The meez Podcast
Josh Sharkey (Entrepreneur, professional chef, and founder/CEO of meez, the culinaryOS for food professionals) interviews world class entrepreneurs in the food space that are shifting the paradigm of how we innovate and operate in our industry.
The meez Podcast
Kenny Warner on How AI Models Really Work, Why Clean Data Wins, and the Future of Restaurant AI
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#144
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.
Links and resources 📌
Visit meez: https://www.getmeez.com
Follow meez on Instagram: https://www.instagram.com/getmeez
Follow Josh on Instagram: https://www.instagram.com/joshlsharkey/?hl=en
Follow Josh on LinkedIn: https://www.linkedin.com/in/joshua-sharkey-406965b/
Follow Kenny Warner on LinkedIn: https://www.linkedin.com/in/kennywarner/
Visit Blanket: https://www.blanket.app/
Follow Michael on Instagram: @michaeljacober
Timestamps
08:04 Kenny's Path From College To Startup Founder
12:01 Raising Capital And Hitting Hard Times
15:01 Building A Model Versus Fine-Tuning One
16:11 What Tokens And Embeddings Really Are
18:45 How Attention Works In Language Models
26:01 Why Clean, Trusted Data Wins
29:05 The Case For Smaller, Purpose-Built Models
41:03 How AI Reads A Hundred Million Rows
43:21 MCP And The Rise Of Agentic Tools
50:01 What All This Means For Restaurants