Sacks's AI equivalent: a competitive market with five frontier labs is the best natural check; monopolization is the scenario to prevent. Gurley lands the sharpest historical counterpunch. Pope Leo XIII's 1891 encyclical *Rerum Novarum* warned that the industrial revolution would harm workers — and was wrong on every metric. From 1891 to today: the work week fell from 60+ hours to 34, real wages rose 8–10x, the median worker now earns more than a doctor did in 1891, global GDP per capita went from $1,500 to $20,000, child labor in the US dropped from 18% to zero, workplace deaths fell 40x, life expectancy rose 60%, and global poverty dropped from 75% to under 10%. > *"All those things happened because of technology, innovation, and capitalism, which is exactly what Leo the 13th was warning against. So he got it dead wrong. He got the whole thing precisely wrong."* ## [26:54] Anthropic's Digital God: Do they believe they are creating a superior species? Gurley delivers what becomes the most-quoted segment of the episode: his "Dr. Frankenstein theory" of Anthropic. He had previously held a simpler regulatory-capture theory — Anthropic stirs up AI fear to lock in regulation that entrenches incumbents. But after spending 30 days reading everything he could find about the company, he has a darker read. He describes meeting people inside Anthropic who he believes genuinely think they are not writing software but "midwifing a deity." The evidence trail: Anthropic chief philosopher Amanda Askell's podcasts, Chris Olah's 80-page Constitutional AI document, and Dario Amodei's own essay "Machines of Loving Grace," which envisions a post-AGI economy where AI systems allocate resources to humans based on an AI-determined reward function. Chamath calls it "a computational reward function for humans — it decides how much you're worth." Jason calls it "the ultimate delusions of grandeur." Gurley corrects him: he didn't say it, Dario did. Sacks steelmans Anthropic briefly — they probably see themselves as responsible builders who take the power of this technology seriously enough to guard it — then immediately notes this framing is textbook regulatory capture: brand yourself the safe player, characterize competitors as reckless, let regulation shut down the recklessness. Both Sacks and Chamath converge on the structural danger: a singular AI value system that decides how humans live is catastrophically fragile. The answer is decentralization and competing systems, not one algorithmic authority. > *"I don't think they think they're writing software. I think they're midwifing a deity here. And I don't know which one I'm more afraid of — the regulatory capture or this second theory I call the Dr. Frankenstein theory."* ## [38:32] AI sovereignty, the next era of privacy, open-source crackdown coming? Jason introduces "intelligence sovereignty" as the successor to data privacy. Data privacy was about who can see your photos and messages. Intelligence sovereignty is about who gets to interpret your world — whether the AI shaping your information feed is a centralized system with a particular political philosophy, or something you control. He flags the paradox: China's Communist Party is leading the open-weight model movement while the United States is centralizing. Chamath presents his portfolio company Abacus as evidence that Fortune 1000 buyers are responding to this anxiety: they want a control plane that can hot-swap between frontier models, plus on-prem options that remove dependence on any one provider's terms of service. He gives a concrete example — a Canadian hospital that supports its country's euthanasia laws could be shut off by an American frontier model whose constitution prohibits that content. Sacks connects the dots to a regulatory threat he has been watching build: the regulatory-capture playbook leads, in his read, to a ban on open-source or open-weight models. The justification will be safety — open models let users strip guardrails. Gurley reaches the same conclusion in his P3 Institute post. If a ban succeeds, the United States effectively exiles itself from the open ecosystem while the rest of the world — including China — runs on open models. > *"I think where it's all leading to is an effort to ban open source models or open weight models. There's a lot of breadcrumbs leading here."* ## [59:56] The Great AI Jobs Debate: Dario and Sam Altman flip their rhetoric, Goldman CEO says no AI job apocalypse The chapter opens with a news roundup of the week's narrative shift. Cloudflare's Matthew Prince, Zuckerberg at Meta, Jack Dorsey at Block, and Andy Jassy at Amazon all cited AI when announcing major layoffs. But Goldman Sachs CEO David Solomon published a New York Times op-ed with three counterpoints: AI will automate 25% of work hours, not 25% of jobs; bank tellers increased after ATMs; the US labor market creates and destroys 25–35 million jobs annually so gross churn dwarfs net losses. Simultaneously, Fortune reported that Dario Amodei and Sam Altman are both walking back prior doom-and-gloom rhetoric — with Chamath noting the timing cannot be separated from upcoming frontier-lab IPOs that need a jobs-creation narrative. Sacks is unambiguous: he has been making the non-consensus case against the jobs apocalypse for over a year and considers himself vindicated. Yale Budget Lab found no discernible labor-market disruption over three years of the AI wave. Software engineering — the single breakout AI use case — saw job postings rise 15% year-over-year and hit a three-year high. The 4.3% unemployment rate is near record lows. Most of the high-profile layoffs, he argues, are AI washing: CEOs who over-hired during COVID found AI to be a convenient narrative for long-overdue downsizing. The Jack Dorsey / Block 50% cut was immediately flagged by financial analysts as a company that had been overstaffed relative to peers for years — pure AI washing. Jason pushes back. He insists cab drivers, truck drivers, and package-sorters — roughly 20 million American workers — face real structural displacement over the next decade regardless of current aggregate statistics, and accuses the panel of elitism: "We are elite performers. These people are going to lose their jobs and they may not get a job very quickly." He draws a distinction between the short-to-medium term, where he expects acceleration, and the long run, where a Cambrian explosion of startups built by AI-enabled founders creates new categories. By the end, he shifts toward Sacks's territory — acknowledging the aggregate data is less alarming than his anecdotes suggested. Gurley threads the needle with the same historical argument from the Leo XIII discussion: innovation has always, on net, created more prosperity than it destroyed. His practical advice to people at risk: get ahead of your peers on the tools now; if your job is going away, plan your pivot toward trades (he plugs MicroWorks, which provides free scholarships for plumbers, welders, and electricians) or toward something you find genuinely fascinating. > *"I think the best way to protect yourself from AI is to be the most AI enabled version of yourself you can be. Know what it's capable of in your field. Get out there."