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SemiAnalysis puts 40-50% of 2027's new capacity under OpenAI and Anthropic contracts, with revenue per megawatt now well clear of deployment cost. That makes inference a residual market.
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Two of Patel's per-megawatt figures do not sit comfortably together. He puts Anthropic's peak at $50M per megawatt against a deployment cost of $10-15M [5][4], a multiple of roughly three to five [1], where serving GPT-4 on Hopper had run at negative gross margin [6]. He then gives an end-2027 base case of $70-80M per megawatt blended across the whole company [18]. But only about 40% of a lab's compute budget serves inference, with the rest in training and research [14]. Divide the blended figure by that share and the serving megawatts have to be clearing $175-200M each [2]. Either the mix swings hard back toward inference, or those two numbers are not measured on the same base.
The floor is easier to pin down. Anyone can stand up open weights, Kimi on vLLM or SGLang, at $10-15M per megawatt and clear positive revenue through OpenRouter [10], so a megawatt has a clearing price that owes nothing to what a frontier lab earns on it. Patel's own arithmetic needs that price to move to $25-50M for the two labs to hold 100 GW in 2028 [9][11], which is 67% to 233% above the top of today's base [4]. Price then tracks who can pay most at the margin rather than what the capacity cost to build.
What breaks the story is not demand. From the middle of 2026, marginal revenue per new megawatt flattened even as absolute capacity kept climbing [16], and Patel reads that as marginal megawatts going into research rather than serving [15]. For a buyer of tokens that is the worse reading: compute absorbed into internal R&D does not appear as available capacity at any price. The shelving of Astra and of Anthropic's Model 2 works the same way [17]. The models that would justify $100M per megawatt are the ones not being shipped.
Supply will not rescue the residual buyer either. New York State has banned data center construction and Texas has paused approvals, with Ohio making data centers pay property tax on surrounding land [20], and the tooling chain upstream cannot be widened on a two-year horizon even with capital thrown at Carl Zeiss [22]. The silicon going in during 2026 runs three to five times the performance per watt of the last generation [12], so half the physical megawatts is more than half the usable FLOPs. Meta and SpaceX can build with no end customer and then choose between using the capacity and reselling it at $25-50M per megawatt [19].
Every number here is Patel's, relayed second hand in a summary of the conversation [24]. The structural claim survives that. In 2024 and 2025 the hardware chain took nearly all the gross margin while the model layer ran negative [21], and compute was at least sold to whoever turned up with money. On these figures, within one to two years most of the world's compute is owned by or serving two labs [3], and everyone else trades in what they leave.
Ranked by verification strength, evidence, and original report placement.
Dylan Patel, founder of SemiAnalysis, says that based on already-signed contracts, Anthropic and OpenAI will take 40-50% of global new compute added in 2027.
Patel judges that by the end of 2027 half of all new global compute will belong to Anthropic and OpenAI, and because total global compute roughly doubles each year, new compute soon constitutes most of the installed base, making it real within one to two years that most of the world's compute is owned by or serving those two labs.
For the two labs to secure 100 GW in 2028, the unit price of compute must rise from $15M per megawatt to $25M, $30M or even $50M per megawatt, so they can outbid every other buyer.
If the trend continues, Patel expects the two labs to absorb 70-80% of global new compute by 2028, reaching around 100 GW of combined physical capacity.
Against the consensus that most compute goes to inference, Patel argues labs will allocate a declining share to inference over time and more to training and internal R&D.
Patel's base case, absent regulatory blockage, is $70-80M per megawatt of revenue for Anthropic and OpenAI on a blended whole-company basis by the end of 2027, with more than $100M per megawatt not exaggerated if models keep improving.
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Evidence-backed comparisons of source perspectives and observed adoption signals. Read the methodology
Which Builder, Operator, and Investor concerns the observed source mix emphasized—not a truth score.
Evidence, demonstrated adoption, hype gap, incentives, and confidence are assessed independently, each on its own current evidence. How these are measured.
Single translated recap, no primary data
Every quantity in the cluster traces to one item: a community-posted Chinese-language summary of a podcast conversation. There are no contracts, filings, price indices, vendor quotes, benchmarks or regulatory dockets, the text is a translation that ends mid-sentence, and no second publisher carries or checks the figures. Only the provenance of the material and the fact that these are Patel's stated positions are firmly established.
Large buildouts reported, none independently confirmed
The recap does describe concrete deployment activity - multi-gigawatt capacity ramps at both labs, Anthropic on Google TPUs with Fluidstack, the 2026 GB300 / TPUv7 / Trainium3 generation entering service, a priced B300 capacity sale, and state-level restrictions reshaping supply. That is substantive real-world activity rather than pure speculation, but all of it is relayed by one secondary source with no operator, vendor or regulator confirmation, so the observed adoption signal is weak relative to the scale claimed.
Sweeping forecasts on one unverified retelling
The headline propositions - two firms owning or serving most of the world's compute within one to two years, 70-80% of 2028 additions, $70-80M and then $100M+ per megawatt of revenue - are near-maximal claims about the shape of the compute market, yet the supporting record is a single translated podcast recap with no contracts, financials or price data. The forecasts are internally coherent and the underlying activity is real, which keeps the gap short of extreme, but the certainty of framing runs well ahead of what the supplied evidence and confirmable adoption can carry.
Forecaster sells research on the market he is repricing
The source itself identifies the forecaster as the founder of SemiAnalysis, a firm whose commercial product is research on the semiconductor and compute supply chain; a narrative of accelerating scarcity and rising compute prices directly raises the value of that research, and the recap discloses no such interest. The recap also notes sponsor ad reads inserted into the conversation, and the carrier is an unedited community post rather than an outlet applying independent verification. The incentive is structural and visible, not evidence of distortion.
Low - one voice, one carrier, no corroboration
Confidence is limited by a single-source, single-publisher cluster in which the only firm findings are what Patel asserted and where the assertion was published. Attributed forecasts are documented, but the factual claims that would make them checkable - shares, prices, margins, profitability dates and permitting actions - have no corroboration anywhere in the supplied material, and the translated text is truncated.
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1 article · August 25, 2026