Tesla has pioneered the use of single-piece castings for the front and rear sections of their vehicles, thanks to its innovative Gigapress process. Many automakers are now following suit, as this approach allows the crash structure to be integrated directly into the casting.
This makes the castings not only safer but also easier to manufacture in a single step, reducing costs and improving repairability. For example, replacing the entire rear frame of a Cybertruck is estimated to cost under $10,000 USD, with most of the expense coming from labor, according to estimates shared on X after high-speed rear collisions.
These insights come from Sandy Munro’s interview (posted below) with Lars Moravy, Tesla’s VP of Vehicle Engineering, highlighting how these advancements contribute to the improvements in Tesla’s latest vehicles, including the New Model Y.
However, with the new Model Y, Tesla has decided to go a different route and eliminated the front gigacast.
No Front Casting
Tesla’s factories aren’t equipped to produce both front and rear castings for the Model Y. Only Giga Texas and Giga Berlin used structural battery packs, but these were quickly phased out due to the underwhelming performance of the first-generation 4680 battery.
Tesla has gone back to building a common body across the globe, increasing part interchangeability and reducing supply chain complexity across the four factories that produce the Model Y. They’ve instead improved and reduced the number of unique parts up front to help simplify assembly and repair.
There is still potential for Tesla to switch back to using a front and rear casting - especially with their innovative unboxed assembly method. However, that will also require Tesla to begin using a structural battery pack again, which could potentially happen in the future with new battery technology.
Rear Casting Improvements
The rear casting has been completely redesigned, shedding 7 kg (15.4 lbs) and cutting machining time in half. Originally weighing around 67 kg (147 lbs), the new casting is now approximately 60 kg (132 lbs).
This 15% weight reduction improves both vehicle dynamics and range while also increasing the rear structure’s stiffness, reducing body flex during maneuvers.
Tesla leveraged its in-house fluid dynamics software to optimize the design, resulting in castings that resemble organic structures in some areas and flowing river patterns in others. Additionally, manufacturing efficiency has dramatically improved—the casting process, which originally took 180 seconds per part, has been reduced to just 75 seconds, a nearly 60% time reduction per unit.
Advancements in die-casting machines and cooling systems have allowed @Tesla to dramatically reduce cycle times and improve dimensional stability. pic.twitter.com/WB5ji67rvV
Tesla’s new casting method incorporates conformal cooling, which cools the die directly within the gigapress. Tesla has been refining the die-casting machines and collaborating with manufacturers to improve the gigapress process.
In 2023, Tesla patented a thermal control unit for the casting process. This system uses real-time temperature analysis and precise mixing of metal streams to optimize casting quality. SETI Park, which covers Tesla’s manufacturing patents on X, offers a great series for those interested in learning more.
The new system allows Tesla to control the flow of cooling liquid, precisely directing water to different parts of the die, cooling them at varying rates. This enables faster material flow and quicker cooling, improving both dimensional stability and the speed of removing the part from the press for the next stage.
With these new process improvements, Tesla now rolls out a new Model Y at Giga Berlin, Giga Texas, and Fremont every 43 seconds—an astounding achievement in auto manufacturing. Meanwhile, Giga Shanghai operates two Model Y lines, delivering a completed vehicle every 35 seconds.
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With Tesla’s first major expansion of the Robotaxi Geofence now complete and operational, they’ve been hard at work with validation in new locations - and some are quite the drive from the current Austin Geofence.
Validation fleet vehicles have been spotted operating in a wider perimeter around the city, from rural roads in the west end to the more complex area closer to the airport. Tesla mentioned during their earnings call that the Robotaxi has already completed 7,000 miles in Austin, and it will expand its area of operation to roughly 10 times what it is now. This lines up with the validation vehicles we’ve been tracking around Austin.
Based on the spread of the new sightings, the potential next geofence could cover a staggering 450 square miles - a tenfold increase from the current service area of roughly 42 square miles. You can check this out in our map below with the sightings we’re tracking.
If Tesla decides to expand into these new areas, it would represent a tenfold increase over their current geofence, matching Tesla’s statement. The new area would cover approximately 10% of the 4,500-square-mile Austin metropolitan area. If Tesla can offer Robotaxi services in that entire area, it would prove they can tackle just about any city in the United States.
From Urban Core to Rural Roads
The locations of the validation vehicles show a clear intent to move beyond the initial urban and suburban core and prepare the Robotaxi service for a much wider range of uses.
