In a stunning achievement, Hansjörg von Gemmingen’s Model S has reached a jaw-dropping 2 million kilometers (1.25 million miles) of distance traveled. He hit this milestone during a cross-country trip from northern Morocco to Mauritania.
This is a remarkable achievement, and we’ll take a deep dive into Hansjörg’s story and experience with having such a long-lived Tesla.
The 2 Millionth Kilometer
Hansjörg’s journey began in the northern Moroccan city of Tangier. Nestled on the coast of the Atlantic Ocean, Tangier is a bustling metropolis known for its vibrant culture, scenic ocean views, and modern infrastructure, including an international airport and a thriving business district. As a seasoned traveler and EV enthusiast, Hansjörg has been all across Morocco, embracing its diverse landscapes and challenging terrains.
Rallye Rive Maroc
His adventures included participating in the Rallye Rive Maroc, a grueling 7-day electric vehicle rally. This event is designed to push both drivers and their vehicles to the limit, with participants covering approximately 300 kilometers a day on three of the seven days. The rally navigates through harsh deserts, rugged mountains, and remote locations, presenting a formidable challenge that tests the vehicles' endurance, skill, and durability. The routes are not only demanding but also showcase the breathtaking beauty of Morocco's natural landscapes.
Rallye Rive Maroc's participants in 2023.
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In his latest endeavor, Hansjörg set out from Tangier with the ambitious goal of reaching the 2-million-kilometer mark. His route took him across Morocco, heading towards the southern border with Mauritania. This milestone journey was more than just a personal achievement; it represented a significant moment for Tesla owners worldwide, demonstrating the potential and reliability of electric vehicles over extensive distances and diverse conditions.
Reaching the 2-million-kilometer milestone during this trip highlighted the endurance of his 2013 Tesla Model S, a testament to both Tesla’s amazing engineering and Hansjörg's dedication to pushing the boundaries of electric vehicle travel. His journey serves as an inspiring example for EV enthusiasts and a beacon of what is possible with determination, innovation, and the right infrastructure.
Battery Life
Of course, electric vehicle batteries do inevitably degrade, and Tesla is no exception. However, they’ve proven their longevity – with reports showing only 15% degradation at 350,000km (200,000 mi). Hansjörg drives a 2013 Tesla Model S 85+, with a single motor. He’s seen several battery replacements throughout the vehicle’s lifespan, but the vehicle is still going strong. Keep in mind Tesla had only begun producing the Model S in 2012. It was Tesla’s first vehicle that was designed and created in-house.
1. Hansjörg replaced the first battery at 290,000 km (180k miles)
2. The second battery lasted 670,000 km (416k miles), before being replaced due to cell in-balance – it had suffered only a 20% loss of range
3. The third battery lasted for 550,000 km (341k miles)
4. The vehicle is currently on its fourth battery, which has already clocked 250,000 km (155k miles), but remains going strong
There is a 150,000 km gap above, which was due to the vehicle using a temporary loaner battery offered by Tesla while the vehicle’s battery was repaired.
These are some amazing numbers to see – real-life battery degradation under high-stress conditions, over a decade. Tesla, vehicle owners, and future customers couldn’t ask for a better data set in this case.
The two million kilometer Model S, in all its glory.
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Charging in Morocco and Africa
Ali Lakrakbi, who brought this amazing feat to our attention, operates a network of EV charges in Morocco. He’s been working on opening an electric road – one lined with EV charging stations – from Morocco to the rest of Africa. This is an amazing initiative and one that is bound to help Africa replace its mobility access with EVs. Promoting EV adoption is difficult enough in North America, Europe, and Asia, but doing so in Africa is leagues harder, and a feat worthy of admiration.
Currently, Ali's network includes a significant number of slower AC (Level 1) chargers, which provide essential charging options for travelers. These chargers are crucial for establishing the foundational infrastructure needed to support the initial wave of EV users in regions where such facilities are scarce.
However, Ali does recognize the need for faster and more efficient charging solutions, and he is actively working to increase the availability of faster DC (Level 2 and Level 3) chargers. These high-speed chargers (such as Superchargers) can significantly reduce charging times, making long-distance travel more practical and appealing for EV owners.
The development of this electric road is more than just a technical achievement; it represents a vision for a connected and accessible Africa. By facilitating easier and quicker EV charging, Ali's project aims to encourage more people to switch to electric vehicles, thus reducing the continent's reliance on fossil fuels and lowering greenhouse gas emissions. Moreover, this network of chargers can stimulate economic growth by creating new business opportunities and fostering a green technology sector.
Ali hopes to one day see Superchargers like these in Morocco.
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Some African Challenges
Ali has faced some challenges in establishing his network in Morocco and Africa, given the lack of EV adoption there. People have made fun of EV owners – who have not had much success before Ali in deploying chargers, even free of charge – to locations. However, Ali has incentivized the process by demonstrating the benefits – which in part helps bring in travelers who will spend money locally.
Even getting basic chargers installed in cities can be difficult – much less the high-power capabilities needed for something like an L3 Supercharger. An L2 charger in Morocco can cost as much as an L3 in Europe or North America, but the charging network is beginning to spread. One positive note is that the weather is milder – no deep winters – this means that vehicles have better range even without preconditioning.
Another challenge is the lack of subsidies in Africa. Europe and North America have plenty of subsidies. Ali identified that vehicles are considered luxury items in Africa – and providing subsidies for EVs is next to impossible considering other challenges faced. However, he mentions tax subsidies may be a good option in Morocco – and Africa in general.
Since adoption is slower In Morocco and Africa, Ali and others have one big advantage – they get to learn from North America’s and Europe’s mistakes. Adoption is smoother – their chargers and vehicles use a single standard, and they don’t have challenges with RFID cards or other government regulations.
The shortest point between Morocco and Spain is just 9 miles
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It is also possible to import Teslas from Europe since Morocco is so close to Spain. Many Moroccans also work in Europe and return, and vice versa. There are plenty of Moroccans who experience EVs in Europe as the growth for EVs grows in the country. Ali finances his charger deployments through the sale of Teslas to Moroccans.
However, Tesla doesn’t send parts to Morocco meaning it requires a chain of third-party companies to send over any parts necessary. It can be difficult since Tesla doesn’t officially support the vehicle in the country. Ali hopes that one day Tesla will support owners in Africa, not only with parts but also with training personnel and navigation data as well.
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.
