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Dylan Patel projects AI capital expenditures could require $11 trillion by 2030. Hasib Qureshi argues that as human population growth peaks, AI labor will become the primary driver of economic expansion, necessitating a significant increase in the global money supply.
Dylan Patel states that AI infrastructure accounted for most of US GDP growth last year. Global capital expenditure will rise from over one trillion dollars this year to more than two trillion dollars by 2028.
Dylan Patel reports that Anthropic and OpenAI expanded their compute from under two gigawatts to over five gigawatts in 2024. Next year, they are projected to secure 45% to 50% of all incremental global compute.
Dylan Patel reveals that Anthropic transitioned to profitability in Q2 2024, with OpenAI expected to follow in Q3. Their revenue generation has reached up to $50 million per megawatt, compared to a base compute cost of $10 to $15 million.
Dylan Patel notes that newer chips like the GB300, TPU v7, and Trainium 3 deliver three to five times more performance per watt than prior generations. This hardware efficiency acts as a performance multiplier on newly deployed gigawatts.
Dylan Patel highlights a massive economic mismatch where six billion dollars of fab capital expenditure generates one gigawatt of compute annually, which translates to one hundred billion dollars in end-user revenue. Supply remains bottlenecked by specialized tooling like ASML EUV mirrors.
Dylan Patel argues that safety regulations and deployment restrictions slow down frontier labs more than open-source competitors. Anthropic has withheld safety-assessed models, and local rules in New York, Texas, and Ohio threaten to restrict data center capacity.
Dylan Patel predicts labs will allocate a smaller percentage of compute to inference, prioritizing training and R&D to achieve artificial general intelligence. Historically, pre-training runs like Anthropic's Mythos used less than 200 megawatts of active compute.
Dylan Patel details a dramatic shift in global compute distribution since 2022, with US deployment rising to 70% of global watts while China's share fell below 10%. China relies on smuggled chips and domestic fabs that lag in performance.
Dylan Patel estimates that domestic fab expansions will allow China to deploy up to 30 gigawatts of compute by 2028. However, due to lower-quality domestic hardware, 50 gigawatts of Chinese compute in 2029 may only equal 20 gigawatts of US compute.
Dylan Patel forecasts eleven trillion dollars in total capital expenditure from 2024 to 2029 to build out AI infrastructure. With six trillion dollars funded by corporate cash, five trillion dollars of new credit must be raised, driving interest rates higher.
Dwarkesh Patel and Dylan Patel argue that higher interest rates will raise discount rates and suppress equity values for traditional companies. This crowding-out effect threatens to default debt-heavy developing nations like Pakistan and Nigeria that lack domestic AI revenue.