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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.

Third Point के CEO और CIO Dan Loeb, All-In Besties के साथ अपनी पूरी यात्रा साझा करते हैं — 1990 के दशक में याहू फाइनेंस पर गुमनाम ट्रोल से लेकर 30 अरब डॉलर के मल्टी-स्ट्रैटेजी हेज फंड तक। उनका तर्क है कि शॉर्ट सेलिंग — जो वर्षों से सुप्त थी — अब फिर ज़रूरी हो गई है, कि किसी भी गंभीर निवेशक के लिए AI की समझ अब बुनियादी शर्त है, और पोर्टफोलियो प्रबंधन में इंसान की भूमिका अपूरणीय है क्योंकि उसे एजेंट नकल नहीं कर सकते। बातचीत के अंत में Loeb बताते हैं कि उन्होंने Ross Ulbricht की राष्ट्रपति माफी दिलाने में कैसे मदद की — इसे वे आपराधिक न्याय सुधार और शिक्षा समानता के प्रति अपनी व्यापक प्रतिबद्धता के हिस्से के रूप में देखते हैं। ## [00:00] Dan Loeb Besties से जुड़े! यह शुरुआती खंड इंटरव्यू के बाद के हिस्सों से लिए गए चुनिंदा अंशों की एक तेज़-रफ़्तार झलक है — Loeb की सबसे पैनी बातों की पूर्वावलोकन क्लिप। Loeb घोषणा करते हैं कि शॉर्ट सेलिंग वापस आ गई है और यह "बिल्कुल ज़रूरी" है, जबकि होस्ट स्टॉक पिकर्स मार्केट और क्रेडिट मार्केट पर मज़ाकिया टिप्पणियाँ करते हैं। Third Point के शुरुआती एक्टिविस्ट हथियार के रूप में शर्म और हास्य का उनका किस्सा यहाँ आता है, और उनकी यह सपाट टिप्पणी भी: "प्रॉक्सी कॉन्टेस्ट के बिना एक्टिविज़्म वैसा ही है जैसे नर्क के बिना कैथोलिकवाद।" > *"शॉर्ट सेलिंग की खोई हुई कला वापस आ गई है और यह बिल्कुल ज़रूरी है।"* ## [00:34] निवेश की यात्रा: मैसेज बोर्ड और वॉल स्ट्रीट की खिंचाई से अरबों डॉलर के हेज फंड तक Loeb ऑनलाइन निवेश संस्कृति के शुरुआती दिनों का ज़िक्र करते हैं। Reddit के अस्तित्व में आने से पहले, वे याहू फाइनेंस और Silicon Investor पर एक छद्म नाम से पोस्ट करते थे — 1990 के दशक के अंत में जिन्हें वे "बेहद धोखाधड़ी वाली कंपनियाँ" कहते हैं उनकी पोल खोलते, प्रबंधन को ललकारते, और कभी-कभी जीत भी हासिल करते। वे खुद को "OG" नहीं बल्कि "OT" — ओरिजिनल ट्रोल — बताते हैं, हालाँकि इसे वे द्वेष से नहीं बल्कि एक बेलगाम अनियंत्रित माहौल में भाप छोड़ने वाले युवा निवेशक के रूप में पेश करते हैं। Act Trade की कहानी उस दौर को दर्शाती है: एक बार-बार ठगी करने वाला व्यक्ति रेफ्रिजरेटर की प्राप्य राशियों को TADS नामक एक मालिकाना तकनीक बताकर बेच रहा था, और शेयर बुक वैल्यू के कई गुना पर कारोबार कर रहे थे। > *"जब हम छोटे थे, तो हमारा मुख्य हथियार शर्म और हास्य था।"* ## [03:15] Third Point के शुरुआती दिन: गुरु और बाज़ार की उथल-पुथल Loeb अपनी औपचारिक निवेश शिक्षा का सिलसिला बताते हैं — किशोरावस्था में Paine Weber की एक शाखा में किताबें रखने की नौकरी से, जहाँ उन्हें संदेह है कि कुछ प्रतिभूति कानून टूटे होंगे — Warburg Pincus, एक रिस्क आर्बिट्राज फर्म, और अंततः Jefferies के डिस्ट्रेस्ड डेट डेस्क तक। वे पारंपरिक गुरु-शिष्य कथा को नकारते हैं: उनकी गहरी सीख अपने साथियों से और उन ग्राहकों को देखकर आई, खासकर David Tepper को — उनकी सोच की प्रक्रिया को उल्टा सुलझाते हुए। शुरुआती Third Point इवेंट-ड्रिवन निवेश पर बनी थी — अधिग्रहण, स्पिन-ऑफ, दिवालियेपन, डीम्युचुअलाइज़ेशन — जहाँ विकल्प-निर्धारण की अवधि में प्रबंधन की जानबूझकर कम आँकना उन सह-निवेशकों के लिए व्यवस्थित अल्फा बनाता था जो इस अपारदर्शिता और उत्प्रेरकों को समझते थे। वे Jesse Livermore को उद्धृत करते हैं: "सूरज के नीचे कुछ भी नया नहीं है।" > *"मुझे उनकी सोच की प्रक्रिया देखने को मिली और मैं उस चीनी कंपनी जैसा था जो नकल करती है, रिवर्स इंजीनियरिंग करती है, सब कुछ अंदर लेती है और अपना ज्ञान का डेटाबेस और अपना ऑपरेटिंग सिस्टम बनाती है।"* ## [08:47] रणनीति में बदलाव: इवेंट-ड्रिवन से क्वालिटी और AI की ओर आज Third Point एक मल्टी-स्ट्रैटेजी प्लेटफ़ॉर्म है: फ्लैगशिप लॉन्ग/शॉर्ट फंड, एक CLO बिज़नेस, प्राइवेट क्रेडिट, डायरेक्ट लेंडिंग, और एक बीमा कंपनी जो बुक के इन्वेस्टमेंट-ग्रेड हिस्से को तैनात करती है। Chamath पूछते हैं कि जब एजेंट बढ़ेंगे तो दस साल बाद Dan Loeb की भूमिका कैसी दिखेगी — Loeb का जवाब है कि इंसानी नेटवर्क, किसी की आँखों में देखकर बात करने की क्षमता, AI कभी नहीं दोहरा पाएगी। निवेश के मोर्चे पर उन्होंने सस्ती प्रतिभूतियों-उत्प्रेरक वाले दृष्टिकोण से हटकर वास्तविक खाई वाले टिकाऊ-गुणवत्ता वाले कारोबारों की ओर रुख किया है — यह मानते हुए कि पहले निवेशक IBM, AOL और Yahoo के इर्द-गिर्द खाइयों के बारे में खुद को धोखा देते थे। अब मुख्य फिल्टर प्रबंधन की अनुकूलनशीलता है: व्यवधान से आगे रहने में सिद्ध टीम किसी भी मौजूदा उत्पाद लाभ से ज़्यादा मायने रखती है, और Loeb मानते हैं कि तीस साल बाद भी यह मूल्यांकन पैटर्न पहचान है, कोई मापने योग्य पैमाना नहीं। > *"आप तकनीकी रूप से अनपढ़ हो सकते थे या बस कह सकते थे कि मैं यह नहीं करता — और GFC तक मुझे लगता है कि आप कमोबेश आर्थिक रूप से अनपढ़ होकर भी खूब पैसा कमा सकते थे। और अब मैं इनमें से कोई भी नहीं बनना चाहूँगा।"* ## [16:01] शॉर्ट सेलिंग की कला और होमबिल्डर ट्रेड Loeb शुद्ध वैल्यूएशन-आधारित शॉर्टिंग को नकारते हैं — बहुत सारे "मूर्खतापूर्ण वैल्यूएशन" शॉर्ट Reddit भीड़ या मीम मोमेंटम से निचुड़ जाते हैं। उनका पसंदीदा तरीका संरचनात्मक है: ऐसे उद्योग खोजें जिनमें कोविड के बाद इन्वेंट्री का बोझ हो, ऐसी लागत मुद्रास्फीति जिसे मार्जिन सहन न कर सके, और छुपी हुई बैलेंस शीट देनदारियाँ। होमबिल्डर इस थीसिस पर फिट बैठते थे — वे NVR की तरह एसेट-लाइट होने का दावा कर रहे थे जबकि विशाल, व्यावहारिक रूप से प्रतिबद्ध भूमि विकल्पों पर बैठे थे, और खरीदार मौजूदा वित्तपोषण माहौल में महामारी-युग की कीमतें वहन नहीं कर सकते थे। समूह फिर निजी पोज़ीशन कब वितरित करें — इस चिरपरिचित सवाल पर आता है: Loeb ने Palantir 20s में बेचा ("बड़ी गलती"), Upstart में B राउंड लीड करने के बाद Enphase की ज़्यादातर बढ़त चूक गए, और Enphase को एक डॉलर से कम पर बेचा जो अंततः 4 अरब डॉलर बन सकता था। Nvidia पर वे स्पष्ट हैं: लॉन्ग/शॉर्ट पॉड्स इसे संरचनात्मक रूप से "सुरक्षित" शॉर्ट की तरह इस्तेमाल कर रहे हैं जैसे कभी Google और Amazon को शॉर्ट किया जाता था, और उन्हें उम्मीद है कि यह टूट जाएगा। > *"Nvidia एक सुरक्षित शॉर्ट जैसा लगता है। वैसे, Google एक सुरक्षित शॉर्ट था। Amazon एक सुरक्षित शॉर्ट था। ऐसा होता है और कभी-कभी ये एक वैल्यूएशन पर लंबे समय तक रुकेंगे फिर टूट जाएंगे।"