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SpaceX schedules Starship’s first triple-Raptor static fire test
A photo posted by CEO Elon Musk confirms that SpaceX has successfully installed three functional Raptors on Starship SN8 just hours before road closure notices revealed the company’s first triple-engine static fire schedule.
Technically, it’s incorrect to say that Starship serial number 8 (SN8) is the first prototype to receive three Raptor engines. Back in late-September 2019, in the lead-up to Musk’s promised Starship update event, the company installed three Raptors on the first full-scale prototype, known as Starship Mk1. The engines were only installed as an apparent fit test or even a photo opportunity, however – evidenced by the fact that they weren’t actually plumbed to the Starship’s propellant tanks.
Even then, in September 2019, Starship Mk1 was far from ready to make use of Raptor engines and was more than a month away from attempting its first pressure and cryogenic proof tests – tests it quickly failed. As such, Starship SN8 – having more or less successfully passed its ‘cryo proof’ by October 9th – is undoubtedly the first ship to have a shot at igniting multiple Raptor engines at once.
Curiously, SpaceX remained quiet for several days after Starship SN8 passed its first big test. Whereas with past Starship prototypes SpaceX has often filed test plans (appearing in the form of road closures) even before the current phase of testing is complete, the company waited until Tuesday, October 14th to file closure notices for “SN8 static fire” testing.
Same as Starships SN4, SN5, and SN6, all of which successfully graduated from cryo proof to static fire testing (and even flight tests for the latter two), SpaceX began Starship SN8’s test campaign with a cryo proof. It took three days and at least as many attempts but SN8 ultimately “passed cryo proof” according to Elon Musk, likely meaning that the ship reached sustained pressures of 7.5 bar (~110 psi) or more.
Cryo proof complete, SpaceX installed Starship SN8’s engines – the first time multiple Raptors have been fully integrated with a rocket or test stand – in preparation for another Raptor first: multi-engine static fires. While modern computation fluid dynamics (CFD) and modeling mean that the great unknowns of rocket propulsion are rarely as opaque as they used to be, the first test of multiple powerful engines in close proximity is still a guaranteed recipe for surprises.
Thanks to expertise hard-won from nearly 100 Falcon 9 and Falcon Heavy launches, SpaceX is likely the world’s foremost expert in the challenges and dynamics of the proximity operation of more than two rocket engines. At the same time, though, Raptor is a dramatically different engine than Merlin 1D and while Starship will only have six engines at most, those six engines will produce thrust equivalent to almost two entire Falcon 9 boosters.
In other words, even with a (relatively) simple three-Raptor static fire, SpaceX will be treading new ground and will almost certainly end up learning one or several things about Raptor’s design and operation. More likely than not, SpaceX will begin Starship SN8’s static fire test campaign with a wet dress rehearsal (like a cryo proof but with real liquid methane and oxygen propellant) and transition into a Raptor spin prime (turbopump spin-up) or preburner test (a turbopump spin-up but with partial combustion) if the WDR goes smoothly. If all three Raptor engines appear healthy, SpaceX may recycle and attempt the first static fire just an hour or two later.
Starship SN8’s triple-Raptor static fire test window opened at 9pm CDT on October 14th and closes at 6am on the 15th, with an identical 9pm-6am backup on the 15th and another window from 8am to 4:30pm on the 16th. LabPadre (below) will continue to offer 24/7 views of Starship, including any static fire testing, while NASASpaceflight.com will likely provide live coverage once testing begins in earnest.
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
