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The all-American EV startup is cutting costs to survive.

America’s most interesting electric-vehicle company is about to have the defining year of its life.
On Wednesday, the company reported that it lost $1.58 billion in the fourth quarter of last year, bringing its net annual losses to $5.4 billion. It announced that it is laying off about 10% of its salaried employees, but — at the same time — promised that it has a plan to achieve a small profit by the end of this year.
Rivian does not seem to be in trouble — not quite yet, at least. But the earnings made clear what electric-vehicle observers have known for a long time: Either the company will emerge from this year poised to be a winner in the EV transition, or it will find itself up against the wall.
That’s partially because Rivian has a stomach-turning number of corporate milestones coming up. Over the next 11 months, it plans to unveil an entirely new line of vehicles, shut down its factory for several weeks for cost-saving upgrades, break ground on a new $5 billion facility in Georgia, and — most importantly — turn a profit for the first time. It also expects to manufacture and deliver roughly another 60,000 vehicles to customers.
Any one of these goals would be difficult to achieve in any environment. But Rivian is going to have to execute all of them during a time defined by “economic and geopolitical uncertainties” and especially high interest rates, its CEO R.J. Scaringe told investors on Wednesday. Since 2021, Rivian’s once robust stockpile of cash has been cut in half to about $7 billion; at its current burn rate, the company will run out of money in a little more than two years.
Although Rivian’s situation is dire, it’s not experiencing anything out of the ordinary. As I’ve written before, the electric truck maker is crossing what commentators sometimes call “the EV valley of death.” This is the challenging point in a company’s life cycle where it has developed a product and scaled it up to production — thereby raising its operating expenses to eye-watering levels — but where its revenue has not yet increased too.
During this vulnerable period, a company essentially burns through its cash on hand in the hope that more customers and serious revenue will soon show up. If those customers don’t arrive, then it either needs to raise more cash … or it runs out of money and goes bankrupt.
It’s a frightening time, but once a company crosses the valley of death, it can reach an idyll. Not so long ago, Tesla found itself in something like Rivian’s position as it prepared to launch the Model 3. Seven years later, it is the most valuable automaker in the world.
Once Rivian’s revenue exceeds its costs, its problems will get easier, or at least more straightforward: Instead of fighting for its survival and watching its cash reserves dwindle, Scaringe will be able to make more strategic trade-offs. Should the company cut costs to expand its profit margin and reward investors, or should it pass the savings along to customers in the form of lower prices, thus growing its market share? Scaringe can’t make these types of decisions until his firm is safely out of the valley.
Claire McDonough, Rivian’s chief financial officer and a former J.P. Morgan director, has a plan for crossing that canyon — an aptly if strangely named “bridge to profitability” that it will attempt to build this year. Rivian’s survival, she said, will depend above all on cutting the unit costs of producing its vehicles, including by using fewer materials to make every car. Other savings will come from making more vehicles faster. That’s what makes the shutdown plan, though it might seem extreme, worth it; McDonough said those improvements alone will get the company about 80% of the way to profitability.
Another 15% will come from marketing more “software-enabled products” to Rivian drivers and by selling air-pollution credits to other carmakers, whose vehicles are not as climate-friendly. This is a tried-and-true technique; Tesla first turned a profit in 2021 by selling regulatory credits needed to comply with federal and California state-level rules to other, dirtier automakers. But that same year, Tesla also debuted an entirely new vehicle: the Model Y crossover, which quickly became its top seller in the United States. Tesla, in other words, finally started to make money by cutting costs, finding new revenue sources, and releasing new products.
New products, however, are becoming a weak point for Rivian. The company says that high interest rates will keep demand for its vehicles flat this year. It expects to make about 60,000 of them, about 20,000 fewer than what it had once anticipated. The Rivian R1S, a three-row S.U.V., has become the company’s flagship; it is selling better and is cheaper to manufacture than Rivian’s pickup, the R1T. It also costs at least $75,000, or nearly $600 a month to lease. The highest-tier models can cost $99,000. Turns out, it’s difficult to sell a lot of $70,000 trucks when even the cheapest new-car loans hover around 6%.
Rivian once had a first-to-market advantage in the electric three-row SUV market, but that may be fizzling out, too. Kia is now selling its own all-electric three-row SUV, the EV9, for $18,000 less than the R1S; in fact, the Kia EV9’s most expensive trim costs $76,000, which is only slightly more than the cheapest R1S. The Kia SUV can also charge faster than the Rivian under ideal conditions. It remains an open question how many rich suburbanites are still interested in buying Rivians, especially now that the Tesla Cybertruck and Ford F-150 Lightning are competing directly with Rivian’s pickup truck.
