You’re out of free articles.
Log in
To continue reading, log in to your account.
Create a Free Account
To unlock more free articles, please create a free account.
Sign In or Create an Account.
By continuing, you agree to the Terms of Service and acknowledge our Privacy Policy
Welcome to Heatmap
Thank you for registering with Heatmap. Climate change is one of the greatest challenges of our lives, a force reshaping our economy, our politics, and our culture. We hope to be your trusted, friendly, and insightful guide to that transformation. Please enjoy your free articles. You can check your profile here .
subscribe to get Unlimited access
Offer for a Heatmap News Unlimited Access subscription; please note that your subscription will renew automatically unless you cancel prior to renewal. Cancellation takes effect at the end of your current billing period. We will let you know in advance of any price changes. Taxes may apply. Offer terms are subject to change.
Subscribe to get unlimited Access
Hey, you are out of free articles but you are only a few clicks away from full access. Subscribe below and take advantage of our introductory offer.
subscribe to get Unlimited access
Offer for a Heatmap News Unlimited Access subscription; please note that your subscription will renew automatically unless you cancel prior to renewal. Cancellation takes effect at the end of your current billing period. We will let you know in advance of any price changes. Taxes may apply. Offer terms are subject to change.
Create Your Account
Please Enter Your Password
Forgot your password?
Please enter the email address you use for your account so we can send you a link to reset your password:
Inside Climeworks’ big experiment to wrest carbon from the air

In the spring of 2021, the world’s leading authority on energy published a “roadmap” for preventing the most catastrophic climate change scenarios. One of its conclusions was particularly daunting. Getting energy-related emissions down to net zero by 2050, the International Energy Agency said, would require “huge leaps in innovation.”
Existing technologies would be mostly sufficient to carry us down the carbon curve over the next decade. But after that, nearly half of the remaining work would have to come from solutions that, for all intents and purposes, did not exist yet. Some would only require retooling existing industries, like developing electric long-haul trucks and carbon-free steel. But others would have to be built from almost nothing and brought to market in record time.
What will it take to rapidly develop new solutions, especially those that involve costly physical infrastructure and which have essentially no commercial value today?
That’s the challenge facing Climeworks, the Swiss company developing machines to wrest carbon dioxide molecules directly from the air. In September 2021, a few months after the IEA’s landmark report came out, Climeworks switched on its first commercial-scale “direct air capture” facility, a feat of engineering it dubbed “Orca,” in Iceland.
The technology behind Orca is one of the top candidates to clean up the carbon already blanketing the Earth. It could also be used to balance out any stubborn, residual sources of greenhouse gases in the future, such as from agriculture or air travel, providing the “net” in net-zero. If we manage to scale up technologies like Orca to the point where we remove more carbon than we release, we could even begin cooling the planet.
As the largest carbon removal plant operating in the world, Orca is either trivial or one of the most important climate projects built in the last decade, depending on how you look at it. It was designed to capture approximately 4,000 metric tons of carbon from the air per year, which, as one climate scientist, David Ho, put it, is the equivalent of rolling back the clock on just 3 seconds of global emissions. But the learnings gleaned from Orca could surpass any quantitative assessment of its impact. How well do these “direct air capture” machines work in the real world? How much does it really cost to run them? And can they get better?
The company — and its funders — are betting they can. Climeworks has made major deals with banks, insurers, and other companies trying to go green to eventually remove carbon from the atmosphere on their behalf. Last year, the company raised $650 million in equity that will “unlock the next phase of its growth,” scaling the technology “up to multi-million-ton capacity … as carbon removal becomes a trillion-dollar market.” And just last month, the U.S. Department of Energy selected Climeworks, along with another carbon removal company, Heirloom, to receive up to $600 million to build a direct air capture “hub” in Louisiana, with the goal of removing one million tons of carbon annually.
Two years after powering up Orca, Climeworks has yet to reveal how effective the technology has proven to be. But in extensive interviews, top executives painted a picture of innovation in progress.
