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Tesla’s race to autonomy: No one said it would be easy
Need to type up a quick memo before work? Forgot to eat breakfast before driving to school? In just a few years, driving may be a more hands-off endeavor than ever before if companies like Tesla, Uber, Volvo, Alphabet, General Motors, or Ford have anything to do about it. You could be a passenger in your own self-driving car, weaving in and out of traffic with ease and parallel parking like a pro every time. It seems like most every company even tangentially related to cars is pouring money into the race for autonomy.
The freedom of self-driving cars is still heavily dependent on regulatory whim and technological availability, but some are setting demanding goals in an effort to finish first in that race. Tesla for example, plans to showcase its Full Self-Driving Capability by driving one of its fleet cars from California to New York, without human involvement, by the end of this year. But their competitors are moneyed, motivated and many.
The Self-Driving Battle Arena
For Uber, success in autonomous driving research could be a sweet distraction from the recent troubles of the company. Its self-driving program has been based in Pittsburgh, right next to Carnegie Mellon with its highly regarded robotics program since it began in 2015. Then-CEO Travis Kalanick was determined to stay on top of the industry. “It starts with understand that the world is going to go self-driving and autonomous,” Kalanick said in a 2016 interview with Business Insider. “So if that’s happening, what would happen if we weren’t a part of that future? If we weren’t part of the autonomy thing? Then the future passes us by basically, in a very expeditious and efficient way.”
Plagued by lawsuits, investigations, and subsequent executive upheaval that saw Kalanick’s resignation from the enterprise he founded, Uber is still one of the best places for researchers and engineers to work on their projects. The company has armies of vehicles across the country, vast datasets of information from the millions of miles its cars have covered through its ride-hailing branch, and the money to fund its engineers’ work.
This does not mean that Uber’s self-driving program has remained untouched. Waymo, the autonomous car division of Google’s parent company, Alphabet, is currently suing Uber over files allegedly by Anthony Levandowski when he moved from Waymo to Uber. According to Reuters, in recent court filings, Waymo has claimed that Uber knew of the stolen intellectual property and even conspired with Levandowski to use it. Uber denies the allegations and actually fired Levandowski on May 30, claiming he had not cooperated with their internal investigation– and probably hoping to win some goodwill from the judge who has already said Waymo had produced a convincing case.
It is unlikely the scandals will affect the decisions of most researchers to stay with the company. As Wired’s Aarian Marshall points out, the long timeline of building a safe autonomous car makes engineers less likely to leave at a moment’s notice in a period of executive instability. And the branch’s position in Pittsburgh rather than Silicon Valley means the roiling news is less sensationalized and the researchers less affected. The ride-sharing company’s failure to live up to certain promises, including backing one of Pittsburgh’s federal grant proposals or hiring from neighborhoods near its test tracks, have drawn ire from many local activists and politicians, as reported by the New York Times. Even so, it has helped the city break away from its steel past and into a high-tech future.
Meanwhile, Uber’s main competitor in the ride-sharing industry, Lyft, has been making strides to continue chipping away Uber’s monopoly in any field, including self-driving cars, as Uber deals with scandal after scandal. As reported by Recode, Lyft is steadily gaining ground on Uber in terms of the share of ride-hailing app downloads as its ratings in the IOS App Store rise and Uber’s falls. This recent shift in market share comes as Waymo and Lyft start a new partnership that will combine Waymo’s advanced technology with Lyft’s vast amounts of data on people, where and how they drive. “Lyft’s vision and commitment to improving the ways cities move with help Waymo’s self-driving technology reach more people, in more places,” a Waymo spokesperson told Wired. Extending Waymo’s dataset beyond the few cities, including Phoenix and Pittsburgh, allows the enterprise to collect the small details of average people’s driving habits much faster and accurately than its test drives around Silicon Valley will.
But despite Waymo’s eight years of self-driving research, it still has to play catch up to Uber in some regards. Waymo just started testing autonomous trucks earlier this month, while Uber first used a self-driving truck to deliver a shipment last August, advancing its technology quickly after it snatched up the self-driving truck startup Otto—founded by Anthony Levandowski after he left Waymo— in January of 2016. Yet, Waymo has the benefit of its parent company’s huge cash reserves and data.
