Tying earnouts to revenue alone misses engineering quality. Add these 5 metrics.
Standard earnout: revenue targets only. Better earnout: revenue + engineering health metrics that predict sustainable growth.
Add: PDI score (must stay below 2.0), Innovation ratio (must stay above 40%), Key person retention (80%+ for 18 months), Deployment frequency (must not regress), Customer incident rate (must not increase).
These ensure the acquired team doesn't sacrifice long-term health for short-term revenue targets.
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14 minThe three integration patterns and when each one leads to failure.
10 minCarving out a technology business costs 2-5x what the financial models predict.
9 minThe compounding gross margin penalty incurred when variable LLM inference query costs scale faster than subscription revenue, shifting server hosting into variable Cost of Goods Sold (COGS).
Read Concept →The executive discipline bridging engineering velocity, financial P&L contribution, and product margin strategy to prevent technical debt and AI COGS from destroying business valuation.
Read Concept →The systemic financial risk created when high-velocity AI code generation produces massive volumes of un-audited, low-trust technical debt that inflates future maintenance liabilities.
Read Concept →The engineering debt accumulated when developers accept AI-generated code based on superficial execution ("vibes") without understanding underlying architectural assumptions or edge cases.
Read Concept →A degenerative process where AI models experience severe performance degradation after being iteratively trained on synthetic data generated by other models.
Read Concept →The financial discipline of managing, projecting, and optimizing the per query token costs associated with running large language models in production.
Read Concept →The critical threshold where the operational cost of maintaining a codebase and resolving technical debt exceeds the engineering capacity available for new feature development.
Read Concept →The compounding maintenance burden and operational friction incurred when new technology is deployed without decommissioning legacy systems, effectively taxing all future engineering velocity.
Read Concept →The non-linear increase in communication overhead, alignment meetings, and process friction that occurs when scaling engineering organizations, ultimately degrading per-capita execution capacity.
Read Concept →The systemic masking of growing software maintenance liabilities (OpEx) behind inflated velocity metrics and new feature launches, creating a fragile engineering economy that requires constant new capital to sustain.
Read Concept →The analytical framework for determining the precise point where the ongoing maintenance cost of a software feature exceeds its marginal revenue value, necessitating immediate deprecation.
Read Concept →The systemic erosion of traditional SaaS gross margins caused by the integration of generative AI features, as variable compute and API costs scale linearly or exponentially with user engagement, fundamentally altering software unit economics.
Read Concept →The principle that heavily AI-augmented teams of ten elite engineers can now achieve execution parity with traditional enterprise engineering organizations of over one hundred, fundamentally altering the economics of software creation.
Read Concept →The architectural pattern of storing and reusing similar LLM query results using vector embeddings to bypass redundant frontier model API execution and eliminate variable COGS.
Read Concept →Zombie Code refers to deprecated or unused features that continue to run in production, consuming maintenance budget, compute resources, and engineering focus. The Sunset Protocol is the structured mechanism for financial remediation through systematic deletion.
Read Concept →The strategic shift of migrating high-volume inference tasks from commercial Frontier APIs (OpenAI, Anthropic) to local Small Language Models (SLMs) to achieve financial breakeven on variable COGS.
Read Concept →The analytical process of converting standard engineering performance metrics (Deployment Frequency, Lead Time, MTTR, Change Failure Rate) into direct financial liabilities and capitalization impacts on the P&L statement.
Read Concept →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.
Read Definition →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.
Read Definition →The AI Economist - Quantifying engineering economics for technology leaders, PE firms, and boards.
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Richard Ewing - AI Economist & Capital Auditor