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SpaceX backup Starship reaches full height after nosecone installation
SpaceX has installed another Starship’s nosecone, all but completing the second full-size prototype a matter of days before the first fully-assembled Starship’s risky launch debut.
Over the last two months, SpaceX has effectively put Starship number 8 (SN8) through an almost nonstop series of tests, completing at least four separate cryogenic proof tests, four Raptor engine static fires, and much more. The company’s South Texas team have also dodged an array of technical bugs; installed, plumbed, and wired what amounts to ~40% of Starship (the nose section) while fully exposed to the coastal elements; and even narrowly avoided a potentially catastrophic failure.
In spite of the many hurdles thrown up and delays resultant, CEO Elon Musk announced earlier this week that Starship SN8 is scheduled to attempt its 15-kilometer (~50,000 ft) launch debut as early as Monday, November 30th. Musk, however, does not see success as the most probable outcome.
Why, then, push to launch Starship SN8 when, in Musk’s own words, the probability of success is as low as “33%”? As previously discussed many times in the history of Teslarati’s BFR and Starship coverage, SpaceX’s attitude towards technology development is (unfortunately) relatively unique in the aerospace industry. While once a backbone of major parts of NASA’s Apollo Program moonshot, modern aerospace companies simply do not take risks, instead choosing a systems engineering methodology and waterfall-style development approach, attempting to understand and design out every single problem to ensure success on the first try.
The result: extremely predictable, conservative solutions that take huge sums of money and time to field but yield excellent reliability and all but guarantee moderate success. SpaceX, on the other hand, borrows from early US and German rocket groups and, more recently, software companies to end up with a development approach that prioritizes efficiency, speed, and extensive testing, forever pushing the envelope and thus continually improving whatever is built.
In the early stages of any program, the results of that approach can look extremely unusual and rudimentary without context (i.e. Starhopper, above), but building and testing a minimum viable product or prototype is a very intentional foundation. Particularly at the start, those minimal prototypes are extremely cheap and almost singularly focused on narrowing a vast range of design options to something more palatable. As those prototypes rapidly teach their builders what the right and wrong questions and design decisions are, more focused and refined prototypes are simultaneously built and tested.
Done well, the agile approach is often quite similar to evolution, where prototype failures inform necessary design changes and killing off dead-end strategies, designs, and assumptions before they can be built upon. In many cases, compared to cautious waterfall-style development, it will even produce results that are both better, cheaper, and faster to realize. SpaceX’s Starship program is perhaps the most visible example in history, made all the more interesting and controversial by the fact that it’s still somewhere in between its early, chaotic development phase and a clear path to a viable product.
On the build side of things, SpaceX has created a truly incredible ad hoc factory from next to nothing, succeeding to the point that the company is now arguably testing and pushing the envelope too slowly. As of November 2020, no fewer than eight full-size Starships and the first Super Heavy booster prototype are visibly under construction. Most recently, Starship SN9 was stacked to its full height, kicking off nosecone installation while still at the build site (unlike SN8). SN10’s completed tank section is likely ready to begin flap installation within the next few days, while Starship SN11 is perhaps a week or two behind that. Additionally, large tank sections of Starships SN12, SN13, SN14, SN15, and (most likely) SN16 are already completed and have all been spotted in the last few weeks.
Some ~90% of the above work was likely started after Starship SN8 first left the factory and rolled to the launch pad on September 26th. In many regards, SN8 has been the first to reach multiple major milestones, largely explaining the relatively plodding pace of its test program compared to SN4, SN5, and SN6.
Ultimately, SN9’s imminent completion – effectively a superior, more refined copy of SN8 – means that Starship SN8’s utility to SpaceX is rapidly deteriorating. The company would almost assuredly never skip an opportunity to learn, meaning that there’s no plausible future in which SN8 testing doesn’t continue, but that doesn’t mean that SpaceX can’t turn its risk tolerance to 11. In essence, accept a 67% (or higher) chance of Starship SN8’s violent destruction but learn as much as possible in the process. As long as good data is gathered, SN8’s launch debut will be a success for Starship whether the rocket lands in one or several pieces.
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