* ## Entities - **Bill Gurley** (Person): General partner at Benchmark; author of *Running Down a Dream*; founder of P3 Institute; guest filling in for David Friedberg - **Jason Calacanis** (Person): All-In host; angel investor; founder of LAUNCH; argues for worker empathy and short-term displacement risk - **David Sacks** (Person): All-In host; Craft Ventures founder; most vocal critic of AI jobs-apocalypse narrative this episode - **Chamath Palihapitiya** (Person): All-In host; Social Capital CEO; coined "intelligence sovereignty"; co-founder of Abacus - **Dario Amodei** (Person): Anthropic CEO; subject of Gurley's "Dr. Frankenstein theory"; walked back jobs-doom rhetoric this week alongside Sam Altman - **Pope Leo XIV** (Person): Catholic Pope; released *Magnifica Humanitas*, a 235-page AI encyclical warning against technology concentration - **David Solomon** (Person): Goldman Sachs CEO; published New York Times op-ed arguing AI job apocalypse is overblown - **Anthropic** (Organization): Frontier AI lab; subject of Gurley's regulatory-capture and "Dr. Frankenstein" theories; maker of Claude - **P3 Institute** (Organization): Bill Gurley's new policy and philanthropy institute; published post defending open-source AI - **Goldman Sachs** (Organization): Investment bank; CEO's NYT op-ed became the week's anchor data point against the jobs-apocalypse narrative - **Abacus** (Software): Chamath's Social Capital portfolio company; builds on-prem AI hardware stacks for Fortune 1000 enterprises seeking model independence - **Intelligence sovereignty** (Concept): Jason's term for the next frontier of privacy — not who sees your data, but which AI system is allowed to shape your interpretation of the world - **Dr. Frankenstein theory** (Concept): Gurley's characterization of Anthropic's worldview: senior staff believe they are midwifing a deity or superior species rather than writing software, as described in Dario Amodei's "Machines of Loving Grace" essay - **Regulatory capture** (Concept): The strategy of branding oneself the "safe" AI company, amplifying public fear, and lobbying for regulation that locks in incumbents and targets open-source competitors

Sacks is out, Gavin Baker (Atreides Management) sits in. The panel walks through Andrej Karpathy's surprise move to Anthropic, debates why the public mood on AI has flipped, tears apart SpaceX's $2T S-1, and asks why Nvidia's blowout earnings still saw the stock sold. Friedberg and Chamath also flag warning signals from inflation, oil, and bond yields, and close on what — if anything — came out of the US-China summit. ## [00:00] Gavin Baker joins the show! Jason opens episode 274 noting Sacks is out and welcomes Gavin Baker from Atreides Management for the week. They tee up the agenda: SpaceX and OpenAI IPOs, Karpathy to Anthropic, and Nvidia's earnings. > *"Sachs is out today, but we're very lucky to have Gavin Baker from Atreides Management joining us. The spicy takes must flow."* ## [00:30] Andrej Karpathy joins Anthropic; hypergrowth and profitability The Karpathy hire is read as a major strategic win for Anthropic — Chamath frames it as continuity of the Richard Sutton "bitter lesson" school of scaling that Karpathy executed at Tesla FSD and OpenAI. Gavin layers in financial context: Anthropic was EBIT-positive in the last quarter per the WSJ, which combined with hypergrowth makes the recent funding rounds look very different from a capital-burn narrative. Friedberg pushes back on the framing that models will soon "feed themselves" into context windows to self-improve, but flags that papers (one from MIT) suggest large efficiency gains are on the horizon. Chamath uses the moment to argue the podcast itself has to start telling the upside story of AI — the doctors, the scientists, the unlock — because the dominant public narrative has gone negative. > *"He was probably the first person that really commercialized the Richard Sutton bitter lesson essay when he was leading FSD at Tesla."* ## [12:42] Why Americans have turned on AI, anti-human perception Gavin shares a personal story: his daughter has a rare disease, and a Stanford scientist he funded is months away from what he believes is a complete cure, made tractable by AI-accelerated biology. He uses it to argue for an optimistic posture — a future where work is optional and disease is solvable — and warns that the people pushing for AI regulation are also shaping how the public feels about the technology. Friedberg goes deeper into the cultural mechanics: AI is being framed as anti-human in a way that mirrors anti-nuclear and anti-industrial backlashes of the 20th century. He argues the United States can't unilaterally slow down because China and others won't — and tries to separate genuine safety concerns from elite class anxiety. Chamath then makes a pointed observation that none of the survey data on AI job loss actually asks the truck drivers, package sorters, and ICU nurses themselves how they feel about the tools. > *"We're listening too much to the inventors of AI. They're geniuses. They're smart. We need to be listening to the frontline factory workers who are using AI saying, 'Wow, I was able to add a third shift.'"* ## [27:22] Trump pulls AI EO, US-China AI relationship, dystopian AI layoffs A Trump AI executive order was scrubbed at the last minute — the panel walks through what was reportedly in it (review of frontier-model training runs) and whether any pre-release regulatory framework is workable. Jason argues a state-by-state patchwork is the more likely outcome regardless of what Washington does. The conversation pivots to Meta's latest round of layoffs and the way they were communicated. Gavin and Jason agree the messaging — leaning on "AI productivity gains" as the public reason — landed badly even with people who accept the underlying logic, and Jason argues it became a case study in how *not* to message AI-driven workforce changes. > *"Because the reality is that if this is the way that you're going to message something as critical as this, I think you did a horrible job."* ## [45:19] SpaceX S-1 tear down! Breaking down the three major businesses and the case for a $2T valuation SpaceX filed its S-1 on Wednesday. Jason breaks the company into three businesses: launch (which could be hundreds of millions of paying subscribers via Starlink), Elon Web Services / xAI / Colossus compute, and rockets. The AI-cloud line item alone is around $15B and growing roughly 2x year over year, anchored by an Anthropic deal Gavin calls "extraordinary." Gavin then makes the case that Colossus matters because raw gigawatt-class data centers are now the binding constraint, and SpaceX-adjacent build velocity is the moat. He uses Cursor's Composer 2.5 release — Pareto-dominant on three or four weeks of RL training — as evidence that whoever owns the compute owns the next model generation, and walks through why rapid reusability on Starship compresses the unit economics of getting payload to orbit faster than any competitor can model. > *"If you look at who's actually capable of delivering a gigawatt data center, these guys are the closest, like an actual gigawatt."