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SpaceX Starship to test landing upgrades after two explosions
After two Starship prototypes aced their high-altitude launch debuts only to suffer last-second landing failures for unique reasons, SpaceX is gearing up for a third launch as early as this week.
Crucially, though Elon Musk’s levelheaded realism (or pessimism) has often seemed to underestimate the actual odds of success, the SpaceX CEO is substantially more confident on Starship’s third launch attempt than he was on the first flight two months ago. Back when Starship serial number 8 (SN8) was preparing to attempt the program’s first high-altitude launch, Musk pegged the probability of a successful launch, freefall, and landing at ~33%.
As it turned out, he wasn’t wrong, but Starship SN8 ultimately made far closer to a total success than almost anyone – inside SpaceX or not – expected it to get on the first try. Less than two months later, Starship SN9 suffered a similar last-second landing failure more than six minutes into the flight, though the root causes of both failures were unique.
In other words, both flight tests served their nominal purpose, uncovering two failure modes that would have eventually reared their heads one way or another. With SN8, Starship was unable to maintain enough pressure in its secondary landing fuel tank to supply two Raptor engines with enough fuel for a landing burn. Starship SN9 failed a few seconds before SN8 when one of the two Raptor engines needed for a flip and landing burn never ignited, causing the rocket to smash into the ground at an angle relative to SN8’s tail-down impact.
As previously discussed on Teslarati, Elon Musk eventually revealed his opinion that SN9’s engine-out failure was potentially avoidable and that SpaceX would change the way future Starships attempt to land in a bid to add more redundancy.
“While SpaceX obviously hasn’t spun around and fixed a complex Starship propulsion issue in a matter of days, Musk eventually revealed his opinion that he, his engineers, or some combination of both “were too dumb” to exploit one obvious way to mitigate the risk of engine failure during [Starship SN9’s] flip and landing. That ‘obvious’ tweak: reignite all three of Starship’s available landing engines, not just two.”
By igniting not just two – but all three – Raptor engines during Starship’s flip burn, SpaceX could essentially perform a midair static fire, giving the rocket’s flight computer a few seconds to analyze performance and downselect to the two healthiest engines for the final landing burn. With that change implemented, Starship would theoretically have enough redundancy to land if only two of its three sea-level Raptors performed nominally.
Currently installed on one of two ‘suborbital stands’ at SpaceX’s South Texas launch pad, Starship SN10 will be the first high-altitude prototype to attempt that three-engine flip burn and on-the-fly downselect. Musk says his confidence that SN10 will successfully land is now 60%, an almost twofold improvement over SN8. Starship SN10 could potentially fly as early as this week, though the prototype still needs to complete a nominal three-engine static fire test and the launch has yet to receive FAA approval.
Further down the road, Musk says that SpaceX is working hard to improve Raptor’s deep throttle performance, potentially allowing future Starships to burn two – or even three – engines all the way to touchdown for even more redundancy. Deep-throttling large, complex rocket engines is extraordinarily difficult, though, so that upgrade is likely no less than several months away. In the meantime, Starship SN11 is effectively complete and Starships SN15 through SN18 are being assembled to support a relentless flight test campaign as SpaceX works towards orbital flights.
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.
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
