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SpaceX’s thin-skinned Starship ‘test tank’ passes first trial
CEO Elon Musk says that a new thin-skinned Starship ‘test tank’ just passed its first trial, taking advantage of delays to Starship SN9’s planned high-altitude launch debut.
Delayed by a lack of FAA approval for unknown reasons, Starship SN9’s 12.5-kilometer (7.8 mi) launch debut (virtually identical to SN8’s 12.5 km launch last month) is in limbo pending an “FAA review” according to Musk. SpaceX thus found itself with at least 24 hours of guaranteed inactivity for Starship SN9, time the company rapidly chose to fill with crane transportation and, more importantly, the first Starship ‘test tank’ stress test in months.
Known as Starship SN7.2, SpaceX’s latest ‘test tank’ is the third to carry the SN7 moniker and appears to have been built primarily to test refinements to the rocket’s structural design. Following test tanks SN7.0 and SN7.1, both used to qualify the use of a new steel alloy on an otherwise unchanged design, SN7.2 – likely built out of the same alloy – is instead focused on determining if SpaceX can begin trimming the margins of an increasingly mature technology.
Curiously, SN7.2 is a sort of fusion of its predecessors: combining the stout stature of SN7.0 with SN7.1’s use of an aft thrust dome, but without SN7.1’s Starship-style skirt (the three rings at its bottom). Welded directly to its black test stand, it’s unclear why SpaceX chose to give SN7.2 a thrust dome, given that the thrust of Raptor engines can only be simulated with hydraulic rams if the tank is installed on one of two Starship launch mounts.
Regardless, whether SpaceX actually tests that aspect of SN7.2, the tank’s most important task is determining if future Starships (and perhaps Super Heavy boosters) can be built out of thinner, lighter steel rings. Its domes appear to be identical to past ships but writing on the exterior of the tank strongly implied that its three rings were built out of 3mm steel rather than the 4mm sheets that have made up every Starship built in the last 12 months.
SpaceX began loading the thin-skinned tank with liquid nitrogen (used to simulate cryogenic propellant without the risk of an explosion) around 9am CST and spent around three hours performing an “initial pressure test.” It’s unclear what that test entailed but it most likely involved raising the tank’s internal pressure to levels achieved by SN7.0 and SN7.1 Musk has previously said that that 6 bar was the bare minimum necessary for orbital flight, translating to 7.5-8.5 bar to achieve an industry-standard safety margin of 25-40%.
That SN7.2 survived that initial pressure test bodes well for the significant mass reductions SpaceX will need to optimize Starships for efficient orbital flight, potentially shaving 5-10 metric tons off the dry mass of future ships. For orbital rocket stages, every single kilogram of mass reduction translates to an extra kilogram of cargo capacity, whereas boost stages (i.e. Super Heavy) offer far more lenient ratios on the order to 10:1, meaning that adding 5-10 kilograms of rocket hardware reduces maximum payload capacity by just ~1 kg.
Depending on when SpaceX is allowed to launch Starship SN9, the company’s next test could involve pressurizing SN7.2 until it bursts, determining if the tank’s thinner skin substantially impacts its performance as a pressure vessel.
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