* ## [71:22] Nvidia smashes earnings but stock falls, why people are shorting chips Nvidia blew out earnings again — 20% sequential growth would be a high-growth print for any other company, the dividend was raised 25x, and the CFO committed to returning 50% of free cash flow. Yet the stock sold off, and Leopold Aschenbrenner's reported pivot away from chip exposure is being read as a smart-money signal. Gavin takes the bear case apart: at current PE Nvidia is cheap relative to growth, and the segment breakdown obscures how much the "AI clouds" line is dragging the multiple. He flags that the true useful life of a GPU is closer to two years than five, which means the reported profits of every hyperscaler running these chips are overstated — a real concern, not a stock-killer. He also notes Nvidia's CPU business is on track to do $20B this year, making it overnight one of the largest CPU manufacturers in the world. > *"The true lifespan of a GPU is more like two years and therefore the profits of all these businesses are overstated."* ## [82:25] Market update: Flashing red signals, oil, inflation, yields up The macro snapshot: May inflation expected at 4.2%+, Fed rate-hike odds back on the table, UK yields at the highest since the great financial crisis, oil and gold both moving. Chamath warns that when the currency-debasement mechanism finally breaks, the downside is non-linear. Gavin counters with relative optimism on the US: America is self-sufficient in energy, the AI build-out is structurally good for re-industrialization, and even in an ugly global scenario the US is the least-bad place to be invested. He flags AI fundamentals also have a seasonality that investors are starting to model — the same way e-commerce and subscription businesses do. > *"While it's terrible for everyone, it is relatively the best for America because we are self-sufficient in energy."* ## [92:45] China trip flops, or was progress made behind the scenes? A 48-hour US tech-CEO-plus-president trip to Beijing produced thin public deliverables: some soybeans, some H100/A200 sales to Chinese players. The panel asks whether that's the real story or just the visible surface, and whether the immediate China-Russia bonding moment afterward says more about the trajectory than any handshake photo. Gavin argues the more important read is structural: keeping America ahead in AI requires keeping the trans-Pacific relationship just stable enough to avoid a full decoupling shock, and that's a defensible strategic logic even if the optics are unsatisfying. He also paints a what-if scenario around the Strait of Hormuz to make the point that energy independence is what gives the US the option to act asymmetrically. Jason closes with thanks to Gavin and an invite back to the Summit. > *"There's sound arguments that this is stabilizing for the world and is the best highest probability path for keeping America ahead in AI."* ## Entities - **Jason Calacanis** (Person): Host, LAUNCH founder, MC of this episode. - **Chamath Palihapitiya** (Person): Host, Social Capital CEO; pushed the "listen to frontline AI users" framing. - **David Friedberg** (Person): Host, The Production Board CEO; led the cultural / historical analysis of the AI backlash. - **Gavin Baker** (Person): Guest host, Atreides Management founder/CIO; carried the investing thread across SpaceX, Nvidia, and macro. - **Andrej Karpathy** (Person): Joining Anthropic's new pre-training team; OpenAI co-founder, ex-Tesla FSD lead. - **Anthropic** (Organization): Hired Karpathy; EBIT-positive last quarter per WSJ; $15B AI-cloud deal with SpaceX-adjacent compute. - **SpaceX** (Organization): Filed S-1; three businesses (launch/Starlink, Elon Web Services compute, rockets); $2T valuation case. - **Nvidia** (Organization): Earnings blowout but stock sold off; $20B CPU run-rate; $5.3T market cap. - **Cursor** (Software): Composer 2.5 model release used as proof of fast RL-driven catch-up dynamics. - **Richard Sutton's bitter lesson** (Concept): Scaling beats clever architectures — framing for why Karpathy's move matters. - **GPU useful life** (Concept): Closer to ~2 years than ~5, so hyperscaler reported profits are overstated. - **Strait of Hormuz scenario** (Concept): Energy-independence-as-strategic-option argument for the US in the China game.

Marc Benioff, CEO de Salesforce, se une a Jason Calacanis, David Friedberg y Chamath Palihapitiya (con David Sacks ausente) en un episodio de amplio alcance anclado en dos historias de actualidad: la primera cumbre Trump-Xi desde 2017 y el acelerado embate de la IA sobre las valoraciones del software empresarial. Benioff, presente en la cena de estado saudí, el Castillo de Windsor y esta delegación de la cumbre, ofrece una visión de primera fila sobre la diplomacia comercial entre Estados Unidos y China, antes de girar hacia la revalorización existencial de su propia compañía: argumenta que la infraestructura de datos y la plataforma de agentes de Salesforce la sitúan en el lado correcto de la disrupción por IA. La segunda mitad cubre el choque entre OpenAI y Apple, el demo multimodal en tiempo real de Thinking Machines, los alarmantes datos de El Niño de Friedberg y la ofensiva de Anthropic contra los esquemas de SPV en capas. ## [00:00] ¡Marc Benioff, CEO de Salesforce, se une al show! Sacks no está esta semana y Benioff ocupa su lugar. Jason pregunta de inmediato sobre el posicionamiento político de Benioff: exdonante demócrata, ahora asistiendo a cenas de estado saudíes y aparentemente bienvenido en la administración actual. Benioff descarta por completo el encuadre partidista. > *"No soy demócrata ni republicano. Soy americano."* Chamath señala que Benioff acumuló invitaciones al Castillo de Windsor, la visita del Príncipe Carlos a Estados Unidos y la cena de estado saudí en rápida sucesión: el raro CEO tecnológico que navega entre administraciones sin fricciones. El contexto presenta a Benioff como una voz excepcionalmente creíble sobre la cumbre que se desarrolla en tiempo real. ## [01:14] Cumbre Trump-Xi, hacer negocios en China como empresa estadounidense, impacto en los americanos y las elecciones de mitad de período El séptimo encuentro cara a cara entre Trump y Xi, demorado dos meses por la guerra con Irán, se abrió en Pekín con Xi advirtiendo que una mala gestión de Taiwán podría poner toda la relación "en una situación extremadamente peligrosa." Polymarket situó la probabilidad de invasión en 2026 en el 6% con 23 millones de dólares en volumen. En materia comercial, Xi se comprometió a comprar soja, GNL estadounidense y 200 aviones Boeing, y pidió una "puerta más abierta" al comercio. La delegación estadounidense parece un consejo de administración corporativo: Jensen Huang vendiendo chips, Kelly Ortberg vendiendo aviones, Brian Sykes de Cargill vendiendo soja, y Visa y Mastercard presionando por acceso al mercado de pagos. Friedberg encuadró la cumbre a través del prisma de la trampa de Tucídides: cuando una potencia en ascenso se encuentra con una en declive, el conflicto es históricamente probable. Aun así, argumentó que un momento de expansión de recursos, impulsado por la IA y la biotecnología, ofrece una salida poco común a ese patrón. > *"Parece que en este momento, cuando estamos viendo estos extraordinarios cambios tecnológicos desbloqueados por la IA, la automatización, la biotecnología y todo lo que podría anunciar una verdadera abundancia por delante, es el momento perfecto para decir que quizá el mundo pueda ser más multipolar."