Neutral mention · rule
That's why I gave you data! METR study was 16 people using Sonnet 3.5/3.7. Data I'm talking about is 10s of thousands of people and is much more up to date. Some counter examples to METR that are in the literature but I'll just say: "rigor" here is very difficult (including METR) because outcomes are high dimensional and nuanced, or ecological validity is an issue. It's hard to have any approach that someone wouldn't be able to dismiss due to some issue they have with the methodology. The sources below also have methodological problems just like METR https://arxiv.org/pdf/2302.06590 -- 55% faster implementing HTTP server in javascript with copilot (in 2023!) but this is a…
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single task and not really representative. https://demirermert.github.io/Papers/Demirer_AI_productivity... -- "Though each experiment is noisy, when data is combined across three experiments and 4,867 developers, our analysis reveals a 26.08% increase (SE: 10.3%) in completed tasks among developers using the AI tool. Notably, less experienced developers had higher adoption rates and greater productivity gains." (but e.g. "completed tasks" as the outcome measure is of course problematic) To me, internal company measures for large tech companies will be most reliable -- they are easiest to track and measure, the scale is large enough, and the talent + task pool is diverse (junior -> senior, different product areas, different types of tasks). But then outcome measures are always a problem...commits per developer per month? LOC? task completion time? all of them are highly problematic, especially because its reasonable to expect AI tools would change the bias and variance of the proxy so its never clear if you're measuring the change in "style" or the change in the underlying latent measure of productivity you care about