Adam Mowafi, a user on the social media platform X, has expressed concerns about Waymo's approach to scaling its robotaxi model. He said that the company's requirement to comprehensively map every area makes it difficult to scale compared with ride networks that can more easily adjust supply.
"I didn't say engineering I said projects with a possibility of scaling, Waymo requires comprehensive mapping of the whole world and every drive way to work," said Mowafi, X user. "That's not going to happen without going back to scratch."
Waymo’s own materials highlight that its driverless service relies on creating highly detailed "custom maps" of each service area. These maps catalog lanes, curbs, signs, and signals before cars begin operating. According to the company, it is expanding into new U.S. cities such as Washington, D.C., Miami, and additional areas in California and Texas. However, each launch requires a period of mapping and validation before full-scale robotaxi service can commence. Critics like Mowafi argue that this mapping-heavy model is structurally harder to scale than ride-hailing networks that can add new human drivers or service zones with fewer upfront constraints.
Recent reports indicate that Waymo operates a sizable but fixed autonomous fleet. Company blogs and industry analyses estimate its commercial robotaxis at around 1,500 vehicles by mid-2025. The fleet distribution includes approximately 800–1,000 cars in San Francisco, 700 in Los Angeles, 500 in Phoenix, 200 in Austin, and about 100 in Atlanta. Reuters and other outlets report that this fleet delivers around 200,000–250,000 paid trips per week across its U.S. markets. Due to centrally controlled vehicle numbers that cannot spike on short notice, AV-only fleets resemble fixed taxi fleets and may face longer queues or unstable estimated times of arrival (ETAs) during unexpected demand surges.
Transport researchers are increasingly studying how mixed fleets of autonomous and human-driven vehicles can smooth out peaks in demand. Modeling of ride-hailing systems with a fixed AV fleet and flexible human drivers suggests that AVs are well-suited for covering predictable base load while human drivers help keep waiting times low during rush hours or special events by entering or leaving the platform based on earnings opportunities. Ride-sourcing platforms like Uber already implement dynamic pricing to encourage more human drivers online when demand spikes, effectively scaling supply within minutes. These findings support the view that a hybrid network can offer more reliable ETAs than a purely AV fixed-fleet model.
Tekedra Nzinga Mawakana serves as co-chief executive officer of Waymo, Alphabet’s autonomous driving subsidiary. She oversees overall company strategy and the commercialization of the "Waymo Driver" robotaxi platform. A Columbia Law School graduate with over two decades of experience in regulatory environments from companies like AOL, Yahoo, and eBay, she has led Waymo's expansion of paid fully driverless services across multiple U.S. metros while promoting their mission "to be the world’s most trusted driver." Debates continue over scalability, fleet design, and long-term integration with broader ride-hailing networks under her leadership.