Josh and Mike sit down with Kenny Warner, VP of Data Science and Engineering at meez, for a conversation that starts with Kenny's unlikely path into tech leaving college after his sophomore year to launch a cause-marketing startup and winds up deep inside the machinery of modern AI. Kenny breaks down what actually happens when a language model reads your data, explaining tokens, embeddings, attention, and inference in plain terms, and why you can't simply point a chatbot at a hundred-million-row database and expect good answers.
He and Josh dig into the real difference between building a model and fine-tuning one, why clean and trusted data is the true competitive advantage, and how smaller, purpose-built models can beat the giants at specific jobs. They also get into where this is all heading for restaurants, from turning recipes, costs, and margins into something a system can reason about to the rise of MCP and agentic tooling, with a detour through Nobu, a Miami chef tour, and a few fun facts along the way. It's a rare, jargon-free look under the hood of AI from someone who builds it every day.
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