Autonomy is steadily moving from “R&D showcase” to real-world deployment, and Waabi’s latest funding round is a strong signal that investors (and platforms like Uber) want scalable self-driving systems that can travel across vehicle types—not just across roads. From Global Martech Alliance’s lens, this is also a “platform moment”: autonomy isn’t only a transportation breakthrough, it’s the emergence of a new, data-rich distribution layer that will reshape customer experience, on-the-move commerce, and the operational backbone behind last-mile promises.
Waabi, the Toronto-based autonomous vehicle startup best known for its self-driving trucking work, announced a $750 million Series C round to accelerate development of what it calls “physical AI”—the core system intended to power both driverless trucks and robotaxis. The round was co-led by Khosla Ventures and G2 Venture Partners. Alongside the Series C, Uber committed an additional $250 million investment tied to future milestones and plans to deploy at least 25,000 autonomous vehicles powered by Waabi’s system exclusively through Uber’s ride-hailing platform.
This pairing—deep venture backing plus a scaled marketplace partner—matters because autonomy’s toughest challenge has rarely been demos; it’s been repeatable deployment across geographies, vehicle platforms, and edge-case conditions while maintaining safety and operational reliability. Waabi’s bet is that its approach, trained and validated heavily through simulation, can shorten time-to-scale and reduce the “brute force” economics that defined earlier generations of autonomous vehicle development.
Waabi’s $750 million raise is positioned as one of the largest single funding rounds for a Canadian tech startup. CNBC also noted Waabi ranked No. 35 on CNBC’s 2025 Disruptor 50 list and operates out of Toronto with some operations in Texas.
Uber’s milestone-based commitment adds a second layer to the financing story: capital is being paired with a deployment pathway, not just a product roadmap. Per the announced arrangement, Waabi’s autonomous system would power at least 25,000 vehicles deployed exclusively on Uber’s ride-hailing network. What’s not yet public (and strategically important) is the vehicle model lineup that will carry Waabi’s robot driver—Waabi has not disclosed which models will feature its systems.
From a martech-and-platform strategy standpoint, Uber’s approach reads like a “network defense” play: autonomy could disintermediate ride-hailing platforms over time, so Uber is partnering with many AV companies globally rather than rebuilding a full in-house AV stack. Axios reported that Uber has opted to work with more than 20 AV partners worldwide to bring AV fleets onto its network alongside human drivers. In that context, a commitment measured in tens of thousands of vehicles is a meaningful escalation from “pilot mindset” to “supply plan,” even if timelines and geographies remain undisclosed.
Waabi’s public narrative here is not “new product line,” but “same brain, new body.” The company argues that a self-driving system trained to handle heavy trucking (including more complex dynamics and safety requirements) can be adapted to light-duty passenger vehicles, provided the underlying intelligence generalizes well and the stack is designed for multiple form factors.
This move also follows the practical evolution of autonomous trucking itself. Axios reported Waabi initially pursued a hub-to-hub model (highway driving with human handoffs for local delivery), but shipper feedback highlighted added friction and costs—pushing Waabi to train trucks on surface streets as well. That street-level capability, Waabi’s CEO told Axios, is what opened the door to urban robotaxis next.
There’s also a career-throughline shaping this partnership. Waabi founder and CEO Raquel Urtasun previously served as chief scientist at Uber’s Advanced Technologies Group (ATG), which worked on autonomous vehicle technology. TechCrunch additionally notes Uber sold ATG to Aurora Innovation in 2020, and the new tie-up brings Urtasun’s work “full circle” back onto Uber’s platform.
Waabi says the new funding will help it adapt its “physical AI” so driverless systems can work in new locations, conditions, and vehicle types with a high level of safety relatively quickly. That claim is supported (at least directionally) by the company’s emphasis on simulation-driven development and validation rather than relying only on large fleets collecting massive real-world miles.
