[epolanski]

If AI doesn't make you more productive you're using it wrong, end of story.

Even if you don't let it author or write a single line of code, from collecting information, inspecting code, reviewing requirements, reviewing PRs, finding bugs, hell even researching information online, there's so many things it does well and fast that if you're not leveraging it, you're either in denial or have ai skill issues period.

[geraneum]

Not to refute your point but I’ve met overly confident people with “AI skills” who are “extremely productive” with it, while producing garbage without knowing, or not being able to tell the difference.

[epolanski]

You're describing lack of care and lack of professionalism, fire these people, nothing to do with the tools, it's the person using it the problem.

[ModernMech]

We're trying very earnestly to create a world where being careful and professional is a liability. "Move fast and break things, don't ask permission, don't apologize for anything" is the dominant business model. Having care and practicing professionalism takes times and patience, which just translate to missed opportunities to make money.

Meanwhile, if you grift hard enough, you can become CEO of a trillion dollar company or President of the United States. Young people are being raised today seeing that you can raise billions on the promise building self driving cars in 3 years, not deliver even after 10 years, and nothing bad actually happens. Your business doesn't crater, you don't get sued into oblivion, your reputation doesn't really change. In fact, the bigger the grift, the more people are incentivized to prop it up. Care and professionalism are dead until we go back to an environment that is not so nurturing for grifts.

[impulsivepuppet]

While I circumstantially agree, I hold it to be self-evident that the "optimal amount of grift is nonzero". I leave it to politicians to decide whether increased oversight, decentralization, or "solution X" is the right call to make.

[ModernMech]

A little grift is expected. The real problem for us is when it's grift all the way down, and all the way up, to the extent even the President is grifting. Leaving it to the politicians in that case just means enabling maximum, economy-scale grift.

[geraneum]

Yea I’m talking about people and that’s honestly what matters here. At the end of the day this tools is used by people and how people use it plays a big role in how we assess its usefulness.

[mrwrong]

this is known as the no true scotsman fallacy

[tick_tock_tick]

I've not really seen this outside of extremely junior engineers. On the flip side I've seen plenty of seniors who can't manage to understand how to interact with AI tools come away thinking they are useless when just watching them for a bit it's really clear the issue is the engineer.

[SchemaLoad]

They just shovel the garbage on someone else who has to fact check and clean it up.

[MangoCoffee]

you can say that about overly confident people with "xyz" skills.

[9rx]

Garbage to whom? Are we talking about something that the user shudders to think about, or something more like a product the user loves, but behind the scenes the worst code ever created?

[geraneum]

A lot of important details/parts of a system (not only code) that may seem insignificant to the end user could be really important in making a a system work correctly as a whole.

[mdavidn]

It sounds like you're the one in denial? AI makes some things faster, like working in a language I don't know very well. It makes other things slower, like working in a language I already know very well. In both cases, writing code is a small percentage of the total development effort.

[epolanski]

No I'm not, I'm just sick of these edgy takes where AI does not improve productivity when it obviously does.

Even if you limit your AI experience to finding information online through deep research it's such a time saver and productivity booster that makes a lot of difference.

The list of things it can do for you is massive, even if you don't have it write a single line of code.

Yet the counter argument is like "bu..but..my colleague is pushing slop and it's not good at writing code for me", come on, then use it at things it's good at, not things you don't find it satisfactory.

[lunar_mycroft]

It "obviously" does based on what, exactly? For most devs (and it appears you, based on your comments) the answer is "their own subjective impressions", but that METR study (https://arxiv.org/pdf/2507.09089) should have completely killed any illusions that that is a reliable metric (note: this argument works regardless of how much LLMs have improved since the study period, because it's about how accurate dev's impressions are, not how good the LLMs actually were).

[johnsmith1840]

It's a good study. I also believe it is not an easy skill to learn. I would not say I have 10x output but easily 20%

When I was early in use of it I would say I sped up 4x but now after using it heavily for a long time some days it's 20% other days -20%

It's a very difficuly technology to know when you're one or the other.

The real thing to note is when you "feel" lazy and using AI you are almost certainly in the -20% category. I've had days of not thinking and I have to revert all the code from that day because AI jacked it up so much.

To get that speed up you need to be truly focused 100% or risk death by a thousand cuts.

[keeda]

Yes, self-reported productivity is unreliable, but there have been other, larger, more rigorous, empirical studies on real-world tasks which we should be talking about instead. The majority of them consistently show a productivity boost. A thread that mentions and briefly discusses some of those:

https://news.ycombinator.com/item?id=45379452

[lunar_mycroft]

Some (partial) counter points:

- I think given public available metrics, it's clear that this isn't translating into more products/apps getting shipped. That could be because devs are now running into other bottlenecks, but it could also indicate that there's something wrong with these studies.