Growing Pains
Tesla is moving its autonomous program forward at an increasingly demanding pace, trying to meet that goal of driving from Los Angeles to New York by the end of this year. It, like Uber, is going through some executive shakeup: after just six months with Tesla, Chris Lattner, Vice President of its Autopilot Software program, left the company after reported tensions with Elon Musk. Tesla explained that the former Apple engineer was not a “good fit.” It stands to mention that working under Musk is notoriously a high-pressure gig. According to LinkedIn Insights, the average tenure of a Tesla employee is only 2.2 years, while companies like General Motors keeps its employees for almost 9. But Lattner’s exit is just one example of many of talented Tesla self-driving engineers leaving the company or being poached by the competition, like Waymo.
While Autopilot can do many impressive things— change lanes, brake before obstacles, and generally act as a rational human driver— it is far from perfect. The program is still technically in “public beta” testing, and rated by the National Transportation Safety Board as a 2 out of 5 on its scale of autonomy.
The fatal crash of a Model S owner Joshua Brown in May 2016 serves as a good reminder that drivers are cautioned to pay attention and keep their hands on the wheel at all times while using Autopilot. Tesla’s driving-assist feature, at the time, could not distinguish the difference between the bright sky and the white truck. Tesla and Autopilot were cleared of responsibility by the NTSB because Brown was given several warnings to take back control of the wheel. But it is a poignant example that Autopilot does not function as a self-driving car and still requires a driver’s full attention. After the accident, Tesla was forced to start developing its own hardware for Autopilot. Mobileye, which previously supplied Tesla’s image processing chips, ended its partnership in a public spat with Musk.
According to Lattner’s public resume, the transition to its own hardware presented “many tough challenges” to the Tesla team. Musk commented to shareholders in June that Tesla is “almost there in terms of exceeding the ability” of the original hardware. All of Tesla’s vehicles in production, including the upcoming Model 3, have the capability to engage Autopilot (for a price) and the necessary hardware to enable full self-driving someday. Autopilot will continue using the camera-based system that Tesla swears by, even as most of the industry focuses on developing LiDAR technology based on light and lasers.
And while Tesla prefers to work mostly alone, the rest of the industry is also pairing up, making deals, partnerships, and contracts between manufacturers, data giants, and service teams. Musk is taking a move out of Steve Jobs’ playbook by vertically integrating everything within the business, from top-to-bottom. Waymo and Honda, Lyft and Waymo, Autoliv and Volvo, Hertz and Apple, Intel and Mobileye, Audi and NVIDIA, and almost every other combination you could think of. Predictions for when the first company will reach the finish line range from within a year to two decades from now. And even if the car is made, there is still the question of if cities and states will allow autonomous vehicles to drive on their streets. The technology is closer than ever, but for now, please keep your eyes on the road.
Elon Musk
Elon Musk teases crazy outlook for xAI against its competitors
Musk’s response was vintage hyperbole, designed to rally supporters and dismiss doubters, something his responses on social media often do.
Elon Musk has never been one to shy away from crazy timelines, massive expectations, and outrageous outlooks. However, his recent plans for xAI and where he believes it will end up compared to its competitors are sure to stimulate conversation.
In a bold and characteristic response on X, Elon Musk fired back at a recent analysis that positioned his AI venture, xAI, as lagging behind industry frontrunners.
The post, from March 14, came as a direct reply to forecaster Peter Wildeford’s assessment, which drew from benchmarks and reporting to rank AI developers.
xAI will catch up this year and then exceed them all by such a long distance in 3 years that you will need the James Webb telescope to see who is in second place
— Elon Musk (@elonmusk) March 14, 2026
Wildeford placed Anthropic, Google, and OpenAI in a virtual tie at the top, with xAI and Meta trailing by about seven months. Chinese players like Moonshot, Deepseek, zAI, and Alibaba were estimated to be nine months behind, while France’s Mistral lagged by about a year and a half.
Musk’s response was vintage hyperbole, designed to rally supporters and dismiss doubters, something his responses on social media often do.