Chief marketing officer Julie Gosalvez told me that Orca is small and climatically insignificant on purpose. The goal is not to make a dent in climate change — yet — but to maximize learning at minimal cost. “You want to learn when you're small, right?” Gosalvez said. “It’s really de-risking the technology. It’s not like Tesla doing EVs when we have been building cars for 70 years and the margin of learning and risk is much smaller. It’s completely new.”
From the ground, Orca looks sort of like a warehouse or a server farm with a massive air conditioning system out back. The plant consists of eight shipping container-sized boxes arranged in a U-shape around a central building, each one equipped with an array of fans. When the plant is running, which is more or less all the time, the fans suck air into the containers where it makes contact with a porous filter known as a “sorbent” which attracts CO2 molecules.

When the filters become totally saturated with CO2, the vents on the containers snap shut, and the containers are heated to more than 212 degrees Fahrenheit. This releases the CO2, which is then delivered through a pipe to a secondary process called “liquefaction,” where it is compressed into a liquid. Finally, the liquid CO2 is piped into basalt rock formations underground, where it slowly mineralizes into stone. The process requires a little bit of electricity and a lot of heat, all of which comes from a carbon-free source — a geothermal power plant nearby.
A day at Orca begins with the morning huddle. The total number on the team is often in flux, but it typically has a staff of about 15 people, Climeworks’ head of operations Benjamin Keusch told me. Ten work in a virtual control room 1,600 miles away in Zurich, taking turns monitoring the plant on a laptop and managing its operations remotely. The remainder work on site, taking orders from the control room, repairing equipment, and helping to run tests.
During the huddle, the team discusses any maintenance that needs to be done. If there’s an issue, the control room will shut down part of the plant while the on-site workers investigate. So far, they’ve dealt with snow piling up around the plant that had to be shoveled, broken and corroded equipment that had to be replaced, and sediment build-up that had to be removed.

The air is more humid and sulfurous at the site in Iceland than in Switzerland, where Climeworks had built an earlier, smaller-scale model, so the team is also learning how to optimize the technology for different weather. Within all this troubleshooting, there’s additional trade-offs to explore and lessons to learn. If a part keeps breaking, does it make more sense to plan to replace it periodically, or to redesign it? How do supply chain constraints play into that calculus?
The company is also performing tests regularly, said Keusch. For example, the team has tested new component designs at Orca that it now plans to incorporate into Climeworks’ next project from the start. (Last year, the company began construction on “Mammoth,” a new plant that will be nine times larger than Orca, on a neighboring site.) At a summit that Climeworks hosted in June, co-founder Jan Wurzbacher said the company believes that over the next decade, it will be able to make its direct air capture system twice as small and cut its energy consumption in half.
“In innovation lingo, the jargon is we haven’t converged on a dominant design,” Gregory Nemet, a professor at the University of Wisconsin who studies technological development, told me. For example, in the wind industry, turbines with three blades, upwind design, and a horizontal axis, are now standard. “There were lots of other experiments before that convergence happened in the late 1980s,” he said. “So that’s kind of where we are with direct air capture. There’s lots of different ways that are being tried right now, even within a company like Climeworks."
Although Climeworks was willing to tell me about the goings-on at Orca over the last two years, the company declined to share how much carbon it has captured or how much energy, on average, the process has used.
Gosalvez told me that the plant’s performance has improved month after month, and that more detailed information was shared with investors. But she was hesitant to make the data public, concerned that it could be misinterpreted, because tests and maintenance at Orca require the plant to shut down regularly.
“Expectations are not in line with the stage of the technology development we are at. People expect this to be turnkey,” she said. “What does success look like? Is it the absolute numbers, or the learnings and ability to scale?”
