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Last time around they were bulwarks for climate action. This time is different.

This story is part of a Heatmap series on the “green freeze” under Trump.
Following Donald Trump’s election in November, climate advocates self-soothed with the conviction that cities and states would continue carrying the banner in the absence of federal climate action. That’s what happened during Trump’s first presidency, after all. When he pulled the U.S. out of the Paris Agreement in 2017, hundreds of local governments declared they were “still in” on climate, and a new wave of state and local climate policies swept the country.
By the time Biden stepped into the White House four years later, many of these communities had climate plans either in place or in progress. When his administration passed the Infrastructure Investment and Jobs Act and the Inflation Reduction Act, setting aside billions of dollars for emissions reduction and climate adaptation projects, they were in a prime position to apply for funding. By November 2024, with most of that money doled out, it was easy to imagine how climate-forward cities could forge ahead, seeded by grants, regardless of what Trump did.
Except then Trump did the thing that many assumed he would not — because he legally could not — do. He froze and is now trying to claw back congressionally appropriated, contractually obligated funds. And in so doing, he has thrown the prospects for cities as a last line of defense into question.
“In this administration, it’s a lot more chaotic,” Barbara Buffaloe, the mayor of Columbia, Missouri, told me. “There’s a lot more happening than I feel like there was in 2017, right at the get-go. Nobody knows what the universe is right now.”
Columbia was among those that joined the “still in” campaign in 2017. It adopted emissions reduction goals in 2018, and passed a climate action and adaptation plan in 2019. The Biden administration awarded the city more than $28 million across three separate federal grants to build electric vehicle charging stations, make electrical upgrades that would allow it to charge electric buses, and redesign its central business loop to be more walkable, bikeable, and safe.
All three of those grants are now up in the air. Buffaloe said she was told by state partners that the $2.1 million business loop planning grant from the Department of Transportation’s Reconnecting Communities program was paused. Columbia was the only city in Missouri to get a Charging and Fueling Infrastructure Grant from the DOT, with the $3.6 million supposed to help pay for EV chargers at the library and the airport. The city is moving ahead with initial activities like environmental reviews and preliminary engineering in the hope that funds to build the actual stations will be unfrozen by the time it’s ready to break ground. Regarding the $23 million bus infrastructure grant, part of a separate DOT program, she said the city hasn’t heard from its grant managers in about a month.
“We don’t know whether or not to continue on the projects,” she told me. “It’s that feeling of uncertainty and trepidation that is causing us the most anxiety. Our construction window is not year-round in Columbia, and because we’re a public institution, it takes a lot longer for us to put out bids and to start projects. We need to know if we have this budget or not.”
It’s not just the funding freeze leaving Columbia in a holding pattern. The city has a municipally-owned electric utility that had been looking to take advantage of “direct pay,” an option for nonprofit entities with no tax liability to collect federal renewable energy incentives as direct subsidies, to help it build more solar farms. But now Republicans in Congress are considering eliminating direct pay.
The funding freeze has put a lot of cities in this position where time-sensitive decisions are stalled. Hundreds of communities were awarded grants from the U.S. Department of Agriculture program to fund tree-planting for carbon mitigation and shade creation, for example. Some recipients have been told their grants were canceled altogether, others are still in the dark — their federal grant managers have been fired and no one is responding to their emails.
“They’re kind of at this point of, hey, do we put in the order for trees? We need to plant at certain times of the year,” Laura Jay, the deputy director of Climate Mayors, a national network of mayors working to address climate change, told me. “For a lot of these cities and programs, there’s key decisions that they have to be making, and when there’s uncertainty around it, it puts the city at a huge risk.” There’s financial risk, she said, in terms of spending money without knowing if it will get reimbursed, but also planning risks. A number of cities were awarded grants to purchase electric school buses, for example, and they need to make sure they are going to have enough to get kids to school.
