Domain expertise has always been the real moat

If that's true, any statements defining what is necessary to do should be ignored in that context. I'm still interested in hearing about what people tried, what results they think they saw, and then trying to apply those findings to my own processes.

Which is to say I don't think the pontificating is pointless, but as statements of Real Truth, I agree they're likely wrong. We're too early in the game.

Tests stick around and prevent future problems, whereas the debugger only shows me something once.

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.

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.