Anthropic has closed one of the largest private financing rounds in the history of technology, raising $65 billion in Series H funding at a $965 billion post-money valuation. The round, announced on May 28, 2026, lands as demand for Claude reaches what the company calls historic levels, and it positions Anthropic to pour fresh capital into safety research, compute, and the products that enterprises now lean on every day.
TLDR
Anthropic raised $65 billion in its Series H at a $965 billion post-money valuation, with Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital leading and Capital Group, Coatue, D1 Capital Partners, GIC, ICONIQ, and XN co-leading, alongside $15 billion in previously committed hyperscaler investment that includes $5 billion from Amazon. The raise follows Anthropic crossing $47 billion in run-rate revenue earlier in May 2026, and it funds three priorities named by CFO Krishna Rao: advancing safety and interpretability research, expanding compute capacity to meet growing Claude demand, and scaling the products and partnerships customers depend on. On the infrastructure side, the company is locking in gigawatt-scale compute through 5 gigawatts with Amazon, 5 gigawatts of TPU capacity via Google and Broadcom, GPU access from SpaceX, and supply from partners Micron, Samsung, and SK hynix, while Claude remains available across all three major cloud platforms, AWS, Google Cloud, and Microsoft Azure, with widespread enterprise adoption across industries.
Thoughts
Start with the number that everyone will fixate on. A $965 billion post-money valuation against $47 billion in run-rate revenue is roughly 20 times sales, and for a company growing this fast that multiple is not the interesting part. The interesting part is that run-rate revenue crossed $47 billion earlier this month, which means the denominator is moving so quickly that the multiple is already stale. Investors are not pricing the business Anthropic is today. They are pricing the slope. A 20x multiple on a number that may double again inside a year is a very different bet than 20x on a flat line, and the lead names here (Altimeter, Dragoneer, Greenoaks, Sequoia, with Capital Group, Coatue, GIC and others co-leading) are not the kind of capital that pays for nostalgia. They are paying for the second derivative.
But the real story is not the valuation. It is the compute. Read the infrastructure list carefully and you see the actual problem this round solves: 5 gigawatts from Amazon, 5 gigawatts of TPU capacity through Google and Broadcom, GPU access from SpaceX, and memory supply locked down with Micron, Samsung, and SK hynix. That is more than 10 gigawatts of secured power and silicon. The constraint on frontier AI in 2026 is no longer talent or even algorithms. It is electricity, land, and the multi-year queue for advanced packaging and high-bandwidth memory. You cannot buy 10 gigawatts on a quarterly basis. You reserve it years out, and you need the balance sheet to make those commitments credible. A $65 billion raise is, in plain terms, the down payment that lets Anthropic sign for capacity nobody can conjure on demand. The money is downstream of the megawatts.
The diversification across that compute stack matters as much as the size. By splitting between Amazon’s infrastructure, Google and Broadcom’s custom TPUs, and SpaceX-supplied GPUs, Anthropic is refusing to become hostage to any single supplier’s roadmap or pricing. Custom silicon through Broadcom in particular is a bet on bending the cost curve, because the long-term economics of serving Claude at this scale depend on dollars per token, not just on raw availability. Anyone who has watched cloud lock-in play out over the last decade understands the move. Optionality at the hardware layer is leverage, and leverage is what keeps margins from being dictated by whoever owns the only fab slot you can reach.
It is worth pausing on the fact that the round explicitly funds safety and interpretability research alongside scaling, and not as a footnote. Most companies treat safety spend as a cost center to be minimized once growth kicks in. Naming it first, ahead of compute and products, is a statement about where Anthropic believes its durable advantage sits. If models keep getting more capable, the binding constraint on deployment inside regulated industries (finance, healthcare, government) becomes trust, not intelligence. Interpretability is the work that turns a black box into something an enterprise risk committee can actually sign off on. Framed that way, safety research is not philanthropy subtracted from the bottom line. It is the thing that unlocks the most lucrative and defensible parts of the market, and pairing it with the scaling budget is the tell.
Finally, look at distribution. Claude now ships on all three major clouds at once: AWS, Google Cloud, and Microsoft Azure. In a market where most frontier labs are tethered to a single hyperscaler, being available everywhere enterprises already run their workloads is a structural edge. It removes the procurement friction of asking a customer to adopt a new vendor relationship, and it means Anthropic competes on the merits of the model rather than on which cloud a buyer happened to standardize on years ago. Combine that omnipresent distribution with the compute reservations and the explicit safety mandate, and the shape of the strategy is clear. This is not a company buying time. It is a company buying the three things that actually compound: capacity that cannot be rushed, trust that cannot be faked, and reach into every place where work already happens.
Key Takeaways
Anthropic raised $65 billion in its Series H funding round, one of the largest private financings in the history of the technology industry.
The round set Anthropic’s post-money valuation at $965 billion, placing the company within reach of the $1 trillion mark.
Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital led the Series H round.
Capital Group, Coatue, D1 Capital Partners, GIC, ICONIQ, and XN served as co-leads on the investment.
The new capital builds on $15 billion in previously committed hyperscaler investments, which includes $5 billion from Amazon.
Anthropic crossed $47 billion in run-rate revenue earlier in May 2026, reflecting the surging commercial demand for Claude.
A core priority for the funding is to advance Anthropic’s safety and interpretability research.
The company will use the capital to expand compute capacity in order to meet growing demand for Claude.
Anthropic plans to scale the products and partnerships that customers depend on across its business.
CFO Krishna Rao said the funding will help Anthropic serve the historic demand it is experiencing, stay at the research frontier, and bring Claude to more of the places where work happens.
Amazon is providing 5 gigawatts of compute capacity as part of Anthropic’s infrastructure expansion.
Google and Broadcom are supplying 5 gigawatts of TPU capacity to power Claude’s growth.
SpaceX is contributing GPU access to Anthropic’s compute footprint.
Micron, Samsung, and SK hynix are partnering with Anthropic on memory and infrastructure to support its scaling needs.
Claude is available on all three major cloud platforms, AWS, Google Cloud, and Microsoft Azure.
Anthropic reports widespread enterprise adoption of Claude across a broad range of industries.
Detailed Summary
The Raise and the Valuation
Anthropic has raised $65 billion in Series H funding, a round that values the company at $965 billion on a post-money basis. The size of the raise places it among the largest private financing events the technology industry has ever seen, and the valuation pushes Anthropic to the doorstep of the trillion dollar mark. The capital arrives at a moment when demand for the company’s Claude models has accelerated sharply, and the round is built to fund the response to that demand rather than simply mark a milestone. Anthropic framed the financing in its Series H announcement as the fuel for staying at the research frontier while scaling the infrastructure and products that customers increasingly rely on.
Who Put In the Money
The Series H was led by Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital, a group that combines deep growth-stage technology experience with conviction in Anthropic’s long-term trajectory. Joining as co-leads were Capital Group, Coatue, D1 Capital Partners, GIC, ICONIQ, and XN, a roster that spans crossover funds, sovereign wealth, and institutional investors. Beyond the new equity, Anthropic pointed to $15 billion in previously committed hyperscaler investment, including $5 billion from Amazon. Taken together, the investor base reflects a mix of financial backers and strategic partners with a direct stake in seeing Claude reach more customers and more compute.
Revenue at $47 Billion Run-Rate
Underpinning the valuation is a business that has scaled with unusual speed. Anthropic crossed a $47 billion run-rate revenue figure earlier in May 2026, a number that signals how quickly enterprises and developers have adopted Claude across their workflows. Run-rate revenue annualizes the company’s most recent performance, and at this level it puts Anthropic firmly among the fastest growing software businesses on record. That financial momentum is the practical justification for both the round’s size and the near trillion dollar valuation investors were willing to support.
The Compute Buildout
A large share of the strategy behind the raise centers on securing compute at enormous scale. Anthropic detailed a set of infrastructure partnerships designed to keep pace with Claude demand. Amazon is providing 5 gigawatts of capacity, while Google and Broadcom together are supplying 5 gigawatts of TPU capacity. SpaceX is contributing GPU access, broadening the range of silicon Anthropic can draw on. Supporting the buildout on the hardware supply side are Micron, Samsung, and SK hynix, the memory and component partners whose output is essential to standing up data centers at this magnitude. The combined picture is a company assembling power, chips, and supply chain commitments measured in gigawatts rather than racks.
Where the Money Goes
Anthropic outlined three priorities for the new capital. The first is to advance safety and interpretability research, continuing the work of understanding how models behave and ensuring they remain reliable as they grow more capable. The second is to expand compute capacity to meet the growing demand for Claude, the practical engine behind the infrastructure commitments above. The third is to scale the products and partnerships that customers depend on, deepening the company’s reach into the tools and platforms where work actually happens. Krishna Rao, Anthropic’s chief financial officer, said the funding “will help us serve the historic demand we are experiencing, stay at the research frontier, and bring Claude to more of the places where work happens.”
Claude Everywhere
The funding lands on top of a distribution footprint that already spans the major cloud ecosystems. Claude is available on all three leading cloud platforms, AWS, Google Cloud, and Microsoft Azure, which means enterprises can reach the models through whichever provider they have standardized on. That availability has translated into widespread enterprise adoption across industries, from software and finance to healthcare and beyond. By being present everywhere developers and businesses already operate, Anthropic positions Claude not as a destination customers must travel to but as a capability woven into the platforms they use every day.
