十万美元签证税冲击：美国科技巨头转向海外揽才

_IEEE Spectrum_ is the flagship publication of the IEEE — the world’s largest professional organization devoted to engineering and applied sciences. Our articles, videos, and infographics inform our readers about developments in technology, engineering, and science. Over the last 19 months, the U.S. federal government has created a series of high-profile barriers to legal immigration by highly-skilled students and workers: Cancelling visa [extensions](https://www.federalregister.gov/documents/2025/10/30/2025-19702/removal-of-the-automatic-extension-of-employment-authorization-documents), cutting [interview opportunities](https://www.federalregister.gov/documents/2025/10/30/2025-19702/removal-of-the-automatic-extension-of-employment-authorization-documents), and attempting to [charge US $100,000](https://www.whitehouse.gov/presidential-actions/2025/09/restriction-on-entry-of-certain-nonimmigrant-workers/) or more for certain immigrant visas. “Whether they go into effect or not, it’s really an unrelenting barrage of government-sponsored propaganda against highly-skilled workers in the U.S.,” says former congressional immigration policy advisor [David Bier](https://www.cato.org/people/david-bier), now an immigration scholar at the Cato Institute, a libertarian think tank. In fact, most companies that might have hired H-1B workers decided against applying for visas for candidates subject to the $100,000 tax, a [March court filing](https://storage.courtlistener.com/recap/gov.uscourts.cand.457426/gov.uscourts.cand.457426.117.1_1.pdf) revealed. The Department of Homeland Security (DHS) recorded collecting fees for only 85 qualifying H-1B applications between the presidential proclamation in September 2025 and 15 February 2026. The department also reported processing 87 percent fewer applications for the H-1B visas subject to the tax than the year before. The government ended up earning about $28 million less than the previous year on H-1Bs, because so few companies were willing to pay the new price for hiring H-1B workers from abroad. “Some of those jobs might just be relocated abroad, or maybe companies will make more efforts to recruit U.S. workers,” says sociologist [Julia Gelatt](https://www.migrationpolicy.org/person/julia-gelatt), an immigration demography scholar at the Migration Policy Institute in Washington, D.C. “Some immigrant workers may have found a different pathway, like the L-1 visa for transfers inside multinational companies, or the highly-skilled may have been able to get O-1 visas for extraordinary ability.” For a few weeks earlier this year, things were looking better for H-1B applicants: In July, the [U.S. Court of Appeals for the First Circuit](https://www.ca1.uscourts.gov/) (covering Maine, Massachusetts, New Hampshire, Puerto Rico, and Rhode Island) upheld a lower court [ruling](https://www.ca1.uscourts.gov/sites/ca1/files/opnfiles/26-1699O-01A.pdf) that the September 2025 H-1B tax was illegal, removing that particular barrier. But a few weeks later, on 25 August, the administration responded with a [proposed rule](https://public-inspection.federalregister.gov/2026-17324.pdf) that the Department of Homeland Security (DHS) charge $103,265 for the 85,000 H-1B visas it issues annually to for-profit companies. If it goes into effect, the rule would apply to a far larger number of people than the overturned 2025 proclamation, because even applicants already in the country would need to pay. While few U.S. tech companies have issued public pronouncements on U.S. immigration policy, their staff are talking about it at a pragmatic level: For instance, the Society for Human Resource Management organized a [webinar on navigating H-1B policy](https://www.shrm.org/events-education/education/webinars/navigating-the-new-h1b-landscape-compliance-workforce-strategy-and-advocacy) soon after the 2025 presidential proclamation. The society [recommended](https://www.shrm.org/advocacy/executive-order-impact-zone/immigration-enforcement) employers plan for longer hiring timelines, especially from countries such as [China](https://spectrum.ieee.org/tag/china), [India](https://spectrum.ieee.org/tag/india), and the [Philippines](https://spectrum.ieee.org/tag/philippines), from which many immigrants are hired. It also notes that companies may want to focus on retention of existing talent by helping them switch from temporary visas to permanent residency. Changes to immigration policy are also reshaping the flow of future workers. As of March, U.S. universities were reporting 20 percent fewer international enrollments in bachelor’s programs than the year before, according to a [survey](https://studyportalsbv.sharepoint.com/:b:/t/External/IQDj9SKpx93-Q60gofchrIivAV5j8QLWnkYkm3VuxBXC558?e=SEhNUp) by [Studyportals](https://studyportals.com/), [NAFSA](https://www.nafsa.org/), and [Oxford Test of English](https://elt.oup.com/feature/global/oxford-test-of-english/?cc=us&selLanguage=en). It was worse for master’s degrees: a 24 percent annual decline. 84 percent of the participating universities listed government policy as a significant obstacle to enrollment. Many U.S. tech companies have historically hired foreign students to work summer internships or part-time while still on their student visas, or in the immediate years after they graduate. Experts say that is good for both workers and companies. “Companies don’t want to have to learn how to recruit their talent from abroad. They want to hire the young smart worker who’s right in front of them,” Gelatt says. Companies also do not appear eager to provoke the federal authorities on whom they depend for visas. Of the 25 U.S. companies _\_IEEE Spectrum\__ contacted for comment, 20 are among [the top 100 users of H-1B visas](https://www.uscis.gov/tools/reports-and-studies/h-1b-employer-data-hub). Only two replied. They both declined to participate in this story. In unpublished, preliminary research, Clemens says, he and other economists have found clues in online job sites that fewer high-skill Indian tech workers are moving to the U.S. than in the past. “People around the world have very clearly gotten the message,” Clemens says. _This story was updated on 15 September._ It was disappointing to see CATO Institute was used as a reference for this article. One of CATO's position is that H-1B creates jobs for Americans; the paper "The Effects of High-Skilled Immigration Policy on Firms:Evidence from Visa Lotteries" (https://gspp.berkeley.edu/archived/files/research/pdf/h1b.pdf) contradicts this position. Companies were already creating offshore centers and hiring outside the US. Mass layoffs in the name of AI means we don't need the alphabet of worker visas any more to fill in gaps, except maybe O visas. Tesla’s original vehicle, the Roadster, seen here in red, evolved from the tZero electric car (yellow). The tZero was the brainchild of Alan Cocconi [second from left]. Its transformation into the Roadster involved a cast of characters including Tom Gage [left], Elon Musk [center right], and Martin Eberhard [right]. In some ways, the tZero was a hit. When a VC would ride shotgun in the car with Eberhard at the wheel, Eberhard would implore them to touch the dashboard. As they reached forward, he’d punch the accelerator. As the car accelerated and the _\_g\__ forces piled up, the VC was literally unable to touch the dashboard. That was how powerful the tZero’s acceleration was, Eberhard would say. Many of the VCs were astounded. Some even questioned whether the car was really electric. Many owned Ferraris or Lamborghinis. They knew sports cars—but this? They could never have imagined it was possible to do this with an electric drivetrain. In 2004, it wasn’t apparent to anyone that Elon Musk had a future in the auto industry. He was notable for cofounding [PayPal](https://www.paypal.com/us/home), which he then sold to eBay in 2002 for a whopping US $1.5 billion. He had already launched Space Exploration Technologies Corp., or [SpaceX](https://www.spacex.com/), with the stated goal of paving the way to a sustainable colony on [Mars](https://spectrum.ieee.org/tag/mars). Musk did love fast cars. He owned a million-dollar silver McLaren F1, one of only 64 road-going F1s in the world, as well as a 400-horsepower BMW M5 sports car and a 1967 XK-E Series 1 Jaguar roadster. But he’d never expressed an interest in building cars or starting an auto company, at least not publicly. Still, Musk’s affinity for fast cars made it almost impossible for him to ignore an email from Gage on 21 January 2004. “Sergey Brin and JB Straubel both suggested you might be interested in driving our tZero electric sports car,” Gage wrote. ([Straubel](https://en.wikipedia.org/wiki/J._B._Straubel) was the young [Stanford](https://spectrum.ieee.org/tag/stanford) engineering graduate who would later serve as Tesla’s chief technology officer.) “The tZero goes quite well,” the email continued. “We ran it against a Viper last Monday and it won four of five sprints on a 1/8th of a mile track. I lost one because I was carrying a 300-pound cameraman. Do you have time for me to bring it by?” Musk quickly replied. “Sure, I would really enjoy seeing it. Don’t think it could beat my McLaren (yet) though I’m in town Feb 2nd through 4th.” “Hmm, a McLaren, boy that would be a feather in my cap,” Gage wrote back. “I can have it there on Feb. 4.” The emails marked the beginning of Musk’s involvement in electric cars and in the auto industry. Gage drove the car to SpaceX headquarters, a warehouse in El Segundo, Calif., about 30 kilometers southwest of Los Angeles. In Musk’s cubicle in the “office” portion of the warehouse, Gage made his pitch. Gage told him it wasn’t for sale. Undeterred, Musk offered a quarter million dollars if AC Propulsion would squeeze its [lithium-ion battery](https://spectrum.ieee.org/tag/lithium-ion-battery) pack into his Porsche. Gage declined again. AC Propulsion needed money to electrify the Toyota Scion, Gage said. Musk shook his head. The idea seemed incredible to him. “Who wants to take an ugly $20,000 car and buy it for $65,000?” he asked incredulously, as he later recalled during an [interview with _\_Vanity Fair\__ magazine](https://www.vanityfair.com/news/2007/05/tesla200705?). “I wouldn’t want to drive it. My wife certainly wouldn’t want to drive it.” Many years later, Musk would tell his biographer Walter Isaacson, “Nobody is going to pay anything near that for something that looks like crap.” Musk believed that the way to start a car company was to build high-priced cars first and then let the technology trickle down to the mainstream. It was a classic Silicon Valley approach. In Musk’s mind, it was all very obvious. He liked fast cars. He liked the tZero and believed it “could change the world.” He couldn’t even imagine why Gage was sitting here trying to sell him on the idea of the eBox. “Gage and Cocconi were sort of madcap inventors,” he told Isaacson. “Common sense was not their strong suit.” Gage concluded that he wasn’t going to convince Musk to invest in AC Propulsion. “Well, if you want to do a sports car, then you should talk to Martin Eberhard,” Gage said. A few weeks later, Gage sent an email to Eberhard introducing him to Musk. “Elon Musk heads up SpaceX, is a car enthusiast,” Gage wrote. “He would be interested in hearing about your activities at Tesla Motors.” Eberhard and Wright flew to Los Angeles on a Friday and met Musk in his cubicle at SpaceX. The meeting was supposed to last a half hour, but Musk’s questions came virtually nonstop, and as the meeting progressed, he repeatedly shouted to his assistant to cancel his next meeting. Over the following weekend, Musk called Tarpenning to get his input about their financial model. “I just remember responding, responding, and responding,” Tarpenning said, according to a [2015 book by Ashlee Vance](https://www.harpercollins.com/products/elon-musk-ashlee-vance?variant=32161254932514). The Tesla founders were all impressed with Musk. He was unlike any of the VCs they’d met with in the previous months. He was technically astute. He’d earned a bachelor’s degree in physics from the [University of Pennsylvania](https://www.upenn.edu/), and in his two-day stint as a Ph.D. student at Stanford, he’d intended to do a dissertation on solid-state [capacitors](https://spectrum.ieee.org/tag/capacitors) for use in electric cars. Moreover, he wasn’t averse to risk—at least not intelligent risk. He loved technical challenges, and he loved proving that the impossible was possible. “You’re presenting an electric car company to this person on the other side of the table, and he’s doing something even crazier,” Tarpenning said later. “He’s building rocket ships.” On the Monday after their first meeting at SpaceX, Eberhard and Tarpenning flew back to Los Angeles. Musk agreed to invest $6.35 million. He would become the biggest shareholder as well as chairman of the company. No one at AC Propulsion believed that Tesla Motors had even a remote chance of success. The whole idea—building and selling electric vehicles and competing against the giants in Detroit, Japan, and Germany—seemed impossible. Even Toyota, which was having so much success with the hybrid Prius, was not planning to build pure electric cars. The prospect of starting any kind of auto company was unbelievably daunting. Automotive startups had been the undoing of many ambitious entrepreneurs, including Henry Kaiser, Preston Tucker, and John DeLorean. Such endeavors required mountains of money, connections, and expertise. There were unseen obstacles around every corner. And the people who’d launched Tesla, as smart as they were, were almost certainly unprepared for what lay ahead. Years later, Musk would contend that their struggles were caused by the fact that Tesla had been founded on “two false premises.” The first was that Tesla’s founders believed they could simply convert an existing gasoline sports car to electric. The second was that they could use the existing AC Propulsion technology with little or no modification. “That turned out to be, in retrospect, staggeringly