* Benioff confirmó que Salesforce no tiene oficinas ni empleados en el continente chino: todos los ingresos de China fluyen a través de una asociación exclusiva con Alibaba para cumplir con la ley de residencia de datos. Espera que la cumbre genere flujo real de pedidos en toda la delegación. Chamath argumentó que la jerarquía confuciana vertical de China hace que la diplomacia a nivel de CEO sea más efectiva que los canales burocráticos, y que los americanos que sienten el aprieto de la inflación necesitan que el acuerdo funcione. ## [18:46] Taiwán, chips, modelos de IA y la paz a través del comercio Benioff rechazó la premisa de que Taiwán es la prioridad central de China, insistiendo en que la prosperidad económica y el crecimiento de la clase media importan más a Xi que la ambición territorial. Ante la pregunta directa de si Estados Unidos debería defender Taiwán ante un bloqueo chino, evitó el planteamiento binario: "Creo que China y Taiwán se reconciliarán." Chamath adoptó una visión estructural: Estados Unidos está a roughly 1-2 nanómetros de la paridad doméstica en chips, punto en el que el valor estratégico de Taiwán pasa a ser económico antes que existencial. > *"Estamos en un punto donde probablemente estamos a 1 o 2 nanómetros de poder hacer lo que necesitamos que Taiwán haga estratégicamente por nosotros. Hoy es económico, y si sacas eso de la mesa, creo que tendremos una actitud muy diferente hacia Taiwán."* La propuesta de Chamath: vender los chips de todos modos, porque dejar que Huawei gane la carrera de semiconductores es peor que dejar que Nvidia venda en China con controles KYC sobre el uso de modelos. Benioff coincidió en que los modelos de IA chinos están cerca de la paridad con los estadounidenses a pesar de las restricciones de chips, lo que socava el argumento del embargo. Friedberg añadió que, a medida que China construye fábricas y equipos de capital propios, la irremplazabilidad de Taiwán se reduce por su propio camino, independientemente de los resultados políticos. ## [31:41] El impacto de la IA en el software: ¿qué SaaS prospera y qué SaaS muere? Jason expuso la revalorización sin rodeos: Salesforce bajó un 37%, ServiceNow un 42%, Workday un 45%, unos 180.000 millones de dólares en capitalización de mercado combinada esfumados ante la suposición de que la IA dejará obsoleto al SaaS gestionado. Benioff salió al frente. > *"No es mi primera apocalipsis del SaaS, honestamente, pero es la apocalipsis del SaaS actual."* Su argumento: el mercado se revalorizó sobre una premisa falsa. La apuesta de Salesforce es Agentforce: agentes de IA anclados en datos empresariales reales, no modelos genéricos propensos a alucinaciones. La adquisición de Informatica por entre 8.000 y 9.000 millones de dólares proporciona la capa de armonización de datos que hace confiables a los agentes: "La IA es muy probabilística; necesita estar anclada en la verdad, en una única fuente de verdad, o simplemente no puede funcionar bien." Benioff añadió que Salesforce gastará roughly 300 millones de dólares en Anthropic este año exclusivamente para agentes de codificación internos, comprimiendo los ciclos de implementación. Chamath dividió el mercado en dos: el extremo bajo ya terminó, las soluciones puntuales genéricas sin relaciones profundas con clientes están muertas; pero el extremo alto, donde opera Salesforce, está posicionado para beneficiarse del examen de retorno de inversión cuando los mercados públicos dejen de estar "eufóricos con la IA" y pregunten qué produjeron 3 billones de dólares en capex. Los supervivientes serán quienes tengan relaciones a nivel de C-suite, churn negativo y la capacidad de ofrecer las capacidades de IA como resultados medibles. ## [47:26] OpenAI considera demandar a Apple por el fracaso de la integración con ChatGPT Bloomberg informó que OpenAI podría demandar a Apple por incumplimiento de contrato: el acuerdo ChatGPT-Siri de 2024 colapsó en la práctica porque Apple dirige las consultas a ChatGPT solo cuando los usuarios dicen explícitamente "ChatGPT," nunca promocionó la integración, y OpenAI nunca vio los ingresos por suscriptores que esperaba. La defensa de Apple apunta a preocupaciones de privacidad sobre las prácticas de datos de OpenAI. Benioff reencuadró la historia como una divergencia estratégica entre los laboratorios de IA: Grok construyó compañeros y "sex bots," OpenAI apostó por Sora y redes publicitarias, Gemini lanzó Nano, y Anthropic ignoró todo eso para centrarse en agentes de codificación, y Anthropic resultó tener razón. Lanzó una pista sobre funcionalidad de codificación nativa en Slack aún sin anunciar. > *"Anthropic dijo que no saben de esos sex bots ni de Nano Banana, pero que van a hacer agentes de codificación. Y resultó que Anthropic tenía razón. Y de repente el cohete despegó."* Chamath planteó la pregunta de fondo: ¿qué le ocurre a Apple si la capa de interacción con la IA se desplaza completamente fuera del dispositivo? Predijo un "momento iPhone" de parte de un fabricante de hardware inesperado: un dispositivo ambiental delgado y siempre encendido que haga irrelevante al MacBook Pro para la inferencia de IA. Friedberg señaló que la estrategia actual de Apple consiste en llenar huecos antes que en tener visión, y que G Suite está quitando silenciosamente cuota empresarial a la pila de productividad de Apple. ## [56:54] Thinking Machines lanza modelo en tiempo real, el futuro de la IA para el consumidor y los modelos multisensoriales Thinking Machines, de Mira Murati, lanzó un modelo multimodal en tiempo real que monitorea el escritorio, escucha el audio ambiente y procesa la entrada de la webcam simultáneamente en intervalos de 200ms a través de dos pipelines paralelos: uno para razonamiento retrospectivo profundo y otro para respuesta en vivo. Al mismo tiempo, Apple ha patentado cámaras en el interior de los AirPods. > *"Los modelos multisensoriales son la próxima gran ola para la IA, aunque todavía no llegaremos a la AGI en ese punto."* Benioff argumentó que los LLM entrenados con lenguaje tienen limitaciones fundamentales: la cognición humana integra visión, audición y propiocepción en paralelo sobre hardware biológico. El anclaje multisensorial es la capa que falta. La economía de tokens es impresionante: el monitoreo ambiental en tiempo real a 8 horas por usuario al día supondría 1000 veces el consumo empresarial actual. Benioff rechazó la carrera de "modelo más grande = mejor," prediciendo que la inteligencia distribuida integrada en apps y dispositivos importará más que la escala bruta del modelo, y señalando espacio para una "nueva empresa prometedora" que combine sensores ambientales con contexto empresarial. ## [62:24] Rincón de Ciencia: Impactos de un El Niño históricamente fuerte en 2026 Friedberg presentó datos de anomalías de temperatura oceánica que muestran temperaturas superficiales del mar encaminadas hacia la mayor desviación de la norma desde 1877, roughly 4°C por encima de la línea base. La energía térmica almacenada: 11 millones de teravatios-hora, frente al consumo humano anual global de 25.000 teravatios-hora. > *"Eso equivale a 500 años de energía humana en este océano. Y en los próximos meses, esa energía se liberará en la atmósfera, lo que, con un 99% de confianza, hará que el próximo año sea el más caluroso registrado por un amplio margen."