* ## [22:15] आपराधिक न्याय सुधार और Ross Ulbricht की माफी Loeb का परोपकार का ढाँचा आय असमानता से शुरू होता है — खासकर कमज़ोर बच्चों को बौद्धिक औज़ार देने में विफलता — जो उन्हें Success Academy में चार्टर स्कूल बोर्ड के काम से आपराधिक न्याय सुधार तक ले गई। वे तीन श्रेणियों की पहचान करते हैं जिनके लिए लड़ना उचित है: झूठे दोषी ठहराए गए, वास्तव में सुधरे हुए, और अनुपातहीन सज़ा भुगत रहे लोग। Ulbricht तीसरी श्रेणी में फिट हुए: Silk Road — शुरुआती क्रिप्टो बाज़ार जहाँ नशीली दवाएं बेची जाती थीं — चलाने के लिए दोहरे आजीवन कारावास और 40 साल की सज़ा, लेकिन हत्या के लिए अनुबंध के उन आरोपों पर कभी मुकदमा नहीं जो सरकार ने बाद में उठाए। Loeb ने Charlie Kirk से संपर्क किया जिन्होंने मामला राष्ट्रपति Trump तक पहुँचाया; Trump के पहले कार्यकाल के आखिरी दिन न्याय विभाग ने धमकी दी कि अगर Trump ने सज़ा माफ की तो बदला लिया जाएगा, इसलिए इसे वापस ले लिया गया। चार साल बाद, Kirk की निरंतर वकालत और व्हाइट हाउस काउंसल David Warrington — जो एक दशक से Ulbricht के वकील थे — की मदद से पूरी माफी मिल गई। Loeb, Olive नामक एक संगठन के ज़रिए व्यक्तिगत मामलों पर काम जारी रखते हैं। > *"किसी आजीवन कारावास की सज़ा पाने वाले को जेल से बाहर निकालने का व्यवस्था के भीतर कोई रास्ता नहीं है। यह केवल राष्ट्रपति की माफी से ही काम करेगा।"* ## प्रमुख व्यक्ति एवं संस्थाएं - **Dan Loeb** (व्यक्ति): Third Point के CEO और CIO; एक्टिविस्ट निवेशक; 1990 के दशक के मध्य में Third Point की स्थापना की; याहू फाइनेंस और Silicon Investor पर शुरुआती ऑनलाइन ट्रोल। - **Third Point** (संगठन): मल्टी-स्ट्रैटेजी हेज फंड; ~30 अरब डॉलर AUM; लॉन्ग/शॉर्ट इक्विटी, CLO, प्राइवेट क्रेडिट, डायरेक्ट लेंडिंग और एक बीमा कंपनी चलाता है। - **Chamath Palihapitiya** (व्यक्ति): होस्ट; Social Capital के CEO; AI व्यवधान, खाई की स्थायित्व और इंसान बनाम एजेंट की भूमिका पर सवाल उठाते हैं। - **Jason Calacanis** (व्यक्ति): होस्ट; LAUNCH के संस्थापक; वितरण निर्णय की चर्चा को आगे बढ़ाते हैं। - **David Sacks** (व्यक्ति): होस्ट; Craft Ventures के संस्थापक; व्हाइट हाउस AI और क्रिप्टो ज़ार; वेंचर पोज़ीशन रखने बनाम वितरित करने पर चर्चा करते हैं। - **David Friedberg** (व्यक्ति): होस्ट; The Production Board के CEO; जाँचते हैं कि प्रबंधन गुणवत्ता आकलन को मापा जा सकता है या नहीं। - **Ross Ulbricht** (व्यक्ति): Silk Road के संस्थापक; दोहरे आजीवन कारावास और 40 साल की सज़ा; 2025 में राष्ट्रपति Trump द्वारा माफ किए गए, जिस गठबंधन प्रयास में Loeb ने मदद की। - **Silk Road** (संगठन): शुरुआती क्रिप्टो-आधारित डार्कनेट बाज़ार; Ulbricht के मुकदमे के केंद्र में। - **Nvidia** (संगठन): चिप कंपनी जिसे Loeb 2-3 साल की कमाई के आधार पर कम मूल्यांकित मानते हैं; नए संरचनात्मक "सुरक्षित शॉर्ट" के रूप में उद्धृत जैसे कभी Google और Amazon थे। - **इवेंट-ड्रिवन निवेश** (अवधारणा): Loeb की शुरुआती रणनीति — अधिग्रहण, स्पिन-ऑफ, दिवालियेपन, डीम्युचुअलाइज़ेशन — प्रबंधन प्रोत्साहन की विसंगतियों और संरचनात्मक विकृतियों का फायदा उठाना। - **एक्टिविस्ट निवेश** (अवधारणा): कॉर्पोरेट प्रशासन बदलाव के लिए दबाव डालने हेतु इक्विटी हिस्सेदारी लेना; Third Point का विशिष्ट तरीका, अब गुणवत्ता-केंद्रित लॉन्ग/शॉर्ट के साथ मिलाकर।

Coatue Management के Thomas Laffont ने All-In पर अपना पहला पॉडकास्ट डेब्यू किया और AI यूनिकॉर्न अर्थव्यवस्था पर एक डेटा-आधारित स्थिति-विवरण प्रस्तुत किया — इसमें बताया कि 2024 का AI कोहोर्ट पिछले हर दौर से बड़ा क्यों हो सकता है, हर लॉन्च के साथ SpaceX की वैल्यू कैसे बढ़ती है, और क्यों $4 ट्रिलियन के AI IPO एक ऐसे दौर में पब्लिक मार्केट में आने वाले हैं जो निवेशकों ने पहले कभी नहीं देखा। Besties ने Power Law की एकाग्रता की समस्या, उस दुनिया में VC के भविष्य जहाँ पूंजी तीन नामों की तरफ दौड़ती है, और इतनी बड़ी तरलता की बाढ़ का सिलिकॉन वैली के इकोसिस्टम पर क्या असर होगा — इन सवालों को गहराई से खंगाला। ## [00:00] Coatue के Thomas Laffont Besties में शामिल हुए! Laffont ने शुरुआत में बताया कि उन्होंने अपने पॉडकास्ट डेब्यू के लिए All-In को क्यों चुना — बाकी सभी प्लेटफॉर्म को मना करके वे सिर्फ इसी का इंतजार कर रहे थे। Sacks ने Coatue को पिछले दो दशकों के सबसे सफल हेज फंडों में से एक बताया, जिसके पास $55 बिलियन की संपत्ति है। Laffont ने अपने तैयार डेक में जाने से पहले एक वाक्य में Coatue की खासियत बताई। > *"हम एक आइडिया बिज़नेस में हैं। और जब आपके पास सच में क्रांतिकारी आइडिया होता है, तो वह बहुत बड़ा हो सकता है।"* ## [00:30] AI के दबदबे में पब्लिक मार्केट की वापसी और "यूनिकॉर्न इकोनॉमी" Laffont ने Coatue के अपने यूनिकॉर्न इकोनॉमी डेटा को सामने रखा। सितंबर 2024 से यूनिकॉर्न इकोनॉमी औसतन 70% ऊपर है, जो मोटे तौर पर NASDAQ की चाल से मेल खाती है — फंडरेज़िंग में AI का हिस्सा साल-दर-साल बढ़ता जा रहा है, लेकिन तस्वीर बदल गई है: नए यूनिकॉर्न बनने की रफ्तार काफी कम हो गई है, और हर यूनिकॉर्न 2021 के मुकाबले 5 गुना ज़्यादा पूंजी जुटा रहा है। 2021 विंटेज कोहोर्ट एक सबक है: उस साल 479 कंपनियाँ बनीं, लेकिन 20 तिमाहियों बाद केवल 20% ने एग्ज़िट लिया या नया राउंड उठाया — जबकि ZIRP से पहले के दौर में सिर्फ 73 कंपनियों में से 80% स्वस्थ थीं। असली सवाल यह है कि नया 2024 AI कोहोर्ट किस खाँचे में फिट होगा। एग्ज़िट के मोर्चे पर 2026 अच्छी राह पर है, हालाँकि अभी 2021 के शिखर तक नहीं पहुँचा। उन्होंने एक "Magnificent 8" प्राइवेट इंडेक्स का विचार पेश किया — SpaceX, Stripe, Anthropic, Databricks, Revolut, ByteDance, Anduril — जो करीब $4 ट्रिलियन की वैल्यू रखता है और पारंपरिक Mag 7 को प्रदर्शन में पीछे छोड़ चुका है। > *"अगर मैं यह इंडेक्स अगले एक दशक और उससे भी अधिक समय के लिए रख सकूँ, तो मुझे काफी आराम महसूस होगा।"