AI MythBusters
AI MythBusters
A Practical Guide to AI Reality vs. Hype
About the Book
Stop falling for AI hype. Start making informed decisions.
While vendors promise AI will revolutionize everything, executives face a different reality: hallucinating chatbots, failed deployments, and demos that don't survive
production.
AI MythBusters cuts through the noise with:
• Real case studies — From the lawyer fined $5,000 for AI-fabricated citations to the $1.7B Healthcare.gov disaster
• Practical frameworks — The CFCES prompt structure, production readiness checklists, and vendor evaluation criteria
• Monday Morning Action Plans — Concrete experiments you can run this week
Based on 30 years in technology and direct experience with AI failures, this book equips business leaders to separate genuine capability from marketing fiction.
You'll learn:
- Why AI "thinks" nothing (and what it actually does)
- How to spot the 80% of work hiding beneath impressive demos
- When AI agents deliver ROI — and when they drain budget
- The verification discipline that prevents expensive mistakes
For executives, managers, and decision-makers who need clarity, not hype.
About the Author
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Table of Contents
- Acknowledgments
- Chapter 1: Introduction - Why Everyone’s Confused About AI
- You’re Not Alone
- The Hype Cycle Has Outpaced Reality
- Who I Am (And Why I Wrote This)
- What This Book Will (and Won’t) Do
- Who This Book Is For
- How This Book Is Structured
- A Note on Tone
- One Last Thing Before We Begin
- Chapter 2: Myth - “AI Can Think” / Reality - “AI Completes Patterns”
- The Intelligence Illusion
- What Is AI, Really?
- The Autocomplete Mental Model
- The Critical Difference: Prediction vs. Reasoning
- We’ve Known This for Sixty Years
- Why “AI Can Think” Is a Dangerous Myth
- So What Can AI Actually Do?
- The Mental Model That Works
- Why This Matters for Productivity
- Monday Morning Action Plan
- The Bottom Line
- Chapter 3: Myth - “AI Will Replace You” / Reality - “AI Augments Your Work”
- The Question Everyone’s Asking
- The Short Answer
- What History Actually Tells Us
- Why AI Follows the Same Pattern
- The Copilot Model (Not Autopilot)
- What AI Is Actually Good At
- What Still Requires You
- The Real Productivity Numbers
- The Uncomfortable Truth
- The Hybrid Advantage
- Monday Morning Action Plan
- The Bottom Line
- Chapter 4: Myth - “AI Agents Are Autonomous” / Reality - “They’re Scripted Workflows with LLM Calls”
- The AGI Illusion
- What People Think AI Agents Are
- What AI Agents Actually Are
- The Real Use Cases (Where Agents Actually Work)
- What Doesn’t Work (Yet)
- The Economics Don’t Add Up (Usually)
- The Vendor Sleight of Hand
- Monday Morning Action Plan
- The Bottom Line
- Chapter 5: Myth - “AI Is Always Right” / Reality - “Hallucinations and Limitations”
- The Alice Test
- Why Confidence Doesn’t Equal Correctness
- Hallucinations: When AI Invents Reality
- The Skeleton Principle (Revisited)
- When AI Gets It Wrong: Pattern Recognition
- Practical Mitigation Strategies
- The Hidden Cost of Hallucinations
- Automation Bias: The Rubber Stamp Problem
- When to Trust AI, When to Verify Harder
- The Mindset Shift
- Monday Morning Action Plan
- The Bottom Line
- Chapter 6: Myth - “Legal AI Is Ready for Practice” / Reality - “Hallucinations Get Lawyers Sanctioned”
- Why This Chapter Matters (Even If You’re Not a Lawyer)
- The Hallucination Crisis in Legal Practice
- Zero-Tolerance Professions: Legal and Medical AI
- The Four Critical Challenges Beyond Hallucinations
- Understanding the Legal AI Vendor Landscape
- Vendor Selection Framework: The Questions You Must Ask
- The Implementation Roadmap: From Decision to Deployment
- What Actually Works in Legal AI (And What Doesn’t)
- Monday Morning Action Plan
- The Bottom Line
- Chapter 7: Myth - “Just Prompt and Go” / Reality - “Engineering Good Results”
- The Magical Thinking Problem
- The Skeleton Principle (Redux)
- Garbage In, Garbage Out
- The Five Elements of Strong Prompts
- Common Prompt Failures (and Fixes)
- Overcoming AI Limitations Through Structure
- The Right Tool for the Job
- The Human-in-the-Loop Mandate
- Building Your Prompt Library
- Monday Morning Action Plan
- The Bottom Line
- Chapter 8: Myth - “Demos Equal Production Ready” / Reality - “The Vibe Coding Gap”
- The Two-Hour Miracle
- What Is Vibe Coding?
- Context Engineering: The Alternative Approach
- The Iceberg Problem
- The Four Failure Modes Executives Must Recognize
- Why Vibe-Coded Demos Are Deceptively Fast
- The 80% You Can’t See (Yet)
- The Technical Debt Time Bomb
- Choosing the Right Approach
- The Production Readiness Checklist
- Real-World Time Estimates
- How to Use AI Development Effectively
- Monday Morning Action Plan
- The Bottom Line
- Chapter 9: Myth - “AI Wrote It, Not Me” / Reality - “You Own Every Line”
- The Chatbot That Cost Air Canada a Lawsuit
- Data & Security Risks
- Technical Risks
- Financial Risks
- Legal & Regulatory Risks
- Accountability
- Monday Morning: The AI Risk Audit
- The Bottom Line
- Epilogue: Writing This Book with AI
- The Irony Isn’t Lost on Me
- This Book Has an Expiration Date
- The 60-Second Decision Framework
- What I Hope You Do Next
- A Final Note on Hype
- The Real Opportunity
- Appendix: Case Study Index
- Legal and Professional Services
- Consumer Safety and Product Integrity
- Technology and Product Development
- AI Washing and Fraud
- Enterprise AI Liability
- AI Bias and Discrimination
- Algorithmic Trading and Financial Services
- Autonomous Vehicle Liability
- Security and Cryptography
- Further Resources
- AI Governance and Regulatory Compliance
- Practical AI Usage
- AI Security
- AI Research and Benchmarks
- Executive Strategy Reports
- Future of Work
- Regulatory Frameworks
- Glossary of Key Terms
- Table of Figures
- Bibliography
- Academic Papers and Research
- Books
- Court Cases
- Legal and Regulatory Sources
- News and Industry Sources
- Films
- Image Sources
- Index
- A
- B
- C
- D
- E
- F
- G
- H
- I
- J
- K
- L
- M
- N
- O
- P
- Q
- R
- S
- T
- U
- V
- W
- X–Z
- Frameworks Quick Reference
- About the Author
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