The case for staying technical isn't that product leaders should be writing production code. It's narrower: if you can't independently estimate what something costs to build, every prioritization decision you make depends entirely on someone else's estimate.
That's not a knock on engineers' estimates. It's that you lose the ability to ask a second question.
Where I'm coming from
I spent years as an independent full-stack developer — JavaScript, React, Node, MongoDB, REST APIs — before co-founding Sunbots. I didn't come to product from business strategy. I came from building things.
I've kept enough of that to still be dangerous in a codebase, and it shows up in specific, repeatable ways.
What it actually changes
You can tell a weekend from a quarter
Someone proposes an AI feature. The version where the model classifies incoming messages is two days. The version where it acts on them, handles failures, and reverses mistakes safely is two months.
Those sound like the same feature in a roadmap doc. They are not remotely the same commitment. Recognizing which one you're being asked to approve — before scoping — changes what you say yes to.
You can test the idea yourself
The gap between "I wonder if the model can do this" and "here's the answer" collapses when you can just try it. No handoff, no context-switch for an engineer, no meeting to explain what you meant.
Most of my product opinions about AI come from having run the thing against real data at 11pm, not from reading benchmarks.
You know which technical objections are real
Engineers sometimes say "that's hard." Sometimes it means genuinely hard, sometimes it means tedious, sometimes it means "hard the way we've currently built it."
Those require completely different responses. Without technical judgment, they all sound identical and you either defer on everything or push on everything. Both are bad.
You don't get sold
Vendors pitch AI capabilities constantly. Understanding what's a genuine capability versus a wrapper around a public API is worth an enormous amount in procurement conversations — and in not building a roadmap around something that turns out to be three lines of code.
The honest limitations
I'm not a better engineer than my engineers. I'm slower, my code is worse, and I don't hold the whole system in my head the way they do.
There's also a real failure mode here: the technical product leader who does the engineering team's thinking for them, over-specifies solutions, and hands down implementation details instead of problems. That's worse than being non-technical. Technical fluency should expand the questions you can ask, not narrow the decisions the team gets to make.
The line I try to hold: I prototype to learn, then hand over the problem — not the prototype.
What non-technical leaders should do instead
Not everyone has this background, and it isn't a prerequisite for good product leadership. But the underlying capability — independent judgment about cost and feasibility — is.
You can build it without writing code: sit in on technical design discussions, ask engineers to explain trade-offs rather than conclusions, keep a mental record of estimates versus actuals, and learn enough about your specific stack's constraints to know where the expensive parts are.
The skill isn't coding. It's not being fully dependent on a single source for your understanding of what's possible.
Why it matters more with AI
AI makes this sharper because the gap between demo and product is unusually wide. A model can look like it solves your problem in ten minutes and take six months to make reliable.
Leaders who can't judge that gap either commit to timelines that were never real, or dismiss things that were genuinely close. Both cost you more than the time it takes to stay a little bit hands-on.
FAQ
Should product managers know how to code? Not necessarily, but they need independent judgment about implementation cost and feasibility. Coding is one way to build that; sustained technical engagement is another.
Does being technical make a product leader worse at product? It can, if it leads to over-specifying solutions instead of defining problems. Technical fluency should expand the questions you ask, not narrow your team's decisions.
Why does technical judgment matter more for AI products? Because the gap between a working demo and a reliable product is unusually large with AI, and misjudging it produces timelines that were never achievable.
How can a non-technical product leader build this judgment? Sit in on design discussions, ask for trade-offs rather than conclusions, track estimates against actuals, and learn where your specific stack is expensive.
I'm Ravi Jadav, Chief Product Officer and Co-Founder at Sunbots Innovations, where I still prototype. Get in touch.