Summary

In the paper Measuring AI Ability to Complete Long Software Tasks (Kwa & West et al. 2025), METR defined an AI model's 50% time horizon as the length of tasks (measured by how long they take human professionals) that it can complete autonomously with 50% probability. We estimated the time horizon of frontier models released since 2019 on a benchmark combining three sets of software and research tasks ranging from 1 second to 16 hours in length-for-humans (HCAST, RE-Bench, and SWAA, henceforth METR-HRS). METR found that the time horizon has doubled every 7 months, possibly accelerating to every 4 months in 2024.

One important limitation was the task domain: all involved software engineering or research, but AI capabilities are known to vary greatly between different task types.[1] Here we explore whether similar trends apply to different task distributions including self-driving and agentic computer use, using a methodology that [...]

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Outline:

(00:10) Summary

(03:48) Methodology

(07:31) Benchmarks

(08:58) Results

(09:01) Trends on other domains

(09:58) Main takeaway

(10:27) More observations

(13:23) Soundness of the time horizon metric

(18:22) Video length is a poor predictor of difficulty

(21:16) SWE-Lancer task value is a poor predictor of difficulty

(22:25) Robustness checks

(24:23) Limitations and future work

(27:22) Conclusion

(28:50) Appendix

(28:53) Potential future experiments

(30:06) Details for individual benchmarks

(35:48) Other plots

(35:52) Raw data

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First published:

Source:

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Narrated by TYPE III AUDIO.

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