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SpaceX rocket ready for second rideshare launch with 100+ small satellites
For the second time in six months, a SpaceX Falcon 9 rocket is ready to launch around 100+ small satellites into low orbit as part of a rideshare mission for dozens of companies and institutions.
Known as Transporter-2, the mission is SpaceX’s second dedicated launch under the Smallsat Rideshare Program it established in 2019. In 2020, SpaceX began its first launches under the program and delivered eight Earth observation satellites to orbit for Planet and BlackSky as co-passengers on three Starlink missions. A fourth Starlink rideshare was later completed with payloads from Capella Space and Tyvak in May 2021.
In January 2021, Falcon 9 successfully launched 143 small satellites into orbit on a mission known as Transporter-1, setting an all-time record for the number of spacecraft flown on a single rocket and emphasizing just how serious and competitive SpaceX’s Smallsat Program really is.
Six months after that milestone mission, Transporter-2 is now ready to launch. On June 22nd, a Falcon 9 rocket was loaded with hundreds of tons of liquid oxygen and rocket-grade kerosene (RP1) and flight-proven booster B1060 successfully ignited its nine Merlin 1D engines, completing a routine wet dress rehearsal (WDR) and static fire test. Transporter-2 will be B1060’s eighth spaceflight and orbital-class launch in less than a year, representing an average of one flight every ~45 days or six weeks.
Now cleared for flight, SpaceX will have since brought Falcon 9 horizontal and rolled the rocket back to its Cape Canaveral LC-40 pad’s integration hangar. Once there, Transporter-2’s payload ‘stack’ – already encapsulated in a payload fairing – will be installed on top of Falcon 9’s expendable second stage and the rocket will be rolled back out to the pad and brought vertical a second time.
It remains to be seen what exactly Transporter-2 will be carrying to orbit. Transporter-1 carried 133 customer spacecraft and 10 of SpaceX’s own Starlink satellites with a collective liftoff mass of around five metric tons (~11,000 lb). The true mass is unknown but the Falcon 9 booster supporting the mission had to land on a drone ship ~550 km (~340 mi) downrange. Transporter-2, however, will reportedly involve an increasingly rare return-to-launch-site (RTLS) landing for Falcon 9 booster B1060, implying that its payloads may be substantially lighter than its predecessor’s.
Based on a rough accounting of known Transporter-2 payloads from rideshare managers Spaceflight, Exolaunch, and others, the mission could feasibly launch with 100+ small satellites onboard. Relative to Transporter-1, the most obvious weight-saving solution would be to exclude Starlink satellites, which likely represented more than a third of missions payload mass at liftoff. Given that SpaceX also appears to be preparing for a flurry of dedicated polar Starlink launches from its West and East Coast pads that could begin as early as July, it’s fairly safe to assume that Transporter-2 will be Starlink-free.
Ultimately, Transporter-2 appears to be on track for a 2:56 pm EDT (18:56 UTC) launch on Friday, June 25th. Stay tuned for updates and webcast details.
Tesla’s Head of AI, Ashok Elluswamy, says that something that was expected with version 14.3 of the company’s Full Self-Driving platform has already partially shipped with the current build of version 14.2.
Tesla and CEO Elon Musk have teased on several occasions that reasoning will be a big piece of future Full Self-Driving builds, helping bring forth the “sentient” narrative that the company has pushed for these more advanced FSD versions.
Back in October on the Q3 Earnings Call, Musk said:
“With reasoning, it’s literally going to think about which parking spot to pick. It’ll drop you off at the entrance of the store, then go find a parking spot. It’s going to spot empty spots much better than a human. It’s going to use reasoning to solve things.”
Musk said in the same month:
“By v14.3, your car will feel like it is sentient.”
Amazingly, Tesla Full Self-Driving v14.2.2.2, which is the most recent iteration released, is very close to this sentient feeling. However, there are more things that need to be improved, and logic appears to be in the future plans to help with decision-making in general, alongside other refinements and features.
On Thursday evening, Elluswamy revealed that some of the reasoning features have already been rolled out, confirming that it has been added to navigation route changes during construction, as well as with parking options.
He added that “more and more reasoning will ship in Q1.”
🚨 Tesla’s Ashok Elluswamy reveals Nav decisions when encountering construction and parking options contain “some elements of reasoning”
More uses of reasoning will be shipped later this quarter, a big tidbit of info as we wait v14.3 https://t.co/jty8llgsKM
— TESLARATI (@Teslarati) January 9, 2026
Interestingly, parking improvements were hinted at being added in the initial rollout of v14.2 several months ago. These had not rolled out to vehicles quite yet, as they were listed under the future improvements portion of the release notes, but it appears things have already started to make their way to cars in a limited fashion.
Tesla Full Self-Driving v14.2 – Full Review, the Good and the Bad
As reasoning is more involved in more of the Full Self-Driving suite, it is likely we will see cars make better decisions in terms of routing and navigation, which is a big complaint of many owners (including me).
Additionally, the operation as a whole should be smoother and more comfortable to owners, which is hard to believe considering how good it is already. Nevertheless, there are absolutely improvements that need to be made before Tesla can introduce completely unsupervised FSD.
Elon Musk
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.”
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.
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.
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.
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.
Tesla AI Head says future FSD feature has already partially shipped
Tesla’s Elon Musk: 10 billion miles needed for safe Unsupervised FSD
