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SpaceX begins installing Raptor engines on first Super Heavy booster
SpaceX has installed a Raptor engine on a Super Heavy booster prototype for the first time, defying expectations and setting the rocket up for two major tests as early as this week.
On Thursday, July 8th, SpaceX briefly filled Super Heavy Booster 3’s (B3) propellant tanks with benign nitrogen gas. The vehicle seemingly came to life for the first time that morning when it was spotted using its tank vents – a generally incontrovertible sign that the complex mechanical system that is a rocket is functional. Later that day, the public highway and beach adjacent to SpaceX’s launch site were briefly closed for what was expected to be an ambient pressure and/or cryogenic proof test.
Booster 3 never got to the cryogenic proof test – easily confirmed thanks to the frost that forms on most rockets’ exteriors as main tanks are filled with extremely cold liquid nitrogen. No such frost formed, no major venting occurred, and the road was only closed for the first two hours of a six-hour test window.
According to Next Spaceflight’s Michael Baylor, SpaceX did complete a “brief ambient proof” during that relatively short closure, though very little activity was visible during the test. Friday’s 14-hour test window was canceled the next morning, leaving SpaceX the rest of the weekend to prepare the first functional Super Heavy booster for its first truly challenging test – cryo proof.
Instead, late on Saturday, July 10th, SpaceX rolled Raptor 57 (R57) from build site to launch pad and began installing the engine on Booster 3 just a few hours later. Prior to Raptor 57’s installation, most prominent (albeit unofficial) voices in the SpaceX fan community anticipated no more than cryogenic proof testing for Booster 3 – no static fires, in other words.
However, it was fairly apparent that Super Heavy Booster 3 and the modified suborbital launch mount it was installed on were both outfitted for testing more complex than a cryo proof alone. Notably, B3 rolled to the pad with multiple labeled methane pressure vessels (COPVs), extensive plumbing, and autogenous pressurization control panels installed – all of which continued to be actively worked on after the booster was installed at the launch site.
While it’s technically not impossible to build a ground testing Starship prototype that’s capable of a wide variety of tests but never actually used to its full extent, doing so would be well out of character for SpaceX and make little sense in general. As such, it’s not a major surprise that SpaceX has now begun to install Raptor engines on Super Heavy Booster 3. What is surprising is that SpaceX is installing Raptor engines on a first-of-its-kind Super Heavy prototype before any fully integrated booster has completed cryogenic testing.
Based on Starship’s ~18-month test history, there is a real possibility Super Heavy B3 will fail during cryogenic proof testing. Even accepting that SpaceX’s testing processes and expertise have matured dramatically after dozens of Starship tests on the ground and in flight, the chance remains. In other words, SpaceX’s decision to begin installing Raptors on Super Heavy before ensuring structural and mechanical integrity implies some combination of unusual confidence in a prototype as unproven as Booster 3 and a distinct lack of concern at the prospect of losing at least two Raptor engines in a hypothetical test failure.
Knowing SpaceX and CEO Elon Musk’s goals for Raptor, the latter implication isn’t much of a surprise but it’s always interesting to have direct visual evidence that Raptor is, in fact, so cheap to build and easy to install that the minor effort and few days of possible delays required to reduce the risk of losing multiple engines just aren’t worth it.
As such, it’s now clear that Super Heavy Booster 3 will have at least one or two Raptor engines installed during its very first cryogenic proof test – currently no earlier than 12pm to 8pm CDT (UTC-5) on Monday, July 12th. Assuming SpaceX’s confidence is well-placed and Booster 3 passes its first cryogenic tests without issue, the real question now is how many Raptors will be installed and ignited during Super Heavy’s first static fire test?
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
