Exxon Mobil is in talks with Tesla and other automakers to become a lithium supplier
The Carter Center
For years Elon Musk has been urging entrepreneurs to get into lithium mining. It appears someone was listening, but it's not a start-up. Exxon Mobil, the oil and gas giant and a name synonymous with the fossil fuel industry is reportedly engaged in early-stage discussions with Tesla and other automakers, including Ford and Volkswagen, to become a supplier of lithium.
Lithium is a key component in electric vehicle batteries. Tesla recently broke ground on its own lithium mining operation to address the critical element. Now that a major oil player is getting in the game, it signifies a significant shift in Exxon's strategic outlook as it embraces the inevitable transition towards more sustainable energy sources.
Harnessing Lithium: Exxon's Answer to EV Growth
A report from Bloomberg states that Exxon has been actively exploring the lithium business to diversify beyond fossil fuels. Its recent initiative involves the development of over 6,100 acres of lithium-rich land in Arkansas in partnership with Tetra Technologies Inc. The oil giant's strategic venture into the lithium sector underscores its commitment to securing the assets needed for EV battery production.
This move isn't merely a diversification strategy for Exxon; it presents a timely response to the meteoric rise of the EV sector. The increasing adoption of EVs has amplified the demand for lithium, posing a significant challenge to Exxon's core oil production and refining businesses. In turn, Exxon seeks to secure its position by harnessing a vital resource in this emerging market.
The company's entrance into the lithium market is not just about survival but also about leveraging its industry expertise for new business opportunities. Exxon has been considering extracting lithium from underground saltwater. This method aligns with the company's extensive experience in oil and gas extraction while also promising to be more cost-effective and environmentally friendly.
A Turning Point in the Energy Landscape
While the details of the discussions remain confidential, Exxon's engagement with Tesla and other automotive heavyweights signals a remarkable convergence of interests. By teaming up with major automakers, Exxon is positioning itself as a critical link between traditional and renewable energy sectors. This cooperation could foster a mutually beneficial relationship that bridges the gap between these diverse energy domains, encouraging knowledge sharing and expediting the adoption of sustainable practices.
Though Exxon has yet to announce whether it will independently undertake lithium production or seek partnerships, its ambitious goal of extracting 100,000 tons of lithium annually attests to its commitment to becoming a significant player in the lithium business. The corporation is also reportedly in talks with other lithium market participants, including Albemarle Corp, further emphasizing its intent to become a leading figure in the EV revolution.
Exxon Mobil's potential collaboration with Tesla and other automakers represents more than a strategic pivot; it is an emblematic turning point in the energy landscape, marking an era where traditional oil companies and EV manufacturers might work hand-in-hand to accelerate the transition towards a more sustainable future.
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In our continued series exploring Tesla’s patents, we’re taking a look at how Tesla automates data labeling for FSD. This is Tesla patent WO2024073033A1, which outlines a system that could revolutionize how Tesla trains FSD.
We’ll be approaching this article the same way as others in the past, by breaking it down into easily digestible portions.
Training a sophisticated AI model like FSD requires a tremendous amount of data. But all of that data needs to be labeled - and traditionally, this process has been done manually. Human reviewers have to go in and categorize and tag hundreds of thousands of data points across millions of hours of video.
This isn’t just laborious and rote work, it's time consuming, expensive, and prone to human error. The perfect job to hand off to AI.
Tesla’s Automated Solution
Tesla’s patent introduces a model-agnostic system for automated data labeling. Just like their previous patent on the Universal Translator, this will function for any AI model - but FSD is really what it is for.
The system works by leveraging the vast amounts of data collected by Tesla’s fleet to create a 3D model of the environment, which is then automatically used to label new data.
Three Step Process
This process has three steps, so we’ll look at each individually.
High-Precision Mapping
The system starts by creating a highly accurate 3D map of the environment. This involves fusing data from multiple Tesla vehicles equipped with cameras, radar, and other sensors. The map includes detailed information about roads, lane markings, buildings, trees, and other static objects.
It's like creating a digital twin of the real world, and this is exactly the simulation data that Tesla uses to rapidly test FSD. The system continuously improves its accuracy as it processes more data and also generates better synthetic data to augment the training dataset.
Multi-Trip Reconstruction
To refine the 3D model and capture dynamic elements of the environment, the system analyzes data from multiple trips through the same area. This allows it to identify moving objects, track their trajectories, and understand how they interact with the static environment. This way, you have a dynamic, living 3D world that also captures the ebb and flow of traffic and pedestrians.
Automated Labelling
Once the 3D model is sufficiently detailed, it becomes the key to automated labeling. When a Tesla vehicle encounters a new scene, the system compares the real-time sensor data with the existing 3D model. This allows it to automatically identify and label objects, lane markings, and other relevant features in the new data.
