I imagine this stuff is probably really good at iterative changes to improve objective benchmarks like CPU or RAM use. I'm thinking of little contained optimizations that you can understand in and of themselves, that you maybe have done yourself before in other places, found really quickly and applied uniformly. Stuff you can confirm to be what you expect it to be by mostly just scanning. Low hanging fruit for sure, but something where you can actually know what is a fruit and what is crap, and only keep the fruit, and develop some confidence in the process, if that makes sense?

Or trying to reduce complexity, increasing readability and coherence of variable names (the opposite of code golf if you will), while staying within a certain limit of performance regression (e.g. "make this code as nice as you can while making it at most 0.5% slower").

Making the stuff millions of people have been using for decades better, in a way that also makes it better for humans when they read the code. Surely that's possible, some people are probably doing it but it doesn't go viral as much, because it's too mundane.

And of course, making new stuff is more exciting. I mean, you could hit on something with a vibe coded thing, and then know it's now worth to make a non-sloppy version, but you won't get much fame for making ffmpeg twice as fast by prompting an LLM. Though on the other hand, it's like a safe investment (if not in "fame", then in "improving the stuff we all have to use daily"), because you know ffmpeg and many many other things will still be around, whereas a vibe coded thing that wasn't special will be 100% forgotten the next day, or have just the one user forever.

Is that a good thing? I think getting stuck is an intrinsic part of the learning process and sometimes it's good that there isn't an immediate answer from a senior. For some things you'd never have solved yourself, sure. But pain and suffering is a big and important chunk of learning and I fear we just throw it all out of the window with asking AI.

I am skeptical. This is a testable hypothesis, and I know there have been tons of research done trying to provide evidence of claims like these. However I have yet to see a convincing evidence. If this statement was indeed true, I think experimental data backing it up would be old news at this point, it however is not, and I suspect that is because junior developers are by and large in fact not learning faster or more with the help of AI.

AI is turning 1x engineers into 0.3x engineers claiming to be 100x engineers.

> But the simple fact is there's massive evidence that in skilled hands 10x or 100x engineers are possible. We're seeing evidence of it across major open source project as well. And definitely behind closed doors across companies.

Each of these three sentences are in need of some evidence. I'm not actually seing any signs of software velocity notably increasing anywhere. Except perhaps in the AI-reseller sphere, but that seems mostly due to throwing huge amounts of VC money at it and a lack of quality control.

100x is achieving something in two days, what it took an entire year before. I strongly doubt that is happening for an individual.

I can believe that the difference between the slowest programmer in the world and the fastest AI aided programmer in the world is now 100x in terms of lines of code output. Like I can imagine a programmer writing 250 lines of code per week by hand and I can imagine an AI powered person writing 25,000 lines of code per week.

I'm not say it's pure hype. Not in the sense of previous hype waves like the metaverse or NFCs. It clearly has uses.

I do think it is hype as a killer of knowledge work. It can certainly remove a lot of friction in the kind of borderline mechanical work that you'd formerly outsource to the lowest priced denominator, serve as an idea bouncer, remove friction for bug tracing, etc.

Attempts to cross the next line ("no need for architecture discussions, ai plans", "no need to read the code, ai reviews", and so on), nope.

As someone else mentioned, 100x is a couple days producing the outcomes (remember, not output) of a year. Or for a team, iOS delivering in a single year ten times as many features as its entire previous existence. It's not something that doesn't get noticed.

Name a single project that has a 10x increase. I mean real production ready code, not some single person hobby project.

> And definitely behind closed doors across companies.

At my large org (+100 engineers), I'd say it's a mixed bag and the overall impact of AI rollout looks to be slightly negative productivity.

They probably won't say it publicly though.

It's not because some people are more productive with it that all of them are and it certainly doesn't mean that the company itself is more productive either as you have other things than code to take into account.