>I do thoroughly audit all the code that AI writes, and often go through multiple iterations
Does this actually save you time versus writing most of the code yourself? In general, it's a lot harder to read and grok code than to write it [0, 1, 2, 3]. For me, one of the biggest skills for using AI to efficiently write code is a) chunking the task into increments that are both small enough for me to easily grok the AI-generated code and also aligned enough to the AI's training data for its output to be ~100% correct, b) correctly predicting ahead of time whether reviewing/correcting the output for each increment will take longer than just doing it myself, and c) ensuring that the overhead of a) and b) doesn't exceed just doing it myself.
[0] https://mattrickard.com/its-hard-to-read-code-than-write-it
[1] https://www.joelonsoftware.com/2000/04/06/things-you-should-...
[2] https://trishagee.com/presentations/reading_code/
[3] https://idiallo.com/blog/writing-code-is-easy-reading-is-har...
To be honest, I don’t really have a problem with chunking my tasks. The reason I don’t is because I don’t really think about it that way. I care a lot more about chunks and AI could reasonably validate. Instead of thinking “what’s the biggest chunk I could reasonably ask AI to solve” I think “what’s the biggest piece I could ask an AI to do that I can write a script to easily validate once it’s done?” Allowing the AI to validate its own work means you never have to worry about chunking again. (OK, that's a slight hyperbole, but the validation is most of my concern, and a secondary concern is that I try not to let it go for more than 1000 lines.)
For instance, take the example of an AI rewriting an API call to support a new db library you are migrating to. In this case, it’s easy to write a test case for the AI. Just run a bunch of cURLs on the existing endpoint that exercise the existing behavior (surely you already have these because you’re working in a code base that’s well tested, right? right?!?), and then make a script that verifies that the result of those cURLs has not changed. Now, instruct the AI to ensure it runs that script and doesn’t stop until the results are character for character identical. That will almost always get you something working.
Obviously the tactics change based on what you are working on. In frontend code, for example, I use a lot of Playwright. You get the idea.
As for code legibility, I tend to solve that by telling the AI to focus particularly on clean interfaces, and being OK with the internals of those interfaces be vibecoded and a little messy, so long as the external interface is crisp and well-tested. This is another very long discussion, and for the non-vibe-code-pilled (sorry), it probably sounds insane, and I feel it's easy to lose one's audience on such a polarizing topic, so I'll keep it brief. In short, one real key thing to understand about AI is that it makes the cost of writing unit tests and e2e tests drop significantly, and I find this (along with remaining disciplined and having crisp interfaces) to be an excellent tool in the fight against the increased code complexity that AI tools bring. So, in short, I deal with legibility by having a few really really clean interfaces/APIs that are extremely readable, and then testing them like crazy.
EDIT
There is a dead comment that I can't respond to that claims that I am not a reliable narrator because I have no A/B test. Behold, though: I am the AI-hater's nightmare, because I do have a good A/B test! I have a website that sees a decent amount of traffic (https://chipscompo.com/). Over the last few years, I have tried a few times to modernize and redesign the website, but these attempts have always failed because the website is pretty big (~50k loc) and I haven't been able to fit it in a single week of PTO.
This Thanksgiving, I took another crack at it with Claude Code, and not only did I finish an entire redesign (basically touched every line of frontend code), but I also got in a bunch of other new features, too, like a forgot password feature, and a suite of moderation tools. I then IaC'd the whole thing with Terraform, something I only dreamed about doing before AI! Then I bumped React a few majors versions, bumped TS about 10 years, etc, all with the help of AI. The new site is live and everyone seems to like it (well, they haven't left yet...).
If anything, this is actually an unfair comparison, because it was more work for the AI than it was for me when I tried a few years ago, because because my dependencies became more and more out of date as the years went on! This was actually a pain for AI, but I eventually managed to solve it.
But yes, I do think that the efficiency gain, purely in the domain of coding, is around 5x, which is why I was able to entirely redesign my website in a week. When working on personal projects I don't need to worry about stakeholders at all.
Is it ego? Defensiveness? AI anxiety? A need to be the HN contrarian against a highly visible technology innovation?
I don't think I understand... I haven't seen the opposite view (AI wastes a ton of time) get hammered like that.
At the very least, it certainly makes for an acidic comments section.
That’s why when folks say that AI has made them 10x more productive, I ask if they did 10 years worth of work in the last year. If you cannot make that claim, you were lying when you said it made you 10x more productive. Or at least needed a big asterisk.
If AI makes you 10x more productive in a tiny portion of your job, then it did not make you 10x more productive.
Meanwhile, the people claiming 10x productivity are taken at face value by people who don’t know any better, and we end up in an insane hype cycle that has obvious externalities. Things like management telling people that they must use AI or else. Things like developer tooling making zero progress on anything that isn’t an AI feature for the last two years. Things like RAM becoming unaffordable because Silicon Valley thinks they are a step away from inventing god. And I haven’t scratched the surface.
What makes you think one year is the right timeframe? Yet you seem to be so wildly confident in the strength of what you think your question will reveal… in spite of the fact that the guy gave you an example.
It wasn’t that he didn’t provide it, it was that you didn’t want to hear it.
It is actually a very forgiving metric over a year because it is measuring only your own productivity relative to your personal trend. That includes vacation time and sick time, so the year smooths over all the variation.
Maybe he did do 5 weeks of work in 1 week, and I’ll accept that (a much more modest claim than the usual 10-100x claimed multiplier).
Not only that, I think you're misrepresenting his claim:
> I suspect I’m likely 5-10x more productive, though it depends exactly what I’m working on
1) He didn't say 10-100x
2) He said it depended on the work he was doing
Those seem reasonable enough that I can take his experience at face value.
This isn't about you pressure testing his claim, this is about you just being unwilling to believe his experience because it doesn't fit the narrative you've already got in your head.
You’ll note the pattern of the claims getting narrower and narrower as people have to defend them and think critically about them (5-10x productivity -> 4-5x productivity -> 4-5x as much code written on a side project).
It’s not a personal attack, it is a corrective to the trend of claiming 5,10,100x improvements to developer productivity, which rarely if ever holds up to scrutiny.
If I have to "prove" my productivity in a court of law - that is to say, you - I'll down-modulate it to focus on the bits that are most objective, because I understand you will be skeptical. For instance, I really do think I'm 10x faster with Terraform, because I don't need to read all the documentation, and that would have taken absurd amounts of time. There were also a few nightmarish bugs that I feel could have taken me literally hours or infinity (I would have just given up), like tracking down a breaking change snuck in in a TS minor update when I upgraded from 2.8 to latest, that Codex chomped through. But I imagine me handwaving "it's definitely 10x, just trust me" on those ones, where the alternatives aren't particularly clear, might not be an argument you'd readily accept. On the other hand, the 5x gains when writing my website, using tech I know inside and out, felt objective.
I think this is where the lede is buried. Yes, it takes time up front. But then you learn(ed) it and can apply those skills quickly in the future.
In 10 years when all sorts of new tech is around, will you read the docs? Or just count on an LLM?
That being said, I have taught myself a ridiculous amount of tech with AI. It's not always great at depth, but it sure is amazing at breadth. And I can still turn to docs for depth when I need to.
Makes sense. I’d probably be less skeptical if a/ we had a definition of AI and b/ people stopped calling LLMs “AI”
It is really neat tech. It is absolutely “artificial” and it absolutely is not “intelligent”
The trick now is deciding what code to write quickly enough to keep Claude and friends busy.
It used to be "hey I found an issue..", now it is like "here is a pr to fix an issue I saw". The net effort to me is only slightly more. I usually have to identify the problem and that is like 90% of fixing it.
Add to the fact that now I can have an AI take a first pass at identifying the problem with probably an 80%+ success rate.
Does Amazon pay everyone who receives "Not meeting expectations" in their perf review 0 dollars? Did Meta pay John Carmack (or insert your favorite engineer here) 100x that of a normal engineer? Why do you think that would be?
Does anyone know how much he made altogether from Meta?
If you're climbing the exec ladder your pay will scale a little bit better, but again, not 100x or even 10x. Even the current AI researcher craze is for an extremely small number of people.
For some data points, check out levels.fyi and compare the ratio of TCs for a mid-level engineer/manager versus the topmost level (Distinguished SWE, VP etc.) for any given company.
And thus help engineers get paid more in line with their “value”. Albeit with much higher variance.
Which is not to say either one is better or worse! Regular employment does come with much lower risk, as it is amortized over the entire company, whereas startups are risky and stressful. Different strokes for different folks.
I do think AI could create a new paradigm though. With dropping employment and increasing full-stack business capabilities, I foresee a rise in solopreneurship, something I'm trying out myself.
The commenter told you they suspect they save time, it seems like taking their experience at face value is reasonable here. Or, at least I have no reason to jump down their throat... the same way I don't jump down your throat when you say, "these tools are a waste of time in my experience." I assume that you're smart enough to have tested them out thoroughly, and I give you the benefit of the doubt.
If you want to bring up METR to show that they might be falling into the same trap, that's fine, but you can do that in a much less caustic way.
