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  • Gavin Baker on Orbital Compute, TSMC, Frontier AI Models, Anthropic’s Vertical Take Off, and the Coming Wafer Shortage

    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.

    Watch the full conversation here.

  • Bubbles, Parabolas and Speed Crashes: How AI Agents Are Ending Human Market Structure and Why This Is Not the Dot-Com Bubble

    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.

    Watch the full conversation here.