Notable Quotes
This funding will help us serve the historic demand we are experiencing, stay at the research frontier, and bring Claude to more of the places where work happens.
Krishna Rao, CFO at Anthropic, on the purpose of the Series H round.
Advance safety and interpretability research, expand compute capacity to meet growing Claude demand, and scale products and partnerships customers depend on.
How Anthropic describes its use of funds from the round.
For the full details on the round, the lead and co-lead investors, and how Anthropic plans to deploy the capital across safety research, compute, and products, read the full announcement here.
Related Reading
Anthropic, the AI safety and research company behind Claude that raised this Series H round.
Sequoia Capital, one of the lead investors anchoring the financing.
Amazon Web Services, one of the three major cloud platforms where Claude is available and the source of a $5 billion investment.
Google Cloud TPUs, the tensor processing units behind the 5 gigawatts of TPU capacity in the Google and Broadcom partnership.
AI safety, the research field at the center of how Anthropic says it will use the new funding.
Dan Loeb has spent three decades turning a $3 million fund into Third Point, a roughly $25 billion collection of hedge fund, credit, insurance, and venture businesses. In this Invest Like the Best conversation with Patrick O’Shaughnessy, Loeb walks through how he reinvented his strategy from deep value and event-driven trades into quality and thematic investing, why he now believes every serious investor has to be a technology investor, how he reads the AI cycle and the semiconductor melt-up, where activism and corporate governance still pay, and the single mistake that taught him the most. It is a rare, unhurried look at how a famously sharp-elbowed activist actually thinks about markets, businesses, and people.
TLDW
Loeb covers an enormous amount of ground: his daily process for staying ahead of the information firehose, Jensen Huang’s AI stack as a mental model, and why Nvidia, Anthropic, and Elon Musk’s companies are the three most consequential firms he tracks. He traces Third Point’s roots in credit and event-driven investing at Jefferies, the influence of Joel Greenblatt’s “You Can Be a Stock Market Genius,” and his later pivot to quality investing shaped by “The Outsiders” and Lawrence Cunningham’s “Quality Investing.” He argues the AI rally is not a dot-com-style valuation bubble because the leaders generate enormous cash, explains why human judgment and structural market quirks still create alpha, and makes the case that AI will never fully run a capital system. He digs into corporate governance and his father’s influence, the Sotheby’s and Sony activism campaigns, the hard reality of activism in Japan, and what investing in Danaher’s operating system taught him. He names FTX as his hardest lesson, breaks down Third Point’s evolution into a 60-percent-credit platform spanning CLOs, structured credit, reinsurance and annuities, describes how he is pushing his analysts to use AI and Claude daily, and closes on kindness and the friend who let him sleep on a couch before he made it.
Thoughts
The most striking thing about Loeb is that he treats his own strategy as a thing to be disrupted rather than defended. He built his reputation on Greenblatt-style special situations, spin-offs, demutualizations, and post-reorg equities bought cheap because of forced selling and sandbagged guidance. Most investors who win that way spend the rest of their careers protecting the formula. Loeb instead watched the people who stayed rigid about deep value and low multiples underperform or disappear, and deliberately retrained himself and his team around business quality and thematic conviction. The willingness to abandon a winning identity is the actual edge here, more than any single trade. It is the rare investor who can say his current strategy would not fit cleanly on a PowerPoint deck and treat that as a feature.
His AI framing deserves attention because it is unfashionably calm. The bear case on AI is usually about valuation, and Loeb dismantles it on the leaders’ own numbers: these are companies investing off their balance sheets, generating enormous cash, trading at multiples that do not resemble 1999. He was short the dot-com bubble, so he is not a permabull cheering from the sidelines. His real point is subtler, that the danger is expectations, not valuations. The semiconductor index ran up 40 percent on genuinely strong fundamentals, but Micron and Nvidia both put up monster quarters and saw their stocks fall because expectations had simply outrun even great results. That gap between fundamentals and price is where he thinks the human investor still earns a living, precisely because quant strategies, CTAs, and risk-managed pods are forced to sell into weakness rather than buy it.
The governance material is the most quietly radical part of the conversation. Loeb defends shareholder primacy against the Business Roundtable’s softer stakeholder language, but his argument is not the cartoon version where shareholder value means strip-mining a company. It is that boards have one job, accountability for capital allocation and management, and that vague multi-stakeholder mandates become an excuse for directors to avoid the hard work. His read on bad governance is almost always relational: directors who let loyalty to an underperforming CEO override their duty, or who sit on boards for status and income. The Sotheby’s story is the clean illustration, a centuries-old, high-status business run unprofitably because nobody treated it like a business. Loeb’s pattern is to find the gap between claimed status and actual performance and to raise the social cost of coasting.
What is genuinely new in Loeb’s posture is how he talks about AI inside his own firm. He is not pitching it as a moat or a headcount-reduction story. He frames Claude and AI tools as a way to make each person a more autonomous self-improver, something that gives back whatever you put into it, with some analysts running agents overnight and burning tokens while he personally uses it more for queries. Coming from a 30-year fundamental investor, the absence of defensiveness is the signal. He pairs it with Brad Gerstner’s nod to “Essentialism”: the firehose is now infinite, so the scarce skill is deciding what is actually relevant. That is a more honest answer to the AI question than either doom or hype.
Finally, the FTX confession is worth sitting with because of how he frames it. He does not retreat into cynicism about venture or crypto. He notes that Sam Bankman-Fried, fraud aside, had a real nose for value, with stakes in Anthropic, Cursor, and Solana that would have made him a top venture investor of the era. The lesson Loeb extracts is procedural, not philosophical: their due diligence now includes checking bank balances, the most basic verification that would have surfaced the problem. It is a useful reminder that even sophisticated capital can skip boring fundamentals when a company is growing fast and the cap table looks good. The discipline is not in having a grand theory of fraud, it is in never skipping the unglamorous checks.
Key Takeaways
Loeb’s macro focus right now collapses to two variables: where oil goes, dictated by war and geopolitics, and what AI does on the spending and infrastructure front and its impact on society and the economy.
He argues you can no longer punt on technology and focus on industrials or consumer; tech is a big, growing, compounding part of the economy that affects everything else, so every investor has to become a tech investor.
He uses Jensen Huang’s AI stack as a mental model: power and energy at the bottom, then chips and infrastructure, up through large language models, software, and applications.
The three most consequential companies he tracks are Nvidia, Anthropic, and Elon Musk’s companies collectively.
Third Point’s roots are in credit and event-driven investing, shaped by his time at Jefferies watching investors like David Tepper before he founded Appaloosa, Eric Mindich at Goldman, and firms like Angelo Gordon and Farallon.
Joel Greenblatt’s “You Can Be a Stock Market Genius” was his foundational framework: spin-offs, demutualizations, privatizations, and post-reorg equities where a new, illiquid security gets dumped by holders who will not do the work.
Spin-off managers often sandbag guidance because their incentive packages get set at the time of the spin-off, creating a predictable gap between conservative numbers and real value.
From 1995 to roughly 2013-2015, event-driven special situations were Third Point’s bread and butter; those opportunities still exist, but the real edge now is overlaying them with a business-quality lens.
The pivot to quality and thematic investing was influenced most by “The Outsiders” (capital allocation plus great operations) and Lawrence Cunningham’s “Quality Investing” (high-moat, high-return-on-capital businesses to own for years).
AI disruption made last year one of the worst for many apparently high-quality companies, as businesses that looked durable rapidly became less so.
Loeb sees the AI rally as fundamentally different from the dot-com bubble: the leaders invest off their balance sheets, generate enormous cash, and do not carry the valuation excess of 1999.
The danger in semis is expectations, not valuation: Nvidia and Micron posted spectacular quarters yet saw stocks fall because expectations had outrun even great numbers.
Structural forces still create alpha for fundamental investors: quants, CTAs, and multi-strategy pods have risk metrics that force selling on the way down, the opposite of what is rational for long-term holders.
He believes AI will not fully run a capital system; private equity, restructurings, creditor committees, and high-touch negotiation will always need humans.
His interest in governance came from his father, a securities lawyer and corporate governance expert who sat on the boards of Mattel and Williams-Sonoma and pushed ethical sourcing ahead of his time.
Loeb defends shareholder primacy, citing Milton Friedman and Warren Buffett, and criticizes the Business Roundtable’s move away from shareholder value as a distraction from the board’s real duty.
Bad governance usually comes from directors letting loyalty to a weak CEO override fiduciary duty, lacking the knowledge to do the job, or serving for status and income.
Writing is a core activism lever: great writing is clear thinking, and social pressure through writing and PR is one of the most effective ways to move a board, alongside financial and legal levers.
The Sotheby’s campaign targeted a high-status, centuries-old business run unprofitably; Third Point bought 9.9 percent, eventually brought in Tad Smith from MSG, who cleaned up operations and technology before the company sold.
Third Point increasingly prefers to back great companies with excellent management and cheer them on rather than hunt for mismanaged businesses, because bad management tends to cluster into a morass.
