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Uber drivers could become the foundation of a new urban data collection infrastructure, capable of supplying autonomous vehicle companies with real-time information captured on the streets and unprecedentedly expanding the training of artificial intelligence focused on the physical world.
Drivers for Uber could gain a much larger role than just transporting passengers. The company revealed a plan to, in the future, equip partner cars with sensors capable of recording the urban environment in real-time, creating a distributed network for global-scale data collection. The proposal was detailed by the company’s CTO, Praveen Neppalli Naga, during TechCrunch StrictlyVC San Francisco 2026, held in California.
According to the Olhar Digital portal, the point that makes the idea most relevant is that it doesn’t just target Uber’s own operations. If it comes to fruition, this network formed by drivers could supply companies developing autonomous vehicles, as well as companies training artificial intelligence systems focused on the so-called physical world, where machines need to interpret streets, intersections, pedestrians, peak hours, and real traffic situations to make decisions safely.
The strongest detail of the plan lies in the scale that drivers can achieve
The most powerful aspect of the proposal lies in the size of the network Uber already possesses. Instead of relying solely on reduced and expensive fleets to collect information, the company sees in its millions of drivers a ready-made structure to capture data in different cities, countries, times, and traffic conditions.
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In practice, this means that ordinary cars used daily could record urban environment behavior in a much wider variety of scenarios. For companies developing autonomous vehicles, this type of volume has strategic value, because the training of these systems depends precisely on diverse, frequent data obtained in real situations.
The curious twist is that Uber wants to transform ordinary rides into raw material for artificial intelligence
The proposal draws attention because it shifts the center of the technological dispute. Instead of focusing solely on building its own autonomous cars, Uber is now targeting an asset that could be even more valuable at this moment: organized data collection.
This change places drivers at the heart of a much larger technological mechanism. Every journey through the streets can become input for training algorithms, testing automated driving models, and feeding systems that need to learn how the world works outside of laboratories. It is this bridge between real-world driving and machine learning that makes the plan go beyond a simple operational expansion.
The context shows that Uber wants to expand its role in the market without having to compete for everything alone
The initiative is presented as an evolution of AV Labs, a project launched earlier this year and still in its initial stages. Today, this structure operates with its own sensor-equipped fleet, separate from the traditional partner network. The difference is that, in the future, Uber wants to scale this logic with the support of drivers who already circulate daily through numerous regions.
At the same time, the company already maintains partnerships with about 25 companies in the sector. Through these collaborations, a kind of cloud of autonomous vehicles is being formed, focused on data organization and system training. This movement suggests that Uber is trying to occupy a new space in the ecosystem: less as a manufacturer of proprietary technology and more as a platform capable of connecting mobility, data collection, and infrastructure for artificial intelligence.
Why drivers can change the data dispute in real traffic
According to the company executive, the main current bottleneck for autonomous vehicles is no longer just the technology itself, but access to sufficient data to train the systems. An automated car needs to learn to deal with confusing lanes, unexpected pedestrians, busy intersections, motorcycles, buses, construction, and sudden changes in traffic flow.
This is where the network of drivers gains importance. If part of this fleet starts operating with sensors, Uber will be able to significantly expand the volume of information available to the sector. This changes the game because many companies face high costs to collect data on their own. With a global presence and daily circulation, the company now sees its partner base as a competitive advantage capable of fueling the advancement of autonomous vehicles and artificial intelligence applied to urban spaces on a large scale.
What still needs to be confirmed before this network moves from presentations to the streets
Despite its potential, the project still depends on important steps. The CTO himself stated that the company needs to better understand how sensors function in this expanded operation and also address regulatory issues related to data collection and sharing.
This involves rules that differ between cities and countries, as well as clearer definitions regarding the use, storage, and circulation of this information. It is also not yet defined when the proposal can be implemented at scale or how many drivers would be incorporated first. For now, the idea is treated as a promising path, but still surrounded by tests, technical adjustments, and regulatory barriers.
Uber’s signal, however, is already enough to put the company back at the center of a decisive transformation. If millions of drivers truly start functioning as a global network of sensors, streets may cease to be merely the setting for urban mobility and become one of the planet’s largest sources of data for training machines. And it is precisely this possibility that makes the plan go beyond Uber and point to a new phase of artificial intelligence in the physical world.
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