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  • Thomas Laffont of Coatue on the $4 Trillion AI IPO Wave: SpaceX, Anthropic, OpenAI, and Why the New Unicorn Economy Is Healthier

    Thomas Laffont, co-founder of the $55 billion hedge fund Coatue Management, made his All-In Podcast premiere with a data-dense walk through what he calls a once-in-a-generation moment for the unicorn economy. In front of Chamath Palihapitiya, Jason Calacanis, David Sacks, and David Friedberg, he argued that a roughly $4 trillion wave of private value is about to hit the public markets, led by SpaceX, Anthropic, and OpenAI, and that the new AI-driven unicorn economy is actually healthier than the one that came before it. You can watch the full presentation and Q&A on YouTube.

    TLDW

    Laffont presents Coatue’s slide deck on the state of the unicorn economy and argues it has rebalanced after the excesses of 2021. The average unicorn is up about 70 percent since September 2024, AI keeps taking a bigger share of all fundraising, and the model has shifted from many small unicorns to fewer companies each raising far more, with funding per unicorn up roughly 5x since 2021. He introduces a “Magnificent 8” private index (SpaceX, Stripe, Anthropic, Databricks, Revolut, ByteDance, Anduril, and more) worth nearly $4 trillion that has crushed the public Mag 7, then shows that exits are finally thawing as SpaceX heads to an IPO in weeks and Anthropic confidentially files its S1. He lays out Coatue’s “CODE” framework for why SpaceX gets more valuable the more it launches, a counterintuitive finding that the odds of a 10x actually rise as companies get bigger (31 percent for $100 billion-plus centicorns), the explosive revenue ramp of OpenAI and Anthropic past Workday, ServiceNow, Adobe, Salesforce, and now the hyperscalers, a three-pillar map of where AI revenue comes from (consumer, ads, enterprise), and the AI memory thesis. The Q&A with Chamath and Calacanis digs into the power law, K-shaped outcomes, whether these valuations are disconnected from reality, the public market as the great antiseptic, and what happens when trillions in private value finally recycles back through GPs and LPs.

    Thoughts

    The most useful idea in the talk is not the $4 trillion headline, it is the cohort-health chart. Laffont splits unicorns into eras and shows that the pre-2021 cohort was healthy, roughly 80 percent had raised again or exited 20 quarters after minting, while the giant 2021 ZIRP cohort of 479 companies is stuck with under 20 percent doing either. That single comparison reframes the whole AI boom. The bullish read is that the 2024 AI cohort is small, concentrated, and cash-generative, so it looks more like the healthy pre-ZIRP group than the 2021 hangover. The bearish read is that we are watching the same movie with bigger numbers, and the test only comes when these companies face public markets. Laffont is honest that we do not yet know which cohort the AI class resembles, and that intellectual humility is what makes the deck credible rather than promotional.

    The SpaceX “CODE” framework is the sharpest analytical move of the presentation. Most people would assume a launch business gets cheaper per launch as it scales. Laffont shows the opposite, the market pays more per launch as cadence rises, and explains it as a phase change in business quality: from one-time government launch revenue, to a single recurring-revenue constellation, to multiple constellations, to a platform with optional upside in space data centers, the moon, and Mars. It is a clean way to think about any company that climbs from a project business to a platform business, and it applies far beyond rockets. The lesson for investors is that valuation can rationally expand even as unit economics look like they should compress, because the nature of the revenue underneath is changing.

    The counterintuitive 10x odds finding deserves more attention than it got in the room. Conventional wisdom says the bigger you are, the harder it is to grow, so a $100 billion company should be less likely to 10x than a $10 billion one. Coatue’s data says the reverse: centicorns have a 31 percent shot at a 10x, far higher than the 8 percent a unicorn has at becoming a decacorn. Laffont’s explanation is a filtering mechanism, every step up validates a compounding advantage and durability of earnings, so survivors are increasingly the kind of business that keeps compounding. This is essentially a quantitative restatement of quality investing, and it is the intellectual backbone of the LP strategy the besties tease out, just buy whoever reaches $100 billion and hold.