Danny Cullenward, a climate economist and consultant who has studied the integrity of various carbon removal methods, did not find the company’s reluctance to share data especially concerning. “For these earliest demonstration facilities, you might expect people to hit roadblocks or to have to shut the plant down for a couple of weeks, or do all sorts of things that are going to make it hard to transparently report the efficiency of your process, the number of tons you’re getting at different times,” he told me.
But he acknowledged that there was an inherent tension to the stance, because ultimately, Climeworks’ business model — and the technology’s effectiveness as a climate solution — depend entirely on the ability to make precise, transparent, carbon accounting claims.
Nemet was also of two minds about it. Carbon removal needs to go from almost nothing today to something like a billion tons of carbon removed per year in just three decades, he said. That’s a pace on the upper end of what’s been observed historically with other technologies, like solar panels. So it’s important to understand whether Climeworks’ tech has any chance of meeting the moment. Especially since the company faces competition from a number of others developing direct air capture technologies, like Heirloom and Occidental Petroleum, that may be able to do it cheaper, or faster.
However, Nemet was also sympathetic to the position the company was in. “It’s relatively incremental how these technologies develop,” he said. “I have heard this criticism that this is not a real technology because we haven’t built it at scale, so we shouldn’t depend on it. Or that one of these plants not doing the removal that it said it would do shows that it doesn’t work and that we therefore shouldn’t plan on having it available. To me, that’s a pretty high bar to cross with a climate mitigation technology that could be really useful.”
More data on Orca is coming. Climeworks recently announced that it will work with the company Puro.Earth to certify every ton of CO2 that it removes from the atmosphere and stores underground, in order to sell carbon credits based on this service. The credits will be listed on a public registry.
But even if Orca eventually runs at full capacity, Climeworks will never be able to sell 4,000 carbon credits per year from the plant. Gosalvez clarified that 4,000 tons is the amount of carbon the plant is designed to suck up annually, but the more important number is the amount of “net” carbon removal it can produce. “That might be the first bit of education you need to get out there,” she said, “because it really invites everyone to look at what are the key drivers to be paid attention to.”
She walked me through a chart that illustrated the various ways in which some of Orca’s potential to remove carbon can be lost. First, there’s the question of availability — how often does the plant have to shut down due to maintenance or power shortages? Climeworks aims to limit those losses to 10%. Next, there’s the recovery stage, where the CO2 is separated from the sorbent, purified, and liquified. Gosalvez said it’s basically impossible to do this without losing some CO2. At best, the company hopes to limit that to 5%.
Finally, the company also takes into account “gray emissions,” or the carbon footprint associated with the business, like the materials, the construction, and the eventual decommissioning of the plant and restoration of the site to its former state. If one of Climeworks’ plants ever uses energy from fossil fuels (which the company has said it does not plan to do) it would incorporate any emissions from that energy. Climeworks aims to limit gray emissions to 15%.
In the end, Orca’s net annual carbon removal capacity — the amount Climeworks can sell to customers — is really closer to 3,000 tons. Gosalvez hopes other carbon removal companies adopt the same approach. “Ultimately what counts is your net impact on the planet and the atmosphere,” she said.
Get one great climate story in your inbox every day:
Despite being a first-of-its-kind demonstration plant — and an active research site — Orca is also a commercial project. In fact, Gosalvez told me that Orca’s entire estimated capacity for carbon removal, over the 12 years that the plant is expected to run, sold out shortly after it began operating. The company is now selling carbon removal services from its yet-to-be-built Mammoth plant.
In January, Climeworks announced that Orca had officially fulfilled orders from Microsoft, Stripe, and Shopify. Those companies have collectively asked Climeworks to remove more than 16,000 tons of carbon, according to the deal-tracking site cdr.fyi, but it’s unclear what portion of that was delivered. The achievement was verified by a third party, but the total amount removed was not made public.
Climeworks has also not disclosed how much it has charged companies per ton of carbon, a metric that will eventually be an important indicator of whether the technology can scale to a climate-relevant level. But it has provided rough estimates of how much it expects each ton of carbon removal to cost as the technology scales — expectations which seem to have shifted after two years of operating Orca.
