Tesla appears to be preparing to expand its Robotaxi geofence in Austin, Texas, with numerous engineering vehicles taking to the road. One of the most interesting sights, between the short and tall LiDAR rigs, was a Cybertruck validation vehicle, which we don’t often see.
Tesla’s expansion is moving the Robotaxi Network into downtown Austin, a dense urban environment that is currently outside the geofence. It appears Tesla is content with the latest builds of Robotaxi FSD and is ready to take on urban traffic.
The inclusion of a Cybertruck in the validation fleet is noteworthy, as the rest of the vehicles are Model Ys. This suggests that Tesla may be addressing two challenges simultaneously: expanding its service area while also addressing the FSD gap between the Cybertruck and other HW4 Tesla vehicles.
Tesla Validating Downtown Austin before expanding the Robotaxi geo-fence area. pic.twitter.com/ylFATtjcDi
Recent sightings have shown a fleet of Tesla vehicles, equipped with rooftop validation sensor rigs, running routes throughout downtown Austin and across the South Congress Bridge. While these rigs include LiDAR, it’s not a sign that Tesla is abandoning its vision-only approach.
Instead, Tesla uses the high-fidelity data from the LiDAR as a ground truth measurement to validate and improve the performance of its cameras. In short, it essentially uses the LiDAR measurements as the actual distances and then compares the distances determined in vision-only to the LiDAR measurements. This allows Tesla to tweak and improve its vision system without needing LiDAR.
This data collection in a new, complex environment right outside the Robotaxi geofence is an indicator that plans to expand the geofence. Tesla has previously indicated that they intend to roll out more vehicles and expand the geofence slowly. Given that their operational envelope includes the entire Austin Metro Area, we can expect more locations to open up gradually.
Once they expand the operational radius to include downtown Austin, they will likely also have to considerably increase the number of Robotaxis active in the fleet at any given time. Early-access riders are already saying that the wait time for a Robotaxi is too long, with them sometimes having to wait 15 minutes to be picked up.
With a larger service area, we expect Tesla to also increase the number of vehicles and the number of invited riders to try out the service.
After all, Tesla’s goal is to expand the Robotaxi Network to multiple cities within the United States by the end of 2025. Tesla has already been running an employees-only program in California, and we’ve seen validation vehicles as far away as Boston and New Jersey, on the other side of the country.
Cyber FSD Lagging Behind
One of the most significant details from these recent sightings is the presence of a Cybertruck. Cybertruck’s FSD builds have famously lagged behind the builds available on the rest of Tesla’s HW4 fleet. Key features that were expected never fully materialized for the Cybertruck, and the list of missing features is quite extensive.
Start FSD from Park
Improved Controller
Reverse on FSD
Actually Smart Summon
It may not look like a lot, but if you drive a Cybertruck on FSD and then hop in any of the rest of Tesla’s HW4 vehicles, you’ll notice a distinct difference. This is especially evident on highways, where the Cybertruck tends to drift out of the lane, often crossing over the lane markings.
Tesla was testing parts of Downtown Austin, TX with this Cybertruck which had a massive roof rack, and sensors.
We previously released an exclusive mentioning that a well-positioned internal source confirmed with us that a new FSD build for the Cybertruck was upcoming, but we never ended up receiving that particular build, only a point release to V13.2.9. The AI team’s focus had clearly shifted to getting the latest Robotaxi builds running and validated, and while a flagship, the Cybertruck fleet was small and new, and really a secondary task.
The Cybertruck’s larger size, steer-by-wire, rear-wheel steering, and different camera placements likely present a bigger set of challenges for FSD. Deploying it now as a validation vehicle in a complex environment like downtown Austin suggests that Tesla is finally gathering the specific data needed to bring the Cybertruck’s capabilities up to par. This focused effort is likely the necessary step to refine FSD’s handling of the Cybertruck before they begin rolling out new public builds.
When?
Once Tesla’s validation is complete, we can probably expect the Robotaxi Network to expand its borders for the first time in the coming days or weeks. However, we’ll likely see more signs of the expansion, such as Robotaxi vehicles driving themselves around the area, before the expansion actually happens.
Hopefully, the Cybertruck will also learn from its older siblings and receive the rest of its much-needed FSD features, alongside an FSD update for the entire fleet.
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Tesla has unveiled its 6-seat Model Y variant in China, known as the Model Y L. This new variant of one of the world’s best-selling vehicles comes with a longer wheelbase, adjusted C-pillar design, and most importantly, a six-seat interior layout.
The vehicle’s specifications have been officially listed in a filing with China’s Ministry of Industry and Information Technology (MIIT), confirming a launch for this fall.
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The addition of a longer wheelbase and a more spacious third row is a fantastic addition for the Model Y’s family utility, and positions this variant as sort of a mini Model X, but let’s compare the sizes to really know how this new Model Y compares to a Model X.
Meet the Model Y L
The defining feature of the new Model Y L is its six-seat configuration. This layout has previously been exclusive to the larger and more expensive Model X. While Tesla has offered the Model Y in a 7-seat configuration before, the third row was much too small to be utilized by anyone but small children.
Comparing Model Y L to the Model X
@xiaoteshushu on X
Let’s compare this upcoming Model Y L to the regular Model Y and the Model X.