dumb,” Musk said. He also later concluded that their early path almost doomed them. “It ended up being much worse than if we had designed the car from scratch,” he said. But Tesla decided it could not go back and start over. It could only deal with the problem at hand. Another looming issue was the safety of the Tesla lithium-ion battery pack. To test it, Eberhard brought the engineering team to his home, where they dug a pit in his backyard. They took a brick of cells, covered it with a sheet of Plexiglass, and then remotely heated one of the cells with an electrical wire. As they expected, the heated cell [burst into flame](https://spectrum.ieee.org/teslas-lithiumion-battery-catches-fire-), setting neighboring cells on fire. The cells went off one at a time—pop, pop, pop. “We had a conflagration,” Eberhard said. “One cell caught fire, and it blasted right through the pack.” Buyers of sports cars were known to be forgiving. In their quest for performance, they could put up with poor reliability. But the fire hazard was another matter, and one that had the potential to take down the company. Eberhard took the news right to Tesla’s board of directors. “It was my first big oh-shit moment to my board,” he said. “I told them we’ll have a day-to-day schedule stop until we figure this out.” Working with friends from his Stanford days, Straubel began developing a new pack in his garage. The team acquired 7,000 lithium-ion cells from LG Chem, then constructed battery bricks, each with 69 cells, and tested them with different kinds of liquid-cooling systems. By October 2004, they’d finished a prototype pack and used a crane to lower it into the back of a Lotus Elise sportscar. A few months later—in January 2005—the team had completed a working prototype of that first car. At the end of the month, they showed it off at a board meeting, and Musk took it for a spin. Impressed by its performance, he invested $9 million more, and Tesla completed a $13 million round of funding. Now the vehicle had a name—Roadster—and a tentative production schedule. The plan was to begin delivering it to customers in early 2006. Tesla’s struggles with the Roadster were not apparent to the outside world, especially to those invited to the [reveal of the Roadster](https://motorillustrated.com/tesla-roadster-prototype-was-unveiled-14-years-ago-today/53257/) at the Santa Monica Airport in July 2006. By then, the yellow test car, or “mule,” had evolved into two prototypes: a red car and a black car, both of which would be available for drives at the event. The prototypes were more advanced than the mule, with more of a production-type design. But Musk and the team didn’t know what to expect at the reveal. The company was just coming out of stealth mode, and at that point, Tesla had received no media coverage. It had no customers, no deposits, and no sales team. Still, Musk planned a huge party for the unveiling—an “awesome event,” in his words, staged inside the airport’s Barker Hangar. He told his personal assistant to invite 350 guests, including Michael Eisner of [Disney](https://spectrum.ieee.org/tag/disney), movie producer Richard Donner, actor Ed Begley Jr., California Governor Arnold Schwarzenegger, and many other luminaries. All were told to bring their checkbooks in case they wanted to write a $100,000 check to put a deposit on an electric Roadster. Meanwhile, a Roadster prototype zipped around a makeshift road inside the hangar, out the door, down a runway, and back inside again. Musk took center stage, telling the audience that they were witnessing the start of a new era in automotive technology, according to [CNET’s coverage of the event](https://www.cnet.com/roadshow/news/electric-sports-car-packs-a-punch-but-will-it-sell/). “Until today, all electric cars have sucked,” he told the audience. “Electric cars play into the strength of Silicon Valley. A lot of the things inside the car are conventional automobile technology. The magic is the battery technology and the software and the controllers.” Musk’s message was perfect for such an event, especially for the dozens of reporters who were there to publicize the reemergence of the electric car. They adored the [Roadster](https://grubermotors.com/a-brief-tesla-roadster-history/). It was small, powerful, electric, and above all, cool. It was anti-Detroit—a new kind of car that burned no gasoline and was born in Silicon Valley instead of an antiquated factory in Michigan. The night was also a financial success for Tesla, with twenty $100,000 checks gathered from prospective buyers. And the momentum continued. A few days after the event, [Joe Francis](https://en.wikipedia.org/wiki/Joe_Francis), creator of the adult entertainment franchise Girls Gone Wild, sent an armored truck to Tesla’s San Carlos office to drop off $100,000 in cash. A few days after that, Schwarzenegger put his money down, as did actor George Clooney. Within two weeks, Tesla had presold 127 Roadsters. Meanwhile, though, the company’s manufacturing woes continued. The mechanical problems weren’t even the biggest issue. The biggest issue was the supply chain. This was ironic, because some in the media admired Tesla for its global approach. They liked the fact that the battery pack, the motor, the chassis, and the assembly had an international flavor. It was a world car, they thought. But for Tesla, it was a nightmare. The battery pack was being assembled in Thailand by a manufacturer of barbecue grills. The facility was 3 hours from Bangkok, literally in a jungle where the heat was almost unbearable, and the factory building consisted of a truss roof held up by some steel columns. There were no walls because no one there wanted to work indoors. And because the pack assembler was inexperienced, Tesla engineers were repeatedly flying to and from Thailand to direct the effort. They would find animal droppings on the battery packs, which were sitting out in the open air all day and all night. For the umpteenth time, Musk wondered if the company would be able to survive. “We’re doomed if we don’t in-source the battery pack,” he told one of Tesla’s manufacturing engineers, “because we have a supplier in Thailand who is great at making barbecues but not great at battery packs. And the supply chain is so long that it takes six months from when the cells are built to when the battery pack is done and in a car. So that means the capital cost is gigantic because we have to pay for all that inventory and process. And inevitably, there are mistakes in the design or fabrication of the battery pack, and then we have six months’ worth of battery packs that don’t work.” Never mind that this chaotic approach was central to their plan. Tesla had never been envisioned as an old-fashioned, Detroit-style, vertically integrated manufacturer. From the beginning, it had been a Silicon Valley–type enterprise that would rely on others for the bulk of its manufacturing. Only now, as the fledgling company sent [batteries](https://spectrum.ieee.org/tag/batteries) and motors and assembled cars back and forth