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Tech companies, developers, and banks are converging behind “flexible loads.”

Electricity prices are up by over 5% so far this year — more than twice the overall rate of inflation — while utilities have proposed $29 billion worth of rate hikes so far this year, compared to $12 billion last year, according to electricity policy research group PowerLines. At the same time, new data centers are sprouting up everywhere as tech giants try to outpace each other — and their Chinese rivals — in the race to develop ever more advanced (and energy hungry) artificial intelligence systems, with hundreds of billions of dollars of new investments still in the pipeline.
You see the problem here?
In the PJM Interconnection, America’s largest electricity market which includes Virginia’s “data center alley” as part of its 13-state territory, some 30 gigawatts of a projected 32 total gigawatts of load growth through 2030 are expected to come from data centers.
“The onrush of demand has created significant upward pricing pressure and has raised future resource adequacy concerns,” David Mills, the chair of PJM’s board of managers, said in a letter last week announcing the beginning of a process to look into the issues raised by large load interconnection — i.e. getting data centers on the grid without exploding costs for other users of the grid or risking blackouts.
Customers in PJM are paying the price already, as increasingly scarce capacity has translated into upward-spiraling payments to generators, which then show up on retail electricity bills. New large loads can raise costs still further by requiring grid upgrades to accommodate the increased demand for power — costs that get passed down to all ratepayers. PJM alone has announced over $10 billion in transmission upgrades, according to research by Johns Hopkins scholar Abraham Silverman. “These new costs are putting significant upward pressure on customer bills,” Silverman wrote in a report with colleagues Suzanne Glatz and Mahala Lahvis, released in June.
“There’s increasing recognition that the path we’re on right now is not long-term sustainable,” Silverman told me when we spoke this week about the report. “Costs are increasing too fast. The amount of infrastructure we need to build is too much. We need to prioritize, and we need to make this data center expansion affordable for consumers. Right now it’s simply not. You can’t have multi-billion-dollar rate increases year over year.”
While it’s not clear precisely what role existing data center construction has played in electricity bill increases on a nationwide scale, rising electricity rates will likely become a political problem wherever and whenever they do hit, with data centers being the most visible manifestation of the pressures on the grid.
Charles Hua, the founder and executive director of PowerLines, called data centers “arguably the most important topic in energy,” but cautioned that outside of specific demonstrable instances (e.g. in PJM), linking them to utility rate increases can be “a very oversimplified narrative.” The business model for vertically integrated utilities can incentivize them to over-invest in local transmission, Hua pointed out. And even without new data center construction, the necessity of replacing and updating an aging grid would remain.
Still, the connection between large new sources of demand and higher prices is pretty easy to draw: Electricity grids are built to accommodate peak demand, while the bills customers receive are based on a combination of the fixed cost of maintaining the grid for everyone and the cost of the energy itself, therefore higher peak demand and more grid maintenance equals higher bills.
But what if data centers could use the existing transmission and generation system and not add to peak generation? That’s the promise of load flexibility.
If data centers could commit to not requiring power at times of extremely high demand, they could essentially piggyback on existing grid infrastructure. Widely cited research by Tyler Norris, Tim Profeta, Dalia Patino-Echeverri, and Adam Cowie-Haskell of Duke University demonstrated that curtailing large loads for as little as 0.5% of their annual uptime (177 hours of curtailment annually on average, with curtailment typically lasting just over two hours) could allow almost 100 gigawatts of new demand to connect to the grid without requiring extensive, costly upgrades.
The groundswell behind flexibility has rapidly gained institutional credibility. Last week, Google announced that it had reached deals with two utilities, Indiana Michigan Power and the Tennessee Valley Authority, to incorporate flexibility into how their data centers run. The Indiana Michigan Power contract will “allow [Google] to reduce or shift electricity demand to carry out non-urgent tasks during hours when the electric grid is under less stress,” the utility said.