The company’s hopes, in other words, rest on its next product line: the R2, which it will launch on March 7. We know almost nothing about the R2 line, except that it will probably include an SUV, that it will go on sale in 2026, and that it will fall somewhere in the $45,000 to $55,000 price range. (The median new car transaction in the United States now costs $48,200.) Last year, Scaringe told me that the R2’s timing was perfect because it would fit “beautifully with what we see as this big shift” in the American EV market. In today’s market, he said, “a lot of people ask themselves, Am I gonna get an electric car? Well maybe the next one.” He better hope they’ll start buying that next one in 2026.
Even if they do, Rivian may still have to confront the problem that Tesla has changed the EV market before Rivian could get there. When the first Tesla Model 3s were delivered in 2017, the sedan was instantly one of the best EVs on the market — because it was one of the only EVs on the market. Now every automaker in the world has plans to compete at the Model 3’s price point.
Rivian’s fortunes don’t rest entirely on American consumers; it also sells vans to commercial fleet operators, as well as delivery trucks to Amazon. (Amazon owns about 17% of Rivian.) But that business can be lumpy. Rivian’s vehicle growth slowed down last quarter, for instance, almost entirely because of a near pause in sales to Amazon, which sets up fewer new vehicles in the fourth quarter. If Amazon is willing to bail out Rivian, in other words, it’s not yet clear in the data.
None of this is to say that the company’s outlook is dire. Rivian was always going to find itself at a moment like this, when its expenses exceeded its revenue by such a large amount. The automaker already has devoted fans, and many people — myself included — are interested in the R2 as a potential first EV purchase.
And the company has shown that it can make strides in a single year. Twelve months ago, I had never seen a Rivian on the road before; today, one is regularly parked on my block. The company rocketed from a standing start to become the No. 5 best-selling electric car brand in America last year. What the company has done so far is impressive. But now it must prove that it can be great.
Editor's note: This story has been updated to correctly reflect Rivian's cash burn rate.
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What 700 math problems say about the AI economy’s biggest open question
This is Heatmap Daily, an evening news digest written by our executive editor. Subscribe here.
Happy Monday and welcome back.
It’s been a big weekend: I’m fairly certain Saturday Night Live mentioned Heatmap’s data center polling in one of its Weekend Update segments over the weekend. Watch here.
There are 22 days to the midterm elections here in the United States. We’ll soon enter that pre-election wormhole where only national politics can break through. So today I wanted to look at a story that might, on its face, seem like it has nothing to do with climate or energy. And warning: It’s a little bit of a weird one.
Last week, OpenAI released what it described as the solution to more than 700 open problems in mathematics. Many of the problems, such as the Quasi-Riemann hypothesis, had gone unsolved for decades. Some of the solutions came with proofs written in the programming language Lean.
The release made a stir in part because OpenAI had reached the solutions by using an internal model — that is, a version of ChatGPT not yet open to the general public. In a statement, the Association for Human Mathematics lambasted OpenAI for using an internal model to do research, violating what it described as a precept of AI-driven math research. (I’ve since seen speculation that OpenAI may not have even meant to solve the problems, but gave them to the internal model as a test … and then got the answers.) It’s also not clear whether all of OpenAI’s 700 solutions are correct — in many cases the proofs are so intricate that mathematicians are still wrapping their heads around them.
“There are some sad stories related to their users getting scooped [and] conflicts of interest,” Levent Alpöge, a mathematician at Anthropic, posted on X. “It’s obviously the most significant moment in mathematical history.”
On Friday night, the UCLA mathematician Terence Tao gave a pre-planned lecture at Caltech about what he called “Math 2.0”; it considered the revolution the field had seemingly undergone in the previous few days. (He posted the slides to his blog over the weekend, and I’m using them to summarize his talk here.) For most of math’s history, he said, the field operated under “proof scarcity,” in which finding a solution to an open math problem and confirming it as true was “difficult and rare,” requiring “significant human expertise.”
But AI, it has turned out, is well suited to solving math problems. This is partly because math, like the large language models themselves, is entirely conceptual, so confirming a proof as true requires no recourse to the physical. But it’s also because math’s solutions are “objectively verifiable”: “There is broad consensus on what constitutes a correct and complete proof of a given problem,” and thanks to the existence of proof programming languages like Lean, they can even be checked by computer.
The end result is that math is now entering a world of “proof abundance,” he said.
