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SpaceX’s first orbital Starship rocket engine is almost ready for testing
CEO Elon Musk says that SpaceX is “about a month away” from testing a rocket engine that will be essential for Starship and its Super Heavy booster to reach their full potential.
Known as Raptor Vacuum, the engine – as its name suggests – is a variant of the base Raptor engine optimized for maximum performance and efficiency in the vacuum of space. Although Starship could technically still function and likely reach orbit with only sea level-optimized Raptors installed, it would likely significantly limit the amount of payload it could carry into Earth orbit and would especially harm the ship’s performance to higher orbits and other planets.
Back in May 2019, Musk revealed that SpaceX had shifted gears again, forgoing a plan to begin orbital Starship flight operations with only sea level Raptors, gradually designing and phasing in RaptorVac engines much further down the road. Instead, SpaceX restarted (relatively) urgent work on the vacuum variant and Musk hinted that it would “aspirationally” be ready to support launches in the near term. A few weeks shy of a year later, Musk says that Raptor Vacuum testing could begin as early as June 2020.
For a variety of reasons, even if based directly off of an existing design, vacuum-optimized engines are typically much more complex than a comparable sea level variant. While efficiency is always relatively important for rocket engine design, it becomes even more paramount when dealing with vacuum rocketry, as the entire point of a dedicated vacuum-optimized engine is to eke as much efficiency as possible out of a launch vehicle’s orbital stage(s).
For example, even from a purely visual perspective, Merlin Vacuum (MVac) is substantially different when compared to the Merlin 1D engine it’s based on. Due to a number of major and largely unknown differences, the engines’ shared components are largely invisible. It’s unclear how similar they are but it’s safe to say that they share at least ~50% commonality. Obviously, the most apparent part of the difference between a vacuum-optimized engine and an atmosphere-optimized engine is the bell nozzle: MVac has a nozzle that is dramatically larger than M1D.
Raptor will be no different, with the sea-level variant featuring a nozzle about 1m (3.2 ft) in diameter, whereas RaptorVac’s bell will have a diameter closer to 2.5m (~8 ft). With SpaceX’s apparent May 2019 pivot back to working on RaptorVac now, the company has been working on a dedicated vacuum variant of the high-performance methane-oxygen engine for at least a full year. Now, perhaps beginning as early as June or July, Musk suggests that the first RaptorVac engine (SN0? SN1?) is almost ready to commence static fire testing.
The nature of that testing is a bit of a mystery. While it will almost certainly occur at SpaceX’s McGregor, Texas test and development facilities, it’s unclear if Raptor Vacuum’s first static fire test campaign will be attempted with the engine’s extended nozzle installed. Back in October 2019, Musk suggested that yes, Raptor Vacuum version 1.0 would have a nozzle small enough to operate at sea level without destroying itself or its test facilities. With Merlin Vacuum engines, SpaceX performs acceptance tests in Texas but only without their nozzle extensions installed. If Musk’s October 2019 comments remain true, that may not be the case for RaptorVac.
Either way, it will be thoroughly interesting to note the differences between RaptorVac and its sea level-optimized predecessor if or when Elon Musk or SpaceX releases photos of their newest engine as it nears its first major tests. Simultaneously, SpaceX is also readying a sea-level Raptor for its inaugural static fire test while attached to a full-scale Starship prototype, while the first test with three Raptor engines installed could be attempted 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