Benefits
There are three simple benefits to this system, which is what makes it so valuable.
It is far more efficient. Automated data labeling drastically reduces the time and resources required to prepare training data for AI models. This accelerates development cycles and allows Tesla to train its AI on much larger datasets.
It is also scalable. This system can handle massive datasets derived from millions of miles of driving data collected by Tesla's fleet. As the fleet grows and collects more data, the 3D models become even more detailed and accurate, further improving the automated labeling process.
Finally, it is accurate. By eliminating human error and bias, automated labeling improves the accuracy and consistency of the labeled data. This leads to more robust and reliable AI models. Of course, human review is still involved, but that’s only to catch and flag errors.
Applications
While this technology has significant implications for FSD, Tesla can use this automated labeling system to train AI models for various tasks.
Object detection and classification: Accurately identifying and categorizing objects in the environment, such as vehicles, pedestrians, traffic signs, and obstacles.
Kinematic analysis: Understanding the motion and behavior of objects, predicting their trajectories, and anticipating potential hazards.
Shape analysis: Recognizing the shapes and structures of objects, even when partially obscured or viewed from different angles.
Occupancy and surface detection: Creating detailed maps of the environment, identifying occupied and free space, and understanding the properties of different surfaces (e.g., road, sidewalk, grass).
These different applications are all used by Tesla - which uses different AI subnets to analyze all these different things before feeding them into the greater model that is FSD, which means things like pedestrians, lane markings, and traffic controls are all labeled on-vehicle.
In a Nutshell
Tesla's automated data labeling system is a game-changer for AI development. By leveraging the power of its fleet and 3D mapping technology, Tesla has created a self-learning system that continuously improves its ability to understand and navigate the world.
Imagine a world where self-driving cars can label and understand the world around them without human help. This patent describes a system that could make that possible. It uses data collected from many Tesla vehicles to create a 3D model of the environment, which is like a virtual copy of the real world.
This 3D model is then used to label new images and sensor data, eliminating most needs for human intervention. The system can recognize objects, lane markings, and other important features, making it easier to train AI models.
Back in 2021, while Giga Berlin was still undergoing construction, Elon Musk said that he wanted to fill the factory with graffiti artwork. Just months later, Tesla posted a submission link to find local artists for the project.
It remained relatively quiet for about two years until Musk resurfaced with a post congratulating the team on their progress—and revealing that the factory’s concrete would be entirely covered in art. By 2023, that vision was already taking shape. Tesla began by collaborating with local artists, who created much of the artwork seen in the 2023 image above.
The Giga Berlin West Side in 2023
Not a Tesla App
Graffiti at Scale
As expected from Tesla, they didn’t just hire a group of artists to paint and scale the walls. True to their ethos of autonomy, robotics, and innovation, they sought a more futuristic approach. The local crews couldn’t work fast enough or cover enough ground, so Tesla did what it does best—push the boundaries of technology.
Covering an entire factory in art is a massive undertaking, especially when that factory spans 740 acres (1.2 sq mi / 3 km²). With such an immense canvas, Tesla needed a high-tech solution.
More of the awesome digital artwork
@tobilindh on X
Enter a graffiti start-up that had developed a robotic muralist. Tesla partnered with the company, sourcing digital artwork from independent artists while also commissioning pieces from its in-house creative team. Armed with this collection, the robot meticulously printed the artwork directly onto the factory’s concrete, turning Gigafactory Berlin-Brandenburg into a futuristic masterpiece.
The Robot
Not a Tesla App
This ingenious little robot is equipped with a precision printhead and a sophisticated lifting mechanism. It moves using two kevlar cables that allow it to glide up, down, left, and right while a pair of propellers generates downforce to keep it steady against the wall.
The printhead itself is capable of painting approximately 10 million tiny dots per wall, adding up to a staggering 300 million dots just for the west-facing side of Giga Berlin. Each mural features five distinct colors, and the robot carries 12 cans of paint, ensuring it can keep working for extended periods without interruption.
Check out the video below to see the robot action, along with mesmerizing time-lapse footage of the printing process. It’s an exciting glimpse into how Tesla is blending technology and creativity at Giga Berlin—and we can’t wait to see what’s next.
![Tesla’s Giga Berlin Artwork: Where Creativity Meets Autonomy [VIDEO]](https://www.notateslaapp.com/img/containers/article_images/2025/giga_berlin_art_tobias.jpg/074b092609d4477c5cafb71e90dbaf57/giga_berlin_art_tobias.jpg)
![Tesla Shows Off Robotaxi Cleaning Robot [VIDEO]](https://www.notateslaapp.com/img/containers/article_images/2025/tesla_robotic_cleaner.png/08bbe7d309afe2f4bfee710a5df8cf25/tesla_robotic_cleaner.jpg)