In 2021, Climeworks co-founder Jan Wurzbacher said the company aimed to get the cost down to $200 to $300 per ton removed by the end of the decade, with steeper declines in subsequent years. But at the summit in June, he presented a new cost curve chart showing that the price was currently more than $1,000, and that by the end of the decade, it would fall to somewhere between $400 to $700. The range was so large because the cost of labor, energy, and storing the CO2 varied widely by location, he said. The company aims to get the price down to $100 to $300 per ton by 2050, when the technology has significantly matured.
Critics of carbon removal technologies often point to the vast sums flowing into direct air capture tech like Orca, which are unlikely to make a meaningful difference in climate change for decades to come. During a time when worsening disasters make action feel increasingly urgent, many are skeptical of the value of investing limited funds and political energy into these future solutions. Carbon removal won’t make much of a difference if the world doesn’t deploy the tools already available to reduce emissions as rapidly as possible — and there’s certainly not enough money or effort going into that yet.
But we’ll never have the option to fully halt climate change, let alone begin reversing it, if we don’t develop solutions like Orca. In September, the International Energy Agency released an update to its seminal net-zero report. The new analysis said that in the last two years, the world had, in fact, made significant progress on innovation. Now, some 65% of emission reductions after 2030 could be accounted for with technologies that had reached market uptake. It even included a line about the launch of Orca, noting that Climeworks’ direct air capture technology had moved from the prototype to the demonstration stage.
But it cautioned that DAC needs “to be scaled up dramatically to play the role envisaged,” in the net zero scenario. Climeworks’ experience with Orca offers a glimpse of how much work is yet to be done.
Read more about carbon removal:
Log in
To continue reading, log in to your account.
Create a Free Account
To unlock more free articles, please create a free account.
Frontier model developers still keep their energy use largely a secret, but Watershed is proposing a new formula that will at least get you close.
With companies now rapidly adding artificial intelligence into their products and using it across their workstreams, it stands to reason that all that extra energy use might show up in their climate accounting. But to any business that wants to get a sense of how big its AI-related emissions footprint is becoming — and, god forbid, maybe even try to reduce it — I say well, good luck. AI providers mostly keep the data required to make such calculations a secret.
Now Watershed, a startup that helps companies track and estimate their carbon emissions, is proposing a workaround. The firm published a white paper on Wednesday laying out a method for companies to produce rough estimates of their carbon impact from AI, while also encouraging them to demand better data from AI developers.
“We’ve heard from companies that they’re already being asked about AI emissions from investors, from auditors, from regulators, and right now most of them are guessing,” John Bistline, Watershed’s head of science, told me. “We wanted to give them something that was more defensible.”
For most frontier AI models, including those developed by OpenAI and Anthropic, there’s very little information to work with. Google is the only proprietary AI developer that has published a transparent estimate of its model’s operational energy use and related emissions. In a paper last August, researchers at the company found that “the median Gemini apps text prompt consumes 0.24 watts,” which is “less energy than watching nine seconds of television,” and released 0.03 grams of CO2-equivalent. These numbers may be out of date by now, however. In the paper, the authors note that this already represented a 33-fold reduction in energy consumption compared to the previous year.
That’s one challenge with estimating AI-related emissions — tech companies are both growing and innovating rapidly, expanding their energy footprints while also finding greater efficiencies, which may be one reason they don’t disclose this information yet.
Another obstacle is that the exercise involves making a number of carbon accounting decisions, and there’s no consensus yet on best practices. For instance, where do you draw the line on which emissions to include? You could just look at the energy required to operate the model, or you could include the energy used to train the model, or even the emissions related to fabricating and manufacturing the hardware it’s running on. Training a model tends to be more energy-intensive than running it to respond to queries, but it only happens once. If you’re going to include training emissions, the next question is, how should responsibility for those be allotted across the lifetime of the model and its use by hundreds of thousands of customers?
