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Plus AI for powering AI, low-carbon concrete, and more of the week’s big money moves.

This week has already brought a plethora of funding announcements. As my colleague Alexander C. Kaufman highlighted in our AM newsletter, these include $421 million in debt financing for the geothermal unicorn Fervo and a deal between Uber and Rivian that could see the former investing over $1.2 billion in the electric vehicle automaker to support the deployment of up to 50,000 autonomous robotaxis through 2031.
But beyond these headline-grabbing numbers, this week also brings a major offtake agreement for the Massachusetts-based critical minerals startup Nth Cycle, coinciding with the Trump administration’s $500 million push for domestic refining and recycling. There’s also new money for an AI platform for energy data, lower-carbon concrete, and carbon sequestering textiles.
A wave of critical minerals startups has been gaining momentum in the U.S., buoyed by the Trump administration’s push to onshore production in the name of national security. One of those companies, Nth Cycle, recently signed a $1.1 billion, 10-year binding offtake agreement to supply nickel and lithium to global commodities trader Trafigura. The scale of this deal — Trafigura has committed to buying 2,000 metric tons of nickel and 1,500 metric tons of lithium carbonate — underscores the growing geopolitical attraction of breaking away from the Chinese-dominated battery minerals supply chain.
Nth Cycle recovers metals such as nickel, copper, and cobalt from “black mass” — a processed mixture of materials from spent lithium-ion batteries — by dissolving it in a liquid electrolyte and pumping it through an electrochemical cell. Individually-tuned voltages drive specific metal ions to gain electrons and deposit as solid metals onto the electrode’s surface. Unlike large-scale traditional metal refineries, the startup says its smaller, modular system can be deployed at existing partner sites like recycling facilities or scrap handling locations, reducing the cost of refining by up to 70%.
This deal will support the company’s expansion into South Carolina and the Netherlands, where operations are expected to start in 2028. Nth Cycle first started commercial operations a year and a half ago at a facility in Ohio capable of producing 900 metric tons annually of a mixed-metal material that contains nickel and cobalt.
“Speed to power” is the name of the game these days as companies — data centers in particular — need all the help they can get bringing new electricity sources online. Halcyon, which bills itself as “the AI platform for energy,” helps address this issue by helping energy professionals navigate and interpret vast volumes of information from regulatory findings to utility data, enabling more efficient and informed decisionmaking across energy markets and policy landscapes.
This week, the company announced a $21 million Series A round, led by the software-focused climate tech firm Energize Capital, to expand its platform’s capabilities, source even more data, and expand its team. The startup’s AI tool was built and trained on a deep catalogue of energy data from regulators including state public utility commissions, independent system operators, regional transmission organizations, and the Federal Energy Regulatory Commission, enabling users to query its sector-specific database as they would ChatGPT and track developments on particular questions.
“Despite representing as much as 10% of global GDP, multi-billion dollar energy investments are being made with opaque, incomplete, and fragmented information,” Bruce Falck, Halcyon’s co-founder and CEO, said in a company blog post about the new funding. “Halcyon makes complex and fragmented energy information discoverable, actionable, and valuable.” In addition to its main platform, the startup sells subscriptions to datasets such as its Gas Power Plant Tracker and New Substation Development Tracker, which can also inform data center citing decisions, for example.
The world produces roughly 30 billion metric tons of concrete annually, making it the most widely used man-made material on earth — and the source of about 8% of global CO2 emissions. That’s due to the energy-intensive production process of cement, the glue that holds the concrete mixture together. Now though, the London-based startup Cocoon Carbon promises to reduce the emissions intensity of concrete through the addition of industrial byproducts known as “supplementary cementitious materials,” raising a $15 million Series A to help scale production.
SCMs are nothing new — they’ve been a part of standard concrete mixes for decades due to their durability-enhancing properties. But today they’re largely sourced from the byproducts of coal plants and iron blast furnaces — technologies that are falling out of favor as natural gas and renewables scale and electric arc furnaces replace blast furnaces in the steel production process. That’s tightened the market for SCMs, causing prices to double since 2017, at the same time that construction is booming. To address this shortage, Cocoon has developed a rapid cooling system to convert steel slag residue from electric arc furnaces into a cement replacement that it says can reduce concrete emissions by up to 40%.
The startup says its ability to retrofit its system directly onto electric arc furnaces is crucial for cost-competitiveness, drawing a contrast with “other emerging alternatives to cement” — startups Brimstone and Sublime Systems come to mind — which it claims will carry the dreaded “green premium.” Cocoon is planning to use the new capital to build out its first commercial demonstration facility in the U.S., ultimately targeting deployment at 50 steel mills by 2035.