That is, perhaps surprisingly, a problem for math’s practitioners, and it will mandate a new intellectual regime for the discipline. Mathematicians have often used open problems as “lighthouses,” Tao wrote, “not destinations to be reached … but useful guides to explore the mathematical landscape.” Mathematicians often learn more in the solving than in the solution, in other words. But if mathematicians can now generate shortcuts to math problems without knowing how they got there, or even how the problem works, then actual human mathematical understanding will suffer. (One way to understand this is that mathematicians are now dealing with the same problem that math teachers have faced since the invention of the pocket calculator: How do you confirm students actually understand the material? Except the world’s most elite mathematicians themselves are the students in question.)
This disconnect can pose its own AI alignment problems, Tao said, analogizing math to pharmaceuticals. Suppose an advanced AI found what appears to be a cure for cancer that can pass a stage 3 clinical trial: “After a large amount of compute, it produces a cocktail of previously unknown chemicals which it claims, when mixed and injected into a patient, will kill all their cancer cells,” he said. It even generates a programmatic proof of the cocktail’s effectiveness.
“Before injecting this cocktail into your bloodstream,” he asked, “would you find it reassuring to know that there is at least one human cancer expert who understands — even partially — the mechanism behind this cure?” If not, you can see mathematicians’ problem, confronted by a surfeit of proofs that seem to work, but nobody who can explain how or why they do.
Now, Tao’s analogy is somewhat peculiar because something like this actually does happen all the time in medicine: Doctors still don’t fully understand how many drugs work, including acetaminophen, SSRIs, and lithium. (That said, those compounds were identified by trial and error, not by a mechanical superintelligence.) But the broader situation that Tao is painting — as well as the crisis posed by OpenAI’s 700 proofs — get at a significant question with economic importance far beyond just math: Will AI solve the economy’s secular attention shortage or worsen it?
This question has greater import than it may seem — but let me qualify what I mean by “attention shortage.” Writing in 1971, the economist Herber Simon observed the sudden superabundance of information that had exploded in every economic domain. There were more facts, data, news articles, and opportunities than any individual or organization knew what to do with. He pointed out that this newfound “wealth” of information implied an insufficiency of something else: attention.
“What information consumes is rather obvious: it consumes the attention of its recipients,” he wrote in Designing Organizations for an Information-Rich World. “Hence a wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.”
He wrote this, of course, well before the advent of internet, smartphones, sensors, or networked cameras — all of which have worsened the problem. But in the ensuing half century, the economy has learned to deal (or at least intermittently manage) this secular scarcity of attention. Much of what we now call knowledge work is devoted to filtering, siphoning, processing, and otherwise attending to the information superabundance.
AI is, in theory, supposed to help address this same set of problems. AI is commodified attention — or, at least, that is its promise. (The most important machine learning paper of the past decade, after all, is entitled “Attention Is All You Need.”) Many AI products, such as Meta’s Muse, now want you to believe that by ceding control of your email, your calendar, your restaurant reservations, and your digital life to AI, you will gain an attentional edge. The AI will do the grunt-work of attending to boring things for you, and you will feel less overwhelmed. If this vision comes true, the economy’s mismatch of information and attention will prove to be a short-lived historical blip.
But the past week of math news and Tao’s “Math 2.0” regime suggest a different scenario is more likely, at least in the immediate near term. By allowing far faster and more automated information production, AI will create a new, more vicious, and even more abstract form of information overload. Every profession and intellectual discipline will be overwhelmed with new papers, ideas, observations, proofs, or choices. Yet the human understanding required to convert this new information into useful action will become even more scarce. In such a world, AI will not solve the economy’s attention shortage, but worsen it.
I’m not sure which world would be worse for humanity, to be candid. One world suggests the eventual automation of all human effort. But in Tao’s world, many of us might still have jobs — they will just involve even more filtering, sorting, and processing of an ever-deepening pile of automated idea generation than we do today.
And what is the connection to climate and energy? One easy connection might be to the permitting process: If you think a new transit line (at the local level) or transmission line (at the state or federal level) could be overwhelmed with procedural concerns 10 years ago, wait until you see what happens when information itself can be mechanically produced. But that is only one example of a more general problem. Soon, if not already, any process of providing input to an important policy question — on energy, decarbonization, or anything else — will be overwhelmed with facially high-quality analysis. It will be left to policy officials and elected leaders to sort through it.
This past weekend’s storms caused flooding and property damage up and down the coast — and it’s going to get worse.
California has around 3,400 miles of shoreline, ranging from the wild, foggy beaches of the North Coast to the sunbather-studded sands of Orange and San Diego counties. That’s a whole lot of border to share with the Pacific Ocean, especially when the water is as hot and tempestuous a neighbor as it is right now.