    Where the argument gets genuinely contested is valuation, and the panel does not let it slide. The pushback that “these are not fake companies” is true and important, OpenAI and Anthropic are growing faster than any software company in history, and Anthropic reportedly had a profitable month. But growth and reality do not settle the question of price when you are paying 50 to 100 times revenue for trillion-dollar private companies, as Bill Ackman pointed out earlier in the day. Laffont’s answer is the most grounded thing he says all session: the public market is the great antiseptic, it will not care about anyone’s slide deck, and he wants to see these names withstand short sellers and skeptics. That is the right posture. The deck is a thesis, not a verdict, and the verdict arrives roughly six months and one day after the IPOs, once passive flows and supply have washed through.

    The closing thread, that almost every sector is being transformed at once and we still do not have superintelligence, is the part worth sitting with. The risk in a presentation this bullish is treating the trend as destiny. The value is in the framing tools Laffont hands you, cohort health, phase-change business quality, the filtering odds, the three revenue pillars, and the antiseptic of public scrutiny. Use those to interrogate each name rather than to buy the index on faith, and the talk earns its premiere billing.

    Key Takeaways

    • Coatue Management is one of the most successful hedge funds of the last two decades with about $55 billion under management, and is raising roughly another billion dollars specifically to invest in AI.
    • The unicorn economy is up about 70 percent on average since September 2024, and the public market has made a similar move up over the same period.
    • The unicorn economy’s share of the NASDAQ rose significantly after 2015 but has plateaued in recent years, reflecting strong performance from public companies.
    • AI keeps increasing its wallet share of all venture fundraising, multiple years in a row now.
    • The composition of funding has changed. The unicorn “factory” peaked in the ZIRP era of 2021 and has normalized at a much lower level since.
    • Funding per unicorn has increased roughly 5x since 2021. There are fewer unicorns, and each one is raising more.
    • Cohort health, pre-ZIRP group: of about 73 unicorns, 20 quarters after minting roughly 80 percent had either raised a new round or exited, which is healthy.
    • Cohort health, 2021 group: of about 479 unicorns, 20 quarters in, fewer than 20 percent had exited or raised again. Far larger cohort, far worse outcomes.
    • The open question is which cohort the new 2024 AI cohort will resemble.
    • Funding is concentrating: the top 10 companies capture a large share, and it is a small number of AI companies, not all of them, with Anthropic and OpenAI raising massive rounds.
    • Laffont proposes a “Magnificent 8” private index: SpaceX, Stripe, Anthropic, Databricks, Revolut, ByteDance, Anduril, and more, spanning internet, AI, fintech, and space tech.
    • That private index represents almost $4 trillion of value and has crushed the traditional public Mag 7, with almost every name outperforming.
    • Exits are thawing. 2026 is on a good trend for cash returned versus consumed, not quite 2021 levels, with half a year still to go.
    • That trend does not yet include three imminent liquidity events: SpaceX (IPO expected in weeks) and Anthropic (confidentially filed its S1), whose combined value could exceed the prior decade of exits combined.
    • The ecosystem is far more balanced than when Laffont first presented at the 2024 All-In Summit, when it was consuming much more cash than it returned.
    • OpenAI and Anthropic revenue growth is unlike anything previously seen. Starting from January 2025, they passed Workday, then ServiceNow, then Adobe, then Salesforce, and are now bigger than Google Cloud and Azure.
    • On current forecasts, that revenue could pass AWS by the end of the year and exceed all of Microsoft by 2028.
    • Hyperscalers are not sitting still. The largest companies in the world are funding the disruption, investing unprecedented sums to enable the ChatGPT moment.
    • The SpaceX “CODE” framework: the number one driver correlated to SpaceX’s valuation is cadence of launches, and valuation per launch rises as launches increase.
    • Why per-launch value rises: business quality improves through phases, pre-constellation (one-time government revenue), initial ramp (one recurring-revenue constellation), scale (multiple constellations), and platform (space data centers, moon and Mars optionality).
    • Anthropic in particular is scaling like no company seen across the PC, internet, or mobile eras.
    • Counterintuitive 10x odds: a unicorn has about an 8 percent chance of becoming a decacorn, a decacorn has 8 to 13 percent odds of reaching $100 billion, but a centicorn ($100 billion-plus) has a 31 percent chance of a 10x.
    • Value creation has accelerated. It typically takes years to go from $500 billion to $1 trillion in market cap, yet recently three companies did it in one year and two did it in a matter of weeks.
    • Cerebras is the counterexample of slow success: years of dark periods and no new capital developing its technology, then a massive OpenAI contract that quintupled the company’s value ahead of its IPO.
    • Semiconductors are on a generational run, with the sector dramatically outperforming the index since the 2024 All-In Summit.
    • AI memory thesis: the more an AI system knows about you, the more useful it is, so memory per user could quintuple, which helps explain recent moves in memory companies.
    • Where the revenue is: the AI ecosystem is roughly $140 billion today, about $300 billion this year, and is expected to double in 2027.
    • Three revenue pillars: consumer (subscribers times ARPU), ads (about a quarter of Meta and Google ads are AI-enabled today, heading toward 100 percent and roughly $150 billion), and enterprise (tools like Claude Code and Codex inside businesses).
    • Disruption is hitting every sector: software, telco (Starlink-powered global phone calls), semis, energy (data centers reshaping Pennsylvania’s grid), auto (Ferrari’s electric and autonomous stumble), and consumer (GLP-1s reshaping food, alcohol, and wellness).
    • Final takeaways: the new unicorn economy is healthier thanks to AI, winners are compounding faster so the cost of not owning a winner is higher than ever, disruption is everywhere, and we do not even have superintelligence yet.
    • In the Q&A, both Anthropic and OpenAI publicly say they want to be public, and big outcomes now look likely to become liquid within roughly a 12-month window.
    • The valuation pushback: these are not fake companies, they generate substantial revenue at scale and grow faster than anything before, and Anthropic reportedly even had a profitable month.
    • The public market is framed as the great equalizer and antiseptic, but with passive buying the true price discovery may not land on day one, more like six months and a day after listing.
    • A floated LP strategy: wait for whoever reaches $100 billion and concentrate capital there as the least brittle, quickest-return bet, tempered by the warning that valuations are disconnecting from any historical metric (50x to 100x revenue).
    • An open risk: with so much capital, OpenAI and Anthropic could rationally start a price war, the way ride-sharing and food-delivery players once did, though heavy infrastructure spend complicates it.

