behind the meter deals

Gavin Baker of Atreides Management returned to Invest Like the Best with Patrick O’Shaughnessy days after one of the strangest months the AI trade has ever produced. AI and semiconductor names fell 40 to 60 percent in a straight line while, by Baker’s account, not a single quantitative metric on the ground deteriorated. He spent the week in Silicon Valley hunting for a bearish data point and came back with almost nothing except credit. This conversation is the result: a detailed argument that the market has mispriced the gap between contracted compute and spot compute, that open source is growing the infrastructure pie rather than shrinking it, and that the one risk actually worth fearing is political rather than financial.

TLDW

Gavin Baker describes July 2026 as “2022 packed into a single month,” a violent AI and semiconductor drawdown that happened while hyperscaler operating cash flow accelerated from roughly 28 percent growth to 32 percent, or closer to 35 percent adjusting for unusual legal charges. His core claim is that the installed base of GPU compute is locked into long-term contracts priced far below the current spot market, so as those contracts roll off, compute reprices higher, operating cash flow accelerates, and the buildout can be funded internally rather than with the debt that widening credit default swap spreads and a poorly received Meta bond have made look expensive. He walks through each catalyst of the selloff: Meta renting out compute (misread as a capex cut), the open source capability leap from GLM 5.2 and Kimi K3 (misread as deflationary when a token is a token and costs the same flops, watts, and memory to produce), China acquiring a domestic deep ultraviolet lithography machine (real but 25 years behind), and rising real yields (the only genuine negative). He covers the game theory of breaking a memory long-term agreement in a world where market share is set by supply allocations, Nvidia’s new credit wrapper plus revenue share model and why it is misunderstood, the router and fine-tuning stack from Fireworks and Baseten that turns “ChatGPT wrappers” into defensible AI natives, continual learning as the one technical development that could disrupt training demand, SRAM accelerators for disaggregated inference, SpaceX as an underappreciated compute company with orbital ambitions, and his view that regulation, not fundamentals, is the biggest risk because the industry has done a terrible job telling its own story. He also makes an unusual observation about market structure: everyone now feeds news into Claude, and Claude has become a kind of Walter Cronkite for the stock market, collapsing the diversity of interpretation that normally keeps markets stable.

Thoughts

The load-bearing claim in this episode is the spread between contracted and spot compute, and to Baker’s credit it is falsifiable in a way most bull cases are not. He is not arguing that AI will be transformative or that demand feels strong. He is arguing something narrow and checkable: hyperscalers and neoclouds signed multi-year GPU contracts in 2024 and 2025 at prices that assumed a gentle decline, prices instead went vertical, and the installed base is therefore systematically under-earning. A startup rented several thousand B200s in the mid two dollars per GPU hour range and expects to pay just under four dollars for an identical cluster seven months later. If that repricing is real and broad, hyperscaler operating cash flow mechanically accelerates and roughly 700 billion dollars of projected credit demand evaporates. If GPU rental prices roll over and stay down for two consecutive quarters, the thesis is dead. That is the number to watch rather than any earnings headline. The caveat he steps past quickly is that the open source mix shift he describes as bullish does not eliminate margin, it relocates it, out of the frontier labs and down into the infrastructure layer. Excellent if you sell GPUs, power, and memory. Considerably more awkward for the labs whose projected cash flows are the reason anyone believes the compute gets paid for at all.

The Claude as Walter Cronkite observation deserves more attention than it got, where it passed as a joke. Baker is describing a genuine change in market microstructure. Every institutional and retail participant now feeds the same news into roughly the same models, and while those models are probabilistic, they are not producing meaningfully diverse readings of the same headline. He connects this to Michael Mauboussin’s argument that a breakdown in diversity, not leverage alone, is what produces bubbles and crashes. If that is what happened in July, then the Japanese capacitor stock chart he cites, an entire three-year cycle compressed into six weeks before the fundamentals had even arrived, is not a curiosity. It is the signature of a market where thousands of participants share one interpretive engine. That makes drawdowns faster and deeper without making them more informative, which argues for holding through machine-generated narrative cascades rather than trading them.

