News
How SpaceX is able to achieve its amazing rocket landing accuracy
After SpaceX’s successful and uniquely exciting launch of Taiwan’s Formosat-5 remote sensing satellite, Elon Musk took to Twitter to reveal some fascinating details about the launch and recovery of the Falcon 9 first stage.
Unabashedly technical, the details Musk revealed demonstrate the truly incredible accuracy of Falcon 9’s recovery, honed over 20 landing attempts and numerous modifications to the launch vehicle. The accuracy is best understood within the context of Falcon 9’s scale and the general scope of orbital rocketry.
Touchdown:
Vertical Velocity (m/s): -1.47
Lateral Velocity (m/s): -0.15
Tilt (deg): 0.40
Lateral position: 0.7m from target center— Elon Musk (@elonmusk) August 25, 2017
The first stage of Falcon 9 Full Thrust, currently the active version of Falcon 9, stands 140 feet tall and 12 feet in diameter. If you can, for a moment, picture a 737 airliner, the plane most people have likely flown aboard on domestic flights. The first stage of Falcon 9 is the same length or greater and the same diameter as Boeing’s workhorse airliner. If you are now imagining a 737 landing on its tail aboard an ocean-going barge, that is a great start. The most common version of the 737, the -800, has an empty weight of 91,000 lb, while Falcon 9’s empty first stage is a bit more than half as heavy. With a full load of fuel, Falcon 9 S1 (first stage) weighs nearly three times as much as the 737-800. A single Merlin 1D engine out of Falcon 9’s namesake nine rocket engines has nearly ten times the thrust of the airliner. In short, Falcon 9’s first stage is massive, both extremely light and extremely heavy, and has a mind-boggling amount of thrust.
Falcon 9’s ability to land as accurately as it does is due to a combination of multiple technologies and vehicle modifications. Most visible are S1’s cold gas maneuvering thrusters and aluminum or titanium grid fins, both of which are designed to provide some level of control authority and maneuverability to the first stage during its trip within and without Earth’s atmosphere. At the peak of its trips, the first stage is often completely outside of the vast majority of the atmosphere, meaning that aerodynamic forces are no longer relevant or useful for the vehicle. This is where the cold gas thrusters come in: by carrying their reaction mass with them (the gas), Falcon 9 can maneuver outside of the atmosphere. Once the stage descends into thicker atmospheric conditions, the grid fins deploy and are used like wings to guide the stage down to its landing location, be that on land or at sea. While the gas thrusters lose a lot of their utility once in the atmosphere, they can still be used to add a small amount of control authority when needed. They were famously seen fighting a futile battle to save a first stage aboard OCISLY in 2015.
With this in mind, we can take a closer look at Musk’s technical details. First off, we have a photo of the landed booster, Falcon 9 1038, clearly almost dead center on the droneship Just Read The Instructions. More specifically, Musk reports that 1038 landed less than a single meter off the center of the target, and it landed with less than a single meter per second of latent velocity. The first stage thus managed both a soft and deadly accurate landing after traveling to a height of 150 miles – well into what is technically “space” – at a maximum speed of 1.5 miles per second. Without delving further into the details, this is best summarized as “insanely fast”, and is a bit faster than the X-15 rocketplane’s fastest recorded speed. To better put this into context, Falcon 9 1038 traveled to an altitude of 240,000 meters at a top speed of 2,400 meters per second, turned around, and landed on an autonomous barge about two feet off of its optimal target. It is truly difficult to describe how impressive that kind of accuracy is.
The hypersonic X-15 and Falcon 9 S1, with a 737-800 on the right. All vehicles are to scale. (Wikipedia, SpaceX)
Mr. Musk nevertheless did not let 1038 steal all the fanfare, and revealed that the first stage responsible for launching BulgariaSat-1, 1029, had the honor of being the fastest first stage yet, clocking in at at a truly staggering Mach 7.9, or 2,700 meters per second. That speedy mission marked the stage’s second flight and was SpaceX’s second successful reuse of a Falcon 9. Indicative of the intense speed and heat the core experienced, one of the vehicle’s grid fins was noted to have almost completely melted through. Aluminum’s melting point begins at 1,221°F.
-
- The central aluminum grid fin of 1029 features a dramatic lack of several vanes, likely melted off during the intense heat of reentry. Expending older boosters is likely helping SpaceX learn how to preserve Block 5 rockets for multiple high-energy missions. (Reddit, u/thedubya22)
-
- SpaceX will move to titanium grid fins in the future, first trialed during 1036’s launch of Iridium-2. (SpaceX)
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.”
News
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