Another decision is how to account for differences in user behavior. A model’s energy intensity can vary widely depending on whether the user is asking a simple question, requesting complex research, generating images, or dispatching agents to conduct multiple tasks simultaneously. Models capable of “reasoning” use an estimated 30 times more electricity than those without that ability, according to research by HuggingFace, a company that creates tools for AI developers. A per-prompt emissions average would not capture these differences, and therefore would not give companies actionable information to help them reduce their emissions.
A “per token” average might be more useful in that sense. When AI models process queries, they break the sentence or code down into smaller components called tokens. One token might be just the first few letters of a word. When the model generates a response, it also processes it in terms of tokens. Estimating emissions per token is not a perfect system either, however, since a token’s value can vary across AI providers. Input tokens, i.e. user questions, also tend to be less energy-intensive than output tokens, or user responses, and a single per-token average will conceal that difference.
Then there’s the question of how to get from a model’s energy intensity to an emissions estimate. Should you use the real-world average carbon intensity of the electric grid? What about any clean energy agreements the AI company may have signed? And how should you factor in companies that decide to bypass the grid entirely and build their own on-site generation, which tends to use natural gas?
The Watershed paper proposes some answers to these questions, and also offers guidance for how companies can develop emissions estimates based on the data available to them.
While most of the published research on AI emissions to date has calculated energy intensity on a per-query basis, Watershed advocates for a per-token approach. — i.e. “kilowatt-hours per thousand tokens.” The authors reason that electricity use scales more directly with the number of tokens used than the number of queries submitted. AI application customers are also often billed based on their token usage, so there’s a business case for tracking tokens and trying to use them more efficiently.
For those companies working with essentially zero data — not even the number of tokens they’re using per year — Watershed recommends they approximate their AI emissions using a “spend-based” method. This means simply multiplying the amount they spend per year on AI services by an emissions factor of 0.134 kilograms of carbon dioxide equivalent per U.S. dollar, which is based on U.S. Bureau of Economic Analysis numbers for the data processing sector of the economy.
The Watershed paper concedes that whatever number this method spits out will be wrong, noting that it “can misestimate true AI emissions by several times in either direction,” and advising companies to treat this as a “provisional placeholder.” But publishing these numbers, even though they are wrong, could help push AI companies toward more transparency if they want to correct the record.
For companies that do track their token volumes, Watershed has a more rigorous solution. The paper proposes a formula companies can use to calculate their AI emissions, accounting not just for inference energy use, but also training emissions, embodied emissions of the data processing equipment, and a figure known as “power usage effectiveness.” This captures the energy consumed by cooling systems, power conversion, and other data center infrastructure that’s not directly serving AI processing. Since model-specific values for the various inputs to the formula are mostly not available today, Watershed has provided default values gathered from previous studies, including papers by Microsoft and Google. Companies can substitute the actual numbers disclosed by AI providers into the formula as that information becomes available.
I reached out to Google, Microsoft, Anthropic, and OpenAI to ask why they didn’t share token carbon intensity, and whether they planned to in the future. Only Microsoft responded to my inquiry, pointing me to its blog post and peer-reviewed paper estimating general AI energy use across frontier models.
To get the most accurate estimate, companies would also need to know where, geographically, their AI queries are being serviced, since emissions from the electric grid varies by region. In some cases, companies may be able to actually choose where their queries are being processed, offering another lever by which they could potentially reduce their emissions.
The right data, disclosed in sufficient detail, will unlock companies’ ability to reduce their AI-related emissions, Watershed argues. Employees would have more reason to choose the most appropriate model for a given task, for example, like avoiding using energy-intensive reasoning models for basic questions.
