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NVIDIA and Bosch partner on AI self-driving car supercomputer
NVIDIA CEO Jen-Hsun Huang announced to attendees at the Bosch Connected World conference in Berlin this week that they have partnered with Bosch to producing an artificial intelligence supercomputer aimed at the self-driving car industry.
“I’m so proud to announce that the world’s leading tier-one automotive supplier — the only tier one that supports every car maker in the world — is building an AI car computer for the mass market,” said Huang. “We’ve really supercharged our roadmap to autonomous vehicles. We’ve dedicated ourselves to build an end-to-end deep learning solution. Nearly everyone using deep learning is using our platform.”
The announcement made by NVIDIA comes on the heels of this week’s announcement that the world’s leading chipmaker Intel will be acquiring ex-Tesla Autopilot partner Mobileye for $15 billion.
NVIDIA’s Drive PX platform with Xavier technology can process up to 30 trillion deep learning operations a second while drawing just 30 watts of power. It is intended to provide Level 4 autonomy, where a vehicle equipped with the technology can drive on its own.
Huang noted that a wide variety of companies are actively working on self-driving solutions. From carmakers like Audi, Ford, BMW, and Tesla, to technology companies such as Waymo, Uber and China’s Baidu.
As the self-driving car industry continues to take shape, vehicles will require an unprecedented level of computing power to make instantaneous decisions on nearly an infinite number of scenarios that can take place in a real world environment. Though vehicles on the road today are equipped with driving-assist features like Tesla Autopilot that allows the car to detect object and handle acceleration and braking when needed, the requirements for autonomous driving are dramatically more demanding. Cars that stray from their lanes, objects that fall onto the roadway, rapid shifts in weather conditions, deer that dart across the road. The permutations are endless, said Huang.
Despite the positive outlook on a self-driving future being presented at Bosch Connected World, the conference also revealed a significant difference of opinion between the companies in attendance regarding when they expect full Level 5 autonomy – when a vehicle can drive entire on its own without human involvement – to become widely available. Huang told the conference he expects to have chips available that will permit Level 3 automated driving which still requires a human driver to intervene, by the end of this year. He sees those chips being incorporated into customers’ cars and on the road by the end of 2018. The following year will see chips capable of Level 4 full autonomy on the road. The distinction between Level 4 and Level 5 full autonomy is that Level 4 does not cover every driving scenario.
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Elmar Frickenstein, the head of autonomous driving at BMW, told the conference his company will be ready to offer cars with Level 3 capability in 2021 with Level 4 and Level 5 autonomy following shortly thereafter. He thinks self-driving cars may first be produced in small numbers for fleet customers like Uber, Waymo, and Baidu.
Surprisingly, Bosch CEO Volkmar Denner told the attendees his timeline for fully self-driving cars for mainstream customers is not before 2025, if then.
Fully self driving cars that can operate in all environments require enormous computing power, Huang told the conference. “No human could write enough code to capture the vast diversity and complexity that we do so easily, called driving,” he said.
The conference highlighted the differences between traditional car companies, which think full autonomy is still 7 to 10 years away, and chip companies like NVIDIA who see a much shorter timeline. Huang thinks companies like his will drive the pace of change faster than predicted. “In the near future, you’re going to see these schedules pull in,” he says.
Tesla, which uses a supercomputer made by NVIDIA on Model S, Model X and the upcoming Model 3 that are equipped with Autopilot 2.0 full self-driving hardware, is perhaps the most optimistic of all when it comes to having fully autonomous vehicles on the road. Elon Musk believes every car equipped with the Hardware 2 package will begin seeing Full Self-Driving capabilities as early as this year, barring regulatory approval.
Tesla’s Full Self-Driving Capability to arrive in 3 months, “definitely” by 6 months, says Musk
Tesla is accumulating driving data from billions of miles of real world driving each day and using that information to improve its algorithm for Autopilot.
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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.”
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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.
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.
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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.
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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.
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.
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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.
Tesla has one big financial question to answer for investors: Morgan Stanley
SpaceX AI investment gamble will make it a big winner, firm says