The middle of the conversation contains the most consequential business idea in it, and it is one that got almost no coverage during the selloff: memory long-term agreements and Nvidia’s credit wrapper are the same move executed at two different layers of the stack. Both trade near-term upside for durability. The memory companies stopped maximizing spot price and started signing prepaid agreements with floors and ceilings, and the reason those agreements will hold is that the penalty for breaking one has changed category. Apple could renege on memory pricing for years because its volume was overwhelming and it had no equivalent competitor. In a world with four buyers that matter and where AI market share is set by supply allocation rather than product quality, a supplier can answer a broken price agreement by breaking the volume commitment and handing your allocation to a rival, in an industry where oversupply is always followed by undersupply. Nvidia is running the same play one layer up. The credit wrapper with a revenue share above a price floor converts a cyclical one-time chip sale into a royalty on recurring compute revenue, financed on someone else’s balance sheet, which is a materially better business than selling hardware. It also widens the moat, because a startup accelerator pays more at the foundry, pays more for high bandwidth memory, and cannot finance its chips at Nvidia’s rate. Baker is right that this is misunderstood, and it is a strange thing for a stock at a ten-year-low forward multiple to be quietly doing.

The technical material in the back half reveals an asymmetry worth naming. Baker treats two efficiency developments very differently. Continual learning and sample efficient learning, which several labs believe are close, would collapse the token budget required to produce a capable model, and he handles this by asserting that training asymptotes to a small but nonzero share of compute and that the outcome would be wonderful for the world anyway. SRAM-based accelerators for disaggregated inference, running prefill on one chip, attention on a high-memory chip, and the feed forward network on SRAM, he embraces enthusiastically as a return-on-investment improvement across the installed base. Both are efficiency gains. One is treated as neutral, the other as clearly positive, and Jevons paradox is doing all the work in both directions. That is probably correct given everything we have observed so far, but it is an assumption rather than a finding, and it is the assumption on which the entire “cheaper compute is bullish for compute” framework rests. Worth noting too that the SRAM disaggregation point is genuinely underdiscussed: those chips sit on older nodes and do not compete for leading-edge capacity, so they are additive supply rather than substitute supply.

The final twenty minutes hold both the largest unpriced upside and the largest unpriced risk, and neither is in consensus estimates. On the upside, only the hyperscalers, CoreWeave, Crusoe, and SpaceX have ever brought more than 500 megawatts online in a single year, and SpaceX has done it fastest and cheapest. When it dumped a large block of compute into the market, the market absorbed it without a blip, which tells you more about demand than any survey. Baker’s sanity check on orbital compute is the sharpest reasoning move in the episode: Benchmark, from entirely outside the Elon ecosystem and without the benefit of internal launch costs, funded StarCloud at a real valuation, so the set of people who would all have to be wrong keeps growing. On the downside, regulation is the risk he names first and it is the one his own framework cannot arbitrage. New York’s data center moratorium is not a fundamentals problem, and no amount of operating cash flow acceleration fixes a permitting ban. His diagnosis is that the industry finds the benefits so obvious that it never learned to explain them, which is how a water usage figure overstated by four orders of magnitude became conventional wisdom. Proposing a foundation that buys World Series ad time is a tell about how far behind he thinks the industry is. Every other risk in this conversation is priced somewhere. That one is not.

