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SpaceX rolls Super Heavy booster to orbital launch mount
For the third time in four months, SpaceX has rolled the first potentially flightworthy Super Heavy booster towards Starbase’s orbital launch mount.
Combined with a large crane – fitted with a jig solely used to lift boosters – moving to a spot just beside the booster, it’s clear that SpaceX is preparing to reinstall Super Heavy Booster 4 (B4) on the orbital launch mount. In the context of its unusual history, though, what happens next to the first more or less finished prototype of the largest rocket booster ever built is less clear.
After a shockingly quick assembly over the course of six summer weeks, Super Heavy Booster 4 rolled out of Starbase’s ‘high bay’ facility and headed to the nearby orbital launch complex, where it was installed on a custom ‘mount’ designed to support booster testing and orbital launches. It’s now clear that during that early August photo opportunity and fit test, Booster 4 was nowhere close to finished. Nor, apparently, was it anywhere close to complete one month later when it returned to the orbital pad for the second time after another few weeks of work back at the high bay.
Three months (almost 14 weeks or 100 days) after the Super Heavy prototype’s second trip to the pad, SpaceX has yet to attempt to put the booster through a single proof test. There also appears to be a significant amount of work left to finish installing external ‘aerocovers’ and a heat shield meant to enclose all 29 of its Raptor engines. In the three-year history of Starbase, there isn’t a single prototype of the roughly two-dozen SpaceX has built, tested, and even flown that’s spent even half as long as Super Heavy B4 between apparent structural completion and its first test. Perhaps the fact that Booster 4 is a first-of-its-kind pathfinder explains SpaceX’s uncharacteristic sluggishness or reluctance to actually test the rocket.
In every other instance, SpaceX’s approach to Starship development has been to move incredibly quickly, build a large number of prototypes, and rapidly test those prototypes – often resulting in catastrophic failures. Data is gathered from those failures (SN1, SN3, SN4, SN8, SN9, SN10, SN11, and half a dozen smaller test tanks serve as examples), changes are made, and then the new and improved prototypes that follow repeat the process until SpaceX arrives at a successful design.
Super Heavy B4’s circuitous path has been almost nothing like those of its predecessors. That could also be partly explained by the unavailability of a stand or facilities capable of truly proof testing a Super Heavy, which necessitates a supply of around 3200 tons (7M lb) of liquid nitrogen (LN2; for a cryogenic proof test with full tanks), another 3200 tons of a combination of liquid methane (LCH4) and oxygen (LOx), and the ability to ignite – and survive – as many as 29 to 33 Raptor engines. The suborbital stands SpaceX has used to proof Starships and even Super Heavy Booster 3 don’t even have half the storage capacity required to fully test a booster and the mounts and their surroundings would likely be catastrophically damaged or destroyed by the thrust and blast created by dozens of Raptors.
Still, SpaceX could have theoretically put Booster 4 through a partial cryoproof and maybe fired up as many as nine Raptors at once – not a replacement for full proof testing but still plenty to ensure Super Heavy’s structural integrity and gather invaluable data on clustered Raptor performance. Instead, of course, Super Heavy B4 has sat at Starbase’s former landing zone for more than three months while teams removed engines, reinstalled engines, half-installed a full Raptor heat shield; and installed two of six or seven ‘aerocovers’ needed to protect heat exchangers, racks of pressure vessels, and hydraulic systems installed on the booster’s aft.
This is all to say that from the outside looking in, Booster 4’s path towards testing and flight has been almost entirely different from that of any other Starship prototype. While still quick in comparison with other launch vehicle development programs, relative to other Starship and Super Heavy prototypes, the rate of B4 progress has been far slower – strongly implying that something is seriously wrong with the booster, that SpaceX no longer feels that partial testing is worth the effort, that finishing Booster 4 just hasn’t been a priority for several months, or some combination of the above.
What that ultimately means is that it’s almost impossible to predict what Super Heavy B4’s future holds beyond the clear evidence that SpaceX will soon reinstall to reinstall it on an orbital launch mount that’s much closer to completion than it was the last time B4 was installed. At this point, it’s just as likely that the booster’s third launch mount installation will just be another mechanical fit test, though the hope is that it will kick off full-scale pneumatic and cryogenic proof testing. It could even culminate in the static fire of some or all of its 29 Raptor engines, which have been installed for several months.
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
