Big Tech will spend nearly $700 billion on AI this year. No one knows where the buildout ends

Welcome to Eye on AI, with AI reporter Sharon Goldman. In this edition: SoftBank plans to list a new AI and robotics company in the US…AI model’s goblin habit, explained…Putting Google’s AI to the test as a trip planner.

If Big Tech’s AI spending spree were like climbing Mount Everest, they would still be ascending toward the summit, getting dizzy from the altitude.

In quarterly earnings, estimates from Alphabet, Amazon, Meta and Microsoft put combined capital expenditures at more than $130 billion for the quarter, driven by buildouts of data centers and other infrastructure. That spending could surpass $700 billion this year, up sharply from about $410 billion last year. While only Alphabet has explicitly pointed to further increases beyond this year, all four companies signaled sustained high levels of investment as demand for AI infrastructure continues to grow.

The market reaction has been mixed. Shares of Meta fell sharply after its earnings report as investors focused on the scale of its AI spending plans, and Microsoft also slipped. By contrast, Alphabet and Amazon rose on strong cloud growth—highlighting a growing divide on Wall Street over whether this buildout is justified or getting ahead of itself.

There’s no doubt that AI companies—from the hyperscalers to startups like OpenAI and Anthropic—are hungry, if not starving, for more computing power. The scale of today’s AI systems, which require far more hardware, energy, and coordination than earlier generations of software, means that more is almost never enough. The result is a surge in spending unlike anything the industry has seen before: : McKinsey research from last year found that by 2030, AI capex is projected to require $6.7 trillion worldwide to keep pace with the demand for compute power. 

Spending big on physical infrastructure

It’s important to understand how much of that spending is going directly into the physical infrastructure that supports AI—both training frontier models and running them. But it can be hard to wrap your mind around the scale of this buildout. 

It starts with chips—the specialized silicon semiconductors designed to perform the calculations used in AI. A single GPU from Nvidia, for example,........

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