    Detailed Summary

    The unicorn economy has rebalanced after 2021

    Laffont opens by reframing a market many assume is frothy. The average unicorn is up about 70 percent since September 2024, and the public market has tracked a similar climb, so private and public value are moving together rather than diverging. The unicorn economy’s share of the NASDAQ rose sharply after 2015 and then plateaued, which he reads as a sign of how strong public companies have become. Underneath the headline, the structure of funding has changed. The 2021 ZIRP era was a unicorn factory that minted enormous numbers of companies, and that machine has since normalized to a much lower level. The result is a barbell: fewer new unicorns, but each raising far more, with funding per unicorn up roughly 5x since 2021. AI sits at the center of this, taking a steadily larger share of all venture dollars for several years running.

    Cohort health is the real story

    The deck’s most important slide measures the health of the ecosystem by cohort. The pre-ZIRP cohort, about 73 unicorns, looks healthy: 20 quarters after becoming unicorns, roughly 80 percent had either raised a new round or exited. The 2021 cohort tells the opposite story. It is enormous, about 479 unicorns, and 20 quarters in, fewer than 20 percent had raised again or exited. That contrast sets up the central question of the talk. A new 2024 cohort of AI companies is forming, and no one yet knows whether it will resemble the healthy pre-ZIRP group or the bloated, stuck 2021 group. Laffont’s framing leans optimistic because the AI cohort is small and concentrated, but he is careful not to declare the answer.

