Governments are starting to impose requirements on AI models based on how
much compute was used to train them. For example, the EU AI Act imposes
requirements on providers of general-purpose AI with systemic risk, which
includes systems trained using greater than $10^{25}$ floating point operations
(FLOP). In the United States’ AI Diffusion Framework, a training compute
threshold of $10^{26}$ FLOP is used to identify “controlled models” which face
a number of requirements. We explore how many models such training compute
thresholds will capture over time. We estimate that by the end of 2028, there
will be between 103-306 foundation models exceeding the $10^{25}$ FLOP
threshold put forward in the EU AI Act (90% CI), E 45-148 models exceeding
the $10^{26}$ FLOP threshold that defines controlled models in the AI Diffusion
Framework (90% CI). We also find that the number of models exceeding these
absolute compute thresholds each year will increase superlinearly — that is,
each successive year will see more new models captured within the threshold
than the year before. Thresholds that are defined with respect to the largest
training run to date (for example, such that all models within one order of
magnitude of the largest training run to date are captured by the threshold)
see a more stable trend, with a median forecast of 14-16 models being captured
by this definition annually from 2025-2028.

Questo articolo esplora i giri e le loro implicazioni.

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