Book 1: The Narrative
Forthcoming
The 98% Win Rate That Failed
What Seven Versions of Wrong Turns Taught Us About AI Investment Risk
This book documents how an AI system that achieved a 98.2% win rate in training proved financially useless in reality—and what that failure revealed about how boards should evaluate AI investments.
What This Book Documents
- The Seven-Version Journey: From over-engineering, to false confidence, to catastrophic regression (42% win rate), to validated learning
- Hidden Failure Modes: Feature leakage, state aliasing, reward hacking, evaluation bugs that survived weeks of review
- Leadership Lessons: When to invest in AI, how to structure skeptical reviews, and when stopping is the correct decision
Board Value: Provides a structured lens to judge whether an AI initiative deserves continued capital—or should be stopped early.
Supports: Problem Framing Risk · Evaluation & Evidence Risk · Learning & Incentive Risk
This is not a success story. It is a map of dead ends, written so others don't have to discover them the hard way.
Written for: Board Members · CFOs and CROs · Technology Leaders overseeing AI initiatives
Book 2: The Principles
In Preparation
Principles of AI Capital Risk
How Boards Should Think About Go/No-Go and Continue/Stop Decisions
This book addresses how boards and investment committees should reason about AI capital allocation under uncertainty—when evidence is ambiguous and momentum is strong.
What This Book Provides
- Mental Models: How to think about AI risk at the governance level—without requiring technical depth
- Decision Logic: Frameworks for Go/No-Go decisions before capital is committed, and Continue/Stop decisions when projects stall
- Governance Failures: Patterns of oversight breakdown observed in real AI initiatives
- Capital Consequences: How AI failures translate to financial exposure and organizational risk
Board Value: A decision framework for evaluating AI investments before commitment and during execution.
Supports: All five governance dimensions
This book defines decision logic, not technical methodology. It is written for those accountable for capital allocation, not system implementation.
Written for: Board Members · Independent Directors · CEOs and CFOs · Investment Committees
How These Books Relate
Book 1 provides the empirical foundation—the documented failures, the specific versions, the lessons extracted under pressure. It establishes credibility through transparency.
Book 2 transforms those technical experiences into governance principles. It abstracts from the specifics to address the universal challenge: how boards should think about AI investments when evidence is ambiguous and momentum is strong.
Together, they represent two levels of the same insight: AI systems can optimize metrics perfectly while guaranteeing capital destruction—and governance must be designed to detect this before it becomes irreversible.
"In AI, the most valuable asset is not the model that worked—it's the record of what failed, why it failed, and how early it could have been detected."