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Elon Musk talks upgrades after SpaceX Starship launches, explodes in midair
SpaceX has completed its fourth Starship test flight in as many months, offering the latest glimpse into the often frustrating reality of a highly iterative, hardware-rich rocket development program.
Right on schedule, SpaceX Starship prototype serial number 11 (SN11) lifted off from Boca Chica, Texas at exactly 8am CDT (UTC-5) – all but completely cloaked in a thick layer of fog. While unfortunate for any unofficial observers (and possibly SpaceX’s own desire to gather video footage of a test flight), SpaceX has experience launching rockets (namely Falcon 9) in thick fog thanks to its Vandenberg Air Force Base launch site on the California coast.
As such, fog theoretically poses no fundamental threat to rockets like Starship, but SN11 still took the opportunity to explore new and exciting failure modes shortly before touchdown. CEO Elon Musk himself didn’t take long to weigh in and has even offered some details and a schedule for upgrades planned for SpaceX’s next-generation launch vehicle – upgrades hoped to alleviate whatever issues led to Starship SN11’s premature demise.
First and foremost, due to the fog, the general public saw virtually nothing throughout the launch attempt. Remote streaming cameras set up near SpaceX’s launch facilities – now, excitingly, with the company’s own permission – did manage to catch some level of detail, providing the bare minimum level of insight needed to speculate on SN11’s failed landing attempt.
Per an official webcast and NASASpaceflight’s unofficial “Danger-Close Camera,” installed a few hundred feet from the launch site with SpaceX’s permission, Starship lifted off at exactly 8am and had a seemingly nominal ascent, reaching a familiar 10 km (6.2 mi) apogee around four minutes later. SN11 then arced over onto its belly and free-fell for ~100 seconds. Aside from a few intermittent fires burning on some of the rocket’s three Raptor engines, not an uncommon sight since SN8 first flew, nothing appeared particularly out of the ordinary.
At T+5:49, however, things rapidly went wrong. Still belly-down, Starship SN11 attempted to reignite all three of its Raptor engines to propulsively flip into a vertical landing position. After at least one seemingly successful reignition, SpaceX immediately lost onboard video and telemetry feeds. Based on NASASpaceflight’s pad-adjacent camera, a substantial explosion followed one or two seconds after that attempted ignition, ending Starship SN11’s test flight around 20 seconds earlier than any of its three late siblings.
Debris began to visibly hit the ground another 5-10 seconds after that explosion was first heard, all but guaranteeing that Starship SN11 exploded in midair. At this time, it’s impossible to know what exactly went wrong, but there are two clear possibilities. Starship SN11 could have failed to reignite two or even all three Raptor engines, triggering onboard flight termination system (FTS) explosives designed to prevent the rocket from straying beyond a safe zone of operations. More likely, Starship suffered a substantial failure during that reignition and flip attempt, triggering an almost immediate explosion that tore the rocket apart around half a kilometer (~1500 ft) above the pad and landing zone.
Shortly after, Musk said that Raptor “engine #2 had issues on ascent” that were notable but not enough to explain a violent midair failure and confirmed that whatever went wrong came “shortly after landing burn start.”
Musk offers Starship upgrade schedule, details
Having suffered a failure a bit less than six minutes after launch, Starship SN11 – the fourth three-engine, high-altitude prototype – was ironically the farthest from a successful landing before something went wrong: one step forward, two steps back. While unfortunate, SpaceX still got some amount of data and uncovered one or several new failure modes – arguably the two of the most important primary goals of any developmental flight test program.
Further, Musk revealed that SpaceX intends to complete and roll Starship SN15 to the launch pad just “a few days” from now – certainly earlier than expected. While the SpaceX CEO didn’t go much into detail, he reaffirmed that SN15 would bring substantial upgrades, stating that “it has hundreds of design improvements across structures, avionics/software, & engine[s].”
Musk also touched on SpaceX’s near-term plans after SN15’s upgrade path, confirming that Starship prototypes from SN20 onwards will be “orbit-capable” with even more improvements. That seemingly delineates three clear ‘blocks’ of Starship prototypes, beginning with SN8 through SN11, proceeding with SN15 through SN19, and (nominally) gearing up for true orbital-class test flights with prototype SN20 and its successors. All told, SN11’s midair demise appears likely to be just a small blip in front of a jam-packed, well-structured series of Starship upgrades and flight tests just over the horizon.
News
Nvidia CEO Jensen Huang explains difference between Tesla FSD and Alpamayo
“Tesla’s FSD stack is completely world-class,” the Nvidia CEO said.
NVIDIA CEO Jensen Huang has offered high praise for Tesla’s Full Self-Driving (FSD) system during a Q&A at CES 2026, calling it “world-class” and “state-of-the-art” in design, training, and performance.
More importantly, he also shared some insights about the key differences between FSD and Nvidia’s recently announced Alpamayo system.
Jensen Huang’s praise for Tesla FSD
Nvidia made headlines at CES following its announcement of Alpamayo, which uses artificial intelligence to accelerate the development of autonomous driving solutions. Due to its focus on AI, many started speculating that Alpamayo would be a direct rival to FSD. This was somewhat addressed by Elon Musk, who predicted that “they will find that it’s easy to get to 99% and then super hard to solve the long tail of the distribution.”
