AI Raises the Hiring Bar
I used to think AI would make me more willing to hire less experienced engineers.
The argument seemed reasonable. Software engineering is changing fast. The tools are new, and the habits aren't settled yet. Someone early in their career may be more open to rebuilding their workflow around AI, while someone with ten years of experience may keep trying to fit AI into an older way of working.
I still think that can happen. But for a startup, I no longer think it's the important part.
A startup isn't trying to maximize the amount of code written. It's trying to solve a problem before it runs out of time, money, or patience. The engineer you want isn't the one who can produce code the fastest. It's the one who can move the product toward the right answer with the least unnecessary weight.
That distinction matters more now, not less.
A Ticket Is Only a Guess
A ticket looks precise, but it usually isn't.
It says: build this flow, match this design, support these states. In practice, it's only one proposed path toward a product outcome.
There are usually several acceptable versions of the same feature. One matches the design exactly but needs a complicated implementation. Another changes a small interaction and removes half the complexity. A third looks easy but confuses users later.
The work isn't just implementing the ticket. It's recognizing those differences.
I often write fallback notes in tickets: if this is expensive, we can do X instead. That helps, but it also exposes a problem. If I have to spell out every acceptable simplification, every product tradeoff, and every implementation boundary, then the whole system depends on me noticing everything in advance.
That doesn't scale.
In a startup, a small unnecessary detail can become a permanent maintenance cost. One overbuilt component is fine. A hundred of them becomes the architecture.
This is why I now care so much about product judgment in engineers. I need people who can read the intent behind the ticket, not just the words inside it.
AI Rewards People Who Already Know How to Delegate
Before AI, the best senior engineers were already using leverage.
They didn't scale by typing faster. They scaled by breaking down ambiguous problems, mentoring other engineers, reviewing work, catching wrong assumptions early, and deciding when a simpler solution was good enough.
That's a very specific skill. You have to describe the goal clearly. You have to know which details matter. You have to look at partial work and notice what's off. You have to correct the direction without getting dragged into every line of the implementation.
It's also a lot of what working well with AI looks like.
AI didn't invent this way of working. It just made it more direct.
A strong senior engineer can give AI a problem, evaluate the result, reject the parts that are subtly wrong, and steer toward a simpler design. Their experience shows up in the questions they ask, the constraints they add, and the solutions they refuse to ship.
A less experienced engineer can generate code quickly too. But speed isn't the scarce resource anymore. Knowing what should exist is.
Taste Is Part of Engineering
So the word I keep coming back to is taste.
When I say I want engineers with taste, I don't mean visual polish. I mean the ability to connect product value, design quality, and engineering cost in the same decision.
Should we match the design exactly here, or is the difference invisible to users? Is this edge case part of the core product, or are we building a support burden for one hypothetical customer? Is this abstraction making future work easier, or just making today's code feel more elegant?
These are engineering questions. They're also product questions. And in a startup, the same person often needs to answer both.
That's why my hiring bar has moved away from "smart junior engineer who's good with AI." That profile can still be valuable, but it's not the center of what I'm looking for.
The profile I want is closer to this:
- Senior enough to understand long-term cost
- Smart enough to learn quickly
- Tasteful enough to make product and design tradeoffs
- Close enough to the market to understand why the feature exists
AI didn't lower my hiring bar. It raised it, from writing code to knowing what should exist.