This is not entirely wrong, but oddly describes the major flaw in its own argument: software engineering has tacit knowledge just like every other domain of expertise! And just as you can't become a doctor by just reading textbooks, or an architect by just looking at plans, you can't become a software engineer by just reading a bunch of code.

Successful software results from the intersection of expertise in two domains: the application domain, and software engineering.

It's not just domain expertise. Sustainbly great work is done by people with expertise _and_ ownership/accountability.

Wouldn't the model have as much domain expertise as pretty much any human? I'm assuming the average project manager isn't reading the entire internet

there is a big difference between seeing that pairs of input and output are correct, and knowing that the system is correct. there are invinitely many in and output pairs and only checking some while you vibe code your tool is never going to be a reliable method

This is a nice theory, but is it true in practice? (At scale; I’m sure at least 10% of domain experts will find they enjoy writing software too)

So "the real moat" are BIs and PMs equipped with LLMs? Man will I enjoy seeing this tower of babel crashing down on some heads. Unfortunately, software has tendency to survive for a long time even the shittiest conditions. Taking into account average turnover of 2 years, we're at least 3-5 generations before it collapses, so the people who started this madness will most likely be elsewhere and won't see the reckoning.

A non technical domain expert might usually lack thought clarity. They might know what is right once they see it but they seldom know how to reach there, even with AI. They will write themselves into slop in 3 days.

The real moat I believe is the ability to hold the the problem in the head, isolate it and mentally design a way to structurally solve it iteratively.

Very few people have it. Much less common with domain experts.

I would rather bet on educating domain to the engineer than teaching a domain expert to architect software.

[flagged]

Sure, producing code has become cheap. Yet again the taste matters and LLMs do not have taste - they will apply patterns that are unnecessary or not extendible, producing unmaintainable systems that nobody understands. Capturing domain knowledge was the crux of development process, but so was verifying, documenting, ensuring that multiple systems work together, maintaining uniformity. I don't know where the assumptions, done by developers, that they only need to produce code that just works or goes brrr fast comes from.

Domain expert can develop working code, but they will not be able to ensure above.

These guys live in their heads, so when the world changes, they invent reasons why they’re still relevant.

What’s the truth, though? Are we still relevant?

My experience is that three years ago, when this kind of AI work first started becoming usable, I had to talk to the AI a lot. I had to review a lot. I had to change a lot.

These days, I talk to the AI less and fix less, while the amount and quality of the output we make together has gone up and the time required has gone down.

That suggests to me that AI is like a coworker coming up through the ranks. At first, it was like a capable, hard-working junior: useful, maybe even like a small team, but still making lots of mistakes and needing a lot of communication. Now it’s mostly on board. It almost always knows what I’m talking about, but not always. I have to fix less, but taste, architectural judgment, and domain knowledge still matter.

I’m aware of the value of my domain knowledge in browser instrumentation, and the nuances of CDP commands that may never have been documented anywhere. The commands are documented, but their quirks, behaviors, and the way you can combine them to create a working system are not. I can still suggest things to agents that help them.

I don’t know if that gap is closing. I do know that I’m learning less new domain knowledge because I don’t have to be in the code as much. But I also know my hard-won technical nuance and architectural lessons still matter. Maybe agents will eventually be able to hit iteration repeatedly until they figure all of that out. That seems more likely as they get more capable. But that’s still a hypothesis. I haven’t seen it directly yet, just a vague sense of where the capability is going.

With advances in memory and the models themselves, I don’t see why they don’t end up with something like that. And I agree with the top comments: the goalposts are always moving for the people trying to redefine their own relevance in a changing world.

The main pattern I’ve noticed in myself is that I spent years, really a decade, chasing down random bugs in the web platform, JavaScript frameworks, and browser instrumentation. I was very deep in that for a long time. That helped me build the products I built.

But over the last three years, I’ve started growing in a new direction: big-picture business, go-to-market, sales, and marketing. I guess that’s adaptation. You spend a decade building technical IP assets, and then you can build more of the same because you have the domain knowledge, while working with agents to massively increase the speed of production.

The situation feels analogous to having hired a small team of capable juniors three years ago who have now grown into A-players. If that had happened, we’d have the capability we’re operating at today. It’s just that we’re using AI and paying a lot less for it.

That’s my experience building a set of large, highly nuanced technical tools around the web platform.

AI changed the shape of my company. I migrated into a role where I’m not just doing the taxes every year or writing all the code myself, but thinking seriously about marketing, GTM strategy, and sales. For me personally, my evolution is in that direction now.

That doesn’t mean I’m not still growing in product sense or technical judgment, but it’s very different from the deep technical stuff I was in before. Now I’m freed up to focus on other parts of the business.

A fun benefit is that I get more time to rapidly build things that interest me: little side bets that may just be fun, or may actually become cool products.

The transition into sales and marketing is new to me, but I welcome it in 2026.

I think AI may be easier to deal with if you have your own small company than if you’re watching it affect your job inside a workplace. I’m not sure. I’ve heard people say good things about that too.

I’m not making an argument. I’m not trying to convince anyone. I’m just sharing my experience.