Quick take
Your AI features are accumulating debt in places your existing tooling can’t see: prompts nobody versions, data nobody validates, models nobody benchmarks after deploy. Treat it like any other dependency: track it, test it, or pay for it later at 10x the cost.
I spend a lot of my time helping teams integrate AI into financial infrastructure: open-source ledger systems, strict correctness requirements, and environments where “it usually works” is not an acceptable quality bar. What I’ve learned is that AI technical debt is sneakier than the regular kind.
Traditional tech debt is familiar. We all know what it looks like: rushed code, missing tests, dependencies you should have updated six months ago. AI debt is different. It accumulates silently because the system keeps producing outputs that look plausible. By the time you notice something is wrong, you’re already deep in the hole.
The Five Flavors of AI Debt
At a fintech company, I started categorizing the debt I kept seeing across teams. It clusters into five buckets, and they overlap in annoying ways.
Model debt. Nobody knows which model version is running in production. Nobody benchmarked the current version against the previous one. The model provider shipped an update, behavior shifted, and three weeks later someone noticed the outputs were slightly worse. By then, good luck figuring out what changed.
Prompt debt. Prompts scattered across files, notebooks, Slack messages, and someone’s local branch. Duplicated logic. No review process. One engineer tweaks a system prompt on Tuesday, another tweaks the same prompt on Thursday, and by Friday they’re debugging each other’s changes without knowing it.
Data debt. Unknown provenance. “Where did this training data come from?” “I think Jake downloaded it from somewhere.” Weak validation, unmeasured drift. The inputs your model sees in production look nothing like what it was tested on, and nobody is tracking the gap.
Evaluation debt. This is the most dangerous one. No baseline. No regression suite. The team ships a change, eyeballs a few outputs, and declares it good. Then three weeks later users start complaining and there’s nothing to compare against.
Infrastructure debt. Brittle integrations, no fallbacks, and cost attribution that amounts to “the AI line item went up, who knows why.” In fintech, where we deal with financial transactions, this kind of opacity is unacceptable. But I see it everywhere.
The Warning Signs
You’re already in debt if any of these sound familiar:
- Outputs differ between staging and production and nobody can explain why
- You ship prompt changes without running any automated evaluation
- You can’t answer “which model version and prompt version are in production right now?” in under thirty seconds
- Your data sources are described as “the usual ones” in documentation that doesn’t exist
- Your AI costs went up 40% last month and the best explanation is “more usage, probably”
If you nodded at three or more, you have a problem. If you nodded at all five, you have a fire.
What Actually Works
Version Everything
Prompts are code. Full stop. At one fintech company, we moved all prompts into version-controlled templates with required code review for changes. It felt like overhead for about a week. Then someone caught a regression in review that would have taken days to debug in production.
Models are dependencies. Pin them. Track deployment dates. Record benchmark results at deploy time so you have a comparison point when behavior drifts.
Build Your Eval Suite Before You Need It
A lightweight evaluation set – even 30 representative inputs with expected outputs – will save you more debugging time than almost any other investment. Run it before every deploy. Run it on a schedule against production. When it catches something, you’ll be glad you spent the half-day building it.
Make Cost Attribution Explicit
If you can’t attribute AI costs to specific features and workflows, you’re flying blind. At one fintech company, we tag every API call with the feature path that triggered it. When costs spike, we know exactly which workflow is responsible within minutes, not days.
Monitor Drift, Not Just Uptime
Traditional monitoring asks “is it up?” AI monitoring also needs to ask “is it still correct?” Track output distributions. Flag anomalies. Set up alerts when the model’s behavior shifts beyond your tolerance band. This isn’t optional – it’s the equivalent of testing in production, which you’re already doing whether you admit it or not.
Paying It Down
The approach I recommend is the same one I use for regular tech debt: risk-driven, regular, and documented.
Pick the highest-risk debt category. For most teams, that’s evaluation debt because it blocks your ability to safely address everything else. Stabilize it. Then move to the next.
Write down every decision. Not a novel – a paragraph. “We pinned model version X because benchmark Y showed regression on task Z.” When future-you is debugging at 2 AM, these notes are the difference between a thirty-minute fix and an all-nighter.
AI systems can be reliable. But only if you treat invisible debt with the same seriousness as the kind your linter can catch.