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Tesla’s $25k car and dedicated robotaxi will be Cybertruck-inspired: report
Tesla’s $25,000 car and the company’s dedicated robotaxi past will both feature a design that’s inspired by the Cybertruck. This was, at least, according to Musk biographer Walter Isaacson.
Citing an excerpt from Isaacson’s upcoming book, Axios noted that Musk was so focused on self-driving robotaxis that it took the collective effort of Tesla executives to persuade him to pursue an affordable car as well. As noted by the publication, the CEO only relented when his aides revealed a plan to build both the $25,000 car and Tesla’s robotaxi side by side.
Tesla is aiming to reach 20 million vehicles per year in 2030. To achieve such a goal, the company would need a vehicle that it could build quickly at scale. This means that Tesla needs a car that would far exceed the Model 3 sedan and Model Y crossover in output. Tesla’s next-generation platform, which was discussed during Investor Day earlier this year, seems designed for this purpose.
As per Isaacson, Musk gathered his top five lieutenants for dinner in Austin for a brainstorming session in November 2021. The agenda for the day was a basic robotaxi that could be produced in high volumes. Musk and his team would reportedly end up debating for almost a year whether they should play it safe and build a car with traditional controls, or opt for a vehicle that’s all-in on autonomy, with no steering wheel or pedals.
Several Tesla engineers reportedly pushed for a more conservative approach. Tesla Chief Designer Franz von Holzhausen, during a meeting on August 18, 2022, even suggested that the robotaxi could be a vehicle with steering wheels and pedals that could be removed later. “If we go down a path of having no steering wheel, and FSD is not ready, we won’t be able to put them on the road,” the Chief Designer said.
Musk, however, was reportedly adamant. “No mirrors, no pedals, no steering wheel. This is me taking responsibility for this decision,” Musk reportedly said. The CEO also highlighted Tesla’s focus on autonomy. “Let me be clear. This vehicle must be designed as a clean robotaxi. We’re going to take that risk. It’s my fault if it f–ks up. But we are not going to design some sort of amphibian frog that’s a halfway car. We are all in on autonomy,” Musk said.
Musk would later relent, however, following a meeting in September 2022. During the meeting, von Holzhausen and several others presented data indicating that a small, “global car” would be needed for Tesla to achieve its ambitious growth targets. The team also convinced the CEO that both the $25,000 car and the robotaxi could be built on the same next-generation platform. Both vehicles could be produced using the same assembly lines as well.
Ultimately, Musk was reportedly convinced of the $25,000 car after von Holzhausen placed models of the $25,000 Tesla and the robotaxi side by side in Tesla’s Design Studio. Both vehicles were unlike other cars on the road today. As per Isaacson, both vehicles had a futuristic design that was inspired by the Tesla Cybertruck. Musk loved the vehicles.
“When one of these comes around a corner, people will think they are seeing something from the future,” Musk said.
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Tesla’s Head of AI, Ashok Elluswamy, says that something that was expected with version 14.3 of the company’s Full Self-Driving platform has already partially shipped with the current build of version 14.2.
Tesla and CEO Elon Musk have teased on several occasions that reasoning will be a big piece of future Full Self-Driving builds, helping bring forth the “sentient” narrative that the company has pushed for these more advanced FSD versions.
Back in October on the Q3 Earnings Call, Musk said:
“With reasoning, it’s literally going to think about which parking spot to pick. It’ll drop you off at the entrance of the store, then go find a parking spot. It’s going to spot empty spots much better than a human. It’s going to use reasoning to solve things.”
Musk said in the same month:
“By v14.3, your car will feel like it is sentient.”
Amazingly, Tesla Full Self-Driving v14.2.2.2, which is the most recent iteration released, is very close to this sentient feeling. However, there are more things that need to be improved, and logic appears to be in the future plans to help with decision-making in general, alongside other refinements and features.
On Thursday evening, Elluswamy revealed that some of the reasoning features have already been rolled out, confirming that it has been added to navigation route changes during construction, as well as with parking options.
He added that “more and more reasoning will ship in Q1.”
🚨 Tesla’s Ashok Elluswamy reveals Nav decisions when encountering construction and parking options contain “some elements of reasoning”
More uses of reasoning will be shipped later this quarter, a big tidbit of info as we wait v14.3 https://t.co/jty8llgsKM
— TESLARATI (@Teslarati) January 9, 2026
Interestingly, parking improvements were hinted at being added in the initial rollout of v14.2 several months ago. These had not rolled out to vehicles quite yet, as they were listed under the future improvements portion of the release notes, but it appears things have already started to make their way to cars in a limited fashion.