The fashion industry is slyly one of the world’s largest emitters, with textile production alone accounting for about 1.2 billion tons of annual carbon dioxide emissions — roughly equivalent to Germany, the U.K., and France’s annual emissions combined. But the materials company Rubi thinks it’s found a more sustainable approach to fashion, raising a $7.5 million seed round to support the production of textile fibers such as viscose and lyocell from captured carbon dioxide. The company has already piloted its process with partners such as Walmart, Reformation, and H&M, the latter of which also participated in this latest funding round.
To produce raw material for textiles, the company sources captured carbon from industrial flue gas and feeds it into its enzyme reactor, where a cascade of chemical reactions converts the CO2 into cellulose polymers. The resulting cellulose pulp is then recovered and supplied to Rubi’s partners, who spin it into fibers and yarns using existing textile manufacturing infrastructure. The startup’s approach differs from traditional fermentation and chemical methods, which rely on either microbes like yeast or fossil-derived feedstocks such as natural gas to produce polymers, systems the startup says are less efficient, more emissions intensive, and costlier than its own.
Rubi plans to use this latest funding to develop an industrial demonstration system capable of producing commercial quantities of its materials for its growing customer base, from which it’s already secured $60 million in non-binding offtake agreements. While clothing brands are Rubi’s first customers, the company plans to expand into other industries such as consumer packaged goods, aerospace, and chemicals.
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The data center boom is everywhere you look in U.S. economic and emissions data.
This is an edition of Heatmap Daily, an evening review of the day’s news written by our executive editor. Sign up for it here.
It isn’t exactly a new thought, but I’ve been struck recently by how many trends in America’s economic and environmental data are fundamentally about the data center boom and the return of electricity demand:
First, the Energy Information Administration reported this week that U.S. emissions grew by more than 2% last year, driven by surging electricity demand and an increase in coal-fired generation.What caused that higher power demand? New factories and data centers — as well as record summertime cooling demand.
Second, many of the new factories driving that higher power demand are themselves producing goods that are … let’s say … data center-adjacent. There are the enormous new semiconductor fabs, of course. But Ford and General Motors have also set up new production lines (or repurposed old ones) to manufacture grid-scale batteries to meet power demand.
Third, take a look at the recent U.S. spending on private non-residential construction — in other words, everything American companies are building that is not houses, condos, or apartments.
The construction industry’s spent almost $60 billion on data centers over the past year, which is more than it spent on all other office buildings combined (and more than it spent building warehouses, too). Just a handful of categories — data centers, power plants, electricity infrastructure, and certain kinds of electronics manufacturing — now make up a third of all U.S. private non-residential construction investment. They’ve never made up such a large share of construction spending since data collection began in 2014.
As The New York Times recently noted, the American economy is unusually dependent on the American stock market right now — and the stock market is unusually dependent on artificial intelligence. This week, investors started to balk at the enormous spending hyperscalers are planning to keep building out the AI boom; Alphabet’s shares dropped 8% this week after it boosted its planned 2026 capital expenditure and signaled 2027 will be even bigger. If the data center boom started to slow down in earnest, then more than just that budget will change.
Speaking of which, my colleague Emily Pontecorvo wrote earlier this week about how many businesses are struggling to even estimate their carbon emissions from artificial intelligence. The carbon accounting startup Watershed recently unveiled a new formula to help companies get a sense of their AI-related emissions.
But even that formula is still limited by the amount of data hyperscalers publish — and they don’t publish that much. Google, for instance, is the only AI company that has (laudably) provided estimates of its emissions on a per-prompt basis. Yet no company has published its per-token emissions, or how emissions sync up with particular models or regions.
So Emily asked Google: Why aren’t you — or any other model provider — disclosing this kind of data yet?
The tech company didn’t get back to us until after we’d published Emily’s story. But its response was interesting enough that I wanted to quote some of it here.
The problem is “industry consensus,” Cooper Elsworth, a Google spokesperson, told us. “There is currently very little consensus on how to comprehensively and fairly measure the serving environmental impact of generative AI (such as text generation),” he wrote. “Without standardized, ‘apples-to-apples’ frameworks, it is difficult to compare different providers accurately.”
That’s partly because energy use — and emissions data — can vary from site to site and depend on “custom-built hardware, software compilers, and advanced inference techniques.” And he claimed Google doesn’t always have the measurement hardware in place to provide such specific estimates: “Providing precise, repeatable data requires highly advanced measurement infrastructure,” he said. “For example, software-based energy monitoring tools often suffer from sampling biases. For our study, we had to step away from top-down averages and directly measure actual energy at the physical power supply unit (PSU) level across our deployed fleet. Not all providers have the telemetry or data sets required to benchmark their operations at this level of granularity.”
Read Emily’s story to understand the other reasons why estimating — or even “guesstimating” — AI-related carbon emissions is so challenging.
