One little detail. The models are already pre trained on similar system implementations. Likely whatever you are building has been built in some form or shape in the past and the ambiguity is resolved by someone in the training set.

> There’s no skill file that contains the tacit knowledge of a person who has reconciled a thousand payrolls.

That person has zero skill in actually making tight automation that doesn't just fall over. And I have yet to see an AI agent that tells them "look, your requirements are contradictory, given this and that, these two cannot coexist".

Those little sycophants will just go and try to please the domain expert and placate him in all ways possible. Bend backwards rather then forcing them to reassess their assumptions.

Software engineering is a domain on its own, just a technical, not a business one. Good luck with looking for a once in a million data race or deadlock bugs. Good luck with synchronizing system distributed over a whole globe. And good luck with keeping your domain knowledge up-to-date while developing and maintaining your vibecoded system. Assuming you can even provide enough details and deterministic specification to AI agent, because understanding business and having knowledge is not equal to being able to write down specific and concise rules to make a system out of it.

And if you think LLM are (or will be) good enough to not care about software part, what makes you think that your domain will not be completely resolved by AI?

Knowing the correct answer to one million instances of a+b, and validating this, does not guarantee that an oracle will use the + operation that's correct over a domain billions of times larger, which you can't exhaustively check.

In the past few months, I've used agents to brute force and reverse engineer solutions to problems I would never have economically have figured out on my own. I did it by putting agents in loops, connected to hardware and the internet, reading technical documentation, and relentlessly trying.

The code was shit. But it's much better to start with working shit and make it correct than spend weeks frustrated that nothing works.

I get that being a domain expert and instantly knowing the output is shit is important, but even if the output looks great, the code can be shit, and it takes looking at the code and knowing something about it to figure that out.

The solution to shit output is not (always, sometimes it is) just another if statement.

Even in a very well specified OSS effort, where I have some expertise, and I carefully reviewed the AI's output every goddamn step of the way, bugs slip through that the agents confidently tell me can't happen, and when shown proof they… add just another if, instead of really questioning assumptions.

You either know what you're doing, or you don't.

More generally, I think it’s more specialized knowledge.

If you have particularly specific knowledge in pretty much any domain, combining that with AI can lead to huge gains.

"to build software you need someone who can make requirements and someone who can build systems"

revelations never stop coming do they

This is exactly my experience vibe coding as a security architect of 20 years https://open.substack.com/pub/rakkhi/p/vibe-coding-as-securi...

'course, software development itself is a domain.

> Domain Expertise Has Always Been the Real Moat

Yes, and the Big AI companies are currently hoarding data about all domains out there.