He claimed xAI would “catch up this year,” meaning by the end of 2026, erasing that seven-month deficit against the leaders. But he didn’t stop there.
Musk escalated his vision to 2029, predicting xAI would “exceed them all by such a long distance” that observers would need the James Webb Space Telescope, NASA’s orbiting observatory stationed about 930,000 miles from Earth, to spot whoever lands in second place. This analogy underscores Musk’s confidence in xAI’s trajectory, implying an astronomical lead that could redefine the AI landscape.
Breaking down these claims reveals Musk’s strategic optimism. First, the short-term catch-up: xAI, launched in 2023, has already released models like Grok, but recent benchmarks, including those for Grok 4.2, have shown it falling short in capabilities compared to rivals.
Anthropic’s Claude series, Google’s Gemini, and OpenAI’s GPT models dominate in areas like reasoning, coding, and multimodal tasks. Musk’s assertion suggests aggressive scaling in compute, talent, or architecture, perhaps leveraging xAI’s ties to Tesla’s Dojo supercomputers or Musk’s vast resources, to close the gap swiftly.
The longer-term dominance by 2029 paints an even more audacious picture. Musk envisions xAI not just parity but supremacy, outpacing competitors in innovation speed and model sophistication.
This could involve breakthroughs in energy-efficient training, real-world integration, like Tesla’s robotics, or ethical AI alignment, aligning with Musk’s stated goal of “understanding the universe.”
Critics, however, point to parallels with Tesla’s Full Self-Driving delays; one reply highlighted Musk’s 2023 promise of FSD readiness. Musk has made this promise for many years, and although the system has been strong and improving, it is still a ways off from the completely autonomous operation that was expected by now.
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Musk’s comment highlights the intensifying U.S.-centric AI race, with xAI challenging the “three-way” dominance noted by Wharton professor Ethan Mollick, whom Wildeford quoted. As geopolitical tensions rise—evident in the Chinese firms’ lag—Musk’s tease could spur investment and talent wars.
Yet, it also invites scrutiny: Will xAI deliver, or is this another telescope-needed mirage? In an industry where timelines slip but stakes soar, Musk’s words keep the spotlight on xAI’s ambitious path forward.
Elon Musk
Tesla Terafab set for launch: Inside the $20B AI chip factory that will reshape the auto industry
Tesla set to launch “Terafab Project: A vertically integrated chip fabrication effort combining logic processing, memory, and advanced packaging.
Tesla is making one of the boldest bets in its history. On March 14, Elon Musk posted on X that the “Terafab Project launches in 7 days,” pointing to March 21, 2026 as the start date for what he has described as a vertically integrated chip fabrication effort combining logic processing, memory, and advanced packaging.
Tesla first confirmed Terafab on its January 28, 2026 earnings call, where Musk told investors the company needs to build a chip fabrication facility to avoid a supply constraint projected to materialize within three to four years. But the seeds were planted even earlier. At Tesla’s annual general meeting last year, Musk warned that even in the best-case scenario for chip production from their suppliers, it still wouldn’t be enough, and declared that building a “gigantic chip fab” simply had to be done.
While there has been no official announcement on where Tesla plans to break ground on the massive Terafab, all signs point to the North Campus of Giga Texas in Austin.
Months of speculation has surrounded Tesla’s North Campus expansion at Giga Texas, where drone footage captured by observer Joe Tegtmeyer revealed massive construction site preparation just north of the existing factory on a scale that rivals the original Giga Texas footprint itself.
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The project is projected to produce 100–200 billion AI and memory chips annually, targeting 100,000 wafer starts per month, at an estimated cost of $20 billion. Tesla is targeting 2-nanometre process technology and anticipated to be the most advanced node currently in commercial production. Dubbed the Tesla AI5 chip, the chip will pack 40x–50x more compute performance and 9x more memory than AI4, and will be among the first products Terafab factory is set to produce. This highly optimized, and massively powerful inference chip is designed to make full self-driving (FSD) and Tesla’s Optimus robots faster, safer, and with full autonomy.
(Credit: Tesla)
This is where Terafab becomes a genuine game-changer. If Tesla successfully builds a 2nm chip fab at scale, it becomes one of only a handful of entities that’s capable of producing AI silicon in-house, with competitive implications that extend far beyond Tesla’s own vehicles, and potentially positioning Tesla as a chip supplier or licensor to other industries.
