Chips, Watts, and Diplomacy: AI’s new power struggle

⚡ TL;DR
AI’s voracious appetite for power is turning kilowatt-hours into geopolitical currency. As companies like OpenAI quietly diversify cloud suppliers and Washington eases chip restrictions on China, Wall Street is buying up electric utilities in anticipation of surging demand. The race for computing power—once about silicon alone—now hinges just as much on securing reliable energy sources.

The coming "cold chain" for AI infrastructure will reshape economies, diplomacy, and sustainability for decades to come.

1. Compute Diplomacy Is Back

1.1 OpenAI Goes Multi-Cloud

OpenAI quietly added Google Cloud as an infrastructure supplier in May, ending its de facto exclusivity with Microsoft Azure and signaling that “who owns the flops” is now a board-level risk factor. The updated partner list on OpenAI’s site shows it now relies on services from Google in addition to Microsoft, Oracle and CoreWeave. The Google deal – finalized after months of talks – underscores how massive computing demands to train and deploy AI models are reshaping competitive dynamics, and marks OpenAI’s latest move to diversify its compute sources beyond primary backer Microsoft. Earlier this year OpenAI partnered with SoftBank and Oracle on the $500 billion Stargate infrastructure program and signed multi-billion-dollar agreements with CoreWeave to hedge against chronic GPU shortages. By moving to a multi-cloud strategy (including a new deal with Google that had been blocked until its prior Microsoft lock-in expired in January), OpenAI is ensuring that “who owns the FLOPs” doesn’t become a single point of failure for its business.

1.2 Nvidia’s H20 Chip Re-Enters China

The race for advanced AI chips has made GPU export controls a major geopolitical bargaining chip.
On July 16, U.S. officials signaled that licenses will again allow Nvidia’s downgraded H20 GPUs into China – folding the decision into broader rare-earth mineral negotiations. The planned resumption is a reversal of export controls imposed in April to keep top-end AI chips out of Chinese hands. Commerce Secretary Howard Lutnick confirmed the H20 move was “put in the trade deal with the magnets,” referring to an agreement by President Trump to restart rare-earth exports from China. The U-turn illustrates how export controls have become a bargaining chip rather than a hard barrier. It could restore billions in Nvidia sales (the company had estimated a $15 billion revenue hit from the curbs) and has already set off a scramble among Chinese tech firms to order the chips. U.S. lawmakers voiced concern, noting the H20 still provides substantial AI capability (albeit legally pared-down for export) and works with Nvidia’s dominant software ecosystem. Nvidia’s CEO Jensen Huang, visiting Beijing, argued that completely cutting off China would only accelerate domestic alternatives (Huawei, etc.) and erode U.S. leadership. The significance of this policy shift ultimately depends on volume: “If China is able to get a million H20 chips, it could significantly narrow – if not overtake – the U.S. lead in AI,” one Washington analyst noted.

1.3 Federal Megabucks for Electro-Compute

At a Pittsburgh summit on July 15, President Trump touted a package of $90 billion in AI-and-energy projects across Pennsylvania, bundling data-center campuses with new power plants and grid upgrades. The inaugural Pennsylvania Energy & Innovation Summit saw executives from energy and tech giants (Blackstone, SoftBank, Google, ExxonMobil, etc.) announce tens of billions in planned investments. Taken together, Trump said, these moves show the state “reclaiming its industrial heritage” at the forefront of an AI-powered industrial revival. Among the headline commitments: Blackstone pledged $25 billion for new data centers in northeast Pennsylvania alongside natural gas generation to power them; Westinghouse promised to build 10 new nuclear reactors (a plan estimated at $75 billion economic impact) beginning in 2030; FirstEnergy announced $15 billion to upgrade the electric grid; and Google unveiled a $3 billion program to modernize hydroelectric plants in partnership with Brookfield, adding 670 MW of renewed capacity. In addition, state officials highlighted projects converting retired coal facilities into gas-fired “AI campuses” and efforts to fast-track permits for transmission lines. Political fanfare aside, some of these investments will take years to materialize – stretching into the 2030s – but they underscore a strategic alignment of compute and kilowatts. Notably, private capital is dovetailing with policy: Blackstone’s utility JV with PPL, for example, aims to supply power under long-term contracts to the very hyperscalers building AI data centers. Take-away: Access to both advanced chips and predictable electrons is emerging as a key lever of statecraft and corporate strategy.

