Karam challenges the notion that AI's technological advancement ensures its survival, arguing that misalignments in attention, data supply, economics, trust, and incentives can lead to obsolescence despite technical competence.

Five Failure Mechanisms:

1. The Attention Trap: Introduces the "Attention-to-Value Decay Loop," explaining that AI features may become invisible as initial novelty fades. As competitors catch up and user expectations evolve, products lose feedback loops, leading to decline.
2. Data Supply Collapse: The "Data Supply Half-Life Model" highlights that AI systems depend on stable data ecosystems. Changes in privacy regulations and consent can disrupt data sources, causing models to degrade as inputs no longer match historical patterns.
3. Cost Curve Crisis: The "Marginal Cost Reality Check" shows that while training costs are stable, inference costs rise with usage. Businesses may achieve technical success but face economic collapse as costs grow faster than revenue.
4. Trust Collapse Triggers: Misinformation and accountability gaps erode trust. AI outputs may seem confident but lack verifiable sources, leading to diminishing trust faster than improvements can restore it.
5. Incentive Endgame: Adoption stalls when incentive structures misalign. Systems lose momentum due to rising compliance costs and unclear liability, leading to bureaucratic drift instead of technical failure.

Karam emphasizes that ecosystems around AI data governance, cost structures, and incentives are as crucial as algorithms. Improvement doesn’t guarantee survival; AI can falter if attention shifts, data access contracts, costs rise, trust erodes, or regulations favor alternatives.

The book concludes with a practical suggestion for a 48-hour risk audit to identify and mitigate issues in AI use cases. Karam, a CTO with over 35 years in AI, emphasizes practical implementation over theoretical potential, suggesting that AI's decline may happen gradually as support wanes.