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It took the market about a week to catch up to the fact that the Chinese artificial intelligence firm DeepSeek had released an open-source AI model that rivaled those from prominent U.S. companies such as OpenAI and Anthropic — and that, most importantly, it had managed to do so much more cheaply and efficiently than its domestic competitors. The news cratered not only tech stocks such as Nvidia, but energy stocks, as well, leading to assumptions that investors thought more-energy efficient AI would reduce energy demand in the sector overall.
But will it really? While some in climate world assumed the same and celebrated the seemingly good news, many venture capitalists, AI proponents, and analysts quickly arrived at essentially the opposite conclusion — that cheaper AI will only lead to greater demand for AI. The resulting unfettered proliferation of the technology across a wide array of industries could thus negate the energy efficiency gains, ultimately leading to a substantial net increase in data center power demand overall.
“With cost destruction comes proliferation,” Susan Su, a climate investor at the venture capital firm Toba Capital, told me. “Plus the fact that it’s open source, I think, is a really, really big deal. It puts the power to expand and to deploy and to proliferate into billions of hands.”
If you’ve seen lots of chitchat about Jevons paradox of late, that’s basically what this line of thinking boils down to. After Microsoft’s CEO Satya Nadella responded to DeepSeek mania by posting the Wikipedia page for this 19th century economic theory on X, many (myself included) got a quick crash course on its origins. The idea is that as technical efficiencies of the Victorian era made burning coal cheaper, demand for — and thus consumption of — coal actually increased.
While this is a distinct possibility in the AI space, it’s by no means a guarantee. “This is very much, I think, an open question,“ energy expert Nat Bullard told me, with regards to whether DeepSeek-type models will spur a reduction or increase in energy demand. “I sort of lean in both directions at once.” Formerly the chief content officer at BloombergNEF and current co-founder of the AI startup Halcyon, a search and information platform for energy professionals, Bullard is personally excited for the greater efficiencies and optionality that new AI models can bring to his business.
But he warns that just because DeepSeek was cheap to train — the company claims it cost about $5.5 million, while domestic models cost hundreds of millions or even billions — doesn’t mean that it’s cheap or energy-efficient to operate. “Training more efficiently does not necessarily mean that you can run it that much more efficiently,” Bullard told me. When a large language model answers a question or provides any type of output, it’s said to be making an “inference.” And as Bullard explains, “That may mean, as we move into an era of more and more inference and not just training, then the [energy] impacts could be rather muted.”
DeepSeek-R1, the name for the model that caused the investor freakout, is also a newer type of LLM that uses more energy in general. Up until literally a few days ago, when OpenAI released o3-mini for free, most casual users were probably interacting with so-called “pretrained” AI models. Fed on gobs of internet text, these LLMs spit out answers based primarily on prediction and pattern recognition. DeepSeek released a model like this, called V3, in September. But last year, more advanced “reasoning” models, which can “think,” in some sense, started blowing up. These models — which include o3-mini, the latest version of Anthropic’s Claude, and the now infamous DeepSeek-R1 — have the ability to try out different strategies to arrive at the correct answer, recognize their mistakes, and improve their outputs, allowing for significant advancements in areas such as math and coding.
But all that artificial reasoning eats up a lot of energy. As Sasha Luccioni, the AI and climate lead at Hugging Face, which makes an open-source platform for AI projects, wrote on LinkedIn, “To set things clear about DeepSeek + sustainability: (it seems that) training is much shorter/cheaper/more efficient than traditional LLMs, *but* inference is longer/more expensive/less efficient because of the chain of thought aspect.” Chain of thought refers to the reasoning process these newer models undertake. Luccioni wrote that she’s currently working to evaluate the energy efficiency of both the DeepSeek V3 and R1 models.
Another factor that could influence energy demand is how fast domestic companies respond to the DeepSeek breakthrough with their own new and improved models. Amy Francetic, co-founder at Buoyant Ventures, doesn’t think we’ll have to wait long. “One effect of DeepSeek is that it will highly motivate all of the large LLMs in the U.S. to go faster,” she told me. And because a lot of the big players are fundamentally constrained by energy availability, she’s crossing her fingers that this means they’ll work smarter, not harder. “Hopefully it causes them to find these similar efficiencies rather than just, you know, pouring more gasoline into a less fuel-efficient vehicle.”
