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Given enough context for a business problem, sure. But LLMs are not in a condition to judge how you should pick the technical solution to a business problem with several stakeholders, risks, and so on.

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With the speed AI moves, a lot of technical decisions become reversible. And while engineering often makes decisions that could lie elsewhere in the business, outside of engineering, I could imagine those decisions moving elsewhere in a fully AI world.

Do you have examples of things that would be hard to train for? One that could be compensated for with changes elsewhere in the business process?

Yes, if Alex from BizDev is a scheming moron who consistently lies about the priority of features, it’s hard to keep an LLM on the loop about it when transcribing meetings and feature requests. If your boss is gonna be unavailable for a month and that means that a junior devs garbage PRs will be getting merged because the second in command is much laxer then you need to be aware for that and so on.

What I mean is that these things decant into technical decisions and even with all the AI in the world running a DB schema migration does not become any more trivial.

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