According to Sawyer Merritt, U.S. auto safety regulators have provided Tesla with a five-week extension to respond to an ongoing National Highway Traffic Safety Administration (NHTSA) investigation into potential traffic law violations associated with Tesla's Full Self-Driving (FSD) AI system. The revised deadline for Tesla’s key responses is now set for February 23, 2026. This regulatory development highlights the increasing scrutiny on the real-world safety and compliance of AI-powered autonomous vehicle technologies, which could influence future regulatory frameworks, liability standards, and commercial deployment strategies for AI-driven mobility solutions. Businesses in the autonomous driving sector should closely monitor regulatory responses to AI system performance, as these outcomes will shape market entry strategies, risk management, and product development priorities. (Source: Sawyer Merritt on Twitter)

According to @SawyerMerritt, Tesla's Full Self-Driving (FSD) AI system has been credited with saving numerous animal lives, as evidenced by user-submitted videos showcasing the technology’s real-world safety applications (source: x.com/drjsc/status/1992660682660315488). The practical deployment of FSD to detect and avoid animals on the road highlights advanced AI perception and decision-making capabilities. For businesses, this demonstrates strong market potential for AI-powered safety solutions in autonomous vehicles, emphasizing both regulatory compliance and consumer trust as competitive advantages (source: Sawyer Merritt on Twitter).

According to @aelluswamy, Tesla's VP of AI, in his latest ICCV 2024 presentation, Tesla leverages its global vehicle fleet to collect the equivalent of 500 years of driving data daily, giving it a significant data advantage in autonomous vehicle AI development (Sawyer Merritt, 2025). The curse of dimensionality is tackled by compressing billions of tokens generated from 8 high-frame-rate cameras into essential correlations between sensor input and control actions. Tesla’s smart data triggers capture rare driving scenarios, ensuring robust model training beyond routine highway driving. The company’s custom Gaussian splatting system enables rapid, high-fidelity 3D scene reconstruction for enhanced debugging and interpretability, while natural language querying allows engineers to examine model decisions. Tesla also introduced a proprietary neural video simulator that can generate fully synthetic multi-camera data for reinforcement learning and adversarial testing, supporting iterative model improvement and real-world robustness. These advancements are paving the way for Tesla's global robotaxi rollout, the Cybercab two-seater, and even humanoid robots powered by the same neural networks, opening new business opportunities in mobility and robotics (ICCV 2024, YouTube).