The productivity gains are flowing to capital holders instead of the workers generating them. The old framework said credentials lead to knowledge, knowledge leads to value. But value is concentrating in weird corners now; places where someone has to be sued, where platforms control distribution, where tacit knowledge can’t be extracted from text.

Inference workloads now account for 80% of AI compute spending. The hierarchy in tokens is no longer about information density—it’s what happens when the token leads somewhere wrong.

When building becomes trivially easy, every software market becomes a red ocean. A browser from scratch in one week. 3 million lines of code. This isn’t an anomaly; it’s the new baseline.

Own the chips or the customers. Everything else is a footnote.

The build manifest: 90 days from foundation to production-ready autonomy. What to ship in what sequence.

Alignment as a runtime surface, policy enforcement without retraining. Team practices that ship.

Model outputs are hypotheses that need verification pipelines to catch errors before users do.

Memory and hands for the model: retrieval that doesn’t hallucinate. Tools that don’t break production.

The physics of production AI: latency engineering that keeps humans in the loop. Token economics that don’t bankrupt you.

Before you build: the mental models for human-AI collaboration. Why L1 copilots need different infrastructure than L4 autonomous agents.