Google has long been an innovator in energy procurement — it famously pioneered the power purchase agreement structure that has helped finance many a renewable energy development — and already has its fingers in many pots when it comes to grid flexibility. The company’s chief scientist, Jeff Dean, is an investor in Emerald AI, a software company that promises to help data centers work flexibly, while its urbanism-focused spinout Sidewalk Infrastructure Partners has backed Verrus, a demand-flexible data center developer.
Hyperscale developers aren’t the only big fish excited about data center flexibility. Financiers are, as well.
Goldman Sachs released a splashy report this week that cited Norris extensively (plus Heatmap). Data center flexibility promises to be a win-win-win, according to Goldman (which, of course, would love to finance an AI boom unhindered by higher retail electricity rates or long interconnection queues for new generation). “What if, thanks to curtailment, instead of overwhelming the grid, AI data centers became the shock absorbers that finally unlocked this stranded capacity?” the report asks.
The holy grail for developers and flexibility is not just saving money on electricity, which is a small cost compared to procuring advanced chips to train and run AI models. The real win would be to build new data centers faster. “Time to market is critical for AI companies,” the Goldman analysts wrote.
But creating a system where data centers can connect to the grid sooner if they promise to be flexible about power consumption would require immense institutional change for states, utilities, regulators, and power markets.
“We really don’t have existing service tiers in place for most jurisdictions that acknowledges and incentivizes flexible loads and plans around them,” Norris told me.
When I talked to Silverman, he told me that integrating flexibility into local decision-making could mean rewriting state utility regulations to allow a special pathway for data centers. It could also involve making local or state tax incentives contingent on flexibility.
Whatever the new structure looks like, the point is to “enshrine a policy that says, ‘data centers are different,’ and we are going to explicitly recognize those differences and tailor rules to data centers,” Silverman said. He pointed specifically to a piece of legislation in New Jersey that he consulted on, which would have utilities and regulators work together to come up with specific rate structures for data centers.
Norris also pointed to a proposal in the Southwest Power Pool, which runs down the spine of the country from the Dakotas to Louisiana, which would allow large loads like data centers to connect to the grid quickly “with the tradeoff of potential curtailment during periods of system stress to protect regional reliability,” the transmission organization said.
And there’s still more legal and regulatory work to be done before hyperscalers can take full advantage of those incentives, Norris told me. Utilities and their data center customers would have to come up with a rate structure that incorporates flexibility and faster interconnection, where more flexibility can allow for quicker timelines.
Speed is of the essence — not just to be able to link up more data centers, but also to avoid a political firestorm around rising electricity rates. There’s already a data center backlash brewing: The city of Tucson earlier this month rejected an Amazon facility in a unanimous city council vote, taken in front of a raucous, cheering crowd. Communities in Indiana, a popular location for data center construction, have rejected several projects.
The drama around PJM may be a test case for the rest of the country. After its 2024 capacity auction jumped came in at $15 billion, up from just over $2 billion the year before, complaints from Pennsylvania Governor Josh Shapiro led to a price cap on future auctions. PJM’s chief executive said in April that he would resign by the end of this year. A few months later, PJM’s next capacity auction hit the price cap.
“You had every major publication writing that AI data centers are causing electricity prices to spike” after the PJM capacity auction, Norris told me. “They lost that public relations battle.”
With more flexibility, there’s a chance for data center developers to tell a more positive story about how they affect the grid.
“It’s not just about avoiding additional costs,” Norris said. “There’s this opportunity that if you can mitigate additional cost, you can put downward cost on rates.” That’s almost putting things generously — data center developers might not have a choice.
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What 700 math problems say about the AI economy’s biggest open question
This is Heatmap Daily, an evening news digest written by our executive editor. Subscribe here.
Happy Monday and welcome back.
It’s been a big weekend: I’m fairly certain Saturday Night Live mentioned Heatmap’s data center polling in one of its Weekend Update segments over the weekend. Watch here.
There are 22 days to the midterm elections here in the United States. We’ll soon enter that pre-election wormhole where only national politics can break through. So today I wanted to look at a story that might, on its face, seem like it has nothing to do with climate or energy. And warning: It’s a little bit of a weird one.
