ByteDance is considering capital expenditures of up to $70 billion in 2026 on AI data centers and chips, Bloomberg reported on May 27 — up from roughly $25 billion in 2025. The company plans to self-fund most of the spend from the approximately $50 billion in profit it earned last year, sidestepping the external-financing pressure that has constrained other Chinese AI players. A separate deal to buy millions of Qualcomm chips is also in the works to back its agentic AI services.
The number puts ByteDance in roughly the same capex tier as Meta and Microsoft, and ahead of Anthropic and OpenAI on hardware spend alone. That is the operative comparison: a single Chinese platform company, blocked from buying NVIDIA's flagship chips at scale, is matching US frontier-lab infrastructure budgets through a mix of Huawei Ascend silicon, Qualcomm parts, and domestic alternatives.
It also signals that the export-controls thesis — that US chip restrictions would slow Chinese AI development — is partially failing on the spend side, even where it is succeeding on the silicon side. Chinese hyperscalers are not running out of money; they are running out of NVIDIA. As long as they can buy alternative chips and pour profit into building data centers, the gap between US and Chinese compute capacity narrows in dollars even when it widens in FLOPS per chip.
Takeaway for learners: AI infrastructure is now the bottleneck for the entire field. Whichever country, company, or lab can field the most efficient compute at scale will set the pace for model size, training cadence, and ultimately capability. If you are choosing a corner of AI to specialize in, the boring layers — power, cooling, networking, inference economics — are where careers will be made over the next five years.