s. So I Write / shelper

artificial intelligence

4 ENTRIES

AI 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.

artificial intelligenceagents11 min read

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.

artificial intelligencesmall models14 min read

Can AI Learn How to Search?

Part 5 of AI Beyond the Model. Part 4 introduces the specialist-building system. In the previous article, I imagined a general model that discovers repetitive tasks, creates specialized components, and coordinates them. That still leaves one of the deepest questions from my August discussion unanswered: who designed the space in which the system searches for solutions? Choosing among existing models is one kind of intelligence. Training a new model for a specified task is another.

artificial intelligencemeta-learning9 min read

From Next-Token Prediction to Persistent World Models: Notes on the Future of AI

Part 1 of AI Beyond the Model. On September 2 and 3, I had a conversation that began with a practical question: if so many language models seem to share the same architecture, why can one inference engine run so many of them? It ended somewhere less practical and more interesting: perhaps the next step in AI is not one model that directly answers every question about its input, but a system that builds and maintains a model of the world, then lets other components reason over it.

artificial intelligencedeep learning14 min read