Comment by bopbopbop7

To add to your point:

If the M stands for Meta, I would also like to note that as a user, I have been seeing increasingly poor UI, of the sort I'd expect from people committing code that wasn't properly checked before going live, as I would expect from vibe coding in the original sense of "blindly accept without review". Like, some posts have two copies of the sender's name in the same location on screen with slightly different fonts going out of sync with each other.

I can easily believe the metrics that all [MF]AANG bonuses are denominated in are going up, our profession has had jokes about engineers gaming those metrics even back when our comics were still printed in books: https://imgur.com/bug-free-programs-dilbert-classic-tyXXh1d

Oh yes all of this I agree with. I had tried to clarify this above but your examples are clearer: my point is: all measures and studies I have personally seen of AI impact on productivity have been deeply flawed for one reason or another.

Total speed up is WAY less than 10x by any measure. 2x seems too high too.

By data alone it’s a bit unclear of impact I agree. But I will say there seems to be a clear picture that to me, starting from a prior formed from personal experience, indicates some real productivity impact today, with a trajectory that suggests these claims of a lot of SWE work being offloaded to agents over the next few years seems not that far fetched.

- adoption and retention numbers internally and externally. You can argue this is driven by perverse incentives and/or the perception performance mismatch but I’m highly skeptical of this even though the effects of both are probably really, it would be truly extraordinary to me if there weren’t at least a ~10-20% bump in productivity today and a lot of headroom to go as integration gets better and user skill gets better and model capabilities grow

- benchmark performance, again benchmarks are really problematic but there are a lot of them and all of them together paint a pretty clear picture of capabilities truly growing and growing quickly

- there are clearly biases we can think of that would cause us to overestimate AI impact, but there are also biases that may cause us to underestimate impact: e.g. I’m now able to do work that I would have never attempted before. Multitasking is easier. Experiments are quicker and easier. That may not be captured well by e.g. task completion time or other metrics.

I even agree: quality of agentic code can be a real risk, but:

- I think this ignores the fact that humans have also always written shitty code and always will; there is lots of garbage in production believe me, and that predates agentic code

- as models improve, they can correct earlier mistakes

- it’s also a muscle to grow: how to review and use humans in the loop to improve quality and set a high bar

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

That negates everything else? If you have a tool that can boost you for 80% of your work and for the other 20% you just have to do what you’re already doing, is that bad?

There's a reason why sunk cost IS a fallacy and not a sound strategy.