Frontier AI Labs' Trillion-Dollar Compute Spending Race

SemiAnalysis founder Dylan Patel frames the AI buildout as a two-step jump: labs went from spending tens of billions of dollars a year to hundreds of billions, and are now forecasting trillions of dollars a year by the end of the decade ^[1]. The aggregate numbers back up the trajectory. Total AI industry capex is approaching roughly $1 trillion annualized in 2026, about 1 percent of gross world product and 3 percent of US GDP, and Patel projects it will cross $2 trillion by 2028 ^[4]. OpenAI alone just raised its planned compute spending through 2030 to roughly $750 billion, a 25 percent jump from the approximately $600 billion figure it had set earlier in the year, as it shifts from renting cloud capacity from Azure, Oracle, and CoreWeave to owning data centers outright ^[5]. Big Tech's hyperscalers are on a similar curve - combined AI infrastructure capex is on track to approach $700 billion this year, up from about $410 billion the year before, roughly $200 billion in 2024, and just $162 billion in 2022 ^[6][7]. McKinsey has put the cumulative worldwide AI capex bill needed by 2030 at $6.7 trillion ^[6]. Patel laid out the trajectory in a podcast conversation with Dwarkesh Patel, which also drew commentary on X tying the spending directly to US GDP growth.

The spending forecasts arrive alongside a parallel claim: that AI is close to automating the research that justifies the spending. Jerry Tworek, who led OpenAI's reasoning-model research for nearly seven years before departing in January 2026, says coding agents are so good right now it's getting them to the moment where they can automate AI research, and then have the models research better models ^[2]. He has gone further, raising the possibility that current frontier models may be the last generation humans design largely unaided - 'maybe we are at the last model that humans could have figured out' ^[2]. Tworek left OpenAI saying this kind of fundamental research was no longer possible there, and founded Core Automation, a startup explicitly aimed at building the most automated AI lab in the world ^[3]. Clips of Tworek describing the shift circulated widely on X, with one summarizing his view that AI researchers now tell each other they have only days of meaningful work left, and that he gives it about two years before humans stop being a meaningful part of AI research - a claim that read across social platforms as alarm rather than skepticism, in contrast to the more mocking tone reserved for the spending numbers themselves.

Patel's numbers imply a further consequence: that OpenAI and Anthropic could end up controlling most of the world's usable compute by 2028 ^[1]. Getting there has produced an unusual tangle of cross-competitor dependencies. Anthropic, whose gigawatt capacity is growing from under 2GW to above 5GW within a year per Patel's tracking ^[1], is paying rival xAI $1.25 billion a month for Colossus capacity through May 2029, has signed a 20-year, $19 billion lease with TeraWulf, and has committed $50 billion to custom US infrastructure with Fluidstack ^[7]. Compute suppliers are scaling to match: Oracle's capex jumped from $21.2 billion in fiscal 2025 to $55.7 billion in fiscal 2026, and it is guiding to roughly $70 billion in fiscal 2027 ^[7]. A podcast conversation between Dylan Patel and Dwarkesh Patel walking through this dynamic - labs racing to lock up compute even from direct competitors - drew significant engagement on X, reflecting how much attention the mechanics of compute concentration are now getting relative to the headline dollar figures.

Not everyone is convinced the spending curve is sustainable. Some analysts, including at Goldman Sachs, JPMorgan, and Wedbush, argue current AI investment levels are justified by expected productivity gains, while others point out that AI revenue run-rates remain far below the roughly $650 billion in annual revenue that would be needed to justify current capex at a modest 10 percent return ^[8]. That gap is where skepticism concentrates most visibly: on Reddit, threads tallying the multi-year compute commitments piling up across suppliers against current annual revenue treated the trillion-dollar framing with open mockery rather than alarm, and a widely upvoted thread reacting to a frontier lab's own long-run revenue projections argued the figures implied a share of global GDP no single company's product could plausibly capture. The recurring theme across that skepticism is not that the spending isn't happening - the dollar figures above are not in dispute - but that normal return-on-investment math may not apply to an arms race where falling behind on compute is treated as the bigger risk than overspending on it.