During his Q&A, Nvidia CEO Jensen Huang was asked about the difference between FSD and Alpamayo. His response was extensive:
“Tesla’s FSD stack is completely world-class. They’ve been working on it for quite some time. It’s world-class not only in the number of miles it’s accumulated, but in the way it’s designed, the way they do training, data collection, curation, synthetic data generation, and all of their simulation technologies.
“Of course, the latest generation is end-to-end Full Self-Driving—meaning it’s one large model trained end to end. And so… Elon’s AD system is, in every way, 100% state-of-the-art. I’m really quite impressed by the technology. I have it, and I drive it in our house, and it works incredibly well,” the Nvidia CEO said.
Nvidia’s platform approach vs Tesla’s integration
Huang also stated that Nvidia’s Alpamayo system was built around a fundamentally different philosophy from Tesla’s. Rather than developing self-driving cars itself, Nvidia supplies the full autonomous technology stack for other companies to use.
“Nvidia doesn’t build self-driving cars. We build the full stack so others can,” Huang said, explaining that Nvidia provides separate systems for training, simulation, and in-vehicle computing, all supported by shared software.
He added that customers can adopt as much or as little of the platform as they need, noting that Nvidia works across the industry, including with Tesla on training systems and companies like Waymo, XPeng, and Nuro on vehicle computing.
“So our system is really quite pervasive because we’re a technology platform provider. That’s the primary difference. There’s no question in our mind that, of the billion cars on the road today, in another 10 years’ time, hundreds of millions of them will have great autonomous capability. This is likely one of the largest, fastest-growing technology industries over the next decade.”
He also emphasized Nvidia’s open approach, saying the company open-sources its models and helps partners train their own systems. “We’re not a self-driving car company. We’re enabling the autonomous industry,” Huang said.
Elon Musk
Elon Musk confirms xAI’s purchase of five 380 MW natural gas turbines
The deal, which was confirmed by Musk on X, highlights xAI’s effort to aggressively scale its operations.
xAI, Elon Musk’s artificial intelligence startup, has purchased five additional 380 MW natural gas turbines from South Korea’s Doosan Enerbility to power its growing supercomputer clusters.
The deal, which was confirmed by Musk on X, highlights xAI’s effort to aggressively scale its operations.
xAI’s turbine deal details
News of xAI’s new turbines was shared on social media platform X, with user @SemiAnalysis_ stating that the turbines were produced by South Korea’s Doosan Enerbility. As noted in an Asian Business Daily report, Doosan Enerbility announced last October that it signed a contract to supply two 380 MW gas turbines for a major U.S. tech company. Doosan later noted in December that it secured an order for three more 380 MW gas turbines.
As per the X user, the gas turbines would power an additional 600,000+ GB200 NVL72 equivalent size cluster. This should make xAI’s facilities among the largest in the world. In a reply, Elon Musk confirmed that xAI did purchase the turbines. “True,” Musk wrote in a post on X.
xAI’s ambitions
Recent reports have indicated that xAI closed an upsized $20 billion Series E funding round, exceeding the initial $15 billion target to fuel rapid infrastructure scaling and AI product development. The funding, as per the AI startup, “will accelerate our world-leading infrastructure buildout, enable the rapid development and deployment of transformative AI products.”
The company also teased the rollout of its upcoming frontier AI model. “Looking ahead, Grok 5 is currently in training, and we are focused on launching innovative new consumer and enterprise products that harness the power of Grok, Colossus, and 𝕏 to transform how we live, work, and play,” xAI wrote in a post on its website.
Elon Musk
Elon Musk’s xAI closes upsized $20B Series E funding round
xAI announced the investment round in a post on its official website.
xAI has closed an upsized $20 billion Series E funding round, exceeding the initial $15 billion target to fuel rapid infrastructure scaling and AI product development.
xAI announced the investment round in a post on its official website.
A $20 billion Series E round
As noted by the artificial intelligence startup in its post, the Series E funding round attracted a diverse group of investors, including Valor Equity Partners, Stepstone Group, Fidelity Management & Research Company, Qatar Investment Authority, MGX, and Baron Capital Group, among others.
Strategic partners NVIDIA and Cisco Investments also continued support for building the world’s largest GPU clusters.
As xAI stated, “This financing will accelerate our world-leading infrastructure buildout, enable the rapid development and deployment of transformative AI products reaching billions of users, and fuel groundbreaking research advancing xAI’s core mission: Understanding the Universe.”
xAI’s core mission
Th Series E funding builds on xAI’s previous rounds, powering Grok advancements and massive compute expansions like the Memphis supercluster. The upsized demand reflects growing recognition of xAI’s potential in frontier AI.
xAI also highlighted several of its breakthroughs in 2025, from the buildout of Colossus I and II, which ended with over 1 million H100 GPU equivalents, and the rollout of the Grok 4 Series, Grok Voice, and Grok Imagine, among others. The company also confirmed that work is already underway to train the flagship large language model’s next iteration, Grok 5.
“Looking ahead, Grok 5 is currently in training, and we are focused on launching innovative new consumer and enterprise products that harness the power of Grok, Colossus, and 𝕏 to transform how we live, work, and play,” xAI wrote.
Nvidia CEO Jensen Huang explains difference between Tesla FSD and Alpamayo
Elon Musk confirms xAI’s purchase of five 380 MW natural gas turbines