    The Magnificent 8 and a $4 trillion private index

    Funding is not just flowing to AI, it is flowing to a handful of AI names, with the top 10 capturing a large share and Anthropic and OpenAI raising the biggest rounds. From this concentration Laffont builds a private index he half-jokingly calls the Magnificent 8, a number he expects to shrink as companies go public. The members span sectors: SpaceX, Stripe, Anthropic, Databricks, Revolut, ByteDance, and Anduril, covering internet, AI, fintech, and space tech. He says he would be comfortable owning that index for the next decade-plus. Collectively it represents almost $4 trillion of value and has outperformed the public Mag 7, with nearly every constituent beating that benchmark.

    Exits are thawing and a wall of liquidity is coming

    One of Laffont’s recurring concerns at past summits has been balance: the unicorn economy is great at consuming cash, but a healthy ecosystem must also return it. On that score 2026 is trending well, not quite 2021, but solid with half a year left. Crucially, that figure does not yet include three imminent events. SpaceX is expected to go public within weeks, and Anthropic confidentially filed its S1 the day of the talk. Adding those up, just a few companies could deliver more liquidity than the prior ten years combined. The takeaway is that the ecosystem that was dangerously out of balance in 2024 is now meaningfully more balanced, and improving.

    The revenue ramp past the hyperscalers

    The growth rates of OpenAI and Anthropic, Laffont argues, are unlike anything previously seen. Charting from January 2025, the leading AI labs passed Workday, then ServiceNow, then Adobe by year end, then Salesforce by January, and are now bigger than Google Cloud and Azure. On forecast, that revenue could surpass AWS by the end of the year and exceed all of Microsoft by 2028. He stresses that the hyperscalers are not passive bystanders, they are actively funding the disruption, pouring unprecedented capital into enabling the change that began with the ChatGPT moment.

    The SpaceX CODE framework

    Laffont devotes real time to how Coatue thinks about SpaceX. The single factor most correlated with SpaceX’s valuation is cadence of launches, which is intuitive for a launch business. The surprise is that valuation per launch has risen rather than fallen as cadence climbed. His explanation, the CODE framework, is that the quality of the business model improves the more SpaceX launches. In phase one, pre-constellation, you are simply proving rockets, with a few government customers and lumpy, unpredictable one-time revenue. In the initial ramp you stand up a constellation, which is an end market and a recurring-revenue business that grows with every satellite and subscriber. At scale you operate multiple constellations, and Laffont expects companies, governments, and militaries to want to own their own. Ultimately it becomes a platform, with new businesses layered on top, from space data centers to the optionality of the moon and Mars.

    Counterintuitive odds and the speed of value creation

    Coatue bucketed companies and asked the odds of a 10x within each. A unicorn has roughly an 8 percent chance of becoming a decacorn. A decacorn has 8 to 13 percent odds of reaching $100 billion. But a centicorn, $100 billion or more, has a 31 percent chance of a 10x, counting both public and private companies. The bigger you are, the better your odds, which inverts intuition. Laffont pairs this with the sheer speed of recent value creation. Going from $500 billion to $1 trillion in market cap normally takes years, yet three companies did it in a single year and two did it in a matter of weeks. He also offers Cerebras as the patient counterexample, a chip company that endured years of dark periods and no new capital before a massive OpenAI contract quintupled its value ahead of IPO, part of a broader generational run for semiconductors.

    AI memory and where the revenue actually comes from

    A throughline from the day’s other speakers is that the more an AI knows about you, the more useful it is, from your restaurant preferences to your work context. Laffont turns that into a thesis: memory per user could quintuple based on what these systems require, which helps explain recent moves in memory companies. He then tackles the most contested question, where is the revenue. He sizes the AI ecosystem at about $140 billion today, roughly $300 billion this year, and doubling in 2027, built on three pillars. Consumer is subscribers times ARPU. Ads are the pillar people forget, with about a quarter of Meta and Google ads already AI-enabled and penetration heading toward 100 percent, a roughly $150 billion opportunity. Enterprise is the breakthrough category, exemplified by tools like Claude Code and Codex operating inside businesses.

