The Little Helper Paradox: LLM Between Promise and Responsibility (KI-Macherreihe 2026) Kindle Edition

Can you guarantee that an AI answer is correct? No. Then you cannot sell it as a managed service.This book asks one simple question: Can Large Language Models function as managed services for critical applications? The answer, developed across 16 chapters with legal analysis, SLA falsification tests, and a running drone development scenario, is: No. Not because the technology is bad. Because the architecture makes it impossible to guarantee what a managed service must guarantee—correctness.The Little Helper Paradox: The more an LLM helps, the more you trust it. The more you trust it, the less you verify. The less you verify, the more dangerous the trust becomes.Michael Schmid brings 43 years of IT experience in critical infrastructures—telecommunications, energy, defense. He has written managed service contracts, negotiated SLAs, and watched providers promise what no contract could deliver. His method is simple: Try to write the contract. If you cannot write one that protects both sides, the business model does not work.What this book covers:Why SLAs can guarantee availability but never correctnessWhy wrong answers are not bugs but expected behavior of stochastic systemsWhat the EU AI Act, Product Liability Directive, and CLOUD Act mean for your contractsWhy the core risk of AI outputs is currently uninsurableWhat actually works: the Researcher Model, version pinning, SLAs for the peripheryPractical tools: Template SLA, compliance checklist, GPU infrastructure planning guideWho should read this: CIOs, CTOs, IT decision-makers, legal and compliance professionals in regulated industries—anyone who must decide whether and how to deploy AI in environments where errors have consequences.What this book is not: Not anti-AI. The author used Claude from Anthropic as research partner and co-author. The collaboration is documented transparently in the appendix. This book is a plea for honesty—not against technology, but against misplaced trust. Read more