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SpaceX’s next Starship gets frosty to prepare for first launch
One week after the rocket was rolled from the factory to the launch pad, SpaceX appears to have successfully put Starship serial number 9 (SN9) through two routine pre-launch tests.
On December 22nd, significantly less than two weeks after Starship SN9 suffered a significant handling or production accident that caused it to tip several degrees and impact the walls of its production facility, SpaceX wrapped up speedy repairs and transported the rocket about 1.5 miles down the road.
In some combination of a minor miracle and Starship’s exceptionally sturdy design, the rocket – standing ~50 meters (~165 ft) tall and weighing around 75 to 100 metric tons (175,000-220,000 lb) – tipped sideways onto two of its four pre-installed flaps. Despite being subjected to off-nominal forces, the far stronger structural mechanisms connecting those flaps to Starship’s main airframe were seemingly unharmed and SpaceX was able to remove and replace the crumpled control surfaces mere days after the incident.
On December 28th, that work began in earnest with what is generally known as an ambient temperature pressure test, filling Starship SN9’s propellant tanks with benign air-temperature nitrogen gas. Used to check for leaks, verify basic vehicle valve and plumbing performance, and ensure a basic level of structural integrity, SN9 appeared to pass its ambient proof test without issue – albeit late in the window.
Testing wrapped up on Monday shortly after the ambient proof and was followed by the main event – a cryogenic proof test – a bit less than a day later on Tuesday. The exterior of Starship SN9 began to develop a coating of frost after SpaceX started loading its oxygen and methane tanks with liquid nitrogen around 2:30 pm CST (UTC-6). While used similarly to verify structural integrity like an ambient pressure test, a ‘cryo proof’ adds the challenge of thermal stresses to ensure that Starship can safely load, hold, and offload supercool liquids.
In SN9’s case, it’s unclear if SpaceX fully or only partially loaded the rocket’s main propellant tanks with liquid nitrogen, while a lack of frost at the tip of its nose implies that the Starship’s smaller liquid oxygen ‘header’ tank wasn’t filled as part of the test. Altogether, Starship should be capable of holding roughly 1200 metric tons of liquid nitrogen if fully loaded.
The lack of SN9’s LOx header tank participation in Tuesday cryo proof testing is intriguing on its own, as it implies that SpaceX will either perform a second cryo proof later this week or is confident enough in LOx header tank and transfer tube performance to forgo any testing. In the latter case, SpaceX would likely just use the build-up to Starship SN9’s first Raptor static fire test as a wet dress rehearsal (WDR) and a cryo proof for the smaller tank system.
According to NASASpaceflight’s managing editor, if Monday and Tuesday’s ambient and cryo proof tests were as uneventful and successful as they seemed, SpaceX may move directly on to triple-Raptor static fire preparations. In a first, Starship SN9 was transported to the launch pad last week with two of three central Raptor engines already installed and had that missing third engine installed within a few days of arrival. SN9 is also the first Starship to attempt its first proof tests with any Raptor – let alone three – installed.
If SpaceX does move directly from cryo proof testing to a three-engine static fire, that will mark another first for the Starship program and signal growing confidence and a desire for speedier preflight tests – both of which will help accelerate flight testing. As of now, SpaceX has yet to cancel a road closure scheduled on Wednesday, December 30th but it’s far more likely that a trio of 8 am to 5 pm CST closures requested on January 4th, 5th, and 6th will host Starship SN9’s first static fire attempt(s). According to NASASpaceflight.com, Starship SN9 is expected to attempt a 12.5 km (~7.8 mi) launch similar or identical to SN8’s as early as a few days after that static fire. Stay tuned for updates!
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
