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Alphabet CEO Sundar Pichai
(Alain Jocard/Getty Images)

Unpacking Alphabet’s $75 billion capex plans

A spending spree on data centers, custom chips, and undersea cables.

Jon Keegan

Investors were disappointed with Alphabet’s Q4 earnings yesterday, evidenced by the stock’s subsequent tumble.

While Alphabet delivered fairly strong earnings, the issue was a slowdown in revenue growth. Sales grew 12% from Q4 2023, which is far below last quarter’s 15% growth rate.

But there were some interesting details from the earnings call. Despite expectations that 2025’s capital expenditures would be a slight bump up from 2024’s $52.5 billion, Alphabet CEO Sundar Pichai told investors that the company has since upped that number to $75 billion. This comes just weeks after Meta CEO Mark Zuckerberg announced that his company would spend up to $65 billion on AI-related capex. Big Tech companies are all following suit with jumbo-sized capex plans for 2025.

What is Alphabet gong to be spending that big pile of money on? According to CFO Anat Ashkenazi, “The majority of that is going to go towards our technical infrastructure, which includes servers and data centers.”

Ashkenazi said that current computing demand is exceeding supply and the company is racing to increase capacity. Pichai said that in 2024, the company broke ground on new data center campuses in South Carolina, Indiana, and Missouri. The company also announced plans for seven new subsea cables to strengthen global infrastructure.

Much like the rest of the industry, Alphabet is also investing in its own Trillium TPU AI chips, lest they become too dependent on market leader Nvidia. But they are still making sure they can sell cloud computing access to Nvidia’s popular products.

“We also continue our strong relationship with Nvidia . We recently delivered their H200-based platforms to customers. And just last week, we were the first to announce a customer running on the highly anticipated Blackwell platform.”

Pichai said that Google Cloud customers are using “eight times the compute capacity for training and inferencing than they were 18 months ago.”

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