As a larger, wealthier city, Columbia is in a better position than others. It collects revenue through a capital improvement tax that Buffaloe said could be used for climate projects. “We’ll do as much as we can,” she told me.
But in more rural areas, these grants represented a rare opportunity to modernize and build more equitable access to infrastructure.
“We’re in Southeast Ohio, which traditionally has been left behind when it comes to larger infrastructure projects,” Andrew Chiki, the deputy service-safety director in Athens, Ohio, told me. “We don’t have an interstate highway.”
Chiki helped lead a regional effort to apply for a Charging and Fueling Infrastructure Grant, the same program Columbia won funding from that is now frozen. He and his partners were awarded $12.5 million to build a corridor of electric vehicle chargers in 16 communities between Athens and Dayton. “One of our attempts with this was to answer the question, if EV adoption takes off the way that we are envisioning, how do we allow an on-ramp for communities that are already disadvantaged to be able to adopt?”
Chiki said they were still waiting to hear whether they could move forward with the project or not. Athens passed a resolution declaring a climate emergency in 2020, and adopted a target to reduce emissions by 50% over 10 years. The city has made some strides, Chiki said, by making buildings more energy efficient and installing solar on city-owned facilities. “We are still committed to doing as much as we can,” he told me.
But if the EV charging grant falls through, the smaller villages and towns between Athens and Dayton that don’t have the staff resources or capacity to apply for these types of grants will lose out, he said. “We would probably look at other types of funding sources, but it would make it incredibly difficult and not be nearly as broad as we want.”
There are some pots of money for local climate projects that have flown under the Trump administration’s radar. Last year, the South Florida ClimateReady Tech Hub, a consortium of local governments, schools, labor groups, and companies working to accelerate the development of climate technologies, won a $19.5 million grant from the Department of Commerce’s Economic Development Administration. The money came from the Biden-era CHIPS and Science Act, a law that Trump is pushing Congress to scrap but that Republicans have thus far defended. Tech Hub will use the funds to scale low-emissions cement that can be used for adaptation projects, energy efficiency, and workforce development, among other things.
Francesca Covey, the chief innovation and economic development officer for Miami-Dade County and regional innovation officer for the Tech Hub, told me the group has continued to have quarterly check-ins with federal partners and haven’t gotten any signal that the funding is in jeopardy. “It’s really been more business as usual,” she said. Covey also mentioned two pilot projects to build artificial reefs and seawalls in the area that had funding from the Department of Defense and were moving forward.
Still, the Tech Hub has adjusted its language to stay competitive in the new political environment. The group changed its name to the Risk and Resilience Tech Hub two weeks ago, Covey told me. “We wanted to underscore the economic imperative of the work,” she said, when I asked what motivated the name change. “Right now we’re finding that where we are getting the best traction with the private and public community is around risk. We wanted to make sure we were couching it in the right way.”
Ithaca, New York, on the other hand, which passed its own Green New Deal in 2019, is committed to its climate and equity-centric messaging. “We are not intending to change the narrative around what we’re doing,” Rebecca Evans, the city’s sustainability director, told me. “It’s still clean energy, and it is still because climate change is a threat to human existence. We are still going to prioritize black and brown populations and populations that experience poverty at various levels because they are most vulnerable to climate change.”
About 85% of Evans’ Green New Deal budget comes from federal sources, and at first she worried that was all at risk. In 2022 and 2023, Ithaca had received funding from what’s called “congressional directed spending,” or “earmarks,” in two federal appropriations bills, meaning that New York state lawmakers fought to get money set aside for the city. The first grant, worth $1 million, was for a hydrogen production and fueling project. The second, worth $1.5 million, was for a wide-ranging program to decarbonize the school system and enhance a local workforce development program to include new energy efficiency certifications. Both programs included explicit diversity, equity, and inclusion-related objectives, so Evans assumed they would be targeted by the Trump administration.