    Every sector is being transformed at once

    What makes this era different, Laffont says, is that nearly every sector is being transformed simultaneously. Software is obvious, but look at telco, where he believes Starlink will soon power a device that lets you make a phone call anywhere on earth, attacking the global telco and broadband profit pool with a better product. Compute is driving massive change in semis, data centers are reshaping the energy equation in places like Pennsylvania, and the auto business is being upended, as Ferrari’s stumble introducing electric and autonomous technology showed. In consumer, GLP-1 drugs are profoundly changing consumption of food and alcohol and the broader focus on wellness. His takeaways close the loop: the new unicorn economy is healthier thanks to AI, winners are compounding faster so the cost of missing them is higher than ever, disruption is everywhere, and superintelligence has not even arrived yet.

    The Q&A: power law, valuation, and the public market test

    Chamath and Jason Calacanis press Laffont on what this means for allocators. The recurring theme is the power law and K-shaped outcomes, with gains consolidating into a small number of companies. The positive side, Laffont notes, is that outcomes are enormous and increasingly liquid within a 12-month window, and both Anthropic and OpenAI say they want to be public. The hard part is valuation. The besties cite Bill Ackman’s framing that investors are making venture bets on trillion-dollar companies at 50 to 100 times revenue. Laffont’s pushback is that these are not fake companies, they generate substantial revenue at scale and grow faster than anything before, and Anthropic reportedly had a profitable month. But he embraces the discipline ahead: the public market is the great antiseptic and will not care about anyone’s presentation, though with heavy passive buying, true price discovery may take roughly six months and a day rather than landing on day one. Asked whether the compounding is a market inefficiency or survivor bias, he declines to over-read a small sample, noting that Anthropic before Claude Code was a completely different company than after. The conversation closes on what happens when trillions recycle from GPs to LPs, the case for simply owning whoever crosses $100 billion, the risk of everyone crowding into three names, and the possibility of an eventual OpenAI versus Anthropic price war.

    Notable Quotes

    “So we have fewer unicorns that are each raising more.”

    Thomas Laffont, summarizing how funding per unicorn has risen roughly 5x since 2021

    “The reason is that the quality of SpaceX’s business model increases the more you launch.”

    Thomas Laffont, explaining the CODE framework and why valuation per launch rises with cadence

    “The winners are compounding faster than ever, which means the costs of not being in a winner are higher than ever.”

    Thomas Laffont, on the central risk of a power-law market

    “And by the way, we don’t even have super intelligence yet.”

    Thomas Laffont, closing his takeaways on how early the transformation still is

    “These are companies generating substantial revenue at scale that are growing faster than anything we’ve ever seen.”

    Thomas Laffont, pushing back on the idea that AI valuations rest on fake companies

    “It will be the great antiseptic. It will not care about my presentation.”

    Thomas Laffont, on the public market as the ultimate test for SpaceX, OpenAI, and Anthropic

    “Anthropic pre-cloud code was a completely different company than post cloud code.”

    Thomas Laffont, on why he won’t over-read a small sample of hyper-compounders

    “The power law rules our lives. All the great gains are being consolidated into small numbers of companies.”

    An All-In host, framing the Q&A on concentration in private markets

    This is a curated set of highlights. To hear the full presentation, the slide walkthrough, and the complete Q&A with Chamath and Jason Calacanis, watch the full conversation here.

    Related Reading

    • Coatue Management. Primary source for Thomas Laffont’s firm and the technology investing strategy behind the deck.
    • The All-In Podcast. The show and summit where Laffont made this premiere presentation.
    • Power law (Wikipedia). Background on the distribution Laffont and the hosts say governs venture and public-market returns.
    • The Magnificent Seven (Wikipedia). The public-market benchmark Laffont’s private “Magnificent 8” index is measured against.
    • Cerebras Systems. The AI chipmaker Laffont cites as the slow-grind IPO that was eventually transformed by a major OpenAI contract.

