[parpfish]

i've seen a number of articles claiming things like "devs self report they'er +x% more productive with AI, but actually they're -y% LESS efficient!". and i think that this is explanation for why.

as a boss (or researcher) i'm going to measure productivity based on amount of output per hour that i'm paying you; as a workers, i'm going to measure productivity based on amount of output relative to the amount of effort i'm putting in.

so what may be happening is that bosses see that output is at 80% (productivity down!) but workers see that they can give that 80% output with 40% effort (productivity up!).

[thewebguyd]

Not sure among devs, but I do know that in other positions in typical corporate bureaucracy, people have a propensity to not report their own automations or productivity gains upward, because the reward structure isn't there.

Early on in my days as a sysadmin, I automated a ton of my role when the rest of the team was still doing ClickOps. The reward for doing so was more work and expectations without the additional pay increase to justify my new found productivity. That happens all over the workforce, and so people will just keep it to themselves. I learned my lesson at that first job real fast that if I'm able to have the same, or greater output, for half the time, I keep that to myself so I can use the automation to free up my own time instead of have it filled by the company.

I wonder how much of that is happening now with AI in non-technical roles.

[toomuchtodo]

https://www.youtube.com/watch?v=OwfNjGxa_D4

[Aurornis]

> so what may be happening is that bosses see that output is at 80% (productivity down!)

If an initiative produces only 80% of the previous results and you’re paying large token bills on top of the same wages, the AI is going to get cut off.

> i've seen a number of articles claiming things like "devs self report they'er +x% more productive with AI, but actually they're -y% LESS efficient!".

Are you thinking of the old METR evals? Their more recent evals showed an actual performance improvement.

The old report is still circulated as bait for AI skeptics.

[raini]

I think the old report you're referencing is this [1] from July 2025, but I can't find a new report. This [2] links to a new dataset at the bottom (that maybe shows improvements?) but it seems like they chose not to write it up because of perceived flaws in their study. Is there a more relevant report I'm missing?

[1]: https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...

[2]: https://metr.org/blog/2026-02-24-uplift-update/#wider-adopti...

[oudlys]

I read this today and found it super valuable in evaluating METRs research.

https://arachnemag.substack.com/p/the-metr-graph-is-hot-garb...

[Ifkaluva]

> so what may be happening is that bosses see that output is at 80% (productivity down!) but workers see that they can give that 80% output with 40% effort (productivity up!).

So why is it that the bosses are the ones that are so enthusiastic about adoption?

[jofla_net]

Well the obvious is that, monkey-see-monkey-do, and they don't want to miss out. But the insidious externality is that they are the most likely ones (and higher ups) to be invested in nvidia and others, so when they push you to use, it creates a viscous cycle.

[therealdrag0]

Do you have any studies in the last 6 months showing performance decreases?

[oudlys]

March 2026:

https://www.faros.ai/blog/ai-acceleration-whiplash-takeaways

Faros would tell you shops are shipping 16% more PRs with heavy AI use.

https://unessays.substack.com/p/talk-is-cheap

I think that's wrong because it doesn't adequately account for the quality and rework their data show.