Aaron Newton burns through close to a billion tokens a week. Sterling's head of HR, who has never written a line of code, built a training app that replaced their existing one, complete with user roles, tracking, and a GitHub repo. This is what happens when you stop asking AI for answers and start letting it cook. In Episode 2 of AI for Restaurants, Sterling and Aaron move past the "should I use AI" question and get into the "how". The conversation opens with both hosts comparing token usage like a scoreboard, and the numbers tell a story: the more you use AI, the more value you extract, and the cost is almost comically low compared to the output. Aaron describes paying roughly $70 a week for what amounts to a full software development team. From there, they dig into vibe coding and why it's both overhyped and genuinely powerful. Sterling's HR example is the proof point: a non-technical person built production software that the whole company now uses daily. Aaron shares how his head of marketing used AI to pull every competitive deal transcript from Gong, analyze what customers said, and build battle cards that actually reflect real sales conversations. The key insight in both cases is that the data had to be accessible first. If your call recordings, CRM data, and internal docs are scattered across platforms, AI can't help you. If they're organized, the results are immediate. The episode's standout segment is Aaron's explanation of Skills using the Matrix helicopter scene: Trinity needs to fly a helicopter, someone loads the knowledge, and she instantly can fly it. Skills work the same way for AI. They're text files written in plain English that teach your AI agent how to do specific jobs, from querying Salesforce to sending Slack messages. They're token efficient, easy to iterate on, and unlike MCPs, you don't need to understand Docker or authentication protocols to get started. Both hosts describe how skills have become the backbone of how they work. Sterling then walks through his personal AI task management system. He took a to-do app, embedded AI agents into it, and now when he adds a task, an agent picks it up, invokes the right skills, and either completes it autonomously or enters planning mode with him for anything complex. Aaron reframes it perfectly: "You took an app that manages your to-do list and turned it into an app that manages Claude's to-do list." The conversation wraps with both hosts discussing how they give their AI agents personalities based on real people they've worked with, and Aaron introduces Sherlock, his company's AI analyst that doesn't just answer questions but investigates them, chases down related threads, and flags its own assumptions through an adversarial review agent called Moriarty.
