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It will get better, but until then, the dongles are killing me.

Last year, a great streamlining of electric vehicle charging infrastructure looked imminent. One by one, the major automakers committed to using the North American Charging Standard, or NACS, which was formerly Tesla’s proprietary plug. The moves would allow EV drivers of all stripes to use Tesla’s Supercharger network and would move the industry toward a single standard where things worked seamlessly. Earlier this month, GM joined the ranks of Ford and Rivian in having its vehicles officially able to visit nearly 18,000 Supercharger stations.
All of the GM vehicles built up to this point, however, carry the previous charging standard for non-Tesla EVs. You know what that means: dongles.
Drivers in combustion cars choose between regular, plus, and premium gas, but they don’t worry that they’ll pull into a station and the pump won’t fit their car. EVs, meanwhile, still have to deal with a mess of competing plug standards and confusing customer interfaces at charging stations. This situation is the inescapable result of a fast-moving, fledgling industry, yes. But the complexity is an annoyingly sticky barrier to EV adoption.
The adapter necessary to make a GM EV work with a Tesla plug, for instance, is available. But there’s a waiting list, and the piece costs $225 — effectively a $225 early adopter penalty for buying your EV back before everyone agreed on how to cooperate. When Ford transitioned to NACS earlier this year, it had difficulty extracting enough adapters from Tesla to meet the demand, dragging out the process for months for some of its EV drivers. GM had been slated to join the Supercharger network months earlier and could not because of the dongle delays.
Not all the eligible cars just work, either. After GM electric vehicles were welcomed to Tesla Superchargers, it turned out that lots of Chevrolet Bolts made in 2019 and 2020 (when they were the best-selling non-Tesla EVs) needed to visit the dealership for a software update before they could link up with a Tesla plug.
Software patches and dongles may be an annoyance, a kind of Band-Aid to make two systems that weren’t meant to work together play nice, but at least a quick fix is possible. A bigger issue for streamlining charging stations is that the locations of charging ports on EVs themselves are far from standardized.
All Tesla models have ports in the rear on the driver’s side; Supercharging stations are typically built for drivers to back in and then find the appropriate cord right next to their charging port. A Chevy Bolt’s port, however, is found on the driver’s side but on the front. A Hyundai Ioniq 5’s is in the back, but on the passenger side. When Rivian revealed the R2 and R3 designs, their ports were on the passenger side rear because the brand thought that location would fit into its existing network of chargers and make it easier to plug into street-side plugs. Then came an outcry from fans distraught at how difficult it would be to use a Tesla Supercharger if the port were on the wrong side and the cable had to wrap all the way around the back of the vehicle. Rivian changed its mind.
Thank goodness for that, because the situation at Superchargers is poised to get messy. I’ve been to ones where Tesla plugs were available, but I could not park my Model 3 within reach of one because other EVs parked incorrectly in order to plug in. Tesla’s lead engineer for the Cybertruck had to warn people not to use extension cords at Superchargers since that might lead to electrical shorts.
Some relief is on the way. In the coming years, most car companies will build the NACS standard into their electric vehicles, negating the need for expensive adapters and dongles. With so much emphasis on using the Supercharger network, it’s likely the brands will feel pressure to follow Rivian’s lead and just put the port where Tesla puts it.
But then there’s the last piece of the puzzle: the interface. Tesla beat the competition at charging not only by building a bigger and far more reliable network, but also by inventing a seamless way to pay for electricity: When you plug in, the system knows it’s your car and charges the credit card on file. Non-Tesla drivers are beginning to experience this convenience when they stop at the Supercharger.
Competing systems, though, rely on a variety of phone apps that may or may not work, especially in places with spotty cell coverage. Tech companies are trying to solve this problem with, you guessed it, AI. Revel, which used to offer rentable mopeds around New York City, has tried to reposition itself as an EV charging company. It just partnered with a computer vision company to announce a kind of facial recognition system for your car so that the charging station knows it’s you.
Of course, one could just copy Tesla’s idea and have the charging cord auto-identify each vehicle, or even simply install a camera to read the car’s license plate instead of overcomplicating the basic task of IDing a car. But those solutions don’t use the magic technology of the moment.
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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.