* La cascada: los vientos alisios alterados impulsan ríos atmosféricos hacia California y la costa del Golfo; las cúpulas de calor se extienden sobre Phoenix e interior de Canadá; los monzones indios fallan con alta probabilidad, amenazando a 150 millones de agricultores y 1.500 millones de personas dependientes de la alimentación; las exportaciones agrícolas de Brasil a Indonesia y Filipinas colapsan; los precios del trigo suben globalmente. Phoenix ya estaba a 106°F en mayo. Los mercados de materias primas cotizan activamente la exposición a El Niño. El lado positivo parcial de Friedberg: la genética de cultivos ha mejorado la resiliencia a la sequía y las tierras agrícolas de Siberia se están expandiendo, pero esas ganancias no rescatan la ventana de cosecha de 2026. ## [71:40] Anthropic arremete contra las "Dark SPVs" Anthropic señaló formalmente a las plataformas que venden SPVs en múltiples capas a inversores minoristas, el modelo de "dentistas a los que cobran comisiones de carga del 10%," y declaró que anulará las acciones vendidas a través de estructuras no autorizadas. Chamath lo respaldó sin reservas: cada empresa pre-IPO debería seguir el ejemplo, avanzar hacia los mercados públicos y dejar que estas estructuras desaparezcan. > *"Una vez que SpaceX salga a bolsa, una vez que Anthropic salga a bolsa, una vez que OpenAI salga a bolsa, veremos una letanía de demandas en todas direcciones entre los promotores de estas SPVs. No deberían estar permitidas."* Chamath predijo una oleada de consecuencias legales cuando las principales empresas de IA salgan a bolsa y los inversores minoristas en SPVs descubran que los números no cuadran. El capítulo cierra con Benioff hablando del modelo filantrópico 1-1-1 de Salesforce, el 1% de acciones, el 1% de beneficios y el 1% del tiempo de los empleados desde la fundación, que hoy da servicio gratuito a 50.000 organizaciones sin ánimo de lucro en la plataforma, y un emotivo recuerdo de Susan Wojcicki. ## Entidades - **Marc Benioff** (Persona): Presidente y CEO de Salesforce; invitado en este episodio; arquitecto del modelo filantrópico 1-1-1 y la plataforma de agentes de IA Agentforce - **David Friedberg** (Persona): Presentador; CEO de The Production Board; expuso el rincón de ciencia sobre El Niño - **Chamath Palihapitiya** (Persona): Presentador; CEO de Social Capital; defendió la supervivencia del SaaS de gama alta de Salesforce y la proliferación de chips de Nvidia - **Salesforce / Agentforce** (Software): CRM empresarial y plataforma de agentes de IA; la apuesta de Benioff de que los agentes anclados en datos son lo contrario a una sentencia de muerte para el SaaS - **Anthropic** (Organización): Empresa de seguridad en IA; proveedor preferido de agentes de codificación por Benioff (~300 millones de dólares en gasto planificado en Salesforce); también tomando medidas contra estructuras de SPV no autorizadas - **OpenAI** (Organización): Considera demandar a Apple por el fracaso de la integración ChatGPT-Siri; pivotando hacia agentes de codificación tras el éxito de Anthropic - **Thinking Machines / Mira Murati** (Organización): Lanzó un modelo multimodal ambiental en tiempo real que procesa escritorio, audio y webcam simultáneamente en intervalos de 200ms - **Trampa de Tucídides** (Concepto): Marco de ciencia política sobre el ciclo de conflicto entre potencia en ascenso y potencia en declive, invocado por Friedberg para enmarcar la oportunidad de abundancia cooperativa en la cumbre EE.UU.-China - **Dark SPVs** (Concepto): Vehículos de propósito especial en múltiples capas que venden capital pre-IPO en empresas privadas de IA a inversores minoristas, a menudo con altas comisiones y legitimidad jurídica cuestionada

Charles Koch y su hijo Chase se sientan con David Friedberg para contar cómo Koch Inc. creció 9,000 veces: de una empresa petrolera de 300 empleados en Oklahoma en 1961 a un conglomerado privado de 130,000 empleados que abarca energía, química, productos forestales, bienes de consumo y capital de riesgo, sin salir jamás a bolsa. La conversación gira en torno al Principle-Based Management (PBM): el marco de 41 principios que guía cada decisión de contratación, adquisición y cambio cultural en Koch. Charles y Chase también abordan la caricatura política que rodea al nombre Koch, explicando su giro desde el libertarismo partidista hacia la coalición más amplia Stand Together, enfocada en la reforma educativa y el florecimiento humano. El episodio cierra con IA y capitalismo: ambos ven la innovación sin restricciones y el empoderamiento de abajo hacia arriba como el único camino creíble ante las presiones económicas que se avecinan. ## [00:00] David Friedberg da la bienvenida a Charles y Chase Koch David Friedberg abre la conversación en un evento de Forbes señalando que él y Chase Koch se conocen desde 2013 a través de la industria agrícola y que desde entonces han sido socios comerciales. Enmarca a Koch Inc. como "la historia sin contar" de la empresa americana: posiblemente el negocio familiar privado más rentable del mundo, pero prácticamente invisible en comparación con sus pares que cotizan en bolsa. La apertura también establece las expectativas para la audiencia del All-In Podcast: una extensa conversación en vivo con el presidente y el presidente de la próxima generación de Koch Inc., registrada en directo. > "Siempre sentí que Koch Industries era esa historia sin contar: probablemente el negocio familiar privado más rentable del mundo." > — David Friedberg ## [01:04] Panorama de Koch Inc.: escala, líneas de negocio e historia Friedberg ofrece la base estadística: si Koch cotizara en bolsa, sus ingresos la situarían entre las 25 primeras de la Fortune 500. Fundada en 1940 por Fred Koch en Wichita, Kansas, la empresa opera hoy en 60 países con más de 120,000 empleados en energía, agricultura, química, productos de construcción, bienes de consumo, computación en la nube y un activo portafolio de inversiones minoritarias. Koch reinvierte el 90% de sus ganancias en el negocio, una decisión estructural que la distingue de las empresas públicas que optimizan para resultados trimestrales. Charles señala de qué va a tratar realmente la conversación: no de hitos de ingresos, sino de los principios y los fracasos que hicieron posible el crecimiento sostenido. > "Un modelo operativo muy singular, que incluye principios de innovación disruptiva, reinversión del 90% de las ganancias en nuevos negocios y crecimiento, y valores meritocráticos." > — David Friedberg ## [02:21] Construyendo el negocio: los primeros días y la