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 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
To be fair, I’ll take a non-biased 16 person study over “internal measures” from a MAANG company that burned 100s of billions on AI with no ROI that is now forcing its employees to use AI.
I don't speak for bopbopbop7, but I will say this: my experience of using Claude Code has been that it can do much longer tasks than the METR benchmark implies are possible.
The converse of this is that if those tasks are representative of software engineering as a whole, I would expect a lot of other tasks where it absolutely sucks.
This expectation is further supported by the number of times people pop up in conversations like this to say for any given LLM that it falls flat on its face even for something the poster thinks is simple, that it cost more time than it saved.
As with supposedly "full" self driving on Teslas, the anecdotes about the failure modes are much more interesting than the success: one person whose commute/coding problem happens to be easy, may mistake their own circumstances for normal. Until it does work everywhere, it doesn't work everywhere.
When I experiment with vibe coding (as in, properly unsupervised), it can break down large tasks into small ones and churn through each sub-task well enough, such that it can do a task I'd expect to take most of a sprint by itself. Now, that said, I will also say it seems to do these things a level of "that'll do" not "amazing!", but it does do them.
But I am very much aware this is like all the people posting "well my Tesla commute doesn't need any interventions!" in response to all the people pointing out how it's been a decade since Musk said "I think that within two years, you'll be able to summon your car from across the country. It will meet you wherever your phone is … and it will just automatically charge itself along the entire journey."
It works on my [use case], but we can't always ship my [use case].
I could have guessed you would say that :) but METR is not an unbiased study either. Maybe you mean that METR is less likely to intentionally inflate their numbers?
If you insist or believe in a conspiracy I don’t think there’s really anything I or others will be able to say or show you that would assuage you, all I can say is I’ve seen the raw data. It’s a mess and again we’re stuck with proxies (which are bad since you start conflating the change in the proxy-latent relationship with the treatment effect). And it’s also hard and arguably irresponsible to run RCTs.
All I will say is: there are flaws everywhere. METR results are far from conclusive. Totally understandable if there is a mismatch between perception and performance. But also consider: even if task takes the same or even slightly more time, one big advantage for me is that it substantially reduces cognitive load so I can work in parallel sessions on two completely different issues.
I bet it does reduce your cognitive load, considering you, in your own words "Give up when Claude is hopelessly lost". No better way to reduce cognitive load.
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 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