Third Point is a collection of businesses; the flagship hedge fund grew from $3 million to about $9 billion and is roughly 30 percent credit, with the broader firm closer to 60 percent credit.
The firm spans a roughly $7 billion CLO business, structured and corporate credit, an insurance company, asbestos liabilities, a small private credit unit, and a venture capital arm.
The unifying thread is valuing enterprises across early, mid, and mature stages and investing in whichever fulcrum security offers the best risk-reward, from equity to senior debt.
Loeb cites buying Twitter’s financing debt near 96-97 cents at a 12 percent yield when most credit investors were scared, and a difficult xAI debt financing, as examples of cross-discipline conviction.
He is the portfolio manager only of the hedge fund; the credit, CLO, structured credit, and high-yield businesses have their own PMs and investment committees he does not sit on.
The Sony campaign saw Third Point own up to 7 percent and push to separate the conglomerate; management resisted for years before spinning out the semiconductor and financial services businesses.
He learned that activism in Japan is hard, but the government often wants reform; he co-wrote a paper with Larry Lindsey and Niall Ferguson urging corporate governance and return on invested capital as a fourth arrow of Abenomics, picked up as a Wall Street Journal editorial.
Investing in Danaher was his most instructive experience, teaching him how the Danaher Business System drives continuous improvement (Kaizen) and how the company celebrates rather than shames underperformance because problems are fixable.
FTX was his hardest lesson; it looked great and was verifiable on the blockchain, but was not what it appeared, and now Third Point’s diligence includes checking bank balances.
He notes that, fraud aside, Sam Bankman-Fried had a strong nose for value with stakes in Anthropic, Cursor, and Solana.
Recent mistakes also include shorts where Third Point thought certain info-services businesses would resist AI disruption; he still expects a shakeout with some phoenixes rising from the ashes.
He is pushing his whole team to use AI daily, hiring native computer scientists and system integrators, and describes Claude as a tool that makes you autonomous and gives back whatever you put into it.
Third Point’s distinctive edge is optimism about AI creating net jobs and the ability to default into credit investing during stressed times, as it did with investment-grade credit in 2020.
Credit is hard to copy because it runs on relationships, not electronic trading; that is why Third Point built into CLOs and eyes the roughly $6 trillion structured credit market rather than treating it as tourism.
The great analyst has changed: 20 years ago it was someone who could model fast and crack a complex restructuring (Loeb made a career-defining bet on Drexel Burnham claims); today it is a Gavin Baker type who deeply understands an industry, like the analyst who flew to Texas and realized Casey’s General Stores was really a pizza chain.
Outside the US, Loeb is more bullish on Korea, Taiwan, and Japan as hunting grounds, finds Europe tough on regulation (though he owns Rolls-Royce and ASML), and finds the Middle East the most vibrant region.
What worries him most is not the business but running out of time for family, surfing, and reading; what excites him is incorporating everything relevant about the world and forming relationships with people building interesting things.
His closing reflection is on kindness as a top-tier value, and the friend, Carter, who let him sleep on a couch and seeded his early fund, echoing a Palmer Luckey line that money cannot buy friends who believed in you when you had nothing.
Detailed Summary
Staying ahead of the firehose and reading the macro
Loeb opens by admitting he does not have a perfectly organized system for processing the modern flood of information. He checks the news for what is relevant to the economy and to Third Point’s positions, tries not to obsess over minute-to-minute moves, and leans more tactical than strategic. When people ask him about macro, he says the usual government-reported metrics (growth, unemployment, inflation, rates, currencies, gold, crypto) are trumped right now by two things: where oil goes, which depends on war and geopolitics, and what AI does on the spending and infrastructure side and its impact on society and the economy. To understand technology, he leans on Jensen Huang’s framing of the AI stack and talks to smart people regularly, and he watches three companies above all: Nvidia, Anthropic, and Elon Musk’s companies as a group.
From event-driven roots to quality investing
Third Point’s DNA comes from Loeb’s time as a credit investor at Jefferies, where he watched some of the best distressed, event-driven, and risk-arbitrage investors operate, from David Tepper to Eric Mindich to firms like Angelo Gordon and Farallon. His first lens was event-driven: spin-offs, demutualizations, privatizations, and post-reorg equities, where a newly created and illiquid security gets dumped by holders who will not do the work, and management sandbags guidance because incentive packages are set at the spin date. He barely thought about moats or returns on capital; he just wanted to buy something genuinely cheap with those characteristics. That was the firm’s bread and butter from 1995 until roughly 2013-2015. Those opportunities still exist, but Loeb describes deliberately evolving toward business quality and thematic investing, influenced by “The Outsiders” on capital allocation and Lawrence Cunningham’s “Quality Investing” on durable, high-return businesses. He organized the team around industry experts rather than generalists. The twist: AI disruption recently turned many apparently high-quality companies into much lower-quality ones, fast.
The AI cycle, bubbles, and the human edge
Loeb resists the bubble narrative. He was short the dot-com bubble and remembers the valuation excess; today’s AI leaders, by contrast, invest off their balance sheets and generate enormous cash, so unless you believe the capex yields no return, the earnings and multiples do not look like 1999. The real driver of volatility, he argues, is expectations: the semiconductor index ran up 40 percent on strong fundamentals, but Nvidia and Micron both delivered blowout quarters and still saw their stocks fall because expectations had run too high. That dynamic is exactly where a fundamental investor earns a living, because quants, CTAs, and risk-managed pods are structurally forced to sell into weakness. He also doubts AI will ever fully run a capital system, since private equity, restructurings, creditor committees, and high-touch credit always need humans. He cites “Reminiscences of a Stock Operator” and Ecclesiastes: there is nothing new under the sun, and human nature, with its bubbles, panics, and extremes, does not change.
Governance, his father, and the duty of boards
Loeb traces his governance interest to his father, a securities lawyer and corporate-governance expert who served on the boards of Mattel and Williams-Sonoma and championed ethical sourcing before it was common. He calls the American board system beautiful: directors are answerable to shareholders and accountable for strategy and key financial decisions. Governance breaks down when directors lose sight of their fiduciary duty, lack the knowledge or talent diversity to do the job, or prioritize things other than shareholders. He invokes Milton Friedman and Warren Buffett to argue that caring about communities, employees, and conduct is not inconsistent with shareholder value but part of it, and criticizes the Business Roundtable for muddying the board’s core duty. The most common failure he sees is directors letting loyalty to an underperforming CEO override their duty. Most of the time Third Point redirects existing boards without even taking a seat; the extreme proxy fights are the exception.
Activism, writing, Sotheby’s, and Sony
Great writing, Loeb says, is clear thinking and organizing your thoughts to get a desired outcome, and it is one of activism’s most effective levers alongside financial and legal pressure. Social pressure through writing and PR can move a board on its own. He sees a pattern in his campaigns: targets that hold themselves out as high status but are not living up to it. Sotheby’s is the clean example, a centuries-old, high-status business run unprofitably, where Third Point bought 9.9 percent, gave the existing CEO a year, then helped install Tad Smith from MSG, who modernized operations and technology before the company was sold. Sony was a two-act campaign in which Third Point owned up to 7 percent and pushed to break up the conglomerate; he recounts sharing the thesis with Andrew Ross Sorkin at the New York Times under embargo, the panic it caused, and how management resisted for years before spinning out the semiconductor and financial services units. The lesson: activism in Japan is genuinely hard, even though the government wanted reform. He co-authored a paper with Larry Lindsey and Niall Ferguson arguing corporate governance and return on invested capital should be a fourth arrow of Abenomics, which ran as a Wall Street Journal editorial.
The Danaher operating system
Loeb calls Danaher his most instructive investment. He and his partner persuaded the company to compress its five-day Danaher Business System training into a single day, and he came away with a deep appreciation for how a real operating system drives continuous improvement. The standout lesson was cultural: Danaher holds people individually accountable, but when it finds someone underperforming it celebrates rather than shames, because the problems are addressable and fixable, and it does this relentlessly across operations and working capital. He also points to the diaspora of Danaher executives, including Larry Culp and the leadership at Ingersoll Rand, as evidence of the system’s depth. The investment worked for about four years before COVID-era order surges and inventory swings turned tailwinds into headwinds; Third Point sold and has recently bought back in modestly.
The structure of Third Point and the fulcrum security
Third Point is not one fund but a collection of businesses. The flagship hedge fund grew from $3 million to about $9 billion and is roughly 30 percent credit, generically around 110 percent long and 30-40 percent short on the equity side. Across the firm the credit weight is closer to 60 percent, spanning a roughly $7 billion CLO business, several billion in structured and corporate credit, an insurance company, a couple billion in asbestos liabilities, a small new private credit unit, and a venture arm. The unifying thread is valuing enterprises at any stage and investing in whichever fulcrum security (the one with the best risk-reward) makes sense. Loeb illustrates with Credit Suisse’s takeover by UBS, where the holdco paper proved the fulcrum, and with buying Twitter’s resold financing debt near 96-97 cents at a 12 percent yield when other credit investors were scared, plus a difficult xAI debt financing that few credit people wanted. He pushes back on the idea that he sits atop everything: he is the PM only of the hedge fund, while the other businesses have their own PMs and committees he is not on.
