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SpaceX installs first ‘Mechazilla’ arm on Starship launch tower
One month after SpaceX stacked Starship’s South Texas ‘launch tower’ to its full height, the company has installed the first arm on what amounts to the backbone of ‘Mechazilla.’
At the end of July, after less than four months of work, a team of SpaceX workers and contractors installed the final prefabricated section of a ~145m (~475 ft) tall tower meant to support orbital Starship launches. Above all else, SpaceX’s first custom-built ‘launch tower’ is a sort of backbone or anchor point for several massive, mechanical arms that will accomplish the actual tasks of servicing – and, perhaps, catching – Starships and Super Heavy boosters.
Work on all three of the arms expected to make up what SpaceX CEO Elon Musk has described as “Mechazilla” has been visibly underway since the last week of June as a small army of welders carefully assembled dozens of sections of heavy-duty steel pipe into house-sized frames. Almost exactly two months later, SpaceX has installed the first of those three arms on the exterior of Starship’s skyscraper-sized launch tower.
Known as the tower’s quick-disconnect or QD swing arm, the standalone structure is reportedly designed to accomplish a few different tasks. First, as its unofficial name might suggest, the QD arm will hold a quick-disconnect umbilical connector that will temporarily attach to the base of Starships to load them with fuel, oxidizer, and other consumables and link them to ground power and networking. For years, it appeared that SpaceX planned to fuel Starship upper stages through their Super Heavy boosters, which will themselves be connected to umbilical panels on a table-like launch mount that sits beside the tower.
However, once work began on Starship S20, the first potentially space-capable prototype, it was clear that SpaceX had foregone the umbilical plate normally installed at the base of Starship skirts and moved that connection to the ship’s lower back. Musk later confirmed as much in interviews and tweets, revealing that longstanding plans to dock Starships aft to aft for in-space refueling were also up in the air. As of late, aside from reiterating that the launch pad itself (“Stage Zero,” per Musk) is even more complex and difficult than Starship or Super Heavy, SpaceX’s CEO has also repeatedly stated a desire to offload as many systems as possible onto the launch pad – seemingly regardless of the complexity of the alternative.
Enter the building-sized robot informally known as Mechazilla. While the relatively simple swinging ‘QD arm’ that will fuel Starship and stabilize both stages of the rocket is a common feature of rockets and launch pads, the only experience SpaceX itself has with umbilical swing arms is the Crew Access Arm (CAA) that allows astronauts and cargo to board Dragon spacecraft after Falcon 9 goes vertical – a structure with near-zero umbilical utility. Technically, the transporter/erectors (T/Es) that cradle Falcon rockets, lift them vertical, and fuel them before launch have some similarities with swing arms but SpaceX has always used simpler and more reliable passive mechanisms whenever possible.
A step further, though, SpaceX has also seemingly foregone the installation of a basic crane on top of its Starship tower and Musk himself has developed an almost infamous aversion to the inclusion of something as seemingly simple as landing legs on Super Heavy boosters – and, eventually, perhaps even (some) Starship variants. Instead of adding rudimentary legs to Super Heavy prototypes, Musk has seemingly pushed SpaceX to turn Starship’s launch tower into a complex, vulnerable, and fragile rocket recovery system. Beyond the comparatively mundane QD arm, Musk says that SpaceX will ultimately install a pair of massive house-sized steel arms mounted on a sort of external elevator. Those arms will apparently be capable of actuating and moving up and down the tower with the speed, precision, and reliability needed to quite literally catch Super Heavy boosters – and, eventually, Starships – out of mid-air.
The team tasked with designing and building those rocket-catching arms have affectionately deemed them “chopsticks” – a nod towards the kind of nuanced actuation they’ll need to recover the world’s largest rocket boosters and upper stages without missing or destroying them. Having really only just perfected propulsive vertical landing with Falcon 9 and Falcon Heavy boosters, SpaceX thus intends to throw a few extra points of failure into the mix.
To SpaceX and Musk’s credit, whether the company’s second attempt at catching rockets goes as well as the first, some version of the massive ‘chopstick’ arms SpaceX is working on was likely going to be necessary just to rapidly turn around boosters and Starships – and do so regardless (within reason) of weather conditions. By replacing a tower crane with giant arms, SpaceX will hopefully be able to stack Starship on Super Heavy (and Super Heavy on the launch mount) even in the high winds that are almost always present on the South Texas Gulf Coast. If SpaceX can also reliably catch boosters with those arms, it could be a significant upgrade for the operations side of Starship reusability. For now, though, only time will tell.
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
