[warkdarrior]

> I almost feel less productive using modern GPT-5/Claude Sonnet 4 for software dev than prior models, precisely because they are more hands off and less supervised.

That is because you are trained in the old way to writing code: manual crafting of software line by line, slowly, deliberately, thoughtfully. New generations of developers will not use the same workflow as you, just like you do not use the same workflow as folks who programmed punch cards.

[_se]

No, it's because reading code is slower than writing it.

The only way these tools can possibly be faster for non-trivial work is if you don't give a shit enough about the output to not even read it. And if you can do that and still achieve your goal, chances are your goal wasn't that difficult to begin with.

That's why we're now consistently measuring individuals to be slower using these tools even though many of them feel faster.

[KronisLV]

> No, it's because reading code is slower than writing it.

This feels wrong to me, unless we qualify the statement with: "...if you want the exact same level of understanding of it."

Otherwise, the bottleneck in development would be pull/merge request review, not writing the code to do something. But almost always, it's the other way around - someone works on a feature for 3-5 days, the pull/merge request does not really spend the same time in active review. I don't think you need the exact same level of intricate understanding over some code when reviewing it.

It's quite similar with the AI stuff, I often nitpick and want to rework certain bits of code that AI generates (or fix obvious issues with it), but using it for the first version/draft is still easier than trying to approach the issue from zero. Ofc AI won't make you consistently better, but will remove some of the friction and reduce the cognitive load.

[mwigdahl]

"Consistently"? Is there more than just the one METR study that's saying this?

[_se]

I have measured it myself within my organization, and I know many peers across companies who have done the same. No, I cannot share the data (I wish I could, truly), but I expect that we will begin to see many of these types of studies emerge before long.

The tools are absolutely useful, but they need to be applied in the right places and they are decided not a silver bullet or general-purpose software engineering tool in the manner that they're being billed at present. We still use them despite our finding, but we use them judiciously and where they actually help.