Top AI companies announced plans to dramatically ramp up their spending on AI infrastructure.
What’s new: Alphabet, Amazon, Meta, Microsoft, and others will boost their capital spending dramatically in 2025, pouring hundreds of billions of dollars into data centers where they process AI training, the companies said in their most recent quarterly reports. The surge suggests that more-efficient approaches to training models won’t dampen the need for greater and greater processing power.
How it works: Capital expenditures include long-term purchases like land, buildings, and computing hardware rather than recurring costs like salaries or electricity. The AI leaders signaled that most of this spending will support their AI efforts.
- Amazon has budgeted $105 billion to capital expenditures in 2025, 35 percent more than last year. CFO Brian Olsavsky attributed the increase to the company’s need to satisfy demand for AI services and tech infrastructure. CEO Andy Jassy emphasized that it reflects strong demand for AI and dismissed concerns that cheaper alternatives like DeepSeek would reduce overall spending. (Disclosure: Andrew Ng is a member of Amazon’s board of directors.)
- Alphabet allocated $75 billion to capital expenditures, up from $52.5 billion last year, to support growth in Google Services, Google Cloud, and Google DeepMind. The company indicated that most of this money would go to technical infrastructure including data centers and networking.
- Meta’s annual capital expenditures will amount to $65 billion, a huge jump from $39.2 billion last year. CEO Mark Zuckerberg argued that such spending on AI infrastructure and chips is needed to assure the company’s lead in AI and integrate the technology into its social platforms.
- Microsoft said it would put around $80 billion — a figure that analysts expect to rise to $94 billion — into capital expenditures in 2025, another big jump following an 83 percent rise from 2023 to 2024. Most of this investment will support cloud infrastructure, servers, CPUs, and GPUs to meet demand for AI.
- OpenAI, Oracle, SoftBank, and others announced Stargate, a project that intends immediately to put $100 billion — $500 billion over time — into data centers that would support development of artificial general intelligence. Elon Musk claimed in a tweet that the investors “don’t actually have the money,” raising questions about the announcement’s veracity.
Behind the news: DeepSeek initially surprised many members of the AI community by claiming to have trained a high-performance large language model at a fraction of the usual cost.
- Specifically, DeepSeek-R1 reportedly cost less than $6 million and 2,048 GPUs to train. (For comparison, Anthropic’s Claude 3.5 Sonnet cost “a few $10Ms to train,” according to CEO Dario Amodei, and GPT-4 cost about $100 million to train, according to CEO Sam Altman.) Follow-up reports shed light on DeepSeek’s actual infrastructure and noted that the $6 million figure represented only DeepSeek-R1’s final training run, a small fraction of the total development cost.
- Furthermore, while initial reports said DeepSeek piggy-backed on a 10,000-GPU supercomputer owned by its parent company High-Flyer, a hedge fund, research firm SemiAnalysis questioned whether DeepSeek relied on High-Flyer’s hardware. DeepSeek has spent around $1.6 billion on a cluster of 50,000 Nvidia GPUs, Tom’s Hardware reported.
- Initial excitement over the company’s low training costs gave way to concerns about data sovereignty, security, and the cost of running DeepSeek-R1, which generates a larger number of reasoning tokens than similar models.
Why it matters: DeepSeek-R1’s purported training cost fueled fears that demand for AI infrastructure would cool, but the top AI companies’ plans show that it’s not happening yet. A possible explanation lies in the Jevons Paradox, a 19th-century economic theory named after the English economist William Stanley Jevons. As a valuable product becomes more affordable, demand doesn’t fall, it rises. According to this theory, even if training costs tumble, the world will demand ever greater processing power for inference.
We’re thinking: DeepSeek’s low-cost technology momentarily rattled investors who had expected the next big gains would come from the U.S. rather than China. But DeepSeek’s efficiency follows a broader pattern we’ve seen for years: The AI community steadily wrings better performance from less processing power.