010Will AI compute run short or long through 2030?

What the base case says

Short through 2026. Today’s market is short. One-year H100 rental contracts rose about 40% between October and March and the B200 rental index is up about 28% this year. Every large cloud says demand exceeds what it can supply, and NVIDIA says its supply supports about 70% revenue growth in its year to January 2028, against demand growth nearer 100%. We assume demand at today’s prices runs 20% above what is being served. The fourth quarter adds about 7 GW, mostly Blackwell racks and new TPUs, while inference demand grows about 56% in the quarter, so the year ends 19% short.

2027 brings supply about level with demand. About 38 GW comes online, 60% more than the 24 GW switched on in 2026. Most of it is Vera Rubin racks and Google’s eighth-generation TPUs, plus Blackwell racks delivered late in 2026. Each gigawatt does about 4.6× the inference work of today’s average and 2.2× the training work. Supply grows about 3.1× during the year in compute terms and demand about 2.6×, so by December supply is about level with demand (−1%): 37.9 GW switched on against 38.3 GW needed. Month by month, with last year’s late deliveries switched on first, the market comes within 3 points of balance in September 2027 and stays there until August 2028. That fits Goldman’s view that supply and demand rebalance in the second half of 2027 at the earliest and more likely in the first half of 2028; Amazon expects to be short through 2027. It needs about 38 GW of power in 2027, above the top of our power research’s range: its high case is 30 GW in 2027 and 40 GW in 2028. At that high case, 2027 ends 15% short.

Then demand pulls ahead. In compute terms supply grows 2.2×, 1.9× and 1.8× in 2028–30, and demand 2.4×, 2.1× and 1.9×. Capacity switched on rises only from 43 GW to 51 GW a year, because AI capex grows 11–18% a year on street estimates. Meanwhile what customers spend on inference grows 35–70% a year in our base case, and training budgets keep growing. The market ends 2028 9% short and 2030 21% short. By 2030, 86 GW of new capacity would be needed against 51 GW switched on, because each year’s shortfall carries into the next. Either capex keeps rising faster than analysts expect, or prices rise until demand fits. The demand and supply cases move this a long way; see the controls above and “What tips the balance” below.

Supply: chips, power and money

The chips keep coming. Epoch AI counts 11.3M H100-equivalents delivered in the first half of 2026. We estimate 33M for the year, above pack 009’s 24–27M, which extended the first half’s pace: NVIDIA’s guidance implies a much larger second half, with Vera Rubin, rated at 8.8 H100-equivalents a chip, shipping from the fourth quarter. In 2027 the chipmakers could deliver about 100M as Rubin ramps, Google ships its eighth-generation TPUs and AMD its MI455X racks; NVIDIA would be about 56% of that and Google 27%. NVIDIA says supply limits its revenue at least through January 2028, Broadcom expects its AI revenue to double to about $115B in fiscal 2027 and again in 2028, and TSMC says AI demand still exceeds what it can make. Rated at about 26 GW, 2027’s chips need about 52 GW to run; allowing a quarter to install them, about 47 GW could be switched on during 2027. After 2027 we let accelerator spending rise from about $1T to $1.25T a year and each generation’s gains do the rest: 170M H100-equivalents in 2028 and 350M in 2030.

Power doesn’t keep up. About 24 GW came online for AI in 2026, by our estimate from chip deliveries; we assume power for about 27 GW was ready. For 2027 Morgan Stanley expects hyperscalers and neoclouds, 60–95% of new capacity, to add 32 GW. Goldman counts 2.7 times as much US capacity scheduled for 2027 as for 2026 but expects only half to 60% of it on time. Neither says whether it counts IT power or the whole facility. Trackers found most US projects due in 2027 not yet under construction in April, new gas turbines are being booked for delivery as late as 2031 and large transformers take more than three years. We expect about 38 GW of all-in capacity to come online in 2027, rising to 58 GW by 2030. That is within the range those forecasts imply once they are put on an all-in, worldwide basis, and above the top of our own power research’s range through 2029. Its central estimate, which applies project trackers’ delay rates, is about 23 GW in 2027 rising to 35 GW in 2030, with a high case rising from 30 GW in 2027 to 60 GW in 2030. It starts from a lower 2026, about 16 GW, partly because it takes Epoch’s end-2025 installed base at 2.5× chip power where we use 2×. Our path leaves chips for about 9 GW more than can be switched on in 2027; a consensus estimate cited by Elon Musk in August put that at about 15 GW.