But on Tuesday, she was told by federal partners on the hydrogen grant that congressionally directed spending was not subject to Trump’s executive orders and got the greenlight to move into the next phase. Evans still hasn’t heard back from her federal partners on the second grant, but she’s more hopeful now that it will move forward.
Back when I first spoke to Evans, when things were more up in the air, she told me she worried that the Trump administration’s actions would cause advocates to lose hope. “I think anger can be a positive thing, but it’s the loss of hope, even if it’s marginal, that is truly, truly dangerous to this movement.”
Perhaps that’s why Evans, like all of the other local leaders I spoke with, projected optimism when I asked what they could accomplish over the next four years without federal support. She was already trying to find the money elsewhere, she said. “We can’t do all of the amazing things that we wanted to do, but we can still make progress,” she said.
“Cities are incredibly nimble and innovative,” Jay, of Climate Mayors, told me. “I think that they’re eager to and committed to keeping the work going. What that looks like, I think, is hard to figure out right now, because everyone’s kind of caught in the chaos of trying to figure out if they still have this funding or not. But they’re fully committed to making sure that this work is continuing.”
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The spinoff of Lawrence Livermore National Lab has a new 10-point plan to get onto the grid by the 2030s.
One of fusion energy’s newest startups, Inertia Enterprises, is betting that the fastest route to commercial fusion runs through one of the field’s oldest ideas. The company, which raised a $450 million Series A earlier this year, plans to build a power plant based on the laser-driven fusion system pioneered at Lawrence Livermore National Laboratory’s — the only tech yet to have produced more energy from a fusion reaction than it took to initiate it. Now, Inertia has shared its commercialization roadmap exclusively with Heatmap, detailing the 10 near-term capabilities it must demonstrate before this landmark experiment can become a grid-scale power plant by the mid-2030s.
The roadmap offers a route from the national lab’s impressive but commercially impractical fusion demonstrations to an economical power plant capable of producing electricity for the grid. At its core are a set of milestones — mostly aimed at developing cheap, mass-manufacturable components — that Inertia says it must clear before those individual systems can be integrated into a working plant. This road is not necessarily linear, however, as various teams will likely be working on many of these goals simultaneously.
At least the physics of Inertia’s approach are already proven, the startup’s CEO Jeff Lawson told me, pointing to the fusion experiments at Lawrence Livermore’s National Ignition Facility as a proof-of-concept. The lab’s demonstration of net energy gain caps more than six decades and $30 billion (in 2026 dollars) of U.S. fusion research. The remaining challenges, he argued, are all engineering-related, requiring “elbow grease, hard work, and smart people” rather than breakthroughs in fusion science.
"It seems to us like a startup or a commercial company of any variety should be focused on commercializing a proven scientific result, as opposed to actually trying to demonstrate the basic science to begin with," Lawson told me. Basic science, he argues, is better left to national labs and universities, where researchers can pursue "unbounded problems" that don’t align with the expectations and timelines of venture-backed startups.
Indeed, no fusion startup has yet achieved scientific breakeven, the milestone Lawrence Livermore first hit in 2022, and has since repeated numerous times. But leading players such as Commonwealth Fusion Systems and Helion Energy maintain that it’s only a matter of time before they validate the physics behind their own reactor designs, which they claim will be highly cost-competitive.
Lawson, on the other hand, readily acknowledged that Lawrence Livermore’s tech is uneconomical in its current form. His bet is simply that the more predictable path to a commercial reactor is to drive down the cost of the lab’s validated fusion approach, known as inertial confinement. This system relies on high-powered lasers firing at a millimeter-scale pellet of fusion fuel, compressing it to extreme temperatures and pressures until the atoms fuse. Today, the National Ignition Facility makes each individual fusion target by hand, a workable solution given that it only uses about a dozen per year.
That production model, however, isn’t remotely plausible for a grid-scale power plant. Because each fusion reaction lasts just a fraction of a billionth of a second, a commercial facility must fire its lasers at a fresh target about 10 times per second to generate continuous electricity — requiring the production of hundreds of millions of targets each year.
