In a significant intervention in the ongoing debate over the future of self-driving cars, former Tesla AI director Andrej Karpathy has stated that Google's Waymo would be fundamentally incapable of replicating a recent coast-to-coast autonomous drive achieved by a Tesla using its Full Self-Driving (FSD) system. This assertion highlights the deep technological divide between the two leading approaches to autonomous vehicle technology.
Modular vs. End-to-End: The Core AI Dispute
The comments were made by Karpathy on December 31 on the social media platform X, formerly Twitter. He was responding to a direct question about whether Waymo could accomplish a similar 2,732-mile journey undertaken by Tesla enthusiast David Moss using Tesla's FSD V14.2. Karpathy confirmed, "Yes exactly, that's correct in my understanding," agreeing that Waymo's architecture would prevent such a feat.
This exchange underscores the key philosophical split. Waymo's system is built on a modular approach, relying on a combination of high-definition (HD) maps, LiDAR sensors, 5G connectivity, and multiple neural networks working together. Tesla, championing a vision Karpathy calls "Software 2.0," is developing a single, end-to-end neural network. This system aims to translate raw camera input directly into steering, braking, and acceleration commands, learning from billions of miles of real-world human driving data rather than relying on pre-coded logic for every scenario.
Karpathy pointed to a real-world example of the potential brittleness of the modular model: a San Francisco power outage where traffic lights went dark. In that situation, Waymo's HD maps did not reflect the live conditions, causing its vehicles to become stranded. Tesla's vision-based system, in theory, would interpret the dark traffic lights as a human would, navigating cautiously based on visual cues and traffic rules.
Praise for Milestone Amid Public Tension with Musk
Despite his departure from the company, Karpathy celebrated the coast-to-coast drive as a "special" achievement. He noted it was "a major goal for the autopilot team from the start." He reminisced about intense work sessions, saying, "We had marathon clip review sessions late into the night," analyzing instances where human intervention was needed and planning projects to eliminate those edge cases entirely.
This praise comes amidst a public and somewhat tense exchange with Tesla CEO Elon Musk. Earlier in December, Musk had sharply criticized Karpathy's perspective as "dated," asserting that Tesla's AI software had "advanced vastly beyond what it was when he left" in 2022. Musk even made a public overture, posting, "Andrej, my long lost brother, let us work together again!" However, Karpathy has given no indication of planning a return to Tesla.
What This Means for the Future of Self-Driving Cars
The debate between these two technological paths is more than academic; it will shape the scalability and global applicability of autonomous driving. Tesla's end-to-end neural network approach argues for a system that can theoretically drive anywhere a human can, without reliance on expensive, pre-mapped geofenced areas. Waymo's method offers extremely high reliability and safety within its meticulously mapped operational domains but may face challenges scaling to arbitrary locations quickly.
Karpathy's analysis, coming from a key architect of Tesla's early AI strategy, adds significant weight to the argument that the future of widespread autonomy may hinge on creating a generalized driving intelligence, much like a human driver, rather than a collection of specialized, map-dependent modules. The race is now not just about who drives autonomously first, but about how they achieve it and which method proves to be the most robust and scalable on a global stage.
