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SpaceX Mars landing expert talks Starship recovery challenges in new interview

Starship Mk1 is in the late stages of assembly and integration at SpaceX's Boca Chica, Texas facilities. (SpaceX)

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Formerly responsible for developing Falcon 9 (and Heavy) into the routinely-landing reusable rocket it is today, senior SpaceX engineer Lars Blackmore says he now has one primary focus: figuring out how to land Starship on Earth, the Moon, and Mars.

A graduate of University of Cambridge and MIT, the latter of which interviewed him on October 23rd for an “Alumni Stories” blog, Lars Blackmore has become famous for his groundbreaking work in guidance, navigation, and control (GNC). After graduating with honors from Cambridge and earning a PhD from MIT, Dr. Blackmore joined NASA in 2007 and immersed himself in “precision Mars landing”, part of a more general focus on figuring out how to autonomously control vehicles in uncertain conditions.

In his last year at NASA, Blackmore co-invented an algorithm known as G-FOLD (Guidance for Fuel Optimal Large Divert) that should theoretically enable precision landings on Mars, improving the state of the art by two full orders of magnitude (+/- 10 km to +/- 100 m). In 2011, he departed NASA and joined SpaceX, where he lead the development of the GNC technology needed to successfully and reliably recovery Falcon 9 boosters. Although the same could be said for any number of critical, groundbreaking systems that had to be developed, the onboard software that autonomously guides Falcon 9 landings on the fly is one of many things that booster recovery and reuse would be wholly impossible without.

After numerous failed attempts, all part SpaceX’s preferred learning process, Falcon 9 successfully landed for the first time on December 21st, 2015. As they say, the rest is history: in the roughly four years since that milestone landing, SpaceX has successfully completed 57 orbital launches, recovered boosters 43 more times, and reused flight-proven boosters on 23 launches. Since that first success, more than half of all SpaceX launches have been followed by a successful booster landing (or two).

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Three of SpaceX’s thrice-flown Falcon 9 boosters are pictured here: B1046, B1048, and B1049. (Tom Cross & Pauline Acalin)

Back to Mars

In 2018, Dr. Blackmore officially took on a new full-time role as SpaceX’s Principal Mars Landing Engineer. As the namesake suggests, this meant handing (now semi-routine) Falcon 9 and Heavy GNC development to a strong team and beginning to tackle an array of new problems that will need to be solved for SpaceX to reach the Moon, Mars, and beyond.

Following radical design modifications made to Starship in 2018 and again in 2019, SpaceX is pursuing a radically different method of recovery with Starship (the upper stage), while Super Heavy will more directly follow in the footsteps of Falcon 9/Heavy. Starship, however, is being designed to perform a guided descent more akin to a skydiver falling straight down, using flaps at its nose and tail (explicitly “not wings”) to accurately guide its fall.

As little as a few hundred meters above the ground, Starship will then perform a radical maneuver, igniting its Raptor engines to flip around, burn in the opposite direction to counteract that sideways boost, and finally coming in for a precise landing on Earth/Mars/the Moon.

Beyond the new GNC software and knowledge needed to make that maneuver real, Blackmore is also responsible for Starship atmospheric entry, no less critical to enabling precise, repeatable landings from orbital velocity to touchdown. In his recent interview with University of Cambridge staff, Lars revealed that his role as Principal Mars Landing Engineer involved a far wider scope than his previous GNC-centered work, with the goal instead being to design a launch vehicle (Starship) from the ground up to be easily recovered and reused. Falcon 9 Block 5 may be radically different than the ‘V1.0’ rocket that debuted in 2010, but it’s still ultimately a product of retroactive engineering.

With Starship and Super Heavy, SpaceX instead wants to take the vast wealth of knowledge and experience gained from F9/FH and build the vehicle from the ground up to be optimized for full reuse. Ultimately, Dr. Blackmore stated that “landing Starship will be much harder than landing Falcon 9, but if [SpaceX] can do it, it will be revolutionary.”