Last week, OpenAI released what it described as the solution to more than 700 open problems in mathematics. Many of the problems, such as the Quasi-Riemann hypothesis, had gone unsolved for decades. Some of the solutions came with proofs written in the programming language Lean.
The release made a stir in part because OpenAI had reached the solutions by using an internal model — that is, a version of ChatGPT not yet open to the general public. In a statement, the Association for Human Mathematics lambasted OpenAI for using an internal model to do research, violating what it described as a precept of AI-driven math research. (I’ve since seen speculation that OpenAI may not have even meant to solve the problems, but gave them to the internal model as a test … and then got the answers.) It’s also not clear whether all of OpenAI’s 700 solutions are correct — in many cases the proofs are so intricate that mathematicians are still wrapping their heads around them.
“There are some sad stories related to their users getting scooped [and] conflicts of interest,” Levent Alpöge, a mathematician at Anthropic, posted on X. “It’s obviously the most significant moment in mathematical history.”
On Friday night, the UCLA mathematician Terence Tao gave a pre-planned lecture at Caltech about what he called “Math 2.0”; it considered the revolution the field had seemingly undergone in the previous few days. (He posted the slides to his blog over the weekend, and I’m using them to summarize his talk here.) For most of math’s history, he said, the field operated under “proof scarcity,” in which finding a solution to an open math problem and confirming it as true was “difficult and rare,” requiring “significant human expertise.”
But AI, it has turned out, is well suited to solving math problems. This is partly because math, like the large language models themselves, is entirely conceptual, so confirming a proof as true requires no recourse to the physical. But it’s also because math’s solutions are “objectively verifiable”: “There is broad consensus on what constitutes a correct and complete proof of a given problem,” and thanks to the existence of proof programming languages like Lean, they can even be checked by computer.
The end result is that math is now entering a world of “proof abundance,” he said.
That is, perhaps surprisingly, a problem for math’s practitioners, and it will mandate a new intellectual regime for the discipline. Mathematicians have often used open problems as “lighthouses,” Tao wrote, “not destinations to be reached … but useful guides to explore the mathematical landscape.” Mathematicians often learn more in the solving than in the solution, in other words. But if mathematicians can now generate shortcuts to math problems without knowing how they got there, or even how the problem works, then actual human mathematical understanding will suffer. (One way to understand this is that mathematicians are now dealing with the same problem that math teachers have faced since the invention of the pocket calculator: How do you confirm students actually understand the material? Except the world’s most elite mathematicians themselves are the students in question.)
This disconnect can pose its own AI alignment problems, Tao said, analogizing math to pharmaceuticals. Suppose an advanced AI found what appears to be a cure for cancer that can pass a stage 3 clinical trial: “After a large amount of compute, it produces a cocktail of previously unknown chemicals which it claims, when mixed and injected into a patient, will kill all their cancer cells,” he said. It even generates a programmatic proof of the cocktail’s effectiveness.
“Before injecting this cocktail into your bloodstream,” he asked, “would you find it reassuring to know that there is at least one human cancer expert who understands — even partially — the mechanism behind this cure?” If not, you can see mathematicians’ problem, confronted by a surfeit of proofs that seem to work, but nobody who can explain how or why they do.
Now, Tao’s analogy is somewhat peculiar because something like this actually does happen all the time in medicine: Doctors still don’t fully understand how many drugs work, including acetaminophen, SSRIs, and lithium. (That said, those compounds were identified by trial and error, not by a mechanical superintelligence.) But the broader situation that Tao is painting — as well as the crisis posed by OpenAI’s 700 proofs — get at a significant question with economic importance far beyond just math: Will AI solve the economy’s secular attention shortage or worsen it?