* ## [05:15] $4 ट्रिलियन AI IPO विस्फोट SpaceX कुछ हफ्तों में पब्लिक होने वाला है; रिकॉर्डिंग के दिन Anthropic ने गोपनीय रूप से S1 दाखिल किया। अकेले SpaceX, OpenAI और Anthropic के एग्ज़िट से पिछले दस वर्षों के कुल IPO से ज़्यादा तरलता आएगी — इकोसिस्टम लगभग रातोंरात पूंजी-खपत से पूंजी-वापसी की ओर पलट जाएगा। Laffont ने जनवरी 2025 से OpenAI और Anthropic की राजस्व वृद्धि का ग्राफ दिखाया: कुछ ही महीनों में उन्होंने Workday, फिर ServiceNow, Adobe, Salesforce को पार किया और अब Google Cloud और Azure से भी बड़े हैं — अनुमान है कि Anthropic अकेले साल के अंत तक AWS को और 2028 तक पूरे Microsoft को पीछे छोड़ सकता है। उन्होंने नोट किया कि हाइपरस्केलर्स इस उथल-पुथल को केवल देख नहीं रहे: वे इसे फंड कर रहे हैं, और दुनिया की सबसे बड़ी कंपनियों की पूंजी प्रतिबद्धताएँ "सच में अभूतपूर्व" हैं। > *"इसकी एक वजह यह है कि OpenAI और Anthropic की ग्रोथ रेट जैसी हमने पहले कभी नहीं देखी।"* ## [07:48] SpaceX का तर्क: कंपाउंडिंग लॉन्च मोनोपॉली और Starlink Laffont ने Coatue का आंतरिक CODE फ्रेमवर्क पेश किया जिससे समझाया कि लॉन्च की रफ्तार बढ़ने के साथ SpaceX की प्रति-लॉन्च वैल्यू क्यों बढ़ी है — यह एक वॉल्यूम बिज़नेस के लिए उलटी बात लगती है। जवाब यह है: SpaceX का बिज़नेस मॉडल स्केल के साथ और मजबूत होता है। पहला चरण पूरी तरह लॉन्च बिज़नेस है — अनिश्चित, सरकारी अनुबंध से आय। दूसरा चरण एक कॉन्स्टेलेशन (Starlink) जोड़ता है, जो लॉन्च को बार-बार आने वाले सब्सक्राइबर राजस्व में बदल देता है। तीसरे चरण में कई कॉन्स्टेलेशन और एक प्लेटफॉर्म आता है, जहाँ कॉर्पोरेट और सेनाएँ अपनी कक्षीय क्षमता चाहती हैं। इससे आगे अंतरिक्ष डेटा सेंटर, चाँद और मंगल में संभावनाएँ हैं। > *"आप जितना ज़्यादा लॉन्च करते हैं, SpaceX का बिज़नेस मॉडल उतना ही बेहतर होता जाता है।"* ## [10:38] 10x विरोधाभास: अभूतपूर्व स्केलिंग के पीछे क्या है कंपनी के विभिन्न चरणों में 10 गुना रिटर्न का डेटा चौंकाने वाला है: यूनिकॉर्न के डेकाकॉर्न बनने की संभावना 8% है; डेकाकॉर्न के $100B तक पहुँचने की संभावना 13% है; लेकिन सेंटाकॉर्न ($100B+) के 10 गुना होने की संभावना 31% है। स्केल रिटर्न को बढ़ाता है, कमज़ोर नहीं करता। तीन पब्लिक कंपनियाँ एक साल में $500B से $1T तक गईं; दो ने यह काम हफ्तों में किया। Laffont ने Cerebras — एक Coatue पोर्टफोलियो कंपनी जिसके वे बोर्ड पर रहे — को एक विपरीत उदाहरण के रूप में रखा: नई पूंजी के बिना अंधेरे के साल, चिप आर्किटेक्चर पर मेहनत, और फिर एक बड़े OpenAI अनुबंध ने लगभग रातोंरात कंपनी की वैल्यू पाँच गुना कर दी। 2024 All-In Summit के बाद से सेमीकंडक्टर सेक्टर ने हर इंडेक्स को पीछे छोड़ा है। राजस्व-संशयवादियों की बहस पर: Coatue का अनुमान है कि आज कुल AI इकोसिस्टम $140B का है, इस साल $300B होगा, 2027 में फिर दोगुना होगा — और यह तीन स्तंभों पर टिका है: उपभोक्ता सब्सक्रिप्शन, एंटरप्राइज़/क्लाउड कोड प्रोडक्टिविटी टूल्स, और AI-सक्षम विज्ञापन (अभी Meta और Google पर 25% पेनेट्रेशन, अनुमान है 100% तक जाएगा)। > *"Anthropic विशेष रूप से उस तेज़ी से स्केल कर रहा है जो हमने किसी भी कंपनी में पहले नहीं देखी।"* ## [15:33] AI बाज़ारों का वर्गीकरण और भविष्य का प्रभाव विज्ञापन सेगमेंट वह है जिसे अधिकांश विश्लेषक नज़रअंदाज़ करते हैं: अगर AI-संचालित विज्ञापन Meta और Google पर 25% से 100% पेनेट्रेशन तक जाए, तो अकेले इसमें $150B का अतिरिक्त मूल्य है। एंटरप्राइज़ कोड टूल्स (Claude Code, Codex) एक और स्तंभ जोड़ते हैं। पूरी अर्थव्यवस्था में उथल-पुथल एक साथ हो रही है — टेलीकॉम (Starlink कॉल ड्रॉप को इतिहास बना रहा है), कंप्यूट (डेटा सेंटर पेन्सिलवेनिया के एनर्जी ग्रिड को नया आकार दे रहे हैं), ऑटो (Ferrari EV-ऑटोनॉमस बदलाव से जूझ रहा है), और उपभोक्ता (GLP-1s खाद्य और शराब की खपत को नए सिरे से ढाल रहे हैं)। Laffont का सारांश: नई यूनिकॉर्न इकोनॉमी संरचनात्मक रूप से ज़्यादा स्वस्थ है, विजेता पहले से कहीं तेज़ी से कंपाउंड करते हैं, और इसलिए किसी विजेता से बाहर रहने की कीमत पहले से कहीं ज़्यादा है — और यह अभी superintelligence के बिना ही है। > *"उथल-पुथल वैश्विक अर्थव्यवस्था के हर हिस्से को प्रभावित कर रही है। और वैसे, अभी हमारे पास superintelligence भी नहीं है।"* ## [18:32] Bestie Q&A: AI में Power Law, VC का भविष्य, राजस्व के स्रोत, तरलता विस्फोट Jason ने सीधे पूंजी-आवंटक का सवाल पूछा: अगर सेंटाकॉर्न डेटा कहता है कि एकाग्रता जीतती है, तो क्या LP को बस तीन सबसे बड़े प्राइवेट नामों में पैसा लगा देना चाहिए? Laffont का जवाब: वैल्यूएशन बहुत ज़्यादा लगती है, लेकिन ये असली कारोबार हैं जो ऐतिहासिक रूप से कम earnings multiples पर असली राजस्व बना रहे हैं — "पब्लिक मार्केट सबसे बड़ा antiseptic है।" Chamath ने नोट किया कि IPO के पहले दिन नहीं, बल्कि छह महीने बाद असली price discovery हो सकती है, पैसिव-बाइंग फ्लो की लहर को देखते हुए। Chamath ने सवाल उठाया कि सेंटाकॉर्न की तेज़ी संरचनात्मक कुशलता है या survivor bias। Laffont ने Claude Code को Exhibit A बताया: "Anthropic Claude Code से पहले एक बिल्कुल अलग कंपनी थी, और Claude Code के बाद कुछ और। एक घटना ने पूरे उद्योग की दिशा बदल दी।" कमोडिटी-मॉडल की कहानी, उन्होंने कहा, "काफी हद तक गलत साबित हो चुकी है।" Sacks ने 31% सेंटाकॉर्न-से-10x के आँकड़े को आगे बढ़ाया: ट्रिलियन-डॉलर कंपनी के लिए क्या संभावना है? उनका अनुभव — 30% से ज़्यादा, शायद कहीं अधिक। Friedberg ने earnings की स्थायित्व फ़िल्टर जोड़ा: हर स्केल टियर कंपाउंडिंग लाभ को चुनता है, इसलिए शीर्ष पर यह फ़िल्टर कमज़ोर नहीं, बल्कि मज़बूत होता है। बातचीत का अंत इस सवाल पर हुआ कि $3-4T की तरलता GP और LP के ज़रिए इकोसिस्टम में वापस आने पर क्या होगा। Laffont ने सबसे उलटा जोखिम उठाया: OpenAI बनाम Anthropic के बीच price war, जहाँ प्रचुर पूंजी राइड-शेयरिंग-शैली की मूल्य निर्धारण लीवर सक्षम कर सकती है। उन्होंने दो साल में All-In पर वापस आने और यह बताने का वादा किया कि क्या सही रहा और क्या नहीं। > *"क्या हम OpenAI और Anthropic के बीच price war देख सकते हैं? अगर इन कंपनियों के पास इतनी पूंजी है, तो क्या कोई एक दूसरे से मुकाबला करने के लिए कभी मूल्य लीवर खींचेगा?"