Tesla has filed four patents for wireless charging. Previously, Tesla acquired the experience and skills of Wiferion’s engineering team in an acquisition in June 2023. Tesla quickly sold off the company but kept the company’s engineers. This was an acqui-hire, where large companies rapidly onboard top talent by purchasing a company for human capital, rather than products, services, or intellectual products.
Following the Wiferion acquisition, Tesla showed off a wireless charging mat several times and even confirmed they are working on wireless inductive home charging. We expected this to arrive with the Cybertruck, but now we hope to see more details at the upcoming Robotaxi reveal event in October, at the Warner Bro.’s studio in Hollywood.
Wireless Charging Patents
First Patent
The first of the four patents looks at temperature sensors and applications for wireless charging. Tesla intends to track the temperatures across both the wireless charging pad, as well as the inductive charging coils in the vehicle. The third patent details how they’re planning to use this in an innovative way.
WIPO
Second Patent
The second patent is a shorting switch, which is designed to prevent or reduce leakage of energy to ground sources near the wireless charging mat. This shorting switch is used to create a short with the receiving side of the inductive charging, essentially making energy transfer faster and more efficient – while also reducing the amount of energy that is leaked out elsewhere. We’ll leave this one for the electrical engineers to explain in our forums.
Third Patent
The third patent is actually the wireless charging circuit itself, and how to manufacture it. This one is straightforward and explains how Tesla intends to design and build the prototype inductive wireless charging mats. Beyond the basic design, this document also details how Tesla intends to lay out the inductive charging coils and how it expects the entire assembly to function – which includes a small cable that runs to a wall-mounted energy source.
Fourth Patent
The final patent is for intelligently estimating and adapting to variations in wireless charging layouts and systems. This one is probably the most interesting of the four patents, as it involves adjusting the coils in the mat to dynamically account for the positioning of the vehicle, as well as responding to the information from the first patent – the temperature sensors.
Essentially, by varying the coil induction across the mat, Tesla intends to dynamically aim the wireless charger at different parts of the inductive coils in the vehicle, thereby reducing heat. This patent will also help Tesla to determine the adequate charge speed and other key information they may need to relay to the vehicle, or back to fleet management software.
Cybertruck Inductive Charging
While the Cybertruck never actually arrived with inductive charging, it was noted in the Service Manual to have 2 Inductive Charging Headers, so it is ready to be upgraded to have Inductive Charging support. We expect that Tesla will release this as a purchasable upgrade for the Cybertruck sometime in the future – likely as a package with the Inductive Charging Mat.
Robotaxi Inductive Charging
The Robotaxi will likely be the first of Tesla’s vehicles to feature inductive charging – which. makes sense given its autonomous nature. Plugging in a NACS charger will likely be fairly difficult to automate, and the 5-10% efficiency loss of wireless charging isn’t a huge deal. It’s possible Tesla chose this route due to cost, as having an automated NACS cable like the “snake” prototype we saw years ago could be costly.
Having a simple parking stall with an inductive mat on it, alongside all the tech and supplies needed to automatically clean and sanitize the Robotaxi makes far more sense. This means that a Robotaxi will be able to roll in, charge and clean itself, and then roll out to go pick up more customers. Tesla is planning a super-efficient system here that requires minimal intervention from real humans.
Essentially, by effectively managing the Robotaxi fleet, Tesla can offset the increased time that wireless charging would take, and also reduce the amount of waste energy by charging when energy is cheapest. While this could prevent Tesla from taking advantage of the sheer battery mass of a Robotaxi fleet – by selling power when it’s expensive and travel demand is low – this doesn’t make it impossible.
Smartphone manufacturers have demonstrated reverse wireless charging with the inductive charging coils already present in modern smartphones. We could see Tesla low-power bi-directional charging in the future too.
We’re looking forward to seeing Tesla unveil this at the upcoming Robotaxi event. It’ll be something special, for sure.
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Tesla launched two FSD updates simultaneously on Saturday night, and what’s most interesting is that they arrived on the same software version. We’ll dig into that a little later, but for now, there’s good news for everyone. For Hardware 3 owners, FSD V12.6.1 is launching to all vehicles, including the Model 3 and Model Y. For AI4 owners, FSD V13.2.4 is launching, starting with the Cybertruck.
FSD V13.2.4
A new V13 build is now rolling out to the Cybertruck and is expected to arrive for the rest of the AI4 fleet soon. However, this build seems to be focused on bug fixes. There are no changes to the release notes for the Cybertruck with this release, and it’s unlikely to feature any changes when it arrives on other vehicles.
FSD V12.6.1 builds upon V12.6, which is the latest FSD version for HW3 vehicles. While FSD V12.6 was only released for the redesigned Model S and Model X with HW3, FSD V12.6.1 is adding support for the Model 3 and Model Y.
While this is only a bug-fix release for users coming from FSD V12.6, it includes massive improvements for anyone coming from an older FSD version. Two of the biggest changes are the new end-to-end highway stack that now utilizes FSD V12 for highway driving and a redesigned controller that allows FSD to drive “V13” smooth.
It also adds speed profiles, earlier lane changes, and more. You can read our in-depth look at all the changes in FSD V12.6.
Same Update, Multiple FSD Builds
What’s interesting about this software version is that it “includes" two FSD updates, V12.6.1 for HW3 and V13.2.4 for HW4 vehicles. While this is interesting, it’s less special when you understand what’s happening under the hood.
The vehicle’s firmware and Autopilot firmware are actually completely separate. While a vehicle downloading a firmware update may look like a singular process, it’s actually performing several functions during this period. First, it downloads the vehicle’s firmware. Upon unpacking the update, it’s instructed which Autopilot/FSD firmware should be downloaded.