across two oceans, was its plan beginning to appear untenable. “We had this misguided idea that everything must be cheaper and better if built in Asia,” Straubel later said. From the beginning, Musk had never been optimistic about Tesla’s chance of success. He repeatedly said he thought it was approximately 10 percent. “In 2004, the idea of starting a car company was extremely stupid,” he said. “The idea of starting an electric car company was stupid squared.” As he watched Tesla struggle with its supply chain, his earlier words were starting to look prescient. The only chance, the engineering team concluded, was to bring the manufacturing of all of the subsystems, such as the battery packs, motors, and [inverters](https://spectrum.ieee.org/tag/inverters), in-house. They disassembled their overseas operations and moved them to California, starting with battery pack manufacturing. Assembly stations were loaded into huge shipping containers, transported back to one of the company’s new facilities on Bing Street in San Carlos, and then reassembled there. It took five and a half months. They also redesigned the battery packs and developed machines for automating their assembly. Musk concluded that the key to success was not the design of the car itself but rather the manufacturing. Henry Ford had reached the same conclusion a hundred years earlier. It was “the realization of how important it is to build the machine that builds the machine,” Musk said at the Tesla Annual Shareholder Meeting in 2016. “And how much harder it is to build the manufacturing system that builds the product, than it is to create the product in the first place. You can create a demo version of a product…with a small team in maybe three to six months. But to build the machine that builds the machine takes at least a hundred to a thousand times more resources and difficulty.” Cyberattacks can change how robots act without obvious signs of failure, requiring a new approach to safety that combines AI model and vulnerability scanning, simulation-based validation, and continuous monitoring to help secure robots from development through operation. Robot safety has traditionally asked: Can a machine remain safe when something goes wrong? Physical AI raises a harder question: Can a machine remain safe when an attacker changes what it sees, decides, or does even when nothing appears to have failed? As AI and robotics continue to advance at an unprecedented pace, modern robots perceive through multimodal sensors, interpret context using [AI models](https://spectrum.ieee.org/tag/ai-models), and translate those interpretations into physical action. As they move into dynamic environments, their safety increasingly depends on the integrity of the data guiding their decisions. That dependence creates risks that conventional safety assessments may not fully capture. Recent research has demonstrated that manipulating what a robot sees, hears, or interprets can influence its behavior without requiring direct control. So a critical safety question today is whether Physical AI models remain within their task and safety boundaries under adversarial conditions. [Simulation tools](https://vicone.com/company/press-releases/vicone-turns-def-con-34-robot-hacking-research-into-free-nvidia-isaac-sim-extension?utm_source=ieee-spectrum&utm_medium=sponsored-content&utm_campaign=2026-09) such as [NVIDIA](https://spectrum.ieee.org/tag/nvidia) Isaac Sim, when paired with [VicOne Radeis](https://vicone.com/products/radeis?utm_source=ieee-spectrum&utm_medium=sponsored-content&utm_campaign=2026-09), can test the effects of manipulated inputs before deployment. In this case, the components may still function as designed. What has changed is the trustworthiness of the commands flowing through the system. Vulnerability management can help teams identify known risks before deployment, while continuous monitoring can surface emerging threats. In each case, the camera may still work, the model may still run, and the controller may still respond. Yet the resulting behavior can be unsafe because the robot is acting on manipulated perception or reasoning. Runtime assurance must therefore look beyond whether individual components remain available and assess whether cyber events are beginning to affect physical behavior. Security event correlation, behavioral-impact assessment, and policy-bounded response supported by edge AI, can help contain the affected path without unnecessarily stopping the entire robot fleet. This requires assurance across the robot’s lifecycle. During design, teams need to understand which cyber risks could invalidate assumptions behind intended behavior. Before deployment, they should test whether realistic attacks can cause a robot to deviate from its task or safety boundaries. In operation, monitoring should identify whether cyber events are beginning to affect behavior, contain the affected path, and preserve safe operation where possible. For a deeper look at the cybersecurity risks and defense strategies shaping autonomous robotics, download our whitepaper “[Securing the Rise of AI Robots: Cyber Risks, Real-World Threats, and Defense Strategies](https://info.vicone.com/ai-robotics-security-risk-whitepaper?utm_source=ieee-spectrum&utm_medium=sponsored-content&utm_campaign=2026-09).” This white paper gives distribution engineers and utility teams a practical overview of the substation exit, the short and high-consequence section where station capacity divides into individual feeders. It explains how covered, spacer-supported overhead construction can reduce common contact-driven faults while leaving room for future circuits. > _A microwave box creates a surprisingly tricky vision system challenge: there’s a picture of a microwave on the side of the box, and that microwave has a window. From the camera’s perspective, it’s a box inside a box inside a box._ > _As the evolution of the UR Series, the g?Series is designed to accelerate how robots are integrated into complete automation systems. It connects more directly with cameras, higher power EOATs, and control systems, eliminating external cabling and hardware to streamline integration. This enables cleaner and faster deployment of advanced automation._ This Rivian might look like any other electric SUV in this affluent town, with its concentration of tech bros, venture capital, and startups. But inside this boxy EV is something special: an Autonomy+ system that will allow owners to enter an address, sit back, and let the vehicle drive to any mapped destination in the U.S. and Canada. This point-to-point system is one of the most advanced semiautonomous-driving systems coming to market. It is also a precursor of the company’s bid to make [self-driving cars](https://spectrum.ieee.org/tag/self-driving-cars) a reality, for [robotaxis](https://spectrum.ieee.org/tag/robotaxis) and—eventually—for everyday car buyers. After years of incremental advances and frustrating setbacks, self-driving has been swept up in the great AI resurgence, and is now a top priority for [investors and global automakers](https://www.wsj.com/video/series/wsj-the-future-of-everything/how-uber-plans-to-win-the-self-driving-car-race/10F91546-7884-4404-8B65-E252B6C514A3), who envision vast new streams of profits. Video released by Rivian shows the company’s R1 SUV being driven on a variety of urban and rural roads, according to the company. Rivian plans to introduce this self-driving system to compete with Tesla’s offering before the end of 2026.Rivian Impressive as it is, Autonomy+ is only a Level 2+ system in the classification system established by the Society of Automotive Engineers. Level 2+ means that a human driver must pay full attention and be ready to retake control at any moment. Rivian, along with a horde of deep-pocketed rivals, is aggressively working toward more impressive (and potentially lucrative) levels of autonomy. At Level 3, drivers could “check out” behind the wheel for limited periods, to scroll through emails or watch a movie—but not to sleep. Robotaxis currently roaming select cities in the U.S., China, and the Middle East have proved that driverless, Level 4 autonomy is possible. These cars operate in relatively tiny numbers in a [couple of dozen cities](https://insideevs.com/news/776837/china-us-robotaxi-fleet-comparison/), and within the specific constraints of commercial services. Now Rivian and its many rivals—including Tesla, [Toyota](https://www.toyota.com/cars/), Mercedes, [Volkswagen](https://www.vw.com/en/corporate.html), and [China’s BYD](https://www.byd.com/en)—are racing to bring that level of self-guided mobility to the masses. Rivian’s strategy combines a suite of cameras, radar, and [lidar](https://spectrum.ieee.org/tag/lidar); a custom set of silicon chips, developed in-house, to process sensor data; and an AI autonomy model running on those chips. With $1.25 billion in backing from [Uber](https://spectrum.ieee.org/tag/uber), Rivian plans to graduate to a fleet of self-driving, Level 4 robotaxis starting in 2028. Those taxis, along with Rivian’s consumer fleet, will be the literal training wheels for extending Level 4 ability to consumer vehicles. Meanwhile, millions of connected cars, as they cruise every nook and cranny of the globe, are already sending data to train automakers’ systems. The race is on to funnel those data through fast-improving [AI models](https://spectrum.ieee.org/tag/ai-models) with “end to end” capability: an AI architecture, powered by [deep learning](https://spectrum.ieee.org/tag/deep-learning), that processes raw sensor data directly into physical vehicle commands. So equipped, engineers anticipate they’ll be able to solve the tricky edge cases—tangled urban streets, unique geographies, swarms of pedestrians, inclement weather—that skeptics once deemed intractable. Despite the company’s high media profile, including a spotlight on RJ Scaringe, its MIT-doctorate founder and chief executive, Rivian holds a relatively tiny slice of the U.S. passenger-vehicle market. It sold just [42,000](https://rivian.com/newsroom/article/rivian-releases-fourth-quarter-full-year-2025-financial-results) vehicles last year across its three models, the adventure-minded R1S SUV and R1T pickup, and the Electric Delivery Van. Tesla sold about 1.6 million units. Toyota, the world’s largest automaker, sold more than 11 million. Rivian’s underdog strategy is to leverage software and tech to make itself a serious player. Volkswagen, among the world’s largest automakers, saw enough value there to invest up to $5.8 billion in a joint venture called Rivian and Volkswagen Group Technologies. The joint venture gives Rivian crucial capital for development. It gives Volkswagen access to Rivian’s electrical architecture and to the software for the R2, new-generation Rivian SUV that went on sale in June. “Rivian developed an architecture so important that VW is spending billions to buy it, as opposed to trying to re-create it themselves,” says [Bryan Reimer](https://ctl.mit.edu/people/reimer-bryan), a research scientist in MIT’s [Center for Transportation and Logistics](https://ctl.mit.edu/). But the joint venture doesn’t give VW access to Rivian’s autonomous tech. In March, that R2 architecture underpinned Rivian’s [$1.25 billion deal](https://apnews.com/article/uber-rivian-robotaxi-autonomous-019439a7e5dd3c855c7171f8de3e9ce9) to supply Uber with up to 50,000 robotaxis. The companies plan to initially deploy 10,000 taxis, beginning in San Francisco and Miami in 2028, before expanding across 25 cities in the U.S., Canada, and Europe. Rivian’s vulnerabilities include struggles with reliability, along with expensive body repair costs that the company says it strove to reduce for its new R2. As impressive as Rivian’s in-house tech may appear, the company has miles to go to catch up with Tesla, which recently announced it has 1.1 million active users of its FSD system. Toyota is also jumping into the game; its [Woven by Toyota](https://woven.toyota/en/) subsidiary has partnered with the Alphabet-owned [Waymo](https://robotsguide.com/robots/waymo) to develop an autonomy platform for robotaxis _\_and\__ consumer cars. Until recently, most observers would have gone all-in on Tesla as the winner of the autonomous race. Elon Musk’s company has begun operating a small test fleet of [Model Y robotaxis](https://www.tesla.com/robotaxi) in three Texas cities and in Florida. Tesla has also begun producing a dedicated autonomous vehicle, the [Cybercab robotaxi](https://www.bloomberg.com/news/articles/2026-04-24/musk-says-tesla-has-begun-production-of-its-cybercab-robotaxi). But in April, Musk pushed back his timeline for Level 4 autonomy for general consumers: “I’m just guessing here, but probably in the fourth quarter” of 2026, [he said](https://electrek.co/2026/04/22/tesla-elon-musk-unsupervised-fsd-consumer-cars-q4-delay-again/). It was the latest in a series of deflating walkbacks from the man who once promised 1 million robotaxis on the road by 2020. Scaringe, during our drive of his company’s make-or-break R2 SUV at a Utah state park, says that showroom Rivians will start adopting some of its robotaxis’ Level 4 capabilities no later than 2030, perhaps beginning with self-parking functions. Like most autonomous cars, Rivian’s system fuses data from multiple sensors to create a robust picture of a fast-moving environment and its obstacles. Data is fed to a neural network—what Rivian refers to as its “[Large Driving Model](https://www.wardsauto.com/news/rivian-announces-new-ai-hardware-software-autonomy-day-event-r2/807844/),” or LDM—that churns through hundreds of trillions of operations per second to interpret and fuse data from cameras, radar, and lidar. That network is [end to end](https://ieeexplore.ieee.org/document/8576190), meaning that it processes multiple streams of raw sensor data (such as camera pixels) and outputs driving controls (for steering, braking, and acceleration) through a single data pipeline. More traditional systems coded distinct steps for data collection, feature extraction, prediction, and decision-making. That