The one chart to take from the book: how deep each AI station's profit pool runs, its quality (real / circular / borrowed), and where it migrates. A pyramid with its peak upstream — and four arrows showing depth moving from silicon to watts, realness compounding from subscriptions into rent.

Over $800B of circular financing: chipmakers invest in customers, customers commit to purchases, purchases justify buildouts, buildouts flow back to chipmakers. The loop's real cost isn't fraud — it's that price discovery dies, and nobody knows how much demand is real. Lucent's ghost, and where the chain breaks.

The biggest capex in history is buying the shortest-lived infrastructure in history. ~$725B in 2026 — but a GPU's economic life is 3–5 years, not the century of rails or fiber. The depreciation–replacement scissors, who carries the debt, and why the burst reshapes who owns the assets.

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.

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.