Stanford MS&E435 Economics of the AI Supercycle | Spring 2026 | The GPU Economy

AI inference represents a fundamental shift from the near-zero incremental costs of traditional software to a compute-intensive paradigm where power and memory are the primary constraints. As models transition from simple pre-training to inference-time reasoning and autonomous agents, token consumption is growing exponentially. Specialized, deterministic architectures—such as the SRAM-based chips developed by Grok—are essential to maximizing token output within fixed power footprints, a necessity that drove NVIDIA’s recent acquisition of the company. While initial AI business models faced negative gross margins, the rapid improvement in model capabilities has triggered a surge in enterprise adoption and revenue. Ultimately, this era of bionic productivity is democratizing access to high-level intelligence, shifting the value of human labor away from rote cognitive tasks toward emotional intelligence, leadership, and the strategic orchestration of these powerful new tools.