Insurance, the FTX lesson, and recent mistakes
Loeb started a Bermuda reinsurance company in 2010, backed by himself, Kelso, and Pinebrook, on a barbell thesis of investing the float in Third Point and treasuries to defer taxes and lever capital. The reinsurance side soured, and about three years ago he concluded they had the right idea but the wrong vehicle, that plain-vanilla annuities (which can only invest in credit) would have fit better. Third Point merged the reinsurer into its UK closed-end fund, Third Point Offshore Investors, reincorporated from Guernsey to Cayman, and repurposed it into an insurance company managing private credit, structured credit, whole-loan mortgages, real estate lending, and investment-grade debt. His hardest lesson was FTX: it looked great, was verifiable on the blockchain, and had a strong cap table, but was not what it seemed; diligence now includes checking bank balances. He notes Sam Bankman-Fried, fraud aside, had a great nose for value (Anthropic, Cursor, Solana). Other recent mistakes were shorts where Third Point bet certain info-services businesses would resist AI disruption; he still expects a shakeout with some survivors rising from the ashes.
AI inside the firm, the analyst of the future, and kindness
Loeb is pushing his entire team to use AI daily, hiring native computer scientists and system integrators, and describes Claude as a tool that makes you an autonomous self-improver and gives back whatever you put into it, with some analysts running agents overnight while he uses it more for queries. He pairs this with Brad Gerstner’s recommendation of “Essentialism”: you cannot do it all, so you must decide what is most relevant. The great analyst has changed: 20 years ago it was someone who could model fast and crack a complex restructuring, as Loeb did with the Drexel Burnham bankruptcy claims early in his career; today it is a Gavin Baker type who deeply understands an industry and its technology, like the analyst who flew to Texas and realized Casey’s General Stores was really a pizza chain in disguise. On the rest of the world, he is more bullish on Korea, Taiwan, and Japan, finds Europe tough on regulation (while owning Rolls-Royce and ASML), and finds the Middle East the most vibrant region. He closes on what worries and excites him (time with family, surfing, and reading versus the joy of incorporating everything relevant about the world), and on kindness, crediting his friend Carter, who let him sleep on a couch and seeded his early fund, and echoing Palmer Luckey’s line that money cannot buy friends who believed in you when you had nothing.
Notable Quotes
“I think you have to be a tech person today. It’s a big and growing and compounding part of the economy. It affects everything else.”
Dan Loeb, on why no serious investor can punt on technology anymore
“Hold on to your seats because things are only going to accelerate from here.”
Dan Loeb, recounting a 2013 Davos warning about technological change he now applies to AI
“Maybe that’s where the human element comes in, to understand and to be able to make those tough trading decisions when fundamentals are going one way and stock prices are going the other way, and to be able to take the pain of losses in the short run.”
Dan Loeb, on where a human investor still has an edge over machines
“It’s very different from the dot-com bubble, which we were short going into. You don’t have the valuation bubble now on those companies that you had back in those days.”
Dan Loeb, on why he does not see the AI rally as a 1999-style bubble
“When they found someone that was underperforming, it was celebrated instead of shamed, because look at all these things you’re doing wrong, we can fix those. And they did.”
Dan Loeb, on the accountability culture he learned from the Danaher Business System
“I would have to say our investment in FTX. It looked great. The company was growing fast. We could verify it all on the blockchain.”
Dan Loeb, naming his hardest investment lesson
“Be kind to people you have no idea how it will ever benefit you. And sometimes it will and sometimes it won’t.”
Dan Loeb, on elevating kindness in your hierarchy of values
“The one thing money doesn’t buy you is friends that believed in you when you had nothing.”
Dan Loeb, quoting Gavin Baker quoting Palmer Luckey, on the friend who seeded his early fund
Gavin Baker, founder and CIO of Atreides Management, returns to Patrick O’Shaughnessy’s Invest Like the Best for his sixth appearance. He calls the current AI moment the most extraordinary moment in the history of capitalism, walks through what Anthropic’s vertical takeoff in revenue actually means, lays out why orbital compute is closer than skeptics believe, dissects the TSMC bottleneck that may be the only thing standing between today’s market and a full-on AI bubble, and rates every hyperscaler on how they have positioned for a world where frontier model providers may stop selling API access altogether.
TLDW
Anthropic added eleven billion dollars of ARR in a single month, which is roughly the combined business of Palantir, Snowflake, and Databricks built over a decade. That is the setup. From there Gavin Baker covers the March and April selloff, the contrarian read that a closed Strait of Hormuz was actually bullish for American manufacturing competitiveness, why Anthropic and OpenAI multiples may be misleadingly cheap on an unconstrained run rate basis, why Elon Musk’s discipline on SpaceX valuation created a superpower of permanent access to capital, the practical engineering case for orbital compute as racks in space rather than Pentagon sized space stations, why TSMC’s capacity discipline is the single most important variable in whether the AI cycle becomes a bubble, what Terafab in Texas changes, why the Pareto frontier of AI models has flipped from Google dominance to Anthropic and OpenAI dominance in nine months, the shift from all you can eat AI subscriptions to usage based pricing and what that means for revenue scaling, Richard Sutton’s bitter lesson as the largest risk to the AI trade, why frontier tokens still capture an overwhelming share of economic value, the role of continual learning as the third great open question, why most new chip startups should not try to build a better GPU, why Cerebras did something different and hard, why disaggregated inference may extend GPU useful lives to ten or fifteen years and rescue the private credit industry, why being in the token path is the new venture filter, the new prisoner’s dilemma around releasing frontier models via API, an honest rating of Google, Meta, Amazon, and Microsoft, why personal safety is becoming a real AI era risk, and why he remains an AI optimist maximalist who believes this could be the next Pax Americana.
Key Takeaways
Anthropic added eleven billion dollars of ARR in one month, more than the combined businesses of Palantir, Snowflake, and Databricks built across a decade. There is no precedent for this in the history of capitalism.
The SaaS and cloud revolution created between five and ten trillion dollars of value over twenty years. AI is replaying that compression on a timeline measured in months.
The March selloff was a drawdown driven by disagreement with price action, not invalidated thesis. That is the kind of drawdown an investor can lean into.
Deep Seek Monday in January 2025 was a similar setup. By the day of the selloff, AWS Asia GPU prices had already doubled, GPU availability had fallen, and it was obvious reasoning models would be vastly more compute hungry at inference. The market priced the opposite.
The Strait of Hormuz closing was actually positive for America. US natural gas (the primary input into US electricity, which feeds AI) fell twenty percent on Bloomberg while Asian and European natural gas doubled or tripled. American manufacturing competitiveness improved overnight.
The US is now the world’s largest producer and exporter of oil and gas. The economy is dramatically less energy intensive than in the 1970s. The shortage trauma comparison does not hold.
Tech as a sector traded as cheaply versus the rest of the market in early April as at any point in the last ten years, into the single most bullish moment for AI fundamentals on record.
Anthropic is dramatically more capital efficient than OpenAI, having burned roughly eighty percent less to reach a similar revenue scale. They have very different structural returns on invested capital.
Anthropic at roughly nine hundred billion for fifty billion of ARR (growing a thousand percent) is striking. Adjusted for compute constraint, the unconstrained run rate could be one hundred fifty to two hundred billion, putting the implied multiple closer to five times.
Claude Opus generates roughly seventy percent fewer tokens for the same question than previously, with token quantity tied to answer quality. Subscribers on flat-fee plans are getting a lobotomized model.
Elon Musk’s superpower is twenty years of making investors money. He never pushes valuation. SpaceX compounded low thirty percent per year for a decade because Musk treats fair pricing as a sacred covenant.
Capitalism will solve the watts shortage. The current bottleneck has shifted from chips and energy to zoning and political approval. Many capex decisions are paused until after the US midterms.
The watts shortage probably begins to alleviate in 2027 and 2028. Orbital compute solves it longer term.
Orbital compute is not Pentagon sized data centers in space. It is racks in space. A Blackwell rack is three thousand pounds, eight feet tall, four feet deep, three feet wide. SpaceX has shown a satellite roughly that size.
The satellites operate in sun synchronous orbit so solar wings (around five hundred feet per side) always face the sun and the radiator on the dark side always points to deep space.
Starlink V3 satellites already run at around twenty kilowatts. A Blackwell rack runs at one hundred kilowatts. SpaceX engineers express genuine confidence they have already solved cooling and radiator design at these scales.
Racks in space are connected with lasers traveling through vacuum, the same lasers already on every Starlink. SpaceX operates the world’s largest satellite fleet and, via xAI Colossus, the world’s largest data center on Earth.
Inference will move to orbit. Training will stay on Earth for a long time. Terrestrial data centers remain valuable for the rest of an investor’s career.
The wafer bottleneck is structural and political. TSMC is essentially Taiwan’s GDP, water, and electricity. The leaders see themselves as inheritors of Morris Chang’s sacred legacy and they do not behave like a Western public company.
Jensen Huang has never had a contract with TSMC. The relationship is run on handshakes and the assumption that things will be fair over time.
If TSMC did everything Jensen wanted, Nvidia could be selling two to three trillion dollars of GPUs in 2026 and 2027. TSMC’s discipline is the single largest factor preventing a true AI bubble.
