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SpaceX Starship wraps up nosecone ‘cryo proof’ and first of several Raptor static fires
SpaceX has successfully ‘cryoproofed’ the first fully-assembled Starship prototype’s nose-based propellant tank and used that same tank to fire up a Raptor engine, crossing off one of the last major tests before the rocket’s 15-kilometer (~9.5 mile) launch debut.
On November 4th, after a few false-starts, Starship Serial Number 8 (SN8) kicked off its first round of testing after becoming the first prototype to have a nose section permanently installed. On that Wednesday evening, SpaceX most likely put the rocket through a partial cryogenic proof test explicitly focused on SN8’s new nosecone and a small secondary propellant tank situated in its tip. Designed to act as a secondary reservoir for the relatively small amount of propellant Starships need to land, SN8’s two header tanks were likely loaded with cryogenic liquid nitrogen – a safe, nonreactive stand-in for liquid oxygen and methane.
Having proven that Starship SN8’s newly-installed liquid oxygen header tank and associated plumbing is capable of loading, managing, and offloading dozens of tons of cryogenic fluid while navigating a 40-meter-tall (~130 ft) vertical pipe, SpaceX was ready to move onto the next step: a wet dress rehearsal (WDR) and Raptor static fire.
While SpaceX has technically completed eight successful Raptor static fires on four separate prototypes, including the first three-Raptor static fire ever attempted with Starship SN8, the company has never attempted a static fire while solely drawing propellant from header (landing) tanks. All but essential for Starships to be able to reliably reignite their Raptor engines in flight and keep cryogenic landing propellant liquid for hours, days, weeks, and even months, much smaller header tanks make it easier to keep propellant highly pressurized and in the right place to supply Raptors.
After several days of test windows come and gone and an aborted attempt on November 9th, Starship SN8 finally ignited one of its three Raptor engines, feeding the engine with liquid methane and oxygen stored in two separate header tanks. Oddly, a second or two after startup and ignition, Raptor’s usual exhaust plume was joined by a burst of shiny firework-like debris. A relatively normal five seconds later, the Raptor cut off, though the engine appeared to remain partially on fire for another ten or so seconds – also somewhat unusual.
Ultimately, the observed anomaly could be as simple as debris accidentally left in the vicinity of Raptor’s plume or, while less likely, concrete erosion. There’s also a chance that it was pieces of Raptor’s complex turbopumps or preburners, although it’s also unlikely that the engine would have continued running (as it did) if it had lost that much internal hardware.
(Update: Thankfully, NASASpaceflight.com reporter Michael Baylor says that the cloud of debris observed on November 10th “is not a [Raptor performance] concern,” making pad debris the likely source.)
SpaceX has canceled another static fire window on November 11th, leaving the next opportunity for a second (of three) expected static fire between 9am and 9pm CST (UTC-5) on Thursday, November 13th.
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
