Buttigieg argues that while AVs can save thousands of lives, a conservative regulatory approach is paradoxically the fastest path to adoption. A handful of highly-publicized accidents can destroy public acceptance, so ensuring safety upfront is critical for long-term success, even if it slows initial deployment.
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In contrast to the 'move fast' ethos of tech rivals, GM views its intense focus on safety as a core business strategy. The company believes that building and retaining customer trust is paramount for new technologies like autonomous driving. It sees a single major incident as catastrophic to public perception, making a slower, safer rollout a long-term competitive advantage.
As AI-powered sensors make the physical world "observable," the primary barrier to adoption is not technology, but public trust. Winning platforms must treat privacy and democratic values as core design requirements, not bolt-on features, to earn their "license to operate."
Top Chinese officials use the metaphor "if the braking system isn't under control, you can't really step on the accelerator with confidence." This reflects a core belief that robust safety measures enable, rather than hinder, the aggressive development and deployment of powerful AI systems, viewing the two as synergistic.
Avoid deploying AI directly into a fully autonomous role for critical applications. Instead, begin with a human-in-the-loop, advisory function. Only after the system has proven its reliability in a real-world environment should its autonomy be gradually increased, moving from supervised to unsupervised operation.
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.
The transition to AVs won't be a sudden replacement of human drivers. Uber's CEO argues that for the next two decades, a hybrid network where humans and AVs coexist will be a more efficient and effective solution, allowing for a responsible transition while serving diverse customer preferences.
Despite rapid software advances like deep learning, the deployment of self-driving cars was a 20-year process because it had to integrate with the mature automotive industry's supply chains, infrastructure, and business models. This serves as a reminder that AI's real-world impact is often constrained by the readiness of the sectors it aims to disrupt.
The public holds new technologies to a much higher safety standard than human performance. Waymo could deploy cars that are statistically safer than human drivers, but society would not accept them killing tens of thousands of people annually, even if it's an improvement. This demonstrates the need for near-perfection in high-stakes tech launches.
With Waymo's data showing a dramatic potential to reduce traffic deaths, the primary barrier to adoption is shifting from technology to politics. A neurosurgeon argues that moneyed interests and city councils are creating regulatory capture, blocking a proven public health intervention and framing a safety story as a risk story.
The lack of widespread outrage after a Waymo vehicle killed a beloved cat in tech-skeptical San Francisco is a telling sign. It suggests society is crossing an acceptance threshold for autonomous technology, implicitly acknowledging that while imperfect, the path to fewer accidents overall involves tolerating isolated, non-human incidents.