A conversation with Emma Uridge of the Kansas Health Institute.
This week’s conversation is with Emma Uridge, analyst with the Kansas Health Institute. Uridge spent copious hours analyzing state and local laws on data center development to best understand how policymakers are responding to the potential environmental public health impacts of large AI infrastructure, including power and water. The report, which came out this week, also goes in depth into those health impacts. I reached out to her to discuss what she sees as must-watch territory for our readers on this emerging policy arena.
Our conversation was lightly edited for clarity.
What is actually being done on policy when it comes to data centers — beyond moratoria of course?
So first I’d like to just talk about the point of moratoria. It’s helpful to talk about how these policies emerge in the first place. One area where moratoria are helpful is when a data center is proposed but the county has no approach for how they’d like to potentially regulate them. That’s temporary, most of the time. It lets local governments conduct research on the various impacts and also negotiate community benefits, ones that can mitigate any potential negative impacts — like Lancaster Pennsylvania, which instituted a community benefit agreement that maximized the potential benefits of development while mitigating what large data centers can do. That agreement looked at capping municipal water use at 20,000 gallons per day and requiring 100% clean energy. It had financial penalties for non-compliance. The company also committed $20 million to their local economic development and clean energy fund. There are ways to negotiate with developers.
We also see amendments to existing zoning. Data center proposals are increasingly popping up in rural areas, many of which are unzoned, so there’s no way a county can negotiate unless there’s a moratorium in place.
Other policy solutions include different performance standards or requiring on-site renewable energy, like what Jefferson County, Missouri, looked at. Also setback requirements, mandatory noise buffers, ending by-right zoning.
Where are local governments getting ideas for regulating data centers?
A lot of the technical information comes from developers. That can in cases be seen as a biased source of information. I wouldn’t say there’s a dedicated group providing assistance to local governments when a project is proposed — which is a similar story to wind industry development, where we have only a handful of consultants who provide technical advice. It can be really helpful to get a multi-disciplinary approach to hearing information. It can be helpful to have the utility commission, public health folks, those in academia, as well as the developer.
As of right now, especially in rural areas, local governments have a hard task of balancing pushback while getting the most accurate, evidence-based, neutral information to make decisions. That balance can be contentious.
What is the federal government doing on data center policy? How is the Trump administration approaching it?
A few things there. In the early days, the drive was for AI expansion and to be competitive with foreign adversaries. Now due to the amount of public pushback in red and blue localities and a more cautious approach.
I’m not seeing a lot of actual policy movement at this time.
I know the EPA is looking at the chemicals used in cooling data centers because when that water is cycled through the system, some of it is discharged into the water system, so they’re looking at the Toxic Substances and Control Act for monitoring that.
How much of an impact does this minimal federal role have on industry behavior?
Y’know, this isn’t specific to data centers. This is true for all kinds of large-scale development: there’s a need to require some sort of federal monitoring and regulation.
That’s where I see an emerging role for public health. At the federal level, there could be policy movement towards requiring some sort of environmental monitoring at data centers to make sure they’re operating responsibility. Looking at specific water use relative to water availability and what happens when there’s a time of severe, persistent drought. With air quality too — we’ve seen areas where the grid isn’t as reliable so their diesel generators are kicking on more and affecting air quality for residents.
We’re just not seeing all of that right now. We need corporate disclosure.
What do you see as the most important public health impacts from data center development?
It varies by localities. The most discussed obviously is water usage. One thing I’d note about my conversations with folks enthusiastic around emerging tech is, there are still questions that need to be asked about the capacity of localities to support a data center. Like a small town in Kansas may only be using 40% of their water for their utility needs. If a data center came online, how much of that water goes to the data center?
One area underexplored within the public health discipline is energy poverty and energy security. The ability of a household to meet the needs of everything energy provides in our lives. It’s known we have an aging electric grid but we’re not talking enough about large-scale blackouts when the grid is not sufficient to support some of these new data centers.
Plus more of the week’s big development fights.
1. Laramie County, Wyoming — Meta is fighting the fine it received in the Cheyenne data center water pollution controversy, and the conflict between the tech giant and the city’s small board of public utilities is continuing to spill out into the public.
2. Niagara County, New York — This county just rejected a solar project’s highway work permits in a show of retaliation against the state’s Office of Renewable Energy Siting.
3. Barron County, Wisconsin — The anti-solar protest is the new campaign stop in deep red Wisconsin.
4. Chesapeake, Virginia — A large battery storage project on the Virginia coastline is on the rocks amidst rampant local opposition.
5. Lewis County, West Virginia — West Virginia is now a key battleground in the fight over transmission, as a line spanning all of West Virginia and Maryland — and cutting through Data Center Alley in Virginia — causes compounding consternation.