While the FSD firmware is separate, the vehicle can’t download any FSD update. The FSD version is hard-coded in the vehicle’s firmware that was just downloaded. This helps Tesla keep the infotainment and Autopilot firmware tightly coupled, leading to fewer issues.
What we’re seeing here is that HW3 vehicles are being told to download one FSD version, while HW4 vehicles are being told to download a different version.
While this is the first time Tesla has had two FSD versions tied to the same vehicle software version, the process hasn’t actually changed, and what we’re seeing won’t lead to faster FSD updates or the ability to download FSD separately. What we’re seeing is the direct result of the divergence of HW3 and HW4.
While HW3/4 remained basically on the same FSD version until recently, it is now necessary to deploy different versions for the two platforms. We expect this to be the norm going forward, where HW3 will be on a much different version of FSD than HW4. While each update may not include two different FSD versions going forward, we may see it occasionally, depending on which features Autopilot is dependent on.
Thanks to Greentheonly for helping us understand what happened with this release and for the insight into Tesla’s processes.
At the 2025 Consumer Electronics Show, Nvidia showed off its new consumer graphics cards, home-scale compute machines, and commercial AI offerings. One of these offerings included the new Nvidia Cosmos training system.
Nvidia is a close partner of Tesla - in fact, they produce and supply the GPUs that Tesla uses to train FSD - the H100s and soon-to-be H200s, located at the new Cortex Supercomputing Cluster at Giga Texas. Nvidia will also challenge Tesla’s lead in developing and deploying synthetic training data for an autonomous driving system - something Tesla is already doing.
However, this is far more important for other manufacturers. We’re going to take a look at what Nvidia is offering and how it compares to what Tesla is already doing. We’ve done a few deep dives into how Tesla’s FSD works, how Tesla streamlines FSD, and, more recently, how they optimize FSD. If you want to get familiar with a bit of the lingo and the background knowledge, we recommend reading those articles before continuing, but we’ll do our best to explain how all this synthetic data works.
Nvidia Cosmos
Nvidia’s Cosmos is a generative AI model created to accelerate the development of physical AI systems, including robots and autonomous vehicles. Remember - Tesla’s FSD is also the same software that powers their humanoid robot, Optimus. Nvidia is aiming to tackle physical, real-world deployments of AI anywhere from your home, your street, or your workplace, just like Tesla.
Cosmos is a physics-aware engine that learns from real-world video and builds simulated video inputs. It tokenizes data to help AI systems learn quicker, all based on the video that is input into the system. Sound familiar? That’s exactly how FSD learns as well.
Cosmos also has the capability to do sensor-fused simulations. That means it can take multiple input sources - video, LiDAR, audio, or whatever else the user intends, and fuse them together into a single-world simulation for your AI model to learn from. This helps train, test, and validate autonomous vehicle behavior in a safe, synthetic format while also providing a massive breadth of data.
Data Scaling
Of course, Cosmos itself still requires video input - the more video you feed it, the more simulations it can generate and run. Data scaling is a necessity for AI applications, as you’ll need to feed it an infinite amount of data to build an infinite amount of scenarios for it to train itself on.
Synthetic data also has a problem - is it real? Can it predict real-world situations? In early 2024, Elon Musk commented on this problem, noting that data scales infinitely both in the real world and in simulated data. A better way to gather testing data is through real-world data. After all, no AI can predict the real world just yet - in fact, that’s an excellent quantum computing problem that the brightest minds are working on.
Yun-Ta Tsai, an engineer at Tesla’s AI team, also mentioned that writing code or generating scenarios doesn’t cover what even the wildest AI hallucinations might come up with. There are lots of optical phenomena and real-world situations that don’t necessarily make sense in the rigid training sets that AI would develop, so real-world data is absolutely essential to build a system that can actually train a useful real-world AI.
Tesla has billions of miles of real-world video that can be used for training, according to Tesla’s Social Media Team Lead Viv. This much data is essential because even today, FSD encounters “edge cases” that can confuse it, slow it down, or render it incapable of continuing, throwing up the dreaded red hands telling the user to take over.
Cosmos was trained on approximately 20 million hours of footage, including human activities like walking and manipulating objects. On the other hand, Tesla’s fleet gathers approximately 2,380 recorded minutes of real-world video per minute. Every 140 hours - just shy of 6 days - Tesla’s fleet gathers 20 million hours of footage. That was a little bit of back-of-the-napkin math, calculated at 60 mph as the average speed.
Generative Worlds
Both Tesla’s FSD and Nvidia’s Cosmos can generate highly realistic, physics-based worlds. These worlds are life-like environments and simulate the movement of people and traffic and the real-life position of obstacles and objects, including curbs, fences, buildings, and other objects.
Tesla uses a combination of real-world data and synthetic data, but the combination of data is heavily weighted to real-world data. Meanwhile, companies who use Cosmos will be weighting their data heavily towards synthetically created situations, drastically limiting what kind of cases they may see in their training datasets.
As such, while generative worlds may be useful to validate an AI quickly, we would argue that these worlds aren’t as useful as real-world data to do the training of an AI.
Overall, Cosmos is an exciting step - others are clearly following in Tesla’s footsteps, but they’re extremely far behind in real-world data. Tesla has built a massive first-mover advantage in AI and autonomy, and others are now playing catch-up.
We’re excited to see how Tesla’s future deployment of its Dojo Supercomputer for Data Labelling adds to its pre-existing lead, and how Cortex will be able to expand, as well as what competitors are going to be bringing to the table. After all, competition breeds innovation - and that’s how Tesla innovated in the EV space to begin with.
