预测到2028年，将有103至306个模型超欧盟AI法案算力阈值。
Trends in Frontier AI Model Count: A Forecast to 2028
- 基于算力阈值预测未来模型数量变化趋势
- 2028年超10²⁵ FLOP的模型达103-306个，超10²⁶的达45-148个
- 算力阈值设定方式影响监管稳定性，按最大训练规模倍数更平稳
各国正依据模型训练所用算力制定监管要求。例如，欧盟《人工智能法案》对训练算力超过10²⁵浮点运算（FLOP）的通用人工智能系统施加系统性风险管控；美国《人工智能扩散框架》则以10²⁶ FLOP为阈值识别“受控模型”。本文预测，到2028年底，将有103–306个基础模型超过欧盟阈值（90%置信区间），45–148个超过美国阈值。同时发现，每年超过绝对算力阈值的模型数量呈超线性增长——即每年新增数量高于前一年。而采用相对最大训练规模的阈值（如最大训练规模的10倍以内）则趋势更稳定，2025–2028年每年预计约14–16个模型被纳入监管范围。
原文摘要 · Abstract (English)
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), and 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.
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