> Domain Expertise Has Always Been the Real Moat

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

"Go get one."

Yes, and its price law all the way down to the metal, hasn't it always been?

I think this article is stating the obvious. In software, it has always been a requirement to learn the domain, and then capitalize on that in any way the software can be written (by hand, as a tech lead, or managing others, or lately, using ai).

This is why we have product managers!

I bet there were textile workers who would have written articles like this if the internet had existed back then.

To me, the bottleneck feels like it's shifted. It's no longer 'can we build this?' but 'should we build this, and why?' Strategy might be the last thing AI can't do for us at least for now

There's a lot of people agreeing and disagreeing with the article, but on what grounds? How do you know "domain expertise" is a "moat"? Vibes? Has there been ongoing, persistent attacks by AI on domain expertise where we can say the moat holds, economically speaking? So far it seems quite the opposite.

So far the evidence seems to be pointing to a different adage, Sutton's Bitter Lesson, which (generalized) says to not bring human expertise to a problem that can be "solved" with unfathomable volumes of data. Because the latter has historically slaughtered the former for decades. But somehow people believe this time it's different?

I will counter there is one thing that is a persistent moat, and it's not domain expertise; it's sales. Convincing other humans to part with their money. Humans have shown they will trust a person/human touch to part with their money more than an AI.

But I'm not convinced today's AI or tomorrow's won't be able to replicate domain expertise in domain X for any X.

> the binding constraint has moved from can you build it to can you tell whether it’s right.

My suspicion is we are still moving up along a continuum of capability.

Models didn’t used to produce coherent sentences (GPT-2 era) and now they can. Past models (GPT-3 era) made syntax errors and now models can write well structured code. Past models didn’t reliably emit correct syntax to request a tool call, or track context across multiple tool calls - and now they can.

Frontier models can’t write code without glaring security flaws, even as they already follow other best practices. So on those two criteria of code quality we are still in need of models improvements.

All these forms of correctness lie along a continuum. Today’s models can’t assess what’s needed in a domain for work to be “good” - but if current trends hold it’s just a matter of time.

Brilliant framing. If you understand a domain and understand software architecture then you are in an extremely strong position right now.

I started an agentic experience app in domaining , people don’t seem to take it that well , they are afraid of letting AI run on its own.

Domain expertise is neccessary but not sufficient.

It takes a LOT of time to validate every path, possibly an infinite amount of time, depending on the complexity of the domain.