预测算力放缓下人工智能时间视野的延后，发现能力提升可能推迟7年。
Forecasting AI Time Horizon Under Compute Slowdowns
- 基于算力与算法模型，推导出时间视野随算力线性增长
- 2019–2025年算力与时间视野均呈恒定增速，验证理论一致性
- 若算力投入放缓，1个月可靠时间视野将延迟7年
METR的时间视野指标自2019年以来随算力呈指数增长。然而，算力扩展能否持续至2030年尚不明确，这引发对算力放缓如何影响人工智能代理能力预测的疑问。结合算力与算法共同决定时间视野的模型，以及算力投资推动算法进步的机制（排除纯软件奇点可能性），并基于2019–2025年算力与时间视野均保持恒定增长率的实证事实，我们推导出时间视野增长必须与算力增长成正比。提供有限实验证据支持该理论。利用该模型在OpenAI算力预测下进行投影，发现部分情况下存在显著延迟：在80%可靠性下，1个月时间视野的实现将比简单趋势外推晚7年。
原文摘要 · Abstract (English)
METR's time horizon metric has grown exponentially since 2019, along with compute. However, it is unclear whether compute scaling will persist at current rates through 2030, raising the question of how possible compute slowdowns might impact AI agent capability forecasts. Given a model of time horizon as a function of training compute and algorithms, along with a model of how compute investment spills into algorithmic progress (which, notably, precludes the possibility of a software-only singularity), and the empirical fact that both time horizon and compute have grown at constant rates over 2019--2025, we derive that time horizon growth must be proportional to compute growth. We provide additional, albeit limited, experimental evidence consistent with this theory. We use our model to project time horizon growth under OpenAI's compute projection, finding substantial projected delays in some cases. For example, 1-month time horizons at $80\%$ reliability occur $7$ years later than simple trend extrapolation suggests.
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