Amazon, Google, Meta, and Microsoft have spent the past year locking in natural gas to power their AI data centers, but a new forecast from energy research firm Noreva suggests those bets could carry a hefty price tag. Noreva projects that natural gas prices could triple in some U.S. regions over the coming years as hyperscaler demand collides with slower supply growth and rising liquefied natural gas (LNG) exports.
“I think everyone in the energy markets has been lulled into a sense that gas prices can’t go up,” Peter Gardett, CEO of Noreva, told TechCrunch. “You just need simple arithmetic to get to a much tighter gas market than you were in just a few years ago.”
Hyperscalers’ big bets on gas
The shift marks a notable departure for companies that historically avoided heavy capital expenditures in physical infrastructure. In March, Meta announced plans for a 7.5-gigawatt natural gas power plant in Louisiana to supply its Hyperion data center. Days later, Microsoft and Google each said they would build gigawatt-scale gas plants in Texas. Amazon followed with a 7.6-gigawatt gas plant in Texas.
These projects are part of a broader trend where tech giants are becoming direct participants in energy markets rather than simply buying power from utilities. Gardett said at least one investor he spoke with was “surprised” by the level of natural gas price risk hyperscalers are willing to take on.
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“They’re doing things that are not normal for an off-taker to do,” he said.
The scale of these investments is substantial. For comparison, a typical large utility-scale gas plant is around 1 gigawatt. Meta’s and Amazon’s planned plants are more than seven times that size, reflecting the enormous power demands of AI data centers.
Why prices could spike
Noreva expects natural gas prices to rise above $10 per million BTUs at certain hubs, or delivery points for futures contracts. Today, prices range from about $2 to $4.50 per million BTUs, with the widely traded Henry Hub in Louisiana priced at just under $3.
Fuel represents roughly half the cost of electricity from a large power plant, so a doubling or tripling of natural gas prices could make “bring your own power” AI data centers significantly more expensive to operate. That could drive up token costs for AI services, or push hyperscalers to connect to the grid, which would raise electricity prices for other consumers.
Gardett attributes the current price stability to years of relatively flat demand and steady supply additions, which have offset declining production at older wells. However, he expects energy companies will add new supplies at a slower rate than before, and new wells are becoming more expensive to drill.
“That alone wouldn’t change the economics here. What’s changing the number is that finally we’re connecting the domestic gas market to the global gas market,” he said. “And the second is the AI demand pull.”
The connection between domestic and global markets is key. In West Texas, most wells have focused on oil, with natural gas as a byproduct that historically lacked pipeline capacity to reach major markets. Producers sold it at a discount to anyone who could use it. That is changing as new pipelines connect the region to export terminals.
“They’ve finally built some pipelines out there, and a lot of that is headed towards export markets,” Gardett said.
As West Texas becomes more integrated into national and international gas markets, demand there will influence prices elsewhere, and vice versa. Even modest price swings near hyperscalers’ data centers could be magnified in other regions.
“You will get places where you get a lot of gas next to someplace where there’s none, and so you’ll get those big differentials,” Gardett said.
What higher gas prices mean for AI and consumers
Under Noreva’s scenario, even if hyperscalers can absorb higher fuel costs, their natural gas consumption could add a new dimension to the existing backlash against data centers. A recent survey found that 80% of consumers are worried about data centers’ impact on their utility bills, mostly related to electricity. That concern could extend to natural gas bills as well.
The prospect of higher operating costs also raises questions about the economics of AI services. If energy costs rise significantly, companies may need to pass those costs to consumers through higher subscription fees or usage-based pricing. Alternatively, they could optimize workloads to run during off-peak hours or shift to regions with cheaper power.
For now, natural gas futures contracts are not pricing in major changes, suggesting the market sees stability ahead. Gardett acknowledged that the hyperscalers’ bet is “not an unreasonable bet,” but he remains skeptical.
“On future Alphabet earning calls, you will hear them talk about the correlation between natural gas pricing and Google results, which is strange, but that’s where we are,” he said.
The coming years will test whether hyperscalers’ pivot to fossil fuels proves to be a prudent hedge or a costly miscalculation. With AI demand showing no signs of slowing, the intersection of tech and energy markets is likely to remain a focal point for investors and policymakers alike.
This article is for informational purposes only and does not constitute financial advice. Energy markets are volatile and forecasts are subject to change. Readers should conduct their own research before making investment decisions.