Money binds first. AI capex is about $1T in 2026 (Goldman’s estimate). On street estimates for the hyperscalers, Oracle, SpaceX and the neoclouds, plus China and other buyers, it reaches about $1.4T in 2027 and $2.05T by 2030, growing 11–18% a year after 2027. All-in, a gigawatt of IT power costs about $45B to build today (Epoch puts a GB200 site at $38B, NVIDIA says $50–60B), rising to about $52B by 2030 as chips carry more value per watt. That pays for about 38 GW in 2027 and 51 GW in 2030, at or below our power path, so money is the limit in the base case from 2027. Both sit well below what the chipmakers could ship. The money includes the chips, so when it binds, fewer chips get bought than could be made, or chips wait for power. If these estimates are right, from 2027 new AI capacity is limited less by the fabs than by power and the money to build.

Why gigawatt totals mislead

Today’s fleet is still about a fifth Hopper. On agentic inference a GB300 rack does about 12.6× an H200’s work per MW, and Vera Rubin about 2.1× a GB300’s (SemiAnalysis, measured on pre-release software). So the capacity switched on in 2027 does about 4.6× today’s average per gigawatt at inference, and 2030’s about 19× if Rubin Ultra and Feynman deliver what their roadmaps suggest. Training gains ride on FLOPs per watt, which improve about 30% a year: 2.2× today’s average for 2027’s capacity and 5.0× for 2030’s. These are peak 8-bit FLOPs, the way Epoch counts them. The training throughput our efficiency research expects newer chips to achieve is lower for capacity switched on through 2029; counted that way, 2027 would end 11% short and 2030 25% short. That research labels its tables by install year. We read them as each year’s deliveries, because its 2026 mix includes Rubin, which only ships from the fourth quarter; read as install years, 2027 would end 3% long and 2030 17% short.

Cheaper inference also raises demand. Capacity switched on in 2028 does about 58% more inference work per dollar than 2027’s, and about 60% and 52% more again in 2029 and 2030. If prices follow costs, each dollar customers spend buys that much more, so after 2027 inference demand rises with hardware gains just as supply does. Faster hardware therefore moves the balance mainly through training, which we count in FLOPs, and through the years to 2027, when demand follows usage.

Who gets the new hardware matters too. We split each year’s hardware between inference and training in the same proportion. Newer hardware’s edge is larger at inference (4.6× today’s average in 2027, against 2.2× at training). If it went to inference first and older chips did the training, the market would serve about 6–7 points more of demand each year. If the newest went to training first, as frontier labs train on their newest clusters, it would serve about 8–12 points less.

Demand to 2030

Inference to 2027 is pack 009’s: OpenRouter’s compute on pack 007’s three paths. To scale it to the whole market we compared OpenRouter’s growth with six disclosed token counts over the same windows. The market grew at 0.32–0.86 of OpenRouter’s pace, measured in doublings, with a median of 0.63; we use 0.6. We apply it to OpenRouter’s compute rather than its tokens, because OpenRouter’s mix is probably moving to small, cheap models faster than the market’s; applied to tokens, demand at the end of 2027 would be about 28% lower. That puts the market’s inference demand at 4.3× today’s by the end of 2027, about 2.8× during 2027 alone. Pack 009’s headline, about 47 GW of new GB300 racks by December 2027, assumed the whole market grew at OpenRouter’s full pace. At 0.6 of it, measured as pack 009 does from October’s average to December’s, inference needs about 15 GW of GB300s, or about 10 GW of the capacity switched on in 2027, before counting today’s shortage.

