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SpaceX makes rocket fairing catch look easy with “autopilot” recovery
SpaceX has made Falcon 9 rocket fairing recovery look easy in a video of the latest nosecone catch, published hours after the company’s successful Starlink-10 launch.
Posted on Twitter by Elon Musk not long after a SpaceX webcast host and engineer revealed that one of two fairing catch attempts had been successful, the video offers the best in-action view yet of an operational fairing recovery. Backed by elevator music, it also certainly carries a clear signature of the CEO’s humor, carrying the torch from previous hits like “How Not to Land an Orbital Rocket Booster“, “Grasshopper vs. Cows“, and the successful launch of a Tesla Roadster and spacesuit-wearing mannequin into interplanetary space.
Lackadaisical theme song aside, Musk also shed some light on the actual process of catching Falcon fairings with giant ships and nets. Those new details point towards a major improvement made in the last six or so months that’s helped enable an unprecedented three successful fairing catches in less than 30 days.
According to Musk, SpaceX caught the Starlink-10 fairing half with both recovery ship GO Ms. Tree and the parasailing fairing half “operating on (SpaceX) autopilot.” While his comments leave a great deal of room for interpretation, they seem to imply that SpaceX has found ways to make fairing recovery almost as automatic as Falcon booster landings. During Falcon first stage recovery, the booster and drone ship technically operate as if the other doesn’t exist – the ship simply station keeps in a very specific location and the booster targets that same specific location.
Fairing recovery, as SpaceX would quickly find out, was a dramatically more complex and touchy ballet of humans, machinery, and rocket parts. Little is known about the specifics of fairing recovery beyond the fact that fairing halves have cold gas thrusters for positioning in vacuum and use GPS-guided parafoils to travel towards a rough landing zone. For most prior attempts, it’s believed that one or several crew members were responsible for manually maneuvering the recovery ship during catch attempts.
Including controlled helicopter drop tests, SpaceX failed a dozen or more consecutive fairing catch attempts and even shipped the entire operation from California to Florida before the first successful catch finally came in June 2019. In an apparent fluke, SpaceX managed to catch another fairing half less than two months later. Five months later, SpaceX secured its third fairing catch – possibly the very same fairing half caught on Monday. Another six months after #3, SpaceX hit a major milestone, simultaneously catching both halves of a Falcon fairing with two separate ships on July 21st, 2020.
Now, just 29 days after that spectacular double catch, SpaceX has caught another Falcon 9 fairing half – tempered only by the fact that sister ship Ms. Chief missed her own catch attempt. While it could certainly be a fluke of luck akin to SpaceX’s back-to-back STP-2 and Amos-17 catches, Musk’s note that “fairing chute control & ship control are closing the loop locally” points to cautious optimism.
Cryptic as ever, the comment seems to imply that SpaceX has debuted – or at least recently introduced – a kind of cooperative, autonomous navigation system that allows Falcon fairings and their recovery ships to communicate and function as a unit. For now, we’ll have to wait for the next catch attempt to get a better idea of just how much of a step forward SpaceX has made. SAOCOM 1B, SpaceX’s next Falcon 9 fairing recovery (and launch), is currently scheduled no earlier than (NET) August 27th.
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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