proprietary AI driver identifies features in images and point clouds, groups them into objects, and tracks them across frames, time-stamped to the millisecond to account for differing frame rates. The AI thus builds confidence over time, acting on object detections that persist across several frames, rather than, say, slamming the brakes due to a camera blip on a single frame. The virtual driver can then navigate safely even when sensors disagree, by favoring the persistent data. The output— commands for [electric motors](https://spectrum.ieee.org/tag/electric-motors) and other systems—is backed by redundant hardware for by-wire systems such as steering and brakes. During my demo of Rivian’s point-to-point Autonomy+ system, a company test driver sits behind the wheel. Nick Nguyen, the engineer who directs Rivian’s products and programs related to autonomy, watches from the back seat. Compared to, say, a large language model that writes news or fiction, Nguyen says, an autonomous-driving AI is less subjective and easier to evaluate, so there’s little room for error. “We want cliché. We want boring. Just safe, repeatable driving,” he says. Rivian’s core LDM currently ingests cloud data from up to 125,000 cars for analysis and validation, which then fine-tunes the model through simulations. Onboard computing is smart enough to trigger recording only for unusual scenarios. Owners have to agree explicitly to data collection beforehand. Updated LDMs will be beamed back to customer cars via monthly over-the-air updates. part of that self-reinforcing data flywheel. It’s part of what Scargine calls the “data flywheel,” the self-improving AI loop that continuously refines the system. As is true for some of its rivals, Rivian no longer needs to fully rely on an onboard high-definition map or even a cellular link to pinpoint the car for navigational purposes. That strategic shift reduces data demands, and ensures steady driving in urban canyons or tunnels with no connections. Instead, the Rivian recognizes and responds to its surroundings through recognition and repetition, just as a human would do interpreting street signs, following lane markers, being alert to hazards. The Rivian R2 features 11 high-definition cameras and five radars. It will integrate a lidar unit early next year to lay the groundwork for future autonomy. That miniaturized lidar will integrate smoothly into the R2’s existing roofline, an improvement over the [bulky, drag-producing](https://www.tangramvision.com/blog/sensing-breakdown-waymo-jaguar-i-pace-robotaxi) units seen on Waymo Jaguars, and older partially autonomous models. [Vidya Rajagopalan](https://www.sonatus.com/resources/vidya-rajagopalan-of-rivian/), Rivian’s senior vice-president of [electrical engineering](https://spectrum.ieee.org/tag/electrical-engineering) hardware, says lidar costs have fallen from above $10,000 to a few hundred dollars in under a decade. A mix of sensors plays up the strengths and diminishes the weaknesses of each, Rajagopalan says. Cameras capture color and texture and can distinguish between objects, but they struggle in darkness and low-contrast lighting. Lidar is unaffected by darkness or blinding sunlight, and senses shapes in three dimensions. This inherent 3D capability makes lidar more reliable for slowing or halting a car for random objects—“a tire in the road, or maybe a large dinosaur,” Nguyen quips. Multiple cameras can further contribute 3D data, after a short delay for processing. Sensors with 360-degree vision can outperform human senses in key situations. Radar and lidar can spot nighttime pedestrians or animals hundreds of meters down the road, something no human can do. But lidar can be thrown off by dust, fog, and snow. Radar can “see” through rain or snow, but with relatively low spatial resolution. To handle the flood of sensor data, Rivian has taken on an ambitious challenge: designing its own custom autonomy chip in-house. The Rivian Autonomy Processor (RAP1) is a 5-nanometer processor that can execute 800 trillion operations per second (TOPS), three times as fast as the Nvidia [Jetson Orin](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/) chip used in its earlier models. The chip will be built to Rivian’s specs by Taiwan [Semiconductor Manufacturing](https://spectrum.ieee.org/tag/semiconductor-manufacturing) Co. , which also makes custom chips for Tesla. On paper, a single AGX Thor chip is slightly faster in terms of TOPS, at 1,000 trillion operations per second. But Rivian combines a pair of chips in each autonomy module, giving it 1,600 TOPS and execution rates around 5 billion pixels of data per second, versus 3.5 billion for Nvidia’s Thor. Rajagopalan says developing the chip and AI software simultaneously shaved a critical full year from development. Experts say it’s the kind of fast-to-market speed that China has mastered and that legacy automakers are struggling to match. The in-house design allows Rivian to custom-tailor its software to the chip, and vice versa. Nvidia’s general-purpose chip, designed to satisfy multiple customers with various needs, must devote computing power to onboard infotainment, displays, or other systems. Rivian’s chip is designed to run autonomy and nothing but. Early fusion preserves the richest sensor data for maximum accuracy in self-driving. But it demands the enormous computing power the RAP1 can deliver. Raha says the approach helps the multimodal system degrade gracefully and continue to operate with certainty even if, say, a camera’s lens gets covered with mud. Rivian’s next planned milestone toward self-driving will be Level 3 autonomy—a hands-off and _\_eyes-off\__ system, but for highways only. (Remember, Tesla’s current FSD is technically a Level 2 system: hands off but _\_not\__ eyes off.) On the freeway, Nguyen points out, drivers would be spared the drudgery of dealing with stop-and-go traffic, allowing them to boost productivity or just goof off. Some autonomy critics are leery of Level 3, envisioning a limbo zone in which drivers are lulled into a [false sense of security](https://www.autonews.com/technology/an-automakers-turn-to-level-3-autonomy-amid-robotaxi-hype-0116/) when a car drives for long stretches with no human attention required. Ford and GM are among the automakers pivoting toward limited eyes-off functions. Rivian’s senior vice-president of autonomy, James Philbin, sees Level 3 as an inevitable stepping-stone to Level 4. The company expects it will initially be limited to highways, not the cut-and-thrust of city traffic. If a driver fails to respond to alerts, the system will slow the vehicle, pull off on a shoulder, or call 911. Rivian has not announced a timeline for introducing limited Level 3 capability. Ready or not, these much more autonomous systems are coming, a natural evolution of today’s semiautonomous helpers. In developed markets, adoption of showroom cars with partial-to-full automation is projected to jump from 8 percent in 2024 to 28 percent by 2030, [according to Morgan