TechCrunch described Waabi’s closed-loop simulator “Waabi World” as central to how the Waabi Driver is trained, tested, and validated, including building digital twins from data, simulating sensors in real time, generating stress-test scenarios, and enabling learning without constant human intervention. Waabi’s pitch is that this architecture helps the system generalize from fewer examples than more traditional approaches, which typically demand enormous fleets, long time horizons, and extensive manual scenario engineering.
On the hardware side, Waabi’s CEO told CNBC the company does not compromise on the technology it uses, noting a multi-sensor setup that includes lidar, cameras, and radar. The argument is straightforward: different sensors fail differently, so redundancy increases robustness across conditions. In practical terms, this matters for commercialization because reliability isn’t a single KPI—it’s the sum of thousands of “small correctness” moments (weather shifts, occlusions, unpredictable road users) that operators and regulators will scrutinize.
Investors are also leaning into the “capital efficiency” framing. CNBC reported Vinod Khosla said Khosla Ventures is backing Waabi because it has taken a “capital efficient” approach to “physical AI” and benefits from being a later mover. CNBC further reported Khosla suggested Waabi can achieve what earlier AV efforts did—with thousands of engineers and billions spent—for a fraction of the cost.
Uber’s additional $250 million commitment is milestone-based, and key details are still open—where, when, and which vehicles will be used. Axios explicitly flagged that while Waabi would supply the robot “driver,” it wasn’t yet clear what vehicles would be used, where deployments would happen, or what performance milestones unlock Uber’s funding. TechCrunch similarly said the companies did not provide a timeline for deploying 25,000+ robotaxis at that scale.
Still, Uber’s platform strategy is getting more structured. TechCrunch reported that Uber launched a new division called “Uber AV Labs” intended to collect driving data for AV partners using its vehicles. If executed responsibly, this kind of data flywheel could become a critical “martech-like” advantage: better measurement, faster iteration, and more predictable rollouts—except the “campaigns” are vehicle behaviors and the “conversion” is safe, completed trips at reliable unit economics.
Importantly, Waabi is not entering an empty field. CNBC pointed to competition in autonomous trucking from Aurora, Kodiak AI, Bot Auto, and Tesla, and noted Tesla is poised to produce more Semi electric trucks in 2026 while promising self-driving systems for them. CNBC also described the robotaxi market as fiercely competitive, listing players such as Waymo (Alphabet-owned), Nuro, WeRide, and automakers pursuing their own systems including Tesla and Rivian in the U.S., as well as Xiaomi and BYD in China.
Autonomy at scale changes how brands think about “where attention lives” and “how fulfillment happens,” and those two themes—media and logistics—are increasingly inseparable in modern growth strategy. When robotaxis become normal, the vehicle becomes a managed environment: consistent trip flows, predictable dwell time, and platform-level governance over in-cabin experience. That has implications for brand safety, experiential marketing, local partnerships, and the next evolution of out-of-home into addressable, ride-based formats.
On the trucking side, the strategic shift is equally marketing-relevant, just less visible. CNBC reported Waabi has operated its own driverless trucks hauling customer cargo, but is now shifting toward a “driver as a service” business model. That kind of model tends to accelerate adoption if it reduces friction for customers—because buyers don’t need to become autonomy experts; they “consume capability” while the vendor manages complexity behind the scenes.
From a Global Martech Alliance viewpoint, the key thread to watch is whether “generalizable autonomy” becomes real in the way generalizable software platforms did: one core engine, multiple verticals, and a partner ecosystem that makes distribution easier than going direct. TechCrunch quoted Urtasun emphasizing that the opportunity is a single solution that can do multiple verticals at scale, not “two programs, two stacks.” Axios also framed Waabi’s thesis as a shared AI “brain” that can operate trucks and robotaxis, with a longer-term view toward other “physical AI” categories.
Finally, there’s a real ecosystem story here: Waabi’s round included additional investors such as NVentures (Nvidia’s venture arm), Volvo Group Venture Capital, and Porsche Automobil Holding SE. Those names hint at a future where autonomy is increasingly co-designed across chips, sensors, OEM integration, and platform distribution—meaning competitive advantage may come less from any single component and more from how tightly the system is productized, validated, and operationalized at scale.