“I think about a John von Neumann test here,” Bistline said, referring to the mathematician and proto-computer scientist. “You wouldn’t ask an advanced model like Fable anything that you would be embarrassed to ask John von Neumann, or Marie Curie, right? You wouldn’t want to ask ‘how many R’s are there in Strawberry?’ or ‘which restaurants would you recommend I go to in Miami?’”
Of course, companies can already implement this recommendation today, but there will be no way to account for and prove that they are reducing their emissions as a result until AI providers disclose distinct model-based energy estimates.
As Bistline mentioned, this information isn’t just nice-to know — companies are already being asked for it. Upcoming regulations in California and the European Union will require large companies to disclose their total direct emissions, and will eventually require them to disclose indirect emissions like AI energy use. The EU’s AI Act will also require AI companies to disclose a breakdown of the energy consumption of its general purpose AI models.
“There are customer-side disclosure rules and provider-side ones developing in parallel,” Bistline said, “and right now there’s no agreed methodology connecting the two, which is the gap we’re trying to address with our AI emissions framework.”
Average U.S. gasoline prices have slipped back above $4 a gallon.
A decade ago, the Princeton economists Alan Blinder and Mark Watson published a paper about a fact that they called “not nearly as widely known as it should be”: The U.S. economy has done better under Democratic presidents than Republican presidents.
Blinder was not a completely impartial observer — he served on President Bill Clinton’s Council of Economic Advisers, and Clinton later appointed him vice chair of the Federal Reserve — but he and Watson compiled a lengthy list of statistics to back up their claim. The U.S. economy has grown faster, produced more jobs, had a lower unemployment rate, seen higher corporate profits and investment, and experienced better stock market performance under Democrats than Republicans. While the original paper described this divergence from 1947 to 2013, recent research has shown that it held through the subsequent Obama, Trump, and Biden administrations.
The only metric where the two parties come close is inflation, but Democrats still seem to have a tiny edge there, even after the Biden-era inflation.
Why? Blinder and Watson found that it didn’t entirely come down to timing. (Other observers have disputed this, arguing that Republicans tend to get elected at the peak of economic booms, while Democrats win during or just after recessions.) Instead, Blinder and Watson found that a few factors — oil shocks, productivity growth, a more favorable international growth environment, and perhaps better consumer confidence — could explain much of the divergence.
Of course, these factors can’t be entirely separated from a president’s record in office. Oil shocks, for example, tend to drag down global growth, which in turn slows the U.S. economy. And as Watson and Blinder write, some of those oil shocks “may have been induced by [American] foreign policy.” By that mechanism, presidential bellicosity in the Middle East can translate into poorer economic outcomes. This belligerence may even be, as the writer Matt Yglesias contended earlier this year, Republican presidents’ “worst economic policy.”
Why am I recounting all this? Because average U.S. gasoline prices have slipped back above $4 a gallon, according to AAA. (As I write, they stand at $4.01.) The collapse of the ceasefire with Iran — and President Trump’s inability to figure out how to end a war he started — are once again driving up fossil fuel prices.
The numbers add up. Defense Secretary Pete Hegseth told Congress today that the Iran War has cost $37.5 billion so far, but according to a tracker from Brown University researchers, Americans have already paid nearly double that — $71 billion! — on more expensive gasoline and diesel fuel. A billion here, a billion there, and pretty soon you’re talking about real economic underperformance. That estimate suggests the burden of higher energy prices from the Iran War has wiped out the expected $65 billion consumer boost from the One Big Beautiful Bill Act’s expanded tax refunds.
Of course, from a decarbonization perspective, higher gas prices are good, in theory. They encourage people to drive less and to switch to more fuel-efficient — or even fully electrified — vehicles, reducing carbon emissions. (This is part of why I joke about Degrowth Donald, raising fuel prices as he goes.) But short-term oil shocks are the second worst kind of emissions reductions after recessions: They are unlikely to last; they will probably not lead to real decarbonization; and they produce a lot of human misery along the way.
