Michael Barnes

1 bookScience & TechnologyBusiness & EntrepreneurshipArtificial Intelligence

Michael C. Barnes has spent more than 30 years working with computers, and he has watched almost every stage of personal computing firsthand. He served six years in the U.S. Army, three as a Morse code interceptor and three as a linguist. He built his first computer around a Motorola 6802, programming it in octal and saving his work to cassette tape, and went on to the Atari 800 and the Atari ST. He spent 13 years at Sun Microsystems. Across all those years, the tools kept getting better, but the way people worked changed only a little at a time. AI was the first change that was different. That view shapes his writing: AI is already capable enough, and the real gains come from using it the right way, with your own documents, real tools, and answers you can verify. Today he is VP of Government Sales at Digantara U.S., a space company. He wrote You Are Using AI Wrong after teaching a course on AI in India, where a student told him, "I have never seen anyone use AI this way."

Book

YOU ARE USING AI WRONG

Why fluent models fail, and how to build systems that actually work

Science & Technology
Business & Entrepreneurship

You are not talking to an oracle. You are talking to a fluency engine. Large language models produce confident answers because they are trained to sound right, not to be right. Treating them as a source of facts burns budget, invents citations, and quietly ages your knowledge out of date. Conversations do not teach the model anything durable. Multi-agent chaos does not multiply productivity. It multiplies attack surface. This book explains how the interface trap works, why sycophancy and hallucination are features of preference tuning, and why model weights never become a living memory of your company. Then it offers an operational alternative: keep language models at the perimeter as transformers, not as the truth store. Put deterministic code, explicit contracts, and strict schemas at the core. Feed verified retrieval. Let smaller models refuse and escalate. Keep humans as the scarce product that writes the rules and signs the last decision. If you spend money on AI and still cannot trust the output, this is the architecture you were missing.

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