In her recent Substack post, Su described three possible futures when it comes to AI’s role in the clean energy transition. The ideal is that AI demand scales slowly enough that nuclear and renewables scale with it. The least hopeful is that immediate, exponential growth in AI demand leads to a similar expansion of fossil fuels, locking in new dirty infrastructure for decades. “I think that's already been happening,” Su told me. And then there’s the techno-optimist scenario, linked to figures like Sam Altman, which Su doesn’t put much stock in — that AI “drives the energy revolution” by helping to create new energy technologies and efficiencies that more than offset the attendant increase in energy demand.
Which scenario predominates could also depend upon whether greater efficiencies, combined with the adoption of AI by smaller, more shallow-pocketed companies, leads to a change in the scale of data centers. “There’s going to be a lot more people using AI. So maybe that means we don’t need these huge, gigawatt data centers. Maybe we need a lot more smaller, megawatt-size data centers,” Laura Katzman, a principal at Buoyant Ventures, told me. Katzman has conducted research for the firm on data center decarbonization.
Smaller data centers with a subsequently smaller energy footprint could pair well with renewable-powered microgrids, which are less practical and economically feasible for hyperscalers. That could be a big win for solar and wind plus battery storage, Katzman explained, but a boondoggle for companies such as Microsoft, which has famously committed to re-opening Pennsylvania’s Three Mile Island nuclear plant to power its data centers. “Because of DeepSeek, the expected price of compute probably doesn’t justify now turning back on some of these nuclear plants, or these other high-cost energy sources,” Katzman told me.
Lastly, it remains to be seen what nascent applications cheaper models will open up. “If somebody, say, in the Philippines or Vietnam has an interest in applying this to their own decarbonization challenge, what would they come up with?” Bullard pondered. “I don’t yet know what people would do with greater capability and lower costs and a different set of problems to solve for. And that’s really exciting to me.”
But even if the AI pessimists are right, and these newer models don’t make AI ubiquitously useful for applications from new drug discovery to easier regulatory filing, Su told me that in a certain sense, it doesn't matter much. “If there was a possibility that somebody had this type of power, and you could have it too, would you sit on the couch? Or would you arms race them? I think that is going to drive energy demand, irrespective of end utility.”
As Su told me, “I do not think there’s actually a saturation point for this.”
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On the Greenhouse Gas Reduction Fund, Canada’s new prime minister, and CERAWeek
Current conditions: Firefighters successfully controlled brush fires in Long Island that prompted New York Gov. Kathy Hochul to declare a state of emergency • Brisbane, Australia, recorded its wettest day in more than 50 years • Forecasters are keeping an eye on a storm system developing across the central U.S. that could pack a serious punch this week.
The nonprofit Climate United filed a lawsuit over the weekend against the Environmental Protection Agency and Citibank for withholding $7 billion in climate funds awarded as part of the Biden administration’s Inflation Reduction Act. The move escalates a dispute over some $20 billion in grants from the IRA’s Greenhouse Gas Reduction Fund, which was designed to help mobilize private capital toward clean energy and climate solutions. President Trump’s EPA Administrator Lee Zeldin has been on a mission to claw back the funds, claiming their distribution was rushed and mismanaged. In its lawsuit, Climate United says it has been unable to access the $7 billion it was awarded, and that the EPA and Citibank have given no explanation for this. It wants a judge to order that the money be released. “We’re not trying to make a political statement here,” Beth Bafford, chief executive of Climate United, toldThe New York Times. “This is about math for homeowners, for truck drivers, for public schools — we know that accessing clean energy saves them money that they can use on far more important things.” The Trump administration has reportedly demanded that the eight organizations tapped to receive the money turn over records to the FBI and appear in federal court later this month.
Canada’s Liberal Party has elected Mark Carney, a net-zero finance advocate, to succeed Justin Trudeau as prime minister. Carney is not a career politician. Instead, he comes from the financial world, having overseen both the Bank of Canada and the Bank of England, and is an evangelist for green investment and a net-zero financial sector. He was the UN Special Envoy for Climate Action and Finance in 2019, and “has made clean energy, climate policies and economic prosperity for Canada some of the central facets of his campaign,” CNN reported. If he wins the upcoming general election, Carney will be tasked with navigating President Trump’s tariffs and making key decisions about the future of Canada’s vast natural resources, including fossil fuels and rare minerals.
The U.S. has withdrawn from yet another global climate initiative, this one aimed at helping developing nations recover from natural disasters. The United Nations loss and damage fund was one of the biggest wins to come out of COP28 in 2023, with nearly 200 countries signing on in support. It’s expected to start funding projects this year. About $740 million has been pledged so far, and the U.S. has said it will give about $17.5 million, though it’s unclear if that money will actually be handed over now. “This decision, made by the nation with the largest historical responsibility for climate change, jeopardises vital support for vulnerable countries facing irreversible climate impacts,” said Ali Mohamed, the chair of the African Group of Negotiators.
