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There isn’t one EV transition. There are two.

This has not been a good week for the electric-vehicle transition. On Wednesday, General Motors scrapped a self-imposed plan of building 400,000 electric vehicles by the middle of next year. Then it jettisoned plans with Honda to build a sub-$30,000 EV. On Thursday, Mercedes Benz announced that its profits had fallen in part due to turbulence in the EV market, and Hertz ditched a plan to have EVs make up 25% of its fleet by 2024.
Nor has the past month been much better. Ford has slowed down its EV factory build-out. Elon Musk announced that Tesla was taking a wait-and-see approach to opening its next plant, in Mexico, and The Wall Street Journal has reported that EV demand is proving weaker than once expected. Higher interest rates and, perhaps, a continued lack of public chargers now seem to be impairing the EV transition.
It’s an odd time, because while the day-to-day news is bad, the overall trend remains good — surprisingly good, even. More than 1 million EVs have been sold in America this year, and the country is likely to record 50% year-over-year EV market growth for two years in a row. That is not the usual sign of an industry in trouble. The industry is faltering, yes, but only compared to the rapid scale-up that companies once aimed for — and that the Paris Agreement’s climate targets demand. And at a global level, the news is better: The economics of batteries and trends in the Chinese and European markets leave little doubt that EVs will eventually win.
So how to make sense of this moment? Automakers, it seems, are not doubting whether the EV transition will happen; they are pausing to figure out how best to proceed. Journalists often talk about the “EV transition,” but this is something of a misnomer — there are really at least two different transitions, two different bridges to the EV future.
One of those transitions must be navigated by the legacy automakers, such as Ford and GM. The other must be completed by the new electric-only upstarts, such as Tesla and Rivian. Both transitions are, today, half-complete. What is notable about this moment is that both transitions are also in flux — and the companies and executives tasked with navigating them are struggling with their next steps.
The first bridge must be built by Ford, GM, Toyota, Volkswagen, and every other legacy automaker heavily invested in the U.S. market. You can think of it as a bridge made of cross-subsidies — subsidies not from the government, but from other cars in their product line.
Right now, many automakers earn their biggest profits by selling big, gas-burning vehicles: crossovers, SUVs, and pickup trucks. They lose money, meanwhile, on each EV that they sell. So over the next few years, these companies must take the huge profits from their SUV-and-truck business and reinvest them into scaling up their EV business.
You can see how difficult this will be by looking at Ford, which conveniently reports earnings from its internal combustion business separately from its electric vehicle business. During the first half of 2023, Ford’s global gas and hybrid sales earned $4.9 billion before interest or taxes. Ford’s EV business, meanwhile, lost $1.8 billion before interest or taxes.
During this same period, Ford sold nearly half a million trucks and SUVs in the U.S. alone, and roughly 25,000 electric vehicles. By one calculation, Ford lost $60,000 for every EV that it sold during the first quarter of this year.
This is the narrow bridge that Ford and its ilk must walk: They must remain mature businesses, delivering consistent profits to shareholders, even as they overhaul their entire product line and manufacturing system. And while these legacy automakers have certain advantages — brand cachet, a network of dealerships, and an understanding of how to make car bodies — they lack the deep familiarity with software or battery chemistries that underpin the EV business. What’s more, their current business rests on uneasy foundations: Because their profits are so heavily concentrated in just a few SUVs and trucks, a sudden shift in consumer tastes, fuel prices, or regulation could undercut their whole hustle.
We’ve already seen one consequence of this concentration in the United Autoworkers strike. By focusing its strikes on just a few factories at first, and then gradually expanding them to include each company’s most profitable facilities, the UAW was able to make its strike fund go further than outside commentators initially estimated. That strategy resulted in record high pay raises for workers in the UAW’s tentative deal with Ford; strikes continue at GM and Stellantis.
But this is, of course, only the first bridge to the EV future. Other companies — including Tesla, Rivian, and the early-stage EV startups Canoo and Fisker — have to build a different path across the river. You can think of this as the bridge of scaling up, although some auto-industry analysts give it a different name: crossing the EV valley of death.
