Technical debt used to be priced mostly in human engineering time.
Now it also shows up as AI-agent operating cost.
When a repository has unclear ownership, weak failure tests, tangled boundaries, generated code without explanation, dependency drift, or large context-heavy modules, AI coding agents do not magically move faster.
They search more files.
They burn more context.
They retry more patches.
They need more human review.
They cost more to operate.
The Token Tax
AI coding agents do not only charge for generation.
They also charge for inference.
If the repo does not clearly answer basic questions, the agent has to reconstruct the answers:
- where is the source of truth?
- which module owns this behavior?
- what tests prove failure behavior?
- is this duplicate logic intentional?
- is this generated code safe to modify?
- which dependency boundary is allowed?
- what can be deleted without breaking production?
Every unclear answer becomes extra context, search, retries, and review work.
That is AI token debt.
What An Audit Should Show
A useful technical debt audit should not be a scanner dump.
It should show:
- exact source evidence
- active debt vs accepted risk
- false positives and scope classification
- AI-token-debt drivers
- smallest safe remediation path
- owner and priority
- proof required after cleanup
The goal is not to shame a codebase.
The goal is to make the next change cheaper.
That next change might be made by a human engineer.
It might be made by an AI coding agent.
Either way, the repository has to be easier to reason about.
The Practical Ask
Send one repo.
Identify the files, boundaries, tests, generated-code areas, dependency risks, and ownership gaps that make humans and AI agents burn unnecessary time, tokens, and review cycles.
Then reduce the debt and produce a before/after proof report.
That is where technical debt becomes an operating cost discussion.