But by the way, METR also used Cursor Pro and Claude 3.5/3.7 Sonnet. Cursor had smaller context windows than today's toys and 3.7 Sonnet is no longer state of the art, so I'm not convinced the paper's conclusions are still as valid today. The latest Codex models are exponential leaps ahead of what METR tested, by even their own research.[1]
[1]https://metr.org/blog/2025-03-19-measuring-ai-ability-to-com...
Imagine that you've built an app with libraries A, B, and C and conceptually understand all that's involved. But now you're required to move everything to X, Y, and Z. There won't be anything fundamentally new or revolutionary to learn, but you'll have to sit and read those docs, potentially for hours (cost of task switching and all). Getting the AI to execute the changes gets you to skip much of the tedium. And even though you still don't really know much about the new libs, you'll get the gist of most of the produced code. You can piecemeal the docs to review the code at sensitive boundaries. And for the rest, you'll paint inside the frames as you normally would if you were joining a new project.
Even as a skeptic of the general AI productivity narrative, I can see how that could squeeze a week's worth of "ever postponed" tasks inside a day.
That is one of the assumptions that pro-AI people always bring. You don't read the new docs to learn the domain. As you've said, you've already learn it. You read it for the gotchas. Because most (good) libraries will provide examples that you can just copy-paste and be done with it. But we all know that things can vary between implementations.
> Even as a skeptic of the general AI productivity narrative, I can see how that could squeeze a week's worth of "ever postponed" tasks inside a day.
You could squeeze a week inside a day the normal way to. Just YOLO it, by copy pasting from GitHub, StackOverflow and the whole internet.
I-know-what-kind-of-man-you-are.jpeg
You come off as a zealot by branding people who disagree as "haters".
Edit: AI excels at following examples, or simple, testable tasks that require persistence, which is intern-level work. Doing this narrow band of work quickly doesn't result in 10x productivity.
I'm yet to find a single person who has shown evidence to go through 10x more tasks in a sprint[1], or match the output of the rest of their 6-10-member team by themselves.
1. Even for junior level work
> I'm yet to find a single person who has shown evidence to go through 10x more tasks in a sprint[1], or match the output of the rest of their 6-10-member team by themselves.
If my website, a real website with real users, doesn't qualify, then I'm not sure what would. A single person with evidence is right in front of you, but you seem to be denying the evidence of your own eyes.
All of the work you described is essentially manual labor. It's not difficult work - just boring, sometimes error prone work that mostly requires you to do obvious things and then tackle errors as they pop up in very obvious ways. Great use case for AI, for sure. This and the fact that the end result is so poor isn't really selling your argument very well, except maybe in the sense that yeah, AI is great for dull work in the same way an excavator is great for digging ditches.
If you ever find yourself at the point where you are insulting a guy's passion project in order to prove a point, perhaps have a deep breath, and take a step back from the computer for a moment. And maybe you should look deep inside yourself, because you might have crossed the threshold to being a jerk.
Yes, my site has issues. You know what else it has? Users. Your comments about FOUC and waterfalls are correct, but they don't rank particularly high on what are important to people who used the site. I didn't instruct the AI to fix them, because I was busy fixing a bunch of real problems that my actual users cared about.
As for loading slowly -- it loads in 400ms on my machine.
I'm just calling spade a spade. If you didn't want people to comment on your side project given your arguments and the topic of discussion, you should just not have posted it in a public forum or have done better work.
I can understand to some degree it would be chafing that I described myself as working at a SF Series C startup etc. The only intent there was to illustrate that I wasn't someone who started coding 2 weeks ago and had my mind blown by typing "GPT build me a calculator" into Claude. No intent at all of calling myself a mega-genius, which I don't really think I am. Just someone who likes doing fun stuff with AI.
And, BTW, if you reread my initial comment, you will realize you misread part of it. I said that "Advanced Programming" is the exact opposite of the type of work I am doing.
The problem is that your project has basic performance issues - FOUC, render waterfalls - that are central concerns in modern React development. These aren't arbitrary standards I invented to be mean. They're fundamental enough that React's recent development has specifically focused on solving them.
So when you say I'm inventing quality standards (in your now-deleted comment), or that this is just a passion project so quality doesn't matter, you're missing the point. You can't argue from professional authority that AI makes you more productive without compromise, use your work as proof, and then retreat to "it's just for fun" when someone points out the quality issues. Either it demonstrates your workflow's effectiveness or it doesn't. You can't have it both ways.
