Uber's CPO Reveals the AI and Autonomous Vehicle Strategy Reshaping How We Think About Platform Businesses

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Uber's CPO Reveals the AI and Autonomous Vehicle Strategy Reshaping Platform Businesses

Uber is not trying to be everything for everyone — and according to its Chief Product Officer, that restraint is exactly the point.

In a wide-ranging interview with TechCrunch's Connie Loizos, Sachin Kansal walked through Uber's evolving product vision: financial services ambitions, a deepening but complicated relationship with Waymo, a new autonomous vehicle data operation called AV Labs, and the specific ways AI is beginning to surface inside the Uber experience for both riders and drivers. The picture that emerges is of a company that has learned, sometimes the hard way, that platform discipline matters more than platform sprawl.

What Uber Is Actually Building Right Now

Kansal's interview covers a lot of ground, but a few threads stand out for anyone watching how large tech companies are operationalizing AI.

First, Uber is pushing into financial services. The company has long processed enormous volumes of payments, and it appears to be exploring ways to turn that financial infrastructure into a product layer — think credit, earned wage access for drivers, or embedded banking features. This is not a pivot; it is a natural extension of a platform that already owns the transaction relationship with tens of millions of users.

Second, the Waymo relationship is described as "increasingly complicated." Uber and Waymo partner on robotaxi deployments in certain markets, but they are also, in important respects, competitors. Kansal's candor about this tension is notable. It signals that Uber is not betting its future entirely on any single autonomous vehicle partner — which is why the AV Labs initiative matters.

AV Labs is Uber's move to become a data provider and infrastructure layer for the broader autonomous vehicle industry. By collecting and packaging real-world ride data, Uber positions itself as an indispensable resource for AV companies that need the kind of dense, messy, real-world training data that lab environments cannot replicate. It is a smart hedge: if robotaxis eventually reduce the need for human drivers, Uber wants to own the data pipes that make those robotaxis smarter.

Third, and perhaps most relevant for everyday users, Kansal described how AI is starting to show up in visible ways — better route suggestions, smarter driver-matching, and improved customer support interactions. These are not headline features, but they are the kind of compounding quality improvements that build loyalty over time.

The "Not Everything for Everyone" Lesson for Business Teams

The most instructive part of Kansal's interview for business leaders is the philosophical framing: Uber has made a conscious decision to resist feature sprawl. After years of experiments — food delivery, freight, helicopters, alcohol delivery — the company appears to be sharpening its focus on mobility, the services that directly support mobility, and the data infrastructure that makes mobility smarter.

This is a lesson that scales down to small and mid-sized businesses just as well as it applies to a company with Uber's resources. The temptation to bolt on new tools, new channels, and new capabilities is constant, especially as AI makes it cheaper and faster to build. But undisciplined expansion dilutes operational focus and creates compounding complexity in your workflows and team structure.

The better question, which Uber seems to be asking itself, is: what do we do better than anyone else, and how do we use AI to do that thing even better? For AI tools for business, this framing is essential — the goal is augmentation of your core strengths, not substitution of focus with feature volume.

For teams evaluating their own automation strategy, the Uber model offers a useful template. Map your core transaction or interaction — the thing your customers actually pay you for — and build AI and automation layers around that specific value. Resist the gravitational pull toward building adjacencies before the core is truly excellent.

What This Means for SMBs Watching the AI Space

Uber's AV Labs pivot also carries a signal worth noting: data is increasingly the moat, not the application. If you are running a business that generates operational data — customer interactions, fulfillment patterns, service histories — that data has strategic value that most SMBs are not yet capturing or organizing in a way that compounds over time.

Platforms like WRRK.ai are built around exactly this principle, helping business teams structure their workflows and AI interactions so that the intelligence they generate does not evaporate after each task but accumulates into something that makes the next decision faster and smarter.

Uber is a large company making large bets, but the underlying logic is accessible at any scale: focus your AI investment on your core, build data discipline early, and let compounding do the rest.

Original reporting by Connie Loizos for TechCrunch, published July 13, 2026. Read the full interview at TechCrunch.

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Frequently Asked Questions

What is Uber's AV Labs and why does it matter?

AV Labs is Uber's initiative to collect, package, and monetize real-world ride data for use by autonomous vehicle companies. Because AV systems need massive volumes of real-world driving data to improve, Uber is positioning itself as a critical infrastructure provider for the broader robotaxi industry — regardless of which AV company ultimately wins the market.

How is AI being used inside Uber's platform today?

According to CPO Sachin Kansal, AI is showing up in practical ways for both riders and drivers, including improved route optimization, smarter driver-to-rider matching, and enhanced customer support experiences. These incremental improvements are designed to compound over time rather than serve as flashy one-time features.

What can small businesses learn from Uber's AI strategy?

The core lesson is focus. Uber has pulled back from trying to expand into every possible category and is instead using AI to deepen its advantages in its core mobility business. For SMBs, this means resisting the urge to automate everything at once and instead identifying the one or two workflows where AI can meaningfully improve the thing customers already value most about your business.