| So, one is, it probably does depend on workflow. Some folks probably are doing things that can be accelerated by AI, and I think if you’re a small team with a good product head and know what you’re doing then AI probably helps a lot. But what if the problem you’re trying to solve is the altogether too often problem of like getting teams that are dependent on you to upgrade the library they use. And what if the library is a breaking change, and last year they upgraded to the library on your advice and it broke production and now they’re suss and want to accept all changes, and integrating that library change isn’t in their critical path so they’re just not going to spend time on it, even if you submit the MR them. Even if you show them their tests pass after the change. Importantly to the above, you probably need more devs to do more of the above in parallel. You don’t hire devs to write more code, you hire more devs to carry on the mental load of a broader scope of work. Even in the before times, so much code got stuck at the integration step. But because all that is hard, instead you go and codegen to fix an obscure bug that sure makes a few customers happy, but no one thought was a limiting factor for paying your company more money. It’s not that I don’t think AI can help, I think it’s a prerequisite for the job and everyone should use it. It’s more that I think in the grand scheme of things, people will bias towards using it for tasks that aren’t in the critical path - refactors, tech debt, bug smashing, tool building; and I think it could really help devex and that’s good. But I think people are bad at knowing the difference between “my job feels a bit easier” or “I’m more productive” and “this task had an impact on the bottom line” and when you extrapolate that out to a whole engineering org, that’s where the productivity statistics get lost. I’ll addd one data point to this is like this thread itself. So many people on AI skepticism threads point to their own subjective experience as evidence we’re not in a bubble, and sort of ignore the entire concept of economics. I’m not saying we’re in 100% in a bubble, but subjective experience isn’t great evidence of it. And this is just sort of one of the factors, what about the increased cost and mental load of supporting more software? What about junior engineers who feel pressured to ship work but don’t actually learn the software engineering? What about lost context from not intimately understanding your software? |
Although if this theory is true — that AI helps with coding but coding is not the friction point in organizations with multiple humans, even that should allow faster iteration by allowing one human to do more coding therefore reducing the size of teams required to make some programs. You should see good acceleration in solo shops too.