Cognitive Debt: The Hidden Risk in Your AI-Powered Team
Its already lurking in your documents, your code and your team. When will it strike?
Technical debt is easy to spot. It’s the messy code. The shortcuts. The “we’ll clean it up later.” You can see it. You can point to it.
But there’s another kind of debt building inside teams right now — and it’s much harder to detect.
Cognitive debt.
It shows up when a team builds faster with AI… but understands less of what they’re building.
I was reading about research from Margaret-Anne Storey where a student team used AI coding tools and absolutely flew at the beginning. Velocity was incredible. They were shipping features that would have taken weeks in days. The demos looked fantastic. Everyone felt like they’d unlocked a superpower.
Seven weeks later?
They
were
stuck.
The code ran. The tests passed. But when one team member left and the others tried to modify the system, they hit a wall. No one could explain how it worked. No one could trace the dependencies. No one could confidently change anything without breaking something else (and they couldn’t predict what would break).
That’s not a classroom problem.
That’s a boardroom problem.
It’s happening quietly inside companies right now.
Steve Yegge calls it “The AI Vampire.” You feel 10x productive. The company captures the upside. You’re drained by mid-afternoon. The output is real — but so is the exhaustion, and it’s not just physical. It’s cognitive.
You’re managing code you didn’t fully author and don’t fully understand.
And in the age of Claude CoWork and other agentic tools, this is expanding outside of technical teams faster than ChatGPT can obsessively praise you for the way you worded that email to your boss.
Here’s what I keep coming back to—and more and more leaders need to keep this top-of-mind:
AI doesn’t eliminate jobs. It eliminates the easy parts.
And when the easy parts go away, what’s left?
Judgment. Architecture. Trade-offs. Explanation. Ownership.
Speed is rented. Understanding is owned.
The developer who can walk into a room and say, “Here’s how this system works, here’s why we made these decisions, and here’s what will break first” — that’s the most valuable person in the building. Not the fastest shipper. The clearest thinker.
And for hiring managers: if you optimize purely for speed, you’re stacking cognitive debt your team won’t be able to refinance later.
The teams that will win aren’t the ones using AI to produce more. They’re the ones using AI to understand more.
So what do you actually do about this?
Enforce the explain-back test. Before merging AI-assisted code, have the author walk someone through it without looking at the screen. If they can’t, it’s not ready.
Create modification pressure. Rotate people through refactoring tickets on code they didn’t write. Cognitive debt surfaces fast when someone else has to touch it.
Measure understanding, not just output. In 1-on-1s, ask: “What did you learn this week?” not just “What did you ship?”
If you’re leading right now, ask yourself something uncomfortable: If the AI tools stopped working tomorrow, could your team explain what they built?
If the answer is “not really,” that debt is compounding. And debt has a way of showing up at the worst possible time.
AI should increase intelligence. Not outsource it.
Curious what you’re seeing on your teams. Are people getting sharper with AI — or just faster? (Hit reply. I actually read these.)