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SpaceX CEO Elon Musk talks Starship explosion: “We were too dumb”
Two days after a last-second failure caused Starship SN9 to smash into the ground and explode, SpaceX CEO Elon Musk has returned to Twitter with some harsh preliminary reactions.
Right off the bat, in response to a question about why Starships SN8 and SN9 both attempted their unsuccessful landings with only two of three available Raptor engines, Musk frankly stated that “we were too dumb.” At face value, it’s a decent question, given that there are no obvious showstoppers to explain why Starships couldn’t make the most of the redundancy their three Raptor engines can offer.
After completing an otherwise flawless 6.5 minutes launch, ascent, and belly-flop descent, Starship SN9 began a critical ~120-degree flip maneuver, sequentially igniting two Raptor engines and using that thrust to flip from a belly-down attitude to a tail-first landing configuration. Unfortunately, though the first Raptor did fire up and put in a good effort, the second engine failed to ignite, leaving the building-sized rocket to impact the ground traveling far too quickly.
Ironically, more than three years ago, Musk himself revealed in a Reddit Ask Me Anything thread that he and his engineers had decided to modify Starship’s (then known as BFS) design by adding a third Raptor to its central cluster of two engines.
“Btw, we modified the [Starship] design since IAC [2017] to add a third medium-area-ratio Raptor engine partly for that reason (lose only 1/3 thrust in engine out) and allow landings with higher payload mass for the Earth to Earth transport function.”
Elon Musk – Reddit AMA – October 2017
Primarily meant to enable more efficient landings in Earth’s atmosphere, adding a third engine to that cluster would logically increase the chances of a successful (or at least survivable) landing in the event that one engine fails. Greater thrust and an improved thrust-to-weight ratio both during launch and landing would fundamentally improve the efficiency of Starship, likely making up for most or all of the added weight.
In retrospect, it’s not entirely surprising to learn that a three-engine landing burn is probably the most logical option if three landing-class engines have been included in the design. In SpaceX and Musk’s defense, however, there are also several good reasons to use as few Raptor engines as possible.
It was foolish of us not to start 3 engines & immediately shut down 1, as 2 are needed to land— Elon Musk (@elonmusk) February 4, 2021
Throttling high-performance rocket engines is exceptionally difficult and Raptor is not yet a fully mature engine, meaning that it’s throttle capabilities are likely less than optimal. That’s relevant because the higher a rocket’s thrust-to-weight ratio during landing, the more aggressive its landings have to be. SpaceX is apparently extremely conservative with Starship in this regard, prioritizing slow, gentle landings by only using two of three available engines.
Ironically, it’s possible that that attempt at risk reduction resulted in harder landings for both Starship SN8 and SN9, as three-engine landing burns could have potentially slowed them down significantly more before impact.
At the same time, though it may have mitigated the severity of both landing failures, three-engine landing burns would not have resolved the fundamental issues that caused them. In SN8’s case, low fuel header tank pressure doomed the Starship, while SN9 is more ambiguous. Aside from the clear Raptor ignition failure, which a three-engine burn could have resolved by downselecting to two healthier engines, the one Raptor that did ignite appeared to suffer some kind of uncontained failure seconds before landing.
Impressively, despite that apparent combustion chamber or preburner failure, the engine’s landing burn seemed to continued uninterrupted until the moment of impact. As such, it’s hard to say if that lone Raptor was still producing substantial thrust or if it was in the throes of a catastrophic failure. If it could have held on for another 5-10 seconds and the third Raptor (the engine that didn’t reignite) was able to restart and perform without issue, a three-engine landing burn could have easily made SN9’s demise less violent or even have enabled a soft landing.
While a three-engine burn all the way to touchdown appears to be extremely risky or impossible for present-day Starships, Musk implied that there was nothing preventing SpaceX from reigniting all three engines during the initial flip and landing burn and using that time to determine the health of all three engines. If all three were healthy, Starship would shut down one for a soft landing. If one engine failed to restart or lost thrust shortly after ignition, the other two would already be active and able to take over.
Musk says that Starship SN10, already at the launch pad and likely days away from its first tests, will attempt to adopt that approach on an upcoming test flight expected as few as 2-3 weeks from now.
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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
