The Mach33 AI Compute Model is a market-clearing model of the AI compute build-out from 2026 to 2040, expressed twice — as a 46-tab live-formula Excel workbook and as a Python engine — with every run comparing the two across 513 values, and all 400 assumptions living in a single write-point register that carries bear/base/bull bands, evidence tiers, and sources, including explicit "Mach33 judgment — no external source" labels. Rather than being learned tab by tab, the package is built to be handed to an LLM: point a session at the folder and it runs the model, builds counterfactuals, and answers in plain English with the basis stated, with an MCP connector to live Mach33 research coming next.

An interactive brief on the AI compute market from 2026–2040: use grows 180× while the price of a fixed unit of compute falls 96%, so annual infrastructure revenue rises to about $3.2T. It explores market pressure, orbital vs terrestrial cost routes, demand response, and why demand assumptions drive most of the outcome range.

We modeled the AI compute market from 2026 to 2040 as one connected system: what a delivered unit of compute costs by build route, how demand responds as prices fall, and where new capacity actually gets built. The model treats orbital datacenters as a build route inside the same framework as terrestrial, tests every assumption across its full range, and stress-tests the story under thousands of simulated futures. This Premium article is a presentation of the key takeaways.

AI companies have announced roughly 96 gigawatts of data center capacity. About 15 gigawatts run today. Mach33 tracks the gap facility by facility: ~240 sites and 65 supply deals, each project discounted by how likely it is to get built, against a price tape that turned in early 2026 and now sits ~40% above its October 2025 low. Six buyers have signed build programs that together exceed the entire installed base operating today.