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SpaceX’s first orbital-class Starship ‘tank farm’ is almost finished
Roughly six months after the process began, SpaceX has installed the seventh and final custom-built propellant storage tank at Starbase’s first orbital-class Starship launch site.
Built out of the same factory and parts as the steel tanks that make up most of the two-stage Starship rocket’s structure, SpaceX completed the first two of those ‘ground support equipment (GSE)’ tanks in April and wasted no time installing both at Starbase’s orbital launch site (OLS). However, after a strong start, GSE tank work seemingly halted for several months and it wasn’t until August that SpaceX first enclosed one of the then three installed tanks with a sleeve designed to insulate their cryogenic contents. Since then, progress has picked back up and SpaceX has built and installed another three (for a total of six) storage tanks over the last two months.
That work effectively culminated on September 7th with the transport of the farm’s seventh and final GSE tank from build site to launch pad.
Unintuitively known as GSE-8 after SpaceX chose to scrap one of the original seven planned tanks earlier this year, the company wasted no time installing it shortly after its two-mile trip down the highway. GSE-8 is the second of two liquid methane (LCH4) tanks now installed at the orbital launch site and joins another three liquid oxygen (LOx) and two liquid nitrogen (LN2) tanks for a total of seven.
Combined, the OLS tank farm should be able to store more than 2400 tons of LCH4 and 4000 tons of LOx, as well as 2600+ tons of LN2 to be used for ‘subcooling’ (and thus densifying) that propellant well below its boiling point. Ultimately, that means that despite the massive scale of Starbase’s first orbital-class tank farm, it will still only hold enough propellant for a single orbital Starship launch and have to be almost fully restocked after each flight.
Given the logistical nightmare of arranging something like 100+ tanker trucks for each tank farm ‘refill,’ a process that could easily take a week or more on its own, it should come as no surprise that SpaceX is also building a dedicated liquid oxygen and nitrogen plant adjacent to its Starbase factory. On top of liquid natural gas (LNG) refinery and tenuous plans to potentially tap local natural gas wells, SpaceX is clearly well aware of the logistical challenges of regular Starship launches.
While there are no clear signs of the inevitable permitting and environmental reviews it would require, it’s likely that SpaceX will eventually create a brief above or below-ground cryogenic pipeline connecting its propellant factory to Starbase’s orbital launch site(s). If or when implemented, that would allow SpaceX to resupply its two planned orbital tank farms with minimal effort or human intervention beyond the process of producing the propellant.
For the time being, SpaceX will likely rely on a slow but simple parade of tanker trucks to gradually fill its first orbital tank farm. Before even that process is possible, though, SpaceX will need to finish plumbing GSE-8 and several other tanks, install the last two insulative ‘cryoshells,’ and finally fill the annuli between all seven tanks and their shells with an insulative foam-like material known as perlite. Dozens of bags of perlite and several kilns (used to expand the material into low density insulation) are already distributed around the orbital tank farm.
Meanwhile, SpaceX also continues to slowly fill the first two completed OLS tanks (nominally meant to hold LOx) with liquid nitrogen, serving both to test the tanks and pad plumbing and to clean their interiors for liquid oxygen service. Ultimately, while a good amount of work remains, Starbase’s first orbital-class tank farm could be fully ready to support its first Super Heavy booster proof and static fire test campaign just a few weeks from now.
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