Tesla Full Self-Driving v14.2 – Full Review, the Good and the Bad
As reasoning is more involved in more of the Full Self-Driving suite, it is likely we will see cars make better decisions in terms of routing and navigation, which is a big complaint of many owners (including me).
Additionally, the operation as a whole should be smoother and more comfortable to owners, which is hard to believe considering how good it is already. Nevertheless, there are absolutely improvements that need to be made before Tesla can introduce completely unsupervised FSD.
Elon Musk
Tesla’s Elon Musk: 10 billion miles needed for safe Unsupervised FSD
As per the CEO, roughly 10 billion miles of training data are required due to reality’s “super long tail of complexity.”
Tesla CEO Elon Musk has provided an updated estimate for the training data needed to achieve truly safe unsupervised Full Self-Driving (FSD).
As per the CEO, roughly 10 billion miles of training data are required due to reality’s “super long tail of complexity.”
10 billion miles of training data
Musk comment came as a reply to Apple and Rivian alum Paul Beisel, who posted an analysis on X about the gap between tech demonstrations and real-world products. In his post, Beisel highlighted Tesla’s data-driven lead in autonomy, and he also argued that it would not be easy for rivals to become a legitimate competitor to FSD quickly.
“The notion that someone can ‘catch up’ to this problem primarily through simulation and limited on-road exposure strikes me as deeply naive. This is not a demo problem. It is a scale, data, and iteration problem— and Tesla is already far, far down that road while others are just getting started,” Beisel wrote.
Musk responded to Beisel’s post, stating that “Roughly 10 billion miles of training data is needed to achieve safe unsupervised self-driving. Reality has a super long tail of complexity.” This is quite interesting considering that in his Master Plan Part Deux, Elon Musk estimated that worldwide regulatory approval for autonomous driving would require around 6 billion miles.
FSD’s total training miles
As 2025 came to a close, Tesla community members observed that FSD was already nearing 7 billion miles driven, with over 2.5 billion miles being from inner city roads. The 7-billion-mile mark was passed just a few days later. This suggests that Tesla is likely the company today with the most training data for its autonomous driving program.
The difficulties of achieving autonomy were referenced by Elon Musk recently, when he commented on Nvidia’s Alpamayo program. As per Musk, “they will find that it’s easy to get to 99% and then super hard to solve the long tail of the distribution.” These sentiments were echoed by Tesla VP for AI software Ashok Elluswamy, who also noted on X that “the long tail is sooo long, that most people can’t grasp it.”
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Tesla earns top honors at MotorTrend’s SDV Innovator Awards
MotorTrend’s SDV Awards were presented during CES 2026 in Las Vegas.
Tesla emerged as one of the most recognized automakers at MotorTrend’s 2026 Software-Defined Vehicle (SDV) Innovator Awards.
As could be seen in a press release from the publication, two key Tesla employees were honored for their work on AI, autonomy, and vehicle software. MotorTrend’s SDV Awards were presented during CES 2026 in Las Vegas.
Tesla leaders and engineers recognized
The fourth annual SDV Innovator Awards celebrate pioneers and experts who are pushing the automotive industry deeper into software-driven development. Among the most notable honorees for this year was Ashok Elluswamy, Tesla’s Vice President of AI Software, who received a Pioneer Award for his role in advancing artificial intelligence and autonomy across the company’s vehicle lineup.
Tesla also secured recognition in the Expert category, with Lawson Fulton, a staff Autopilot machine learning engineer, honored for his contributions to Tesla’s driver-assistance and autonomous systems.
Tesla’s software-first strategy
While automakers like General Motors, Ford, and Rivian also received recognition, Tesla’s multiple awards stood out given the company’s outsized role in popularizing software-defined vehicles over the past decade. From frequent OTA updates to its data-driven approach to autonomy, Tesla has consistently treated vehicles as evolving software platforms rather than static products.
This has made Tesla’s vehicles very unique in their respective sectors, as they are arguably the only cars that objectively get better over time. This is especially true for vehicles that are loaded with the company’s Full Self-Driving system, which are getting progressively more intelligent and autonomous over time. The majority of Tesla’s updates to its vehicles are free as well, which is very much appreciated by customers worldwide.
Tesla AI Head says future FSD feature has already partially shipped
Tesla’s Elon Musk: 10 billion miles needed for safe Unsupervised FSD
