# AI Verification Verification, not generation, is the bottleneck. [[Large Language Model - LLMs]] can generate outputs in seconds. Humans verify them in hours. This asymmetry destroys ROI. The formula is simple: > **Net Value = Efficiency Gain - Verification Cost** Most AI implementations fail because they optimize generation while ignoring verification. 97% of enterprises can't demonstrate AI ROI. This is why. The [[AI Capex Super-Cycle]] keeps pouring money into infrastructure, but returns stall at the verification layer. ## The Numbers Knowledge workers spend 4.3 hours per week verifying AI outputs. At $100/hour, that's ~$22k per employee annually. For a 100-person company: $2.2M in hidden verification costs. The market opportunity: 100M+ knowledge workers globally. $2.2 trillion in verification time that could be saved. Current penetration under 1%. ![[Screenshot 2025-11-24 at 00.40.15.png]] ![[Screenshot 2026-01-23 at 18.52.49.png]] ## What Works - **Built-in verifiability**: MIT's SymGen approach embeds source references directly in outputs. Hover over text, see the source. 20% faster verification. - **Multi-agent systems**: Break complex queries into sub-tasks. Each agent verifies its piece. Aggregate results. 85-92% accuracy vs 68% with standard RAG. This is where [[AI agents]] and [[Autonomous Agents]] start earning their keep: not just executing, but cross-checking each other. - **Hierarchical verification**: Not everything needs the same scrutiny. High-stakes (legal, medical) get full review. Low-stakes (internal summaries) get automated checks only. 60-70% cost reduction. - **Domain-specific validators**: Legal clause checkers, financial calculation validators, medical terminology verifiers. 10-20% higher accuracy than generic approaches because they're specialized. [[knowledge graphs]] help here by structuring domain knowledge for reliable cross-referencing. ![[Screenshot 2025-11-24 at 00.46.20.png]] ## Detection Methods - [[Semantic Entropy]] measures uncertainty about meanings, not text. Catches hallucinations caused by knowledge gaps. - [[SelfCheckGPT]] generates multiple samples and scores consistency. If the model knows something, responses should agree. - [[LLM-as-Judge]] uses one model to evaluate another. Works, but can inherit biases. - [[RAG-based verification]] cross-references outputs against trusted sources. Reduces hallucinations, doesn't eliminate them. ## Investment Angle Verification creates defensible moats. See [[AI era Defensibility]] and [[Defensibility Principles MOC]] for the broader framework. The moats here: - Data flywheel: more usage improves verification models - Workflow lock-in: embedded verification is sticky - Domain expertise: specialized knowledge is hard to replicate The companies optimizing for verification cost, not generation quality, will win. We're at the inflection point where "verification-first" becomes the standard. Understanding where this sits in [[The AI Stack - Building Blocks]] matters: verification is an application-layer problem that infrastructure alone can't solve. ![[Screenshot 2026-01-23 at 18.49.09.png]] ![[Screenshot 2026-01-23 at 18.46.05.png]] ![[Screenshot 2025-11-24 at 00.48.04.png]] --- Links: - [[How AI Verification Tools Actually Work - A Technical Deep Dive]] - [[AI Agents Stack]] - [[AI Inference Infrastructure]]