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《AI and Compute》 is a statistical report released by OpenAI in 2018, looking at how much compute it took to train an AI model. It tallied the compute used in historical model trainings and found that compute demand has long grown exponentially, doubling over time, and that several famous capability breakthroughs appeared right after a big leap in compute.

When you hear a vendor today say "we stacked ten times the compute and the model got stronger," the framework established by this report is what you use to judge whether that claim is credible: there is indeed a historical correspondence between compute investment and capability gains. Later analyses have only updated the numbers on top of its statistics; the framework itself hasn't been replaced.

If a field's data has been exhausted or has hit a physical limit, don't use it to make judgments — doubling compute again won't buy an equivalent improvement. The report itself is an observational statistic, not a law, and the trend could be interrupted by algorithmic progress at any time.

《AI and Compute》(2018) | Next review 2027-09-20