Key Takeaways

Baker characterizes July 2026 as “2022 in a month,” with AI names down 40 to 60 percent from their highs in a straight line while underlying fundamentals improved.
He spent the week in Silicon Valley explicitly hunting for a negative quantitative metric and found essentially one: third-party data suggesting Anthropic’s growth curve came slightly off trajectory, a data point Anthropic shareholders reportedly dispute.
Nvidia was trading at its lowest forward price to earnings multiple in ten years at the time of recording. The only cheaper moments were the DeepSeek shock and Liberation Day, both of which proved to be V-bottoms.
A low forward multiple means the market believes these companies are significantly over-earning. Baker’s counter is that they are under-earning because their installed compute is contracted below spot.
Combined operating cash flow at Microsoft, Meta, and Amazon accelerated from roughly 28 percent to 32 percent growth, or to about 35 percent after adjusting for an unusual quarter of legal and regulatory charges.
Nobody in 2024 or 2025 modeled old GPU prices going vertical in 2026. The bull case assumed a slow decline in rental rates and the bear case assumed a steep one.
A concrete example: a well-known startup rented several thousand Blackwell B200s in the mid two dollars per GPU hour range and expects to pay just under four dollars for an identical cluster seven months later, a 50 to 60 percent increase.
One inference cloud stated publicly that it plans to pay roughly 100 percent more for Blackwells when its current contract expires.
Neoclouds were often forced into below-market long-term contracts because they needed an offtake agreement to finance the GPUs in the first place.
Consensus models hyperscalers monetizing Blackwell and Rubin at roughly Ampere rates, two generations behind, producing about 1.3 to 1.4 trillion dollars of hyperscale operating cash flow. Assuming monetization merely at a discount to current Blackwell rates pushes that closer to two trillion and removes roughly 700 billion dollars of credit demand.
The credit concerns are real and undeniable: real yields are up, spreads have widened, credit default swap levels for the large buyers have blown out, and a recent Meta bond did not price where a Meta bond should price.
Baker’s response is that debt-fueled buildouts demand immediate repayment and unwind violently, which is what happened in the internet buildout, but this buildout is still overwhelmingly funded from operating cash flow.
If credit is not available, he argues the existing flops simply become more valuable, which is self-correcting rather than catastrophic.
The Meta selloff catalyst was a misread. Meta renting out compute was interpreted as excess capacity and a capex cut. Meta did not cut capex, and the actual motivation appears to have been demonstrating strong internal rates of return on a small slice of capacity ahead of a capital raise.
The open source panic was also a misread. Open source taking token share moves margin dollars out of the frontier model layer, but a token still requires the same flops, memory, and watts to produce, so infrastructure demand rises rather than falls.
Frontier tokens carry gross margins somewhere in the 80 to 95 percent range. Open source tokens might carry 30 percent. The customer’s savings come almost entirely out of that margin, not out of compute consumption.
Baker calls open source “dark matter to the public markets,” growing rapidly through GLM 5.2, Kimi K3, and Nvidia’s Nemotron, but nearly impossible for public investors to measure since it runs through private inference clouds.
Jensen Huang being the world’s loudest supporter of open source is itself evidence that open source is good for Nvidia’s business.
Enterprises that blow through their AI budget in three months set up a router, which cuts their spend but often increases total GPU hours consumed by shifting volume to cheaper open source tokens.
Adoption is happening in staggered waves: AI natives are all in and hiring very few humans, coastal public companies are optimizing, East Coast and non-coastal companies have barely adopted, and Europe is trying to regulate AI before using it.
Roughly 500,000 people worldwide use agentic AI, and perhaps half that number use it seriously, yet the world is already in an acute compute shortage. The relevant question is what happens at 100 million or 500 million users.
Token spend at the most AI-forward companies now runs 20 to 25 percent of total compensation spend, with individual examples at 30 percent and reports as high as 50 percent, against a roughly 25 trillion dollar global knowledge work market.
Founder-controlled companies are not conducting large-scale layoffs, which suggests the cash flow to pay for AI is expected to come from growth rather than from labor substitution.
Memory is the dominant variable in token economics. More memory per unit of compute yields more tokens out, which lowers cost per token, which is why demand has shown no negative elasticity to memory pricing.