In the west, validation fleet vehicles have been spotted as far as Marble Falls - a much more rural environment that features different road types, higher speed limits, and potentially different challenges.
In the south, Tesla has been expanding towards Kyle, which is part of the growing Austin-San Antonio suburban corridor spanning Highway 35. San Antonio is only 80 miles (roughly a 90-minute drive) away, and could easily become part of the existing Robotaxi area if Tesla obtains regulatory approval there.
In the East, we haven’t spotted any new validation vehicles. This is likely because Tesla’s validation vehicles originate from Giga Texas, which is located East of Austin. We won’t really know if Tesla is expanding in this direction until they start pushing past Giga Texas and toward Houston.
Finally, there have been some validation vehicles spotted just North of the new expanded boundaries, meaning that Tesla isn’t done in that direction either. This direction consists of the largest suburban areas of Austin, which have so far not been serviced by any form of autonomous vehicle.
Rapid Scaling
This new, widespread validation effort confirms what we already know. Tesla is pushing for an intensive period of public data gathering and system testing in a new area, right before conducting geofence expansions. The sheer scale of this new validation zone tells us that Tesla isn’t taking this slowly - the next step is going to be a great leap instead, and they essentially confirmed this during this Q&A session on the recent call. The goal is clearly to bring the entire Austin Metropolitan area into the Robotaxi Network.
While the previous expansion showed off just how Tesla can scale the network, this new phase of validation testing is a demonstration of just how fast they can validate and expand their network. The move to validate across rural, suburban, and urban areas simultaneously shows their confidence in these new Robotaxi FSD builds.
Eventually, all these improvements from Robotaxi will make their way to customer FSD builds sometime in Q3 2025, so there is a lot to look forward to.
For years, the progress of Tesla’s FSD has been measured by smoother turns, better lane centering, and more confident unprotected left turns. But as the system matures, a new, more subtle form of intelligence is emerging - one that shifts its attention to the human nuances of navigating roads. A new video posted to X shows the most recent FSD build, V13.2.9, demonstrating this in a remarkable real-world scenario.
Toll Booth Magic
In the video, a Model Y running FSD pulls up to a toll booth and smoothly comes to a stop, allowing the driver to handle payment. The car waits patiently as the driver interacts with the attendant. Then, at the precise moment the toll booth operator finishes the transaction and says “Have a great day”, the vehicle starts moving, proceeding through the booth - all without any input from the driver.
If you notice, there’s no gate here at this toll booth. This interaction all happened naturally with FSD.
While the timing was perfect, the FSD wasn’t listening to the conversation for clues (maybe one day, with Grok?) The reality, as explained by Ashok Elluswamy, Tesla’s VP of AI, is even more impressive.
It can see the transaction happening using the repeater & pillar cameras. Hence FSD proceeds on its own when the transaction is complete 😎
FSD is simply using the cameras on the side of the vehicle to watch the exchange between the driver and attendant. The neural network has been trained on enough data that it can visually recognize the conclusion of a transaction - the exchange of money or a card and the hands pulling away - and understands that this is the trigger to proceed.
The Bigger Picture
This capability is far more significant than just a simple party trick. FSD is gaining the ability to perceive and navigate a world built for humans in the most human-like fashion possible.
If FSD can learn what a completed toll transaction looks like, it’s an example of the countless other complex scenarios it’ll be able to handle in the future. This same visual understanding could be applied to navigating a fast-food drive-thru, interacting with a parking garage attendant, passing through a security checkpoint, or boarding a ferry or vehicle train — all things we thought that would come much later.
These human-focused interactions will eventually become even more useful, as FSD becomes ever more confident in responding to humans on the road, like when a police officer tells a vehicle to go a certain direction, or a construction worker flags you through a site. These are real-world events that happen every day, and it isn’t surprising to see FSD picking up on the subtleties and nuances of human interaction.
This isn’t a pre-programmed feature for a specific toll booth. It is an emergent capability of the end-to-end AI neural nets. By learning from millions of videos across billions of miles, FSD is beginning to build a true contextual understanding of the world. The best part - with a 10x context increase on its way, this understanding will grow rapidly and become far more powerful.
These small, subtle moments of intelligence are the necessary steps to a truly robust autonomous system that can handle the messy, unpredictable nature of human society.
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