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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. Reutersreported 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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Geothermal is getting closer to the big time. Last week, Fervo Energy — arguably the country’s leading enhanced geothermal company — announced that its Utah demonstration project had achieved record production capacity. The new approach termed “enhanced geothermal,” which borrows drilling techniques and expertise from the oil and gas industry, seems poised to become a big player on America’s clean, 24/7 power grid of the future.
Why is geothermal so hot? How soon could it appear on the grid — and why does it have advantages that other zero-carbon technologies don’t? On this week’s episode of Shift Key, Rob and Jesse speak with a practitioner and an expert in the world of enhanced geothermal. Sarah Jewett is the vice president of strategy at Fervo Energy, which she joined after several years in the oil and gas industry. Wilson Ricks is a doctoral student of mechanical and aerospace engineering at Princeton University, where he studies macro-energy systems modeling. Shift Key is hosted by Robinson Meyer, the founding executive editor of Heatmap, and Jesse Jenkins, a professor of energy systems engineering at Princeton University.
Subscribe to “Shift Key” and find this episode on Apple Podcasts, Spotify, Amazon, or wherever you get your podcasts.
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Here is an excerpt from our conversation:
Robinson Meyer: I just wanted to hit a different note here, which is, Sarah, you’ve alluded a few times to your past in the oil and gas industry. I think this is true across Fervo, is that of course, the technologies we’re discussing here are fracking derived. What has your background in the oil and gas industry and hydrocarbons taught you that you think about at Fervo now, and developing geothermal as a resource?
Sarah Jewett: There are so many things. I mean, I’m thinking about my time in the oil and gas industry daily. And you’re exactly right, I think today about 60% of Fervo’s employees come from the oil and gas industry. And because we are only just about to start construction on our first power facility, the percentage of contractors and field workers from the oil and gas industry is much higher than 60%.
Jesse Jenkins: Right, you can’t go and hire a bunch of people with geothermal experience when there is no large-scale geothermal industry to pull from.
Jewett: That’s right. That’s right. And so the oil and gas industry, I think, has taught us, so many different types of things. I mean, we can’t really exist without thinking about the history of the oil and gas industry — even, you know, Wilson and I are sort of comparing our learning rates to learning rates observed in various different oil and gas basins by different operators, so you can see a lot of prior technological pathways.
I mean, first off, we’re just using off the shelf technology that has been proven and tested in the oil and gas industry over the last 25 years, which has been, really, the reason why geothermal is able to have this big new unlock, because we’re using all of this off the shelf technology that now exists. It’s not like the early 2000s, where there was a single bit we could have tried. Now there are a ton of different bits that are available to us that we can try and say, how is this working? How is this working? How’s this working?
So I think, from a technological perspective, it’s helpful. And then from just an industry that has set a solid example it’s been really helpful, and that can be leveraged in a number of different ways. Learning rates, for example; how to set up supply chains in remote areas, for example; how to engage with and interact with communities. I think we’ve seen examples of oil and gas doing that well and doing it poorly. And I’ve gotten to observe firsthand the oil and gas industry doing it well and doing it poorly.
And so I’ve gotten to learn a lot about how we need to treat those around us, explain to them what it is that we’re doing, how open we need to be. And I think that has been immensely helpful as we’ve crafted the role that we’re going to play in these communities at large.
Wilson Ricks: I think it’s also interesting to talk about the connection to the oil and gas industry from the perspective of the political economy of the energy transition, specifically because you hear policymakers talk all the time about retraining workers from these legacy industries that, if we’re serious about decarbonizing, will unavoidably have to contract — and, you know, getting those people involved in clean energy, in these new industries.
And often that’s taking drillers and retraining some kind of very different job — or coal miners — into battery manufacturers. This is almost exactly one to one. Like Sarah said, there’s additional expertise and experience that you need to get really good at doing this in the geothermal context. But for the most part, you are taking the exact same skills and just reapplying them, and so it allows for both a potentially very smooth transition of workforces, and also it allows for scale-up of enhanced geothermal to proceed much more smoothly than it potentially would if you had to kind of train an entire workforce from scratch to just do this.
This episode of Shift Key is sponsored by …
Watershed’s climate data engine helps companies measure and reduce their emissions, turning the data they already have into an audit-ready carbon footprint backed by the latest climate science. Get the sustainability data you need in weeks, not months. Learn more at watershed.com.
