Amazon and Google are winning the AI capex race — but what’s the prize?
TL;DR
Amazon and Google lead in AI capital expenditure with projections over $200B and $175B respectively for 2026, but investors are skeptical despite tech companies' belief that controlling compute is key to AI dominance.
Key Takeaways
- •Amazon projects $200B in 2026 capex (up from $131.8B in 2025), leading the AI spending race, though some expenses go to robotics and satellites.
- •Google follows with $175-185B in 2026 capex projections, significantly higher than competitors like Meta ($115-135B) and Oracle ($50B).
- •Tech companies view high-end compute as a future scarce resource, driving massive investments to control AI infrastructure.
- •Investors are unconvinced, causing stock prices to drop across companies, even those with clear AI monetization strategies like Microsoft and Amazon.
- •Despite investor pressure, tech firms are unlikely to scale back AI ambitions, believing AI's transformative potential justifies the costs.
Sometimes, it can seem like the AI industry is racing to see who can spend the most money on data centers. Whoever builds the most data centers will have the most compute, the thinking goes, and thus be able to build the best AI products, which will guarantee victory in the years to come. There are limits to this way of thinking — traditionally, businesses eventually succeed by making more money and spending less — but it’s proven remarkably persuasive for large tech companies.
If that is the game, Amazon does seem to be winning.
The company announced in its earnings on Thursday that it projects $200 billion in capital expenditures throughout 2026, across “AI, chips, robotics, and low earth orbit satellites.” That’s up from the $131.8 billion in capex in 2025. It’s tempting to attribute the whole capex budget to AI. But unlike most of its competitors, Amazon has a significant physical plant, some of which is being converted for use by expensive robots, so the non-AI expenses aren’t so easy to wave away.
Google is close behind. In its earnings on Wednesday, the company projected between $175 billion and $185 billion in capital expenditures for 2026, up from $91.4 billion the previous year. It’s significantly more than the company spent on fixed assets last year, and significantly more than most of its competitors are spending.
Meta, which reported last week, projected $115 billion to $135 billion in capex spending for 2026, while Oracle (once the poster child for AI infrastructure) projects a measly $50 billion. Microsoft doesn’t have an official projection for 2026 yet, but the most recent quarterly figure was $37.5 billion, which pencils out to roughly $150 billion, assuming it keeps up. It’s a notable increase, and one that has led to investor pressure on CEO Satya Nadella — but it still puts the company in third place.
From within the tech world, the logic here is simple. The revolutionary potential of AI is going to turn high-end compute into the scarce resource of the future, and only companies that control their own supply will survive. But while Google, Amazon, Microsoft, Meta, Oracle, and others are frantically prepping for the compute desert of the future, their investors aren’t convinced. Each company saw its stock price plummet as investors balked at the hundreds of billions of dollars being committed, and companies with higher spends tended to drop more.
Crucially, this isn’t just a problem for companies like Meta that haven’t figured out their AI product strategy yet. It’s everyone — even companies like Microsoft and Amazon with a robust cloud business and a straightforward take on how to make money in the AI era. The numbers are simply too high for investor comfort.
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Investor sentiment isn’t everything — and in this case, it may not do much to change the industry’s mind. If you believe AI is about to change everything (and the argument is pretty compelling at this point), you’d be a fool to change course just because Wall Street got jumpy. But going forward, Big Tech companies will be under a lot of pressure to downplay how expensive their AI ambitions really are.