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What happens when a search engine is driven by a text file of hand-written rules? You get a Jaguar car ranking first for an iPod query on eBay, and you get the perfect setup for a practical tour of how AI actually creates value. We unpack the journey from brittle if-then logic to machine learning that learns relevance from real outcomes.

In this episode, we break down AI, machine learning, and large language models (LLMs) in clear terms, showing how they fit together and where they differ. We discuss in depth the first wave of AI systems that solve specific problems from detecting credit card fraud, to ranking search results, to recommending the movies we watch. These AI systems are everywhere -- most everything you use at Meta, Google, Microsoft, Amazon, and Apple is driven by machine learning and AI at its core.

This episode also discusses the fundamental truth that great AI starts with fresh, comprehensive, clean data, and a well-defined target. We explain that most companies still aren't getting this right and that no AI system will be effective if there's garbage data.

The most surprising lesson might be the most useful: sometimes the right answer is not to use AI. Hear the story of a 99%‑accurate model that surfaced a company’s fax number as customer support, and why a small human team delivered safer, cheaper, 100%‑correct results. We also explore why LLMs feel like a revolution—real breakthroughs plus a brilliant, accessible UX—and how that shift is changing how people find information.

If you care about building reliable AI products, avoiding unforced errors, and making smarter trade-offs, this conversation will sharpen your instincts. Listen, share with a colleague who loves a good data debate, and subscribe so you don’t miss part two, where we dive into deep learning, transformers, and what’s coming next.