Uber has spent the last year quietly pushing beyond the two businesses most people associate it with. There’s ride-hailing, of course, and delivery, but spend time in the app and you’ll now find hotel bookings powered by Expedia, “shop for me” concierge features, and boat rentals in Europe.
Under the hood, so to speak, there’s also a lot happening. Think debit cards for drivers, a data-labeling side hustle for these same earners looking to make more moolah, and a six-month-old, business unit called AV Labs, which is developing a fleet of sensor-equipped vehicles that’s separate from Uber’s regular driver network and designed to gather ever-larger amounts of driving data. Uber frames the initiative as a way to strengthen its relationships with autonomous vehicle partners, several of which it also holds equity in, but it sure looks like a hedge, as well. Uber competes directly with some of those same partners, with Waymo chief among them, and owning the data layer gives Uber both some leverage and optionality.
Whether Uber becomes a full-blown “everything app” similar to some Asian super-apps like Grab, remains an open question. But in this conversation, Uber Chief Product Officer Sachin Kansal walks TechCrunch through the company’s financial-services ambitions, its increasingly complicated relationship with Waymo, its new AV Labs data operation, and how AI is starting to show up in ways riders and drivers will actually notice.
This interview has been edited for length and clarity.
Uber’s Ambitious Reimagining: From Rides to an “Everything App” Ecosystem
Key Takeaways:
- Broadening Horizons: Uber is aggressively expanding beyond traditional ride-hailing and food delivery into travel (hotels, boats) and concierge shopping, aiming to become a comprehensive “everything app” for consumer needs, particularly around travel.
- Building an Ecosystem: The company is deepening its financial services for drivers/merchants, leveraging its Uber One membership to drive cross-platform engagement, and ensuring key verticals like Uber Eats are independently profitable.
- Strategic Autonomy Play: Uber is positioning itself as an orchestrator of autonomous vehicle (AV) services, fostering a “hybrid network” of human drivers and AVs while strategically collecting data via its AV Labs unit to maintain leverage and optionality with its AV partners and competitors.
Uber, the company synonymous with on-demand mobility, is quietly undergoing a profound metamorphosis. While most users still associate it primarily with rides and food delivery, a deeper dive into its offerings reveals a sprawling ambition to become a far more pervasive presence in daily life. This strategic pivot, steering Uber towards the elusive “everything app” status seen in some Asian super-apps like Grab, is spearheaded by Chief Product Officer Sachin Kansal, who recently shed light on the company’s multi-faceted expansion.
Beyond Mobility: The “Everything App” Vision Takes Shape
Earlier this year, Uber unveiled a suite of new features that firmly planted its flag in the broader travel sector. Hotels, powered by a deep partnership with Expedia, emerged as a headline announcement, complemented by boat rentals in Europe and a “shop for me” concierge service allowing users to order from virtually any local store. Kansal emphasizes this move as the logical “third leg of the stool” for Uber, following rides and eats. He points to compelling internal data: a staggering 1.5 billion trips on the Uber platform each year occur outside a user’s home city, highlighting the natural synergy between mobility, food, and travel accommodations.
This expansion isn’t a one-size-fits-all approach. While the Expedia hotel integration involved Uber building the entire user interface in-house, some newer ventures, like boat rentals, initially leverage a “hand-off” model where users complete bookings on a partner’s platform. This strategic flexibility allows Uber to test new verticals efficiently, with the potential for deeper integration if a service gains significant traction. It’s a pragmatic pathway to rapid diversification without overcommitting resources prematurely.
Powering the Ecosystem: Financial Services & Membership as Growth Engines
Underpinning Uber’s expanding consumer front is a growing focus on financial services, particularly for its vast network of drivers, couriers, and increasingly, merchants. The “Uber Pro card,” a debit card for earners to receive and manage their earnings, is a prime example of this internal financial infrastructure. Experiments are also underway to extend similar products to merchants in select global regions. For consumers, Uber’s “Uber credits” act as a loyalty currency, intrinsically linked to its successful Uber One membership program.
The Uber One membership, now boasting 51 million members and accounting for roughly half of all bookings, is a cornerstone of Uber’s ecosystem strategy. Kansal confirms its effectiveness in driving both increased frequency within a user’s primary service (e.g., more deliveries for a delivery-focused member) and, crucially, cross-sell to other services (e.g., a mobility-only user starting to use delivery). This sticky membership model ensures users are deeply embedded within the Uber universe. Moreover, Kansal proudly notes that Uber Eats, after early struggles, has evolved into an independently profitable business, no longer leaning on ride-hailing to stay healthy—a significant milestone for its diversified portfolio.