The kids' artwork comparison doesn't work either. You're not a child showing me a crayon drawing - you're a professional developer using your work as evidence in a technical argument about AI productivity. If you want to be treated as an experienced developer making authoritative claims, your evidence needs to support those claims.
I'm genuinely not trying to be cruel here, but if this represents what your AI workflow produces when you're auditing the output, it raises serious questions about whether you can actually catch the problems the AI introduces - which is the entire crux of your argument. Either you just aren't equipped to audit it (because you don't know better), or you are becoming passive in the face of the walls of code that the AI is generating for you.
Let's talk a little about FOUC and the waterfall. I am aware of both issues. In fact, they're both on my personal TODO list (along with some other fun stuff, like SSR). I have no doubt I could vibe code them both away, and at some point, I will. I've done plenty harder things. I haven't yet, because I was focusing on stuff that my moderators and users wanted me to do. They wanted features to ban users, a forgot password feature, email notifications, mobile support, dark mode, and a couple of other moderation tools. I added those. No one complained about FOUC or the waterfall, and no one said that the site loaded slowly, so I didn't prioritize those issues.
I understand you think your cited issues are important. To be honest, they irk me, too. But no one who actually uses the site mentioned them. So, when forced to prioritize, I added stuff they cared about instead.
> You can't argue from professional authority that AI makes you more productive without compromise, use your work as proof, and then retreat to "it's just for fun" when someone points out the quality issues
You seem to have missed the point of saying "it's just for fun". My point was this: You are holding a week-long project done with AI to professional standards. Nothing ever done in a week is going to be professional level! That is an absurd standard! You are pointing at the rough edges, that of course exist because it was done on the side, as some insane gotcha that proves the whole thing is a house of cards. "This is "dull work"! You should "have done better work" if you wanted to talk with us"! For FOUC?!? C'mon.
And if it's producing an intern-level artifact for your frontend, what's to say it's not producing similar quality code for everything else? Especially considering frontend is often derided as being easier than other fields of software.
The site looks great to me. Your comment is actually offensive, despite you typing "no offence".
The combination of which, deep training dataset + maps well to how AI "understands" code, it can be a real enabler. I've done it myself. All I've done with some projects is write tests, point Claude at the tests and ask it to write code till those tests pass, then audit said code, make adjustments as required, and ship.
That has worked well and sped up development of straightforward (sometimes I'd argue trivial) situations.
Where it falls down is complex problem sets, major refactors that cross cut multiple interdependent pieces of code, its less robust with less popular languages (we have a particular set of business logic in Rust due to its sensitive nature and need for speed, it does a not great job with that) and a host of other areas I have hit limitations with it.
Granted, I work in a fairly specialized way and deal with alot of business logic / rules rather than boiler plate CRUD, but I have hit walls on things like massive refactors in large codebases (50K is small to me, for reference)
You are stuck in a very low local maximum.
You are me six months ago. You don’t know how it works, so you cannot yet reason about it. Unlike me, you’ve decided “all these other people who say it’s effective are making it up”. Instead ask, how does it work? What am I missing.
Every time I try to use AI it produces endless code that I would never have written. I’ve tried updating my instructions to use established dependencies when possible but it seems completely averse.
An argument could be made that a million lines isn’t a problem now that these machines can consume and keep all the context in memory — maybe machines producing concise code is asking for faster horses.
When people say coding is slow, that usually means they're working on some atrocious code (often of their own making), while using none of the tools for fast feedback (Tests, Linters,...).
This hits the nail on the head, IMO. I haven't seen any of the replies address this yet, unless I missed one.
I don't even like AI per se, but many of the replies to this comment (and to this sentiment in general) are ridiculous. Ignoring the ones that are just insulting your work even though you admitted off the bat you're not an "advanced" programmer... There are obviously flaws with AI coding (maintainability, subtle bugs, skill atrophy, electricity usage, etc). But why do we all spring immediately to this gaslighting-esque "no, your personal experience is actually wrong, you imagined it all?" Come on guys, we should be better than that.
Though I do think "advanced software team" is kind of an absurd phrase, and I don't think there is any correlation with how "advanced" the software you build is and how much you need AI. In fact, there's probably an anti-correlation: I think that I get such great use out of AI primarily because we don't need to write particularly difficult code, but we do need to write a lot of it. I spend a lot of time in React, which AI is very well-suited to.
EDIT: I'd love to hear from people who disagree with me or think I am off-base somehow about which particular part of my comment (or follow-up comment https://news.ycombinator.com/item?id=46222640) seems wrong. I'm particularly curious why when I say I use Rust and code faster everyone is fine with that, but saying that I use AI and code faster is an extremely contentious statement.