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Tesla’s Safety Score system will be key to in-house insurance’s affordability
Tesla’s Safety Score system may still be in its early stages, but it seems to be a key part of some of the company’s upcoming products. This is particularly true for the Safety Score system, which was recently launched ahead of FSD Beta’s expansion. As observed by Tesla owners who signed up for the company’s in-house insurance in Texas, their rates would be directly tied to their respective Safety Scores.
This is something that would be expected as Tesla expands its in-house insurance service to other states, and perhaps even abroad. As mentioned by CFO Zachary Kirkhorn during the recently-held Q3 2021 earnings call, insurance costs are generally not the fairest thing in the market, with low-risk owners typically overpaying on their rates to subsidize drivers who were riskier on the road. Kirkhorn explained that the car insurance market, in its current state, uses limited data. Fortunately, data just happens to be one of Tesla’s biggest strengths.
“We entered the insurance market kind of unintentionally, I would say. Our customers were coming to us, complaining that the price of traditional insurance was too high and it was reducing the affordability of a Tesla… As we started to do more research, essentially, the tools by which the insurance is traditionally calculated are optimized based upon the existing data, but the existing data is limited. So there’s a focus on things like marital status or age or other attributes like that. Accident history is a good one, etc.
“But what essentially happens here is customers who are low risk and don’t actually file many claims end up overpaying on their insurance relative to their cost. That overpayment then goes to riskier customers who are essentially being subsidized. And, you know, as we looked at this and we looked at the data, we thought this just doesn’t seem like it’s fair. At Tesla, because our cars are connected, because they are essentially computers on wheels, there’s enormous amounts of data that we have available to us to be able to assess the attributes of a driver who’s operating that car and whether those attributes correlate with safety because we do get a signal when a car has been in an accident,” the CFO remarked.
Tesla then proceeded to analyze literally billions of miles of driving history from its fleet, and from this study came a model that was able to predict with decent accuracy the probability of collisions over time. But this was just the beginning. Tesla has learned and is continuing to learn a lot more about its fleet, particularly as the company rolled out its Safety Score system and the FSD Beta Enrollment Program.
“We have almost 150,000 cars currently using a safety score. And I believe the latest data is over 100 million miles of driving. So we’ve been able to go back and analyze that data. And we’ve learned two things coming from that. The first is that the probability of collision for a customer using a safety score versus not is 30% lower. It’s a pretty big difference. It means that the product is working and customers are responding to it. The second thing that we’ve looked at is what is the probability of collision based upon actual data as a function of a driver safety score.
“And that is aligning with our models. Most notably, if you’re in the top tier of safety compared to lower tiers, there’s multiple X difference in probability of collision based upon actual data. So this is a very new and very exciting frontier for us. I know that was long-winded, but I — we spent a lot of time on this and we put a lot of thought into it… So we’re very excited about it. We’re excited about individual risk-based pricing. We’re excited about the ability for folks to become safer and, as a result, save money. And it feeds into our priority of a company — of building the safest products in the world,” Kirkhorn concluded.
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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.
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
