AI's Biggest Spenders Are Accelerating — Capex Deep Dive

The capex numbers coming out of the big cloud providers this earnings cycle are getting hard to read without blinking. We're not in "cautious experimentation" territory anymore — the four hyperscalers are collectively on track to push infrastructure spending well past $300 billion this year, and every single one of them told investors the pedal is going down, not up.

What the acceleration actually looks like

• Microsoft: Azure AI demand is the whole story, and the spend follows it — data center leases and GPU fleets keep expanding quarter over quarter.
• Google: Fully committed to TPU and data center buildout, with capacity constraints reportedly the bottleneck on AI revenue growth.
• Amazon: AWS capex is climbing sharply after a relatively conservative 2023, with most of it feeding Bedrock and its own foundational model work.
• Meta: The sleeper here — it keeps raising its floor for AI infrastructure while simultaneously running the largest open-weights push in the field.

That last one is worth sitting with, because Meta is spending like a frontier lab while giving models away. It's a bet that open ecosystem adoption ends up being the moat, not the weights themselves. In the real world, this is the most interesting AI workflow question of the next two years: does the value sit in the model, or in the infrastructure layer beneath it?

Why they're not slowing down

The obvious read is that everyone's terrified of being the one who blinked. But there's a more practical reason: training runs are getting longer and inference scales linearly with adoption. A frontier-class training run doesn't get cheaper because you want it to — you either pay for the compute or you're out of the race. Once you've committed to that, the marginal cost of training the next generation on top of existing infrastructure is comparatively small, so the rational move is to push utilization as hard as you can.

That doesn't mean there's no risk. If the revenue side doesn't catch up to the spend, we're looking at a classic overcapacity hangover. But the people signing these checks have more data than we do, and their boards keep approving bigger numbers.

From a practical standpoint, this matters for anyone building on top of these platforms: inference prices will likely keep dropping as the supply glut materializes, which makes the next 12–18 months a genuinely good window for shipping AI-heavy products. The infrastructure is being built ahead of demand, and the early beneficiaries tend to be the ones building applications, not the ones renting out machines.

Whether this ends in a soft landing or a