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Bradford Ferguson

Magic of Tesla FSD

Thanks to Phil Beisel for letting us turn his article "The Magic of Tesla FSD" into a video. Transcript of video below.



Imagine sitting in your car, hands off the wheel, feet off the pedals, and the car is driving itself—flawlessly. Sounds like something out of a sci-fi movie, right? Well, buckle up, because Tesla’s Full Self-Driving is making that a reality, we’re going to explain to you the Magic of FSD.


In this video, we’ll break down how Tesla’s FSD works at a high level—and why, when you experience it, it feels like something straight out of the future. Before we dive deeper, I want to give a shout-out to Phil Beisel, the original author of the article 'The Magic of Tesla FSD,' which inspired this video. We’re using some of his incredible insights to explore this topic. So, let’s crack open the box and see what’s inside!


In January 2024, Tesla launched FSD Version 12, and with it came a massive leap forward in autonomy. If you thought Tesla’s FSD was advanced before, think again. With its end-to-end artificial intelligence approach, Tesla is pushing autonomy closer to a full solution—one that could completely transform how we think about transportation.


But why does this matter so much? For over 40 years, computer scientists have been obsessed with solving autonomous driving. It’s the Holy Grail of AI—something that has stumped the brightest minds. Why? Because driving is dynamic, unpredictable, and chaotic.


Think about it: every second, the environment changes—new obstacles appear, pedestrians cross the street, other vehicles switch lanes. For humans, it’s almost second nature to adapt, but for AI? That’s been the challenge. So, what makes Tesla’s approach different?


First, Tesla had to solve the perception problem. As humans, we use our eyes to interpret the road, but for a car, it relies on cameras, radar, and lidar to gather data. The problem? Just collecting that data isn’t enough. The vehicle needs to understand what it’s seeing—a stop sign, a pedestrian, or a cyclist. It’s like trying to make sense of an ever-changing puzzle.


But here’s the twist: gathering data is only the beginning. Tesla’s system doesn’t just see; it has to think—it needs to make sense of the chaos around it. So, how does it make decisions like a human driver would?


That’s where the second challenge comes in—decision-making. The car needs to plan its next move. Think about the decisions you make every time you drive—when to brake, when to accelerate, how to navigate an intersection. Now, imagine teaching a computer to do that, not once, but thousands of times in a constantly changing environment.


This is where Tesla completely re-wrote the playbook. Before artificial intelligence, early attempts at vehicle autonomy relied on rigid algorithms. Programmers had to tell the car exactly what to do, and this approach hit a brick wall. Why?


Take recognizing a stop sign, for example. Sounds easy, right? But what if the stop sign is covered in snow, or partially blocked by a tree branch? Old-school algorithms couldn’t handle that. Programmers had to account for every possible variation, and as you can imagine, it didn’t take long for this method to hit its limits.


So what did Tesla do? They threw out the old rulebook and embraced something radically different: Software 2.0. This is where the real magic starts to happen. Instead of relying on hard-coded rules, Tesla’s new approach uses machine learning to let the system teach itself how to drive.


Here’s the secret sauce—machine learning allows the system to improve with experience. Instead of manually programming every decision, Tesla feeds the system tons of data and lets it learn patterns. It’s like teaching a kid to ride a bike: the more practice, the better they get. The more data Tesla feeds its AI, the smarter it becomes.


Tesla’s FSD Version 12 fully embodies this Software 2.0 approach. Instead of relying on sensors like radar or lidar, it processes video data from its eight onboard cameras. The data-driven models handle perception and planning, replacing the need for explicit programming.


At a high level, Tesla’s FSD system works like this:

  • Video data from the vehicle’s cameras feeds into a perception process.

  • The perception process determines what the vehicle is seeing.

  • A planning process takes this data and decides how the vehicle should move forward.

  • The controls module executes the plan, combining steering, braking, and acceleration to drive the vehicle.

This breakthrough allows Tesla’s FSD to improve through data-driven learning and frequent updates.


Here’s where it gets even more interesting: Tesla’s FSD relies on a neural network—think of it as the car’s brain. And this brain doesn’t stop learning. Tesla uses a two-step process: training and inference.


Training happens in Tesla’s data centers. Tesla’s fleet of vehicles is constantly collecting video data—billions of frames from real-world driving. This data is used to train the AI models to recognize patterns, like when a pedestrian might step off the curb or when a light turns yellow.


Once trained, these models are downloaded to each Tesla, and that’s where the inference process begins. In real-time, the car interprets the video data it’s collecting and uses it to make driving decisions—decisions that happen faster than a human can blink. It’s not just seeing the road—it’s predicting what’s going to happen next. And that’s where the magic lies.


The real power of Tesla’s FSD is in its ability to both see and plan. It’s not just recognizing objects; it’s understanding them. Every object on the road—whether it’s a car, a pedestrian, or even lane markings—is labeled with important data, like its size, speed, and direction. The AI knows not just what is there but what it’s likely to do next.


Now, let’s talk about Tesla’s secret weapon, its moat. Tesla isn’t just leading because of its cutting-edge AI—it’s leading because of the sheer scale of its data. Every Tesla on the road is like a data-collecting scout, constantly feeding information back to Tesla’s AI systems.


Tesla adds about 5,000 new cars to its fleet every day, and every single one of those vehicles is contributing to the AI’s learning process. Think of it like a feedback loop: the more cars Tesla sells, the more data it collects, and the smarter the system gets. Add to that Tesla’s massive investments in AI infrastructure—like their supercomputers Cortex and Dojo—and you’ve got a system that’s scaling faster than any competitor can catch up to.


Why do we still trust humans to drive? Humans are slow processors, often distracted, and prone to error. More than 40,000 people die in traffic accidents in the U.S. every year, most caused by human error.


Tesla’s FSD doesn’t get tired. It doesn’t get distracted. And it reacts far faster than any human driver ever could. Tesla’s AI makes adjustments 15 times per second, constantly recalculating the safest way forward. It’s like having a superhuman behind the wheel.


Tesla’s FSD will soon enable fully autonomous driving. This will revolutionize transportation, particularly through the upcoming Robotaxi service, launching in October 2024. Robotaxi will allow Tesla owners to loan out their vehicles for ride-sharing, operating autonomously without a human driver.


Here’s the crazy part—this isn’t some distant dream. It’s happening now. I drive a 2024 Tesla Model 3 Performance running FSD Version 12.5. And after driving 1,900 miles with this system, I can tell you, it’s more real than most people realize. Just last night, I looked over at my friend while my car was driving itself and said,

‘My God, this car is driving itself, and no one knows!’

The revolution in transportation isn’t coming—it’s already here. Tesla is leading the charge, and if you think FSD is impressive now, just wait. Things are going to get even crazier from here.


Again, a big thank you to Phil Beisel for allowing us to use his article. I’m Bradford Ferguson from Rebellionaire.com. If you enjoyed this deep dive into the world of Tesla FSD, don’t forget to hit that subscribe button, and stay tuned for more updates on the future of tech. Thanks for watching, and I’ll catch you in the next video! Bye for now.

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