* ## इकाइयाँ - **Thomas Laffont** (व्यक्ति): Coatue Management ($55B AUM) के सह-संस्थापक; Cerebras के बोर्ड सदस्य; All-In Summit 2026 में अपने यूनिकॉर्न इकोनॉमी शोध प्रस्तुत किया - **Chamath Palihapitiya** (व्यक्ति): होस्ट, Social Capital के CEO; सेंटाकॉर्न की तेज़ी के लिए संरचनात्मक बनाम survivor-bias स्पष्टीकरण पर सवाल उठाया - **Jason Calacanis** (व्यक्ति): होस्ट, LAUNCH के संस्थापक और एंजेल निवेशक; पूंजी-आवंटन और Power Law एकाग्रता के सवाल उठाए - **David Sacks** (व्यक्ति): होस्ट, Craft Ventures के संस्थापक और व्हाइट हाउस AI & Crypto Czar; सेंटाकॉर्न-से-डेकाकॉर्न संभावना को आगे बढ़ाया - **David Friedberg** (व्यक्ति): होस्ट, The Production Board के CEO; Power Law डेटा पर Ben Graham-शैली की earnings स्थायित्व फ्रेमिंग लागू की - **Coatue Management** (संगठन): ग्रोथ और हेज फंड मैनेजर; यूनिकॉर्न इकोनॉमी डेटासेट और SpaceX वैल्यूएशन के CODE फ्रेमवर्क का स्रोत - **Anthropic** (संगठन): AI लैब; रिकॉर्डिंग के दिन गोपनीय S1 दाखिल किया; इतिहास में सबसे तेज़ राजस्व वृद्धि, बताया जाता है एक महीने मुनाफे में रही - **OpenAI** (संगठन): AI लैब; साल के अंत तक AWS और 2028 तक पूरे Microsoft को पीछे छोड़ने का अनुमान; Anthropic के साथ $4T IPO लहर का ट्रिगर - **SpaceX** (संगठन): रॉकेट और सैटेलाइट कंपनी; रिकॉर्डिंग के समय IPO आसन्न; Coatue के CODE फ्रेमवर्क से कंपाउंडिंग लॉन्च वैल्यू और Starlink की टेलीकॉम प्रॉफिट-पूल कैप्चर का विश्लेषण - **Cerebras** (संगठन): AI चिप कंपनी (IPO हो चुकी); Coatue ने Series B का नेतृत्व किया; एक OpenAI अनुबंध से रातोंरात वैल्यू पाँच गुना होने से पहले धैर्यशील पूंजी के अंधेरे दौर का केस स्टडी - **Claude Code** (सॉफ्टवेयर): Anthropic का कोडिंग असिस्टेंट, जिसे एकमात्र ऐसी प्रोडक्ट घटना बताया गया जिसने "पूरे उद्योग की दिशा बदल दी" - **Starlink** (संगठन): SpaceX का सैटेलाइट इंटरनेट नेटवर्क; $200-400B के वैश्विक टेलीकॉम प्रॉफिट पूल को संबोधित करने का अनुमान - **Power Law** (अवधारणा): रिटर्न का कुछ चुनिंदा कंपनियों में बढ़ता केंद्रीकरण — Coatue डेटा दिखाता है कि 10x की संभावना हर स्केल टियर पर बढ़ती है: 8% (यूनिकॉर्न), 13% (डेकाकॉर्न), 31% (सेंटाकॉर्न) - **यूनिकॉर्न इकोनॉमी** (अवधारणा): Coatue का फ्रेमवर्क जो $1B+ कंपनियों के प्राइवेट-मार्केट इकोसिस्टम को ट्रैक करता है — फंडिंग स्वास्थ्य, एग्ज़िट वेग और समय के साथ कोहोर्ट व्यवहार

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.

Salesforce CEO Marc Benioff joins Jason Calacanis, David Friedberg, and Chamath Palihapitiya (David Sacks absent) for a wide-ranging episode anchored by two real-time stories: the first Trump-Xi summit since 2017 and AI's accelerating assault on enterprise software valuations. Benioff — who has attended the Saudi state dinner, Windsor Castle, and this summit delegation — offers a front-row view of US-China commercial diplomacy, then turns to his own company's existential rerate, arguing Salesforce's data infrastructure and agent platform put it on the right side of AI disruption. The back half covers OpenAI's blowup with Apple, Thinking Machines' real-time multimodal demo, Friedberg's alarming El Niño data, and Anthropic's crackdown on layered SPV schemes. ## [00:00] Salesforce CEO Marc Benioff joins the show! Sacks is out this week, and Benioff fills the seat. Jason asks immediately about Benioff's political positioning — past Democratic donor, now attending Saudi state dinners and apparently welcome in the current administration. Benioff brushes off the partisan framing entirely. > *"I'm not a Democrat or Republican. I'm an American."* Chamath notes Benioff collected invites to Windsor Castle, Prince Charles's US visit, and the Saudi state dinner in quick succession — the rare tech CEO who moves across administrations without friction. The setup frames Benioff as an unusually credible voice on the summit unfolding in real time. ## [01:14] Trump-Xi summit, doing business in China as a US company, impact on Americans and the midterms Trump and Xi's seventh face-to-face meeting — delayed two months by the Iran war — opened in Beijing with Xi warning that mishandling Taiwan could put the entire relationship "in an extremely dangerous situation." Polymarket put the 2026 invasion probability at 6% on $23M in volume. On trade, Xi committed to buy soybeans, US LNG, and 200 Boeing jets, and called for a "wider door" on commerce. The US delegation reads like a corporate board: Jensen Huang selling chips, Kelly Ortberg selling planes, Brian Sykes of Cargill selling soybeans, Visa and Mastercard pushing for payment market access. Friedberg framed the summit through the Thucydides trap lens — as a rising power meets a declining power, conflict is historically likely — but argued that a resource-expansive moment, turbocharged by AI and biotech, offers a rare exit from that pattern. > *"It seems like in this moment when we are seeing these extraordinary technology shifts unlocked by AI and automation and biotech and all of these kind of moments of which could be true abundance ahead of us, it seems like the perfect moment to say maybe the world can be more multipolar."