The law mandating advanced drunk driving prevention in new cars allows for delays. The National Highway Traffic Safety Administration (NHTSA) will only issue a binding mandate when the technology is proven ready, which it currently is not, making the 2027 date a soft target.

Traditional vehicle safety (e.g., Euro NCAP) used a checklist of specific test cases with binary pass/fail answers. For AI systems, this is insufficient. The new paradigm is statistical validation, where the goal is to prove reliability to a certain number of "nines" across a vast range of scenarios.

While more data seems better, comprehensive imaging scans can be problematic. Each measurement carries a false positive risk, so the cumulative probability of receiving a disruptive, incorrect result becomes material, leading to unnecessary stress and follow-up procedures.

A key risk in deploying AI is its inability to generalize to 'long-tail' or out-of-distribution events. Models trained on vast but finite data often fail when encountering novel situations common in the open-ended real world, such as a self-driving car mistaking a stop sign on a billboard for a real one.

Even with available AI detection software, professors are hesitant to take punitive action like failing a student. The risk of even a small number of false positives is too high, making anything less than perfect reliability unusable for accountability.

Drawing from his Tesla experience, Karpathy warns of a massive "demo-to-product gap" in AI. Getting a demo to work 90% of the time is easy. But achieving the reliability needed for a real product is a "march of nines," where each additional 9 of accuracy requires a constant, enormous effort, explaining long development timelines.

A technology like Waymo's self-driving cars could be statistically safer than human drivers yet still be rejected by the public. Society is unwilling to accept thousands of deaths directly caused by a single corporate algorithm, even if it represents a net improvement over the chaotic, decentralized risk of human drivers.

A pre-drive lockout system, while well-intentioned, fails to account for nuanced emergencies. For instance, it could prevent a driver who has had alcohol from evacuating during a tsunami warning, raising serious ethical and safety questions about rigid, automated decision-making.

Achieving near-perfect AV reliability (99.999%) is exponentially harder than getting to 99%. This final push involves solving countless subtle, city-specific issues, from differing traffic light colors and curb heights to unique local sounds like emergency sirens, which vehicles must recognize.