There are a lot of ways that AI speeds up software development processes that aren't the actual software development.
I am finding that lately I do not allow LLMs to write any code I am interested in maintaining. Or if they do, I have to micromanage them and it usually takes longer. They produce mediocre solutions, and often add redundant state ("Why did you add that state?" "Because we might need it in the future")
That said, they are extremely good at:
- Dev tools: creating debug tooling, debug screens, scripts that get the job done
- Auxiliary development: landing pages, "what's new" screens, tedious boilerplate, gathering strings for localization
- Prototyping: building full implementations quickly so you can see all the problems rather than having to anticipate them
- Pure transformation: porting from one language or paradigm to another
So while I agree with the article that the actual spec of the feature you are building needs just as much human thought, regardless of AI, the speed-ups around that are worth exploring
An example I have from a recent feature development is adding CarPlay support to an existing app. We could have talked about it and designed it for weeks, but with an LLM I was able to get it running in my car in an hour, go for a drive, and feel it to understand whether it was a valuable direction.
The code was a mess, most of it had to be thrown away, and the LLM couldn't even get the initial build functional (not much CarPlay training data, I expect). But it was an accelerator to answer the question "is it worth investing more time in this?"