AI Prediction Index

Epoch AI

AI research institute; white paper with EPRI, 'Scaling Intelligence: The Exponential Growth of AI's Power Needs'

@EpochAIResearchepoch.ai

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“Aggregate cash capital expenditure across the largest hyperscalers (Microsoft, Amazon, Alphabet, Meta and Oracle) is on track to overtake their operating cash flow around Q3 2026 — capex is growing roughly 70% per year against about 23% annual growth in operating cash flow, pushing most hyperscalers toward external financing.”

AI research institute, Data Insights series

Q3 2026 results are not reported until late October 2026, so the crossover cannot yet be confirmed; corroborating evidence is mounting (FactSet reported in July 2026 that hyperscaler free cash flow was under mounting pressure, with incremental annual debt rising from 9% of capex in FY24 to 32% by mid-2026).

“Overall, we conclude that power demand for frontier training will likely grow by 2.2x to 2.9x per year in the coming years, implying that the largest training runs will reach 4-16 GW by 2030.”

AI research institute; white paper with EPRI, 'Scaling Intelligence: The Exponential Growth of AI's Power Needs'

Largest known training runs were in the low hundreds of MW as of 2025; the 4-16 GW range resolves at the end of 2030.

“GPU energy efficiency has been improving by about 40% annually and is likely to continue at a similar pace — partially offsetting continued growth in training compute, which we expect to persist at 4-5x per year [through 2030].”

AI research institute tracking trends in machine learning inputs

Resolves against measured FLOP/watt trends for frontier AI accelerators through 2030.

“In our median projection, we expect enough manufacturing capacity to produce 100 million H100-equivalent GPUs for AI training by 2030, sufficient to power a 9e29 FLOP training run — even after accounting for GPUs being split between multiple AI labs and in part dedicated to serving models.”

AI research institute; report 'Can AI scaling continue through 2030?'

Said Aug 20, 2024Deadline Dec 31, 2030Epoch AI, 'Can AI scaling continue through 2030?', Aug 2024

Epoch's uncertainty range spans 20M-400M H100-equivalents (1e29-5e30 FLOP); resolves against actual AI accelerator production and single-run scale in 2030.

“The model estimates a 10% chance of transformative AI — defined as 'AI that if deployed widely, would precipitate a change comparable to the industrial revolution' — being developed by 2025, and a 50% chance of it being developed by 2033.”

AI research institute; compute-based ('direct approach') model of transformative AI timelines

The model's 10%-by-2025 leg did not come in — no industrial-revolution-scale transformation occurred by 2025 — but the headline 50%-by-2033 estimate resolves at the end of 2033. TIME notes Epoch researchers themselves expect slower progress than the model implies.