I can see an argument that the productivity gains are illusory / don’t translate to economic productivity. I’m not denying the possibility.

However, most of the engineers I respect have gone from being skeptics a year ago to convinced today. I don’t personally know any true holdouts any more. If there are studies that disprove productivity gains more than six months ago, I’m happy to believe that it was true of the AIs that were available at the time. But I’m going to need something much more recent before I disbelieve my lyin’ eyes where it pertains to the AIs available today.

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oudlys 6 hours ago | parent | next [-]

There is an observational study that was published in March 2026 that followed 4000 teams over 2 years. It shows, in my view, exactly that the productivity gains don't translate into economic value.

Here is the report:

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

And my commentary:

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

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asdfasgasdgasdg 3 hours ago | parent [-]

If it was published in March 2026, even if the data was collected up to the day the study was published, 7/8ths of it would fail my “within the last six months” test. But I am looking forward to the results of future studies on this topic!

	▲	oudlys 2 hours ago \| parent [-]
		I get wanting to wait for more data. And thinking that LLMs have improved enough that this will change. My view is that it's not really about how good the models are - it's about how we're using them. Understanding what you've built is an important part of value creation, and LLMs eliminate that.

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dminik 5 hours ago | parent | prev | next [-]

Its funny, I've noticed the same thing, but did not come to the same conclusion.

I currently don't have work access to Claude Code, but most of my teammates do. Watching from the outside, the cycle seems to look like this:

1. Experience some success, which hooks you into relying on AI.

2. The AI keeps failing at some task, but you don't want to stop. Keep trying over and over again.

3. Run out of tokens and take a break.

Now, sometimes 1 doesn't happen. Sometimes 2 doesn't happen. 3 is a certainty though.

Now, if you told me that the productivity gain from 1 is enough to offset the loss from 2 and 3, I could believe you. But I also wouldn't be surprised if it didn't.

	▲	chillacy 4 hours ago \| parent [-]
		As I work with Claude more and gain a feel for its capabilities, I tend to run into 2 far less often, as I'll decompose my messages more for the current model limitations. The threshold also changes each release.

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techblueberry 2 hours ago | parent | prev [-]

I’m going back to being a holdout, but it’s nuanced - My theory into why LLMs don’t lead to the colloquial definition of productivity would be something like - if code was never the bottleneck than generating code faster doesn’t result in more meaningful output.

Even if you take for granted that AI is as good as the best people say in writing code. And Ive spent a lot of time generating codes, I won’t disagree - Then the question becomes - does this change your daily incentives such that you reach for code as the solution to your problems rather than something else (coordinating with your colleagues? Product management? Planning and Design?

So from a holistic perspective, I think intentionally limiting your own AI usage is the best approach for maximum long-term productivity.