Historically, foundational technologies always get a bubble. Railroads, canals, the internet. The current AI buildout is overwhelmingly funded out of operating cash flow, GPUs are running at one hundred percent utilization, and that is fundamentally different from the year 2000 fiber overbuild.
If one of Intel or Samsung Foundry catches up at the leading node, the other will follow, and TSMC’s discipline collapses. Watch TSMC capacity decisions to predict a bubble.
Terafab, the SpaceX and Tesla joint venture to build the world’s largest fab in America, has a partnership with Intel that grants access to fifty years of institutional foundry knowledge. The A teams at ASML, KLA, Lam Research, and Applied Materials will follow Elon’s reputation in hardware engineering.
The hiring playbook for Terafab includes building Taiwan Town, Japan Town, and Korea Town next to the fab. Recruit the engineers and import their families, their restaurants, and their staff.
Frontier tokens still capture an overwhelming share of all economic value created at the model layer. This is surprising and is one of the three big open questions for AI investing.
The Pareto frontier of intelligence versus cost has flipped. Nine months ago Google’s TPU dominated every point on the frontier. Today Anthropic and OpenAI dominate, with Grok 4.3 on the frontier and Gemini 3.1 hanging on.
Google’s conservative TPU V8 design (partly an attempt to reduce dependence on Broadcom and Nvidia) is the leading explanation for the loss of per token cost leadership.
AI pricing is shifting from all you can eat to usage based, mirroring the cellular and long distance industries. Cellular stopped being a great growth industry when it went all you can eat. AI just made the opposite move.
OpenAI and Anthropic together could exceed two hundred billion in ARR this year if compute keeps coming online and frontier token pricing holds.
The two hundred fifty dollar a month consumer AI plan is no longer enough to evaluate frontier capability. Enterprise plans with usage based billing are required because rate limits are now severe.
The three biggest open questions for AI investors are: violation of the bitter lesson via ASI or human ingenuity, whether frontier tokens keep commanding their premium, and when continual learning arrives.
Today’s continual learning is crude reinforcement learning during mid training on verifiable tasks. True continual learning means weights updating dynamically, like a human who learns the first time they touch fire.
Trying to build a better GPU is a losing strategy. Jensen will copy any one to three percent share design. Startups should target one percent share, do something different, and make it hard enough that Nvidia cannot fast follow.
Disaggregated inference (separating prefill and decode) opens new design canvases. Prefill is memory capacity bound. Decode is memory bandwidth bound. Each can be optimized independently.
Cerebras did something different and hard with wafer scale computing. Three generations of chips and real grit to get there.
Disaggregation of inference may stretch GPU useful lives to ten or fifteen years, dropping financing costs from low sevens to five or six percent, mathematically lowering the cost of the AI buildout and likely saving the private credit industry from its SaaS loan exposure.
Sellers of shortage outperform buyers of shortage. But owning the largest installed base of what is currently in shortage (hyperscaler CPU fleets, for example) is also a strong position.
Most of the economic value at the application layer of AI has been destroyed, not created. The exceptions are companies in the token path or in niches small enough that frontier labs ignore them.
Coding may be the shortest path to ASI. If you can write code, you can write code that does anything. Cursor, Cognition, and Anthropic correctly focused on it.
Jensen could probably get close to the frontier with his own Nemotron family of models whenever he wants. The fact that he chooses not to is a strategic decision about not commoditizing his customers.
The new prisoner’s dilemma in AI is whether frontier labs release their best model via API. If everyone agrees not to, Chinese open source falls behind. If anyone defects, the defector pulls ahead on revenue and resources, forcing everyone else to defect.
Google still owns the largest compute installed base. Without TPU’s prior cost advantage, this matters more. YouTube data has real value in a world of robotics. GCP is going crazy.
Meta deserves credit for becoming AI first internally faster than any other internet giant. Musa, their first MSL model, is impressively close to the Pareto frontier.
Amazon is strong because of Trainium and robotics driven retail P&L efficiency. Nova is better than it gets credit for.
Microsoft flinched on capex in early 2025 and lost position. Satya Nadella’s current decision to use Microsoft compute for Microsoft products rather than reselling to OpenAI is a courageous and probably correct call, even at the cost of an eight hundred dollar stock price.
The hyperscalers most engaged with startups are Amazon and Nvidia by a mile, followed by Google. Broadcom is the favorite ASIC partner. AMD, Microsoft, and Meta have minimal startup engagement and that will cost them as the best teams are now at startups.
Personal safety in an AI era requires a family or company safe word that cannot be socially engineered. Deepfake voice and video extortion at the speed of FaceTime is already feasible.
Ukraine is winning largely on the back of having the best battlefield AI outside America and Israel. Adversaries are starting to internalize what AI dominance means geopolitically.
An optimistic read is that this becomes a new Pax Americana, the way the post 1945 American nuclear monopoly was used to rebuild Germany and Japan rather than dominate.
AI cured a friend’s daughter’s rare disease by spinning up a research effort that identified a market drug capable of impacting her condition. That is the upside that keeps Gavin an AI optimist maximalist.
Detailed Summary
The most extraordinary moment in the history of capitalism
Gavin’s framing of the current moment is unusually direct. Anthropic added eleven billion dollars of annual recurring revenue in a single month. The three highest profile SaaS companies of the last decade plus, Palantir, Snowflake, and Databricks, took a decade and tens of thousands of employees collectively to build the combined business that Anthropic added in thirty days. He has been investing through every major tech cycle and says there is no historical analog. Not the dotcom era, not the cloud transition, not mobile. This is its own thing.
The market response, then, was peculiar. The NASDAQ sold off into the single most bullish moment for AI fundamentals on record. Tech traded at roughly its widest discount versus the rest of the market in a decade. Investors who said they wished they had bought into AI during 2022, during COVID, or during Deep Seek Monday got the same valuation setup again in early April, this time with an even clearer inflection.
Why the Strait of Hormuz closing was secretly bullish for America
One reason the macro fear in March may have been mispriced is that the same geopolitical event that drove the selloff was, in practice, a relative benefit to the United States. American natural gas, the input into American electricity, which is the input into American AI training and inference, fell roughly twenty percent. Asian and European natural gas prices doubled or tripled. The US emerged with sharply improved relative manufacturing competitiveness, which is exactly what the current administration cares about.
The 1970s comparison does not hold. The US economy is dramatically less energy intensive, it is now the world’s largest producer and largest exporter of oil and gas, and there are no shortages, only price moves. That backdrop made it easier for disciplined investors to stay focused on AI fundamentals through the volatility.
Anthropic and OpenAI valuations on an unconstrained run rate
Anthropic at roughly nine hundred billion for fifty billion of ARR sounds rich until you adjust for the fact that the company is severely compute constrained. Gavin estimates that, unconstrained, Anthropic might be at one hundred fifty to two hundred billion in run rate revenue, putting the implied multiple closer to five times. He also points out that Claude Opus now generates roughly seventy percent fewer tokens for the same question than it used to. Token quantity correlates with answer quality, and Anthropic is rate limiting and shrinking outputs to ration capacity across its user base.
Anthropic and OpenAI are also structurally very different. Anthropic has burned around eighty percent less cash than OpenAI to reach a comparable revenue scale. That implies very different long term returns on invested capital, though OpenAI has done a better job locking in compute and Sarah Friar is one of the most exceptional CFOs Gavin has worked with.
Why neither lab is raising at a three trillion dollar valuation
The answer Gavin gives is that both labs are deliberately leaving valuation on the table the way Elon has done for two decades. SpaceX compounded at low thirty percent annually for a decade because Elon never pushed price. The result is a permanent superpower of access to capital. Investors trust him because they have made money with him for twenty years. That is a moat that compounds with every round.
Anthropic could probably raise at a one hundred percent premium to its rumored latest mark. They are choosing not to. In an uncertain world (Ukraine, Russia, Iran, Taiwan), preserving the ability to raise more capital later at fair prices is more valuable than maximizing this round.
Watts and wafers, the two real constraints
Capitalism is solving the watts problem. The leading PE infrastructure investors now say zoning and political approval, not chips or energy, are the gating factors. Companies are deferring big capex announcements until after the US midterms. Turbine capacity is being doubled at the manufacturers. Companies like Boom Aerospace are repurposing jet engines for grid use. Watts probably ease meaningfully in 2027 and 2028 and then orbital compute does the rest.
Wafers are the harder problem because they live in Taiwan, run on handshakes, and depend on a corporate culture that does not respond to public market incentives. TSMC is essentially the GDP, water consumption, and electricity consumption of Taiwan. Its leadership treats the company as the legacy of Morris Chang. The Silicon Shield doctrine is real and internal.
Orbital compute as racks in space
The biggest mental update Gavin asks listeners to make is to stop picturing data centers in space as Pentagon sized space stations. A Blackwell rack is three thousand pounds and roughly the size of a refrigerator. SpaceX has shown a concept satellite of about that size. Solar wings extend five hundred feet to each side and the radiator extends hundreds of feet behind, both possible because the orbit is sun synchronous and the orientation is fixed relative to the sun.