When it comes to broader consumer financial products like “buy now, pay later,” Uber maintains a strategic distance, preferring to partner with industry experts rather than build proprietary solutions. This philosophy underscores a measured approach to expansion, focusing on core competencies while leveraging partnerships for specialized services.
Navigating Autonomy: Partnerships, Data, and the Hybrid Future
Uber’s relationship with autonomous vehicle (AV) technology is complex and multi-layered. While it partners with leading AV developers like Waymo, it also finds itself in direct competition with them in certain markets. Kansal clarifies Uber’s role: it’s not striving to be an L4 autonomy provider itself, but rather to “lay down the race tracks” for multiple players. The vision is a “hybrid network” where human drivers and AVs coexist in the same cities, balancing demand and supply efficiently.
Crucially, Uber has established AV Labs, a six-month-old unit dedicated to developing a fleet of sensor-equipped vehicles separate from its regular driver network. This initiative is designed to gather vast amounts of driving data, which Uber frames as a way to strengthen relationships with its AV partners (many of whom Uber holds equity in). However, it undeniably functions as a strategic hedge, providing Uber with a critical data layer that offers both leverage and optionality in a rapidly evolving autonomous landscape, especially against direct competitors like Waymo. The recent decision to wind down a Waymo pilot in Phoenix while scaling operations in Austin and Atlanta exemplifies this dynamic, nuanced strategy.
The Competitive Landscape & User-Centric Innovation
Uber operates in an intensely competitive global market, facing rivals like Lyft, Didi, Bolt, Ola in mobility, and DoorDash, Delivery Hero in food delivery. Even Airbnb indirectly enters the fray with its own airport transfer partnerships. Yet, Kansal asserts that his primary focus remains internal: ensuring Uber provides maximum value to its users. This user-centric approach, rather than an obsession with competitors, guides the product roadmap and innovation strategy, from the convenience of “shop for me” to the seamless experience of Uber One.
The Bottom Line
Uber’s transformation is far more than an incremental update; it’s a strategic redefinition of its identity. By aggressively diversifying into travel and financial services, buttressing its core offerings with a powerful membership program, and intelligently navigating the complex world of autonomous vehicles, Uber is building a robust, interconnected ecosystem. This ambitious push to become a ubiquitous “everything app” positions it not just as a leader in on-demand services, but as a central orchestrator of daily life, leveraging data, partnerships, and an unwavering focus on user value to drive its next phase of global growth.
Key Takeaways:
- Data and Operational Expertise Drive Autonomy: Uber is leveraging its vast network of sensor-equipped vehicles and 10 million earners’ operational know-how to provide autonomy partners with critical, long-tail driving data and solve complex real-world challenges like pickups, drop-offs, and lost items.
- New Revenue Stream in Ethical AI Data Sales: Uber is actively selling labeled data – collected ethically through paid earner transcription (never during active trips) – to Gen AI companies, marking a “bullish” new commercial avenue separate from its AV Labs initiatives.
- AI Enhances User Experience Today, Prepares for “Agentic” Future: From smart earner routing and grocery list generation to complex voice-activated ride requests, AI is already making Uber’s platform more efficient and convenient, with a future vision of a fully “agentic” system capable of planning entire trips.
Uber’s Strategic Playbook: Unpacking AI, Autonomy, and the Future of Mobility
In the rapidly evolving landscape of artificial intelligence and autonomous vehicles, Uber stands at a unique crossroads. Not just a ride-hailing and delivery giant, the company is strategically positioning itself as a critical enabler for the future of mobility, leveraging its unparalleled scale and real-world operational insights. We recently had the opportunity to delve into Uber’s vision with an executive, discussing everything from their distinct offering to autonomy partners and their burgeoning AI data sales business, to the tangible ways AI is already transforming user experience and the careful steps being taken toward an “agentic” future.
Beyond the Code: Uber’s Unique Value Proposition for Autonomy Partners
When it comes to autonomous vehicle (AV) development, data is king. But not just any data—it’s the breadth and depth of real-world, often unpredictable, scenarios that truly accelerate progress. Uber, through its AV Labs initiative, is offering something most autonomy partners simply can’t replicate: an immense, diverse, and operationally-rich data stream.
“We are going to be equipping hundreds of cars with sensors, deployed through our fleet partners, and through that we’ll be collecting millions of miles worth of driving data,” an Uber executive explained. This vast collection directly addresses what’s known as the “long-tail problem” in AV development – the challenge of encountering and learning from rare, unexpected edge cases that go beyond the typical 95th or 99th percentile of driving conditions. The sheer volume and variety of data collected across Uber’s global operations are invaluable for training robust autonomous systems.