Just ask the city of Long Beach, which this weekend was on the receiving end of some Super El Niño mischief. Swells from the storm briefly dubbed Hurricane Rachel “annihilated” beachfront mansions and flooded city streets, causing pieces of the local boardwalk to “shred away.” “It’s never happened like this before,” one resident of 64 years told the New York Post.
But while things might look bad in California at the moment, they can still get much worse. One disaster modeler recently estimated the state could see as much as $3 billion in flooding and mudslide-related losses alone this winter — not to mention coastal erosion, king tides, and storm swell. Governor Gavin Newsom declared a state of emergency at the end of September, while many cities, including San Diego, have declared their own local emergencies.
El Niño will likely peak in December, meaning that towns and cities across the state have just a few remaining weeks to finalize projects, whether that means improving drainage in landslide-prone environments such as Rancho Palos Verdes or distributing NOAA weather radios in areas likely to lose power, as emergency managers are in Santa Cruz. The approaching winter storms, combined with El Niño, represent “a humbling reminder of the ongoing effects of climate change and the importance of emergency preparedness,” Laath Martin, the public affairs lead for the city of Long Beach, wrote to me in an email.
El Niño is a cyclical phenomenon, occurring when temperatures in a particular stretch of the Pacific Ocean rise half-a-degree Celsius above the average of the preceding 30 years. But there are signs these natural variations are happening less predictably because of climate change, and we know for sure that a warmer atmosphere makes the baseline for its impacts worse: king tides, Kelvin waves, and storm surge piggyback on top of rising sea levels to result in more severe coastal flooding and erosion. A warmer atmosphere also holds more water, which means more rain — perhaps 200% above normal — which, in turn, results in flooding and mudslides further inland, as well.
“Based on what I am seeing now, I am officially concerned,” Daniel Swain, a climate scientist at the University of California Agriculture and Natural Resources, said last week after reviewing the latest outlook. There’s a reason this year’s El Niño is being called a Godzilla.
In general, the weather pattern causes the jet stream to shift south in the winter, bringing wetter and snowier conditions to the lower U.S. while the northern part of the country stays drier and mild. (It’s also responsible for a weaker Atlantic hurricane season, as we’ve already seen this year.) In addition to El Niño-driven Kelvin waves, warm bulges of water that raise the sea level on the coast by as much as 18 inches, the state of California — and particularly southern California — sits directly in a storm track that will deliver “atmospheric rivers” of heavy rain to a region prone to landslides and flash floods.
In many parts of the state, that rain will test the best-made plans in addition to plans deferred. In Los Angeles, the Department of Sanitation and Environment, or LASAN, has told the City Council it has concerns about the ability of the aging wastewater pipes and plants to handle heavy rainfall associated with a supercharged El Niño. In San Diego, which requires some $3.3 billion to upgrade what is in some places a century-old wastewater system, emergency managers are preparing to work with what they have by clearing debris and brush that could back up in the drains, CalMatters reports. In both cases, the time required for systemwide repairs far exceeds the time remaining before the winter’s supercharged atmospheric rivers are due to arrive. (Rezende told me inland flooding affects more businesses and residents in San Diego than coastal flooding, since the population in the city is set further back from the coastline.)
The city of Roseville, northeast of Sacramento, by contrast, is leaning on decades of flood planning to get through the season. After a series of back-to-back storms in 1986 that killed 13 people and caused $400 million in damage, the city and FEMA invested $39 million to upgrade Roseville’s infrastructure, including widening streams and adding levees and stormwater bypass channels. With more accurate floodplain modeling that has “helped keep new construction at higher elevations,” Helen Dyda, the city manager’s public information officer told me that “less than 7% of the city is within a floodplain” today. Dyda added that the city prepares for winter storms every year — it has some 160 miles of creek shoreline, after all — but that the El Niño-specific preparations are, as a result, more similar to routine maintenance: “clearing more dense vegetation and fallen trees from key creek areas.”
Long Beach is caught somewhere in the middle. After swells from Hurricanes Marie and Polo hammered the city in September, Long Beach hastily developed a seven-point plan to strengthen its peninsula in the near and long term. It will also begin a $11.4 million dredging project this fall that will place 415,000 cubic yards of sand on the beach as a buffer zone. “While this effort will achieve a much-desired result, it will not stop the ocean from pulling sand from the peninsula with every wave, especially during the significant weather systems Long Beach continues to experience,” Martin, the city’s public affairs lead, wrote to me. “Back-passing will need to continue to return sand back to the peninsula each day until more permanent, long-term man-made and environmental solutions can be identified, permitted and implemented.”