- Most devs who say AI speeds them up assert numbers much higher than what those studies have shown. Much of the hype around these tools is built on those higher estimates.

- I won't claim to have read every study, but of the ones I have checked in the past, the more the methodology impressed me the less effect it showed.

- Prior to LLMs, it was near universally accepted wisdom that you couldn't really measure developer productivity directly.

- Review is imperfect, and LLMs produce worse code on average than human developers. That should result in somewhat lowered code quality with LLM usage (although that might be an acceptable trade off for some). The fact that some of these studies didn't find that is another thing that suggests there shortcomings in said studies.

[keeda]

> - Most devs who say AI speeds them up assert numbers much higher than what those studies have shown.

I am not sure how much is just programmers saying "10x" because that is the meme, but if at all realistic numbers are mentioned, I see people claiming 20 - 50%, which lines up with the studies above. E.g. https://news.ycombinator.com/item?id=45800710 and https://news.ycombinator.com/item?id=46197037

> - Prior to LLMs, it was near universally accepted wisdom that you couldn't really measure developer productivity directly.

Absolutely, and all the largest studies I've looked at mention this clearly and explain how they try to address it.

> Review is imperfect, and LLMs produce worse code on average than human developers.

Wait, I'm not sure that can be asserted at all. Anecdotally not my experience, and the largest study in the link above explicitly discuss it and find that proxies for quality (like approval rates) indicate more improvement than a decline. The Stanford video accounts for code churn (possibly due to fixing AI-created mistakes) and still finds a clear productivity boost.

My current hypothesis, based on the DORA and DX 2025 reports, is that quality is largely a function of your quality control processes (tests, CI/CD etc.)

That said, I would be very interested in studies you found interesting. I'm always looking for more empirical evidence!

[hu3]

not OP but I have a hard metric for you.

AI multiplied the amount of code I committed last month by 5x and it's exactly the code I would have written manually. Because I review every line.

model: Claude Sonnet 3.5/4.5 in VSCode GitHub Copilot. (GPT Codex and Gemini are good too)

[lunar_mycroft]

I have no reason to think you're lying about the first part (although I'd point there's several ways that metric could be misleading, and approximately every piece of evidence available suggests it doesn't generalize), but the second part is very fishy. There's really no way for you to know whether or not you'd have written the same code or effectively the same code after reviewing existing code, especially when that review must be fairly cursory (because in order to get the speed up you claim, you must be spending much less time reviewing the code than it would have taken to write). Effectively, what you've done is moved the subjectivity from "how much does this speed me up?" to "is the output the same as if I had done it manually?"

[hu3]

> There's really no way for you to know whether or not you'd have written the same code or effectively the same code after reviewing existing code.

There is in my case because it's just CRUD code. The pattern looks exactly like the code I wrote the month prior.

And this is where LLMs excel at, in my experience. "Given these examples, extrapolate to these other cases."

[Libidinalecon]

I am not even a software engineer but from using the models so much I think you are confined to a specific niche that happens to be well represented in the training data so you have a distorted perspective on the general usefulness of language models.

For some things LLMs are like magic. For other things LLMs are maddeningly useless.

The irony to me is anyone who says something like "you don't know how to use the LLM" actually hasn't explored the models enough to understand their strengths/weaknesses and how random and arbitrary the strengths and weakness are.

Their use cases happen to line up with the strengths of the model and think it is something they are doing special themselves when it is not.

[douglasisshiny]

>No I'm not, I'm just sick of these edgy takes where AI does not improve productivity when it obviously does.

Feel free to cite said data you've seen supporting this argument.

[gitremote]

My company mandates AI usage and logs AI usage metrics as input to performance evaluation, so I use it every day. It's a Copilot subscription, though.

[cujo]

why though? are they just using it as a proxy for "is 'gitremote' working today?"

[epolanski]

Someone in management needs a promotion for his impact in revolutionizing and streamlining development from his charlatan managers.

[porksoda]

The first time i asked it about some code in a busy monorepo and it said "oh bob asked me to do this last week when he was doing X, it works like Y and you can integrate it with your stuff like Z, would you like to update the spec now?"... I had some happy feelings. I dont know how they do it without clobbering the context, but it's great.

[Eldt]

This is probably where they're getting their "90% of code is written with AI!!) metrics from