Is the AI hype
cooling down?
A composite reading of physical build-out and platform economics. Higher means the expansion is still intensifying; lower means the momentum is fading. Here's what goes into it.
What the answer is built from
Five independent signals, each weighted by how directly it reflects real, committed demand. Each is scored from 0 (contracting) to 100 (surging).
The trajectory
Where the money and the megawatts are actually going.
Combined infrastructure spend, per quarter
Billions of dollarsSpend by operator
$B / quarterNew data-center power queued
Gigawatts / yearTotal new power capacity queued
Gigawatts / yearWhere data-center load is landing
GW in queue, top regionsThe picks and shovels
The hyperscalers get the headlines, but the chipmakers, memory houses and foundries behind them are in a capex super-cycle of their own, together laying out well over $130B a year to build the fabs, HBM lines and tools the AI build-out physically runs on. Trace that capital back through their filings and you can see exactly how the build-out got here.
The multi-year climb in chip capex
Annual capex, $B · 2019–2025One line per filing trail (10-K / 20-F / 6-K). Memory (SK Hynix, Micron) shows the deep 2023 downturn cut and then the HBM-driven surge; TSMC keeps setting records; Intel is the one line bending down, pacing its foundry build to demand.
Annual capital expenditure compiled from company filings (10-K / 20-F / 6-K and quarterly reports); the latest point is FY2025 guidance, earlier points are reported actuals, converted to USD at period rates. Fiscal years and treatment differ (Micron’s ends in August; Intel’s figures are gross additions, before the partner offsets and CHIPS grants that lower net capex materially). “Samsung” is its semiconductor (DS) division. Two kinds of company carry small capex by design and should not be read as small spenders: equipment makers, whose revenue is the mirror of everyone else’s capex, and fabless designers (AMD, Broadcom, Nvidia), who buy foundry capacity instead of building it, so R&D is the figure that matters and their volume lands on TSMC’s capex line. Illustrative, not investment advice. Sources: company filings & investor releases (2019–2025).
The sovereign money
It isn't only US hyperscalers writing the checks anymore. Governments and sovereign wealth funds now treat AI compute as strategic infrastructure, and have pledged hundreds of billions to build it, buy into it, or avoid being left behind. Here's who's committing what, and how each region's playbook differs.
Headline announcements, not deployed cash, a mix of state programs, sovereign wealth funds and public-private ventures, so they aren't strictly comparable (European figures overlap; * South Korea's cluster runs to 2047). Illustrative, not investment advice. Sources: government announcements, Global SWF, IEA and press reporting (2024–2026).
Where it could actually jam
Demand is only half the story. Even with the money flowing, the build-out slams into hard limits, from the accelerator itself (a market chokepoint as much as a physical one), through raw silicon and the machines that pattern it, packaging, passives, optics and the network fabric, out to water, power, the skilled trades who build it, and the export politics that can throttle any link overnight. Here's how binding each chokepoint becomes year by year through 2030, and the companies with the most leverage over it.
Company names indicate supply-chain exposure to each bottleneck, illustrative, not investment advice. Trajectory synthesized from IEA, McKinsey, SemiAnalysis, TrendForce, Yole, SK Group and industry commentary (2024–2026).