In certain cities, preparations for the peak of El Niño overlap with ongoing repairs from the last bad storm season. In Santa Cruz County, the 2016 El Niño caused an estimated $9 million in damage, while many in the community of Pajaro are still recovering from the flood damage caused by a series of severe atmospheric rivers in 2023. “We have a list of hazards that the city is vulnerable to, and we plan for all of them not individually, but all together,” Meredith Albert, Santa Cruz’s emergency management manager, told me. She added that many of the city’s preparedness activities and El Niño initiatives, like urging the community to be prepared to spend 72 hours unsupported, overlap with other disasters it works year-round to prepare for, like earthquakes, landslides, and wildfires.
“El Niño turns up the volume a little bit, but it doesn’t necessarily change our hazard landscape,” is how Albert put it to me.
Santa Cruz has not declared a local state of emergency, and Albert told me they won’t until they “run out of resources.” Other cities, like San Diego, have declared one preemptively. “In the past, you’d have to go through a longer review if you wanted to make some headway on projects, and sometimes that would take many weeks or many months of permitting,” Rezende said. “With a state of emergency, we streamline that process and get projects in the queue.”
Both Rezende and Albert told me the governor's statewide emergency declaration mostly helps state agencies position themselves for a faster response or has implications for higher-level permitting processes. (In Carlsbad, for example, which has seven miles of badly eroding beach, just one mile is controlled by the city; the other six are managed by the State Parks.) The state has also teamed up with ICEYE, a satellite imaging company, to receive information about flooding even in the dark or through rain clouds.
No state of emergency, though, can make it so a city can replace a century-old sewer system by Christmas or reengineer a beach by the time January’s storms begin to make landfall. But if a Godzilla analogy fits the onslaught of a super El Niño, it also offers a classic disaster-movie lesson on how to survive it.
Albert’s advice for how to prepare? Ask a neighbor for a cup of sugar. Take their dog for a walk. Build a relationship that could prevent a tragedy. “We know that our city will be cut off for at least a couple of days,” she told me of a hypothetical major disaster, referring back to Santa Cruz’s experience after the 1989 Loma Prieta earthquake, which cut off the city’s power, communication, and supply lines. “And we also know that we can’t build those relationships during the disaster. We’re trying to build them before.”
The city is doing a lot to prepare for the storm, she said. “But the question is also, what are all of us doing?” Albert went on. “You know better than I do, at the city level, who’s not going to receive that alert,” whether that be an elderly or unhoused neighbor or a nonnative English speaker who might not receive or understand emergency communications.
“Check in on a neighbor” is easier said than done — active engagement and connection to one’s community is trending downward in most parts of the U.S. But the people and places that bounce back from disaster and tragedy are almost uniformly the ones that look after each other. Borrowing a cup of sugar, to this introvert, sounds like a nightmare. But there’s never been a better time than a Godzilla El Niño to bake a poorly resourced cake.
Heatmap News is looking for an ambitious, energetic senior editor to join our growing newsroom.
The senior editor will report to the deputy editor of the website and will be tasked with assisting in running all aspects of our coverage. This individual will be the primary editor on many of the site’s stories, including breaking news, features, and long-term projects, on topics ranging from permitting reform to decarbonized fuels to greenhouse gas accounting to the electric vehicle market. This person will also play a role in overseeing and furthering our cross-platform efforts such as newsletters, events, and podcasts. Additional duties will include managing and maintaining the site’s social media presence and digital production on stories, newsletters, and podcasts.
Candidates should be intellectually curious news junkies with a deep interest in climate change and the energy transition. They should have a proven ability to work in a fast-paced digital newsroom, spot great angles, line edit stories, and change hats quickly as needed. They should also be responsible, reliable, and a good colleague.
Candidates should have 5+ years experience editing for a digital publication. A background in climate or energy coverage is an obvious plus, but compelling candidates from other verticals, particularly politics and business, will also be considered. Heatmap is a fully remote workplace, so candidates from all U.S. locales are encouraged to apply.
Above all, candidates should be passionate about Heatmap’s mission and absolutely thrilled about building something from the ground up.
The salary minimum is $100,000 and the maximum is $120,000. Competitive benefits, unlimited paid time off, and a generous equity plan, which gives employees a real stake in the company, are also offered. This position is remote.
Interested candidates should send a brief cover letter and resume to editors@heatmap.news with the subject line ‘Senior Editor Application.’
Heatmap News is a media platform focused on the energy transition.
Heatmap News is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected veteran status, age, or any other characteristic protected by applicable law.
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