Uber’s product chief on hotels, robotaxis, and why the company doesn’t want to be “everything for everyone”

Uber is strategically expanding beyond its core mobility and delivery services, launching new ventures in travel and establishing a dedicated data-gathering unit called AV Labs. In an interview, Chief Product Officer Sachin Kansal detailed how the company is integrating hotel bookings via Expedia and other travel-related features, positioning travel as the "third leg of the stool" alongside rides and eats. This diversification signals a deliberate move to leverage its massive user base while simultaneously building a defensible data moat in the burgeoning autonomous vehicle sector, complicating its relationship with partners and competitors like Waymo.

Operationally, Uber's strategy involves both user-facing additions and backend infrastructure plays. While consumers see new options like boat rentals and concierge shopping, the company is also quietly building out financial tools for its drivers, like the Uber Pro debit card. More critically, the six-month-old AV Labs is deploying a fleet of sensor-equipped vehicles to collect millions of miles of driving data. This data is intended to help solve the "long-tail problem" for AV partners by capturing edge cases. Separately, Uber is creating a new revenue stream by using its network of drivers to perform data-labeling tasks for external Gen AI companies, a commercial relationship the company is "extremely bullish about."

Uber's Strategic Initiatives

• Travel Expansion: Deep integration with Expedia for hotel bookings, plus boat rentals and concierge shopping features.
• Financial Services: The Uber Pro card for drivers and couriers, with experiments underway for merchants.
• AV Labs: A new business unit deploying sensor-equipped vehicles to gather vast amounts of driving data for AV partners.
• AI Data Labeling: A commercial service where Uber's earner base labels data, including audio transcription, for Gen AI companies.
• AI-Powered Features: In-app assistants for earners to find demand, grocery cart creation via text, and voice-activated ride requests.

These initiatives position Uber less as a direct builder of autonomous technology and more as an indispensable platform layer for the entire ecosystem. By focusing on owning the "race tracks"—the data, operational expertise, and user interface—Uber gains significant leverage over various L4 autonomy providers. This hybrid network approach, combining human drivers with AVs from multiple partners, allows the company to balance supply and demand while avoiding the immense capital expenditure of developing its own self-driving cars. For the broader AI market, Uber's data-labeling service demonstrates how gig economy platforms can be repurposed to feed the massive data appetite of modern AI model development.

Uber's strategy is shifting from being just a logistics network to becoming a data and operations platform, using its massive scale to create new revenue streams in AI training and autonomous vehicle development while carefully avoiding the capital-intensive race to build its own self-driving technology.