The PM’s Guide to Managing AI Debt - by Sairam Sundaresan

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The PM’s Guide to Managing AI Debt<br>The hidden cost of shipping AI fast, and how to control it.

Sairam Sundaresan<br>Jun 26, 2026

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AI debt is more than technical debt. It’s options debt: losing your ability to respond when AI systems break in production. This is Part I of a series that describes the tools PMs and AI product owners can use for managing AI debt.

By the end, you’ll know how to:<br>Identify which kind of AI debt you’re carrying,

Recognize when scaling becomes risky,

Take the right steps without hurting customer trust, cost, or privacy

Maya, two quarters into owning the virtual agent, days before the holiday promo. The loan shark is already in the room.<br>Five days before the holiday promo, the Slack messages start piling up.<br>“The assistant keeps quoting the old return policy.”<br>“Customers stuck in loops asking for a human.”<br>“Order numbers showing up in logs again.”

Maya stares at her screen, coffee growing cold. She’s two quarters into owning the Intelligent Virtual Agent for a mid-sized ecommerce company. Last week’s “quick fix” has already increased wrong-answer complaints by 28%, and the Friday-through-Sunday window will bring three times the normal conversation volume. VIP cancellations spike when customers get bad answers, and finance is monitoring conversation costs closely.<br>Maya is in debt. Not the well-behaved kind of debt you calculate on a spreadsheet, but the unruly kind that kicks in your door when you least expect and demands payment.<br>Every product manager knows about technical debt: choosing a short-term solution in the present costs you in the future. But technical debt is usually well-behaved: you can estimate refactoring work, schedule sprints, and budget the engineering time. It’s like a mortgage: a known principal, manageable interest, and a clear path to pay off.<br>AI debt is different. AI debt is like borrowing from a loan shark. The interest rate is variable and often hidden. Miss one payment (a policy update you didn’t version, a drift you didn’t catch, a prompt chain nobody owns) and your model hallucinates, your assistant quotes a retired policy, your resolution rates tank in production, and customers start leaving.

Technical debt is a bank manager. AI debt is a loan shark. The difference is whether you can see the next payment coming.<br>Worse yet: because AI systems are probabilistic, opaque, and context-dependent, the cause rarely maps cleanly to the effect. Maya’s problem isn’t that her assistant is broken. It’s that her team can’t see what’s breaking, and can’t safely test fixes without risking more customer trust. As a result, Maya’s options are quickly disappearing.<br>Maya’s case illustrates three things.<br>First , AI debt is options debt. Every decision you make with an AI system either removes or preserves your ability to respond when things go wrong. And with AI, things go wrong faster and more mysteriously than with traditional software1.<br>Second , Maya’s case illustrates what I’ll call The Options Principle: the PM who manages options well usually outperforms the PM who manages models well, in most real conditions.<br>Third , Maya’s case illustrates how PMs can manage options well. It’s this third point I’m going to focus on. The previous quarter, Maya had the foresight to build some tools to get herself out of AI debt: three gauges to measure the debt and three levers to pull if things go wrong. Those gauges and levers are what let her climb out of debt in 72 hours instead of flailing for a week.<br>Thanks for reading Gradient Ascent! Subscribe for free to receive new posts and support my work.

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The Control Room

Three gauges, three levers, one sticky-note rule. Everything Maya does this weekend runs through this panel.<br>To understand Maya’s tools, picture a control room. In front of you are three gauges, each measuring a different kind of AI debt: foundation debt, drift debt, and operations debt . Each debt gauge has green, yellow, and red zones. Green means you have options: you can experiment, scale, and recover from mistakes. Yellow means you’re starting to lose flexibility. Red means you’re flying blind, and any move could make things worse.<br>Next to each gauge is a lever which you pull when a gauge goes red. Pulling the lever doesn’t fix the problem. It just buys you time and information so you can fix it without burning customer trust.<br>Governing everything is one rule written on a sticky note:<br>Never scale when any gauge is red or unknown.

Let’s walk through the gauges and the levers.<br>Gauge One: Foundation Debt

Foundation debt is about traceability: when something goes wrong, can you find out what happened? If, say, a customer complains about a wrong answer, can you pull up the conversation, see which version of the policy the assistant was quoting, and re-run it to understand why? If you can’t, you’re fixing blind.<br>Foundation debt isn’t the same as drift. Drift happens...