Vehicle/Dimension
Wheelbase
Overall Length
Model Y
2,890mm / 113.8 in
4,797mm / 188.9 in
Model Y L
3,040mm / 119.7 in
4,976mm / 195.9 in
Model X
2,965mm / 116.7 in
5,060mm / 199.2 in
The new wheelbase of 3,040mm is a significant stretch from the standard wheelbase, and in fact, is longer than the Model X’s wheelbase of 2,965mm. However, the overall length of the vehicle is 84mm (~3 inches) shorter than the Model X. This means the vehicle sits neatly between the current Model Y and Model X, filling a much-needed gap.
While this Model Y L is slightly smaller than the Model X, it doesn’t necessarily mean that it’s smaller inside. The Model X features a much larger front end than the Model Y, accounting for several inches. When you line up the front wheel base of the Model X with this new Model Y, the vehicles are almost exactly the same length.
Tesla has designed this Model Y to be a bit more compact and efficient than the Model X, and likely much cheaper, while featuring the well-loved design of the new Model Y.
Other Specifications and Price
The MIIT filing also provided a detailed look at some additional specifications. The Model Y L is a dual-motor, AWD variant, so it will likely be more expensive than the current Model Y AWD that’s available in China today. Tesla charges an additional $6,500 USD when upgrading the Model X from a 5-seat configuration to a 6-seat layout, so we may see something similar here.
The extra length has been added behind the C-pillar, resulting in a longer rear profile for the Model Y L. To accompany this, Tesla has added an updated rear spoiler, similar to the one found on Performance variants, but not carbon fiber. There is also a new wheel design to complement the updated look, along with unique Model Y L badging and a new light gold paint option.
In classic Tesla fashion, no Tesla is slow - and the Y L has a 0-100 km/h (0-60mph) time of 5.9s, with a top speed of 217km/h. Alongside an 82.5 kWh LFP battery pack, the Model Y L boasts an impressive CLTC range of 688 km (427 mi).
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Launch & Availability
According to posts from Tesla China on Chinese social media, the new Model Y L is scheduled to launch in the fall of 2025. Its official listing in the MIIT database is essentially the final regulatory step required before sales can begin, which means the launch is really just around the corner. For now, it appears that Tesla intends to launch this vehicle only in China, as no other filings have been made in other regions. However, these could be revealed in the coming months.
The new Model Y L is a huge addition to Tesla’s lineup - one that addresses the Chinese preference for vehicles with longer wheelbases and additional passenger room in a compact SUV package. The question is - will this variant make its way to North America and Europe?
Solving real-world artificial intelligence - whether for autonomous driving, real-world robotics, or advanced reasoning - requires an almost unfathomable amount of computational power. To meet this challenge, Tesla has been developing its own custom AI training hardware while simultaneously purchasing hardware in the open market.
Now, the next-generation Dojo 2 chip has reportedly entered mass production with the world’s largest semiconductor manufacturer, TSMC. While many may consider this a side quest, expanding Tesla’s computing base will be necessary to achieve exascale supercomputing, which will be crucial for all of Tesla’s AI ambitions.
Elon Musk called Dojo 2 “a good computer,” and then followed up with a classic computer performance joke - Dojo 2 can indeed play Crysis at a billion frames per second.
While Tesla has effectively utilized powerful third-party GPUs to train its models to date, the Dojo supercomputer is a ground-up, application-specific solution designed for a single purpose. It will efficiently process massive amounts of video data for training neural networks. The Dojo 2 chip itself is the key that unlocks this potential.
Dojo 2 will train the vision-based neural nets that FSD relies on, allowing Tesla to process video from its massive global fleet of vehicles even faster. As Tesla continues to improve FSD, one of the biggest challenges has been the intake of video for handling difficult edge cases.
Hundreds of thousands of miles of training data may pass by before an edge case is identified and trained on, but it all needs to be analyzed, labeled, and processed, which is key for Dojo 2. Each new useful piece of training data will help Tesla proceed down the march of 9s, making FSD just that little bit better every time.
This process requires massive amounts of compute and training time - but it is an absolute necessity to improve FSD. Of course, this goes beyond just FSD in vehicles. Tesla’s humanoid robot, Optimus, also runs on FSD to navigate and interact with the physical world.
While it may be a custom version of FSD, it remains FSD at its core, which means the same neural nets that analyze the environment and build a 3D map of the world for your car perform the same work for Optimus.
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Custom Approach to AI Hardware
Dojo 2’s power doesn’t just come from raw compute; it comes from a series of architectural choices that make it excel at training FSD and differentiate it from general-purpose hardware, or even other AI-specific hardware.
To this end, Tesla is using TSMC’s new Integrated Fan-Out with Silicon-on-Wafer (InFO-SoW) packaging technology. For massive AI workloads, heat and the speed at which data moves between chips are often the biggest bottlenecks.
This new packaging technique allows for high-bandwidth connections directly between processing dies, which lowers latency and dramatically improves heat dissipation, all key to building massive and dense compute clusters.
Unlike general-purpose chips, Dojo 2 is designed with a custom instruction set, specifically built to train FSD. The cores are specifically made to accelerate the exact mathematical operations, like matrix multiples and systolic arrays, which form the backbone of Tesla’s vision-based neural networks.
By building its own hardware, Tesla can then integrate its own software and compilers directly with the silicon, optimizing for specific workloads and avoiding the performance penalties that can result from using third-party software, such as Nvidia’s CUDA.
The start of Dojo 2 may seem like a side quest for some, but it’s actually a key step for Tesla’s AI technologies that give them an advantage over the competition using off-the-shelf hardware. They’ll need to continue investing in custom hardware to improve FSD at a reasonable pace, rather than the current glacial pace we’ve seen over the last few months.