Every company says they're using AI. Some mean chatbots. Some mean automation. Some mean statistics with a new logo. If everything is AI, the word stops meaning anything.

This episode untangles what people actually mean when they say "AI" by breaking the umbrella into its real components. It covers machine learning (systems that learn patterns from data), deep learning (layered neural networks that made modern recognition possible), large language models (text prediction engines driving today's headlines), RAG or retrieval-augmented generation (connecting models to specific documents instead of relying on training alone), and agentic AI (systems that don't just respond but take action). The episode explains why these distinctions matter for risk, why a fraud detection model making probability estimates is fundamentally different from an agent allowed to move money, and how to filter the hype with a simple mental checklist: is this prediction, generation, retrieval, action, or branding?

Whether you're evaluating AI tools for your organization, sitting through vendor demos full of buzzwords, or just trying to have a smarter conversation about what AI can and can't do, Plaintext with Rich sorts the categories.

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