Episode Details
Back to Episodes“Because 8 ≈ e², Anthropic’s researcher uplift is plausibly >2x” by Thomas Kwa
Description
Note: the modeling assumptions and conclusion are Thomas Kwa's opinion, and others at METR disagree. [1] Also, the math was checked by Claude but not a second human.
Introduction
Anthropic's RSI blog post reported that in Q2 2026, Anthropic contributors merged 8× as much code per day as in the 2021-2024 period. What does this imply about the factor by which a researcher's total effective output increased — the (serial) researcher uplift [2] ?
Of course, 8× more code doesn't mean 8× more research, as coding is only part of the job. However, if we assume each line of code (LoC) has equivalent quality and verbosity to pre-2025 code and make standard economic modeling assumptions, we can conclude a surprising amount: all models predict researcher uplift at Anthropic from coding agents alone is >2×. (Researcher uplift could be even higher, because these numbers assume no uplift on non-code tasks.)
- Cobb-Douglas predicts that if pre-AI time spent coding is and code output increases by a factor , then researcher uplift is .
- CES (constant elasticity of substitution) production functions, due to a fun mathematical coincidence, infer a narrow range of about if code is [...]
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Outline:
(00:25) Introduction
(03:09) Economic models
(03:57) Cobb-Douglas model
(04:57) CES model
(05:26) Setup
(06:31) Because 8 ≈ e², the estimate is robust to σ!
(09:15) M = 8 also implies a lower bound on σ
(10:47) Code heterogeneity model: What if AI speeds up high-stakes code less?
(12:22) Caveats: How could Anthropic's uplift be less than 2x?
(12:36) Plausible reasons
(12:39) Verbosity
(14:42) Barely useful code
(16:14) Irrational time allocation, intrinsic desire to use AI, etc.
(17:17) Unlikely reasons
(17:21) Extreme heterogeneity
(18:04) Changing denominator
(18:34) Discussion
(18:37) Prefer code output over code uplift, for estimating overall uplift
(19:40) Why is Anthropic's own estimate much lower?
(21:55) There are reasons to expect even higher uplift
(22:46) Conclusion
(24:03) Appendix: Heterogeneous code model
The original text contained 8 footnotes which were omitted from this narration.
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First published:
July 9th, 2026
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Narrated by TYPE III AUDIO.
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