Memory suppliers have shifted from maximizing near-term price to signing long-term agreements with prepayments, floors, and ceilings, trading short-term upside for durability.
Breaking a memory long-term agreement is now potentially fatal. With four buyers that matter at scale and market share determined by supply allocation, a supplier can respond by breaking the volume commitment and handing your allocation to a competitor.
This is structurally different from the Apple era, when a single dominant buyer could break pricing agreements without consequence.
Nvidia’s new model is best described as a credit wrapper with a revenue share triggered when GPU prices exceed a floor. It is not vendor financing, since a third party lends the money, and it could produce a very large cloud-scale royalty business quickly.
Baker thinks this model is badly misunderstood, meaningfully increases Nvidia’s revenue per gigawatt, and strengthens its competitive position against startup accelerators that pay more at the foundry, pay more for high bandwidth memory, and cannot finance their chips as cheaply.
Nvidia has taken equity stakes across the ecosystem, and Baker’s read is that every time they have not taken a stake it has proven to be a mistake.
The scenario that would genuinely frighten him: hyperscaler operating cash flow stops accelerating, forcing the buildout onto debt, or a sustained sharp contraction in GPU rental prices. Nobody he has spoken to says they have too many GPUs.
Continual learning and sample efficient learning are the technical developments most likely to disrupt training demand, and several new labs including Safe Superintelligence are focused on them. Baker still thinks training asymptotes to a small share of compute rather than to zero, and that the change would be enormously good for the world regardless.
Fireworks launched a product called Nexus that plugs into Claude Code, OpenAI Codex, or Grok in roughly three lines of code, ingests a customer’s data, applies reinforcement learning to a model, and routes queries appropriately.
This stack is what converts an alleged “ChatGPT wrapper” into a defensible company. Shifting 30 to 60 percent of token consumption to a customized open model on top of frontier orchestration produces better outcomes at roughly half the cost.
Cheap, capable open source models may actually inflate the value of the very best frontier model, since a 160 IQ orchestrator becomes more valuable when it has an army of cheap 120 IQ models to direct.
The inference clouds are growing almost as fast as the frontier labs did in their early days while burning very little cash, which is extraordinary by any conventional software metric.
China obtaining a domestic deep ultraviolet lithography machine is a genuine phase transition and should not be dismissed, but the technology is roughly 25 years behind extreme ultraviolet, and lithography progress is learning by doing that cannot be teleported through.
Baker considers regulation the biggest single risk to AI, citing New York’s data center moratorium as the first of many and describing the current environment as post-factual and post-logical.
The public narrative that data centers raise power bills, drain water, and destroy jobs is largely wrong. Behind the meter deals typically lower local electricity prices, and modern community agreements include hospitals, schools, police and fire stations.
The widely cited data center water figure originated in a published error overstating usage by roughly 10,000 times, since acknowledged by the author, which Baker likens to the decimal point error that created the myth that spinach is exceptionally high in iron.
He argues data centers are among the best things to happen to blue collar wages in his lifetime, with ongoing rather than one-time employment from maintenance, replacement, and upgrade cycles.
SRAM-based accelerators built on older nodes and free of high bandwidth memory constraints could substantially improve return on investment by allowing disaggregated inference: prefill on one chip, attention on a high-memory chip, and the feed forward network on SRAM.
SpaceX has improved fundamentally since going public, and Baker believes the market does not yet understand it as a compute company. Only the hyperscalers, CoreWeave, Crusoe, and SpaceX have ever brought on more than 500 megawatts of power in a single year, and SpaceX has done it fastest and cheapest.
A widely circulated report claims SpaceX intends to bring on eight gigawatts of compute in 18 months. Baker doubts the number but notes that at roughly 50 billion dollars of monetization per gigawatt, even a fraction of it dwarfs the current consensus estimate.
When SpaceX dumped a large block of compute into the market, it was absorbed without a blip, which Baker reads as one of the more bullish demand signals of the year.
Orbital compute feels more real every day. Benchmark funding StarCloud, from outside the Elon ecosystem and without access to internal launch costs, functions as a useful sanity check on the idea.
Dark horse names Baker flags for the next phase: Lip-Bu Tan, Lin Qiao at Fireworks, and Scott Wu at Cognition.