This question has greater import than it may seem — but let me qualify what I mean by “attention shortage.” Writing in 1971, the economist Herber Simon observed the sudden superabundance of information that had exploded in every economic domain. There were more facts, data, news articles, and opportunities than any individual or organization knew what to do with. He pointed out that this newfound “wealth” of information implied an insufficiency of something else: attention.
“What information consumes is rather obvious: it consumes the attention of its recipients,” he wrote in Designing Organizations for an Information-Rich World. “Hence a wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.”
He wrote this, of course, well before the advent of internet, smartphones, sensors, or networked cameras — all of which have worsened the problem. But in the ensuing half century, the economy has learned to deal (or at least intermittently manage) this secular scarcity of attention. Much of what we now call knowledge work is devoted to filtering, siphoning, processing, and otherwise attending to the information superabundance.
AI is, in theory, supposed to help address this same set of problems. AI is commodified attention — or, at least, that is its promise. (The most important machine learning paper of the past decade, after all, is entitled “Attention Is All You Need.”) Many AI products, such as Meta’s Muse, now want you to believe that by ceding control of your email, your calendar, your restaurant reservations, and your digital life to AI, you will gain an attentional edge. The AI will do the grunt-work of attending to boring things for you, and you will feel less overwhelmed. If this vision comes true, the economy’s mismatch of information and attention will prove to be a short-lived historical blip.
But the past week of math news and Tao’s “Math 2.0” regime suggest a different scenario is more likely, at least in the immediate near term. By allowing far faster and more automated information production, AI will create a new, more vicious, and even more abstract form of information overload. Every profession and intellectual discipline will be overwhelmed with new papers, ideas, observations, proofs, or choices. Yet the human understanding required to convert this new information into useful action will become even more scarce. In such a world, AI will not solve the economy’s attention shortage, but worsen it.
I’m not sure which world would be worse for humanity, to be candid. One world suggests the eventual automation of all human effort. But in Tao’s world, many of us might still have jobs — they will just involve even more filtering, sorting, and processing of an ever-deepening pile of automated idea generation than we do today.
And what is the connection to climate and energy? One easy connection might be to the permitting process: If you think a new transit line (at the local level) or transmission line (at the state or federal level) could be overwhelmed with procedural concerns 10 years ago, wait until you see what happens when information itself can be mechanically produced. But that is only one example of a more general problem. Soon, if not already, any process of providing input to an important policy question — on energy, decarbonization, or anything else — will be overwhelmed with facially high-quality analysis. It will be left to policy officials and elected leaders to sort through it.
This past weekend’s storms caused flooding and property damage up and down the coast — and it’s going to get worse.
California has around 3,400 miles of shoreline, ranging from the wild, foggy beaches of the North Coast to the sunbather-studded sands of Orange and San Diego counties. That’s a whole lot of border to share with the Pacific Ocean, especially when the water is as hot and tempestuous a neighbor as it is right now.
Just ask the city of Long Beach, which this weekend was on the receiving end of some Super El Niño mischief. Swells from the storm briefly dubbed Hurricane Rachel “annihilated” beachfront mansions and flooded city streets, causing pieces of the local boardwalk to “shred away.” “It’s never happened like this before,” one resident of 64 years told the New York Post.
But while things might look bad in California at the moment, they can still get much worse. One disaster modeler recently estimated the state could see as much as $3 billion in flooding and mudslide-related losses alone this winter — not to mention coastal erosion, king tides, and storm swell. Governor Gavin Newsom declared a state of emergency at the end of September, while many cities, including San Diego, have declared their own local emergencies.
El Niño will likely peak in December, meaning that towns and cities across the state have just a few remaining weeks to finalize projects, whether that means improving drainage in landslide-prone environments such as Rancho Palos Verdes or distributing NOAA weather radios in areas likely to lose power, as emergency managers are in Santa Cruz. The approaching winter storms, combined with El Niño, represent “a humbling reminder of the ongoing effects of climate change and the importance of emergency preparedness,” Laath Martin, the public affairs lead for the city of Long Beach, wrote to me in an email.