2. Kilowatts—The Next Scarce Resource

2.1 Demand Curve

Soaring demand for AI compute is driving a wave of new data centers, expected to consume more power than some countries within a few years.
The International Energy Agency now projects global data-center electricity use to double to ~945 TWh by 2030, more than Japan’s current total power consumption. Generative AI and other machine-learning workloads are the dominant driver of this growth. In fact,AI-specific data centers could quadruple their energy draw by 2030 as companies race to train ever-larger models. Already, an estimated 1,240 U.S. data centers are built or under construction – nearly four times the number a decade ago – with hundreds aimed squarely at AI applications. Business Insider finds these facilities could soon consume more electricity than Poland (population 37 million) uses in a year. If current building trends continue, their power generation needs may triple over the next three years. The public-health externalities are likewise non-trivial: emissions from the plants powering these data centers could cause between $5.7 and $9.2 billion in annual health costs from pollution, by one estimate. In short, AI’s appetite for watts is growing at a breakneck pace – and scaling up electricity supply is now as critical as scaling model size. Some tech firms are responding by co-locating data farms directly next to power stations (even buying decommissioned plants), while others pursue renewables and efficiency gains to offset the surge. But as detailed below, even optimistic efficiency improvements might only bend the curve, not break it; total demand is still set to soar.

2.2 Utilities Become Tech Assets

Wall Street is racing to lock in generation and grid capacity for the AI era. Blackstone’s recent $25 billion bet on Pennsylvania (for data centers plus gas plants) is intended to catalyze another $60 billion in follow-on investment from partners and clients. In essence, data centers, power stations and pipelines are being treated as a single integrated asset class. Broader investor appetite has even turned stodgy utility-sector analysts into unlikely stars, as kilowatt-hour forecasts start moving stock prices. The trend hasn’t gone unnoticed by energy regulators: for example, BlackRock’s attempt to acquire a major Midwestern utility (Minnesota Power) to secure a data-center energy pipeline drew pushback from state officials concerned about rate hikes. Nonetheless, the buying spree continues. Private equity firm KKR recently took a stake in Nextera’s grid business, and Blackstone itself agreed in May to purchase TXU New Mexico’s electric utility serving 800,000 customer. The goal, as one report put it, is simple: profit from the rising demand for electricity from AI data center. This convergence is blurring the line between Big Tech and Big Power. It’s now common to see hyperscale cloud operators signing 20-year power purchase agreements or even funding new power plant construction. Investor presentations by utilities increasingly highlight data-center connections and AI load growth alongside traditional metrics. In the words of Google’s CFO: “We’ve got to get better on power – it’s a limiting factor for the AI economy until then”. Expect more unconventional tie-ups between tech giants and energy firms in the coming months, as each seeks to hedge the other’s critical inputs.

2.3 Cooling the Beast

Density keeps spiking: Nvidia’s latest reference design for AI compute racks runs at 132 kW per rack, far beyond the limits of traditional air cooling. At these power densities (on the order of 30 kW per server chassis), chilled liquid is no longer optional – it’s essential. NVIDIA and Schneider Electric, for instance, have co-developed liquid-cooled “AI-ready” data center blueprints that can handle these loads, promising ~20% cuts in cooling energy usage and 30% faster build-outs thanks to prefab designs. Immersion cooling – submerging servers in specialized fluids – pushes the envelope even further. Studies suggest this approach can yield up to 50% energy savings on cooling and allow 2–3× higher rack density compared to air-cooled setups, though maintenance remains a challenge (e.g. handling fluid evaporation and component servicing). Major operators like Digital Realty are already retrofitting existing facilities with liquid loop systems and exploring immersion for their highest-density clusters. These companies are also hunting for renewables and waste-heat reuse opportunities to meet corporate carbon targets. In Scandinavia and Canada, a few data centers now pipe excess heat to nearby towns or greenhouses – a symbiotic model that groups like the IEA say could meet 5–10% of heating needs in colder regions by 2030. While exotic cooling tech alone can’t solve AI’s energy hunger, it certainly helps: every watt not spent on chilling is one that can go into computation (or reduce emissions). And as high-performance AI silicon (like Nvidia’s upcoming GB200 “Blackwell” GPUs) pushes towards 140 kW+ per rack, the industry’s willingness to embrace novel cooling solutions is no longer in doubt. The real question is how quickly these innovations can be deployed at scale – and whether gains in efficiency can keep pace with the ballooning demand.

3. The “New Cold Chain”: From Silicon to Electrons

Why “cold chain”? In logistics, a cold chain is the end-to-end system that keeps perishables (like vaccines or food) refrigerated from factory to fridge. AI now demands an analogous continuous chain of “cold” infrastructure: spanning semiconductor fabs, export licenses, cloud data-center buildouts, dedicated power plants and cutting-edge cooling tech. A break in any link – be it a GPU quota or a grid outage – can spoil the whole batch, stalling product roadmaps or model deployments. The strategic advantage will go to those who can vertically integrate this AI cold chain and ensure each link is robust.

4. Strategic Playbook