Posted by RyeCombinator 11 hours ago
This may be true, but they followed in May with this [0]:
> Importantly, survey results are not necessarily grounded in reality. There are reasons to be skeptical of people’s responses to counterfactual questions such as about AI’s effect on productivity — for instance, our study in early 2025 found that people overestimated AI’s effect on their time spent on tasks by 40 percentage points on average.
[0] https://metr.org/blog/2026-05-11-ai-usage-survey/#productivi...
Yes yes, shout it from the rooftops! Over the next few years I think we're going to see that companies that get this point will keep doing meaningful things, and stand a chance of weathering this transition period.
I think we're going to see a bunch of companies that went all in on AI for AI's sake go under because they've lost their mission, lost their implementation, and won't have a way to get those back in a reasonable timeframe and at a reasonable cost.
> When a company says “AI made everyone more productive, so we need fewer people”, I want to see the evidence - and I don’t believe it exists today. Show me that x% of your workforce is genuinely idle (or even just underutilised) because the work can now be done by fewer people. Even then: I’ve never seen a product/SaaS company that didn’t have an endless roadmap. If you got a free headcount increase essentially overnight, why wouldn’t you use it to deliver more value to your customers, faster? That should show up as MAU, conversion, revenue.
I see some people calling for calm instead of AI panic by invoking Jevons Paradox. But at least within these companies there's no good evidence of Jevons in action, is there? The roadmap is endless, but when employees are perceived to be idle they get fired instead of being assigned more (or more ambitious) tasks.
To be fair, one could claim Jevons applies to "the market" at large, but at least we can say the evidence from tech companies is not encouraging. So maybe it is, indeed, time to panic a bit?
> Choosing the layoff instead tells me the productivity claim is doing PR work for a decision that was already made for other reasons (over-hiring, investor pressure, take your pick).
Yup, I think we all suspect this. Though it's probably a mix of the two factors.
Anyone relying on a steady paycheck from an employer should panic a bit all the time, because nothing can save them from bad management. The reference to Jevons Paradox doesn't say anything about individual managers responding correctly. If 30% of managers screw up, that's a lot of collateral damage.
Now to respond to your actual point, I don't think software developers should panic. Even if pure software engineering gets hit hard, I'm having trouble imagining a scenario where years of software development skills plus knowledge in a specific domain isn't a good thing for current software developers. This is unlike what happened with international trade, where you had 60-year old textile workers losing their jobs, no alternative jobs, and no policy being offered to compensate them for the effects of trade.
Is it true that the evidence in tech so far is not encouraging? I was pretty worried about the job market a year or so ago, but it seems pretty good for experienced people at the moment, no? (I do have big concerns about the entry level pipeline though!)
Deciding what to build. Reviewing Code. And testing code. Are the new bottleneck.
So of course we don't see massive productivity gains. Because these parts of the SCLC were always bottlenecked but their capacity matched the throughout. We fired all the dedicated QAs years ago. Sr+ engineers that do all the code review are limited.
Teams have not re-organized to match the new code-input velocity.
Engineers don't want to do QA because it's "beneath them".. and most engineers don't like performing or are not Sr enough to do extensive or high quality code review.
My day (excluding the huge amounts of communication overhead) used to progress as a serial operation of: 1. Write some code for one thing, 2. Self review of that thing, 3. Review other peoples' work, 4. Respond to review comments, 5. Get things merged, 6. Back to 1.
Now I have more of a tendency to queue up work on a few things at once, and then the serial steps are the self reviews and reviews of other peoples' work, and some of the review commentary back and forth (though I can automate some of this in parallel as well).
The upshot is that I'm more working in batches now than in serial, which I really do find to be more efficient.
It's not that it has removed all the bottlenecks at all, but no longer being required to focus all my attention for periods of time on physically typing code has removed one important bottleneck, and has changed, and I would say, improved, my workflow significantly.
But what's not as much the case is that if I did an A/B test on the same task that I'd be massively sped up because so much of my day to day work are the things you mentioned as being serialization points. The time I take to figure out what needs doing, what the best approach would be, making sure it was actually the right thing to have done in the first place once I'm done, all that stuff. I use AI assistance for those tasks too but it's not the same effect as when I just hand off the pure implementation phases. So it winds up being "faster" and you'll have to pry my AI assist tools out of my cold, dead fingers - but if I'm being honest with myself by *that* metric it's not a huge gain.
People. Already. Know. This.
It hasn't been the bottleneck for decades for the majority of products.
I’m fine with doing QA. But the fact is that it’s not how management measure my productivity. Spending hours doing QA looks like wasting time to them because it’s not an activity they track. They track my tickets so any hours not spent on them is literally harmful.
Also there’s the fact that you can’t QA your own output. It’s easy to overlook mistakes and defects.
> and most engineers don't like performing or are not Sr enough to do extensive or high quality code review.
Just like QA, code review takes time. It’s easy to justify that time when the submitter has put in the effort to ensure that the contribution is worthwhile. Or can explain the design clearly. Not so much when it’s slop thrown over the wall.
> Deciding what to build. Reviewing Code. And testing code. Are the new bottleneck.
None of those are truly bottleneck. Deciding what to build is obvious: Something that solve a user problem. Reviewing code is easy when the intent of the code is clear (with additional prose if needs be). Testing code is equally easy and should already be automated.
The one slow activity has always been about designing the solution. And it has no relation to code. It’s mostly deep thinking and research. I do it on the sofa or in front of a whiteboard. If I’m typing, I already have a solution in mind.
I'm currently working in an internal team, so I value cost savings estimation, but even before prioritising was also a bottleneck (although a small one compared to architecture and design)
This morning I reviewed a 1,200 LoC PR. Pretty large by pre-AI standards. But most of it was tests. Before AI, it would be a lot smaller, but only because the PR author wouldn't be nearly as diligent with test coverage as AI tends to be.
And to preempt some common rebuttals:
1. I always read the tests to make sure they are meaningful, and rules and subagent review routines in place to make sure stuff like "assert 1 == 1" or "Process.sleep(5000)" never make it in.
2. Tests do add a maintenance burden as well, but I find that it's pretty easy to refactor and condense tests.
I mean, if you give 219 people a free text box and ask them to explain anything, you're extremely unlikely to get the exact same answer twice...
Why?
> Be curious, try the new tools, test the latest models. To not do so is silly. > [...] > you could delay adopting “the cloud” for a couple of years and survive. With AI you might get a few months. The way we work has already changed, and it’s not changing back as far as I can tell.
I really dislike these claims that act like they know the future of engineering, that they’ve been let in on some enlightenment that we haven’t been. What’s going to happen in a few months? Is Sam Altman going to nuke my house from orbit? Or is it because my CTO is going to fire me for not using AI? If it’s the latter, that’s not a curiosity problem, that’s a “there’s a gun to my head” problem.
If you want a more in depth explanation, go look for interviews with devs who were already super-productive before LLMs and now came around to using them everyday.
I still write code manually to keep my trad-coding skills from withering away, but using AI without a doubt has allowed me to better test my existing apps. Create playwright automations I would've never had the time for. Allowed me to search through docs many times faster. And it just making programming more fun when I do use it for more challenging problems, and I actually get something working at the end of the day.
That is why I have created one (Open Honest Slop Audit).