News
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.
Investor's Corner
Tesla has one big financial question to answer for investors: Morgan Stanley
In a new note to investors on Tuesday, Morgan Stanley analyst Andrew Percoco said that Tesla has one big financial question to answer for investors regarding its Robotaxi rollout, Full Self-Driving software, and Optimus.
Percoco said in the note that, for the most part, investors are still very positive about the direction the company is headed. However, there are some things the firm would like to see, and they have to do with financials.
Tesla (TSLA) Q2 2026 earnings results: miss on EPS, beat on revenue
Tesla bulls are more than convinced that the company’s Full Self-Driving software is proof it can develop physical AI. Financially, however, there are still some questions, especially on elevated spending, which CEO Elon Musk said would occur as the company works to roll out Robotaxi faster and continue developing its Optimus robot.
The latter two are where Tesla will have to prove progress to investors, as Percoco writes that both projects “will require clearer evidence that Robotaxi is scaling and more tangible Optimus proof points to support the ROI on elevated capex.”
Percoco said the second quarter earnings call did not change his long-term thesis of where Tesla is positioned in the AI race, which is out in front. However, there are concerns that weaker gross margins and higher R&D spend will stress financials, and that has “sharpened our (and investors’) focus on measurable progress across Robotaxi and Optimus.”
Additionally, Robotaxi still needs to be proven with more operation in existing cities while maintaining safety but improving how many rides it gives in any given time, he said. For Optimus, Percoco wrote that he is “still looking for evidence beyond commentary around SOP.”
Morgan Stanley put Percoco in charge of covering Tesla after long-time analyst Adam Jonas transitioned to the automotive side.
Currently, Morgan Stanley has a $415 price target on Tesla and a ‘Hold’ rating on the stock. It is trading at around $330 at the time of publication, which was 2:30 P.M. on the East Coast.
Investor's Corner
SpaceX AI investment gamble will make it a big winner, firm says
SpaceX’s massive investment in AI will make it a big winner, Argus Research said after the company’s successful earnings call last week.
The firm also upgraded shares to a Buy from Hold and set a $160 price target.
SpaceX (NASDAQ: SPCX) is currently recovering from its heavy AI infrastructure investments, as it spent nearly $16 billion in Q2 alone. The company did this primarily by monetizing high-demand GPU compute capacity at a much faster pace than traditional data center economics would suggest.
Company CFO Bret Johnsen said that SpaceX would be able to pay back anything on new deployments within a year.
There are plenty of ways the company can do this:
Leasing excess compute capacity through contracts
SpaceX has already built Colossus and Colossus II, largely for its own model training. However, much of that capacity is already rented out to third parties. It already has major deals with Anthropic, Google, and Reflection AI. These partnerships are adding billions per month to SpaceX’s spreadsheet.
High utilization driven by industry-wide scarcity
The demand for advanced AI training and inference capacity continues to exceed what is available for use. SpaceX can fill new racks quickly after they come online, so the capital deployed converts into revenue with minimal idle time.
Additionally, management and outside observers have described the new compute capital as behaving more like a cost-of-goods-sold than traditional multi-year capex, especially because of this rapid monetization pattern.
Capacity has already scaled from ~0.4 GW a year to 1.4 GW annually by the end of Q2. There are targets of more than 2 GW by year-end.
High incremental margins on the rental business once capacity is online
GPU cloud providers often operate at strong gross margins. SpaceX can monetize capacity that was already partially built or can be added efficiently. This means that incremental EBITDA margins on the rental revenue are usually high. This accelerates cash recovery relative to the gross capital outlay.
Parallel monetization of its own AI software and applications
Beyond pure infrastructure rental, SpaceX also generates revenue from Grok through subscriptions and usage, from X through ads, data, and other related services, enterprise APIs, and the planned integration of the Cursor coding tools acquisition.
These application layers ride on the same compute infrastructure and provide additional high-margin streams that could offset build-out costs. AI-segment revenue overall rose sharply to about $2.6 billion in Q2, according to Motley Fool. This was driven primarily by the infrastructure contracts, but the software side is also partially responsible.
Efficient, large-scale deployment and vertical integration advantages
SpaceX has emphasized the rapid construction of power and cooling infrastructure and favorable cost-per-megawatt economics relative to industry benchmarks in some disclosures.
Combined with its ability to scale capacity aggressively and the fact that many contracts start generating revenue within months of capacity coming online, the effective payback compresses dramatically compared with more conventional multi-year data-center projects.
SpaceX’s dominant near-term recovery path will turn the AI clusters into a hyperscale-style compute rental business for other leading AI companies while still using a portion for internal models.
News
Tesla headlights cause recall of over 20,000 Model 3 and Model Y
Tesla headlights have caused a recall of over 20,000 of the company’s two most popular vehicles, the Model 3 and Model Y, due to the low-beam bulb exceeding the maximum allowed intensity according to federal standards.
Tesla initiated the recall with the National Highway Traffic Safety Administration (NHTSA) this morning, stating that the low-beam output “exceeds the maximum allowed intensity in the outer upper-right and outer upper-left areas of the 10U and 90U zone, as prescribed in FMVSS No. 108.”
Tesla sourced the impacted headlights from Marelli Automotive Lighting, a Mexico-based company. The recall impacts 2020-2023 Model Y vehicles and 2017-2023 Model 3 vehicles. It is estimated that every VIN in this recall is impacted by the defect.
🚨 Tesla is recalling 20,349 2020-23 Model Y vehicles and 2017-23 Model 3 vehicles due to an excessively bright headlamp low beam.
Currently, there is no remedy plan in place, as it is still being developed. pic.twitter.com/y34cIO2U0B
— TESLARATI (@Teslarati) August 11, 2026
Typically, Tesla would remedy recalls of this nature through an Over-the-Air software update, which has been a major focus of criticism by the company and its supporters because the NHTSA still refers to it as a “recall,” even though it requires no action by the vehicle owner. The fix is shipped over the internet and downloaded to the car.
However, there appears to be a potentially different solution for this problem. Tesla has not developed a remedy for this issue, so it could potentially be on the way. The big issue appears to be the fact that these recalled lamps are out of production, and this is an old body style for both vehicles. The headlights and front-end designs are completely different.
Tesla switched to another supplier when the affected headlight design was discontinued. It plans to begin notifying owners of their remedy options by September 15.
Tesla filed a petition protesting the recall to fix the vehicles’ headlight issue, but the NHTSA denied it. Now, Tesla will come up with a solution to fix it.



