AI: Economy & Learning

Work should buy a life. Learning should enlarge it. AI should return time to and compound capability for human people. Agentic administrators can take care of paperwork so we can get work done. Inferential geniuses can provide the contextual sidecar to solving our biggest problems.
We can't push the river, but we can channel it.

That makes AI a constitutional problem as well as an economic one: who owns the infrastructure, who sets the terms, who watches the rest of us, and who claims the gains?

The Iron Law of Wages treated subsistence as fate. Taylorism showed how organization could multiply production, and how quickly a person could become just a cog in the machine. If you'd like a Senator who knows how they got us in the past—and has personally burned billions of tokens trying to understand how these systems work so they can't get us in the future, read on.

Who owns the gains?

Four tests for whether AI concentrates capital and control—or returns power to people.

Work Should Buy a Life

AI can multiply returns to capital while weakening labor’s claim on the gains. Government can work like a clutch—protecting people and bringing productivity gains into sync with pay, bargaining power, and time as we shift into a different future.

Learning Should Enlarge It

Education cannot become a sorting system between people who direct machines and people directed by them. Give teachers and students tools that expand judgment, authorship, and room to experiment.

Compute Is Capital

Chips, energy, data, and models are productive infrastructure. Public-interest access—including American open models—can widen who builds, scrutinizes, and benefits.

Shared Gains, Not Smaller People

Automation should return time, not turn work and learning into surveillance. The ordinary-life test is whether people gain capability and choice—or concentrated institutions gain new leverage over them.