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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.
Tesla has reached a notable benchmark in its autonomous driving program after its Robotaxi fleet surpassed one million miles of unsupervised operation. The company made the announcement during its Cybercab event in Austin on September 3.
Tesla Vice President of AI Ashok Elluswamy told attendees he was happy to report the fleet had achieved one million miles of unsupervised Robotaxi operation as a testament to safety.
The new total marked a sharp increase from the 380,000 unsupervised miles Tesla disclosed during its second-quarter 2026 earnings update in late July.
In roughly six weeks, the company added about 620,000 miles. That acceleration followed Tesla’s decision to remove in-vehicle safety monitors from most of its operations outside the San Francisco Bay Area.
Credit: Tesla
Tesla first launched Robotaxi service in Austin in June 2025 with safety drivers present. It later began fully unsupervised rides and expanded into Dallas, Houston, Miami, Orlando, and Tampa. The San Francisco Bay Area remains the exception, where a safety monitor still rides in the vehicle under California permitting rules.
The company has not released a city-by-city breakdown of the one million unsupervised miles.
The milestone arrived as Tesla began offering public Cybercab rides in Austin. The purpose-built vehicle has no steering wheel or pedals and is designed only for autonomous ride-hailing. Production versions joined the existing fleet of modified Tesla vehicles already operating in the service.
Tesla’s unsupervised mileage is growing at a double-digit weekly rate according to earlier company comments, yet its fleet size remains modest compared with established competitors. Waymo has accumulated more than 200 million fully autonomous rider-only miles. Tesla has described its own unsupervised operations as having recorded zero notable incidents in the period leading up to the July update.
The one-million-mile figure reflects Tesla’s shift from supervised testing to broader driverless service in multiple states. It also highlights the company’s strategy of using both existing Model Y vehicles and the new Cybercab to scale its network.
Credit: Tesla
Whether the rapid recent growth continues will depend on further city expansions, regulatory approvals, and the performance of the purpose-built Cybercab in everyday paid rides. Tesla has not specified how many of the latest miles involved the new vehicle versus the rest of the fleet.
The announcement underscores Tesla’s progress toward a larger robotaxi network while illustrating the remaining gap in total autonomous experience relative to longer-operating rivals.
Tesla AI lead Ashok Elluswamy said this week that 24-hour Robotaxi service is close. Replying on X to a rider who wanted Cybercab trips all night, he wrote that the capability would arrive “next month or so” once “the next tech to merge on the v15 plan” is ready.
The comment landed on September 4, one day after Tesla opened public Cybercab rides in Austin. It is the clearest near-term timeline yet for overnight unsupervised operation. Tesla’s paid Robotaxi network currently runs from 6 a.m. to 10 p.m. seven days a week across Austin, Dallas, Houston, Miami, Orlando, and Tampa.
next month or so. the next tech to merge on the v15 plan will enable it.
— Ashok Elluswamy (@aelluswamy) September 4, 2026
That 16-hour window is shorter than the 6 a.m. to 2 a.m. schedule the company used for much of the prior year.
Elluswamy did not name the specific feature or say whether the change would apply first to purpose-built Cybercabs, the existing Model Y fleet, or both. He also offered no city-by-city rollout list. The link to Full Self-Driving v15 is nevertheless significant.
Tesla has described v15 as a step-change architecture with seven parallel improvement tracks and roughly ten times more parameters than earlier builds. Early versions of that software already operate on the Robotaxi fleet and contain about 40 percent of the planned gains.
By July 2026, the unsupervised fleet had logged more than 380,000 miles across six cities in two states with what the company called an impeccable safety record and no notable incidents caused by the vehicles themselves. Tesla has repeatedly argued that camera-based end-to-end neural networks, rather than extra sensors, are the core of the solution.
Overnight service would test that claim in lower-light conditions and would also raise vehicle utilization, a key variable for Robotaxi unit economics. The company has already begun using public Superchargers at night and is building dedicated Robotaxi charging sites.
Riders have asked why software must change if the cars already drive in the dark. The practical answer appears to be reliability and scale: Tesla has held back mass expansion until more of the v15 stack is merged, citing the need for higher confidence before putting thousands of unoccupied vehicles on streets around the clock.
If the next module arrives on the timetable Elluswamy sketched, 24-hour service could begin in October 2026 in at least some markets.
That would mark a shift from a daytime-bounded pilot to a service that can run whenever demand exists, including the late-night hours that have so far remained out of reach.
Tesla is beginning to roll out Full Self-Driving Supervised v14.3.9 with a new active safety layer that can take control even when the driver is operating the car manually.
Tesla AI said the software can activate FSD on the driver’s behalf when an imminent collision is detected and Automatic Emergency Braking may not be enough. It may also engage if the system detects heavy distraction or an accidental FSD disengagement.
FSD Supervised v14.3.9 starting to roll out shortly
This release includes a new active safety feature set: FSD Supervised can now activate on your behalf when an imminent collision is detected and Automatic Emergency Braking (AEB) may not be enough.
It may also engage if we…
— Tesla AI (@Tesla_AI) September 4, 2026
The capability is essentially Automatic Collision Evasion. However, unlike conventional AEB, which mainly applies the brakes in a straight line, this feature can use steering, braking, and acceleration together if the car calculates that stopping alone will not prevent impact and a safer path exists. The system may change lanes or move toward a shoulder when conditions allow, then continue driving after the immediate threat is handled rather than simply coming to a stop.
The intervention is meant as a last-resort safety net, not a replacement for attentive driving.
Tesla Full Self-Driving v14.3.7 early review: FSD saved me from an accident
Tesla’s own description still frames FSD as supervised assistance. Secondary reports on internal release notes say the feature can fire while the car is being driven manually if cabin-camera monitoring suggests the driver is not sufficiently attentive, such as reaching toward the back seat, or if FSD appears to have been turned off unintentionally.
After the emergency maneuver, the car is expected to alert the driver and request a return to manual control.
The safety case is straightforward. Many collisions happen in the last second because a driver is looking away, fumbles a control, or faces an obstacle that braking cannot fully solve. A system that can both recognize that AEB is insufficient and execute a coordinated evasive path can reduce those remaining high-severity events.
Re-engaging after accidental disengagement also addresses a practical failure mode: a small steering nudge that drops FSD at the worst moment. The advantage is a background safety net that uses the same vision stack already running in v14, instead of leaving the car solely to emergency braking once the driver is no longer in command.
The feature still depends on FSD being enabled and, according to reports, an active FSD purchase or subscription. It does not make the vehicle unsupervised. Drivers remain responsible, and Tesla has not published how often the system is expected to intervene or how it will handle false positives.
If the rollout is conservative and the false-alarm rate stays low, the update is a meaningful step: FSD is no longer only a feature the driver turns on. In the rare moments when disaster is already forming, it can step in.
Tesla crosses major Unsupervised Self-Driving milestone
Tesla Robotaxi will be a 24/7 service: here’s when