Scaling production to roughly a million pellets per day and making them inexpensive enough for commercial operation without compromising the strength or precision required for fusion ignition is central to Inertia’s roadmap. That includes goals five, seven, eight and nine — industrializing the manufacturing of the carbon shells that hold the fusion fuel, making the thin films that hold those carbon shells both durable and cheap, scaling up and automating fusion target assembly, and speeding up how fast targets are filled with the requisite deuterium-tritium fuel.
The other central focus of the roadmap is the laser system, which will ultimately consist of 1,000 individual units operating in concert to compress and heat the fusion fuel. Key priorities include reducing the system’s cost (goal two), dramatically increasing its firing cadence (goal three), and bolstering its durability to withstand high-intensity operations (goal four). Goal six also complements these efforts, calling for the development of a control system capable of tracking moving fusion targets to precisely align each laser shot.
Goals one and 10 bookend the journey with some broader milestones. The first focuses on increasing the fusion target’s energy gain — the ratio of fusion energy produced to laser energy delivered — to more than 25 times ignition. Today, the National Ignition Facility’s best-performing laser shot has yielded a gain of just over four times what it took to start the reaction. Goal 10 then zooms out to the ultimate objective: integrating all these technologies into a commercially viable power plant that can deliver either electricity or industrial heat to end customers.
To reach that point, Inertia has embarked on an industrial engineering hiring spree, recruiting folks with experience taking complex hardware systems from prototype to mass production, “not unlike the processes that are used in the semiconductor or consumer electronics world,” Lawson explained. The company has been making progress on its component development goals since the beginning of the year, he told me, and expects to announce the successful demonstration of a few of these milestones in the coming months. Lawson ultimately expects Inertia to complete the core components of its laser and target manufacturing systems by the middle of next year.
The team will spend the next two to three years integrating these individual pieces into two fully operational subsystems, a prototype laser system and a target manufacturing line. Around 2030, the company will begin combining those subsystems into a first-of-a-kind fusion power plant, which will also serve as the proving ground for the target chamber, tritium fuel breeding system, and power conversion system that turns fusion heat into electricity. By the middle of the next decade, Inertia aims to be generating power from this first plant, setting the stage for the company to build and connect additional grid-scale commercial power plants.
There are plenty of engineering trade-offs that the company will have to solve for. Take the decision around how to size the target chamber, for example. “If you make it bigger, your walls have an easier time and survive longer, but it’s more expensive. If you make it smaller, your walls have a tougher time because they’re closer to all the heat and energy that the fusion reaction is creating, but now your power plant costs less to build.”
But to Lawson, this represents exactly the type of problem Inertia was built to solve: complex engineering issues that come to the fore once scientists have demonstrated the fundamental physics are sound. He thinks other fusion companies may someday reach this stage, as well — though he’s unwilling to hazard a guess on exactly what approach or startup is best positioned to do so.
“There have been generations of scientists who’ve made their predictions about fusion energy and gotten it wrong,” he told me. “I’m not going to pretend to be smarter than them. All I’m here to say is, just knowing that one did work, we can commercialize it.”
Current conditions: After forming into Tropical Storm Bertha late Monday, the system is barreling toward the Florida Panhandle as it makes landfall as far west as Texas • In the Pacific, Hurricane Fausto has strength as it heads toward Hawaii but remains a Category 1 storm • Temperatures in Ouargla, Algeria’s southern city in the Sahara desert, are soaring to nearly 120 degrees Fahrenheit this week.
Emissions from the United States’ electrical sector spiked 4% last year as demand for power drove up generation from coal. That’s according to the latest annual assessment published Tuesday morning by the U.S. Energy Information Administration. The report, which has tracked annual emissions data from all power sources since 2010, found that U.S. energy-related carbon dioxide emissions increased by 2%, or about 115 million metric tons, in 2025. But the power sector specifically saw a surge of 4%, or 58 million metric tons, due to a spike in fossil fuel use. Coal-fired generation rose by 13%, even as natural gas-fired power fell 4%. Renewables helped avoid more coal use. While wind generation increased 3%, solar skyrocketed by 34%. Generation from all other sources — including nuclear and the category of “other renewables” that includes hydropower and geothermal — were essentially flat last year.