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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 announces crazy new Full Self-Driving milestone

The number of miles traveled has contextual significance for two reasons: one being the milestone itself, and another being Tesla’s continuing progress toward 10 billion miles of training data to achieve what CEO Elon Musk says will be the threshold needed to achieve unsupervised self-driving.

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Credit: Tesla

Tesla has announced a crazy new Full Self-Driving milestone, as it has officially confirmed drivers have surpassed over 8 billion miles traveled using the Full Self-Driving (Supervised) suite for semi-autonomous travel.

The FSD (Supervised) suite is one of the most robust on the market, and is among the safest from a data perspective available to the public.

On Wednesday, Tesla confirmed in a post on X that it has officially surpassed the 8 billion-mile mark, just a few months after reaching 7 billion cumulative miles, which was announced on December 27, 2025.

The number of miles traveled has contextual significance for two reasons: one being the milestone itself, and another being Tesla’s continuing progress toward 10 billion miles of training data to achieve what CEO Elon Musk says will be the threshold needed to achieve unsupervised self-driving.

The milestone itself is significant, especially considering Tesla has continued to gain valuable data from every mile traveled. However, the pace at which it is gathering these miles is getting faster.

Secondly, in January, Musk said the company would need “roughly 10 billion miles of training data” to achieve safe and unsupervised self-driving. “Reality has a super long tail of complexity,” Musk said.

Training data primarily means the fleet’s accumulated real-world miles that Tesla uses to train and improve its end-to-end AI models. This data captures the “long tail” — extremely rare, complex, or unpredictable situations that simulations alone cannot fully replicate at scale.

This is not the same as the total miles driven on Full Self-Driving, which is the 8 billion miles milestone that is being celebrated here.

The FSD-supervised miles contribute heavily to the training data, but the 10 billion figure is an estimate of the cumulative real-world exposure needed overall to push the system to human-level reliability.

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Tesla Cybercab production begins: The end of car ownership as we know it?

While this could unlock unprecedented mobility abundance — cheaper rides, reduced congestion, freed-up urban space, and massive environmental gains — it risks massive job displacement in ride-hailing, taxi services, and related sectors, forcing society to confront whether the benefits of AI-driven autonomy will outweigh the human costs.

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Credit: Tesla | X

The first Tesla Cybercab rolled off of production lines at Gigafactory Texas yesterday, and it is more than just a simple manufacturing milestone for the company — it’s the opening salvo in a profound economic transformation.

Priced at under $30,000 with volume production slated for April, the steering-wheel-free, pedal-less Robotaxi-geared vehicle promises to make personal car ownership optional for many, slashing transportation costs to as little as $0.20 per mile through shared fleets and high utilization.

While this could unlock unprecedented mobility abundance — cheaper rides, reduced congestion, freed-up urban space, and massive environmental gains — it risks massive job displacement in ride-hailing, taxi services, and related sectors, forcing society to confront whether the benefits of AI-driven autonomy will outweigh the human costs.

Let’s examine the positives and negatives of what the Cybercab could mean for passenger transportation and vehicle ownership as we know it.

The Promise – A Radical Shift in Transportation Economics

Tesla has geared every portion of the Cybercab to be cheaper and more efficient. Even its design — a compact, two-seater, optimized for fleets and ride-sharing, the development of inductive charging, around 300 miles of range on a small battery, half the parts of the Model 3, and revolutionary “unboxed” manufacturing — is all geared toward rapid production.

Operating at a fraction of what today’s rideshare prices are, the Cybercab enables on-demand autonomy for a variety of people in a variety of situations.

Tesla ups Robotaxi fare price to another comical figure with service area expansion

It could also be the way people escape expensive and risky car ownership. Buying a vehicle requires expensive monthly commitments, including insurance and a payment if financed. It also immediately depreciates.

However, Cybercab could unlock potential profitability for owning a car by adding it to the Robotaxi network, enabling passive income. Cities could have parking lots repurposed into parks or housing, and emissions would drop as shared electric vehicles would outnumber gas cars (in time).

The first step of Tesla’s massive production efforts for the Cybercab could lead to millions of units annually, turning transportation into a utility like electricity — always available, cheap, and safe.