* Benioff confirmed Salesforce has zero offices or employees on the mainland — all China revenue flows through an exclusive Alibaba partnership to satisfy data residency law — and expects the summit to generate real order flow across the delegation. Chamath argued that China's top-down Confucian hierarchy makes CEO-level diplomacy more effective than bureaucratic channels, and that Americans who are feeling squeezed by inflation need the deal to work. ## [18:46] Taiwan, chips, AI models, and peace through trade Benioff pushed back on the premise that Taiwan is China's core priority, insisting economic prosperity and middle-class growth matter more to Xi than territorial ambition. On the direct question — should the US defend Taiwan if China blockades it? — he refused the binary: "I think China and Taiwan will reconcile." Chamath took a structural view: the US is roughly 1-2 nanometers away from domestic chip parity, at which point Taiwan's strategic value becomes economic rather than existential. > *"We are at a point where we're probably 1 to 2 nanometers away from being able to do what we need Taiwan to strategically do for us. Today it's economic and if you take that off the table, I think we'll have a very different attitude to Taiwan."* Chamath's prescription: sell the chips anyway, because letting Huawei win the semiconductor race is worse than letting Nvidia sell into China under KYC guardrails for model usage. Benioff agreed Chinese AI models are near-parity with US models despite chip restrictions, undercutting the case for an embargo. Friedberg added that as China builds domestic fabs and capital equipment, Taiwan's irreplaceability diminishes on its own timeline regardless of political outcomes. ## [31:41] AI's impact on software: What SaaS thrives, what SaaS dies? Jason laid out the rerate bluntly: Salesforce down 37%, ServiceNow down 42%, Workday down 45% — roughly $180 billion in combined market cap erased on the assumption that AI will make managed SaaS redundant. Benioff came out swinging. > *"It's not my first SaaS apocalypse, honestly, but it's the current SaaS apocalypse."* His argument: the market rerated on a false premise. Salesforce's bet is Agentforce — AI agents grounded in real enterprise data, not hallucination-prone generic models. The $8-9B Informatica acquisition provides the data harmonization layer that makes agents reliable: "The AI is very probabilistic — it needs to be locked down into the truth, into a single source of truth, or it just cannot work well." Benioff added that Salesforce will spend roughly $300M on Anthropic this year purely for internal coding agents, collapsing implementation cycles. Chamath split the market in two: the low end is finished — generic point solutions with no deep customer relationships are dead — but the high end, where Salesforce operates, is positioned to benefit from the ROI reckoning when public markets stop being "breathless about AI" and ask what $3 trillion in capex produced. The survivors will be those with C-suite relationships, negative churn, and the ability to package AI capability as measurable outcomes. ## [47:26] OpenAI is considering suing Apple over failed ChatGPT integration Bloomberg reported OpenAI may sue Apple for breach of contract: the 2024 ChatGPT-Siri deal collapsed in practice because Apple routes queries to ChatGPT only when users explicitly say "ChatGPT," never promoted the integration, and OpenAI never saw the subscriber revenue it expected. Apple's defense is privacy concerns over OpenAI's data practices. Benioff reframed the story as a strategic divergence among AI labs: Grok built companions and "sex bots," OpenAI pushed Sora and ad networks, Gemini shipped Nano, and Anthropic ignored all of it to focus on coding agents — and Anthropic turned out to be right. He teased unannounced Slack-native coding functionality. > *"Anthropic and they go we don't know about those sex bots and we don't know about Nano Banana but we're going to do coding agents. And it turned out Anthropic was right. And all of a sudden the rocket ship took off."* Chamath raised the deeper question: what happens to Apple if the AI interaction layer moves off the device entirely? He predicted an "iPhone moment" from an unexpected hardware player — a thin, always-on ambient device that makes the MacBook Pro irrelevant for AI inference. Friedberg noted Apple's current strategy is gap-filling rather than visionary, and that G Suite is quietly taking enterprise share from Apple's productivity stack. ## [56:54] Thinking Machines releases real-time model, future of consumer AI, multi-sensory models Mira Murati's Thinking Machines released a real-time multimodal model that watches your desktop, listens to ambient audio, and processes webcam input simultaneously at 200ms intervals across two parallel pipelines — one for deep retrospective reasoning, one for live response. Apple has simultaneously patented cameras inside AirPods. > *"Multi-sensory models are the next big wave for AI and then but we're still not at AGI at that point."