I found myself agreeing with quite a lot of this article

This is (I think) a reference to the 10x engineer, another myth of which I have always been highly dubious ( https://www.simplethread.com/the-10x-programmer-myth/ ).

Thanks for the comment Simon! This is honestly the first one I've read where it feels like someone actually read the article. I'm totally open to the idea that some people, especially those working on the languages/tools that LLMs are good at, are indeed getting a 2x improvement in certain parts of their job.

Yeah. I just need to babysit it too much. Take copilot, it gives good suggestions and blows me away sometimes with a block of code which is exactly what I'd type. But actively letting it code (at least with gpt4.1 or gpt4o) just doesn't work well enough for me. Half of the time it doesn't even compile, and after fixing that it's just not really correctly working either. I'd expect it to work like a very junior programmer, but it works like a very drunk senior programmer that isn't listening to you very well at all.

My biggest takeaway from using AI is that (1) for my day job, it doesn't make me super productive with creation, but it does help with discovery, learning, getting myself unstuck, and writing tedious code. (2) however, the biggest unlock is it makes working on side projects __immensely__ easier. Before AI I was always too tired to spend significant time on side projects. Now, I can see my ideas come to life (albeit with shittier code), with much less mental effort. I also get to improve my AI engineering skills without the constraint of deadlines, data privacy, tool constraints etc..

My experience with GenAI is that it's a significant improvement to Stack Overflow, and generally as capable as someone hired right out of college. If I'm using it to remember the syntax or library for something I used to know how to do, it's great. If I'm using it to explore something I haven't done before, it makes me faster, but sometimes it lies to me. Which was also true of Stack Overflow. But when I ask it to so something fairly complex on it's own, it usually tips over. I've tried a bunch of tests with a bunch of models, and it never quite gets it right. Sometimes it's minor stuff that I can fix if I bang on it long enough, and sometimes it's a steaming pile that I end up tossing in the garbage. For example, I've asked it to code me a web-based calculator, or a 3D model of the solar system using WebGL, and none of the models I've tried have been able to do either.

I think even claims of 2-5x are highly suspect. It would imply that if your team is using AI then all else equal they accomplish 2-5 times as much in a quarter. I don't know about you but I'm certainly not seeing this and most people on my team use AI. [And to those saying we're using it wrong... well I can't argue with something that's not falsifiable]

The thing that I keep wondering about: If the coding part is 2-5x more productive for you, but the stuff around the coding doesn't change... at some point, it'll have to, right? The cost/benefit of a lot of practices (this article talks about code review, which is a big one) changes a lot if coding becomes significantly easier relative to other tasks.

I’ve found I do get small bursts of 10x productivity when trying to prototype an idea - much of the research on frameworks and such just goes away. Of course that’s usually followed by struggling to make a seemingly small change for an hour or two. It seems like the 10x number is just classic engineers underestimating tasks - making estimates based on peak productivity that never materializes. I have found for myself it helps motivate me, resulting in net productivity gain from that alone. Even when it generates bad ideas, it can get me out of a rut and give me a bias towards action. It also keeps me from procrastinating on icky legacy codebases.