> 1. Learn how the subject matter experts do the work.

This will get harder I think over time as low hanging fruit domains are picked - the barrier will be people not technology. Especially if the moat for that domain/company is the knowledge you are trying to acquire (NOTE: Some industries that's not their moat and using AI to shed more jobs is a win). Most industries that don't have public workings on the internet have a couple of characteristics that will make it extremely difficult to perform Task 1 on your list. The biggest is now every person on the street, through the mainstream news, etc knows that it's not great to be a software engineer right now and most media outlets point straight to "AI". "It's sucks to be them" I've heard people say - what was once a profession of respect is now "how long do you think you have? 5 years? What will you do instead?".

This creates a massive resistance/outright potential lies in providing AI developers information - there is a precedent of what happens if you do and it isn't good for the person/company with the knowledge. Doctors associations, apprenticeship schemes, industry bodies I've worked with are all now starting to care about information security a lot more due to "AI", and proprietary methods of working lest AI accidentally "train on them". Definitely boosted the demand for cyber people again as an example around here.

> You are correct! There's lots of information available publicly about certain things like code, and writing SQL queries. But other specialized domains don't have the same kind of information trained into the heart of the model.

The nightmare of anyone that studied and invested into a skill set according to most people you would meet. I think most practitioners will conscious to ensure that the lack of data to train on stays that way for as long as possible - even if it eventually gets there the slower it happens and the more out of date it is the more useful the human skill/economic value of that person. How many people would of contributed to open source if they knew LLM's were coming for example? Some may have, but I think there would of been less all else being equal. Maybe quite a bit less code to the point that AI would of been delayed further - tbh if Google knew that LLM's could scale to be what they are they wouldn't of let that "attention" paper be released either IMO. Anecdotally even the blue collar workers I know are now hesitant to let anyone near their methods of working and their craft - survival, family, etc come first. In the end after all, work is a means to an end for most people.

Unlike us techies which I find at times to not be "rational economic actors" many non-tech professionals don't see AI as an opportunity - they see it as a threat they they need to counter. At best they think they need to adopt AI, before others have it and make sure no one else has it. People I've chatted to say "no one wants this, but if you don't do it others will and you will be left behind" is a common statement. One person likened it to a nuclear weapons arms race - not a good thing, but if you don't do it you will be under threat later.

> This will get harder I think over time as low hanging fruit domains are picked - the barrier will be people not technology. Especially if the moat for that domain/company is the knowledge you are trying to acquire (NOTE: Some industries that's not their moat and using AI to shed more jobs is a win).

Also consider that there exist quite a lot of subject matter experts who simply are not AI fanboys - not because they are afraid of their job because of AI, but because they consider the whole AI hype to be insanely annoying and infuriating. To get them to work with an AI startup, you will thus have to pay them quite a lot of money.