SpaceX engineers Gavin has spoken to at Starbase express genuine confidence that they have solved cooling at these power levels. They have. Starlink V3 satellites already operate at twenty kilowatts. A Blackwell rack is one hundred kilowatts. The same company operates the world’s largest satellite fleet and the world’s largest data center on Earth via xAI Colossus. The racks are connected to each other with lasers traveling through vacuum, technology already deployed in every Starlink. The naysayers, Gavin observes, are armchair skeptics and Larry Ellison’s response (he is out there landing rockets, no one else is) is the right frame.
Terafab in Texas and the threat to TSMC’s discipline
Terafab, the SpaceX and Tesla joint venture, intends to be the largest fab in the world. The partnership with Intel grants access to fifty years of foundry institutional knowledge, allowing Terafab to start three to five quarters behind the leading node rather than fifteen years behind. The A teams at the semicap equipment companies (ASML, KLA, Lam Research, Applied Materials) will follow Elon’s reputation in hardware engineering the same way they followed TSMC twenty years ago when Intel stumbled.
The talent strategy is the part most observers underestimate. Recruit the best engineers globally, then import their families, their restaurants, their staff. Build Taiwan Town, Japan Town, and Korea Town next to the fab. Optimize the human experience for the people whose work matters. Intel and Samsung do not think that way.
Bubble watch and the year 2000 comparison
Every foundational technology in modern history has had a bubble. Railroads, canals, the internet. Carlota Perez documented why. Markets correctly identify the importance, diversity of opinion collapses, supply gets ahead of demand, the bubble crashes. The current cycle has two important differences. The buildout is overwhelmingly funded out of operating cash flow, not debt. Every GPU is running at one hundred percent utilization, while at the peak of the fiber bubble ninety nine percent of fiber was unused.
TSMC discipline is the single largest reason a bubble has not formed. If Jensen could buy everything TSMC could theoretically make, Nvidia could sell two to three trillion dollars of GPUs in 2026 and 2027. At some point that becomes more than the market can absorb. If Intel or Samsung Foundry catches up at the leading node, the other will too. TSMC’s pricing discipline collapses and the bubble starts.
The Pareto frontier and the loss of Google’s cost advantage
The most important chart in AI is the Pareto frontier of model intelligence versus per token cost. Nine months ago, Google’s TPU based models dominated every point on it. OpenAI, Anthropic, and xAI sat inside the frontier. Today the frontier is dominated by Anthropic and OpenAI, with Grok 4.3 on the frontier and Gemini 3.1 hanging on by subsidization more than economics. The most likely cause is Google’s conservative TPU V8 design, an attempt to reduce dependence on Broadcom and Nvidia that sacrificed per token economics.
The bitter lesson, frontier tokens, and continual learning
Three open questions dominate AI investing. The first is whether Richard Sutton’s bitter lesson (more compute beats human algorithmic cleverness) gets violated by ASI itself optimizing for efficiency. Closer observers of AI are more skeptical of a violation. Gavin thinks ASI’s first move will be to make itself more efficient and more resourced, which is technically a temporary violation.
The second is whether frontier tokens keep capturing the overwhelming share of economic value at the model layer. Today they do, surprisingly. Gemini 3.1 Pro was mindblowing nine months ago and is intolerable today. The third is when continual learning arrives. Today’s models need a million fire touches to learn what a human learns from one. True continual learning would mean dynamic weight updates in real time and would produce a fast takeoff.
From all you can eat to usage based AI pricing
AI is shifting from flat fee plans to usage based pricing. The historical analogy is cellular and long distance. Both stopped being great growth industries when they went all you can eat. AI just made the opposite move. The consequence is that flat fee subscribers, even on premium consumer plans, get a rate limited and token throttled version of the frontier model. Enterprise plans with usage based billing are now required to evaluate true capability. Gavin thinks the combination of new compute coming online and usage based pricing is what gets OpenAI and Anthropic past two hundred billion in combined ARR this year.
Chip startups, prefill decode disaggregation, and Cerebras
Trying to build a better GPU is the wrong move. The four scaled players (Nvidia, AMD, Trainium, TPU) have copy capability for any one to three percent share design that looks attractive. The good news for startups is that disaggregated inference (separating prefill and decode) opens a richer design canvas. Prefill is memory capacity bound. Decode is memory bandwidth bound. Each can be optimized independently. Andrew Fox’s analogy is a British naval ship of the eighteenth century. Prefill is loading the cannon. Decode is firing it.
Cerebras is the model. Wafer scale computing is genuinely different and genuinely hard. It took three generations of chips to get right. Andrew Feldman and his team had the grit to keep going through chip one being a failure. The design has a high ratio of on chip compute and memory relative to shoreline IO, which is why Cerebras is now experimenting with putting an optical wafer on top of the compute wafer to solve scale out.
GPU useful lives and the rescue of private credit
One of the strongest claims in the conversation is that disaggregated inference will stretch GPU useful lives to ten or fifteen years. The skeptical narrative (GPUs are obsolete in two years, companies are cooking their depreciation books) is wrong. You can put a Cerebras system or Groq LPU in front of older Hopper or Ampere parts, use them only for prefill, and run them until they physically melt. Private credit, which is in pain from SaaS loans and which underwrote GPU loans on three to four year lives, may be saved by this.
If GPU financing rates can come down from low sevens to five or six percent, the mathematics of the AI buildout improves materially. That is a structural tailwind that compounds for years.
The application layer, the token path, and a new prisoner’s dilemma
Trillions of dollars of value have been destroyed at the application layer, not created. Cursor and Cognition are the rare scaled exceptions, and they got there by focusing on coding very early. As Amjad Masad noted, coding is plausibly the shortest path to ASI because a coding agent can write itself into any new domain. Jamin Ball’s frame is that the new venture filter is whether the company is in the token path. Data Bricks is. Most application layer startups are not.
Jensen could probably get close to the frontier with Nemotron whenever he wants, and the strategic question of whether to do that is a new prisoner’s dilemma. If every frontier lab agrees not to release best models via API, Chinese open source falls steadily behind. If anyone defects, the defector gains revenue and resources, and everyone else has to defect. The same dynamic exists between TSMC, Intel, and Samsung. If Nvidia or AMD ever truly used an alternative foundry, that foundry would catch up rapidly.
Rating the hyperscalers
Google has the largest compute installed base, the YouTube data that matters in a robotics world, and a search business that prints. Their loss of TPU cost leadership is the surprise of the year. If Google IO in five days does not produce a leapfrog model, the Nvidia centric narrative gets even stronger.
Meta deserves real credit. Zuckerberg made Meta AI first internally faster than any other internet giant, paid up for the talent contracts when no one else would, and shipped Musa as a first model from MSL that is close to the Pareto frontier. Amazon is well positioned on Trainium, robotics in retail, and a Nova model line that is better than it gets credit for. Microsoft flinched on capex in early 2025 and lost position. Satya Nadella’s current decision to use Microsoft compute for Copilot rather than reselling to OpenAI is courageous and probably correct, even at the cost of stock price.
The most interesting cross hyperscaler metric is startup engagement. Nvidia and Amazon engage deeply with startups. Google is next. Broadcom is the favored ASIC partner. AMD, Microsoft, and Meta have minimal startup engagement, which Gavin believes will cost them as the best teams now sit at startups.
Personal safety, geopolitics, and the Pax Americana case
The closing section turns darker. Personal safety in an AI era requires a family or company safe word that cannot be socially engineered. Deepfake voice and video extortion via something that looks exactly like your child calling on FaceTime is already feasible. Political violence against AI leaders is a real concern. Geopolitically, Ukraine is winning largely because it has the best battlefield AI outside America and Israel. How adversaries respond to that asymmetry is the next great variable.
Gavin’s optimistic frame is the Pax Americana. After 1945 the US had a nuclear monopoly and could have controlled the world. Instead it rebuilt Germany and Japan, both of which became the most reliable American allies for the next eighty years. If AI dominance plays out similarly, this is a generationally positive story rather than a destabilizing one. The personal anecdote that closes the conversation is a friend whose daughter was diagnosed with a rare genetic condition. He spun up agents, identified a drug already on the market that addresses her mutation, and her life is immeasurably different because of AI. That is the upside.
Thoughts
The Anthropic eleven billion in a month framing is the kind of stat that resets priors. The right way to interpret it is not as a one off but as a measure of how fast value can compound when the underlying technology improves on a curve steeper than the ability of the rest of the economy to absorb it. The skeptical question is whether that ARR is durable or whether it is heavily tied to a customer base of other AI companies that are themselves on a single venture funded year of runway. The bullish answer is that frontier coding, frontier research, and frontier enterprise tasks are not going to stop being valuable, and Anthropic is the best at all three. Both can be true. The number is still extraordinary.
The argument that TSMC discipline is the only thing preventing a bubble is the analytically tightest part of the conversation. The implied trade is to watch TSMC capacity additions like a hawk and to be more, not less, cautious if Intel Foundry or Samsung Foundry ever announce real share at the leading node. The Terafab thesis is more speculative but more interesting. If Elon’s talent recruiting playbook works and the Intel partnership gives Terafab a real seat at the table within five years, the geometry of the global semiconductor industry shifts in a way that is bullish for American manufacturing, bullish for power and water infrastructure in Texas, and ambiguous for TSMC itself.
