The True Cost of AI-Generated Tech Debt — And How to Measure It

Your team shipped 40% more code last quarter. Velocity is up. Sprint burndown charts look beautiful. Management is thrilled.

Nobody has asked why production incidents are also up 35%.

AI coding tools — Copilot, Cursor, Claude Code — have made it trivially easy to generate large volumes of code. The problem is that speed and quality are not the same metric, and most teams are only measuring one of them.

The Speed Trap

Here is the uncomfortable truth about AI-assisted development: it makes you feel productive while quietly building a backlog of problems you do not know about yet.

A developer using Copilot can scaffold an entire service in an afternoon. The code compiles. The tests pass (the ones the AI also wrote). The PR gets approved because it looks reasonable and the reviewer has six other PRs in the queue.

Three months later, someone tries to modify that service and discovers it imports a module that does not exist, calls an API that was deprecated in 2024, and uses a pattern that creates a memory leak under load. The original developer has moved on to another feature. The AI that wrote the code has no memory of it.

The debt was created the moment the code was merged. You just did not know about it until it became expensive.

AI Tech Debt Is Not Traditional Tech Debt

Traditional tech debt is a conscious trade-off. A senior developer says: "I know this is not the right abstraction, but we need to ship by Friday. We will refactor in Q3." The team knows the debt exists. It is on a board somewhere. Someone owns it.

AI tech debt is different. It is unconscious debt — created by a developer who did not know the code was wrong because the AI presented it with total confidence.

The difference matters because traditional debt is visible. It shows up in planning meetings. People budget time for it. AI debt is invisible until something breaks, and by then it has spread across dozens of files because the same AI pattern got copy-pasted (by the AI) into every service it touched.

What AI Debt Looks Like in Practice

These are real examples from Thuban scans. Not contrived. Not edge cases. The kind of thing that ships every day in AI-assisted codebases.

// Phantom import — this module does not exist in the project
import { validateSchema } from '@utils/schema-validator';

// Deprecated API — removed in React 18, still generated by AI
ReactDOM.render(<App />, document.getElementById('root'));

// AI slop — overly verbose, does what a one-liner could do
function isNotEmpty(value) {
  if (value !== null && value !== undefined) {
    if (typeof value === 'string') {
      if (value.trim().length > 0) {
        return true;
      }
    }
  }
  return false;
}

None of these will fail a linter. None will fail a type check (unless you have very strict config). None will fail the tests the AI wrote alongside them. They will all cost you time and money later.

The Compound Interest Effect

All tech debt compounds. That is not new. What is new is the rate of compounding.

If your team ships 3x more code with AI tools, you are also shipping 3x more potential debt. But the compounding is worse than linear because each piece of bad code interacts with other pieces of bad code. A phantom import in one file becomes a pattern that the AI copies into five more files. A deprecated API call in a utility function gets called from thirty places.

Traditional debt compounds at the rate your team writes code. AI debt compounds at the rate the AI writes code. Those are very different numbers.

How to Calculate What It Is Costing You

Most teams have no idea what their AI-generated tech debt costs. They know it exists in the abstract. They have never put a number on it. Here is how.

The Formula

// Immediate fix cost
immediate_cost = issues × avg_fix_time_hours × developer_hourly_rate

// Ongoing incident cost
incident_cost = monthly_incidents × MTTR_hours × team_hourly_rate

// Velocity drag (the hidden killer)
drag_cost = developers × hours_lost_per_dev_per_month × hourly_rate

// Total monthly tech debt cost
total_monthly = immediate_cost + incident_cost + drag_cost

The first two are straightforward. The third — velocity drag — is the one that actually dominates. Every unfixed issue slows down every future change. Developers spend time working around problems, reading confusing code, debugging phantom imports, and wondering why a function behaves differently than its signature suggests.

A Worked Example

Let us use real numbers from a Thuban scan of a mid-size enterprise SaaS codebase.

That is the cost for a single codebase with 87 issues. Not thousands. Eighty-seven. And 99% of them were AI slop indicators — patterns that no human developer would have written, generated by tools that do not check whether their output is correct.