After 2027, spending decides. Pack 007’s paths have OpenRouter’s growth halving every three, six or twelve months. Carried past 2027, the base path has OpenRouter’s compute growing 65% in 2028, 14% in 2029 and 3% in 2030, which describes one marketplace maturing rather than the market. Instead we let inference demand follow what customers spend on it: 1.7× in 2028, 1.5× in 2029 and 1.35× in 2030 in the base case. That is slower than the leading labs plan to grow. OpenAI’s plan takes revenue from $36B in 2026 to $350B in 2030, about 1.8× a year, and Anthropic’s internal forecast of $190–200B in 2028 implies about 1.9× a year from an estimated $55B in 2026. Each dollar buys as much more inference as the capacity switched on that year delivers per dollar, 1.5–1.6× more each year. So inference demand grows about 2.7× in 2028, 2.4× in 2029 and 2.1× in 2030, reaching 57× today’s level by 2030.

Training and research use about 60% of today’s chips, and pack 009 didn’t project them. We let them grow 2.4× in 2027, slowing to 1.6× in 2030, measured in 8-bit FLOPs: 19× today’s by 2030. The anchors: OpenAI’s compute roughly quadrupled in each of 2024 and 2025 (Epoch AI); it ended 2025 with 1.9 GW and targets 30 GW by 2030. Anthropic, at about 2 GW early in 2026, has placed about 5 GW of demand with Broadcom for 2027 and 10 GW for 2028 and could pass 30 GW by 2030 (22V Research). The power behind frontier training runs grows 2.2–2.9× a year (Epoch AI). We assume market-wide training grows more slowly than these labs’ plans: they use less than half of the world’s AI compute.

What would change the numbers

Training and research budgets. The biggest lever. If they grow only 4.5× by 2030, supply ends 2030 43% ahead of demand, a glut. If they grow 66×, the market is 69% short. Much of this is spent by a few labs that are still raising the money.

Power and money. If capacity added stays near 30 GW a year, 2030 is 43% short. At our power research’s central estimate it is 47% short, and at its high case 24% short. If power follows SemiAnalysis’s path, about 90 GW a year by 2029, with capex to match, supply runs 25% ahead by 2030. Chips aren’t what holds supply back in our base case: if power and money kept up with every chip the industry could deliver, supply would run 17% ahead by 2030.

Inference spending. If what customers spend on inference grows only 1.2×, 1.1× and 1.0× in 2028–30, the market stays near balance (+5% in 2030). If it grows 2.35×, 2.0× and 1.7×, faster than even OpenAI’s plan, 2030 is 49% short.

Hardware. If Rubin Ultra slips (SemiAnalysis reported in July that its 144-GPU rack moves to 2028), Feynman disappoints and the 2026–27 mix underdelivers too, 2030 is 43% short. If they beat their roadmaps, it is 11% long. Because cheaper inference also raises demand, hardware moves the balance mostly through training and the years to 2027. Software gains on existing chips help a little: the efficiency research’s central case, software gains of 30% a year against workloads 20% heavier, would leave 2030 18% short instead of 21%.

Who gets the new hardware. If the newest hardware went to inference first, where its edge is largest, 2030 would be 14% short; if it went to training first, 30% short.

Today’s shortage. With no shortage today, supply leads by 19% at the end of 2027 and 2030 ends 6% short. If demand runs 30% above supply today, 2027 ends 8% short.

Inference to 2027. The pace against OpenRouter and inference’s share of today’s chips move 2027 between 12 points up and 13 points down. Letting OpenRouter’s growth fade, as pack 007’s base path does if carried to 2030, would leave 22% more compute than demand by 2030.

What to watch

Gigawatts switched on in 2027. Our base case has about 38 GW. Hyperscalers report capacity added each quarter, and turbine and transformer deliveries lead them. At our power research’s high case, 30 GW, the market ends 2027 15% short.

Capex plans for 2028–30. Street estimates have AI capex growing about 18% in 2028 and 11% by 2030. If lab revenue grows as planned, that is too little; capex rising faster than that is how the gap would close.

Lab revenue against plan. OpenAI’s and Anthropic’s revenue, funding rounds and IPOs decide both what customers spend on inference and what labs spend on training, the two largest demand inputs after 2027.

Rubin in the field. SemiAnalysis measured Rubin at about 2× GB300 per MW on pre-release software. Production results, and when Rubin Ultra ships, set how much each 2027–28 gigawatt is worth.

Rents. In September the B200 rental curve priced the 12-month forward about 12% below spot: the market expects some easing within a year, consistent with supply catching up during 2027.