AI debt
Technical debt is created when we prioritize short-term gains over long-term maintainability. It’s not bad, if it’s intentional. The long term may never come unless you survive the short term. Early on, optimize for momentum first so that you can eventually focus on sustainability.
Let’s skip the generic forms of technical debt – like lack of documentation, copy-pasted code, manual operations, or lacking testing – and focus on AI-specific debt. There are two big themes to consider.
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Data debt. All those hacky scripts and quick fixes that help you run many experiments early on. They become technical debt later. This is often acceptable debt – the kind that lets you move fast when it matters. The key is to recognize when to pay it back before it slows you down.
A subtle but costly variant here is poor logging. Without proper logs, you lose future data. You deprive yourself of feedback loops – so you don’t learn, and you miss the chance to improve. That’s an opportunity cost as much as a technical debt.
Decision tracking — losing the “why”. The second category revolves around decisions that go untracked. Even if choices turn out to be good, without a record they seem accidental and less trustworthy. This includes unreliable evaluation methods, missing model/data versioning, hand-picked hyperparameters. As a result, you’re forced to redo the work, and relearn lessons you already paid to learn. Big waste!
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Technical debt, like financial debt, can be a powerful tool. Used intentionally, with a clear plan for repayment, it provides leverage. Used carelessly, it risks sinking your efforts.