Overview

AI systems are powerful, fast and increasingly embedded in critical decisions but most cannot prove how they work. The Evidential Resilience Ratio (ERR) offers a structural lens for understanding this gap. It doesn't tell engineers how to build systems; it reveals what those systems already are: how traceable, reconstructable, version-consistent and dependency-bound they remain beneath the surface. The future of AI won't belong to the most capable systems, but to the ones that can prove themselves. This book shows why the industry's sub-40% provability is the defining risk of the coming correction, and why the next decade will favour systems that can defend their behaviour under audit, regulation and scrutiny, not just those that perform well in demos. For builders, regulators and investors, ERR provides a clear way to understand AI maturity and resilience. It replaces assumptions with evidence and narratives with structure. The systems that can prove themselves will endure. Everything else is temporary.

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