agents
2 ENTRIESAn AI That Learns to Build Its Own Specialists
Part 4 of AI Beyond the Model. I have been thinking about a different way to organize AI. Instead of asking one ever-larger model to perform every task directly, what if the large model learned to create, teach, select, and coordinate specialists? This idea emerged in several conversations. In July 2025, I considered a practical feedback loop in which a cloud model teaches a smaller model running on a device. In August 2026, I broadened that into heterogeneous AI infrastructure: large models working with detectors, trackers, 3D algorithms, and other specialized components.
The Representation Layer Physical AI Needs
Part 2 of AI Beyond the Model. Start with Part 1. In the first article, I argued that a vision system should maintain a persistent belief about the world rather than answer every question independently from the latest frame. That leaves a harder engineering question: what information should this system expose, and how should other models, applications, and agents use it? An image is rich but expensive to reinterpret. A single classification is cheap but often discards the context needed for a different task.