However, Uber’s contribution extends far beyond raw sensor data. The company boasts “so much know-how from our 10 million earners in terms of how pickups and drop-offs work.” This operational expertise, honed over years of managing millions of daily transactions, is crucial for AVs to integrate seamlessly into urban environments. Consider the complexity of managing 25 million lost items every single year – “how do you operationally handle that in the world of autonomy?” This deep understanding of logistical challenges, customer interaction, and real-world problem-solving represents a powerful, unique asset Uber brings to the table for its autonomy partners.
Navigating New Frontiers: Data Sales and Ethical AI
Beyond supporting AV development, Uber is also exploring new commercial avenues by leveraging its data ecosystem. A significant and “extremely bullish” new part of the business involves commercial relationships with General AI (Gen AI) companies. “We are able to label data for them using our earner base, or through audio collection, and yes, we have commercial relationships with them and we are selling it to them,” the executive confirmed.
This revelation naturally raises questions about data privacy and how such information is collected. The executive was unequivocal in clarifying the process: “No, no, no — I want to be very clear, there’s no conversation being recorded as part of that while they’re on a ride.” Instead, Uber’s earners participate in data labeling tasks when they are *not* on a trip. “When they’re not driving, they’re not delivering, they’re just talking, or they’re listening to a piece of audio and transcribing it. They get paid for doing that, by the way.” This distinction is critical, emphasizing that driver and rider privacy during active trips remains paramount, while new revenue streams are generated through ethical, opt-in data work.
AI’s Present Impact: Enhancing Every Touchpoint
While the long-term visions for autonomy and advanced AI are compelling, AI is already making a tangible difference for both riders and earners on the Uber platform. The company’s strategy isn’t just about futuristic agents; it’s about immediate, practical enhancements.
“If you are an earner on our platform, we have an earner assistant — the number one question on their mind is how do I make more money,” the executive explained. This AI-powered assistant provides real-time demand insights, advising earners, for instance, to “go five miles away where there’s a lot of demand” if their current area is slow. This translates directly into increased efficiency and earnings potential for drivers and delivery personnel.
On the consumer side, AI streamlines daily tasks. For Uber Eats, there’s a “grocery cart assistant where you can say ‘I want milk, eggs, bread’ and it creates the cart very quickly,” significantly simplifying the ordering process. For rides, the convenience extends to complex voice commands: “you’re able to use voice to request a ride — say ‘I’m looking for a ride to the airport, I have six pieces of luggage, six people.'” These features demonstrate how AI is already reducing friction, saving time, and making the platform more intuitive and accessible for its millions of users.
The Agentic Horizon: Planning the Future of Trips
Looking ahead, Uber envisions an even more integrated and intelligent platform—one that can act as a fully “agentic” assistant. This means moving beyond simple requests to a system capable of understanding complex, multi-stage intentions, like “plan and book my whole trip.”
While reluctant to put a specific date on such an ambitious rollout, the executive expressed confidence in AI’s role: “I think AI is going to be a huge enabler of that, where I can leave the complexity to the platform and just tell an agent what exactly I want.” The goal is to offload the cognitive burden of planning from the user to the AI, making travel and logistics seamless. However, there’s a strong emphasis on quality over speed. “Easier said than done — we want to make sure we’re not just checking a box by shipping an agent that maybe doesn’t work that well.” This pragmatic approach underscores a commitment to delivering truly valuable, reliable AI experiences.
A CPO’s Pragmatic Prioritization: Balancing Innovation and Stability
Managing such a diverse portfolio of existing products, emerging technologies, and future visions requires a disciplined approach to prioritization. As Chief Product Officer, the executive shared insights into balancing these demands.
“I would say I spend 70% to 80% of my time making sure that our existing products, or the products we are about to launch, are as solid as possible,” they revealed. This foundational focus ensures that the core services remain robust and reliable, which is paramount for a platform of Uber’s scale. New ideas, while exciting, are treated with careful scrutiny. “All the new ideas are like shiny objects — if you have 100 ideas, maybe five of them are good, and those five then need a lot of cultivation and conviction.” The remaining 20% of time is dedicated to nurturing these promising innovations, ensuring they are thoroughly vetted before significant investment.
Crucially, this prioritization isn’t just theoretical. The CPO maintains a hands-on approach: “including, by the way, I go out and drive and deliver myself, just to see our product from the other side firsthand.” This direct engagement provides invaluable insights, ensuring product development is grounded in the real-world experiences of both earners and consumers.
Bottom Line: Uber’s strategic trajectory is a carefully orchestrated blend of leveraging its monumental operational data, ethically expanding into new AI-driven revenue streams, and a steadfast commitment to enhancing its core platform. By pragmatically balancing existing product solidity with ambitious, agentic AI visions, and grounding innovation in real-world user needs, Uber is not just adapting to the future of mobility—it’s actively shaping it, ensuring its ecosystem remains central to how people move and get things.