incorporación de Charles Koch (1961) Charles Koch se incorporó al negocio familiar en 1961 a los 25 años, recién salido del MIT y de una temporada en Arthur D. Little. El ultimátum de su padre Fred fue directo: "Hijo, o vuelves a dirigir la empresa o voy a tener que venderla, porque mi salud está mal y las empresas no van bien y no me queda mucho tiempo de vida." La empresa tenía entonces unos 300 empleados, dos negocios principales (bandejas de fraccionamiento y recolección de petróleo crudo en Oklahoma) y una disfunción operativa importante. Las lecciones tempranas cristalizaron un principio central de Koch: crecer delimitado por capacidades, no por industrias. El negocio de bandejas de fraccionamiento fracasó en parte porque su presidente era un controlador autoritario que alienó a ingenieros y clientes por igual. Charles empezó a preguntarse no "¿en qué industria estamos?" sino "¿qué podemos hacer mejor que nadie, y en qué punto de la cadena de valor eso crea más valor?" Ese reencuadre, aplicado repetidamente durante décadas, explica la secuencia de industrias aparentemente inconexas que Koch fue entrando. > "Hijo, o vuelves a dirigir la empresa o voy a tener que venderla, porque mi salud está mal y las empresas no van bien y no me queda mucho tiempo de vida." > — Charles Koch, citando a su padre Fred Koch ## [11:31] Fracasos, destrucción creativa y aprender de los errores Charles abre con una provocación: "Si no estás fracasando en todo, es que no estás haciendo nada nuevo." Recuenta pérdidas tempranas, incluyendo un intento fallido de convertir coque de petróleo en carbón activado, y un patrón de entrar en negocios sin las capacidades subyacentes necesarias. El verdadero aprendizaje llegó al diagnosticar por qué ocurrió cada fracaso: casi siempre fue por violar uno de los principios operativos de Koch. Chase añade la perspectiva del portafolio de capacidades: la expansión de Koch desde la recolección de petróleo crudo hacia el gas natural, los químicos, los fertilizantes y finalmente los productos forestales no fue una diversificación aleatoria, sino las mismas capacidades subyacentes redirigidas hacia nuevas aplicaciones. También describe Koch Disruptive Technologies (KDT), que él fundó, como un experimento estructural difícil de hacer consistentemente rentable: una evaluación honesta de su propio fracaso. La decisión de cerrar o hacer un pivot, dice Charles, se reduce a una prueba: ¿hemos perdido la capacidad de crear valor superior para los clientes de una manera por la que seamos recompensados? > "Cuando perdemos suficiente dinero, eso es cuando ya basta. Cuando decidimos que no tenemos la capacidad de crear valor superior para nuestros clientes." > — Charles Koch ## [19:22] Cultura y Principle-Based Management Este es el centro intelectual del episodio. Charles traza los orígenes del sistema PBM en los peores fracasos de Koch, todos con una causa raíz en común: promover a personas con malos valores hacia posiciones de liderazgo. Dos ejemplos casi catastróficos destacan: una operación de trading temeraria que casi quebró la empresa durante la guerra de Oriente Medio en 1973, y un episodio posterior en el que líderes con motivaciones destructivas ocultaban fracasos mientras inventaban éxitos. El antídoto fue contratar primero por valores y segundo por talento, y estructurar una cultura donde la motivación por la contribución, querer tener éxito ayudando a otros a tener éxito, desplace la búsqueda de poder. Chase profundiza con un planteamiento que va al grano: ¿y si todos en la empresa supieran exactamente qué hacer sin que nadie se lo dijera? Ese es el estado objetivo que PBM está diseñado para producir. La estrategia de gestión del cambio evita los mandatos de arriba hacia abajo: se encuentra el subgrupo más ansioso por probar los principios, se demuestran resultados, y se deja que la demanda tire de la transformación hacia el resto de la organización. El conocimiento colectivo reemplaza el juicio de unos pocos inteligentes en la cima. > "¿Y si pudieras tener un negocio y una cultura, pequeños, medianos o grandes, donde todos supieran qué hacer sin que nadie se los dijera?" > — Chase Koch ## [33:53] La adquisición de Georgia-Pacific y la transformación cultural La adquisición de Georgia-Pacific en 2005 fue la mayor apuesta de Koch en ese momento: "una apuesta enorme", dice Chase, cuando la empresa era mucho más pequeña. Charles traza la lógica: Koch vio las operaciones de pulpa y papel de Georgia-Pacific como una extensión natural de sus capacidades en procesos químicos, una conexión que se remontaba hasta la tesis del MIT de Fred Koch sobre la producción de pulpa en Maine. Inicialmente propusieron comprar solo las divisiones de commodities; cuando ese acuerdo no pudo cerrarse por los litigios pendientes, ofrecieron comprar la empresa entera. Lo que siguió fue una transformación cultural de años en una sede de 51 pisos en Atlanta construida sobre la burocracia de arriba hacia abajo. Koch reemplazó el liderazgo, recompensó a los trabajadores que detectaron y corrigieron ineficiencias, y compartió los ahorros de costos con los miembros del sindicato que los encontraron. Chase describe sus propios años en las operaciones de primera línea de Koch, viviendo en un tráiler en un corral de engorde, trabajando en una planta de líquidos de gas, como fundacionales para un liderazgo creíble después. El cambio cultural tarda mucho más de lo que cualquier comprador espera, y casi siempre requiere reemplazar al grupo de liderazgo que sostiene el viejo paradigma. > "Tarda muchísimo más de lo que uno piensa en cambiar la cultura, y en casi todos los casos requiere cambiar al liderazgo que tiene el paradigma del empoderamiento de abajo hacia arriba." > — Chase Koch ## [56:17] Reforma educativa y cambio social Stand Together, la red sin fines de lucro que Charles ha ido construyendo durante 60 años bajo distintos nombres, es hoy una de las organizaciones filantrópicas más grandes de Estados Unidos. Chase dirige la generación de alianzas, y reencuadra su misión: no como cabildeo político, sino como la aplicación de los mismos principios de Koch a los desafíos sociales, empezando por la educación. El COVID-19 cambió drásticamente la opinión pública: antes de 2020, aproximadamente el 20% de las familias estaban abiertas a alternativas a la escuela tradicional; después de ver a sus hijos aprender más en YouTube que en las clases de Zoom, ese número se disparó. Stand Together ha ayudado desde entonces a sembrar más de 5,000 microescuelas. Programas aliados como Alpha School de Joe Limont usan la gamificación y el aprendizaje basado en proyectos para llevar a alumnos que reprobaban al tope de la clase en tres meses. Chase también aplica el principio de la ventaja comparativa a sí mismo: se despidió a sí mismo como presidente de Koch Fertilizer cuando reconoció que otra persona tenía esa ventaja comparativa, y usa esa misma lógica para rediseñar roles en toda la plantilla de 130,000 personas de Koch. > "Antes del COVID, aproximadamente el 20% de las familias estaban abiertas a un nuevo modelo de educación. Durante el COVID todos vieron qué tan roto estaba el sistema: sus hijos habían