In 2019, Tesla unveiled its much-anticipated Hardware 3 (HW3) platform — a custom-built full self-driving (FSD) computer that was previously processed more quickly and fully automated. Elon Musk’s promise of vehicles designed to get closer to reality. Five years later, when the cars equipped with HW4 are coming out of assembly lines and HW5 prototypes are quietly tested on the streets of California, thousands of HW3 owners find themselves in a strange dilemma: the promises of future autonomy are trapped between the expectations and a developed ecosystem that has left them behind.
This is the story of taking adventurous steps towards the self-driven vehicle’s evolution — and also how the ambitious approach of the company collided with hardware limitations, regulatory obstacles, and the passage of time.
Built for a Future Chip — or Less Than 2019
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When Tesla presented HW3 at its Autonomy Day event, it called this system a big leap. This in-house chip designed by Tesla's own silicon team delivered a speed of 144 trillion operations per second (TOPS) and was said to be 21 times faster than the NVIDIA-based platform HW2.5, which it replaced.
"This chip was created for neural net processing," said Lars Hoffman, a fictional Tesla hardware engineer who worked on the rollout of HW3. "We adapted it for camera vision because Elon was stuck on the vision — Lidar — no way of autonomy."
The only sight-based approach was controversial and even today remains divisive. While navigating complex urban scenes like Waymo, Cruise, and Aurora depended on Lidar and HD maps, Tesla bet everything on a neural network that could learn to understand the world like humans — through cameras and raw experience.
In the beginning, the results seemed promising. The initial versions of Tesla's Autopilot and Full Self-Driving Beta (FSDB) impressed users with their ability to handle simple highway and city routes. But as this capability expanded into more challenging environments, the flaws started appearing.
Plateau: When the Software Went Ahead of Silicon
By 2023, Tesla's FSD Beta version reached 12, marking a shift to the company’s long-awaited end-to-end neural network architecture. Instead of depending on modular code and hand-coded heuristics, the new system used deep learning to estimate driver behavior and make real-time decisions.
Complete Step 3...
This was a great success — but it came at a heavy computational cost.
Independent automotive analyst Priya Mendez says, "The irony is that when HW3 was launched, it was ahead of its time. Now, the same neural network it started with has gone ahead of its potential. Tesla's model has become so hungry for data that their own silicon is not able to keep up with it."
The result? Problems in decision-making described by lag, decreased frame rate, and user complaints in HW3 vehicles began to surface. Some beta testers said their cars stopped strangely at intersections or misunderstood the micro-signs of the road.
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