As a global leader in PV and ESS solutions, Sungrow invests heavily in research and development, constantly pushing the boundaries of solar and battery inverter technology. Discover why Sungrow is the essential component of the clean energy transition by visiting sungrowpower.com.
Antenna Group helps you connect with customers, policymakers, investors, and strategic partners to influence markets and accelerate adoption. Visit antennagroup.com to learn more.
Music for Shift Key is by Adam Kromelow.
Why the new “reasoning” models might gobble up more electricity — at least in the short term
What happens when artificial intelligence takes some time to think?
The newest set of models from OpenAI, o1-mini and o1-preview, exhibit more “reasoning” than existing large language models and associated interfaces, which spit out answers to prompts almost instantaneously.
Instead, the new model will sometimes “think” for as long as a minute or two. “Through training, they learn to refine their thinking process, try different strategies, and recognize their mistakes,” OpenAI announced in a blog post last week. The company said these models perform better than their existing ones on some tasks, especially related to math and science. “This is a significant advancement and represents a new level of AI capability,” the company said.
But is it also a significant advancement in energy usage?
In the short run at least, almost certainly, as spending more time “thinking” and generating more text will require more computing power. As Erik Johannes Husom, a researcher at SINTEF Digital, a Norwegian research organization, told me, “It looks like we’re going to get another acceleration of generative AI’s carbon footprint.”
Discussion of energy use and large language models has been dominated by the gargantuan requirements for “training,” essentially running a massive set of equations through a corpus of text from the internet. This requires hardware on the scale of tens of thousands of graphical processing units and an estimated 50 gigawatt-hours of electricity to run.
Training GPT-4 cost “more than” $100 million OpenAI chief executive Sam Altman has said; the next generation models will likely cost around $1 billion, according to Anthropic chief executive Dario Amodei, a figure that might balloon to $100 billion for further generation models, according to Oracle founder Larry Ellison.
While a huge portion of these costs are hardware, the energy consumption is considerable as well. (Meta reported that when training its Llama 3 models, power would sometimes fluctuate by “tens of megawatts,” enough to power thousands of homes). It’s no wonder that OpenAI’s chief executive Sam Altman has put hundreds of millions of dollars into a fusion company.
But the models are not simply trained, they're used out in the world, generating outputs (think of what ChatGPT spits back at you). This process tends to be comparable to other common activities like streaming Netflix or using a lightbulb. This can be done with different hardware and the process is more distributed and less energy intensive.
As large language models are being developed, most computational power — and therefore most electricity — is used on training, Charlie Snell, a PhD student at University of California at Berkeley who studies artificial intelligence, told me. “For a long time training was the dominant term in computing because people weren’t using models much.” But as these models become more popular, that balance could shift.
“There will be a tipping point depending on the user load, when the total energy consumed by the inference requests is larger than the training,” said Jovan Stojkovic, a graduate student at the University of Illinois who has written about optimizing inference in large language models.
And these new reasoning models could bring that tipping point forward because of how computationally intensive they are.
“The more output a model produces, the more computations it has performed. So, long chain-of-thoughts leads to more energy consumption,” Husom of SINTEF Digital told me.
OpenAI staffers have been downright enthusiastic about the possibilities of having more time to think, seeing it as another breakthrough in artificial intelligence that could lead to subsequent breakthroughs on a range of scientific and mathematical problems. “o1 thinks for seconds, but we aim for future versions to think for hours, days, even weeks. Inference costs will be higher, but what cost would you pay for a new cancer drug? For breakthrough batteries? For a proof of the Riemann Hypothesis? AI can be more than chatbots,” OpenAI researcher Noam Brown tweeted.
But those “hours, days, even weeks” will mean more computation and “there is no doubt that the increased performance requires a lot of computation,” Husom said, along with more carbon emissions.
But Snell told me that might not be the end of the story. It’s possible that over the long term, the overall computing demands for constructing and operating large language models will remain fixed or possibly even decline.
While “the default is that as capabilities increase, demand will increase and there will be more inference,” Snell told me, “maybe we can squeeze reasoning capability into a small model ... Maybe we spend more on inference but it’s a much smaller model.”