Credit: @serobinsonjr/X
The next-gen Tesla AI chips will power advancements in Full Self-Driving software, the Cybercab Robotaxi program, and the Optimus humanoid robot line. Musk’s projections for Optimus require chip volumes that no existing external supplier can commit to on Tesla’s timeline.Competitors like Waymo and GM’s Cruise remain dependent on third-party silicon, leaving them exposed to the same supply chain vulnerabilities Tesla is now working to eliminate entirely.
The Terafab launch this week may not mean a factory opens its doors overnight, but it signals Tesla is serious about owning the entire AI stack, from software to silicon.
Elon Musk
What is Digital Optimus? The new Tesla and xAI project explained
At its core, Digital Optimus operates through a dual-process architecture inspired by human cognition.
Tesla and xAI announced their groundbreaking joint project, Digital Optimus, also nicknamed “Macrohard” in a humorous jab at Microsoft, earlier this week.
This software-based AI agent is designed to automate complex office workflows by observing and replicating human interactions with computers. As the first major outcome of Tesla’s $2 billion investment in xAI, it represents a powerful fusion of hardware efficiency and advanced reasoning.
At its core, Digital Optimus operates through a dual-process architecture inspired by human cognition.
Macrohard or Digital Optimus is a joint xAI-Tesla project, coming as part of Tesla’s investment agreement with xAI.
Grok is the master conductor/navigator with deep understanding of the world to direct digital Optimus, which is processing and actioning the past 5 secs of…
— Elon Musk (@elonmusk) March 11, 2026
Tesla’s specialized AI acts as “System 1”—the fast, instinctive executor—processing the past five seconds of real-time computer screen video along with keyboard and mouse actions to perform immediate tasks.
xAI’s Grok model serves as “System 2,” the strategic “master conductor” or navigator, providing high-level reasoning, world understanding, and directional oversight, much like an advanced turn-by-turn navigation system.
When combined, the two can create a powerful AI-based assistant that can complete everything from accounting work to HR tasks.
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The system runs primarily on Tesla’s low-cost AI4 inference chip, minimizing expensive Nvidia resources from xAI for competitive, real-time performance.
Elon Musk described it as “the only real-time smart AI system” capable, in principle, of emulating the functions of entire companies, handling everything from accounting and HR to repetitive digital operations.
Timelines point to swift deployment. Announced just days ago, Musk expects Digital Optimus to be ready for user experience within about six months, targeting rollout around September 2026.
It will integrate into all AI4-equipped Tesla vehicles, enabling parked cars to handle office work during downtime. Millions of dedicated units are also planned for deployment at Supercharger stations, tapping into roughly 7 gigawatts of available power.
Oh and it works in all AI4-equipped cars, so your car can do office work for you when not driving.
We’re also deploying millions of dedicated Digital Optimus units in the field at Superchargers where we have ~7 gigawatts of available power.
— Elon Musk (@elonmusk) March 12, 2026
Digital Optimus directly supports Tesla’s broader autonomy strategy. It leverages the same end-to-end neural networks, computer vision, and real-time decision-making tech that power Full Self-Driving (FSD) software and the physical Optimus humanoid robot.
By repurposing idle vehicle compute and extending AI4 hardware beyond driving, the project scales Tesla’s autonomy ecosystem from roads to digital workspaces.
As a virtual counterpart to physical Optimus, it divides labor: software agents manage screen-based tasks while humanoid robots tackle physical ones, accelerating Tesla’s vision of general-purpose AI for productivity, Robotaxi fleets, and beyond.
In essence, Digital Optimus bridges Tesla’s vehicle and robotics autonomy with enterprise-scale AI, promising massive efficiency gains. No other company currently matches its real-time capabilities on such accessible hardware.
It really could be one of the most crucial developments Tesla and xAI begin to integrate, as it could revolutionize how people work and travel.
Elon Musk teases crazy outlook for xAI against its competitors
Tesla Terafab set for launch: Inside the $20B AI chip factory that will reshape the auto industry
