Judgment is the new bottleneck.

For a long time, the wall between product and engineering wasn’t about org charts or tradition. Building was expensive. If you wanted software made, you needed engineers. Because engineering was expensive, PMs had to get good at framing, prioritizing, clarifying — deciding what mattered before something got built. And engineers, because they lived inside the implementation, had to get good at systems, trade-offs, architecture, reliability — all the invisible stuff underneath the product.

That separation wasn’t perfect. But it forced each side to develop a kind of judgment. The friction was educational.

People say AI makes everyone more technical. Or that AI lets non-engineers build. Or that engineering is becoming commoditized. There’s truth to all of that. But the deeper thing AI changes is this: it makes execution cheap. And when execution gets cheap, the scarce resource changes.

The scarce resource is no longer the ability to produce output. It’s judgment. Judgment about what to build. Judgment about how it should be built. Judgment about which problems are real and which are noise. Judgment about what looks done but is actually fragile. Judgment about what feels impressive but is actually unnecessary.

First version works. Log in. Store entries. Summarize with AI. Tag, search, reflect. Amazing. You keep going. Settings. Syncing. Profiles. Prompt customization. And now weird things happen. Something updates in one place and breaks in another. The data model gets muddy. A fix solves one issue and creates two more. The code technically runs, but you no longer have a clean mental model of why it runs.

So you patch it. Patch it again. Eventually rewrite part of it. Something else becomes unstable. This is where a lot of AI products quietly decay.

Execution is now so cheap you can move from idea to implementation before you’ve earned conviction about the problem itself. You used to need buy-in, process, translation. Now you can just start building. That sounds great until you realize what that friction used to do — it forced contact with ambiguity.

The engineer failure mode isn’t usually poor implementation. It’s false certainty about what deserves implementation. You build something elegant, scalable, modular, technically impressive — that solves the wrong problem. Or the right problem at the wrong level of complexity. Or a problem that only makes sense to someone who enjoys building systems.

In the past, the delay between idea and implementation exposed what you didn’t know. It was also too long in many cases, and it does need to be shortened. But now the model fills the gap so quickly your ignorance can stay hidden longer. And that suppresses the learning cycle. PMs no longer get slowed down in the exact places that used to teach systems intuition. Engineers no longer get slowed down in the places that used to teach product discipline.

The real divide is between people who use AI to extend their judgment and people who use it to bypass it. That’s the line that matters. The people who will stand out are the ones who can move fluidly between outcomes, systems, and judgment. They know how to ask: what problem actually matters here? What’s the simplest thing that could work? What’s fragile underneath? What are we assuming that we haven’t validated? Where am I relying on AI in a way that exceeds my own understanding?