Perhaps this oil spike won’t persist. Perhaps Trump will find a way out of the quagmiring conflict in the Persian Gulf. Perhaps Republican presidential underperformance really does all come down to luck, too. (Or maybe, as a 2020 paper argued, Democratic presidents benefit from a “pre-election growth surge” just before a Republican wins.) But I think it’s worth noting that the recent trickle of news — and the recent and less noticed surge in gas prices — is how an oil interruption results in slower growth overall. If oil shocks really are responsible for GOP presidential underperformance, this is what it would look like.
The irony is that technology finally exists to make the American transportation sector — and the overall economy — less dependent on oil. This technology was developed at the American public’s expense to help manage a scenario much like this one. And the administration has undermined it at almost every opportunity.
The latest forecast from BloombergNEF raises its estimate for AI electricity demand by 83%.
Energy analysts at BloombergNEF predicted last year that U.S. data center electricity demand would reach 106 gigawatts within the next decade. In its latest outlook, released Tuesday, the group increased its forecast by 83%, to 194 gigawatts — enough to light up 150 million homes, or roughly every single household in the country today.
Even that may be a conservative estimate. If data center developers were to max out the total number of the high-powered chips used to train and operate AI models forecast to be delivered by 2035, electricity demand would reach 229 gigawatts.
Over 100 gigawatts of that demand has entered the development pipeline since the beginning of this year, the result of both rising demand for artificial intelligence and shortened construction timelines for data centers. Some developers have oriented their site selection around energy availability, redeveloping brownfield energy generation sites for quick access to electricity and developing relationships with utilities. Others have eschewed grid interconnection entirely and instead relied behind-the-meter power generation.
As Mark Daly, head of technology and innovation at BNEF and a co-author of the report, pointed out to me, a growing share of the project pipeline comes from first-time developers. He and his colleagues project that non-hyperscaler data center capacity will nearly quintuple over the next decade, as hyperscaler capacity almost triples. That could ultimately create pipeline risks, however, as small-scale developers lack the capabilities of more experienced developers to optimize around pre-construction bottlenecks and navigate rapidly growing local opposition. Although local opposition to data centers has become prevalent, historic trends and predictions on how quickly developers are able to navigate hostile environments are built on the proficiency of experienced developers. Because first-time developers may face more challenges, Daly told me that data center projects overall “would see an increase in the number of delays.”
All of this, of course, comes with a big asterisk. The data center sector is rapidly evolving, and therefore highly uncertain. Among leading market research firms, BNEF said, there is a 100-gigawatt spread between the lowest and highest predicted electricity demand from data centers in 2030. Driving this spread are differences in assumptions about the average development timeline for a data center project. Daly told me that BNEF’s “project-based estimate is middle-of-the-road to bearish compared to other outlooks,” but also acknowledged that the fickle nature of local opposition on development timelines may place more constraints on future data center development than currently modeled.
No matter which prediction turns out to be most accurate, hourly U.S. electricity demand will come under intensifying pressure. BNEF predicts that average hourly U.S. electricity demand from AI workloads will grow five-fold over next nine years, reaching 120 gigawatts by 2035. That will put data centers at 12% of total electricity consumption on average by 2030, and 20% in 2035, up from 5% in 2025, according to figures from the International Energy Agency. This will put particular strain on electricity prices in markets like the Mid-Atlantic’s PJM, where data centers already comprise nearly a third of electricity consumption, and Texas’ ERCOT, where data centers currently consume a fifth of the market’s electricity.
Even the most conservative bet on future data center electricity demand is a scenario we’re not prepared for. If the Electric Power Research Institute’s prediction that just 56 gigawatts of new data center capacity will be up and running by 2030 — the lowest estimate BNEF cited — that would still consume the equivalent of Sweden’s total energy supply. Absent investments from utilities into grid resilience and intensive permitting reform to speed up renewable energy siting and development, PJM and ERCOT customers will not be the only ones feeling a serious squeeze in their wallets when their monthly utility bills arrive.