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Tesla’s newest Autopilot Vision head: Who is Andrej Karpathy?
The EV world was rocked when it was announced that Tesla replaced its Vice President of Autopilot Software, Chris Lattner, with Andrej Karpathy. Karpathy is the company’s new Director of AI and Autopilot Vision, but who he is?
Karpathy most recently worked as a research scientist with Elon Musk’s OpenAI, specializing in deep neural networks.
He also had three summer stints at Google. In 2011 and 2013, Karpathy interned for the tech behemoth working on large scale deep learning and video content analysis. In 2015, he was with Google DeepMind, focusing on deep reinforcement learning.
In 2009, Karpathy graduated from University of Toronto with a Bachelor’s of Science in computer science and in physics. From there, he went to the University of British Columbia and got Master’s Degree in computer science and researched motor control, primarily learning controllers for physically simulated figures.
After finishing graduate school in 2011, Karpathy went to Stanford University to pursue a Ph.D in computer science. He researched machine learning, with an emphasis on deep learning for computer vision and natural language processing. He worked under adviser Fei-Fei Li, the director of Stanford’s AI lab and chief director of Google Cloud. According to his Stanford profile, Karpathy graduated in 2015 before jumping to Google DeepMind and eventually OpenAI.
Karpathy has been published in scholarly journals and for conferences, including the International Conference on Learning Representations. He also published a couple of blogs to keep his followers posted on research development and a lighter one on topics such as a survival guide to a Ph.D.
In February of this year he joined the steering committee of distill.pub, a journal focusing on machine learning research.
Karpathy will report directly to Musk, and work closely with chip expert and Vice President of Autopilot Hardware Jim Keller on advancing Tesla’s self-driving technology.
Tesla released the following statement regarding the hiring of Karpathy:
Andrej Karpathy, one of the world’s leading experts in computer vision and deep learning, is joining Tesla as Director of AI and Autopilot Vision, reporting directly to Elon Musk. Andrej has worked to give computers vision through his work on ImageNet, as well as imagination through the development of generative models, and the ability to navigate the internet with reinforcement learning. He was most recently a Research Scientist at OpenAI.
Andrej completed his computer vision PhD at Stanford University, where he demonstrated the ability to derive complex descriptions of images using a deep neural net. For example, identifying not simply that there is a cat in a given picture, but that it is an orange, spotted cat, riding on a skateboard with red wheels on brown hardwood flooring (http://cs.stanford.edu/people/karpathy/main.pdf). He also created and taught “Convolutional Neural Networks for Visual Recognition,” the first and still leading deep learning course at Stanford.
Andrej will work closely with Jim Keller, who now has overall responsibility for Autopilot hardware and software.
To see Karpathy in action discussing deep learning, check him out here:
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
