Opinion |
Technology
AI’s real bottleneck is electricity. Who will pay for it?
As Big Tech turns to nuclear power to fuel its AI ambitions, the cost of keeping data centers running raises a question that extends far beyond Silicon Valley.
By
AlHasan AlSammarraie Founding & Managing Partner - Applab. Published 30 Oct 2026
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.
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?
The views expressed in this article are the author’s own and do not necessarily reflect Al Jazeera’s editorial stance.
* Green emphasis sear's "$70 per month"
AlHasan AlSammarraie
Founding & Managing Partner - Applab
Entrepreneur, visionary, nation builder — building Qatar's digital future, one innovation at a time. Armed with a PhD in Artificial Intelligence and years of experience on both the client and vendor sides, AlHasan has built Applab into the region's leading AI, data, and cloud engineering firm — delivering innovation that touches millions of lives and powers national transformation. He leads with an empowering, flexible, and people-first approach, giving his teams the freedom to innovate while always being present for guidance and support. Known for his humility, he works side-by-side with his people, building trust through transparency and accessibility. His leadership extends beyond internal culture: he sees strategic partnerships as catalysts for national impact — exemplified by the deep, trusted alliance he established between Applab and Microsoft, which has become a cornerstone of Qatar's AI and digital transformation journey.“Technology is not just a tool — it's a catalyst for meaningful change.”
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