The energy industry descends on Houston, Texas, this week, for the annual CERAWeek conference. This year’s event, titled “Moving Ahead: Energy Strategies for a Complex World,” will focus on the changing global energy landscape. Key themes include shifting regulations, the turbulent oil and gas market, electrification and power demand, the rise of AI, managing emissions, and the policy outlook for renewables. According toReuters energy columnist Ron Bousso, fossil fuel executives are going into the conference with a case of “Trump buyer’s remorse” as new tariffs and geopolitical policies from the Trump administration have “created turmoil in financial markets and clouded the outlook for the global economy and energy prices.”
Argentina will observe three days of national mourning after 16 people were killed in flash flooding over the weekend triggered by unprecedented rainfall. Nearly a year’s worth of rain – about 16 inches – fell in just eight hours in the port city of Bahia Blanca in the Buenos Aires province. Many people are still missing. Environment official Andrea Dufourg said the event was a clear example of climate change. “Unfortunately this will continue to take place,” Dufourg said. “We have no other option than to prepare cities, educate citizens, and establish effective early warning systems.”
Twenty-one House Republicans have signed a letter urging the GOP to uphold the Inflation Reduction Act’s clean energy tax credits in their budget bill, warning that gutting the credits would “risk sparking an energy crisis in our country, resulting in drastically higher power bills for American families.” That’s three more than signed a similar letter during the last Congress.
While they’re getting more accurate all the time, they still rely on data from traditional models — and possibly always will.
The National Oceanic and Atmospheric Administration has had a bruising few weeks. Deep staffing cuts at the hands of Elon Musk’s efficiency crusaders have led to concerns regarding the potential closure of facilities critical to data-gathering and weather-forecasting operations. Meteorologists have warned that this could put lives at risk, while industries that rely on trustworthy, publicly available weather data — from insurance to fishing, shipping, and agriculture — are bracing for impact. While reliable numbers are difficult to come by, the agency appears to have lost on the order of 7% to 10% of its workforce, or more than 1,000 employees. NOAA’s former deputy director, Andrew Rosenberg, wrote that Musk plans to lay off 50% of the agency, while slashing its budget by 30%.
Will that actually happen? Who the heck knows. But what we can look at are the small cracks that are already emerging, and who could step in to fill that void.
One thing that’s certain is that the National Weather Service, a division of NOAA, announced last week that it is suspending operations at a weather balloon launch site in Alaska, due to staffing shortages. The data gathered at this remote outpost helped inform the agency’s weather forecasts, which are relied upon by hundreds of millions of people, as well as many of the world’s largest companies and public agencies.
Perhaps to Musk’s department, this looks like a prime opportunity for the private sector to step up and demonstrate some nimble data gathering prowess — and indeed a startup that I’ve covered before, WindBorne, has already offered its services. The company, which makes advanced weather balloons, has offered to provide NOAA with data from its own Alaska launches for six months, at no cost. WindBorne is also one of a number of private companies creating AI-based weather models that have outperformed NOAA’s traditional, physics-based models on key metrics such as temperature, wind speed and direction, precipitation, humidity, and pressure.
All this raises the question, though, of what kind of role the private sector could and should play in the weather forecasting space overall. If the architects of Project 2025 have their way, NOAA would be “broken up and downsized,” and its National Weather Service division would “fully commercialize its forecasting operations.” If the Trump administration achieves these goals, “the Weather Service would cease to function in a way that it could meet its mandate to protect American life and property,” Daniel Swain, a climate scientist at University of California Agriculture and Natural Resources, told me.
But given that heavyweights like Google, Huawei, and Nvidia are already in the AI-based weather prediction game, along with startups such as WindBorne and Brightband, which is making weather predictions tailored to the needs of specific industries such as insurance, agriculture, or transportation, it wasn’t clear to me whether, if NOAA were to crumble, the accuracy of weather forecasts necessarily would, too. I thought that perhaps Musk, the White House’s most notorious AI enthusiast, might be thinking the same thing. So I asked around.
“There’s actually a very good argument that I think would be very uncontroversial to expand the role of the private sector, even to offload certain parts of the workflow to the private sector,” Swain told me, with regards to NOAA and its adoption — or lack thereof — of AI-based weather forecasting. But what nobody wanted was to get rid of free, publicly available government forecasts completely.
