The podcast traces the evolution of AI language models from early recurrent networks like RNNs and LSTMs, which processed sequences step-by-step and faced limitations with long sentences, to the revolutionary Transformer architecture. A key advancement was the introduction of attention mechanisms, allowing models to focus on relevant parts of input dynamically, vastly improving tasks like machine translation. The 2017 paper 'Attention Is All You Need' eliminated recurrence entirely, enabling parallel processing and faster training. This led to the creation of foundational models like BERT and GPT, which scaled into today’s large language models. These LLMs, including ChatGPT and Claude, leverage vast datasets and autoregressive learning, shifting from narrow, task-specific systems to flexible, general-purpose AI capable of handling diverse queries through natural conversation.