The model layer isn't an industry — it's a qualifying race, and there's no third ending: either you escape to become a two-ends company, or you're commoditized. Why bare-API pricing hugs compute cost, how OpenAI and Anthropic escape via opposite routes, why API revenue ≠ bare API, and how to actually value a model company.

"Upstream scarcity" is three different things with three different shelf lives: engineering scarcity (chip design — impregnable until caught), cyclical scarcity (HBM — shelf life printed in expansion announcements), physical scarcity (process nodes, power — you can't design your own physics). Plus a fourth: contract-manufactured scarcity. Physics gets duration, cycles get trades, contracts get discounts.

Follow one dollar of AI spend from end user to power plant: 70% flows upstream. The revenue map and the profit map are two different maps — and the model layer is the hop that can't keep the money. One ruler (profit = scarcity × monopoly × pricing power) explains why.

The price of intelligence fell 280-fold in 18 months: from $20 to 7 cents per million tokens. Three analogies explain intelligence deflation — long-distance calls, recipes, electricity. Why do deflation and exploding revenue coexist? The money settles at both ends of the pipe: upstream earns scarcity, downstream earns lock-in, and selling bare intelligence in the middle gets cheaper forever.

Intelligence is deflating: revenue concentrates in the model layer, but profit settles at the two ends. Why the fastest-growing company in history works for its landlords — one ruler (profit = scarcity × monopoly × pricing power), three falsifiable bets, and a dashboard instead of date predictions. Prologue of a book in progress.