The Dark Side – Job Losses and Industry Upheaval

With Robotaxi and Cybercab, they present the same negatives as broadening AI — there’s a direct threat to the economy.

Uber, Lyft, and traditional taxis will rely on human drivers. Robotaxi will eliminate that labor cost, potentially displacing millions of jobs globally. In the U.S. alone, ride-hailing accounts for billions of miles of travel each year.

There are also potential ripple effects, as suppliers, mechanics, insurance adjusters, and even public transit could see reduced demand as shared autonomy grows. Past automation waves show job creation lags behind destruction, especially for lower-skilled workers.

Gig workers, like those who are seeking flexible income, face the brunt of this. Displaced drivers may struggle to retrain amid broader AI job shifts, as 2025 estimates bring between 50,000 and 300,000 layoffs tied to artificial intelligence.

It could also bring major changes to the overall competitive landscape. While Waymo and Uber have partnered, Tesla’s scale and lower costs could trigger a price war, squeezing incumbents and accelerating consolidation.

Balancing Act – Who Wins and Who Loses

There are two sides to this story, as there are with every other one.

The winners are consumers, Tesla investors, cities, and the environment. Consumers will see lower costs and safer mobility, while potentially alleviating themselves of awkward small talk in ride-sharing applications, a bigger complaint than one might think.

Elon Musk confirms Tesla Cybercab pricing and consumer release date

Tesla investors will be obvious winners, as the launch of self-driving rideshare programs on the company’s behalf will likely swell the company’s valuation and increase its share price.

Cities will have less traffic and parking needs, giving more room for housing or retail needs. Meanwhile, the environment will benefit from fewer tailpipes and more efficient fleets.

A Call for Thoughtful Transition

The Cybercab’s production debut forces us to weigh innovation against equity.

If Tesla delivers on its timeline and autonomy proves reliable, it could herald an era of abundant, affordable mobility that redefines urban life. But without proactive policies — retraining, safety nets, phased deployment — this revolution risks widening inequality and leaving millions behind.

The real question isn’t whether the Cybercab will disrupt — it’s already starting — it’s whether society is prepared for the economic earthquake it unleashes.

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Tesla Model 3 wins Edmunds’ Best EV of 2026 award

The publication rated the Model 3 at an 8.1 out of 10, and with its most recent upgrades and changes, Edmunds says, “This is the best Model 3 yet.”

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Credit: Tesla

The Tesla Model 3 has won Edmunds‘ Top Rated Electric Car of 2026 award, beating out several other highly-rated and exceptional EV offerings from various manufacturers.

This is the second consecutive year the Model 3 beat out other cars like the Model Y, Audi A6 Sportback E-tron, and the BMW i5.

The car, which is Tesla’s second-best-selling vehicle behind the popular Model Y crossover, has been in the company’s lineup for nearly a decade. It offers essentially everything consumers could want from an EV, including range, a quality interior, performance, and Tesla’s Full Self-Driving suite, which is one of the best in the world.

The publication rated the Model 3 at an 8.1 out of 10, and with its most recent upgrades and changes, Edmunds says, “This is the best Model 3 yet.”

In its Top Rated EVs piece on its website, it said about the Model 3:

“The Tesla Model 3 might be the best value electric car you can buy, combining an Edmunds Rating of 8.1 out of 10, a starting price of $43,880, and an Edmunds-tested range of 338 miles. This is the best Model 3 yet. It is impressively well-rounded thanks to improved build quality, ride comfort, and a compelling combination of efficiency, performance, and value.”

Additionally, Jonathan Elfalan, Edmunds’ Director of Vehicle Testing, said:

“The Model 3 offers just about the perfect combination of everything — speed, range, comfort, space, tech, accessibility, and convenience. It’s a no-brainer if you want a sensible EV.”

The Model 3 is the perfect balance of performance and practicality. With the numerous advantages that an EV offers, the Model 3 also comes in at an affordable $36,990 for its Rear-Wheel Drive trim level.

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