OpenAI hints at this possibility, describing their o1-mini as “a smaller model optimized for STEM reasoning,” in contrast to other, larger models that “are pre-trained on vast datasets” and “have broad world knowledge,” which can make them “expensive and slow for real-world applications.” OpenAI is suggesting that a model can know less but think more and deliver comparable or better results to larger models — which might mean more efficient and less energy hungry large language models.
In short, thinking might use less brain power than remembering, even if you think for a very long time.
On Azerbaijan’s plans, offshore wind auctions, and solar jobs
Current conditions: Thousands of firefighters are battling raging blazes in Portugal • Shanghai could be hit by another typhoon this week • More than 18 inches of rain fell in less than 24 hours in Carolina Beach, which forecasters say is a one-in-a-thousand-year event.
Azerbaijan, the host of this year’s COP29, today put forward a list of “non-negotiated” initiatives for the November climate summit that will “supplement” the official mandated program. The action plan includes the creation of a new “Climate Finance Action Fun” that will take (voluntary) contributions from fossil fuel producing countries, a call for increasing battery storage capacity, an appeal for a global “truce” during the event, and a declaration aimed at curbing methane emissions from waste (which the Financial Times noted is “only the third most common man-made source of methane, after the energy and agricultural sectors”). The plan makes no mention of furthering efforts to phase out fossil fuels in the energy system.
The Interior Department set a date for an offshore wind energy lease sale in the Gulf of Maine, an area which the government sees as suitable for developing floating offshore wind technology. The auction will take place on October 29 and cover eight areas on the Outer Continental Shelf off Massachusetts, New Hampshire, and Maine. The area could provide 13 gigawatts of offshore wind energy, if fully developed. The Biden administration has a goal of installing 30 GW of offshore wind by 2030, and has approved about half that amount so far. The DOI’s terms and conditions for the October lease sale include “stipulations designed to promote the development of a robust domestic U.S. supply chain for floating wind.” Floating offshore wind turbines can be deployed in much deeper waters than traditional offshore projects, and could therefore unlock large areas for clean power generation. Last month the government gave the green light for researchers to study floating turbines in the Gulf of Maine.
In other wind news, BP is selling its U.S. onshore wind business, bp Wind Energy. The firm’s 10 wind farm projects have a total generating capacity of 1.3 gigawatts and analysts think they could be worth $2 billion. When it comes to renewables, the fossil fuel giant said it is focusing on investing in solar growth, and onshore wind is “not aligned” with those plans.
The number of jobs in the U.S. solar industry last year grew to 279,447, up 6% from 2022, according to a new report from the nonprofit Interstate Renewable Energy Council. Utility-scale solar added 1,888 jobs in 2023, a 6.8% increase and a nice rebound from 2022, when the utility-scale solar market recorded a loss in jobs. The report warns that we might not see the same kind of growth for solar jobs in 2024, though. Residential installations have dropped, and large utility-scale projects are struggling with grid connection. The report’s authors also note that as the industry grows, it faces a shortage of skilled workers.
Interstate Renewable Energy Council
Most employers reported that hiring qualified solar workers was difficult, especially in installation and project development. “It’s difficult because our projects are built in very rural areas where there just aren't a lot of people,” one interviewee who works at a utility-scale solar firm said. “We strive to hire as many local people as possible because we want local communities to feel the economic impact or benefit from our projects. So in some communities where we go, it is difficult to find local people that are skilled and can perform the work.”
The torrential rain that has battered central Europe is tapering off a bit, but the danger of rising water remains. “The massive amounts of rain that fell is now working its way through the river systems and we are starting to see flooding in areas that avoided the worst of the rain,” BBC meteorologist Matt Taylor explained. The Polish city of Nysa told its 44,000 residents to leave yesterday as water rose. In the Czech Republic, 70% of the town of Litovel was submerged in 3 feet of flooding. The death toll from the disaster has risen to 18. Now the forecast is calling for heavy rain in Italy. “The catastrophic rainfall hitting central Europe is exactly what scientists expect with climate change,” Joyce Kimutai, a climate scientist with Imperial College London’s Grantham Institute, toldThe Guardian.
A recent study examining the effects of London’s ultra-low emissions zone on how students get to school found that a year after the rules came into effect, many students had switched to walking, biking, or taking public transport instead of being driven in private vehicles.