Thuban calculates this estimate automatically in its scan reports. You get the issue count, the severity breakdown, and the projected monthly cost. No spreadsheet required.

The Numbers Get Worse Over Time

That £3,840/month figure assumes the issues stay constant. They do not. Without intervention, AI debt grows because:

1. New AI-generated code adds new issues at a rate of roughly 5–15 per sprint for an active team.
2. Existing issues slow down fixes for other issues, creating a drag-on-drag effect.
3. Developer workarounds become permanent — someone hacks around a phantom import instead of fixing it, and now you have two problems.
4. The AI copies the workaround into the next file it generates, because it learned from your codebase context.

A codebase that costs £3,840/month in June will cost £5,500/month by September if nothing changes. By December, you are looking at £8,000/month. The curve is not linear. It is exponential, because each new issue interacts with every existing issue.

The Fix Window

There is one piece of good news: tech debt is cheapest to fix at the moment of creation.

An issue caught in a PR review takes 5 minutes to fix. The developer has the context. The code is fresh. The change is small.

The same issue caught three months later takes 45 minutes. The original developer does not remember why they wrote it that way (because they did not — the AI did). The code has been built on top of. Other code depends on the broken pattern. The fix requires understanding the blast radius.

Six months later, it takes two hours. And it might break something else.

The cost multiplier from PR-time to production incident is 48x or more. This is why gating PRs matters more than running periodic audits. Catching issues at creation is not just cheaper — it is a different order of magnitude cheaper.

What to Do About It

Knowing the cost is step one. Reducing it is step two. Here is a practical playbook that does not require a six-month initiative or a new hire.

1. Establish a Baseline

Run a scan. Get a number. You cannot improve what you have not measured.

# Scan your codebase, get your baseline
npx thuban scan .

# Output includes:
# - Total issues found
# - Severity breakdown (critical / warning / info)
# - AI slop indicator count
# - Estimated monthly debt cost

Write down the number. Put it on a dashboard. This is your starting point.

2. Gate Your PRs

Stop new debt from entering the codebase. This is the highest-leverage action you can take.

# Add to your CI pipeline (GitHub Actions, GitLab CI, etc.)
npx thuban gate

# Fails the build if new issues are introduced
# Does not fail on existing issues (no boiling-the-ocean required)

The gate command compares the current scan against your baseline. New issues fail the build. Existing issues are tracked but do not block. This means you can adopt it today without fixing everything first.

3. Run Weekly Scans

A weekly scan gives you a trend line. Is your debt going up or down? Are new categories of issues appearing? Which parts of the codebase are getting worse?

# Add to a weekly cron or scheduled CI job
npx thuban scan . --format json > thuban-report-$(date +%Y%m%d).json

Track the issue count week over week. If it is going up, you are creating debt faster than you are fixing it. If it is flat, you are treading water. If it is going down, you are winning.

4. Budget 20% for Debt Reduction

Allocate one day per sprint per developer to fixing existing issues. Not new features. Not refactoring for fun. Targeted fixes on the issues Thuban has flagged, prioritised by severity and blast radius.

At 20% allocation, a team of 8 developers fixing issues at 30 minutes each can clear roughly 60–80 issues per month. That is enough to outpace new debt creation if you are also gating PRs.

5. Measure Improvement

After four weeks of gating PRs and allocating 20% to debt reduction, run another scan. Compare the numbers. The conversation with management shifts from "we need time for tech debt" to "we reduced our monthly debt cost from £3,840 to £2,100 and production incidents dropped 40%."

Numbers win arguments. Feelings do not.

The ROI Case

If your codebase has £46,080/year in projected AI tech debt cost, and you can reduce that by 60% in three months with a combination of PR gating and targeted fixes, you have saved £27,648/year.

The cost of running Thuban on a team of 8 developers is £0 for the free tier (up to 100 files, 5 scans/month) or a fraction of that savings on a paid plan.

This is not a hard ROI case to make. The hard part is getting the baseline number. Once you have it, the rest is arithmetic.

Craig is the founder of Thuban and has spent the last year measuring what AI coding tools actually cost teams in production. The numbers in this post are from real scans of real codebases. The formula works. The only question is whether you want to know your number.