“I don’t want to have to figure out what company to trust. I just want to be able to go and open the National Weather Service and know what’s going on,” John Dean, the CEO and co-founder of WindBorne, told me.
Julian Green, the CEO and co-founder of Brightband, agreed. “The government doesn’t just forecast the weather, but it gives people alerts. And there’s regulation around whether [it tells you that] you should evacuate, or shut your factory down, or so on.” It’s not hard to imagine the ethical quandaries that could arise from a private company with a profit motive deciding who can access potentially life-saving forecasts, and for how much.
WindBorne’s and Brightband’s AI models, as well as those from tech giants such as Google, are significantly less computationally intensive to operate than those from NOAA or the other leading weather forecasting agency, the European Center for Medium-Range Weather Forecasts. These traditional models rely on supercomputers crunching complicated atmospheric equations based on the laws of physics to make their predictions.
But this doesn’t mean the physics-based models are getting replaced by AI now, or potentially ever. Government data and traditional forecasts still make up the backbone of advanced AIs, which are trained on decades of data largely gathered by NOAA satellites, weather balloons, and radar systems, and then interpreted through the lens of standard physics-based models. After training is complete, the AI models can predict what weather patterns will develop, much like ChatGPT predicts the next word in a sequence, but only after being fed a snapshot of initial weather conditions — also pulled from traditional physics-based models.
Essentially, these AI forecasts are built on the backs of the giants, and while their outcomes are hugely promising, they could not exist without that solid foundation. While one day, it might be possible to operate AI forecasting models without relying on traditional models, Dean and Green told me that physics-based models might always be critical for training the AI. So while their companies’ respective models have yielded impressive results, both Dean and Green nixed the idea that their companies could wholly replace the predictions made by the National Weather Service.
All of this is in flux of course, but as Green put it to me in an email, “a good mechanic doesn't throw away good older tools just because you get new tools.” Plus, as Dean explained, there are still conditions under which physics-based models tend to outperform AI, such as “really small-scale and high-res phenomena — let’s say convective events, let’s say severe thunderstorms in the Plains, or tornado formation.”
Even Project 2025’s authors point out that private industry forecasters rely on publicly available NOAA data, though it doesn’t make any reference to AI models or physics models. The document simply says that the agency “should focus on its data-gathering services” and the “efficient delivery of accurate, timely, and unbiased data to the public and to the private sector.”
There are also questions around whether AI models, trained on data from the past, will be able to predict the types of unusual and extreme weather events that are becoming more and more common in a warming world, Swain told me. “Does it fully capture those?” he asked. “There’s a lot of evidence that the answer is no.”
Lastly, NOAA’s weather model, the Global Forecast System, is simply measuring much more than the AI models do today. “It predicts so many different phenomena, like different types of snow, hail, mixing ratios, turbulence,” Dean said. “We’re building up over time to add more and more variables. But for both WindBorne and other models, it’s not the same currently as what GFS does.”
So while the Heritage Foundation might want to delegate all forecasting responsibilities to private companies, the vision I heard from the startups I talked to looked more like a mutually beneficial arrangement than the full commercialization of weather prediction, or even a clean division of labor. “It’s not privatized weather, it’s a public-private partnership,” Dean said of his ideal future, “where you get freely available forecasts from a public institution like NOAA, but they work with our industry to iterate faster and to drive more innovation.”
What everyone seems to want is simply for the government to forecast better, and today that means moving quickly to build AI-based models. NOAA has taken some steps forward, prototyping some models, bolstering its computing capabilities, and even recently partnering with Brightband to optimize its observational data to train AI models. But it remains behind other agencies in this regard. “The Chinese government and the European Center for Medium Range Weather Forecasts have done a far better job at adopting AI-based weather forecasts than NOAA has,” Dean told me. “So something does need to change at NOAA to get them to move faster.”
Indiscriminately laying off hundreds of the agency’s employees may not be the best place to start. But if there’s anything we know Musk loves, it’s AI and private sector ingenuity. So maybe, just maybe, this administration will be able to forge the kind of partnerships that can supercharge federal forecasting, while keeping NOAA’s weather predictions free and open for all. Or maybe we’ll all just be paying the big bucks to figure out when a hurricane is going to hit.
On energy transition funds, disappearing butterflies, and Tesla’s stock slump
Current conditions: Australians have been told to prepare for the worst ahead of Cyclone Alfred, and 100,000 people are already without power • Argentina’s Buenos Aires province has been hit by deadly flooding • Critical fire conditions will persist across much of west Texas through Saturday.
