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Like gas stations, electric car chargers just have to work.

About 14% of American EV drivers experienced a charging fail last year — that is, they stopped somewhere expecting to charge and just couldn’t get the electrons to flow. That number is headed in the right direction, down from 19% just a year prior. Yet it demonstrates how far we have to go. Just imagine the collective rage if it were a yearly occurrence that one in seven gas car drivers pulled into a service station — maybe the only one for miles — and couldn’t get the pumps to work.
For an electrifying nation, it’s not enough to look at the map of high-speed chargers and see enough dots to get you from place to place. Drivers, especially those considering their first try with an EV, need to believe those plugs are going to work seamlessly and without drama. That makes charger uptime the new competition for America’s high-speed charging providers and a crucial concern for carmakers trying to sell electric cars to a still-skeptical general public.
Take what’s happening at Rivian. During the brand’s ascendance, it has been slowly building out the Rivian Adventure Network. While the system is much smaller than Tesla’s Supercharger network in terms of stations and plugs, it has fast-chargers in strategic locations to ensure Rivian drivers can reach popular destinations and far-flung adventure attractions such as national parks. It also focused on making sure those plugs almost always work.
That’s crucial, because not all charger fails are created equal. Plenty of times I’ve tried to plug into a Level 2 destination charger in a parking structure or at a grocery store, only to be thwarted by a card reader that wouldn’t scan my payment method — or by the requirement to download a whole new app just to charge my car, something impossible to do with the cell service in the bowels of a garage. But those are charging sessions of convenience, times it would be nice to add a few miles during a shopping trip. The DC fast-chargers that make road trips possible have to work, no excuses.
When I asked Rivian cofounder and CEO RJ Scaringe about the network during this month’s first drive event for the R2 SUV, he noted that his and Tesla’s are the only EV fast-charging networks in America to achieve uptime north of 99%, and that he’s not stopping there. “The U.S. needs to have more than one great high-speed network,” he said, “and so we’re continuing to build it and we’re continuing to invest in the development of the hardware.”
Rivian could just outsource fast-charging, as legacy carmakers largely have done. Especially now that Rivians use the Tesla-developed NACS plug that is becoming the industry standard, they can charge easily at any of the legion of Superchargers, as well as at the stations run by third parties such as EVgo, Ionna, and Electrify America. But Scaringe says the continued expansion of Rivian’s network remains a core part of the company’s growth. The brand just opened its 1,000th plug, up from just 700 a year ago, while the network has about 150 total charging locations.
The continued investment makes sense. The more affordable R2 is the company’s do-or-die moment, and as Americans consider buying one as the various versions roll out this year and next, they’ll be greeted by a charging map that promises peace of mind — a growing list of Rivian-branded, high-reliability plugs that open up even the lonely places in America, backed up by thousands of accessible stations built by Tesla and others. (It doesn’t hurt that Rivian’s network delivers not only customer confidence, but also corporate revenue: Nearly all Rivian stations are now open to other brands’ EVs, creating a growing revenue stream as the startup finds its financial footing.)
Meanwhile, the rest of the charging industry is catching up. A report by the EV data analysis firm Paren says that while most U.S. states scored between 85% and 92% for charger reliability in the first quarter of 2025, that range of average performance rose to 90% to 95% in the first quarter of this year. In March, when I talked to Sara Rafalson of EVgo, her company was hard at work on a revised technology to make sessions more reliable and foolproof. That will involve “a completely different site layout, a completely different power sharing technology, a different dispenser, a different user interface, different hardware, firmware, software, the whole thing,” she told me.
All the parts matter. Bad interfaces with clunky software or busted hardware like physical buttons or credit card readers caused plenty of charger-fail chaos in the early days of American EVs. Tesla has created the charging gold standard — plug in your Model Y and it just works — but step outside that vertical integration and even Superchargers become a little annoying, as charging a non-Tesla still means having a Tesla account and navigating deep into their app. And too many American EV drivers know the pain of pulling up to a charger to find all the plugs either occupied or busted. Even if that doesn’t count as a failure in the statistics, it still represents a broken experience.
People have always had their reasons for picking which gas station to go to: They hit the one nearest their home, the one where they have a loyalty credit card, or the one that’s always a few cents cheaper than everywhere else in town. They don’t choose based on whose pumps are the most reliable. The gasoline delivery economy is one of those systems so mature it becomes invisible. But as EV charging comes of age, uptime and reliability might be just as important as price and amenities when it comes to planning out stops along the highway.
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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.