I'm about to complete a new non trivial functionality in a project of a costumer of mine. I spent an hour writing the spec. Then I asked Claude (Sonnet 4.6) to check if I missed something. I did, the sort of minor issues one notice after starting writing code, edge cases etc. That made me think about more issues and after a few iterations we settled down on a spec. I asked Claude to make an implementation plan and we ended up with 9 steps. It wrote the code for a step with new automatic tests and I performed some manual QA, which found further issues we didn't think about. We are at step 8 of 9 in about 12 hours of work. I would have needed a week to be there alone, with time spent researching and fixing bugs I created along the way, an inevitable part of our job but not exactly the most pleasant one.

This speedup is great. It improves the overall quality of the product (as perceived by the users) because I can ask Claude to add features that my customers and I would have dismissed because they take too long to implement. We would have settled down with a more basic UX.

So is it a game changer? It is in the same way those HTML / CSS framework like Bootstrap were game changers: suddenly every developer could create a decent and consistent UI in a fraction of the time with a few bells and whistles that we wouldn't have bothered coding. As a side effect a lot of web apps felt look alike mass products and web designers had to reinvent themselves, but the economics leaded inevitably in that direction. Would I spend again one of two weeks doing alone what I could write in a day or two with a LLM? Not anymore, not at this cost ($20 per month.)

> If you use it on stuff that you’re pretty good at, it’s not a gamechanger (and if you’re an expert, it’s a minor boost at best).

This was probably true last year, and it’s a common talking point, but I’ve seen too many examples now of deep experts using Claude & Codex in the last year to solve very big problems, and write or rewrite large systems. The experts do complain that the LLMs can sometimes get stuck or go off the rails and they need to pay attention and actively steer. But nobody I know who’s using it is still claiming the LLMs aren’t a game changer, even quite a few people who were staunch holdouts for a long time. I was skeptical myself, for a long time, but had my oh shit moment late last year.

One caveat - to get expert results, you do need to have some experience using LLMs, you need to use it to write plans and design docs, know how to use ‘skills’ and MCPs, use it to review code, and (for now) you need to understand context compaction and when/why to use sub-agents. If you’re a domain expert but an AI noob, it’s less effective than an expert who knows how to use AI and has experience.

One of the biggest problem with humans is we’re wired to spot patterns and draw conclusions and then we have a really hard time seeing and accepting change and updating our mental rules. The LLMs are getting better. They have already gotten better, and they’re going to continue getting better. It’s too early to draw conclusions, and many conclusions people have already declared are out of date and no longer true.

I think part of it is we often notice bad AI usage. The llm generated "art" by someone with bad taste, or the patches to open source projects by people who cant program at all and are teerrible.

If the use is half decent people just dont notice it.

I find it's a huge boost for my day-to-day work.

If you work on architecture and Claude docs, then you can essentially just have it fill in the gaps. Work then mostly becomes a matter of defining what the next piece of functionality is (which you can also use Claude to help with).

The stuff that used to take days now takes hours. It's not perfect, but if you get your codebase into a good shape then the payoff is huge.

While I think this is true

> If you use GenAI on things that you couldn't approach alone, it's an incredible tool.

I think this isn't true in all cases

> If you use it on stuff that you're pretty good at, it's not a gamechanger (and if you're an expert, it's a minor boost at best).

I think even then there's a divide.

I mostly work greenfield projects (and love it!). For these, AI has been a literal game changer. Our projects are built faster, with one or two orders of magnitude more automated tests, and all quality metrics are up.

Meanwhile, nearly all of my friends complain that AI doesn't help them. But they mostly work in very large existing codebases.

Still, even in large projects I think AI (the expensive variant) has been a complete gamechanger for me. Sure, I spend a lot on tokens, but I just feel happier and enjoy what I do more. The singalong people say about "thinking at a higher abstraction level" is what I feel. I really am thinking about architecture and larger patterns, instead of the boring nitty-gritty (which wasn't boring at all when I was a kid learning to code!...)

I think a key factor in all of this, to me, has been dictation. Most of the time, I don't write -- I use voice-to-text. I don't even read what comes out of it -- the LLMs get it (it is mostly unintelligible to anyone else) .

This means when I'm planning a big feature, I give a gigantic brain dump to the LLM in perfect stream of consciousness way, going through ideas, pros and cons, edge cases, what exists, what doesn't exist, where I'm sure of something, where I'm not sure and want the LLM to browse the state-of-the-art. Sometimes I spend 20 minutes just talking to the microphone before I send the first prompt. When I pair that with Opus, I find that I am able to build much faster and to go through alternative designs much more frequently as well.

I keep trying to tell all my friends: use voice to text and braindump to the computer. But they refuse... I couldn't imagine having to type everything nowadays. Even though I'm a fast typer, it's still much slower than the speed of my thought, which, granted, is still faster than the speed of my voice.

In effect, I filter much less, but I've come to think that's positive for the good LLMs: I throw all the edge cases and what ifs I'm thinking about -- all those years of experience dealing with similar systems.

If I wanted to go back to work in-office, that would be my major problem: I need to be able to talk with my computer all the time, loudly, and pacing through my room.

And in a team setting it can really accelerate tech debt especially if used by people that know just enough to be dangerous.

The dangerous thing is when you’re a novice and can’t identify the BS. That’s why for people with “good” and “expert” skill, it’s not a huge boost. They can identify the BS, and what’s left is modestly helpful.

The highest danger in using AI comes precisely to people who stand the most to gain from it.