Stanford MS&E435 Economics of the AI Supercycle | Spring 2026 | Enterprise Internal Knowledge

Specializing AI models for enterprise needs requires moving beyond general-purpose architectures toward systems optimized for proprietary data and specific business outcomes. While pre-training establishes foundational intelligence, post-training—specifically through reinforcement learning with verifiable rewards—enables models to master domain-specific tasks like menu extraction or automated bug detection. Code and math serve as ideal training grounds because they provide deterministic feedback loops, allowing models to iterate and improve performance efficiently. As the industry shifts from scaling raw compute to maximizing data efficiency, the next frontier involves continual learning, where models adapt to real-world interactions with sparse feedback. Companies that define their own evaluation metrics and integrate these specialized agents into their workflows gain a significant competitive advantage over those relying exclusively on off-the-shelf frontier models.