In this kickoff episode, we go beyond the hype and look under the hood of Large Language Models to understand what they are actually doing when they generate text. We start by tracing the evolution of modern AI, from early probabilistic models like Markov chains to the Transformer architecture that unlocked today’s powerful systems. But we do not stop at the history. We also explore a major shift happening right now in the AI industry: why simply making models bigger is no longer enough. With massive compute costs and data limitations slowing down the era of explosive model scaling, the next frontier of AI is no longer just about size. It is about how we use it. Along the way, we break down key concepts like probability, entropy, and perplexity to explain why AI sometimes “hallucinates,” and why techniques like context engineering and Chain-of-Thought reasoning are becoming essential to building reliable AI systems. If you want a deeper systems-level understanding of how AI works and where it is going, this episode sets the foundation. And if you want to go even further, these ideas are explored in depth in my book, Master Claude Chat, Cowork and Code: From Prompting to Operational AI, where we move from theory into how AI becomes an execution engine in real workflows.
