Within a week, two of the three largest cloud computing companies in the world signed 20-year contracts for nuclear electricity. Amazon has taken 690 megawatts from Constellation’s Calvert Cliffs plant in Maryland, including 190 megawatts of additional generating capacity expected between 2030 and 2032. Google has signed an agreement that will bring 890 megawatts of additional nuclear capacity into the United States. Constellation is investing more than $4.3bn to upgrade 11 reactors across Illinois, Pennsylvania, and New Jersey.

Neither agreement, however, involves building a new nuclear reactor. Rather, they are paying to generate more output from reactors that already exist – some of which are even older than the companies themselves.

A few years ago, such agreements would have seemed inconceivable. Nuclear power had fallen out of favour following accidents at Three Mile Island in 1979, Chornobyl in 1986 and Fukushima in 2011. Germany phased out nuclear power, while Japan shut down its reactors after Fukushima, although it has since begun restarting them. In many democratic countries, advocating for nuclear energy became politically difficult.

Today, an undamaged reactor at Three Mile Island is being brought back into service, with Microsoft contracted to buy its output for 20 years. The US Department of Energy is providing up to $4.4bn in loans to support the restart of three shuttered plants, with the aim of restoring about 2.25 gigawatts. Palisades in Michigan, which was closed in 2022, is contracted to be generating again by March 2027. If successful, it would become the first US commercial nuclear plant to restart after entering decommissioning. What has changed?

The quiet reversal In the last decade, the industry’s answer to AI’s energy problem was efficiency – things such as better chips, smaller models and smarter cooling seemed appealing and feasible. Industry conferences featured discussions about performance per watt, with modern artificial intelligence (AI) chips performing more computation per unit of electricity than their predecessors.

Yet those efficiency gains did not reduce overall electricity demand. Instead, each gain was quickly put towards something larger. Even when DeepSeek demonstrated that a competitive model could be trained at a fraction of the computing cost many had expected, the result was not a reduction in industry spending. Instead, laboratories continued planning more training runs, models that reason for longer, agents that work continuously in the background, and video generation.

This would not be the first time something like this has happened in history. In 1865, William Jevons observed that more efficient steam engines made Britain burn more coal, not less, because cheaper power created uses that had not existed before. The International Energy Agency estimates that global data centre electricity demand will more than double to around 945 terawatt-hours by 2030, even assuming continued improvements in efficiency.

Hence, the industry has changed strategy – unable to shrink demand, companies are now buying up supply. The arithmetic of waiting Money, however, cannot solve every problem quickly.

A new large reactor takes a decade or more from decision to electricity. Tech companies are investing in small modular reactors to power their data centres, although many of these projects are not expected to deliver electricity until the early 2030s. So far, very few of these have been built anywhere in the world. Thus, while reactor restarts and upgrades are the faster option, the “fast option” still means 2027, 2028, 2029, or beyond.

AI capital expenditure, however, does not operate on that same calendar. The chips being installed this year could be overtaken by newer technology long before the 20-year electricity contracts powering them expire. This discrepancy underpins all of these announcements, yet the industry has not addressed the implications if demand weakens while contractual obligations remain. Who pays in the meantime This is where the story stops being about technology.

Most of these deals sit inside PJM, the grid operator serving roughly 67 million people across 13 American states and Washington, DC. PJM uses capacity auctions to secure sufficient electricity-generating resources for future demand, with the associated costs ultimately passed on to consumers through their electricity bills. Capacity prices have reached the regulatory price cap in three consecutive auctions. According to the grid operator’s independent market monitor, data centre demand contributed approximately $29.4bn in capacity costs across the four most recent auctions. In the latest auction alone, around $6.3bn of these costs were attributed to data centre demand, including forecasts for facilities that have yet to be built.

In effect, households are already paying higher electricity capacity charges, partly because of projected demand from facilities that may never become operational. The Natural Resources Defense Council estimates that, by 2028, the typical household within PJM could face an additional $70 per month in electricity costs if current trends continue. These rising costs are also having political consequences. Recent US polling shows growing public dissatisfaction with data centres, with electricity bills among the main concerns. In response, Virginia has established a separate electricity rate class for data centres, Ohio has introduced minimum billing requirements, and Illinois is debating legislation.

The aim of the Google agreement is to tackle this objection. Its underlying principle is that major new electricity consumers should bring additional generation capacity rather than compete with existing users for an already constrained supply. This approach represents a constructive step towards mitigating the pressure that data centres place on electricity systems.

However, the arrangement remains voluntary. In October 2026, US federal regulators rejected most of PJM’s proposal to require large new electricity consumers to provide their own generation capacity. As a consequence, the industry is voluntarily offering a commitment that regulators have so far declined to impose through PJM’s proposed rules. This poses the question: how long will such voluntary commitments remain viable if the costs of fulfilling them begin to rise?

What the Gulf should take from this For Gulf countries preparing their own AI infrastructure expansion, the US experience offers a warning about the costs of getting these arrangements wrong.

For countries with abundant energy resources, the expansion of AI infrastructure presents a significant opportunity.

Qatar’s sovereign fund has committed to a $20bn joint venture with Brookfield for AI infrastructure, beginning with a first phase of roughly 300 megawatts and potentially expanding to near 1.5 gigawatts. Abu Dhabi is pursuing plans for up to five gigawatts of AI data centre capacity. Saudi Arabia is targeting almost two gigawatts through HUMAIN. The region’s competitive advantage is substantial. As demand for AI computing grows, access to electricity is becoming an increasingly important factor in determining where computing infrastructure is built. The Gulf is well positioned to capitalise on this demand.

However, the scale of the proposed developments warrants scrutiny. If the proposed 1.5-gigawatt build-out is completed in Qatar, it would represent electricity demand equivalent to approximately 12 percent of the country’s installed generating capacity. Electricity allocated to AI infrastructure cannot simultaneously serve other purposes, meaning the opportunity costs of these projects deserve serious consideration.

In the United States, the debate over AI infrastructure is becoming increasingly centred on who ultimately bears the cost of its electricity demands, with many of the major commitments already in place. The Gulf is in a rare position of being able to settle that question first: what the industrial tariff is, who funds the new generation, what happens to the contracts when the hardware ages, and what long-term economic benefits the country would see beyond energy revenues and a limited number of jobs.

While hosting AI infrastructure can generate considerable economic value, the greater strategic opportunity lies in determining the conditions under which that infrastructure operates. For Gulf countries, the opportunity to establish these terms exists now, before large-scale commitments constrain their options.