Many foreign aid programs have reportedly received a questionnaire from the Trump administration that they must complete as part of a review, presumably to help the government decide whether or not the groups should receive any more federal funds. One of the questions on the list, according toThe New York Times, is: “Can you confirm this is not a climate or ‘environmental justice’ project or include such elements?” Another asks if the project will “directly impact efforts to strengthen U.S. supply chains or secure rare earth minerals?” President Trump issued an executive order freezing foreign aid on his first day back in office. The Supreme Court subsequently ruled that aid must be released. The Times notes that “many of the projects under scrutiny have already fired their staff and closed their doors, because they have received no federal funds since the review process ostensibly began. … Within some organizations, there are no staff members left to complete the survey.”
The United States has withdrawn from a global financing program aimed at helping poorer nations ditch fossil fuels and shift to clean energy. A spokesperson from the Treasury Department said the Just Energy Transition Partnership does not align with President Trump’s vision of American economic and environmental values. The program was launched in 2021 and has 10 donor nations, including many European countries. Its first beneficiaries were Indonesia, Senegal, South Africa, and Vietnam. The U.S. had committed more than $3 billion to Indonesia and Vietnam and nearly $2 billion to South Africa under the initiative. “The U.S. withdrawal is regrettable,” said Rachel Kyte, the U.K.’s climate envoy. “The rest of the world moves on.” In January, the Trump administration canceled $4 billion in pledges to the Green Climate Fund. “We have to plan for a world where the U.S. is not transfusing funds into the green transition,” Kyte added.
Butterfly populations in the U.S. are rapidly declining due to a combination of climate change, habitat loss, and pesticide exposure, according to a “catastrophic and saddening” new study published in the journal Science. “Butterflies are vanishing from the face of the earth,” one of the study’s co-authors told The Washington Post. The research analyzed data from 77,000 butterfly surveys and found that butterfly numbers have fallen by 22% in just 20 years across the entire country. Of the 342 butterfly species that could be analyzed for trends, 107 plummeted by more than 50% and 22 by more than 90%. Just nine species saw their numbers rise. The researchers say these numbers are likely an underestimate.
The findings underscore the crisis facing all the small, underappreciated insects that pollinate flowers and crops, control pests, maintain soil health, and play a vital role in the food chain. According to the World Wildlife Fund, up to 40% of the world’s insect species may disappear by the end of the century. The study’s lead author, ecologist Collin Edwards, said there is some hope. “Butterflies have fast life cycles,” he said. “At least one generation per year, often two or three. And each of those generations lays a ton of eggs. This means that if we make the world a more hospitable place for butterflies, butterfly species have the capacity to respond very quickly and take advantage of all our efforts.”
The Government Accountability Office yesterday said that Congress can’t review (or repeal) the Environmental Protection Agency’s waiver that lets California set its own vehicle emissions standards. The decision derails plans being spearheaded by Republicans and EPA Administrator Lee Zeldin to use the congressional review process to overturn the waiver. California’s aggressive emissions standards, which have been adopted by many other states, would effectively end the sale of fully gas-powered cars by 2035. Republicans are mulling their next move.
Tesla’s stock price has been taking a beating as resentment grows around CEO Elon Musk’s political meddling. The company’s valuation soared from around $800 billion to $1.5 trillion in December, when it became clear Musk would become the president-elect’s right hand man. Since that moment, the company’s value has fallen by more than $600 million, effectively erasing the bump in Tesla’s market cap. Shares fell by 5.6% yesterday alone, and sales are cratering abroad and in key U.S. markets like California.
As Andrew Moseman explains for Heatmap, a big drop in sales could be a double-whammy for Tesla revenue. “Recall that the company’s most reliable revenue stream is not really its sales of electric cars, but rather the carbon credits generated by those EVs under California’s auto emissions regulatory scheme, which it can sell to other automakers who’ve yet to meet their emissions targets,” Moseman says. “Tesla’s tumbling sales in the wake of Musk’s antics could reduce the amount of credits it could sell to others, since the credits are tied to sales of low-emissions vehicles.” There was more bad news for Musk today: A SpaceX Starship rocket exploded during a test flight, sending flaming debris flying across a large area and disrupting air traffic in Florida.
A new report shows that a year after London expanded its low-emissions zone, air quality in the city has improved, with nitrogen dioxide levels across 2024 down significantly: