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SpaceX rocket catch simulation raises more questions about concept

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CEO Elon Musk has published the first official visualization of what SpaceX’s plans to catch Super Heavy boosters might look like in real life. However, the simulation he shared raises just as many questions as it answers.

Since at least late 2020, SpaceX CEO Elon Musk has been floating the idea of catching Starships and Super Heavy boosters out of the sky as an alternative to having the several-dozen-ton steel rockets use basic legs to land on the ground. This would be a major departure from SpaceX’s highly successful Falcon family, which land on a relatively complex set of deployable legs that can be retracted after most landings. The flexible, lightweight structures have mostly been reliable and easily reusable but Falcon boosters occasionally have rough landings, which can use up disposable shock absorbers or even damage the legs and make boosters hard to safely recover and slower to reuse.

As a smaller rocket, Falcon boosters have to be extremely lightweight to ensure healthy payload margins and likely weigh about 25-30 tons empty and 450 tons fully fueled – an excellent mass ratio for a reusable rocket. While it’s still good to continue that practice of rigorous mass optimization with Starship, the vehicle is an entirely different story. Once plans to stretch the Starship upper stage’s tanks and add three more Raptors are realized, it’s quite possible that Starship will be capable of launching more than 200 tons (~440,000 lb) of payload to low Earth orbit (LEO) with ship and booster recovery.

One might think that SpaceX, with the most capable rocket ever built potentially on its hands, would want to take advantage of that unprecedented performance to make the rocket itself – also likely to be one of the most complex launch vehicles ever – simpler and more reliable early on in the development process. Generally speaking, that would involve sacrificing some of its payload capability and adding systems that are heavier but simpler and more robust. Once Starship is regularly flying to orbit and gathering extensive flight experience and data, SpaceX might then be able refine the rocket, gradually reducing its mass and improving payload to orbit by optimizing or fully replacing suboptimal systems and designs.

Instead, SpaceX appears to be trying to substantially optimize Starship before it’s attempted a single orbital launch. The biggest example is Elon Musk’s plan to catch Super Heavy boosters – and maybe Starships, too – for the sole purpose of, in his own words, “[saving] landing leg mass [and enabling] immediate reflight of [a giant, unwieldy rocket].” Musk, SpaceX executives, or both appear to be attempting to refine a rocket that has never flown. Further, based on a simulation of a Super Heavy “catch” Musk shared on January 20th, all that oddly timed effort may end up producing a solution that’s actually worse than what it’s trying to replace.

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Based on the simulated telemetry shown in the visualization, Super Heavy’s descent to the landing zone appears to be considerably gentler than the ‘suicide burn’ SpaceX routinely uses on Falcon. By decelerating as quickly as possible and making landing burns as short as possible, Falcon saves a considerable amount of propellant during recovery – extra propellant that, if otherwise required, would effectively increase Falcon’s dry mass and decrease its payload to orbit. In the Super Heavy “catch” Musk shared, the booster actually appears to be landing – just on an incredibly small patch of steel on the tower’s ‘Mechazilla’ arms instead of a concrete pad on the ground.

Aside from a tiny bit of lateral motion, the arms appear motionless during the ‘catch,’ making it more of a landing. Further, Super Heavy is shown decelerating rather slowly throughout the simulation and appears to hover for almost 10 seconds near the end. That slow, cautious descent and even slower touchdown may be necessary because of how incredibly accurate Super Heavy has to be to land on a pair of hardpoints with inches of lateral margin for error and maybe a few square feet of usable surface area. The challenge is a bit like if SpaceX, for some reason, made Falcon boosters land on two elevated ledges about as wide as car tires. Aside from demanding accurate rotational control, even the slightest lateral deviation would cause the booster to topple off the pillars and – in the case of Super Heavy – fall about a hundred feet onto concrete, where it would obviously explode.

What that slow descent and final hover mean is that the Super Heavy landing shown would likely cost significantly more delta V (propellant) than a Falcon-style suicide burn. Propellant has mass, so Super Heavy would likely need to burn at least 5-10 tons more to carefully land on arms that aren’t actively matching the booster’s position and velocity. Ironically, SpaceX could probably quite easily add rudimentary, fixed legs – removing most of the bad aspects of Falcon legs – to Super Heavy with a mass budget of 10 tons. But even if SpaceX were to make those legs as simple, dumb, and reliable as physically possible and they wound up weighing 20 tons total, the inherent physics of rocketry mean that adding 20 tons to Super Heavy’s likely 200-ton dry mass would only reduce the rocket’s payload to orbit by about 3-5 tons or 1-3%.

Further, per Musk’s argument that landing on the arms would enhance the speed of reuse, it’s difficult to see how landing Super Heavy or Starship in the exact same corridor – but on the ground instead of on the arms – would change anything. If Super Heavy is accurate enough to land on a few square meters of steel, it must inherently be accurate enough to land within the far larger breadth of those arms. The only process landing on the arms would clearly remove is reattaching the arms to a landed booster or ship, which it’s impossible to imagine would save more than a handful of minutes or maybe an hour of work. SpaceX’s Falcon booster turnaround record is currently 27 days, so it’s even harder to imagine why SpaceX would be worrying about cutting minutes or a few hours off of the turnaround and reuse of a rocket that has never even performed a full static fire test – let alone attempted an orbital-class launch, reentry, or landing.

Put simply, while Starbase’s launch tower arms will undoubtedly be useful for quickly lifting and stacking Super Heavy and Starship, it’s looking more and more likely that using those arms as a landing platform will, at best, be an inferior alternative to basic Falcon-style landings. More importantly, even if everything works perfectly, the arms actually cooperate with boosters to catch them, and it’s possible for Super Heavy to avoid hovering and use a more efficient suicide burn, the apparent best-case outcome of all that effort is marginally faster reuse and perhaps a 5% increase in payload to orbit. Only time will tell if such a radical change proves to be worth such marginal benefits.

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Eric Ralph is Teslarati's senior spaceflight reporter and has been covering the industry in some capacity for almost half a decade, largely spurred in 2016 by a trip to Mexico to watch Elon Musk reveal SpaceX's plans for Mars in person. Aside from spreading interest and excitement about spaceflight far and wide, his primary goal is to cover humanity's ongoing efforts to expand beyond Earth to the Moon, Mars, and elsewhere.

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Tesla seen as early winner as Canada reopens door to China-made EVs

Tesla had already prepared for Chinese exports to Canada in 2023 by equipping its Shanghai Gigafactory to produce a Canada-specific version of the Model Y.

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Credit: Tesla

Tesla seems poised to be an early beneficiary of Canada’s decision to reopen imports of Chinese-made electric vehicles, following the removal of a 100% tariff that halted shipments last year.

Thanks to Giga Shanghai’s capability to produce Canadian-spec vehicles, it might only be a matter of time before Tesla is able to export vehicles to Canada from China once more. 

Under the new U.S.–Canada trade agreement, Canada will allow up to 49,000 vehicles per year to be imported from China at a 6.1% tariff, with the quota potentially rising to 70,000 units within five years, according to Prime Minister Mark Carney. 

Half of the initial quota is reserved for vehicles priced under CAD 35,000, a threshold above current Tesla models, though the electric vehicle maker could still benefit from the rule change, as noted in a Reuters report.

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Tesla had already prepared for Chinese exports to Canada in 2023 by equipping its Shanghai Gigafactory to produce a Canada-specific version of the Model Y. That year, Tesla began shipping vehicles from Shanghai to Canada, contributing to a sharp 460% year-over-year increase in China-built vehicle imports through Vancouver. 

When Ottawa imposed a 100% tariff in 2024, however, Tesla halted those shipments and shifted Canadian supply to its U.S. and Berlin factories. With tariffs now reduced, Tesla could quickly resume China-to-Canada exports.

Beyond manufacturing flexibility, Tesla could also benefit from its established retail presence in Canada. The automaker operates 39 stores across Canada, while Chinese brands like BYD and Nio have yet to enter the Canadian market directly. Tesla’s relatively small lineup, which is comprised of four core models plus the Cybertruck, allows it to move faster on marketing and logistics than competitors with broader portfolios.

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Tesla confirms that work on Dojo 3 has officially resumed

“Now that the AI5 chip design is in good shape, Tesla will restart work on Dojo 3,” Elon Musk wrote in a post on X.

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(Credit: Tesla)

Tesla has restarted work on its Dojo 3 initiative, its in-house AI training supercomputer, now that its AI5 chip design has reached a stable stage. 

Tesla CEO Elon Musk confirmed the update in a recent post on X.

Tesla’s Dojo 3 initiative restarted

In a post on X, Musk said that with the AI5 chip design now “in good shape,” Tesla will resume work on Dojo 3. He added that Tesla is hiring engineers interested in working on what he expects will become the highest-volume AI chips in the world.

“Now that the AI5 chip design is in good shape, Tesla will restart work on Dojo3. If you’re interested in working on what will be the highest volume chips in the world, send a note to AI_Chips@Tesla.com with 3 bullet points on the toughest technical problems you’ve solved,” Musk wrote in his post on X. 

Musk’s comment followed a series of recent posts outlining Tesla’s broader AI chip roadmap. In another update, he stated that Tesla’s AI4 chip alone would achieve self-driving safety levels well above human drivers, AI5 would make vehicles “almost perfect” while significantly enhancing Optimus, and AI6 would be focused on Optimus and data center applications. 

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Musk then highlighted that AI7/Dojo 3 will be designed to support space-based AI compute.

Tesla’s AI roadmap

Musk’s latest comments helped resolve some confusion that emerged last year about Project Dojo’s future. At the time, Musk stated on X that Tesla was stepping back from Dojo because it did not make sense to split resources across multiple AI chip architectures. 

He suggested that clustering large numbers of Tesla AI5 and AI6 chips for training could effectively serve the same purpose as a dedicated Dojo successor. “In a supercomputer cluster, it would make sense to put many AI5/AI6 chips on a board, whether for inference or training, simply to reduce network cabling complexity & cost by a few orders of magnitude,” Musk wrote at the time.

Musk later reinforced that idea by responding positively to an X post stating that Tesla’s AI6 chip would effectively be the new Dojo. Considering his recent updates on X, however, it appears that Tesla will be using AI7, not AI6, as its dedicated Dojo successor. The CEO did state that Tesla’s AI7, AI8, and AI9 chips will be developed in short, nine-month cycles, so Dojo’s deployment might actually be sooner than expected. 

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Elon Musk’s xAI brings 1GW Colossus 2 AI training cluster online

Elon Musk shared his update in a recent post on social media platform X.

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xAI-supercomputer-memphis-environment-pushback
Credit: xAI

xAI has brought its Colossus 2 supercomputer online, making it the first gigawatt-scale AI training cluster in the world, and it’s about to get even bigger in a few months.

Elon Musk shared his update in a recent post on social media platform X.

Colossus 2 goes live

The Colossus 2 supercomputer, together with its predecessor, Colossus 1, are used by xAI to primarily train and refine the company’s Grok large language model. In a post on X, Musk stated that Colossus 2 is already operational, making it the first gigawatt training cluster in the world. 

But what’s even more remarkable is that it would be upgraded to 1.5 GW of power in April. Even in its current iteration, however, the Colossus 2 supercomputer already exceeds the peak demand of San Francisco.  

Commentary from users of the social media platform highlighted the speed of execution behind the project. Colossus 1 went from site preparation to full operation in 122 days, while Colossus 2 went live by crossing the 1-GW barrier and is targeting a total capacity of roughly 2 GW. This far exceeds the speed of xAI’s primary rivals.

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Funding fuels rapid expansion

xAI’s Colossus 2 launch follows xAI’s recently closed, upsized $20 billion Series E funding round, which exceeded its initial $15 billion target. The company said the capital will be used to accelerate infrastructure scaling and AI product development.

The round attracted a broad group of investors, including Valor Equity Partners, Stepstone Group, Fidelity Management & Research Company, Qatar Investment Authority, MGX, and Baron Capital Group. Strategic partners NVIDIA and Cisco also continued their support, helping xAI build what it describes as the world’s largest GPU clusters.

xAI said the funding will accelerate its infrastructure buildout, enable rapid deployment of AI products to billions of users, and support research tied to its mission of understanding the universe. The company noted that its Colossus 1 and 2 systems now represent more than one million H100 GPU equivalents, alongside recent releases including the Grok 4 series, Grok Voice, and Grok Imagine. Training is also already underway for its next flagship model, Grok 5.

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