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SpaceX reveals Starship “marine recovery” plans in new job postings

Super Heavy on YOUR drone ship? It's more likely than you think! (Richard Angle/Teslarati/SpaceX)

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In a series of new job postings, SpaceX has hinted at an unexpected desire to develop “marine recovery systems for the Starship program.”

Since SpaceX first began bending metal for its steel Starship development program in late 2018, CEO Elon Musk, executives, and the company itself have long maintained that both Super Heavy boosters and Starship upper stages would perform what are known as return-to-launch-site (RTLS) landings. It’s no longer clear if those long-stated plans are set in stone.

Oddly, despite repeatedly revealing plans to develop “marine recovery” assets for Starship, SpaceX’s recent “marine engineer” and “naval architect” job postings never specifically mentioned the company’s well-established plans to convert retired oil rigs into vast floating Starship launch sites. Weighing several thousand tons and absolutely dwarfing the football-field-sized drone ships SpaceX recovers Falcon boosters with, it goes without saying that towing an entire oil rig hundreds of miles to and from port is not an efficient or economical solution for rocket recovery. It would also make very little sense for SpaceX to hire a dedicated naval architect without once mentioning that they’d be working on something as all-encompassing as the world’s largest floating launch pad.

That leaves three obvious explanations for the mentions. First, it might be possible that SpaceX is merely preparing for the potential recovery of debris or intact, floating ships or boosters after intentionally expending them on early orbital Starship test flights. Second, SpaceX might have plans to strip an oil rig or two – without fully converting them into launch pads – and then use those rigs as landing platforms designed to remain at sea indefinitely. Those platforms might then transfer landed ships or boosters to smaller support ships tasked with returning them to dry land. Third and arguably most likely, SpaceX might be exploring the possible benefits of landing Super Heavy boosters at sea.

Through its Falcon rockets, SpaceX has slowly but surely refined and perfected the recovery and reuse of orbital-class rocket boosters – 24 (out of 103) of which occurred back on land. Rather than coasting 500-1000 kilometers (300-600+ mi) downrange after stage separation and landing on a drone ship at sea, those 24 boosters flipped around, canceled out their substantial velocities, and boosted themselves a few hundred kilometers back to the Florida or California coast, where they finally touched down on basic concrete pads.

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Unsurprisingly, canceling out around 1.5 kilometers per second of downrange velocity (equivalent to Mach ~4.5) and fully reversing that velocity back towards the launch site is an expensive maneuver, costing quite a lot of propellant. For example, the nominal 25-second reentry burn performed by almost all Falcon boosters likely costs about 20 tons (~40,000 lb) of propellant. The average ~35-second single-engine landing burn used by all Falcon boosters likely costs about 10 tons (~22,000 lb) of propellant. Normally, that’s all that’s needed for a drone ship booster landing.

For RTLS landings, Falcon boosters must also perform a large ~40-second boostback burn with three Merlin 1D engines, likely costing an extra 25-35 tons (55,000-80,000 lb) of propellant. In other words, an RTLS landing generally ends up costing at least twice as much propellant as a drone ship landing. Using the general rocketry rule of thumb that every 7 kilograms of booster mass reduces payload to orbit by 1 kilogram and assuming that each reusable Falcon booster requires about 3 tons of recovery-specific hardware (mostly legs and grid fins) a drone ship landing might reduce Falcon 9’s payload to low Earth orbit (LEO) by ~5 tons (from 22 tons to 17 tons). The extra propellant needed for an RTLS landing might reduce it by another 4-5 tons to 13 tons.

Likely less than coincidentally, a Falcon 9 with drone ship booster recovery has never launched more than ~16 tons to LEO. While SpaceX hasn’t provided NASA’s ELVPerf calculator with data for orbits lower than 400 kilometers (~250 mi), it generally agrees, indicating that Falcon 9 is capable of launching about 12t with an RTLS landing and 16t with a drone ship landing.

This is all to say that landing reusable boosters at sea will likely always be substantially more efficient. The reason that SpaceX has always held that Starship’s Super Heavy boosters will avoid maritime recovery is that landing and recovering giant rocket boosters at sea is inherently difficult, risky, time-consuming, and expensive. That makes rapid reuse (on the order of multiple times per day or week) almost impossible and inevitably adds the cost of recovery, which could actually be quite significant for a rocket that SpaceX wants to eventually cost just a few million dollars per launch. However, so long as at-sea recovery costs less than a few million dollars, there’s always a chance that certain launch profiles could be drastically simplified – and end up cheaper – by the occasional at-sea booster landing.

If the alternative is a second dedicated launch to partially refuel one Starship, it’s possible that a sea landing could give Starship the performance needed to accomplish the same mission in a single launch, lowering the total cost of launch services. If – like with Falcon 9 – a sea landing could boost Starship’s payload to LEO by a third or more, the regular sea recovery of Super Heavy boosters would also necessarily cut the number of launches SpaceX needs to fill up a Starship Moon lander by a third. Given that SpaceX and NASA have been planning for Starship tanker launches to occur ~12 days apart, recovering boosters at sea becomes even more feasible.

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In theory, the Starship launch vehicle CEO Elon Musk has recently described could be capable of launching anywhere from 150 to 200+ tons to low Earth orbit with full reuse and RTLS booster recovery. With so much performance available, it may matter less than it does with Falcon 9 and Falcon Heavy if an RTLS booster landing cuts payload to orbit by a third, a half, or even more. At the end of the day, “just” 100 tons to LEO may be more than enough to satisfy any realistic near-term performance requirements.

But until Starships and Super Heavy boosters are reusable enough to routinely launch multiple times per week (let alone per day) and marginal launch costs have been slashed to single-digit millions of dollars, it’s hard to imagine SpaceX willingly leaving so much performance on the table by forgoing at-sea recovery out of principle alone.

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’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.” 

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Credit: @BLKMDL3/X

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. 

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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.

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Credit: Tesla China

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.

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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.

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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.

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Gage Skidmore, CC BY-SA 4.0 , via Wikimedia Commons

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.

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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.

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