[bigstrat2003]

Also, supposed productivity gains are dubious. I personally experience at best no productivity gains when using LLMs to write code, and sometimes it's an active drain on my productivity. There was that one study a year or so ago showing similar results. People are trying to say the productivity gains are there and undeniable, but that is not true. It is very much a subject of controversy whether AI helps productivity.

[asdfasgasdgasdg]

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.

[oudlys]

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

[asdfasgasdgasdg]

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]

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.

[dminik]

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]

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.

[techblueberry]

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.

[oudlys]

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

I think this is right. They are much better applied as editors than authors, IMO.

The key thing is stay in control of your output. i.e. understand it thoroguhly. I think you let the LLM make decisions you don't really understand, you're increasing the likelihood of introducing defects that are expensive to address.

[asdfasgasdgasdg]

I’m not completely closed to your idea but if code was never the bottleneck why did so many organizations always feel so chronically low on coders? And of course this requires the AI to be no help at all with what is actually the bottleneck.

[techblueberry]

So, one is, it probably does depend on workflow. Some folks probably are doing things that can be accelerated by AI, and I think if you’re a small team with a good product head and know what you’re doing then AI probably helps a lot.

But what if the problem you’re trying to solve is the altogether too often problem of like getting teams that are dependent on you to upgrade the library they use. And what if the library is a breaking change, and last year they upgraded to the library on your advice and it broke production and now they’re suss and want to accept all changes, and integrating that library change isn’t in their critical path so they’re just not going to spend time on it, even if you submit the MR them. Even if you show them their tests pass after the change.

Importantly to the above, you probably need more devs to do more of the above in parallel. You don’t hire devs to write more code, you hire more devs to carry on the mental load of a broader scope of work. Even in the before times, so much code got stuck at the integration step.

But because all that is hard, instead you go and codegen to fix an obscure bug that sure makes a few customers happy, but no one thought was a limiting factor for paying your company more money.

It’s not that I don’t think AI can help, I think it’s a prerequisite for the job and everyone should use it. It’s more that I think in the grand scheme of things, people will bias towards using it for tasks that aren’t in the critical path - refactors, tech debt, bug smashing, tool building; and I think it could really help devex and that’s good.

But I think people are bad at knowing the difference between “my job feels a bit easier” or “I’m more productive” and “this task had an impact on the bottom line” and when you extrapolate that out to a whole engineering org, that’s where the productivity statistics get lost.

I’ll addd one data point to this is like this thread itself. So many people on AI skepticism threads point to their own subjective experience as evidence we’re not in a bubble, and sort of ignore the entire concept of economics. I’m not saying we’re in 100% in a bubble, but subjective experience isn’t great evidence of it.

And this is just sort of one of the factors, what about the increased cost and mental load of supporting more software? What about junior engineers who feel pressured to ship work but don’t actually learn the software engineering? What about lost context from not intimately understanding your software?

[asdfasgasdgasdg]

All good questions. I am not a big believer in claiming I know whether we are in a financial bubble or not. I just put it all in VT and we will see what happens. I know that the AI allows me to write code I couldn’t write before much more quickly than before but I admit that this may not help with organizational friction.

Although if this theory is true — that AI helps with coding but coding is not the friction point in organizations with multiple humans, even that should allow faster iteration by allowing one human to do more coding therefore reducing the size of teams required to make some programs. You should see good acceleration in solo shops too.

[techblueberry]

Yeah, again the answer is definitely not no AI. And I’ve seen the - you know - I can one shot whole applications that would have taken me weeks or months before. But it’s also much easier to one shot apps that aren’t in the critical path. I’m building all sorts of tools to make my job easier, but I still have to do the job.

I’m a platform engineer. The primary failure mode for platform engineers is building tools people don’t want. AI doesn’t really make that easier. Or it can but it can also make it harder by making it easier to chase down ideas that you don’t get traction on. And I think that - net balanced across the organization is probably why productivity gains get sort of averaged out.

For sure I think solo devs who have a system are seeing gains, as long as you can I think have the discipline to have a process that includes feedback and learning and your not just feeding off of dopamine hits of one shotting features but yeah. I mean for solo devs the code was never really the limiting factor, it was product-market fit and marketing.

So solo devs who have a system may be laughing themselves all the way to the bank, but we may not see a lot of net new solo devs.

But if code is cheap now then it’s sort of inherently devalued. 2 8 person startups can probably relatively easily find a dev with AI experience to rocket ship their code generation, which means the basic skills of talking to customers, change management, and building the right thing become even more valuable.

Even solo devs I wonder - almost every post-mortem of a failed company goes “I wish we had spent more time talking to customers and less time writing code”

Again if you can get the discipline right, maybe as a solo devs you can get more work done faster and spend more time with your family. That’s incredibly valuable!

But if you go and add a big new feature, or a second product - unless your community is primed for constant growth(no man’s sky is one community where more more more seems good) you’re just growing the surface area where all the other skills are more necessary.