El Niño is a cyclical phenomenon, occurring when temperatures in a particular stretch of the Pacific Ocean rise half-a-degree Celsius above the average of the preceding 30 years. But there are signs these natural variations are happening less predictably because of climate change, and we know for sure that a warmer atmosphere makes the baseline for its impacts worse: king tides, Kelvin waves, and storm surge piggyback on top of rising sea levels to result in more severe coastal flooding and erosion. A warmer atmosphere also holds more water, which means more rain — perhaps 200% above normal — which, in turn, results in flooding and mudslides further inland, as well.
“Based on what I am seeing now, I am officially concerned,” Daniel Swain, a climate scientist at the University of California Agriculture and Natural Resources, said last week after reviewing the latest outlook. There’s a reason this year’s El Niño is being called a Godzilla.
In general, the weather pattern causes the jet stream to shift south in the winter, bringing wetter and snowier conditions to the lower U.S. while the northern part of the country stays drier and mild. (It’s also responsible for a weaker Atlantic hurricane season, as we’ve already seen this year.) In addition to El Niño-driven Kelvin waves, warm bulges of water that raise the sea level on the coast by as much as 18 inches, the state of California — and particularly southern California — sits directly in a storm track that will deliver “atmospheric rivers” of heavy rain to a region prone to landslides and flash floods.
In many parts of the state, that rain will test the best-made plans in addition to plans deferred. In Los Angeles, the Department of Sanitation and Environment, or LASAN, has told the City Council it has concerns about the ability of the aging wastewater pipes and plants to handle heavy rainfall associated with a supercharged El Niño. In San Diego, which requires some $3.3 billion to upgrade what is in some places a century-old wastewater system, emergency managers are preparing to work with what they have by clearing debris and brush that could back up in the drains, CalMatters reports. In both cases, the time required for systemwide repairs far exceeds the time remaining before the winter’s supercharged atmospheric rivers are due to arrive. (Rezende told me inland flooding affects more businesses and residents in San Diego than coastal flooding, since the population in the city is set further back from the coastline.)
The city of Roseville, northeast of Sacramento, by contrast, is leaning on decades of flood planning to get through the season. After a series of back-to-back storms in 1986 that killed 13 people and caused $400 million in damage, the city and FEMA invested $39 million to upgrade Roseville’s infrastructure, including widening streams and adding levees and stormwater bypass channels. With more accurate floodplain modeling that has “helped keep new construction at higher elevations,” Helen Dyda, the city manager’s public information officer told me that “less than 7% of the city is within a floodplain” today. Dyda added that the city prepares for winter storms every year — it has some 160 miles of creek shoreline, after all — but that the El Niño-specific preparations are, as a result, more similar to routine maintenance: “clearing more dense vegetation and fallen trees from key creek areas.”
Long Beach is caught somewhere in the middle. After swells from Hurricanes Marie and Polo hammered the city in September, Long Beach hastily developed a seven-point plan to strengthen its peninsula in the near and long term. It will also begin a $11.4 million dredging project this fall that will place 415,000 cubic yards of sand on the beach as a buffer zone. “While this effort will achieve a much-desired result, it will not stop the ocean from pulling sand from the peninsula with every wave, especially during the significant weather systems Long Beach continues to experience,” Martin, the city’s public affairs lead, wrote to me. “Back-passing will need to continue to return sand back to the peninsula each day until more permanent, long-term man-made and environmental solutions can be identified, permitted and implemented.”
In certain cities, preparations for the peak of El Niño overlap with ongoing repairs from the last bad storm season. In Santa Cruz County, the 2016 El Niño caused an estimated $9 million in damage, while many in the community of Pajaro are still recovering from the flood damage caused by a series of severe atmospheric rivers in 2023. “We have a list of hazards that the city is vulnerable to, and we plan for all of them not individually, but all together,” Meredith Albert, Santa Cruz’s emergency management manager, told me. She added that many of the city’s preparedness activities and El Niño initiatives, like urging the community to be prepared to spend 72 hours unsupported, overlap with other disasters it works year-round to prepare for, like earthquakes, landslides, and wildfires.
