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Only somebody like Elon Musk could have built Tesla. Now he could destroy it.
Tesla suffered yet another media black eye this week, when Reuters reported that the automaker had built deliberately false range estimates into its electric vehicles. According to the article, under the personal direction of CEO Elon Musk, the range estimation was rigged to exaggerate how far it could go, only triggering more realistic numbers when it got below 50 percent so the car could make it to a charging station. Then when that triggered mass repair requests from customers who thought their cars were broken, the company allegedly set up a “Diversion Team” to automatically close them out as quickly as possible.
This kind of thing is just par for the course for Tesla. Hyperbole, exaggeration, spin, and occasional outright dishonesty were how Musk built the company into a major force in the auto industry. But now his brand of careening irresponsibility is a threat to the company’s future.
Some good background on Tesla’s condition can be found in Ludicrous, an excellent book by automotive journalist Edward Neidermeyer, published back in 2019. He argues convincingly that Tesla’s initial success was precisely because Elon Musk is hilariously unsuited to the auto manufacturing industry. Building cars is an exceptionally challenging business, because of the huge capital requirements, strict safety regulations, and resulting low unit margins.
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Automakers also have to predict both what customers might like to buy several years in advance and predict how many sales they might make of each model, meaning heavy capital risk. And as the industry has evolved — particularly under competition from Japanese manufacturers — customers have come to expect extremely high quality and reliability even from cheaper mass-market vehicles, making success even more difficult.
In short, efficiency, standardization, and consistency are the name of the auto game. As Neidermeyer writes: “Successful automakers are giant, process-driven bureaucracies that rely on rigidly systematized cultures to manage a continent-spanning ballet of manufacturing operations, supply chains, service infrastructure, and regulatory compliance.”
Needless to say, Tesla was not anything like this. It came out of the freewheeling culture of Silicon Valley, with its motto of “move fast and break things,” its dogmatic ideology that every other institution in society but the tech industry is riddled with inefficiency and incompetence, and its belief that any problem can be solved by genius innovators hacking together solutions on the fly.
Musk viewed the stodgy, hyper-bureaucratic auto industry procedures with contempt, and assumed he could do better and cheaper with some good old Silicon Valley magic. He made wild-eyed promises, instructed his team to build factories that would move far faster than the deliberate pace at a traditional factory, and set impossible targets. As a result, Tesla consistently failed to meet its production goals, consistently struggled with factory operations, and suffered consistent quality problems. While Teslas are sleek and fancy-looking, customers have regularly complained of poor body panel alignment, leaks, rattles or other noises, bad service experiences, poor reliability, and other problems.
But Musk is — or was, at least — an hype man. He made grandiose promises about upcoming products and features — often shading into flagrant dishonesty, as shown in the range story above or the time when he oversaw a staged video of Tesla’s Autopilot feature. At the same time, he viciously attacked critics, often singling out journalists by name or even threatening to sue them, stifling much criticism. All this inspired a fervent cult of personality, heroic effort from key workers (though also high employee turnover), and a large cult-like community of investors who boost Tesla’s stock.
Musk also got lucky. He had the advantage that electric drive trains are dramatically simpler than internal-combustion ones, with far fewer parts and far less maintenance required, and also produce maximum torque at idle for breathtaking acceleration. He also got a large, low-interest loan from the federal government under the Obama administration, plus numerous other state and federal subsidies for producing zero-emission cars.
All this allowed Musk to keep raising money and selling stock to fund a consistently unprofitable business for years. His Silicon Valley-brained approach was terrible for actual factory production, but it helped him create a legend. And this really does seem to be the only way you could have built a mass market electric car startup. Realistic promises, careful engineering, and truthful marketing would have run headlong into the nearly impossible economics of the business. Nissan found this out when its Leaf project, in which it invested heavily, failed to live up to expectations, because it was a boringly useful appliance without any utopian dreams attached.
The problem for Tesla was that propaganda is not a sustainable business model. To keep the hype train going, Musk had to keep making more and more fantastical promises, and eventually his credibility started to erode. Meanwhile, the rest of the auto industry got into the EV game, including established fancy brands who took direct aim at Tesla’s aging luxury sedan and SUV models.
Neidermeyer thus predicted that Tesla would eventually stumble into bankruptcy, like every other major car startup since the 1920s. And this wasn’t an implausible idea at the time. Up through mid-2019, the company had posted a quarterly profit on just three occasions in its entire existence.
But a funny thing has happened since then. Starting in 2020, and accelerating through 2022, Tesla has posted consistent large profits, reaching a peak of $3.7 billion in the last quarter of 2022. There are two obvious explanations. The first is the subsidies in the Inflation Reduction Act. Tesla had previously run through its allotment of federal tax credits for its cars, but the law restored them for many of its models, boosting demand. The IRA also has a large subsidy for battery production, which granted the company between $150-250 million in the second quarter of this year.
The second explanation is that Musk is now spending most of his time running Twitter into the ground instead of fiddling with Tesla’s factories and models. As The Wall Street Journalreported back in May, Tesla’s Chief Financial Officer Zach Kirkhorn is now de facto running the company in Musk’s stead. By all accounts, Kirkhorn is exactly the kind of cool-headed, logical, spotlight-averse type of executive the company badly needs. Under his guidance over the last couple years Tesla seems to have focused on the boring nitty-gritty details of factory production, ironed out most of its production kinks, and is now delivering consistent numbers of vehicles. The company’s brand, meanwhile, remains strong enough that a critical mass of customers automatically turn to Tesla when considering an EV, despite it not releasing a new consumer model for the last three years.
Perhaps Musk’s Twitter purchase will be Tesla’s salvation. He’s already lost tens of billions of dollars on the deal, and his increasingly erratic antics on the platform have torched most of what remained of his reputation as a genius innovator. Most recently, he tweeted that he had reinstated the account of a QAnon conspiracy theorist who was banned for, in Musk’s words, “posting child exploitation pictures.” That’s an excuse for the Tesla board to give him the boot if ever there was one.
As a business, Tesla needed Musk’s megalomania and cult of personality to get off the ground. But now he is an existential threat. He remains CEO, and he’s gotten markedly more unhinged since spending hours and hours per day bantering online with antisemitic trolls. He could take back control at any time, demanding disruptive new changes to its factories or promising a new car that will, I dunno, fly into space. (The upcoming new Roadster — which Musk promised in 2017 to be delivered in 2020 and hasn’t been seen since — is supposed to have a package including “cold gas thrusters” from SpaceX.)
If Tesla wants to survive over the long term, it’s time for the adults to take charge.
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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.
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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 …
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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.