The Pareto frontier discussion deserves more attention than it usually gets. Pricing leadership in AI is not a vanity metric. It determines who can subsidize free tier usage, who can absorb compute shortages, who can ship cheaper enterprise plans, and ultimately whose model becomes the default for any given workload. Google losing per token leadership in nine months is one of the most under analyzed events in the sector and it explains a lot about why Anthropic and OpenAI are growing the way they are. If Google IO does not produce a leapfrog model, the implied verdict on TPU V8 design choices gets a lot harsher.
The application layer destruction point is worth sitting with. Founders building on top of frontier models are competing in a world where the model itself moves faster than any moat they can build, where the model lab can absorb their niche if it gets interesting, and where the only protection is either deep token path integration or a niche so small the lab does not bother. That is a much harsher venture environment than the early SaaS era. The compensating opportunity is that one human can now run a hundred agents, so the ceiling on what a small team can build is correspondingly higher. The bet is that productivity per founder rises faster than competitive pressure from the labs. We will find out.
The orbital compute pitch is the section that will polarize listeners. The naive read is that this is science fiction. The closer read is that every component (sun synchronous orbit, laser interconnect, twenty kilowatt satellite buses, ten thousand satellite manufacturing cadence, full rocket reusability) already exists. The remaining engineering problems are repair, maintenance, and radiator scale, all of which are real but tractable on a five to ten year horizon. The strategic implication is that the political and zoning ceiling on terrestrial data centers becomes less binding if orbital compute is a credible alternative for inference workloads. The investor implication is that being short the watts and cooling complex on a five year horizon is a real trade, not a meme.
The host opens this Saturday morning macro and AI markets video with a direct challenge to anyone calling the current move a bubble. The argument is that the market structure itself has changed, that AI agents now dominate trading and capital allocation, and that Charles Kindleberger’s Manias, Panics, and Crashes describes a world that no longer exists. The full hour-long conversation walks through earnings, PEG ratios, capex, the benchmark arbitrage trapping passive investors, the inflation regime shift, and where money is rotating now. Watch the original video here.
TLDW
AI is not a bubble in the Kindleberger sense because the market is no longer dominated by emotional human professionals. AI agents, retail risk-takers, and passive flows are reshaping price discovery while the spend is being funded by free cash flow from the most cash-rich companies in history, not bond-issuance manias like telecoms or oil. Earnings growth is 27 percent, semiconductor sales grew 88 percent year over year in March, OpenAI and Anthropic revenue is on near-vertical curves, Nvidia’s PE is at decade lows even as Cisco’s was 130 at the dot-com peak, and the PEG ratio for the S&P sits at 1.03 with one third of the host’s thematic basket under 1.0 while Microsoft, Amazon, Meta, Apple, and Alphabet all carry richer PEGs. The new regime brings speed crashes instead of multi-year recessions, persistent bottlenecks in power, chips, transportation, and chemicals, inflation pressure that pushes three-month bills below CPI for the first time since the inflation era, and a benchmark arbitrage forcing passive money to chase AI exposure. The host is selling two thirds of his Micron, rotating into Nvidia, Vistra, silver, Bitcoin, and Ethereum, and warning that tokenization launches scheduled for July 26 will be the next major regime change.
Key Takeaways
The word bubble is being misapplied because the same people calling AI a bubble called QE, tariffs, oil, Bitcoin, and passive investing bubbles for fifteen years and were wrong every time.
Kindleberger’s Manias, Panics, and Crashes described a slow, linear, human-emotion-driven world. AI agents have no emotion, no memory of Druckenmiller’s 2000 top, and one goal: make money.
The simplest test for anyone bearish on AI is to ask how much they use artificial intelligence. If they have not used a tool like OpenClaw or similar agentic systems, they are still operating in the old market regime.
This buildout is funded by free cash flow and bond issuance at yields better than US Treasuries from companies with stronger balance sheets than the federal government, unlike the dot-com telecoms or 1970s oil majors.
The S&P 500 is up only 7 percent year to date. The bubble framing is being applied to a handful of names, not to broad indices that remain reasonably valued.
The agentic stage of AI started in late November and accelerated when OpenClaw went viral at the end of January. Token consumption is set to grow 15 to 50 times from the IQ stage.
Anthropic revenue is stair-stepping from 5 to 7 to 9 to 14 to 19 to 24 to 30 billion in annualized run rate, on pace to surpass Alphabet in revenue by mid-2028.
OpenAI’s backlog hit 1.3 to 1.4 trillion in the most recent earnings cycle and the company still does not have enough compute.
Dario Amodei told the world Anthropic was planning for 10 times growth per year. In Q1 they saw 80 times annualized growth, which is why compute is bottlenecked and Anthropic is renting from Amazon, Google, and Colossus.
S&P 500 earnings growth is 27.1 percent year over year. The only quarters that match are those coming out of recessions, and this is not a reopening trade.
320 of 500 S&P companies have reported and the average earnings surprise is 20 percent. Forward estimates are up 25 percent year over year as analysts revise upward against the historical pattern.
Total semiconductor sales grew 88 percent year over year in March. Semis have moved in proportion to earnings, not in excess of them.
Cisco’s PE was 130 at the dot-com peak. Nvidia’s PE today is the lowest of the last decade because professionals cannot run concentrated positions in single names.
The Edward Yardeni PEG ratio for the S&P is 1.03. The hyperscalers are not cheap on PEG: Microsoft 1.4, Amazon 1.66, Meta 1.96, Apple 3, Alphabet near 5. Thirty of ninety-five names in the host’s thematic portfolio carry PEGs under 1.0.
Passive investing creates a benchmark arbitrage. Everyone long the S&P 500 through index funds is structurally underweight Intel, Nvidia, Micron, and every name actually going up. Pension funds and mutual funds are forced to chase AI exposure to keep up.
BlackRock’s Tony Kim at the Milken conference: compute and model layers added 8 trillion in market cap year to date while the service apps that make up two thirds of GDP lost 1.2 trillion. The benchmark arbitrage is already running.
Larry Fink predicted a futures market for computing power. Power plus chips is the oil of the intelligence economy.
Jensen Huang called this a 90 trillion dollar AI physical upgrade cycle. The one big beautiful bill bonus depreciation provision was designed to incentivize this capex magic.
The host is selling two thirds of his Micron position. The reasoning is the memory market started moving in September of last year, the DRAM ETF is the ninth most traded ETF with billion dollar daily volumes, and exhaustion indicators are flashing red.
Money from Micron is rotating into Nvidia, Vistra, silver, Bitcoin, and Ethereum. The view is that the energy and power side of the AI stack is lagging the semis and will catch up next.
Silver versus gold has not moved while Micron has gone parabolic. LME metals are breaking out. China is increasing gold purchases significantly month over month.
The expected CPI print of 3.7 percent will put three-month Treasury bills below CPI for the first time since the post-pandemic inflation era. That is when Bitcoin started its last major run.
Logistics Managers Index hit 69.9 in March, the fastest expansion since March 2022. Transportation prices are surging because there is no capacity. This typically only happens during tax cuts or post-COVID reopenings.
Payroll job creation in information, professional services, and financial activities is negative. AI is already replacing knowledge work. Job creation has shifted to mining, manufacturing, construction, trade, transportation, and utilities, which is structurally inflationary.
Whirlpool says appliance demand is at great financial crisis lows. The consumer PC and laptop market collapse is worse than 2008. AI is pulling capital and pricing power away from legacy consumer categories.
Mike Wilson’s data shows reacceleration across sectors, not just large cap tech. Small caps and median stocks are showing earnings growth too, just at smaller market caps.
Chevron’s CEO says global oil shortages are starting. Jeff Currie warns US storage tanks will run empty. Ships are still not transiting the Strait of Hormuz. Countries that learned this lesson will restock to higher inventory levels permanently.
The Renmac Bubble Watch threshold was crossed on a technical basis. The host considers technical exhaustion a stronger signal than narrative-driven bubble calls.
Goldman Sachs power demand reports, Guggenheim warnings on the power crunch, and BlackRock’s compute intensity research all triangulate on the same conclusion: capex needs are larger than current forecasts.
The thematic portfolio is up roughly 30 percent from March lows. Power, optical fiber, advanced packaging, chemicals, and rack-level infrastructure baskets are leading.
Sterling Infrastructure (STRL), Fluence batteries, ABB electrification, Hon Hai (Foxconn), Vistra, Eaton, and Soitec are highlighted as names lagging the megacaps but inside the same AI infrastructure trade.
John Roque at 22V Research is releasing weekly frozen rope charts, long-base breakouts across power, copper, grid equipment, utilities, natural gas, transportation, capital goods, and agriculture. They all map to the same AI plus inflation regime.
Bitcoin ETF outstanding shares hit new highs. BlackRock, Morgan Stanley, and Goldman are all running competitive products. Boomer and wealth manager allocation is accelerating into year end.
Tokenization rolls out July 26. Wall Street clearing has enlisted 50 firms. A16Z published their case in December 2024. The host considers this underweighted by most investors and is speaking on the topic at the II event in Fort Lauderdale.
