Why Tesla Will Never Make a True Full Reliable Real Autopilot

The article argues that traditional methods of image and video processing, including capsule networks and video transformers, have reached a bottleneck and require enormous resources even for simple tasks. It uses YouTube's Content ID as an example: despite 10 years of development and $100 million, it still cannot efficiently handle short clips. Tesla's Full Self-Driving (FSD) is criticized for remaining at SAE Level 2 despite promises, requiring constant human supervision and facing investigations after accidents. The author claims that modern AI is fundamentally limited because it relies on statistical correlations from large datasets rather than true understanding, leading to failures in rare scenarios. The article introduces TAPe (Theory of Active Perception), a method that represents scenes with structured T-bits encoding geometry and semantics, enabling recognition with minimal resources. For Content ID, TAPe breaks video into scenes described by 48 bytes each, allowing instant matching in archives of any size using a single server without GPUs. The author states that for autopilot, computer vision alone is insufficient, but TAPe solves the vision part completely.