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Energy procurement expert Arushi Sharma Frank wants to apply “connect and manage” to AI development.

Everyone knows now how great Texas is for renewables. Its particular combination of sun, wind, and permissive market structure has led the state to overtake even California in clean energy generation. At the same time, however, the state is nervous about data centers and their effects on the grid, even passing a law this past legislative session to more closely examine the data center industry and to establish protocols for curtailing data center operations when electricity is tight.
But what if you could do for data centers what Texas has done for renewables? That’s roughly the idea Arushi Sharma Frank, who helped bring Tesla’s energy business to Texas and is now an advisor to Nvidia-backed Emerald AI, has come up with.
On Friday, Frank filed a proposal with ERCOT, the electricity market that sits outside federal regulation and covers about 90% of the state, that would reform its rules to allow data centers to connect to the grid much faster. The rough idea is that by applying ERCOT’s existing “connect and manage” system for getting new electricity generators on the grid to new large demand sources like data centers, the data centers can get power more quickly — if they can handle not getting access to the grid sometimes.
The proposal “creates the basis of connect-and-manage of load using the existence system that ERCOT already has for generators and batteries,” Frank told me.
Her idea would reward data centers for being able to modulate how much electricity they need in the interconnection process. This could mean that data centers get credit for curtailment and for having their own generation on site. And crucially, unlike a widely-panned proposal by PJM Interconnection to essentially mandate that some customers be forced to curtail their energy use, the ability to curtail or self-power a data center would result in faster interconnection, not simply the cost of doing business.
Frank pointed to chip designer Nvidia’s recent announcement that it would back a Virginia data center using Emerald AI software to smooth out power usage, saying she wants to be “able to actually do that at scale” for “any developer in Texas.”
Getting power for data centers is one of the biggest barriers to getting them built, and so anything that can deliver faster interconnection without foisting enormous new costs on the system as a whole counts as a win-win. With this system in place, Frank told me, data centers and other large loads could “invest in firming their own power needs before major transmission upgrades get built, enabling them to voluntarily choose to be flexible participants on the grid in exchange for earlier interconnection.”
Texas has been able to deploy wind, solar, and batteries so quickly, many energy policy experts and developers say, precisely because of connect and manage, whereby new generators can get on the grid after just a local grid study, without having to examine their effect on the whole system, which most of the country’s grids require. After these system-wide studies often come expensive transmission upgrades, the costs of which are passed on to all electricity customers in the form of higher bills. This process, Duke University’s Tyler Norris has written, “can often make generators financially unviable, introduce uncertainty for project economics, and delay interconnection by years.”
That level of extensive review is partially responsible for the interconnection delays seen in the rest of the country, which can stretch to as long as several years. Projects in Texas take on average two years to complete the interconnection process, according to the trade group Advanced Energy United.
The trade-off for allowing new resources onto the grid without those upgrades means that they’re more likely to be curtailed if the amount of electricity they generate overwhelms the grid — the “manage” part of “connect and manage.” Frank made an analogy to me between a data center and an 18-wheeler, which might be allowed to start its journey sooner if it agreed in advance to get off the road in the case of heavy traffic.
Frank delivered her proposal along with support from a group of big-name and deep-pocketed stakeholders, including former Loans Program Office chief Jigar Shah, renewables developers like Cypress Creek Renewables, and a number of datacenter developers and technology providers.
In comments on the proposal, Agentic Infrastructure, which works on powering data centers, said that Frank’s plan will allow for “private capital investment to energize with dispatchable service ahead of the timeline required for expansion of firm network service,” which would ensure that “the risks of serving rapid load growth are managed privately while the economics benefits of load growth are socialized to the public rate base.”
In other words, more users of electricity would come online faster, allowing them to make payments to utilities and split up fixed costs among all customers, while the developers would take on the risk of not always being able to power their data centers.
In a best-case scenario, the proposal could be approved at an ERCOT board meeting early next year, Frank said.
Allowing flexible large loads to connect faster is “the most viable way for loads to actually invest with their complex webs of financing and technology partners in creating dispatchability,” she added.
“Everyone is talking about” how important dispatchability is, Frank told me, but “no one is doing anything about it, except for the proposal at PJM and random one-off deals that folks like Google are doing.”
“What makes ERCOT different,” Frank said, “is that it is a place that gets national attention, and it can get national attention because things generally just happen faster there.”
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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.