“El Niño turns up the volume a little bit, but it doesn’t necessarily change our hazard landscape,” is how Albert put it to me.
Santa Cruz has not declared a local state of emergency, and Albert told me they won’t until they “run out of resources.” Other cities, like San Diego, have declared one preemptively. “In the past, you’d have to go through a longer review if you wanted to make some headway on projects, and sometimes that would take many weeks or many months of permitting,” Rezende said. “With a state of emergency, we streamline that process and get projects in the queue.”
Both Rezende and Albert told me the governor's statewide emergency declaration mostly helps state agencies position themselves for a faster response or has implications for higher-level permitting processes. (In Carlsbad, for example, which has seven miles of badly eroding beach, just one mile is controlled by the city; the other six are managed by the State Parks.) The state has also teamed up with ICEYE, a satellite imaging company, to receive information about flooding even in the dark or through rain clouds.
No state of emergency, though, can make it so a city can replace a century-old sewer system by Christmas or reengineer a beach by the time January’s storms begin to make landfall. But if a Godzilla analogy fits the onslaught of a super El Niño, it also offers a classic disaster-movie lesson on how to survive it.
Albert’s advice for how to prepare? Ask a neighbor for a cup of sugar. Take their dog for a walk. Build a relationship that could prevent a tragedy. “We know that our city will be cut off for at least a couple of days,” she told me of a hypothetical major disaster, referring back to Santa Cruz’s experience after the 1989 Loma Prieta earthquake, which cut off the city’s power, communication, and supply lines. “And we also know that we can’t build those relationships during the disaster. We’re trying to build them before.”
The city is doing a lot to prepare for the storm, she said. “But the question is also, what are all of us doing?” Albert went on. “You know better than I do, at the city level, who’s not going to receive that alert,” whether that be an elderly or unhoused neighbor or a nonnative English speaker who might not receive or understand emergency communications.
“Check in on a neighbor” is easier said than done — active engagement and connection to one’s community is trending downward in most parts of the U.S. But the people and places that bounce back from disaster and tragedy are almost uniformly the ones that look after each other. Borrowing a cup of sugar, to this introvert, sounds like a nightmare. But there’s never been a better time than a Godzilla El Niño to bake a poorly resourced cake.
Heatmap News is looking for an ambitious, energetic senior editor to join our growing newsroom.
The senior editor will report to the deputy editor of the website and will be tasked with assisting in running all aspects of our coverage. This individual will be the primary editor on many of the site’s stories, including breaking news, features, and long-term projects, on topics ranging from permitting reform to decarbonized fuels to greenhouse gas accounting to the electric vehicle market. This person will also play a role in overseeing and furthering our cross-platform efforts such as newsletters, events, and podcasts. Additional duties will include managing and maintaining the site’s social media presence and digital production on stories, newsletters, and podcasts.
Candidates should be intellectually curious news junkies with a deep interest in climate change and the energy transition. They should have a proven ability to work in a fast-paced digital newsroom, spot great angles, line edit stories, and change hats quickly as needed. They should also be responsible, reliable, and a good colleague.
Candidates should have 5+ years experience editing for a digital publication. A background in climate or energy coverage is an obvious plus, but compelling candidates from other verticals, particularly politics and business, will also be considered. Heatmap is a fully remote workplace, so candidates from all U.S. locales are encouraged to apply.
Above all, candidates should be passionate about Heatmap’s mission and absolutely thrilled about building something from the ground up.
The salary minimum is $100,000 and the maximum is $120,000. Competitive benefits, unlimited paid time off, and a generous equity plan, which gives employees a real stake in the company, are also offered. This position is remote.
Interested candidates should send a brief cover letter and resume to editors@heatmap.news with the subject line ‘Senior Editor Application.’
Heatmap News is a media platform focused on the energy transition.
Heatmap News is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected veteran status, age, or any other characteristic protected by applicable law.
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