* Benioff argued that LLMs trained on language are fundamentally limited: human cognition runs eyes, ears, and proprioception in parallel on biological hardware. Multi-sensory grounding is the missing layer. The token economics are dramatic — real-time ambient monitoring at 8 hours per user per day would be 1000x current enterprise consumption. Benioff pushed back on the "bigger model = better" arms race, predicting distributed intelligence embedded in apps and devices will matter more than raw model scale, and flagging space for a "hot new company" that aggregates ambient sensing with enterprise context. ## [62:24] Science Corner: Impacts of a historically strong El Nino in 2026 Friedberg presented ocean temperature anomaly data showing sea surface temperatures headed for the largest deviation from normal since 1877 — roughly 4°C above baseline. The stored thermal energy: 11 million terawatt-hours, against global annual human consumption of 25,000 terawatt-hours. > *"That's 500 years worth of human energy in this ocean. And over the next few months, that energy is going to be released into the atmosphere — and that will, with 99% confidence, make the upcoming year the hottest year on record by far."* The cascade: altered trade winds drive atmospheric rivers into California and the Gulf Coast; heat domes extend over Phoenix and interior Canada; Indian monsoons fail at high probability, threatening 150 million farmers and 1.5 billion food-dependent people; Brazil's crop exports to Indonesia and the Philippines collapse; wheat prices spike globally. Phoenix was already at 106°F in May. Commodity markets are actively trading El Niño exposure. Friedberg's partial upside: crop genetics have improved drought resilience, and Siberian farmland is expanding — but those gains don't rescue the 2026 harvest window. ## [71:40] Anthropic goes after "Dark SPVs" Anthropic formally called out platforms selling multi-layered SPVs to retail investors — the "dentists getting charged 10% loading fees" model — and stated it will negate shares sold through unauthorized structures. Chamath gave full-throated support: every pre-IPO company should follow suit, push toward public markets, and let these structures die. > *"Once SpaceX goes public, once Anthropic goes public, once OpenAI goes public, you're going to see a litany of these lawsuits back and forth between the purveyors of these SPVs — they should not be allowed."* Chamath predicted a wave of legal fallout once the major AI companies go public and retail SPV investors discover the math doesn't work. The chapter closes with Benioff discussing Salesforce's 1-1-1 philanthropy model — 1% equity, 1% profit, 1% employee time at founding, now running 50,000 nonprofits free on the platform — and a moving remembrance of Susan Wojcicki. ## Entities - **Marc Benioff** (Person): Chair and CEO of Salesforce; guest on this episode; architect of the 1-1-1 philanthropy model and Agentforce AI agent platform - **David Friedberg** (Person): Host; CEO of The Production Board; delivered the El Niño science corner - **Chamath Palihapitiya** (Person): Host; CEO of Social Capital; made the case for Salesforce's high-end SaaS survival and Nvidia chip proliferation - **Salesforce / Agentforce** (Software): Enterprise CRM and agent platform; Benioff's bet that data-grounded AI agents are the opposite of a SaaS death sentence - **Anthropic** (Organization): AI safety company; Benioff's preferred coding agent provider (~$300M planned spend at Salesforce); also cracking down on unauthorized SPV structures - **OpenAI** (Organization): Reportedly considering lawsuit against Apple over failed ChatGPT-Siri integration; pivoting toward coding agents following Anthropic's success - **Thinking Machines / Mira Murati** (Organization): Released a real-time ambient multimodal model processing desktop, audio, and webcam simultaneously at 200ms intervals - **Thucydides Trap** (Concept): Political science framework (rising vs. declining power conflict cycle) invoked by Friedberg to frame the US-China summit opportunity for cooperative abundance - **Dark SPVs** (Concept): Multi-layered special purpose vehicles selling pre-IPO equity in private AI companies to retail investors, often with high fees and disputed legal standing

