Govern the boundary
Model-based rules assume capability is scarce. As capable models become small and cheap, the durable place to attach policy is the layer that decides what an AI system may do.
Writing
Longer arguments about capable systems, the people who remain responsible for them, and the economics underneath both.
Research threads
Four questions connect the longer arguments. Each is incomplete by design.
- 02
Personal AI
What should remain near the person when AI becomes part of ordinary computing?
Begin with “The computer should be personal again” - 03
Distributed governance
Where does policy attach when computation crosses independently owned machines?
Begin with “Capacity is not consent” - 04
Ownership and incentives
Who decides, who carries the risk, and who remains responsible?
Begin with “What owners notice”
More essays
Arguments that needed more room than an idea.
Capacity is not consent
Distributed compute is usually framed as a problem of finding available machines. The harder question is who is allowed to decide what those machines do.
Probabilistic Intelligence, Deterministic Control
Generative models are for reasoning that is still open. Repeated decisions belong to a narrower model, and repeated actions belong to ordinary software.
What owners notice
Once analysis is cheap, an organization is still defined by who may commit. A note on ownership, beside the technical thread.
The computer should be personal again
Local inference matters less as a benchmark and more as a question of where a person's thinking is allowed to live.
Context is the product
What a model is allowed to see is one boundary. What it is allowed to decide, and what the system is allowed to do, are the other two.