Stanley](https://www.morganstanley.com/insights/articles/self-driving-vehicles-industry-growth). “One in four cars sold globally may be equipped with smart-driving technology in five years, versus one in eight cars now,” wrote [Tim Hsiao](https://www.morganstanley.com/asiaresearch/country-and-region/taiwan.html), a Morgan Stanley analyst, in [a note posted on the company’s website](https://www.morganstanley.com/insights/articles/self-driving-vehicles-industry-growth). Combining cameras, lidar, and radar gives a self-driving car a better view of people and objects in front of it, according to Rivian. The company expects to release a self-driving system before the end of 2026 that will compete with Tesla’s, which uses cameras alone.Rivian MIT’s Reimer believes the self-driving revolution will really begin when the technology migrates from controlled taxi fleets to consumer cars, giving owners back the precious time they waste on commuting. If owners could truly send their autonomous car to safely chauffeur children, keep aged parents mobile, or run errands—while owners keep working or playing—the automakers who first help make that happen will enjoy a massive competitive edge, he says. As automakers struggle to convert buyers to subscription models, Reimer believes that self-driving appears to be the one advance for which consumers might actually pay plenty. But the greatest impediment to that revolution has little to do with technology. Public skepticism over self-driving is rampant; and the fate of fully autonomous testing in [New York City is uncertain](https://www.thecityreporter.nyc/2026/04/06/waymo-driverless-cars-testing-roads-autonomous-vehicle/). Even going from Level 2 to [Level 3](https://www.kbb.com/car-advice/level-3-autonomy-what-car-buyers-need-know/) might shift legal liability for some accidents from drivers to automakers. But with Tesla still fighting lawsuits over its rudimentary Autopilot systems, those questions aren’t anywhere near settled. Experts worry that self-driving cars may become as politicized as EVs. Labor unions are pushing back, fearing job losses from taxis to trucking. A crazy quilt of state or local regulations has failed to create coherent industry guidelines. Publicized failures—even ones that don’t result in injuries, such as Waymos driving onto a flooded street or impeding emergency workers—give the industry a black eye. Companies like Tesla and even Waymo, Reimer says, have too often relied on an arrogant “Trust me” approach, resisting regulation and oversight. Nevertheless, the momentum toward truly self-driving cars, and massive backing from automakers and AI-besotted investors, suggests their time has come. The rest of the journey will depend as much on social and regulatory issues as technical ones, and so Reimer has a bit of advice. “Do it right, and share all your data,” he says. “Earn the right to scale…. It’s about establishing trust, and developing a framework in which we truly believe these systems can operate as a trusted part of our transportation network.” Tsubaki KabelSchlepp Robotrax System provides a specialized three-dimensional cable carrier engineered specifically for complex robotic motion, preventing premature conductor fatigue, insulation breakdown, and costly unplanned production halts. In modern automated manufacturing, six-axis articulated robots perform high-speed, multidirectional maneuvers under demanding operational cycles. However, as robot arms swivel, rotate, and extend, the electrical cables, fiber [optics](https://spectrum.ieee.org/tag/optics), and pneumatic hoses supplying them endure severe mechanical stress. Torsional twist, rapid acceleration, and repeated contact with machine structures often lead to premature conductor fatigue, insulation breakdown, and costly unplanned production halts. Conventional cable carriers often transfer operational movement stress directly onto internal electrical lines and hoses. The Robotrax system changes this dynamic through a central steel cable that runs through the core of every chain link. When robot arms undergo rapid directional shifts and accelerations up to 10 g, this internal steel cable absorbs the primary tensile loads. By isolating electrical and fluid lines from pulling forces, the design preserves conductor integrity and dramatically extends cable service life. Mechanics can easily calibrate and adjust system tension using an integrated clamping piece, ensuring consistent mechanical support throughout long operational cycles. The foundation of the Robotrax system lies in its open, single-piece plastic links featuring spherical snap-on connections on both sides. This geometry allows the carrier to flex smoothly across three axes, providing radial rotation of up to ±450 degrees per meter depending on the model size. To optimize internal organization, carrier links contain up to three distinct chambers. This physical separation prevents signal interference and mechanical abrasion between heavy [power lines](https://spectrum.ieee.org/tag/power-lines), sensitive data channels, and fluid hoses. For standard models (R040 through R100), technicians can press cables directly into the carrier without tools, drastically reducing installation and maintenance time. Larger configurations, such as the R140X, incorporate swiveling crossbars with snap locks alongside vertical and horizontal dividers for customized interior partitioning. 1. Steel cable for transferring extremely high tensile forces 2. Tension piece for locking the chain links 3. Type with toolless opening swivel crossbars and divider module available 4. Open design – Fast cable laying as the cables are simply pressed in – Easy checking of all cables 5. Special plastic for long service life 6. Protective covers or heat shields made from different materials are available for different environmental conditions 7. Quick-release bracket for fixing and continuation 8. Strain relief with LineFix clamps 9. Protection against hard impacts, excessive abrasion and premature wear as well as limitation of the bending radius through protector Large robot work envelopes and high-speed motion trajectories can cause loose cable carrier loops to swing and strike the robot body. To eliminate these destructive collisions, Tsubaki KabelSchlepp integrates the Pull Back Unit (PBU). The PBU serves as an active retraction mechanism that maintains optimal tension on the cable carrier throughout the entire motion cycle. By preventing excess slack and eliminating interfering contours, the PBU minimizes collision risks across complex movement paths. The unit requires zero maintenance on its retraction element and offers standard mounting configurations for leading [industrial robot](https://spectrum.ieee.org/tag/industrial-robot) platforms, including [KUKA](https://spectrum.ieee.org/tag/kuka), [ABB](https://spectrum.ieee.org/tag/abb), and [FANUC](https://spectrum.ieee.org/tag/fanuc). Additionally, external protectors can be retrofitted onto individual chain links. These durable impact shields limit the minimum bending radius to prevent over-flexing while