- AI data centers are overwhelming national networks and driving up energy costs
- Enterprises turn to nuclear options to support power-intensive AI workloads
- OpenAI urges government to massively expand national energy production capacity
Microsoft CEO Satya Nadella drew attention to a less-discussed obstacle in the AI race: the shortage not of processors but of power.
Speaking on a podcast alongside OpenAI CEO Sam Altman, Nadella said Microsoft has “a bunch of chips in inventory that I can’t plug in.”
“The biggest problem we have right now is not an overabundance of compute, but it’s power – it’s sort of the ability to do the builds fast enough and close to power,” Nadella added, “in fact, that’s my problem today. It’s not a chip supply problem; it’s actually the fact that I don’t have any hot shells to plug in.”
Energy limitations are reshaping the AI landscape
Nadella explained that although the supply of GPUs is currently sufficient, the lack of suitable facilities to power them has become a critical problem.
In this context, he described “warm shells” as empty data center buildings, ready to accommodate hardware but dependent on access to adequate energy infrastructure.
This shows that the explosive growth of AI tools has exposed vulnerabilities and that demand for computing capacity has outpaced the ability to build and power new tools. data center sites.
Energy planning in the technology industry is a major problem, and even large companies like Microsoft, which have vast resources, still struggle to keep up.
To address this issue, some companies, including major cloud providers, are now seeking nuclear-based energy solutions to support their rapid expansion.
Nadella’s comments reflect a broader concern that AI infrastructure is pushing the nation’s power grids to their limits.
As data center construction accelerates in the United States, power-intensive AI workloads have already begun to influence consumer electricity prices.
OpenAI has even urged the US government to commit to building 100 gigawatts of new power generation capacity per year.
He says energy security is becoming as important as access to semiconductors in competition with China.
Analysts have pointed out that Beijing’s lead in developing hydropower and nuclear power could give it an advantage in maintaining large-scale AI infrastructure.
Altman also hinted at a potential move toward higher-performance consumer devices that could one day run advanced models like GPT-5 or GPT-6 locally.
If processor and chip innovation enables such low-power systems, much of the predicted demand for cloud-based AI processing could disappear.
This possibility presents a long-term risk for companies that invest heavily in massive data center networks.
Some experts believe such a shift could even accelerate the eventual bursting of what they describe as an AI-driven economic bubble, which could threaten billions of dollars in market value if expectations collapse.
Via TomsHardware
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