I’m starting to realize that LLMs are really good at building low-stakes projects. Your questions mostly presume that the stakes are higher. The software will last a long time; the requirements will evolve; we can’t tolerate mistakes; etc.
The trick to getting good at using LLMs for software is to learn how to make _all_ projects low-stakes.
You don't need LLM for that.
You make _all_ projects low-stakes by working on green field project using (insert buzzword soup of the day) and leaving for a new green field opportunity (that requires experience with buzzword soup of the day) before the project ships.
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No, what you’re describing still requires you to do some actual work, and also, while you work there, there is still some level of accountability. A much, much better grift is coaching.
Like, an AI coaching session for executives at the yearly executive retreat. You show up, spend a few hours going through some nonsense slides ChatGPT put together for you, you charge an eye watering fee for it, HR or whoever organizes it will gladly pay for it because it will make them look all cutting edge in front of the CEO, by the next day everyone will forget about it. No accountability at all!
If there's a viable way to make all projects low-stakes we'd have done it. Consider this: microservices.
This is really insightful, but I think it also extends to making the project either low stakes or low complexity. I have this lurking feeling that the preferable architecture for software will change as a result of LLMs because they're good at working on low complexity modular components more than they are on high complexity million-line code bases.
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You'll just shift complexity to the orchestration of the modular components.
Monoliths vs micro-services.
> The trick to getting good at using LLMs for software is to learn how to make _all_ projects low-stakes.
this doesn't really work in the real world. There are many things that actually matter, engineering is fundamentally about handling them.