Your tech debt is a loan. Do you know the interest rate?

Every quarter, in budget meetings everywhere, the same scene plays out. Engineering asks for time to deal with the debt. The ask is sincere, the debt is real, and the line item dies anyway because "we need to clean things up" names no consequence, and a CFO is paid to cut line items that name no consequence.

Here's the reframe that changes the meeting: technical debt is a loan, and like any loan it has two numbers. The principal is what remediation costs, paid once, by choice, and only if you decide to fix something. The interest is what carrying the debt costs, paid every year, by default, whether or not it appears in any budget. Gartner's research on debt economics puts it in one line: the debt is the invisible principal, the operational friction is the visible interest, and you can't manage the first until you quantify the second.

Almost nobody has quantified the second. McKinsey pegs technical debt at 15–20% of a typical annual IT budget, and reports CIOs estimating debt at 20–40% of the value of their entire technology estate. In Gartner's surveys, 44% of organizations call debt a top challenge while fewer than 20% of engineering leaders rate themselves effective at managing it. A 2025 Gartner survey found 71% of organizations saying debt impacts a quarter to half of their infrastructure estate. The pattern across all of it: the interest payment is enormous, universal, and unmeasured. Which is precisely why it never competes for budget.

Compute the interest from numbers you already know

You don't need a code-scanning platform or a quarter of analysis to get a defensible first number. Three components, all estimable this afternoon:

• Everyday drag. Take your engineering payroll, multiply by the share of time that goes to maintenance and bug work, then by the share of that burden caused by avoidable debt rather than the normal cost of owning software. This is the big one. Stripe's developer survey put average maintenance load at around 42% of the engineering week. Most leaders guess low.
• Debt-driven incidents. Incidents per month attributable to fragile systems, times the engineer-hours each one actually consumes, including response, rollback, cleanup, the works, times your loaded hourly cost. Repeat failures are the tell: if the same class of incident keeps returning, that's debt interest, not bad luck.
• Attrition. The engineers you lost where the codebase was a factor, times the replacement cost: recruiting, ramp, and the tribal knowledge that walked out. Exit interviews rarely say "tech debt." They say "everything was a slog."

Sum those and divide by your total engineering spend. That's your interest rate, and it's usually a shock: a fairly ordinary 80-engineer org with ordinary drag lands around $2M a year, a sixth of its engineering budget, before counting a single lost deal or delayed launch.

Two disciplines make the number credible. First, keep it conservative and say what you left out: revenue lost to outages, roadmap opportunity cost, and compliance-gated markets all make the real number higher, and saying so out loud is more convincing than padding. Second, don't chase precision. Gartner's guidance on debt metrics is explicit that directional accuracy beats precision. Demanding the perfect number is how the conversation gets deferred another year. A CFO doesn't need the third significant digit. They need to know whether it's $200k or $2M.

The line item that didn't exist two years ago

There's now a fourth cost, and it's the reason this decades-old problem has a new deadline. AI coding tools are repriced by the codebase they run in. In well-tested, well-bounded code, agents are a real multiplier. In the legacy core, where tests are missing, contracts are implicit, and seams are tangled, they're useless or confidently wrong, a systems problem we've written about before. Whatever share of your codebase is closed to AI tools is a share of your AI investment producing nothing, and the analysts see the squeeze tightening: Gartner projects that by 2029, half of enterprises scaling generative AI will watch technical debt rise, up from 20% in 2026.

Keep this out of your headline interest number. It's an opportunity cost, not money currently leaving the building. But put it on the same page. For many boards it's the argument that finally lands, because it converts "old problem we've lived with" into "cap on the new thing we just funded."

The number gets you the meeting. The dashboard keeps you funded.

A one-time shock number wins one budget cycle. What sustains funding is a one-page view the business can read every quarter, and Gartner's research on debt tracking is blunt that leaders who can't show remediation progress lose credibility, approval, and funding in that order.

The discipline that makes the dashboard honest is the same one that makes the funding stick: every deferral gets a written rationale and a binding review date, so "later" stops meaning "never." And the scoring behind the priority list, scored on impact, risk, and cost with the business scoring impact, is a conversation we've covered in its own post. Price the interest first, then rank the portfolio. In that order, the two artifacts fund each other.

Say it in the four words

When the number goes upstairs, resist every engineering noun. "Refactoring," "pipeline," and "monolith" each invite the shrug. The interest number is powerful precisely because it's already in the executive vocabulary: time, cost, risk, revenue. "We pay roughly $2.1M a year to carry this debt, it's compounding, and here is the curve if we fund a 10% paydown allocation" is a sentence a CFO can act on. It also survives the hardest question in the room: "why now?" Because the honest answer is that the interest just repriced: the same debt that taxed your engineers now caps your AI leverage too.

The Sigao take

Compute the interest before you ask for the principal. It turns an unfundable cleanup plea into a recurring cost with your company's name on it, and it moves the burden of proof. Suddenly *not* fixing the debt is the expensive choice. We built a free Technical Debt Cost Calculator that does the math in about three minutes: your annual interest in dollars, the risk band it puts you in, the three-year do-nothing curve, and a board-ready PDF brief for the funding conversation. And when the number gets you the meeting, our Tech Debt Paydown engagement is the follow-through: score the portfolio, sequence the paydown, and execute it alongside your teams while you keep shipping.

Sources

• McKinsey & Company, "Breaking technical debt's vicious cycle to modernize your business", 2023: the IT-budget share and technology-estate estimates cited above.
• Gartner, "How to Prioritize and Sell Technical Debt Remediation": the top-challenge and management-effectiveness survey figures (full note behind Gartner's subscription).
• Gartner, "Technical Debt Interest Rate: A New Metric to Quantify the Hidden Cost of Legacy IT Infrastructure" (2026) and "Visualize Your Technical Debt With a Tracking Dashboard" (2023): the interest/principal framing, the 2025 infrastructure survey, the GenAI projection, and the dashboard metrics (behind Gartner's subscription).
• Stripe and Harris Poll, The Developer Coefficient, 2018.