But there's nothing stopping AI from developing domain expertise. If you fine tune a model based on the records of all previous work (effectively "shadowing" the existing workers) then it can easily learn this domain knowledge. The only difference is that AI companies have gone after programming domain knowledge first. Others will come later.

Kernel developer is not the job same as a game developer or an ERP integrator...

But Generalizations aside I think people greatly under estimate how rare is the ability to reduce complex subjects into concrete steps that someone else can follow, human or machine.

Go ask your grandma for a recipe you will find that it never turns out the same, giving her Claude Code is not going to change that.

The article says that domain expertise is more valuable now because agents can code well, but not understand the domain well.

I believe there are domains that are very well encoded. The model very often can know that a shift can't be longer than 11h and if you ask an agent for scheduling software it can surprise the developer by encoding that rule.

Both domain knowledge and coding skills became cheaper.

It might depend on the domain. Highly regulated domains like finance have entire books around how they should work.

However, I agree that verification skills became more important in both areas. A domain expert needs to catch 12h work shifts and experienced programmer needs to catch when the LLM accidentally put a route in a section that doesn't require authentication.

Both require some kind of harness and automatic verifications methods.

> Agentic AI severed the link between the two. You can now produce the software without ever building the model, and that breaks an assumption the whole profession was organized around.

Nah, we’ve always produced software without much understanding of the domain. It’s the premise behind lean: we don’t know much, so get something in front of customers and refine it.

So I don’t believe there’s been a strong decoupling here akin to the degree that understanding the code and writing the code has been.

> The engineer’s advantage, the ability to translate a domain model into working code, is now cheap.

I say this as someone who uses AI a lot. Its still a far cry from cheap, especially with that pesky “working” word in there.

There is a separate aspect of having domain expertise in that it open paths for a software engineer to make a lateral switch to a Business Analyst role. That in turn opens paths to management track on the business side as one gains deeper knowledge. If you have business expertise AND direct SDLC experience, that is a different kind of value you bring to the table.

Obviously it is a very different kind of track, take a long time to develop and means you are no longer programming but then with LLMs, hand rolled programming has been massively reduced anyway.

I have never yet had a set of words that I have grown to hate so much - “taste” and “moat” being at the top of them. It is almost always coming from people who have or know about neither.

(Agree with the article’s general sentiment - but just wanted to make this tangential comment)

I think this misses something.

AI is going to struggle at building a consistent internal model of the domain into the software unless you’re able to give a structured explanation of the domain.

If you’re just giving it a set of inputs and expected outputs, it’s not going to generalise well and fail at out of sample input, unless the AI already understands the domain from its training set.

Being able to give a structured explanation of a domain (and being able to judge if the internal model of the software makes sense) is not the same as having experience in a domain.

Lots of ppl with domain experience can tell a right output from a false one, but can’t tell you why.

Although the idea of the main promise of the article is correct - that teaching domain expert to use AI seems to be faster than teaching senior developer the domain, there is and will be a learning curve to learn AI. It still sounds like this article is trying to sell vibe coding in a fancy wording. However, domain expert working in team with senior developer that utilizes AI will be the ultimate combo.

I feel like this aspect has been discussed on HN many times. The thing that resonates with me strongly is that there’s rarely a clear or fixed set of requirements to begin with - at least from my work experiences. Then, domain expertise helps with being able to proactively call out when they are missing and support in defining them. Still I am of the view that with enough context AI could also replace the best engineers with the best domain expertise.