Measuring AI Ability to Complete Long Tasks

3.2 Time horizon vs. release date

1. What trend did Kwa et al. find in frontier models’ time horizons?
   Exponential growth from 2019 to 2025, doubling about every 7 months (207 days, 95% CI 166–240). GPT-2’s horizon was about 2 seconds; o3’s about 110 minutes. 2024–2025 models doubled faster (about every 4–5 months), but that estimate rests on few models.
   • Kwa et al., 2025, Measuring AI Ability to Complete Long Tasks (arXiv preprint arXiv:2503.14499), §3.2
2. Why are Kwa et al. more confident in the slope than in any single model’s horizon?
   Tasks with the same human time differ widely in difficulty for models, so which tasks are sampled shifts all models’ horizons up or down together. Those errors are strongly correlated, so they largely cancel in the trend.
   • Kwa et al., 2025, Measuring AI Ability to Complete Long Tasks (arXiv preprint arXiv:2503.14499), §3.2
3. How do 80% time horizons compare with 50% horizons?
   They double at about the same rate (204 vs 207 days) but are 4–6 times shorter: even models that sometimes solve long, varied tasks cannot do tasks of moderate length reliably.
   • Kwa et al., 2025, Measuring AI Ability to Complete Long Tasks (arXiv preprint arXiv:2503.14499), §3.2