These companies have to survive long enough to build up economies of scale. You can think of it this way: At the beginning of an EV company’s lifespan, it knows very little about how to mass-produce its EVs, but it has a lot of cash to burn. As it matures, it gets better at making EVs and grows its customer base, and it makes cars more frequently and more cheaply. Eventually, it reaches a point where it can sell lots of EVs for more money than they cost to make — that is, it can be a mature, profitable business.
But in the middle, it faces a hold-your-breath moment where its high costs can overwhelm its meager production. This is the valley of death, “the challenging period between developing a product and large-scale production, when a company isn’t earning much if any revenue, but operating and capital costs are high,” as the journalist Steve Levine puts it at The Information.
Nearly every EV company faces this problem to some extent right now. Elon Musk discussed it during a recent rambling Tesla earnings call. “People do not understand what is truly hard. That’s why I say prototypes are easy. Production is hard,” he said. “Going from a prototype to volume production is like 10,000% harder… than to make the prototype in the first place.”
Now, Tesla seems to have mostly cleared the valley of death with its Model 3 and Model Y this year, allowing it to undertake a campaign of aggressive price cuts that have increased demand while retaining some profitability.
But what Musk was talking about — and what Tesla is clearly struggling with — is the Cybertruck, which will debut next month after a multi-year delay. Musk warned that the company had “dug its own grave” by trying to build the Cybertruck and that there would be “enormous challenges” in producing it profitably and at scale.
But “this is simply normal,” he added. “When you've got a product with a lot of new technology or any brand-new vehicle program, but especially one that is as different and advanced as the Cybertruck, you will have problems proportionate to how many new things you're trying to solve at scale.”
Every other EV company finds itself on the same narrow bridge. Rivian, for instance, is somewhere further behind Tesla in general but is fast making up ground. It scaled up its production of its R1T and R1S models last quarter faster than analysts thought, but was at last report still losing money on each vehicle. Rivian’s CEO, R.J. Scaringe, told me that the company is focusing on making its next line of vehicles, the R2 series, easier and simpler to manufacture to avoid this problem.
Even further behind Rivian are Fisker, which claims to have delivered 5,000 of its Ocean SUVs, and Canoo, which is struggling to stay solvent.
What’s hard about this moment, then, is that the downsides and risks of each approach have never been clearer.
If a legacy company completes its EV transition too quickly, then it risks finding itself with a fleet of electric vehicles that the public isn’t ready to buy. Companies like Ford, GM, Volkswagen, and Toyota must scale up a profitable EV product line at the same time that they sell vehicles from their legacy business.
Worldwide, no historic automaker has transitioned fully to making battery-electric vehicles, although some have come very close: BYD, the Chinese automaker that has surpassed Tesla as the world’s biggest producer of EVs, opted to quit making internal-combustion vehicles last year, but it still sells plug-in hybrids. Volvo, too, is making an attempt: It has promised to stop selling internal-combustion cars by 2030. But Volvo is owned by the Chinese automaker Geely, meaning that both of these companies can sell their cars to a much larger and more EV-interested Chinese domestic market.
Yet the second transition is tough, too. Although it may seem that EV-only companies have a lot of freedom (by lacking a network of EV-skeptical dealerships, for instance), they also have no alternative revenue to cushion themselves through a period of soft demand — they can’t ever cross-subsidize. Although it sold buses and not private vehicles, the American EV-only vehicle maker Proterra is indicative here: It went bankrupt earlier this year after getting stuck halfway through the valley of death.
America is going to have a domestic EV industry. By the mid-2030s, most automakers will be integrated EV companies, building and selling electric vehicles that include some in-house hardware, software, and battery components. Consumers will think of their new vehicles more as technology than as a simple mode of transportation, and they will power them from ubiquitous charging stations, which will be as mundane and abundant as wall outlets are today.
That future is certain. But what kinds of cars will we be driving, and what companies will count themselves among the electric elect? I couldn’t tell you. It will all depend on what happens next — on who makes it across the narrow bridge.
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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.