Ben Bajarin
CEO & Principal Analyst of Creative Strategies, Semiconductors-to-Software Research
NVIDIA's liquid-cooling work with Vertiv can deliver more compute from a fixed power budget. Liquid cooling yields roughly 22% more compute and more tokens from the same power on average, a benefit modeled in an April report. NVIDIA's scale, balance sheet and command of its supply chain make it the economic winner of the AI compute cycle, with Broadcom a close-ish second. x.com x.comÂ
Nvidia's economics win in a power-constrained world, provided it delivers the best tokens per watt per dollar. In that case, every buyer working within a power budget, as all are, is better off with Nvidia; Jensen Huang gave the same answer at GTC. Per Nvidia management at a Morgan Stanley roadshow, 55 of the 80 cloud partners it lists are outside the US, giving it flexibility to go where the power is, increasingly outside the US and out of reach of traditional hyperscalers. x.comÂ
AI data-center spending flows beyond GPUs and operators into networking, optics, power and cooling. Nvidia, AMD, Broadcom and Marvell supply chips; Arista, Nokia and Credo connectivity; Applied Optoelectronics and Lumentum optics; Vistra, Constellation and Bloom power; Vertiv and Modine cooling. Meta's $140B spending this year illustrates the scale, per a commenter with whom the point is agreed. x.com x.comÂ
AI infrastructure bottlenecks could give eight stocks as much as 18x upside. Nvidia supplies frontier-model GPUs; Broadcom and Marvell supply custom silicon and networking; TSMC makes advanced chips; Micron supplies sold-out HBM; Vertiv provides power and cooling; Palantir monetizes enterprise AI; Alphabet spans TPUs, research, cloud and apps. The list tracks where AI infrastructure spending is flowing. x.com x.comÂ
Nvidia's AI profits are real, but its compute demand depends heavily on fragile venture funding. Per Chamath, 1% of firms account for 90% of AI spending, and 72 cents of each dollar comes from unprofitable VC-backed firms. Venture money passes through startups to Nvidia and cloud suppliers; if the next raises fail, concentrated orders could contract together. x.com x.com x.comÂ
AI software and hardware advances make GPUs and power more valuable, creating huge long-term tailwinds for compute providers and AI model and app businesses. Each new software generation increases a GPU's value; each hardware generation increases the value of a gigawatt of power. That favors Nvidia, Broadcom, Intel and AppLovin. x.comÂ
Nvidia's system components can reduce the idle time that erodes returns on expensive AI chips. CPUs, accelerator interconnects and server networking help operators such as Nebius and IREN keep machines doing useful work; solving the next bottleneck could win Nvidia more of the data-center equipment budget even when a rival chip looks appealing alone. Buyers must compare the cost of completed work across the whole system. x.comÂ
Nvidia's neocloud orders increasingly require its own credit support. Per The Information, it is in talks to guarantee billions of Nscale debt for a 460MW data center; Nscale needs an IPO to fund $24B in Nvidia systems committed for 2026-27. Separately, a quoted article says US officials identified Alice Huang, scrutinized for possibly routing Nvidia chips to China, as a Chinese intelligence asset with direct access to Jensen Huang. This is just the tip of a very big iceberg. x.com x.com x.comÂ
Vertiv's power and cooling gear is benefiting from the AI data-center build-out. Q3 net sales rose 61%, operating profit 90% and net income 92%. There is no more direct picks-and-shovels bet on AI infrastructure than Vertiv. Grade: A. x.comÂ
A rising value of compute per watt should benefit AI cloud, chip, connectivity and power suppliers. Elon Musk says that value will rise over time. Nebius has Microsoft and Meta deals; Marvell benefits from custom silicon demand; Credo connects GPUs; Vistra has long-term nuclear supply deals with Amazon and Meta; Nokia's Nvidia partnership could help it capture data-center networking growth. x.com x.comÂ
Low Anthropic spending relative to customer expenses points to an enterprise AI adoption gap that leaves Nvidia reliant on intermediaries able to build their own chips. Anthropic's S-1 says over half the Fortune 500 were $100k+ annual customers as of September, yet spending among its publicly traded customers averaged under 0.1% of expenses in Q3 2026. That measures one provider and cannot show whether AI deployments succeed; Nvidia needs more enterprises buying its compute directly. x.comÂ
AI data-center spending should reach chips first, then racks, power and cooling, benefiting Nvidia, AMD, Vertiv, Bloom Energy, Credo, Marvell, Lumentum and Micron. BofA forecasts the global data-center market at about $1T in 2026, $2.1T in 2028 and nearly $3T by 2030, a 37% annual rise; roughly 80% of a new AI site's $57M per MW cost goes to servers, networking and storage. x.comÂ