The coal surge isn’t unique to the U.S., as my colleague Matthew Zeitlin wrote last year. Worldwide, rising demand for electricity and shrinking supply of natural gas coming through the Strait of Hormuz made for a good year for coal.
Watershed, the software platform focused on corporate sustainability, just published what it called its first comprehensive open framework for estimating the greenhouse gas emissions from companies’ use of AI programs. The framework has three elements: A comprehensive system that includes all phases of a data center’s use, from model training to inference to hardware production; a function unit of kilograms of carbon dioxide equivalent per million tokens; and a three-tier calculation approach “that aligns with companies’ data quality.”
In a statement to my colleague Emily Pontecorvo, Watershed’s science chief John Bistline said he had “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. We wanted to give them something that was more defensible.”
Oil prices spiked again Tuesday after President Donald Trump publicly weighed taking “a nice big fat shot” at Iran’s Pickaxe Mountain, where Israeli intelligence suggests the Islamic Republic moved its uranium-enriching centrifuges last fall. Brent crude, the main European benchmark for the price per barrel of oil, rose nearly 3% to over $91. West Texas Intermediate, the U.S. price signal, saw a 3% hike to just nearly $85. Murban crude — out of the United Arab Emirates, therefore the most sensitive to Persian Gulf disruptions — soared nearly 5% to just under $86 per barrel.
Shakeups among smaller producers, meanwhile, appeared to cancel out each other’s effects on the market. The shot: Kazakhstan, which falls just outside the top 10 oil-producing nations, is halting crude shipments to the Russia ports it relied on to get its hydrocarbons to market now that Ukraine is consistently attacking the Kremlin’s energy infrastructure, according to the Financial Times. The chaser: Norway’s oil output just beat forecasts, per Oil Price.
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Unlike the last man Trump put in charge of the Environmental Protection Agency during his first term in office, Lee Zeldin hadn’t formally worked for the coal industry before serving in government. But the EPA administrator sure made it sound like the industry’s executives are high-priority constituents. At a National Coal Council event in Washington, D.C.’s Willard Hotel that E&E News covered, Zeldin said “many of the items that were on your wish list are now done.” In the coming months, he added, the agency would get to “the remainder of those items,” but said he wouldn’t “prejudge” any rulemaking outcomes. “Between now and your next meeting, I’m excited to be able to share with great optimism, hope, and enthusiasm that you all, again, not prejudging the outcome of any rulemaking, we’ll have a lot to celebrate the next time you all get together again in January,” Zeldin said. One thing the EPA can’t do: Keep the coal plants the Trump administration wants open actually running. As Matthew wrote last year, the big problem with aging coal stations is that they keep breaking down.
Mergers and acquisitions within the global nuclear industry totaled more than $7 billion in value in the first half of 2026, doubling that same figure from a year earlier. That’s according to new data the law firm White & Case LLP shared Tuesday with World Nuclear News. The number of individual deals increased 10%, from 40 to 44. “At the current pace of dealmaking activity, 2026 is set to surpass all years aside from 2024 when a record $29 billion of M&A activity was registered,” the law firm said. More proof that the nuclear dealmaking boom, as Heatmap’s Katie Brigham wrote last year, “is real.”
It’s not just automobiles going hybrid-electric. The startup Electra, which has promised to build a nine-passenger hybrid-electric plane that can take off in as little as 150 feet, is now pumping $850 million into its first aircraft factory in Ohio. The plant, announced Tuesday, will build up to 800 aircraft per year at full capacity. But as Electrek put it, “that’s a big commitment for a plane that hasn’t flown yet.”
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.”