Detailed Summary

A Selloff That Contradicted Every Fundamental

Baker opens by describing July 2026 as 2022 compressed into a single month. AI names fell 40 to 60 percent from their highs in a nearly straight line. What made the month unusual was not the magnitude but the absence of a legible cause. In 2022 the market feared recession, rising rates, and inflation. During the DeepSeek shock and Liberation Day you knew exactly what the market was reacting to. This time the fundamentals moved in the opposite direction from the tape. GPU availability tightened, GPU rental pricing rose, DRAM spot prices rose, and token growth accelerated. Baker asked Patrick, who had also spent the summer in Silicon Valley, whether he had heard a single negative quantitative metric or a single instance of deceleration. The answer was nothing.

Part of the problem is visibility. Public markets cannot see Anthropic or OpenAI directly, and they cannot see the American open source inference clouds like Fireworks, Baseten, Modal, and Together that monetize inference. Everyone stares at the same chart of semiconductor cash flow rising while hyperscaler free cash flow falls, and that chart omits the private companies entirely. It also omits the repricing dynamic Baker considers the most important fact in the market.

The Spot Versus Contract Gap

In 2024 and 2025 every serious forecast assumed GPU rental prices would decline, with the only debate being how fast. Neoclouds locked in long-term contracts partly out of prudence and partly because they needed offtake agreements to finance the hardware at all. The result is a large installed base of contracted compute trading at a steep discount to today’s spot market. Baker’s argument is that as those contracts roll off, compute reprices higher even if spot itself declines from current levels, and that repricing flows directly into hyperscaler operating cash flow.

The anecdotes are stark. A prominent startup rented several thousand B200s in the mid two dollar per GPU hour range and expects to pay just under four dollars for an identical cluster seven months later. One inference cloud said publicly it plans to pay roughly double for Blackwells at contract renewal. Baker’s read is that hyperscalers are therefore under-earning across the board, which is the exact opposite of what a ten-year-low forward multiple implies the market believes.

Financing the Buildout and the Credit Question

Credit is the one bearish input Baker concedes is real. Real yields have risen, spreads have widened, credit default swap levels have blown out across the large buyers, and a recent Meta bond did not price the way a Meta bond should. Sophisticated private capital investors told him this is just banks hedging commitments, but he acknowledges the optics are bad and the facts are undeniable. His concern is the classic capital cycle: debt-financed buildouts demand immediate repayment, so when supply and demand slip out of alignment the unwind is fast and brutal, exactly as it was in the internet buildout.

The math he ran is the counterweight. Consensus effectively models hyperscalers monetizing Blackwell and Rubin at Ampere rates, two generations behind, producing 1.3 to 1.4 trillion dollars of operating cash flow. Assume instead that they monetize merely at a modest discount to current Blackwell rates and the figure approaches two trillion, taking about 700 billion dollars of credit demand off the table. Better cash flow also improves the credit ratios, which makes debt cheaper if they choose to use it. And if credit disappears entirely, the flops already installed simply become more valuable. Microsoft brought on a large slug of capacity in June that did not even appear in second quarter results.

How the Month Actually Unfolded

Baker walks the sequence of catalysts. First, Meta announced it would rent out compute, which the market read as excess capacity and an imminent capex cut. Meta did not cut capex. What Meta appears to have seen was SpaceX selling trading-optimized clusters into the market at an enormous premium to contracted rates, and the plan was likely to demonstrate strong returns on a small slice of capacity before raising equity capital and increasing capex. Shortly afterward Meta released its best model in a long time, overshadowed by a competing release but a clear signal it was not easing off.

Next came the open source freakout. Kimi K3 arrived, the widely watched token index dipped and flattened, and the two were connected: the index captures mix, and a shift from expensive frontier tokens toward open source tokens looks like weakness even when total compute consumption is rising. Then China’s deep ultraviolet lithography news triggered a broad selloff in semicap equipment. Finally, rising real yields and widening spreads gave the market a genuine reason to worry. Baker’s summary is that with the sole exception of credit, every one of these narratives was factually wrong, and a friend at Fidelity described the winning strategy of the past three years as doing the dumbest, most superficial thing as fast as possible and cycling between them.

Open Source as Dark Matter

The most important conceptual argument in the episode is that a token is a token. Regardless of which model produces it, a token consumes the same flops, the same memory, and the same watts. Open source taking share therefore does not reduce compute demand. It transfers margin from the frontier model layer, where gross margins might be 90 percent, to open weights inference at perhaps 30 percent, and the resulting price decline drives elasticity in token volume. Since frontier labs and open source models both run on the same underlying cloud infrastructure at the same compute cost, the effect is to push margin dollars down into the infrastructure layer.

Baker calls open source dark matter to public markets. It is real, it is accelerating on the back of capability leaps from GLM 5.2 and Kimi K3, Nvidia continues to push Nemotron closer to the frontier, and yet none of it appears in audited financials that public investors can underwrite. He also notes the tell that should have settled the debate: Jensen Huang is the world’s most vocal supporter of open source, which would be an odd position for the largest beneficiary of frontier concentration to hold if open source actually threatened the business. Baker adds a normative point, that a world with only one or two dominant frontier models charging 90 percent margins is not good for humanity, and that many models is the better outcome.

Routers, Fine-Tuning, and the End of the Wrapper Insult

The practical mechanism behind the open source surge is the router plus fine-tuning stack. Inference clouds have become genuinely good at supervised fine-tuning and reinforcement learning, so a company can take its proprietary data, customize an open weights model, put it behind a router, and have the router send most queries to that model while escalating to a frontier model for verification or harder work. The result is often slightly better outcomes at half the cost. Fireworks shipped a product called Nexus that connects to Claude Code, OpenAI Codex, or Grok in roughly three lines of code and handles ingestion, reinforcement learning, and routing.

This changes the durability question for AI natives. Two years ago the criticism was that these companies were thin wrappers with no defensibility. Now a company with domain-specific proprietary data can train on it, own the model serving 30 to 60 percent of its tokens, and get off the frontier lab treadmill it previously had no choice but to accept. Baker points to Cursor, Harvey, and others leaning hard into this. He also raises the counterargument fairly: some believe that once a frontier model achieves recursive self-improvement it will serve every intelligence level more cheaply through distillation, leaving no room for open source. He does not dismiss it, but he thinks the proprietary data held by AI natives and the orchestration value of the single smartest model make the multi-model future more likely. Cheap 120 IQ models arguably make a 160 IQ orchestrator more valuable, not less.

Where the Money Comes From

The pushback Baker gets on X is fair: even if hyperscalers are under-earning, where does the customer revenue ultimately come from? Definitionally it must come from faster economic growth through productivity or from labor substitution. He sees labor substitution happening at AI natives, though not through firing. They simply never hire the humans, and gross profit dollars per full-time employee at these companies is vertical compared with prior startup generations. Token spend now runs 20 to 25 percent of total compensation spend at the most aggressive companies, with individual examples at 30 percent and reports as high as 50 percent, against a roughly 25 trillion dollar global knowledge work market.

The encouraging signal is that founder-controlled companies, the ones most likely to move fast on efficiency, are not conducting large-scale layoffs once you adjust for pandemic-era overhiring. That suggests they see continued opportunity for people plus large token budgets rather than a straight substitution. Data from Cognition, Ramp, and Stripe indicates that companies spending the most on AI are growing meaningfully faster, though Baker acknowledges the skeptics’ point that these datasets do not control for industry.

The Memory Supply War and LTA Game Theory

Everything is currently in shortage, and Baker argues the constraint is energizing gigawatts rather than manufacturing. Turbine makers and diesel generator makers are ramping, old aircraft turbines are being stripped and reconditioned for data center power, and regulatory policy is moving favorably. The transition he says he got wrong is the shift, especially in memory, from maximizing short-term pricing to signing long-term agreements with customer prepayments, price floors, and price ceilings.

The reason those agreements will hold is game theory. Memory is the axis around which everything else revolves, because more memory per unit of compute means more tokens out, which lowers cost per token, which is why demand has shown essentially no negative elasticity. Market share among the four buyers that matter (Amazon with Trainium, Google with TPUs, AMD, and an Nvidia bigger than all of them combined) will be determined for years by supply chain allocation. Break a long-term agreement to chase a lower price in an oversupply year and the supplier can break the volume commitment in return and hand your allocation to a competitor. Since oversupply in this industry is reliably followed by undersupply, that is a decision that can end a franchise. Apple could get away with this historically because its volume was overwhelming and it had no equivalent competitor. That world is gone.

Nvidia’s New Playbook

Baker finds Nvidia’s low multiple hard to reconcile with how thoroughly the current environment favors it. If chips need to be financed, nothing on earth is more financeable than an Nvidia GPU. If land and power are the constraint, Nvidia has been playing the matchmaking chess game well. On top of that they have rolled out what Baker describes as a credit wrapper with a revenue share that kicks in when GPU prices sit above a floor. It is not vendor financing, since someone else lends the buyer the money. What it does is give Nvidia a royalty on recurring compute revenue, which could amount to a very large cloud business built entirely out of royalties, while helping bridge the cash flow mismatch between an industry that has gone free cash flow negative and a supplier collecting all the cash.

Asked what he would do as a memory CEO, Baker says he would do exactly what Nvidia is doing: approach GPU and accelerator buyers, participate in the credit wrapper, perhaps put up cash upfront to make lenders comfortable, and take a cut of ongoing revenue. He expects firms like Blackstone and Apollo are pitching variants of this to the memory companies already. He also thinks the arrangement quietly widens Nvidia’s competitive moat, since startup accelerator companies pay more at the foundry, pay more for high bandwidth memory, and cannot finance their chips at Nvidia’s rate. And he notes that essentially every time Nvidia has declined to take an equity stake in something, it has turned out to be a mistake.

What Could Break the Thesis

Pressed for the scenario that would flip him, Baker names two. The first is operating cash flow failing to accelerate, which would force the buildout onto debt and validate the credit bears. That outcome depends largely on whether the combined trajectory of Anthropic, OpenAI, Grok, Cursor, and open source keeps compounding. The second is a sustained sharp contraction in GPU rental prices. The market would react instantly, and it would mean the compute shortage had broken. As of the recording, not a single person he has spoken with says they have too many GPUs.

The technical wildcard is continual learning and sample efficient learning. Many researchers believe both are close. A human learns effectively on something like 20 billion tokens while frontier models train on 300 trillion, so a model that could be trained on 10 trillion tokens and then learn efficiently in the world would represent a discontinuity in training demand. Baker thinks training will asymptote to a small but nonzero share of compute regardless, and that the development would be extraordinarily good for the world. He also notes Nvidia is deeply involved with essentially all of the labs pursuing it.

China, Lithography, and Decoupling

On China’s deep ultraviolet lithography machine, Baker holds both views at once. It is a genuine phase transition, comparable to going from having no propeller plane to having one, because they did not have it before and now allegedly they do. It is also roughly 25 years behind extreme ultraviolet, and lithography is learning by doing, so you cannot teleport through the required cycles. He suspects the market overreacted and that if it ever affects ASML’s order book it will be years out, by which time the market will have forgotten and rediscovered the concern several times.

He is careful about certainty here. It is very hard for an American to have real clarity on what is happening inside China, the people there are extremely capable and work brutally hard, and they consider this existential for the country. There are unverified reports that an extreme ultraviolet machine was smuggled in, which he treats as noise. His larger point is that decoupling is now self-reinforcing on both sides, it is unfortunate, and neither side is going to stop.

Regulation, Data Centers, and a Failure of Storytelling

Asked for the worst thing that could happen to AI, Baker answers regulation without hesitation. New York’s data center moratorium feels like the first of many, and even deep red pro-growth states are telling the industry it is doing a poor job explaining itself. The political narrative among ordinary Americans is that data centers will raise electricity prices, drain water supplies, and eliminate jobs. Baker’s counter is that behind the meter deals generally lower local electricity prices, that community agreements now routinely include hospitals, schools, police stations, and fire stations rather than the old model of buying the fire department new trucks, and that the jobs are ongoing rather than one-time because of continuous maintenance, replacement, and upgrade cycles.

The water claim is the clearest case of a myth outrunning the correction. An author overstated data center water usage by roughly 10,000 times, has acknowledged the error repeatedly, and the figure still circulates. Patrick offers the parallel of the spinach iron myth, created by a misplaced decimal point in an academic text and still believed 80 years later. Baker’s proposed remedy is blunt: a foundation or political action committee running ads during the Final Four, NFL games, and the World Series explaining what a data center actually does for a community, alongside the story of AI accelerating medical research and improving outcomes for people with serious illness. The people building this find the benefits so obvious that they assume everyone already knows, and they cannot process how divergent their view is from most Americans.

SRAM Accelerators and Disaggregated Inference

An underdiscussed development, Baker argues, is what happens when SRAM-based accelerators arrive at scale. These chips are not constrained by high bandwidth memory and are often built on older nodes, so they do not compete for the leading edge capacity that GPUs consume. Inference disaggregates into prefill and decode, and decode splits further into attention and the feed forward network. The holy grail is running prefill on a chip without high bandwidth memory, attention on a high-memory chip, and the feed forward network on SRAM, which nothing beats for that workload. Since workloads keep changing, no single chip can get the ratio of compute to high bandwidth memory to on-die SRAM permanently right, which is precisely the argument for disaggregation. Baker expects this to be strongly positive for the return on investment across the installed base and on new compute.

