Much excitement surrounding Tesla’s Dojo supercomputing cluster has been swirling in recent months since the system went online, and the automaker already expects it to be one of the world’s most powerful supercomputers by early next year. But one reporter recently noted that Dojo could someday have additional uses beyond processing vast amounts of data for Tesla’s Full Self-Driving (FSD) system and humanoid robot.
Dojo can process millions of terabytes of video data per second from the company’s vehicles, training its neural network at an incredible rate. The company has said that the video foundation models input to Dojo would effectively serve as the brain of its vehicles and its Optimus robot.
In a recent video segment about how Dojo is expected to revolutionize self-driving, however, Yahoo Finance reporter Pras Subramanian also talked about the supercomputing cluster’s potential applications beyond FSD and Optimus.
The short discussion, hosted by Seana Smith, details how the Dojo system will use Tesla’s AI model to train FSD. However, Subramanian also says the supercomputer could someday be used for vessels other than cars, including motorcycles, bicycles and boats. The conversation also touched on how Dojo works, the complexity of training AI to handle roads and Ford and Volkswagen’s decision to end Argo AI operations, among other topics.
You can watch a short clip from the video segment below or see the full conversation here.
As for cars, Musk has previously talked about the potential of using Dojo to perform traffic control simply with the system’s inputs for things like accidents, potholes, road closures or other data that would be useful for a Tesla to access in real-time. Musk said in June that Dojo had been “online and running useful tasks for a few months,” helping out with production workloads and spurring on the current round of excitement surrounding the computing cluster.
In June, Tesla posted about Dojo on X, detailing how its neural networks were already being accessed in its cars and adding that the company is “building the foundation models for autonomous robots.” Below is an excerpt from the thread:
“Our multi-modal neural networks are already in customer vehicles—these networks take in arbitrary modalities such as camera videos, maps, navigation, IMU (Inertial Measurement Unit), GPS etc.
Tasks such as Occupancy prediction are already quite general in what they represent—in some ways, they are ontology-free & simply predict the probability that some 3D position is occupied.
Such occupancy can be used for collision avoidance by any robot.
All of this is enabled by fleet scale auto-labelling. By using video data from multiple trips in the same location, we can reconstruct the entire scene
In addition, we’re building off state-of-the-art generative modeling techniques—enabling us to predict possible outcomes given past observations, in a jointly consistent manner across multiple camera views”
These imagined futures can be action-conditioned to produce different outcomes.
For example, the videos below are generated entirely by the neural network by simply using different prompts pic.twitter.com/ZuJEYcLuZK
— Tesla AI (@Tesla_AI) June 21, 2023
In 2021, Musk said that Dojo could likely someday reduce traffic fatalities by 90 percent and eventually it may be able to reduce them by more than 99 percent.
Last month, a report showed that Tesla had doubled its order of D1 Dojo chips for next year from Taiwan Semiconductor Manufacturing Company (TSMC), now totaling 10,000 units. According to the report, the company also plans to increase its order in 2025. Morgan Stanley also said last month that Dojo could add $500 billion to Tesla’s enterprise value.
Tesla Dojo trade secrets lawsuit officially settles out of court
What are your thoughts? Let me know at zach@teslarati.com, find me on X at @zacharyvisconti, or send your tips to us at tips@teslarati.com.
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.”
News
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.
Elon Musk
Judge clears path for Elon Musk’s OpenAI lawsuit to go before a jury
The decision maintains Musk’s claims that OpenAI’s shift toward a for-profit structure violated early assurances made to him as a co-founder.
A U.S. judge has ruled that Elon Musk’s lawsuit accusing OpenAI of abandoning its founding nonprofit mission can proceed to a jury trial.
The decision maintains Musk’s claims that OpenAI’s shift toward a for-profit structure violated early assurances made to him as a co-founder. These claims are directly opposed by OpenAI.
Judge says disputed facts warrant a trial
At a hearing in Oakland, U.S. District Judge Yvonne Gonzalez Rogers stated that there was “plenty of evidence” suggesting that OpenAI leaders had promised that the organization’s original nonprofit structure would be maintained. She ruled that those disputed facts should be evaluated by a jury at a trial in March rather than decided by the court at this stage, as noted in a Reuters report.
Musk helped co-found OpenAI in 2015 but left the organization in 2018. In his lawsuit, he argued that he contributed roughly $38 million, or about 60% of OpenAI’s early funding, based on assurances that the company would remain a nonprofit dedicated to the public benefit. He is seeking unspecified monetary damages tied to what he describes as “ill-gotten gains.”
OpenAI, however, has repeatedly rejected Musk’s allegations. The company has stated that Musk’s claims were baseless and part of a pattern of harassment.
Rivalries and Microsoft ties
The case unfolds against the backdrop of intensifying competition in generative artificial intelligence. Musk now runs xAI, whose Grok chatbot competes directly with OpenAI’s flagship ChatGPT. OpenAI has argued that Musk is a frustrated commercial rival who is simply attempting to slow down a market leader.
The lawsuit also names Microsoft as a defendant, citing its multibillion-dollar partnerships with OpenAI. Microsoft has urged the court to dismiss the claims against it, arguing there is no evidence it aided or abetted any alleged misconduct. Lawyers for OpenAI have also pushed for the case to be thrown out, claiming that Musk failed to show sufficient factual basis for claims such as fraud and breach of contract.
Judge Gonzalez Rogers, however, declined to end the case at this stage, noting that a jury would also need to consider whether Musk filed the lawsuit within the applicable statute of limitations. Still, the dispute between Elon Musk and OpenAI is now headed for a high-profile jury trial in the coming months.
Tesla’s Elon Musk: 10 billion miles needed for safe Unsupervised FSD
Tesla earns top honors at MotorTrend’s SDV Innovator Awards
