How METR measures Long Tasks and Experienced Open Source Dev Productivity
summary
METR time-horizon scores and SWE-bench improvement claim models are getting faster and tackling longer tasks, but a field RCT with 16 experienced open-source devs on mature repos shows no average speedup — videos reveal heavy variance, frequent verification and cleanup, and unreliable self-reported times. Cursor experience split (including a 30–50 hour Cursor bin) shows no clear benefit, and an unpublished greenfield hackathon finds only ~4 percentile points higher judge scores with AI allowed. Data- and deployment-metric tasks routinely fail.
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chapters (18)
- 0:00The Compute-Time Horizon Argument
- 1:43Potential Constraints on AI Scaling (Power & Dollars)
- 4:23The Problem of Eclipsing Evaluation Time
- 6:52Meta's "J-Curve" of Developer Productivity
- 9:12Unreliability of Self-Reported Time Estimates
- 11:43Personal Experiences with AI Tools (Cursor) & Learning Curves
- 14:10METR Study Deep Dive: Scatter Plots & Variance
- 16:48The Controversy of "Conservative" Usage Estimates
- 21:41Unpublished Hackathon Results (AI Allowed vs. Disallowed)
- 25:28Why AI Struggles with Data Science & Messy Enterprise Data
- 30:35Example of AI Failure on Complex Deployment Metrics
- 38:29Quantifying Speed-Up: The Methodological Challenges
- 46:30Future Metrics: "Watched" vs. "Unwatched" Time Horizons
- 52:52Moving Beyond Benchmarks: "In the Wild" Transcripts
- 56:12The "Agent Village" & Fuzzy Goal Measurement
- 58:53The "Neurodivergent AI" Hypothesis & Interface Mismatch
- 1:06:31Software-Only Singularity vs. Hardware Constraints
- 1:13:53AI Applications in Chip Fabrication & Yield Improvement
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