The AI productivity myth is more harmful than you think 

Estimated reading time: 4 minutes

The recent boom in the adoption of AI coding tools comes with hidden debt. 

AI coding assistants like GitHub Copilot and Cursor promise a productivity revolution. Developers can generate entire functions in seconds, ship features faster, and reduce repetitive work. On the surface, it looks like a win.

But here’s the paradox: the more teams lean on copilots, the more hidden costs start to pile up. Output rises, yet so do review queues. Delivery feels faster, yet stability slips. Leaders think they’re gaining velocity, but what they’re building is a new kind of debt.

I call it copilot debt. And unlike technical debt, which is usually visible in the codebase, copilot debt builds silently in three places: knowledge, consistency, and verification. Without being addressed, short-term productivity boosts will quickly turn into burnout and fragility. 

Knowledge debt accumulates quickly

One of the biggest advantages of coding assistants is their ability to generate usable code quickly. In doing so, developers’ time is freed up to work on more complex tasks. However, when a less experienced engineer accepts AI-suggested code they don’t fully understand, it poses a problem six months down the line when that code breaks. If no one knows why the code was written the way it was, the problem becomes infinitely harder to fix.

This is knowledge debt: lines of code with no real owner. Copilot doesn’t care if you understand the output; it just completes the request, creating systems that are made of code that even the author can’t explain.

How to approach this: 

• Use incidents as lessons: When AI code breaks, turn the post-mortem into a short explainer so the next person doesn’t repeat it.
• Assign mentors by area: Don’t over-engineer, just route AI-heavy PRs in critical modules to senior devs who own that part.
• Spot-check, not slow down: Instead of long teaching reviews, ask one clarifying question: “Do you understand why this works?” That surfaces gaps quickly without dragging the process.

AI-generated code isn’t consistent 

Copilots are trained on public internet code. So in one file, you’ll get “user_id”; in another, “userid”; elsewhere, “UID.” The same can occur across error handling, logging, or function structure. It doesn’t seem like a big deal at first, but over time, the codebase starts to look like it was written by five different teams. That patchwork effect makes onboarding harder, slows down reviews, and increases the risk of subtle bugs when patterns don’t line up.

This is consistency debt: small cracks that become onboarding headaches and long-term maintainability nightmares.

What to do:

• Show, don’t tell. Copilots learn best from real examples in your repo, not abstract style guides. Keep a tiny “golden file” or sample scaffold in your system to show it what good looks like. 
• Give the copilot a source of truth. Small scaffolds or helper libraries reduce the likelihood of people using five different ways to do the same thing. 
• Add CODEOWNERS or review gates on core modules so subtle inconsistencies don’t creep into fragile areas.

Code reviews become bigger blockers 

Copilots don’t eliminate code reviews; they multiply them. Developers feel more productive because the AI provides instant output. But reviewers pay the price: more PRs, larger diffs, harder-to-spot subtle bugs.

This is verification debt: the growing gap between the volume of AI-written code and the human capacity to validate it. It doesn’t just slow delivery; it burns out your most senior engineers.

What to do:

• Keep PRs small: Don’t merge 800 lines of copilot code in one go. Set a soft cap (e.g., ~200-300 lines) or break work into multiple PRs tied to smaller tickets. Easier to review, easier to roll back.
• Share the load: If the same people always review AI-heavy code, they’ll get buried. Spread reviews across the team so no one burns out.
• Use AI as the first filter: Tools like CodeAnt, CodeRabbit, and Refacto scan PRs upfront. They flag duplication, outdated dependencies, and basic security gaps, so human reviewers spend their time on what matters, like does the code make sense, does it handle edge cases, and is it the right trade-off?

Why this matters

The temptation is to wave this away. “We’ve always had debt.” But copilot debt is different. It isn’t obvious in metrics like lines of code or tickets closed. In fact, copilots inflate those numbers. Copilots crank out more lines of code and close more tickets, so dashboards show a productivity bump. In reality, those numbers are inflated by code that’s duplicated, inconsistent, or poorly understood.

GitClear’s 2024 analysis of 200M+ lines of code showed a tenfold increase in duplication since AI adoption. Stack Overflow’s 2025 survey found that 45% of developers spend more time debugging AI code than writing it from scratch. This isn’t normal debt; it’s a structural shift in how software gets built.

Ignore it, and you’ll end up with bloated systems, burned-out reviewers, and fragile delivery pipelines.

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The leadership playbook

If you’re running an engineering org today, managing copilot debt should be as much a part of your vocabulary as technical debt:

• Name it. Don’t let these issues stay invisible. Label knowledge, consistency, and verification debt explicitly in retros, post-mortems, and planning.
  
• Measure it. Track indicators: duplicated code percentage, review queue times, and developer trust in AI output through surveys (DX, Jellyfish). Treat spikes as leading indicators of debt.
  
• Balance it. Build guardrails, strong review processes, style enforcement, and developer training to contain the debt while still reaping the benefits.

The takeaway

Copilots are not just changing how code gets written. They’re changing the economics of engineering leadership. Speed is no longer the scarce resource. Trust, comprehension, and consistency are.

If you’re not tracking copilot debt yet, it’s time to start. Because it’s already building in your org, whether you can see it or not.