aprendido más en YouTube que en el salón de clases." > — Chase Koch ## [72:37] IA, desafíos económicos y el futuro del capitalismo Friedberg presiona a Charles para que rinda cuentas de la narrativa política de Koch: las décadas de participación en el Partido Libertario y el giro posterior hacia la coalición más amplia de Stand Together. Charles es sincero: pasó demasiados años trabajando solo con personas que coincidían con él en cada principio, lo que limitó su alcance. La perspectiva de Viktor Frankl, "cada vez más personas tienen los medios para vivir pero no tienen un sentido por el que vivir", reorientó su pensamiento hacia las raíces motivacionales del deterioro social más que hacia remedios puramente políticos. La lección: las estrategias de la libertad no pueden tomar prestado del totalitarismo; someter a una coalición a pruebas de pureza ideológica la destruye. En cuanto a la IA, la posición de Chase es clara: innovación sin restricciones, sistemas abiertos, empoderar a las personas con herramientas de IA en lugar de prohibirlas. Koch está ejecutando PBM como un marco nativo de IA, y Chase creó un asistente de IA para el nuevo libro para que los lectores puedan interactuar directamente con los principios, yendo mucho más allá de lo que Charles anticipaba cuando invitó a Chase a coescribir. El episodio cierra con el legado que Charles declaró querer dejar: que Estados Unidos cumpla más plenamente la promesa de la Declaración de Independencia. > "El problema de hoy es que cada vez más personas tienen los medios para vivir pero no tienen un sentido por el que vivir." > — Charles Koch, citando a Viktor Frankl ## Personajes - **David Friedberg** — Anfitrión; cofundador de The Production Board; socio comercial de Chase Koch desde 2013 a través de la industria agrícola - **Charles Koch** — Presidente y CEO de Koch Inc. desde 1967; ingeniero graduado del MIT; coautor del libro Principle-Based Management; ha liderado el crecimiento de 9,000 veces en valor de Koch - **Chase Koch** — Presidente de Koch Inc.; fundador de Koch Disruptive Technologies; coautor del libro PBM junto a Charles; lidera la generación de alianzas de Stand Together - **Koch Inc.** — Conglomerado familiar privado con sede en Wichita, KS; fundado en 1940 por Fred Koch; más de 130,000 empleados en energía, química, productos forestales, bienes de consumo, software y capital de riesgo - **Principle-Based Management (PBM)** — Marco operativo de 41 principios de Koch; enfatiza la motivación por la contribución, la contratación por valores primero, el empoderamiento de abajo hacia arriba y tratar cada unidad de negocio como un laboratorio - **Georgia-Pacific** — Empresa de productos forestales y de consumo adquirida por Koch en 2005; la mayor adquisición de Koch; caso de estudio principal de transformación cultural bajo PBM - **Koch Disruptive Technologies (KDT)** — Brazo de venture capital fundado por Chase Koch; inversiones minoritarias en empresas tecnológicas disruptivas; descrito como estructuralmente difícil de hacer consistentemente rentable - **Stand Together** — Red filantrópica de Charles Koch activa desde 2003; se enfoca en la reforma educativa, la reducción de la pobreza y el cambio social transpartidista; ha sembrado más de 5,000 microescuelas tras el COVID

In one of their most consequential episodes, the All-In besties dissect SpaceX's surprise compute lease to Anthropic — the deal that may cement Anthropic as AI's dominant platform — and debate whether David Sacks's "Rockefeller" framing is prophecy or paranoia. The group then wrestles with a White House trial balloon about an "FDA for AI," ultimately concluding it was mostly media spin, before closing with a bullish-but-cautious read on the AI-driven market boom. Brad Gerstner fills in for David Friedberg, bringing investor perspective from both public and private markets across the episode's 82 minutes. ## [00:00] Bestie intros! Thoughts on the LA mayor election Jason Calacanis opens with the full crew: Chamath Palihapitiya, David Sacks, and fifth bestie Brad Gerstner joining in for David Friedberg, who is out sick. The warm-up quickly turns to the LA mayoral race, where Spencer Pratt is mounting a surprisingly effective challenge to incumbent Karen Bass. The group praises Pratt's viral debate performance — evisceration of the city council candidate over homeless policy — and Chamath notes the power of a sharp social-media team in modern politics. Brad flags a California ballot initiative that would constitutionally protect retirement savings and ban a wealth tax, reading it as a potential seismic signal. Jason observes that New York City hedge-fund titan Ken Griffin publicly announced he is pulling investment from New York after NYC councilman Zohran Mamdani targeted his home in a campaign video, underlining the tension between aggressive progressive politics and capital flight. > *"If California effectively passes a constitutional amendment protecting retirement savings and personal assets and banning the wealth tax and [Spencer Pratt] gets elected, the message that would send to the country — that's a very non-consensus view that I'm becoming increasingly optimistic about."* — Brad Gerstner ## [04:38] SpaceX-Anthropic deal, Elon Web Services, SpaceX IPO valuation, Anthropic's insane growth trajectory Jason leads with the blockbuster news: SpaceX has leased all of Colossus 1 — its H100-based Memphis data center — to Anthropic, adding over 220,000 Nvidia GPUs and 300 megawatts to Anthropic's supply-constrained capacity. The deal immediately doubled Claude Code's rate limits and removed peak-usage caps for paid users. Chamath frames Anthropic's explosive growth as purely supply-constrained: if unlimited power existed, revenues would be "even more parabolic." He sees the deal as Elon strategically de-risking SpaceX's valuation story — blunting bear cases around delayed orbital data centers while generating near-term revenue to subsidize Grok training. Brad estimates the arrangement adds $4–5 billion in incremental 2026 revenue for SpaceX, calling EWS (Elon Web Services) a genuine fourth hyperscaler alongside AWS, Azure, and GCP. He also warns that organized activists — not organic local opposition — are using the same playbook that stalled nuclear construction in America to delay data-center permitting. David Sacks notes that Anthropic grew from $10B ARR on January 1 to $44B ARR by April — a trajectory he calls unlike anything Silicon Valley has ever witnessed. > *"Nobody in Silicon Valley has ever seen anything like it. Forget about the rest of the country. I mean, all we do in Silicon Valley is deal with exponentials. And still, people have never seen that kind of growth at that level of scale."