News
SpaceX Mars landing expert talks Starship recovery challenges in new interview
Formerly responsible for developing Falcon 9 (and Heavy) into the routinely-landing reusable rocket it is today, senior SpaceX engineer Lars Blackmore says he now has one primary focus: figuring out how to land Starship on Earth, the Moon, and Mars.
A graduate of University of Cambridge and MIT, the latter of which interviewed him on October 23rd for an “Alumni Stories” blog, Lars Blackmore has become famous for his groundbreaking work in guidance, navigation, and control (GNC). After graduating with honors from Cambridge and earning a PhD from MIT, Dr. Blackmore joined NASA in 2007 and immersed himself in “precision Mars landing”, part of a more general focus on figuring out how to autonomously control vehicles in uncertain conditions.
In his last year at NASA, Blackmore co-invented an algorithm known as G-FOLD (Guidance for Fuel Optimal Large Divert) that should theoretically enable precision landings on Mars, improving the state of the art by two full orders of magnitude (+/- 10 km to +/- 100 m). In 2011, he departed NASA and joined SpaceX, where he lead the development of the GNC technology needed to successfully and reliably recovery Falcon 9 boosters. Although the same could be said for any number of critical, groundbreaking systems that had to be developed, the onboard software that autonomously guides Falcon 9 landings on the fly is one of many things that booster recovery and reuse would be wholly impossible without.
After numerous failed attempts, all part SpaceX’s preferred learning process, Falcon 9 successfully landed for the first time on December 21st, 2015. As they say, the rest is history: in the roughly four years since that milestone landing, SpaceX has successfully completed 57 orbital launches, recovered boosters 43 more times, and reused flight-proven boosters on 23 launches. Since that first success, more than half of all SpaceX launches have been followed by a successful booster landing (or two).
Back to Mars
In 2018, Dr. Blackmore officially took on a new full-time role as SpaceX’s Principal Mars Landing Engineer. As the namesake suggests, this meant handing (now semi-routine) Falcon 9 and Heavy GNC development to a strong team and beginning to tackle an array of new problems that will need to be solved for SpaceX to reach the Moon, Mars, and beyond.
Following radical design modifications made to Starship in 2018 and again in 2019, SpaceX is pursuing a radically different method of recovery with Starship (the upper stage), while Super Heavy will more directly follow in the footsteps of Falcon 9/Heavy. Starship, however, is being designed to perform a guided descent more akin to a skydiver falling straight down, using flaps at its nose and tail (explicitly “not wings”) to accurately guide its fall.
As little as a few hundred meters above the ground, Starship will then perform a radical maneuver, igniting its Raptor engines to flip around, burn in the opposite direction to counteract that sideways boost, and finally coming in for a precise landing on Earth/Mars/the Moon.
Beyond the new GNC software and knowledge needed to make that maneuver real, Blackmore is also responsible for Starship atmospheric entry, no less critical to enabling precise, repeatable landings from orbital velocity to touchdown. In his recent interview with University of Cambridge staff, Lars revealed that his role as Principal Mars Landing Engineer involved a far wider scope than his previous GNC-centered work, with the goal instead being to design a launch vehicle (Starship) from the ground up to be easily recovered and reused. Falcon 9 Block 5 may be radically different than the ‘V1.0’ rocket that debuted in 2010, but it’s still ultimately a product of retroactive engineering.
With Starship and Super Heavy, SpaceX instead wants to take the vast wealth of knowledge and experience gained from F9/FH and build the vehicle from the ground up to be optimized for full reuse. Ultimately, Dr. Blackmore stated that “landing Starship will be much harder than landing Falcon 9, but if [SpaceX] can do it, it will be revolutionary.”
Check out Teslarati’s Marketplace! We offer Tesla accessories, including for the Tesla Cybertruck and Tesla Model 3.
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
