Ahead of its IPO, Motive's claim of 99% accuracy for its AI dashcams is powered by a large team of human reviewers in Pakistan paid ~$125/month. They manually verify up to half a million video clips daily, revealing the hidden human labor required to make high-stakes AI systems reliable.

The shift to AI makes multi-sensor arrays (including LiDAR) more valuable. Unlike older rules-based systems where data fusion was complex, AI models benefit directly from more diverse input data. This improves the training of the core driving model, making a multi-sensor approach with increasingly cheap LiDAR more beneficial.

When communities object to surveillance technology, the stated concern is often privacy. However, the root cause is usually a fundamental lack of trust in the local police department. The technology simply highlights this pre-existing trust deficit, making it a social issue, not a technical one.

Municipal police budgets are often inflexible and almost entirely allocated to headcount, leaving no room for technology upgrades. Public-private partnerships, where companies or individuals make relatively small donations, are emerging as a critical model for funding essential tech like drones and AI.

Ring’s founder clarifies his vision for AI in safety is not for AI to autonomously identify threats but to act as a co-pilot for residents. It sifts through immense data from cameras to alert humans only to meaningful anomalies, enabling better community-led responses and decision-making.

California's CalMatters uses an AI called 'Tip Sheet' to analyze public records of politicians, including speeches, votes, and campaign contributions. The AI flags anomalies and potential stories, which it then provides exclusively to human journalists to investigate, creating a powerful human-AI partnership.

The data infrastructure for law enforcement is fragmented and archaic. Until recently, some major US cities ran on paper, and states even outlawed cloud storage. This creates massive data silos that hinder investigations, as criminal activity crosses jurisdictions that don't share data.

While content moderation models are common, true production-grade AI safety requires more. The most valuable asset is not another model, but comprehensive datasets of multi-step agent failures. NVIDIA's release of 11,000 labeled traces of 'sideways' workflows provides the critical data needed to build robust evaluation harnesses and fine-tune truly effective safety layers.

Waive treats the sensor debate as a distraction. Their goal is to build an AI flexible enough to work with any configuration—camera-only, camera-radar, or multi-sensor. This pragmatism allows them to adapt their software to different OEM partners and vehicle price points without being locked into a single hardware ideology.