A recently published Tesla patent application titled “System and Method for Handling Errors in a Vehicle Neural Network Processor” describes a way to safely handle errors encountered in self-driving software. Rather than risking delays in driving responses that result from input data errors, a signal is sent to ignore the bad information and continue processing as usual. Tesla’s application was published May 23, 2019 as International Publication No. WO/2019/099941.
During self-driving operations in Tesla’s program, streams of real-time input data are received and used to both train its neural network and initiate a vehicle response to what’s being processed. If something in the data is erroneous or causes a delay in processing, the real-world impact can be disastrous if not handled properly. For example, in a fast-moving vehicle, sensor data can become stale very quickly and cause the self-driving software to respond to an environment that no longer exists. This can result in accidents, property damage, injury, and/or death. The solution presented in Tesla’s patent application attempts to avoid such processing delays altogether and thus improves the safety of the self-driving software overall.
Tesla’s patent application describes the issue as follows:
“Some types of errors may cause neural network processor to hang or time out. That is, one or more portions of neural network processor may freeze or otherwise remain inactive for more than a predetermined amount of time. When a timeout error is encountered, [the] neural network processor may cease to provide output data and/or respond to input data. Other types of errors, such as program errors and/or data errors, may cause the output data generated by [the] neural network processor to be corrupted. When such errors are encountered, [the] neural network processor may continue to provide output data, but the result may be incorrect, meaningless, and/or otherwise unusable.”
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- Tesla’s self-driving patent application focuses on handling errors found in its neural network. | Image: Tesla/WIPO
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- Tesla’s self-driving patent application focuses on handling errors found in its neural network. | Image: Tesla/WIPO
On its face, the concept behind invention may seem somewhat simple, but likely due to the complexity of neural networks and the field of autonomous driving still being fairly new, Tesla’s solution is unique and innovative. At the international review stage in the patent application process, the Examiner found that Tesla’s patent was novel (new) compared to similar neural network inventions already in the field. Specifically, the following was commented in a Written Opinion:
“Although neural network processors are well known in the art, including in the operation of a vehicle, the addition of having the controller signal that a pending data result is tainted, or incorrect, without terminating the execution of the network, improves upon prior art processors by ensuring the computations of the processor in the vehicle continue while ignoring data determined to be in error, and would require a complexity beyond the ordinary skill, and therefore…meets the…criteria for patentability.”
Concerns about Tesla’s Autopilot software were recently hit by a report published by Consumer Reports wherein the consumer advocacy group concluded that Navigate on Autopilot with autonomic lane changes was more of a liability than an asset. The report stated that, since the feature requires drivers to be one step ahead of the system while it is engaged, it still needs improvement, although the same group found Tesla’s autonomous driving software to be more capable than the competition. However, the report was only focused on how Navigate on Autopilot operates when changing lanes confirmation and warnings are disabled, contrary to scathing headlines which lumped all of Autopilot’s features together with the review.
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This most recent patent application shows that Tesla is continuously improving its self-driving features, if that wasn’t already obvious from the company’s frequent over-the-air software releases.
At Tesla’s Autonomy Day for investors last month, CEO Elon Musk declared that the company’s Full Self-Driving computer was objectively the “best in the world”. As more information becomes available, such as presentations on Tesla’s technology and in patent applications, Musk’s confidence expressed in his statement becomes more clear. Full Self-Driving is expected to be feature-complete this year and will become publicly available as regulatory hurdles are overcome.
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
