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PE/VC9 min read

5 Red Flags PE Firms Miss in Technical Due Diligence

PE firms lose millions because their due diligence stops at revenue metrics. Here are the 5 engineering signals that predict failure.

By Richard Ewing·
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The Due Diligence Blind Spot

PE firms deployed over $800B in tech acquisitions in 2025. ~40% failed to meet their thesis within two years due to hidden engineering liabilities.

Red Flag #1: Maintenance Load Above 65%

Ask the CTO what percentage goes to maintenance vs. new features. If they don't know - that's a red flag. If it's above 65%, you're buying a company spending more on lights-on than value creation.

Red Flag #2: Single Points of Knowledge

Map the "bus factor" for every revenue-critical system. Any system with a bus factor of 1 needs immediate knowledge transfer planning.

Red Flag #3: Undocumented AI Costs

Many companies book AI costs as R&D without breaking them out. A company reporting 80% gross margins might actually have 60% when you properly allocate AI costs to COGS.

Red Flag #4: Deployment Frequency Below Weekly

A 50-person team deploying less than weekly signals fragile code, insufficient testing, or high coordination costs.

Red Flag #5: No Engineering Economic Model

If the CTO can't articulate decisions in economic terms, they're making capital allocation decisions without frameworks. Budget 6-12 months and $200-400K post-acquisition.


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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.

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Richard Ewing: AI Economist & Capital Auditor