Microsoft plans to spend roughly $190 billion this year and still expects to run short on capacity. And Microsoft is not alone: across the four biggest hyperscalers, combined 2026 capital spending is on track to approach $700 billion, nearly double what they spent in 2025.The fiscal third-quarter 2026 numbers fill in the picture: Microsoft reported $31.9 billion in capital expenditures for the quarter and guided to more than $40 billion in the next. About two-thirds of that quarterly spend went to short-lived assets, primarily GPUs and CPUs. Even after all of it, the company expects to stay capacity constrained at least through the year.No classic software company looks like that. A classic software company writes code once and sells it many times, and its biggest worry is talent or distribution, not whether it can physically build enough capacity to meet demand. The cloud was supposed to make infrastructure someone else’s problem.AI breaks that abstraction.The most important thing to understand about the current AI buildout is that tokens are not magic. They are manufactured. Every answer from a model is the output of a physical production system that consumes chips, high-bandwidth memory, advanced packaging, substrates, optics, power, cooling, land, data-center construction, networking, and operations talent. When it works, a user sees a paragraph, a line of code, a summarized contract, or an agent completing a task. Behind the screen, a factory is turning electricity and silicon into intelligence.This is why AI is turning big tech into an industrial business.Calling AI industrial is not new. Mary Meeker built a 340-slide deck around it. Jensen Huang says a version of it on every NVIDIA earnings call. What is new is what Microsoft’s number does to the contract sitting on your desk. Six months ago, an AI vendor agreement was structured like a software agreement. Now that the hyperscalers are spending at this scale and still rationing capacity, your AI vendor agreement is a supply contract in everything but name. It has allocation. It needs capacity terms. It needs a fallback. None of that was a line item a year ago.If that sounds like a problem for the CFO and no one else, consider that my own token spend ran close to 500 million tokens last week. Multiply that across a team and the capacity question stops being abstract.The visible product is still software. ChatGPT, Copilot, Gemini, Claude, Meta AI, and Bedrock all look like applications or APIs, but the constraint underneath them is physical. The strategic question is no longer only, “Who has the best model?” It is, “Who can operate the factory that produces intelligence at scale?”This briefing covers:The bill of materials behind every token. Chips, memory, packaging, power, cooling, construction, and how to tell which one stops your vendor cold before it stops you.Why your vendor contract is now a supply contract. What to actually ask for when the cloud you buy from is competing with you for the same chips: allocation, fallback, reserved capacity, written down.Your CFO is about to inherit a capital cycle. Why utilization is suddenly the metric that matters, and why a 40% throughput gain beats a new data center.Seats are the wrong unit, and almost every plan still uses them. How to forecast in tokens instead, and why an agent that runs for hours belongs in a different budget line than a chatbot that answers questions.The one question that decides whether AI helps your margins or wrecks them. It is not whether tokens are expensive. It is something more specific, and most companies have never asked it.Three instruments to run before the budget meeting. A vendor audit that maps the supply chain under your contract and hands you the language to demand, a forecasting model that sizes demand in tokens instead of seats, and a routing diagnostic that finds the workflows burning premium inference on cheap work.Microsoft has already put $190 billion behind this view of the world. Most companies have not put it into their AI plans at all. What follows is what changes once you do.