Cognition Engineering: The Second Act of Generative Artificial Intelligence

Generative artificial intelligence (GenAI) is moving from “Knowledge Engineering” (2020–2023) to “Cognition Engineering” (since 2024). We examine what drives this shift and what it makes possible. First-act models relied on pre-training and fine-tuning to store and retrieve knowledge; they handled routine tasks but struggled with sustained reasoning. The second act marks a step change: models can reason for long periods—from minutes to hundreds of hours—and function as cognitive tools rather than databases. Three factors underlie this change: better, reasoning-dense data; test-time scaling methods; and advances in self-training. We identify five consequences: greater data efficiency; tighter coupling of pre-training and reinforcement learning; a focus on mining cognitive data; stronger performance on complex, real-world tasks; and interaction as a default mode of intelligence. Data engineering also shifts: “journey learning” replaces “shortcut learning,” capturing full decision paths with reflection, error correction, and backtracking. These changes reshape human-AI collaboration and open new ways to tackle difficult problems.