The LLM Evolution: From Sequence Imitation to Verifiable Reasoning

For the last decade, the recipe for AI was simple: more data, bigger models. By feeding neural networks the entire internet, we taught them to imitate human language with startling accuracy. But as we exhaust the world's high-quality text data, a new question arises: How do we scale intelligence when there is nothing left to imitate? In this talk, we trace the evolution of Language Modeling — from counting words with n-grams, through Word2Vec and the Transformer, to the reasoning paradigm sparked by OpenAI o1 and DeepSeek R1. This "Data Wall" is real — but it ends imitation, not progress: the industry is pivoting from System 1 (fast imitation) to System 2 (deliberate reasoning), opening test-time compute as a second scaling axis where models "think" before they answer. Following the AlphaGo Zero precedent, we'll see how reinforcement learning with verifiable rewards lets models keep improving without new human data, and how coding agents like Claude Code, Codex, and Cursor point that same propose–verify loop at your codebase. Along the way, Verifier's Law and the "jagged edge" of intelligence will tell us which tasks AI will master next — and which it won't. This session is for any developer who wants to understand where this is heading — and how our role evolves from writing syntax to architecting the specs, tests, and feedback loops that guide AI reasoning.