Uber is strategizing to transform its global network of millions of human drivers into a massive sensor grid to support the autonomous vehicle industry. In an interview at TechCrunch's StrictlyVC event in San Francisco, Chief Technology Officer Praveen Neppalli Naga revealed plans to eventually equip drivers' personal vehicles with sensors to collect real-world data. This initiative expands upon Uber's existing AV Labs program, which currently relies on a limited fleet of company-owned sensor-equipped cars. Naga explained that the long-term goal is to turn every driver's car into a rolling data collection platform. While the company has not yet deployed this system widely due to the need to clarify regulatory frameworks regarding sensor equipment and data sharing across different states, the ambition is clear. The driving insight is that the primary bottleneck in autonomous vehicle development is no longer the underlying technology, but the scarcity of diverse real-world data. Unlike AV companies that lack the capital to deploy fleets for data gathering, Uber can leverage its vast existing infrastructure to provide specific scenarios, such as collecting data at particular intersections during specific times of day. Uber has already abandoned its own self-driving car ambitions, a decision co-founder Travis Kalanick has since called a mistake. Instead, the company is positioning itself as the essential data layer for the entire ecosystem. Uber currently maintains partnerships with 25 autonomous vehicle companies, including Wayve, and is building what Naga describes as an "AV cloud." This platform functions as a library of labeled sensor data that partners can query to train their models. Furthermore, partners can utilize the system to run their algorithms in "shadow mode" against actual Uber trips. This allows companies to simulate how their self-driving software would have performed in real traffic without needing to place autonomous vehicles on the road. Although Naga stated that the immediate goal is to democratize access to this data rather than generate direct profit from it, the commercial potential is significant. Uber has already made equity investments in numerous AV players, and its control over high-value training data could grant it substantial influence over a sector that currently depends on Uber's ride-hailing marketplace for customer access. By offering proprietary data at scale, Uber aims to secure a critical role in the future of transportation even as it steps back from developing its own autonomous vehicles.