Yes, I save an incredible amount of time. I suspect I’m likely 5-10x more productive, though it depends exactly what I’m working on. Most of the issues that you cite can be solved, though it requires you to rewire the programming part of your brain to work with this new paradigm.

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.

Use case mapping matters. I use AI tools at work (have for a few years now, first Copilot from GitHub, now I use Gemini and Claude tools primarily). When the use case maps well, it is great. You can typically assume anything with a large corpus of fairly standard problems will map well in a popular language. JavaScript, HTML, CSS, these have huge training datasets from open source alone.

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)

Did you do 5-10 years of work in the year after you adopted AI? If you started after AI came in to existence 3 years ago (/s) you should have achieved 30 years of work output - a whole career of work.

Your site has waterfalls and flashes of unstyled content. It loads slowly and the whole design is basically exactly what every AI-designed site looks like.

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.

Is your redesign live for chipscompo? Because if so, and absolutely no offence meant here, the UI looks like it was built by an intern. And fair enough, you sound like a backend guy so you can't expect perfection for frontend work. My experience with AI is that it's great at producing intern-level artifacts very quickly and that has its uses, but that certainly doesn't replace 95% of software development.

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.

> Yes, I save an incredible amount of time. I suspect I’m likely 5-10x more productive

The METR paper demonstrated that you are not a reliable narrator for this. Have you participated in a study where this was measured, or are you just going off intuition? Because METR demonstrated beyond doubt that your intuition is a liar in this case.

If you're not taking measurements it is more likely that you are falling victim to a number of psychological effects (sunk cost, Gell-Manns, slot machine effect) than it is that your productivity has really improved.

Have you received a 5-10x pay increase? If your productivity is now 10x mine (I don't use these tools at work because they are a waste of time in my experience) then why aren't you compensated as such and if it's because of pointy haired bosses, you should be able to start a new company with your 10x productivity to shut him and me up.

Provide links to your evidence in the replies

> I am the AI-hater's nightmare...

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