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SpaceX stress-tests Starship-catching arms with giant water balloons
SpaceX has begun testing Starbase’s rocket-catching arms with ballast to simulate the weight of Starship and Super Heavy.
SpaceX started the process of proof testing those arms about a week ago, beginning with some basic calibration work. Together, the three arms and launch tower amount to a giant custom-built robot that SpaceX CEO Elon Musk has deemed “Mechazilla.” Controlled with a complex system of hydraulic and electromechanical actuators spread throughout each structure, SpaceX must calibrate all of those devices to enable the full range of motion the arms are meant to be capable of. To do so, SpaceX appeared to actuate both catch arms (also known as “chopsticks”) as far as they were able to move on January 4th, producing data that could be fed back into the system’s control software to properly set limits of motion.
A handful of days later, arm testing continued, with SpaceX lifting the carriage higher than it had traveled before and demonstrating more complex longitudinal movements that required synchronized motion of both arms. On January 9th, SpaceX performed the most ambitious arm testing yet, nearly lifting the arms to the top of their ~140 meter (~460 ft) tall launch tower backbone to simulate the range of vertical motion required to lift and stack Starship and Super Heavy.
SpaceX also installed a temporary frame meant to simulate a Starship or Super Heavy booster, foreshadowing additional testing planned in the coming days. That jig upped the stakes for the longitudinal actuation portion of January 9th’s testing, as anything less than the precise, synchronized movement of both arms could have caused the heavy steel frame to fall hundreds of feet onto a range of equipment and structures directly below it. Thankfully, the arms performed well and returned to their resting position without issue.
On January 11th, SpaceX proceeded to install six ‘water bags’ – three to a side – on the Starship simulator frame. Amounting to giant, heavy-duty water balloons, those bags are routinely used to stress-test large structures and devices by simulating payloads that might be too expensive or inconvenient to use solely for testing purposes. With those seemingly empty bags attached, SpaceX proceeded to move the catch arms up and down the full length of the launch tower at record speed, taking about seven minutes to climb and descend ~120 meters (~400 ft) – averaging a brisk 0.6 mph or 1 km/h.
Here is a video from Giga Texas of this type of mass simulator! pic.twitter.com/uHfah45WVt— Zack Golden (@CSI_Starbase) January 11, 2022
On January 12th, SpaceX filled the balls with water, producing some… interesting… visuals. Ridiculous appearances aside, the six bags SpaceX chose to use could be 20, 35, or 50-ton variants, meaning that all six could weigh anywhere from 120 to 300 tons (264,000-660,000 lb) if fully filled. In other words, perfect for simulating the dry masses of Starship (roughly 80-120 tons) and Super Heavy (150-200+ tons).
SpaceX did appear to fully fill around four of the six bags and partially filled the other two, causing the whole arm structure to visibly sag during the fill process as the weight of the ballast stretched the several-inch-thick steel cable holding the whole device aloft. In the late afternoon, the laden arms lifted around 10-20 meters and rotated left and right, partially demonstrating the process of rotating a lifted Starship or Super Heavy into position for stacking or launch mount installation. They were never lifted high enough to truly demonstrate that ability, though, and were lowered back to the ground soon after.
As of 10pm CST, January 12th, the water bags appear to have been fully drained after their first excursion. It’s likely that load-testing will continue over the next several days or weeks – SpaceX may just want to avoid leaving the arms fully loaded overnight.
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
