Post-QA Verification Engineer

The Cost of Predictivity is a framework coined by Richard Ewing that measures the variable cost of AI accuracy. Unlike traditional software with near-zero marginal costs, AI features have costs that scale with usage and accuracy requirements. The key insight: as AI correctness increases, cost scales exponentially. Moving from 80% accuracy to 95% accuracy often requires a 10x increase in compute and retrieval costs. Moving from 95% to 99% may require another 10x. This creates margin compression that traditional engineering metrics don't capture. A feature that works beautifully at 100 users may be economically unviable at 100,000 users because AI inference costs scale linearly with usage while accuracy improvements require exponentially more resources. The AI Unit Economics Benchmark (AUEB) calculator at richardewing.io/tools/aueb helps companies calculate their Cost of Predictivity and identify their AI margin collapse point.

Orchestration Debt is an emerging form of AI technical debt (2026) created when autonomous AI agents interact with multiple enterprise systems, creating complex dependency chains that are difficult to monitor, debug, and maintain. As organizations deploy agentic AI workflows where agents call other agents, access databases, invoke APIs, and make decisions autonomously, the orchestration layer between these components accumulates debt through: undocumented dependencies, brittle error handling, cascading failure modes, and untested interaction patterns. Orchestration debt is uniquely dangerous because it is invisible — each individual agent may work correctly, but the interactions between agents produce emergent behaviors that no single team designed or tested.

Retrieval-Augmented Generation (RAG) is an AI architecture pattern that combines a language model with a knowledge retrieval system. Instead of relying solely on the model's training data, RAG retrieves relevant documents from a knowledge base and includes them in the prompt, grounding the AI's responses in specific, verifiable information. RAG reduces hallucinations by giving the model factual context to work with. It's the most popular enterprise AI pattern in 2026 because it allows organizations to use their proprietary data with general-purpose language models without fine-tuning. The economics of RAG involve balancing retrieval costs (vector database queries, embedding generation) against the cost of hallucination and the alternative cost of fine-tuning. For most enterprise use cases, RAG is significantly cheaper than fine-tuning while providing better accuracy on domain-specific questions.

Technical debt is the implied cost of future rework caused by choosing an expedient solution now instead of a better approach that would take longer. First coined by Ward Cunningham in 1992, technical debt has become one of the most important concepts in software engineering economics. Like financial debt, technical debt accrues interest. Every shortcut, every "we'll fix it later," every copy-pasted function adds to the principal. The interest comes in the form of slower development velocity, more bugs, longer onboarding times for new engineers, and increased fragility of the system. Technical debt exists on a spectrum from deliberate ("we know this is a shortcut but ship it anyway") to accidental ("we didn't realize this was a bad pattern until later"). Both types compound over time. Organizations that don't actively measure and manage their technical debt risk reaching what Richard Ewing calls the Technical Insolvency Date — the specific quarter when maintenance costs consume 100% of engineering capacity.