agents
3 ENTRIESAI Will Evolve Like a Society, Not Just a Model
A thought has been on my mind: what if we are still looking at AI too much like an individual brain? We often discuss progress in terms of the next model: its benchmark scores, reasoning ability, context window, or parameter count. These things matter. But I suspect that language models and today’s agents are only the beginning. The more consequential evolution may be in how AI systems organize work, share knowledge, use tools, and build on one another’s results.
An 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.