* — David Sacks ## [26:48] Is Anthropic the next great monopoly? Early signals or major overreaction? David Sacks draws an extended analogy between Anthropic and John D. Rockefeller's Standard Oil, arguing that safety-first rhetoric can function as regulatory capture — building a moat that locks in the emerging duopoly of Anthropic and OpenAI while blocking competitors. He notes that if Anthropic sustains its 10× annual growth for just 18 more months it could become "the most powerful monopoly ever created in human history," dwarfing the combined Mag-7 revenue. Brad pushes back hard: Anthropic and OpenAI are still fledgling startups on a GAAP basis, Google and Amazon are producing hundreds of billions in free cash flow to fund competing models, and pre-emptive antitrust action at the starting line of AI would be "a disaster." Jason translates Brad's position as "don't mess with my paper," since Altimeter holds positions in several of these companies. Sacks clarifies his northstar is vigorous competition — but he flags Anthropic's banning of OpenClaw from using its API as a concrete anti-competitive act worth scrutiny. > *"Unless something about their current trajectory changes, Anthropic will be the most powerful monopoly ever created in human history — a trillion dollars of ARR growing at some rate. Dario calls it AGI. I call it the biggest monopoly in human history."* — David Sacks ## [35:21] "FDA for AI" freakout, how the White House thinks about AI safety Reports surfaced that the White House was considering an executive order to create an AI working group that could require pre-release safety reviews for new frontier models — triggered, according to the New York Times, by Anthropic's classified "Mythos" model reportedly alarming national-security officials. NEC Director Kevin Hassett appeared on Fox Business drawing an FDA analogy, while Treasury Secretary Scott Bessent spoke more carefully about balancing innovation and safety. Sacks calls much of it "fake news" amplified by Andrew Ross Sorkin's DealBook column, noting that Susie Wiles, the White House Chief of Staff, issued a statement walking back the FDA framing. He reveals he spoke with Hassett directly and confirms no senior official actually supports a pre-approval regime. He points to the White House's March 20 National AI Regulatory Framework as evidence the administration favors specific solutions over broad regulatory capture. The group converges on one concrete measure: KYC (Know Your Customer) requirements before frontier model API access during preview periods, plus rapid deployment of cyber-capable AI to companies like CrowdStrike and Palo Alto Networks. > *"There is a substantial faction of AI ideologues or doomers who are basically employing the classic 'never let a crisis go to waste' strategy. Yes, we do have this cyber issue that is real — everyone needs to harden their systems now. But what they're trying to do is use that issue to try and create a permanent new infrastructure in Washington."* — David Sacks ## [52:01] Flipping AI's negative perception: Giving, healthcare and education innovation Jason shifts from regulatory defense to offense: how should the tech industry proactively counter negative public perception of AI? He proposes that companies going public — Anthropic, OpenAI, SpaceX — could dedicate 1–5% of IPO proceeds to every American via "Invest America" accounts, creating tangible shared upside. He also calls for serious engagement on minimum wage and universal healthcare, arguing that a financially healthier consumer base is structurally good for capitalism itself. Brad endorses the "Invest America" concept, adding that data center host communities should receive direct benefits like free local electricity. David pivots to political salience data: AI ranks 29th out of 39 voter issues — well below cost of living and economic growth, two metrics where AI is actively deflationary and expansionary. The industry's real message should be economic delivery, not safety governance. Chamath gives tech leaders a "D-minus trending to F" for communications and calls for tangible reinvestment in America at scale. > *"I think that there's a pretty profound vibe shift with respect to tech, tech oligarchs, Silicon Valley, and particularly AI. That vibe shift has already happened on Main Street, and I think that's starting to seep into Washington."* — Chamath Palihapitiya ## [60:04] Trading the AI market, state of the economy Brad leads a comprehensive market check: AWS on a $150B run rate (28% growth), Azure at $108B (39%), Google Cloud at $80B (63%). The S&P 500 is at all-time highs, the 10-year sits at 4.3%, and inflation is under control — far better outcomes than the doom scenarios predicted around tariffs and geopolitical conflicts. S&P 500 operating margins improved from 11% in 2023 to 13% in Q1 2026, and the Mag-5's combined headcount grew only 3% over three years while revenues surged. Chamath urges caution: there is still no direct evidence AI is lifting enterprise profit margins in aggregate, and a reckoning arrives in roughly 500 days when the fork between opex reduction and revenue growth will determine whether the AI boom is real or a mirage. Jason counters that for startups the ROI is already "fait accompli" — AI-generated ad creative at Nike and DoorDash, portfolio companies shipping product at half the headcount. David credits Trump administration policies — rescinding Biden's chip-export licensing and AI-approval regime, unleashing energy permits — for creating the conditions that enabled the boom, and notes that the unemployment rate for recent college graduates has actually improved, contradicting the entry-level-job-loss narrative. > *"I think we have kind of call it 500 days where you just got to be net long. But I think it's literally in the hundreds of days from now that you're going to have to have an important reckoning moment. The people that are paying for all these tokens need to see an actual benefit."* — Chamath Palihapitiya ## Entities - **Jason Calacanis** (Person): Host and moderator; angel investor and podcast co-founder - **Chamath Palihapitiya** (Person): General partner, Social Capital; co-host; contrarian macro voice on AI ROI and market cycles - **David Sacks** (Person): Co-host; former White House AI & Crypto Czar; framed Anthropic as a potential historic monopoly using the Rockefeller analogy - **Brad Gerstner** (Person): Founder & CEO, Altimeter Capital; fifth bestie; bullish on compute stocks and AI market structure - **Dario Amodei** (Person): CEO of Anthropic; referenced as "Daario D. Rockefeller" by Sacks; party to the SpaceX compute deal - **Elon Musk** (Person): CEO of SpaceX and xAI; architect of Elon Web Services and the Colossus 1 compute lease strategy - **Anthropic** (Organization): AI lab behind Claude; grew from $10B to $44B ARR in four months; center of monopoly and FDA debates - **SpaceX / xAI** (Organization): Lessor of Colossus 1 data center to Anthropic; emerging fourth hyperscaler under EWS branding - **Elon Web Services (EWS)** (Concept): SpaceX's compute-leasing business positioned as a hyperscaler competitor to AWS, Azure, and GCP - **Mythos** (Software): Anthropic's classified cyber-capable frontier model that reportedly alarmed White House national-security officials - **KYC for AI** (Concept): Proposal to require identity verification before granting API access to frontier models during preview periods - **Invest America** (Concept): Proposal for IPO-stage tech companies to dedicate a share of proceeds to universal investment accounts for US citizens