shielding the chain body from severe external abrasion. If wear occurs, technicians simply replace the modular protector rather than the entire cable carrier assembly. By combining central load absorption, multi-axis flexibility, and active retraction control, the Robotrax system offers plant engineers and system integrators a reliable path toward maximizing robot uptime and reducing total operational costs. This White Paper gives data engineers and architects a practical overview of how parallel partitioned reads, write-path optimization, and cloud-native bulk loading reduce large-table replication times, and why replication speed has become a business concern as data volumes grow. Beyond policy and research, Radovanovi? writes fiction and poetry. Her short stories have appeared in literary anthologies. She is the author of the short story collection _Divertimento for Four Seasons_ and is completing her first novel, _Counterpoint_. Even before France approved legislation banning social media for children under 15 last January, 13-year-old Benjamin was already wondering what life without social media would look like. “If we want to play football, we won’t be able to organize it. What will we do? Send letters instead?” he joked in [an interview for _Le Monde_.](https://www.lemonde.fr/en/pixels/article/2026/02/18/teens-on-france-s-social-media-ban-for-under-15s-we-re-going-back-to-the-stone-age_6750597_13.html) Second, many children, and often their parents, lack the digital literacy and critical thinking skills needed to navigate online risks safely. My policy research on child online protection in Indonesia, [published earlier this year in _Digital Society_](https://link.springer.com/article/10.1007/s44206-025-00244-0), found substantial gaps that account removals cannot repair: Many children lacked guidance on navigating the internet safely, and large numbers did not know how to report harmful experiences. And third, the platforms have limited independent oversight as they identify underage users, design age-verification systems, and report their own compliance. In Indonesia, platforms themselves are responsible for carrying out age verification, while the Ministry of Communication and Digital Affairs [oversees compliance](https://www.komdigi.go.id/berita/siaran-pers/detail/lindungi-anak-di-era-digital-kemkomdigi-wajibkan-verifikasi-usia-di-platform-digital). TikTok’s appeals process for users flagged as underage, for instance, can require a [government-issued ID and selfies](https://www.tiktok.com/support/faq_detail?id=7611808266502560268&category=web_account), which is a problem because it involves collecting the additional personal data on an ID card, beyond that needed to confirm age. Will government regulators ensure that TikTok handles that data responsibly? Every age-based ban creates an engineering problem: How can a platform reliably determine that a user is old enough, without intruding on other information? Governments and companies may use identity documents, parental authorization, app-store checks, or facial age estimation. Each approach has trade-offs among accuracy, privacy, accessibility, and resistance to circumvention. The challenge should not merely be to “verify age.” It should be to prove that someone is above a threshold, without disclosing their identity, birth date, or other information third parties might use to create a marketing profile. [The European Commission’s age-verification blueprint](https://digital-strategy.ec.europa.eu/en/factpages/blueprint-age-verification-solution-help-protect-minors-online) challenges companies to verify ages without collecting all that additional information. Blocking access to a platform redirects some young people, but not always where expected. Early anecdotal reports in Australia pointed to teenagers migrating to smaller, less-regulated platforms like Yope, a pattern the [Cato Institute](https://www.cato.org/blog/australias-under-16-social-media-ban-warning-online-speech-security-around-world) flagged as a “whack-a-mole” problem for regulators. But industry data collected two months later found [no broad-based shift](https://www.rnz.co.nz/news/world/589514/one-fifth-of-australian-teens-still-use-tiktok-snapchat-after-social-media-ban) of that kind, aside from a small uptick in WhatsApp use. Many teens simply found a way to stay on the banned platforms. For many teenagers, social media workarounds are merely inconvenient. But for isolated, marginalized, disabled, or LGBTQ+ youth who depend on online communities for support that’s otherwise unavailable, displacement can mean losing certain kinds of belonging, or having to move to a platform with even weaker oversight. If blanket social media bans don’t work, then what will? The platforms have created many of the conditions that governments are now trying to contain: engagement-optimized recommenders, intrusive data practices, weak safeguards against unwanted contact, and features such as infinite scroll, autoplay, streaks, and persistent notifications. These design patterns increasingly face regulatory scrutiny, including what’s required under the [European Union’s Digital Services Act](https://www.techtimes.com/articles/320180/20260711/eu-charges-meta-addictive-design-infinite-scroll-violates-dsa-health-rules.htm). A [2026 study from the 5Rights Foundation](https://5rightsfoundation.com/infinite-scroll-groundbreaking-study-reveals-tiktokisation-of-children-online/) that tracked children’s device use minute by minute found that the user interfaces shape children’s attention, sleep, and well-being in real time. Governments should define measurable outcomes and fund independent evaluation, platforms should give researchers meaningful data access, and engineers should audit age-assurance systems for bias and data leakage. Schools, parents, and children themselves need a seat in designing the technology that’s designed to protect children. If policymakers still decide to remove an infrastructure for youth connection, they should offer something better in return. Social media bans may reduce some forms of exposure to harmful content and may be justified for particular ages, services, or risks. But they are just one tool, not a comprehensive substitute for safer design, accountable platforms, digital literacy, institutional capacity, and [noncommercial digital “third places”—](https://digitalserendipities.substack.com/p/if-we-ban-social-media-for-children)moderated communities, creative spaces, and public-interest platforms designed for youth participation rather than profit. The first wave of social media restrictions isn’t enough to keep children safe. Governments are still measuring what’s easiest to count, while neglecting harder-to-measure outcomes such as children’s access to safe third places and [meaningful social connection, both online and offline](https://www.oecd.org/en/data/dashboards/oecd-child-well-being-dashboard.html). Until governments can show evidence that harm has actually declined, they will keep mistaking account removal for safety.