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And four more things we learned from Tesla’s Q1 earnings call.

Tesla doesn’t want to talk about its cars — or at least, not about the cars that have steering wheels and human drivers.
Despite weeks of reports about Tesla’s manufacturing and sales woes — price cuts, recalls, and whether a new, cheaper model would ever come to fruition — CEO Elon Musk and other Tesla executives devoted their quarterly earnings call largely to the company's autonomous driving software. Musk promised that the long-awaited program would revolutionize the auto industry (“We’re putting the actual ‘auto’ in automobile,” as he put it) and lead to the “biggest asset appreciation in history” as existing Tesla vehicles got progressively better self-driving capabilities.
In other Tesla news, car sales are falling, and a new, cheaper vehicle will not be constructed on an all-new platform and manufacturing line, which would instead by reserved for a from-the-ground-up autonomous vehicle.
Here are five big takeaways from the company's earnings and conference call.
The company reported that its “total automotive revenues” came in at $17.4 billion in the first quarter, down 13% from a year ago. Its overall revenues of $21.3 billion, meanwhile, were down 9% from a year ago. The earnings announcement included a number of explanations for the slowdown, which was even worse than Wall Street analysts had expected.
Among the reasons Tesla cited for the disappointing results were arson at its Berlin factory, the obstruction to Red Sea shipping due to Houthi attacks from Yemen, plus a global slowdown in electric vehicle sales “as many carmakers prioritize hybrids over EVs.” The combined effects of these unfortunate events led the company to undertake a well-publicized series of price cuts and other sweeteners for buyers, which dug further into Tesla’s bottom line. Tesla’s chief financial officer, Vaibhav Taneja, said that the company’s free cash flow was negative more than $2 billion, largely due to a “mismatch” between its manufacturing and actual sales, which led to a buildup of car inventory.
The bad news was largely expected — the company’s shares had fallen 40% so far this year leading up to the first quarter earnings, and the past few weeks have featured a steady drumbeat of bad news from the automaker, including layoffs and a major recall. The company’s profits of $1.1 billion were down by more than 50%, short of Wall Street’s expectations — and yet still, Tesla shares were up more than 10% in after-hours trading following the shareholder update and earnings call.
The strange thing about Tesla is that it makes the overwhelming majority of its money from selling cars, but has become the world’s most valuable car company thanks to investors thinking that it’s more of an artificial intelligence company. It’s not uncommon for Tesla CEO Elon Musk and his executives to start talking about their Full Self-Driving technology and autonomous driving goals when the company’s existing business has hit a rough patch, and today was no exception.
Tesla’s value per share was about 33 times its earnings per share by the end of trading on Monday, comparable to how investors evaluate software companies that they expect to grow quickly and expand profitability in the future. Car companies, on the other hand, tend to have much lower valuations compared to their earnings — Ford’s multiple is 12, for instance, and GM’s is 6.
Musk addressed this gap directly on the company’s earnings call. He said that Tesla “should be thought of as an AI/robotics company,” and that “if you value Tesla as an auto company, that’s the wrong framework.” To emphasize just how much the company is pivoting around its self-driving technology, Musk said that “if somebody believes Tesla is not going to solve autonomy they should not be an investor in the company.”
One reason investors value Tesla so differently relative to its peers is that they do, actually, expect the company will make a lot of money using artificial intelligence. No doubt with that in mind, executives made sure to let everyone know that its artificial intelligence spending was immense: The company’s free cash flow may have been negative more than $2 billion, but $1 billion of that was in spending on AI infrastructure. The company also said that it had “increased AI training compute by more than 130%” in the first quarter.
“The future is not only electric, but also autonomous,” the company’s investor update said. “We believe scaled autonomy is only possible with data from millions of vehicles and an immense AI training cluster. We have, and continue to expand, both.”
Musk described the company’s FSD 12 self-driving software as “profound” and said that “it’s only a matter of time before we exceed the reliability of humans, and not much time at that.”
The biggest open question about Tesla is what would happen with its long-promised Model 2, a sub-$30,000 EV that would, in theory, have mass appeal. Reuters reported that the project had been cancelled and that Tesla was instead devoting its resources to another long-promised project, a self-driving ride-hailing vehicle called the “robotaxi.”
Musk tweeted that Reuters was “lying” but never directly denied the report or identified what was wrong with it, instead saying that the robotaxi would be unveiled in August. He later followed up to say that “going balls to the wall for autonomy is a blindingly obvious move. Everything else is like variations on a horse carriage.”
Before the call, Wall Street analysts were begging for a confirmation that newer, cheaper models besides a robotaxi were coming.
“If Tesla does not come out with a Model 2 the next 12 to 18 months, the second growth wave will not come,” Wedbush Securities analyst Dan Ives wrote in a note last week. “Musk needs to recommit to the Model 2 strategy ALONG with robotaxis but it CANNOT be solely replaced by autonomy.”
Anyone who expected to get their answers on today’s call, though, was likely kidding themselves.
Tesla announced today it had updated its planned vehicle line-up to “accelerate the launch of new models ahead of our previously communicated start of production in the second half of 2025,” and that “these new vehicles, including more affordable models, will utilize aspects of the next generation platform as well as aspects of our current platforms.” Musk added on the company’s earnings call that a new model would not be “contingent on any new factory or massive new production line.”
Some analysts attributed the share pricing popping after hours to this line, although it’s unclear just how new this new car would be.
Tesla’s shareholder update indicated that any new, cheaper vehicle would not necessarily be entirely new nor unlock massive new savings through an all-new production process. “This update may result in achieving less cost reduction than previously expected but enables us to prudently grow our vehicle volumes in a more capex efficient manner during uncertain times,” the update said.
Of the robotaxi, meanwhile, the company said it will “continue to pursue a revolutionary ‘unboxed’ manufacturing strategy,” indicating that just the ride-hailing vehicle would be built entirely on a new platform.
Musk also discussed how a robotaxi network could work, saying that it would be a combination of Tesla-operated robotaxis and owners putting their own cars into the ride-hailing fleet. When asked directly about its schedule for a $25,000 car, Musk quickly pivoted to discussing autonomy, saying that when Teslas are able to self-drive without supervision, it will be “the biggest asset appreciation in history,” as existing Teslas became self-driving.
When asked whether any new vehicles would “tweaks” or “new models,” Musk dodged the question, saying that they had said everything they had planned to say on the new cars.
One bright spot on the company’s numbers was the growth in its sales of energy systems, which are tilting more and more toward the company’s battery offerings.
Tesla said it deployed just over 4 gigawatts of energy storage in the first quarter of the year, and that its energy revenue was up 7% from a year ago. Profits from the business more than doubled.
Tesla’s energy business is growing faster than its car business, and Musk said it will continue to grow “significantly faster than the car business” going forward.
Revenues from “services and others,” which includes the company’s charging network, was up by a quarter, as more and more other electric vehicle manufacturers adopt Tesla’s charging standard.
Another speculative Tesla project is Optimus, which the company describes as a “general purpose, bi-pedal, humanoid robot capable of performing tasks that are unsafe, repetitive or boring.” Like many robotics projects, the most the public has seen of Optimus has been intriguing video content, but Musk said that it was doing “factory tasks in the lab” and that it would be in “limited production” in a factory doing “useful tasks” by the end of this year. External sales could begin “by the end of next year,” Musk said.
But as with any new Tesla project, these dates may be aspirational. Musk described them as “just guesses,” but also said that Optimus could “be more valuable than everything else combined.”
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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.