Chamath’s normalized prediction is that in the near term (i.e., the few years after late 2022) the largest purchasers of machine‑learning hardware would be the U.S. government and ultra‑high‑frequency trading (HFT) firms.

Evidence from 2023–2025 instead shows that the dominant buyers of AI/ML hardware (especially GPUs/accelerators) are hyperscale cloud and Big Tech companies, not governments or HFTs:

• Multiple analyses of Nvidia’s customer base identify Amazon, Microsoft, Alphabet/Google, Meta, and Oracle as the biggest buyers of Nvidia’s data‑center AI GPUs. The U.S. government and HFT firms do not appear on these lists. (fool.com)
• Research and reporting on Nvidia’s H100/Hopper boom show that Microsoft and Meta were among the very largest buyers, each acquiring on the order of 150,000 H100 GPUs in 2023 alone, with Amazon and Google also buying tens of thousands. These four Big Tech companies together account for roughly 40% of Nvidia’s revenue, indicating that hyperscalers, not governments or HFTs, dominate demand. (observer.com)
• Capital‑expenditure disclosures and analyst estimates show tens of billions of dollars per year in AI infrastructure spending from Microsoft, Amazon, Google, and Meta—e.g., Microsoft and Amazon each spending on the order of $30–80B+ in annual capex with a large fraction dedicated to AI chips, servers, and data centers. (fool.com)

By contrast, while U.S. government AI spending is rising quickly, it is much smaller and not concentrated purely in hardware:

• A Brookings‑cited analysis found U.S. federal AI‑related contract values grew from about $355M to $4.6B between August 2022 and August 2023, driven mostly by the Department of Defense. Yet the same reporting notes that private‑sector AI investment vastly outstrips public funding, highlighting companies like Meta and Microsoft spending billions annually on AI infrastructure such as high‑performance GPUs. (time.com)
• Large cloud contracts for U.S. government workloads (e.g., AWS’s newly announced $50B AI/HPC build‑out for federal customers) show that even government demand is primarily intermediated through hyperscalers—AWS, Azure, etc.—who themselves are the direct massive purchasers of the hardware. (reuters.com)

For ultra‑high‑frequency/quant trading firms, there are examples of significant GPU clusters—e.g., China’s High‑Flyer hedge fund built a system with about 10,000 Nvidia A100 GPUs—but this is still tiny compared with the hundreds of thousands to millions of GPUs being accumulated by a single hyperscaler like Microsoft. (en.wikipedia.org) There is no credible market data suggesting that HFT firms, in aggregate, rival the big cloud/AI players in total machine‑learning hardware purchases.

Given that nearly three years have passed since the Dec 2022 podcast and the clear consensus of both industry reporting and financial data is that hyperscalers/Big Tech—not the U.S. government or HFTs—are the largest buyers of ML hardware, Chamath’s normalized prediction about who would remain the biggest buyers in the near term is wrong.