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Technical Debt10 min read

The Innovation Tax Audit: Is Your R&D Actually Just OpEx?

Are your software assets still generating value, or are they just burning resources? A guide to auditing your portfolio and killing zombie features.

By Richard Ewing·
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The Reality of R&D Spending

As covered in my latest piece for CIO.com, executives love the idea of R&D. But unfortunately, the vast majority of what organizations classify as "innovation" is just expensive maintenance disguised as progress.

The root problem lies in treating software development as a pure addition game. We pile new features on top of old architectures, never recognizing that every line of code is a liability that exacts an ongoing tax.

Identifying Zombie Assets

Every codebase harbors Zombie Assets—features that are technically alive but functionally dead. They consume compute resources, inflate test suites, and distract engineering attention, yet they deliver zero marginal value to the customer.

These features typically persist because removing them seems riskier than leaving them alone. But the true danger is letting them stay. When an engineering team ignores feature bloat, they eventually reach the Technical Insolvency Date, where maintenance consumes 100% of capacity.

The Rule of Two

How do you identify a Zombie Asset? Audit your portfolio using the Rule of Two: look for features that have not been touched by a user in two months or updated by a developer in two years. If a feature hits both markers, it is a prime candidate for the morgue.

The Scream Test and Sunset Committees

Once identified, how do you kill them safely? The cheapest way is the Scream Test: turn the feature off in staging, then production, and see if anyone complains. Most of the time, there will be silence.

For a more formal approach, establish a Sunset Committee. This is an operational body with one explicit KPI: code retirement. By formalizing asset destruction, you remove the emotional weight of deprecation from the original creators and place it within a structured governance framework.

Real innovation doesn't just come from writing new code. It comes from having the courage to delete the code that no longer serves you.


For a deeper dive into optimizing your organization's engineering economics and conducting your own Innovation Tax Audit, enroll in our AI Economics Curriculum.

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Canonical Frameworks

Technical Insolvency Date

The Technical Insolvency Date (TID) is the specific future quarter when an organization's technical debt maintenance will consume 100% of engineering capacity, leaving zero time for new feature development. Every software organization accumulates technical debt over time — shortcuts taken under deadline pressure, aging infrastructure, deprecated dependencies, and code that nobody understands anymore. This debt isn't free. It requires ongoing maintenance hours: bug fixes, security patches, dependency updates, and workarounds for architectural limitations. The critical insight is that maintenance burden grows faster than most leaders realize. If your team currently spends 40% of its time on maintenance and that percentage is growing 3% per quarter, you can calculate the exact quarter when maintenance reaches 100%. That quarter is your Technical Insolvency Date. At the TID, your engineering team is fully consumed by keeping existing systems alive. Feature velocity drops to zero. No new capabilities. No competitive response. No innovation. Your R&D investment becomes pure maintenance spend — you're paying innovation-era salaries for maintenance-era output. The concept draws from financial insolvency: the point where a company's liabilities exceed its assets and it cannot meet its obligations. Technical insolvency is the same idea applied to engineering capacity — the point where your maintenance obligations exceed your available engineering hours. Most organizations don't realize they're approaching the TID because they track technical debt qualitatively rather than quantitatively. Telling a board "we have technical debt" gets deprioritized. Telling a board "we are 8 quarters from technical insolvency — the point where we can no longer ship any new features" gets immediate action and budget allocation.

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Audit Interview

The Audit Interview is a hiring protocol that tests verification skills instead of code generation skills. In the AI age, the scarce human skill is not writing code — it's catching what AI gets wrong. Traditional coding interviews ask candidates to write algorithms on a whiteboard or in a shared editor. This was a reasonable proxy for engineering skill when humans wrote all the code. But in 2026, AI tools like GitHub Copilot, Cursor, and Claude generate code faster and often more correctly than human candidates under interview pressure. When Anthropic discovered that candidates were using Claude to pass their own coding interviews, it proved that traditional interviews are testing the wrong thing. They're testing a skill that AI performs better than humans under artificial conditions. The Audit Interview flips the model. Instead of asking candidates to generate code, it presents them with AI-generated code that contains hidden flaws — security vulnerabilities, logic errors, performance anti-patterns, edge case failures, and architectural problems. The candidate's job is to find the bugs, rank them by severity, and make a ship/no-ship recommendation. The protocol works like this: candidates receive a realistic code review scenario (500-1000 lines of AI-generated code with 3-5 hidden flaws). They have 10 minutes to review the code, identify issues, and present their findings. The evaluation scores 4 dimensions of engineering judgment: 1. Verification: How many bugs did they find? Did they catch the security vulnerability? 2. Prioritization: Did they correctly rank issues by severity? 3. Communication: Can they explain the risk to a non-technical stakeholder? 4. Judgment: Would they ship this code? Under what conditions? With what caveats? The free Audit Interview tool at richardewing.io/tools/audit-interview generates realistic AI-written code with calibrated flaws for interviewers to use immediately.

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Richard Ewing

The AI Economist — Quantifying engineering economics for technology leaders, PE firms, and boards.