Andrej Karpathy: I am unreasonably excited about self-driving (twitter.com)

Andrej Karpathy — the influential AI researcher and former Tesla/OpenAI director — has publicly said he’s “unreasonably excited” about the current state of self-driving. His endorsement matters because he’s been a leading proponent of scaling end-to-end neural approaches for perception and control; when Karpathy signals enthusiasm, it suggests recent technical progress is moving from hype to tractable engineering. The comment highlights momentum in areas that matter for autonomy: richer sensor fusion, massive fleet data, simulation-driven validation, and better self-supervised pretraining that reduce reliance on hand-labeled corner cases. For the AI/ML community this matters both practically and technically. Practically, improved autonomy techniques mean more realistic benchmarks for robustness, long-tail generalization, and closed-loop evaluation — forcing advances in continual learning, distribution-shift detection, and verification/validation methods. Technically, progress in scalable end-to-end models, differentiable simulators, and efficient on-device inference will ripple into robotics, perception, and multimodal modeling. The implications include new research emphasis on safety-by-design, federated/fleet learning pipelines, and hybrid architectures that blend learned policies with provable fallbacks — all crucial if self-driving is to move from promising demos to deployable, auditable systems.