SpaceX, Orbital Compute, and Dark Horses

Baker does not think the market understands SpaceX as a company yet, and he considers it the most important new public company. The fundamentals have improved since the IPO, and the compute story is the part being missed. Only the hyperscalers, CoreWeave, Crusoe, and SpaceX have ever brought more than 500 megawatts of power online in a single year, and SpaceX has done it fastest and cheapest while building clusters customers actually like. When SpaceX dumped a large block of compute into the market, it was absorbed without a blip, which Baker treats as one of the most bullish demand datapoints available. A circulating Substack report claims eight gigawatts within 18 months. He doubts that figure and quotes it only because it is public, but at roughly 50 billion dollars of monetization per gigawatt against a 73 billion dollar consensus estimate, even partial delivery would overwhelm expectations. There is a well-known New York hedge fund short case built on spot compute prices falling 90 percent.

On orbital compute, Baker says time at Starbase left him thinking it feels more real every day, and the Starship landing reinforced it. His sanity check is that Benchmark, from entirely outside the Elon ecosystem and without the benefit of internal launch costs, chose to fund StarCloud at a real valuation, with SpaceX partnering to provide the Starlink laser technology that orbital compute requires. As he puts it, maybe he is crazy, maybe Elon is crazy, maybe Benchmark is crazy, and maybe the SpaceX engineers are crazy too, but all of that being true simultaneously does not seem probable. Asked for dark horses who could become as consequential as the current giants, he names Lip-Bu Tan, Lin Qiao at Fireworks, and Scott Wu at Cognition. The episode was recorded at Benchmark’s offices, at the table where their dinners are held.

Notable Quotes

“I want to be scared. I don’t want to feel like a lunatic watching these stocks get cheaper thinking the expected forward returns are going up.”
Gavin Baker, on why he spent the week in Silicon Valley hunting for bearish data

“I would describe July as 2022 in a month.”
Gavin Baker, characterizing a 40 to 60 percent drawdown in AI names that happened in a straight line

“Have you heard a single negative quantitative metric about AI? A single instance of deceleration?”
Gavin Baker to Patrick O’Shaughnessy, framing the central contradiction of the month

“A token is a token, and you need the exact same amount of compute to make a token. It takes the same amount of flops, the same amount of memory, the same amount of watts.”
Gavin Baker, on why the open source panic misread infrastructure demand

“Open source is kind of dark matter to the public markets. It’s hard for public markets to measure it.”
Gavin Baker, on why the fastest-growing part of inference demand is invisible in audited financials

“Claude is kind of Walter Cronkite for the stock market and everybody just believes whatever it says. And by the way, it’s really smart, but it’s not always right.”
Gavin Baker, on the collapse of interpretive diversity among investors

“Nvidia is actually, as we record this, at its lowest forward PE of the last 10 years.”
Gavin Baker, noting the only cheaper moments were the DeepSeek shock and Liberation Day, both V-bottoms

“If you break your LTA and then in the next two or three years for any reason leverage shifts back to the memory guys, you’re out of business.”
Gavin Baker, on why long-term agreements will hold through the next memory cycle

“If you need to be able to finance the chips, and you do, nothing’s more financeable than an Nvidia GPU. Nothing.”
Gavin Baker, on why the current environment favors Nvidia more than its multiple suggests

“Data centers are in a lot of ways the best thing to happen for blue collar wages in my lifetime.”
Gavin Baker, on the gap between the political narrative and the local economics

“A lie could go around the world faster than truth gets out of bed.”
Gavin Baker, on a data center water usage figure overstated by roughly 10,000 times that still circulates

“One of Elon’s phrases is we specialize in making the impossible late.”
Gavin Baker, on why he doubts the eight gigawatt figure without betting against SpaceX

Watch the full conversation here: Why the Markets Are Pricing AI Wrong with Gavin Baker on Invest Like the Best.

Tag: behind the meter deals

Why the Markets Are Pricing AI Wrong: Gavin Baker on the July 2026 Selloff, GPU Spot Prices, Memory LTAs, and Nvidia’s Credit Wrapper