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

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

    TLDW

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

    Thoughts

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

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

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

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

    Key Takeaways

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

    Detailed Summary

    Two economies and the money illusion

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

    The AI capital race nobody can stop

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

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

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

    Why crypto lagged and why the worst is over

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

    Layer ones as universal basic equity

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

    Zcash, privacy, and the quantum hedge

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

    Agents, DeFi, and financial rails

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

    The NFT fund and grail digital art

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

    Never sell, and the math of patience

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

    The 2026 to 2027 outlook

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

    Notable Quotes

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

    Raoul Pal, on why capital keeps flooding into AI

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

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

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

    Raoul Pal, on silicon and the universal code

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

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

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

    Raoul Pal, on the entitlement that ruins crypto investors

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

    Raoul Pal, on owning layer one blockchains

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

    Raoul Pal, on why holding beats trading

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

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

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

    Raoul Pal, on the relative value signal he watches

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

    Related Reading

    • Real Vision the financial media platform Raoul Pal co-founded, where his Global Macro Investor research and exponential age thesis live.
    • Metcalfe’s law (Wikipedia) the network-value relationship Pal uses to model the log regression channel for crypto.
    • Reed’s law (Wikipedia) background on the exponential-of-the-exponential growth Pal says AI is the first real-world example of.
    • Technological singularity (Wikipedia) context for the economic singularity Pal argues is now only about four years away.
    • Zcash the privacy coin Pal added in the correction as a Bitcoin-with-privacy and quantum-proof trade.

  • Ray Dalio Warns: The Fed Is Now Stimulating Into a Bubble

    https://x.com/raydalio/status/1986167253453213789?s=46

    Ray Dalio, founder of Bridgewater Associates and one of the most influential macro investors in history, just sounded the alarm: the Federal Reserve may be easing monetary policy into a bubble rather than out of a recession.

    In a recent post on X, Dalio unpacked what he calls a “classic Big Debt Cycle late-stage dynamic” — the point where the Fed’s and Treasury’s actions start looking less like technical balance-sheet adjustments and more like coordinated money creation to fund deficits. His key takeaway: while the Fed is calling its latest move “technical,” it is effectively shifting from quantitative tightening (QT) to quantitative easing (QE), a clear easing move.

    “If the balance sheet starts expanding significantly, while interest rates are being cut, while fiscal deficits are large, we will view that as a classic monetary and fiscal interaction of the Fed and the Treasury to monetize government debt.” — Ray Dalio

    Dalio connects this to his Big Debt Cycle framework, which tracks how economies move from productive credit expansion to destructive debt monetization. Historically, QE has been used to stabilize collapsing economies. But this time, he warns, QE would be arriving while markets and credit are already overheated:

    • Asset valuations are at record highs.
    • Unemployment is near historical lows.
    • Inflation remains above target.
    • Credit spreads are tight and liquidity is abundant.
    • AI and tech stocks are showing classic bubble characteristics.

    In other words, the Fed may be adding fuel to an already roaring fire. Dalio characterizes this as “stimulus into a bubble” — the mirror image of QE during 2008 or 2020, when stimulus was needed to pull the system out of crisis. Now, similar tools may be used even as risk assets soar and government deficits balloon.

    Dalio points out that when central banks buy bonds and expand liquidity, real yields fall, valuations expand, and money tends to flow into financial assets first. That drives up prices of stocks, gold, and long-duration tech companies while widening wealth gaps. Eventually, that liquidity leaks into the real economy, pushing inflation higher.

    He notes that this cycle often culminates in a speculative “melt-up” — a surge in asset prices that precedes the tightening phase which finally bursts the bubble. The “ideal time to sell,” he writes, is during that final euphoric upswing, before the inevitable reversal.

    What makes this period different, Dalio argues, is that it’s not being driven by fear but by policy-driven optimism — an intentional, politically convenient push for growth amid already-loose financial conditions. With massive deficits, a shortening debt maturity profile, and the Fed potentially resuming bond purchases, Dalio sees this as “a bold and dangerous big bet on growth — especially AI growth — financed through very liberal looseness in fiscal, monetary, and regulatory policies.”

    For investors, the takeaway is clear: the Big Debt Cycle is entering its late stage. QE during a bubble may create a liquidity surge that pushes markets higher — temporarily — but it also raises the risk of inflation, currency debasement, and volatility when the cycle turns.

    Or as Dalio might put it: when the system is printing money to sustain itself, you’re no longer in the realm of normal economics — you’re in the endgame of the cycle.

    Source: Ray Dalio on X