Charles Koch और उनके बेटे Chase, David Friedberg के साथ बैठकर बताते हैं कि Koch Inc. कैसे 9,000 गुना बढ़ी — 1961 में Oklahoma की एक 300-employee oil company से आज 130,000 employees वाले एक private conglomerate तक, जो energy, chemicals, forest products, consumer goods और venture capital में काम करता है, और कभी public नहीं गया। इस बातचीत का केंद्र है Principle-Based Management (PBM): 41 principles का वो framework जो Koch में हर hiring decision, acquisition और culture change को drive करता है। Charles और Chase यह भी बताते हैं कि Koch नाम से जुड़ी political image उनकी पूरी कहानी नहीं कहती, और कैसे उन्होंने partisan libertarian politics से pivot लेकर Stand Together coalition बनाई जो education reform और human flourishing पर focused है। Episode का अंत AI और capitalism पर होता है: दोनों मानते हैं कि permissionless innovation और bottom-up empowerment ही आगे का एकमात्र सही रास्ता है। ## [00:00] David Friedberg का Charles और Chase Koch से स्वागत David Friedberg Forbes event में बातचीत शुरू करते हैं। वो बताते हैं कि 2013 से agriculture industry के ज़रिए Chase Koch से उनकी जान-पहचान है और वो business partners भी रहे हैं। वो Koch Inc. को American enterprise की "untold story" बताते हैं, जो शायद दुनिया का सबसे profitable private family business है, पर publicly traded peers की तुलना में बहुत कम visible। यह opening All-In audience के लिए expectations भी set करती है: Koch Inc. के chairman और next-generation president के साथ एक rare, extended live sit-down। > "मुझे हमेशा लगा कि Koch Industries वो untold story है, शायद दुनिया का सबसे profitable private family-owned business।" > — David Friedberg ## [01:04] Koch Inc. का परिचय: Scale, Business Lines और History Friedberg statistical baseline देते हैं: अगर Koch publicly traded होती, तो revenue Fortune 500 के top 25 में होता। 1940 में Fred Koch ने Wichita, Kansas में इसे found किया, अब यह 60 देशों में 120,000 से ज़्यादा employees के साथ energy, agriculture, chemicals, building products, consumer products, cloud computing और एक active minority-investment portfolio में काम करता है। Koch अपने 90% profits business में reinvest करता है, जो इसे quarterly earnings optimize करने वाली public companies से structurally अलग बनाता है। Charles signal देते हैं कि असल बातचीत किस बारे में होगी: revenue milestones नहीं, बल्कि वो principles और failures जिन्होंने sustained compounding को possible बनाया। > "एक बहुत unique operating model जिसमें disruptive innovation, 90% profits को नए businesses में reinvest करना और meritocratic values के principles शामिल हैं।" > — David Friedberg ## [02:21] Business की शुरुआत: Early Days और Charles Koch का 1961 में आना Charles Koch 1961 में 25 साल की उम्र में family business में आए, MIT और Arthur D. Little management consulting के बाद। उनके पिता Fred का ultimatum सीधा था: "या तो वापस आ company चलाने, वरना बेचनी पड़ेगी, मेरी सेहत ठीक नहीं और कंपनियाँ अच्छा नहीं कर रहीं।" तब company में करीब 300 employees थे, दो core businesses थे (Oklahoma में fractionating trays और crude oil gathering) और काफी operational dysfunction था। शुरुआती lessons ने एक core Koch principle crystallize किया: capability-bounded growth, industry-bounded नहीं। Fractionating-tray business एक top-down controller president की वजह से fail हुआ। Charles ने सोचना शुरू किया, "हम किस industry में हैं?" नहीं, बल्कि "हम क्या दूसरों से बेहतर कर सकते हैं और value chain में वो कहाँ सबसे ज़्यादा value create करता है?" इस reframe ने decades में Koch के seemingly unrelated industries में जाने को explain किया। > "बेटे, या तो वापस आ company चलाने, वरना बेचनी पड़ेगी, मेरी सेहत ठीक नहीं और companies अच्छा नहीं कर रही और मुझे ज़्यादा वक्त नहीं है।" > — Charles Koch, अपने पिता Fred Koch को quote करते हुए ## [11:31] Failures, Creative Destruction और गलतियों से सीखना Charles एक provocation से शुरू करते हैं: "अगर आप हर चीज़ में fail नहीं हो रहे, तो कुछ नया नहीं कर रहे।" वो शुरुआती losses बताते हैं जिसमें petroleum coke को activated carbon में convert करने की कोशिश भी शामिल है, और बिना ज़रूरी underlying capabilities के businesses में जाने का pattern। असली सीख आई हर failure को diagnose करने से, जो लगभग हमेशा Koch के operating principles में से किसी एक के violation से जुड़ी थी। Chase capability-portfolio lens add करते हैं: crude oil gathering से natural gas, chemicals, fertilizers और आखिरकार forest products में Koch का expansion random diversification नहीं था, वही underlying capabilities थीं जो नए applications में redirect हुईं। वो Koch Disruptive Technologies (KDT) को, जिसे उन्होंने found किया, एक structural experiment बताते हैं जिसे consistently profitable बनाना मुश्किल साबित हुआ। Shutdown या pivot का decision, Charles कहते हैं, एक test पर आता है: क्या हमने customers के लिए superior value create करने की capability खो दी है? > "जब हम काफी घाटा खा लेते हैं, तब enough is enough होता है। जब हम decide करते हैं कि हमारे पास customers के लिए superior value create करने की capability नहीं है।" > — Charles Koch ## [19:22] Culture और Principle-Based Management यह episode का intellectual center है। Charles PBM system की origins Koch की सबसे बड़ी failures में trace करते हैं, जिन सब में एक root cause था: bad values वाले लोगों को leadership में promote करना। दो near-catastrophic examples सामने आते हैं, एक reckless trading operation जिसने 1973 Middle East war के दौरान company को लगभग bankrupt किया, और एक बाद का episode जिसमें "destructively motivated" leaders ने failures छुपाए और successes fabricate किए। Antidote था पहले values और दूसरे talent पर hire करना और एक ऐसी culture structure करना जहाँ contribution-motivation, power-seeking को displace करे। Chase इसे एक framing से extend करते हैं जो point पर cut करती है: क्या होगा अगर company में हर कोई बिना बताए जानता हो कि क्या करना है? यही target state है जो PBM produce करने के लिए design है। Change-management strategy top-down mandates से बचती है: उस subgroup को ढूँढो जो principles try करने के लिए सबसे eager है, results demonstrate करो और demand को बाकी organization में transformation pull करने दो। > "क्या होगा अगर आपका एक business और एक culture हो, छोटा, मध्यम या बड़ा, जहाँ हर कोई बिना बताए जानता हो कि क्या करना है?" > — Chase Koch ## [33:53] Georgia-Pacific Acquisition और Culture Transformation 2005 में Georgia-Pacific का acquisition Koch का उस वक्त का सबसे बड़ा bet था। Charles logic trace करते हैं: Koch ने Georgia-Pacific के commodity pulp and paper operations को अपनी chemical-process capabilities का natural extension देखा, एक connection जो Fred Koch की MIT thesis तक जाता था। उन्होंने पहले सिर्फ commodity divisions खरीदने का propose किया; जब वो deal pending litigation की वजह से close नहीं हो सकी, तो उन्होंने पूरी company खरीदने का offer दिया। इसके बाद Atlanta के 51-मंज़िला headquarters की, जो top-down bureaucracy पर built था, सालों लंबी culture transformation हुई। Koch ने leadership replace की, उन workers को reward किया जिन्होंने inefficiencies spot और fix कीं, और union members के साथ cost savings share किए। Chase अपने Koch के frontline operations में बिताए सालों को, एक feed yard में single-wide trailer में रहकर, एक gas liquids plant में काम करते हुए, बाद की credible leadership के लिए foundational बताते हैं। Culture change किसी भी acquirer की expectation से कहीं ज़्यादा लंबा लेती है। > "Culture बदलने में आप जितना सोचते हैं उससे कहीं ज़्यादा वक्त लगता है, और लगभग हर case में उस leadership को बदलना पड़ता है जो bottom-up empowerment के paradigm को hold करती है।" > — Chase Koch ## [56:17] Education Reform और Social Change Stand Together, वो nonprofit network जिसे Charles ने 60 साल से अलग-अलग नामों से build किया है, अब United States की सबसे बड़ी philanthropic organizations में से एक है। Chase origination और partnerships run करते हैं, और वो इसका mission reframe करते हैं: political advocacy नहीं, बल्कि social challenges पर वही Koch principles apply करना, education से शुरू करके। COVID-19 ने public opinion में sharp shift ला दी: 2020 से पहले करीब 20% families traditional schooling के alternatives के लिए open थीं; बच्चों को YouTube से Zoom classrooms से ज़्यादा सीखते देखने के बाद, वो number surge हो गई। Stand Together ने तब से 5,000 से ज़्यादा micro-schools seed करने में मदद की है। Chase comparative advantage का principle खुद पर भी apply करते हैं, उन्होंने खुद को Koch Fertilizer के president पद से fire किया जब recognize किया कि किसी और में वो comparative advantage था, और उसी lens से Koch के 130,000-person workforce में roles reshape करते हैं। > "COVID से पहले करीब 20% families education के एक नए model के लिए open थीं। COVID में सबने देखा कि system कितना बुरा है, उनके बच्चों ने classroom से ज़्यादा YouTube पर सीखा।" > — Chase Koch ## [72:37] AI, Economic Challenges और Capitalism का भविष्य Friedberg Charles को Koch की political narrative account करने के लिए push करते हैं। Charles candid हैं: उन्होंने बहुत साल सिर्फ उन लोगों के साथ काम किया जो हर principle पर उनसे agree करते थे, जिसने उनकी reach को cap किया। Viktor Frankl की insight, "ज़्यादा से ज़्यादा लोगों के पास जीने के साधन हैं पर जीने का meaning नहीं," ने उनकी thinking को social breakdown के motivational roots की तरफ reorient किया। Lesson: liberty की strategies totalitarianism से borrow नहीं कर सकतीं। AI पर Chase की position clear है: permissionless innovation, open systems, लोगों को AI tools से empower करना न कि ban करना। Koch PBM को AI-native framework के रूप में run कर रहा है, और Chase ने नई book के लिए एक AI companion build किया ताकि readers principles से directly interact कर सकें। Episode Charles के stated legacy goal के साथ close होता है: कि United States Declaration of Independence के promise को और पूरी तरह live up करे। > "आज की समस्या यह है कि ज़्यादा से ज़्यादा लोगों के पास जीने के साधन हैं पर जीने का meaning नहीं।" > — Charles Koch, Viktor Frankl को quote करते हुए ## व्यक्ति एवं संस्थाएं - **David Friedberg** — Host; The Production Board के co-founder; 2013 से agriculture industry के ज़रिए Chase Koch के business associate - **Charles Koch** — 1967 से Koch Inc. के Chairman और CEO; MIT-educated engineer; Principle-Based Management book के co-author; Koch की 9,000x value growth के architect - **Chase Koch** — Koch Inc. के President; Koch Disruptive Technologies के founder; Charles के साथ PBM book के co-author; Stand Together origination और partnerships lead करते हैं - **Koch Inc.** — Wichita, KS में headquartered private family conglomerate; 1940 में Fred Koch ने found किया; energy, chemicals, forest products, consumer goods, software और venture capital में 130,000 से ज़्यादा employees - **Principle-Based Management (PBM)** — Koch का 41-principle operating framework; contribution-motivation, values-first hiring, bottom-up empowerment और हर business unit को एक laboratory की तरह treat करने पर emphasis - **Georgia-Pacific** — Koch ने 2005 में acquire किया forest और consumer products company; Koch का सबसे बड़ा acquisition; PBM के तहत culture transformation का primary case study - **Koch Disruptive Technologies (KDT)** — Chase Koch द्वारा found किया गया venture arm; disruptive technology companies में minority investments; structurally consistently profitable बनाना मुश्किल बताया - **Stand Together** — Charles Koch का philanthropic network 2003 से active; education reform, poverty reduction और cross-partisan social change पर focus; COVID के बाद 5,000 से ज़्यादा micro-schools seed किए

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