Raoul Pal and Yoni Assia on the end of human trading: AI agents and crypto collide by moving finance from human speed to machine speed. Agents will trade, allocate, hedge, and shift capital through wallets and exchanges. Tokenization means ownership becomes programmable.
The new regime is bubbles, parabolas, and speed crashes. Corrections compress from years into months. The right strategy is to never go to cash, only to rebalance and slow down within the portfolio.
For traders, exhaustion indicators using 5-day and 14-day RSI plus DeMark signals identify potential speed crash setups. Intel and Micron are flashing red on those screens right now.
Detailed Summary
Why this is not Kindleberger’s world anymore
The framing argument of the video is that Manias, Panics, and Crashes described a market dominated by human professionals operating with limited information and lagged feedback loops. When supply and demand fell out of sync, prices collapsed because nobody could see what was happening in real time. That world is gone. AI agents now manage a majority of professional fund flows. Information moves instantaneously. Retail investors trade differently than institutional pros, and the capital structure of the entire market has changed. The host argues that since the Great Financial Crisis, the combination of QE and exponential corporate growth produced the only companies in history worth 25 trillion dollars combined with no net debt. Their AI capex is funded by free cash flow and high-grade bonds, not panicked bond issuance like the dot-com telecoms or oil majors of the 1970s.
The Druckenmiller anchor and why FOMO is the wrong lens
The video reads the Stanley Druckenmiller story of buying six billion in tech at the 2000 top and losing three billion in six weeks. Every professional carries that scar. It has shaped a generation of money managers into seeing parabolic moves and immediately calling bubble. The host’s counter is that recession calls from wealthy professionals are themselves a form of hope. Cash-rich investors root for crashes because crashes give them entry points. If the bubble never breaks the way it broke in 2000, those investors stay locked out, and that is precisely what the AI regime is doing.
Earnings, revenue, and the reality test
The video walks through current numbers in detail. S&P 500 earnings growth is running 27.1 percent year over year, which only happens coming out of recessions. 320 companies have reported with an average 20 percent earnings surprise. Forward estimates were revised up 25 percent year over year, well above the historical pattern of starting-year estimates getting cut. Total semiconductor sales were up 88 percent year over year in March. Anthropic’s revenue trajectory is stair-stepping from 5 to 30 billion in annualized run rate on the back of Claude Opus 4.5, putting it on track to surpass Alphabet by mid-2028. OpenAI is sitting on a 1.3 to 1.4 trillion backlog and still cannot get enough compute. Dario Amodei told the public Anthropic planned for 10 times growth per year and saw 80 times in Q1.
PE, PEG, and the valuation argument
Cisco’s PE at the dot-com peak was 130. Nvidia, the indisputable lead dog of the AI buildout, currently has a PE at the lowest of its last decade. The S&P 500’s PE is roughly where it has been since the post-COVID money printing era, far below the dot-com peak. Edward Yardeni’s PEG ratio for the index sits at 1.03. The host built a PEG screen for his ninety-five name thematic portfolio. Thirty of those names trade at a PEG under 1.0. The hyperscalers everyone holds passively are the expensive ones: Microsoft 1.4, Amazon 1.66, Meta 1.96, Apple 3, Alphabet near 5. The capacity for forward PE compression sits in the names retail and active rotational money are buying, not in the index core.
The benchmark arbitrage trap
Most money is now in passive investing. By construction, an S&P 500 or MSCI World allocation is underweight the names that are actually rising. Pension funds, mutual funds, and any active manager benchmarked to those indices is forced to add AI exposure to keep pace. BlackRock’s Tony Kim made this point at Milken: 8 trillion in market cap has accrued to compute and model layers year to date, while service apps representing two thirds of GDP lost 1.2 trillion. The host calls this benchmark arbitrage and considers it the single most underappreciated driver of the current move.
The 90 trillion dollar physical upgrade cycle
Jensen Huang’s framing of a 90 trillion dollar AI upgrade includes autos, phones, computers, humanoids, robotics, and the military stack. The host considers this a global race between the US and China. The one big beautiful bill included bonus depreciation specifically to incentivize the capex push. Greg Brockman’s interview with Sequoia made the point that demand for intelligence is effectively unlimited, and that every company outside the hyperscalers, Morgan Stanley, Goldman, Eli Lilly, Merck, United Healthcare, needs their own data center compute or their margins will not keep up with competitors. In a capitalist system, that forces broad enterprise AI spending.
Speed crashes replace recessions
The new regime has corrections but they are fast. Since 2020 we have had multiple 20 percent corrections compressed into weeks instead of years. The host expects this pattern to continue for the next decade. Bottlenecks in power, chips, transportation, chemicals, and skilled labor will produce inflation spikes that trigger speed crashes, not traditional credit-cycle recessions. The Logistics Managers Index reading of 69.9 in March, with capacity contraction near record lows, signals exactly this kind of bottleneck environment. The host’s strategy in this regime is to never go to cash, only to rebalance and slow down within the portfolio.
The inflation regime shift and the rotation out of Micron
The expected CPI print of 3.7 percent will put three-month Treasury bills below CPI for the first time since the post-pandemic inflation era, restoring negative real yields. That was the condition under which Bitcoin first launched its major bull moves. The host has sold two thirds of his Micron position despite continued bullish conviction on the name, because the memory market is the most stretched on exhaustion indicators and the DRAM ETF is trading at unprecedented volume. The capital is rotating into Nvidia, Vistra, silver, Bitcoin, and Ethereum. Silver versus gold has not moved while semis went parabolic. LME metals are breaking out. China is increasing gold purchases. The energy and power side of the stack is the next leg up.
AI is breaking the consumer and the labor market
Whirlpool reports appliance demand at financial crisis lows. PCs and laptops are collapsing worse than 2008. Phones, autos, housing, all the categories Kindleberger’s framework was built around are under pressure because AI is pulling capital and pricing power into compute, power, and chemicals. Payroll job creation in information, professional services, and financial activities is negative as AI takes knowledge work. Job creation is rotating into mining, construction, manufacturing, trade, transportation, and utilities, which is structurally inflationary because those sectors require physical capacity and wages. That combination, wage inflation plus commodity inflation, makes it very difficult for the Fed to ease, even with Kevin Warsh likely taking over.
Crypto, tokenization, and AI agents at machine speed
The final section pivots to crypto. Bitcoin ETF outstanding shares hit new highs, BlackRock’s product remains dominant, and Morgan Stanley and Goldman have launched competing vehicles. Wealth managers and boomers are allocating. The Raoul Pal and Yoni Assia conversation on the end of human trading is the host’s headline reference: AI agents will trade, allocate, hedge, and shift capital at machine speed through programmable wallets and exchanges. Tokenization, scheduled for a major launch on July 26 with 50 Wall Street clearing firms onboarded, makes ownership programmable. A16Z laid out the case in December 2024. The host is speaking on tokenization at the II event in Fort Lauderdale May 13 through 15 and considers it the next regime-defining shift after agentic AI.
Thoughts
The strongest argument in this video is structural, not narrative. The shift from human professionals with anchored memories to AI agents and benchmark-driven passive flows is a real change in who sets prices. Whether or not you accept the host’s portfolio calls, the framing should make any investor pause before defaulting to dot-com pattern recognition. Cisco’s PE was 130 with no business model. Nvidia’s PE is at a decade low with a near monopoly on the picks and shovels of the largest capex cycle in industrial history. Those facts cannot both be true and produce the same outcome.
The PEG framework is the cleanest test in the video. If you believe Nvidia, Micron, Intel, and the second-tier AI infrastructure names are bubbles, you are implicitly betting that earnings growth collapses. That bet was viable in 2000 because the companies driving the move had no earnings. It is much harder to bet against earnings growth when 320 companies have just printed a 20 percent average earnings beat and analysts are revising forward estimates up by 25 percent. The host’s argument is not that the prices are reasonable in absolute terms. It is that the bear case requires growth to fall off a cliff, and nothing in the order books, the capex commitments, or the compute backlog suggests that is imminent.
The benchmark arbitrage point deserves more attention than it gets. If the majority of professional money is locked in passive structures that are by definition underweight the leading names, and if those managers are evaluated quarter to quarter against the benchmark they cannot match, the pressure to chase will compound. This is the opposite of the dot-com setup, where active managers were forced to add overpriced tech to keep up with the index. Here, the index itself is structurally underweight the trade, and the active managers chasing it are doing so against names with rational PEG ratios.
The rotation thesis from Micron into power, silver, and crypto is more debatable. The energy and bottleneck story is real, but the timing of when the power trade catches up with the semi trade is the hard part. The host’s discipline of never going to cash and rebalancing through the cycle is a sensible response to a regime that produces speed crashes rather than slow drawdowns. The investors most hurt by this regime will not be the ones who are long the wrong names. They will be the ones who sit out waiting for an entry point that never comes.
Tokenization is the most underappreciated thread in the video. If the July 26 rollout brings 50 clearing firms and real ownership programmability online, the second half of the year could produce a regime shift on top of the AI regime shift. AI agents transacting on tokenized assets at machine speed is the logical endpoint of the trends the host has been tracking, and it is the part of his framework that current market consensus has not yet priced.