The race to deploy robotaxis at scale isn't primarily a vehicle engineering problem; it's an ecosystem problem. According to a legal analysis from US attorney Eric Postow of Holon Law Partners, companies like Waymo may be winning the technology battle but could still lose the war if their cities don't build the supporting infrastructure that autonomous vehicles require to operate reliably.

What's the Real Bottleneck for Robotaxi Expansion?

Most people assume that self-driving cars succeed or fail based on sensor quality, artificial intelligence (AI) algorithms, or safety records. But Postow argues that assumption misses the bigger picture. The progression from Level 2 automation (where a human must stay ready to take over) to Level 4 (full autonomy within a defined area) isn't primarily a sensor or software challenge. It's an ecosystem challenge.

Higher automation levels require rapid decisions using real-time environmental data: traffic conditions, road surface changes, pedestrian behavior, and emergency vehicle movements. While core driving decisions are processed onboard the vehicle itself, reliable performance at scale also depends on map accuracy, infrastructure signals, vehicle-to-everything (V2X) communications, and data ecosystems that no single automaker controls.

This distinction matters enormously for Waymo's long-term prospects. The company operates robotaxi services in specific cities, but expanding those services to new markets requires those cities to have already invested in the digital infrastructure that makes autonomous vehicles viable.

How Are Cities Building the Infrastructure for Autonomous Vehicles?

China has grasped this principle and is building accordingly. The most consequential developments in Chinese automotive AI aren't happening inside the vehicles; they're happening in the cities those vehicles drive through. Consider Shenzhen, which has been transforming its urban fabric into an AI testbed. The city committed ten billion yuan to an AI and robotics industry fund, generated approximately 220 billion yuan from its core AI sector in 2025, and is targeting one trillion yuan in smart terminal output this year.

This isn't coincidence. It's policy. China's national "AI Plus" blueprint embeds AI into healthcare, manufacturing, and urban infrastructure simultaneously. The country's National Integrated Computing Power Network, launched in 2021, pools capacity from data centers, supercomputer clusters, and intelligent computing centers into something closer to a public utility.

The vision is straightforward: computing power will become like electricity, available on demand wherever it's needed. Core driving decisions are processed onboard, but the broader ecosystem,map updates, traffic modeling, fleet coordination,depends on distributed computing at scale.

Steps to Understanding the Infrastructure Gap for Robotaxi Deployment

• Onboard Processing: The vehicle itself handles real-time driving decisions using its own sensors and computing hardware, which is where most computation happens for immediate safety-critical tasks.
• External Infrastructure: Reliable performance at scale requires map accuracy, infrastructure signals, V2X communications, and data ecosystems that depend on city-level investment in digital systems.
• Distributed Computing Networks: Cities need access to shared computing power for map updates, traffic modeling, and fleet coordination, which requires investment in data centers and intelligent computing centers.
• Regulatory and Policy Alignment: National blueprints and city-level policies must embed autonomous vehicle support into urban planning, not treat it as an afterthought.

The practical result of this infrastructure investment is striking. A contact who recently returned from a family trip to China described a detail that stayed with them: a family member had gone to bed. Overnight, their NIO vehicle navigated itself to a nearby battery swap station, exchanged its depleted pack for a charged one, and returned home. By morning the car was ready. This is NIO's Battery as a Service model in practice, with 20 swap stations supporting the Hexi Corridor section from Xi'an to Dunhuang, and the full Silk Road route of over 30 stations projected to connect by September 2026.

"When vehicle, infrastructure, and service network work together seamlessly, the experience stops feeling like technology. It feels like a utility," noted Eric Postow.

Eric Postow, Attorney at Holon Law Partners

The United States and United Kingdom are not starting from nothing. The US is home to Waymo's operational robotaxi service and some of the world's leading AI research institutions. The Brookings Institution's 2025 analysis identified San Francisco and San Jose as dominant across all three AI readiness pillars: talent, innovation, and adoption.

But the same analysis reveals a structural problem. AI activity is heavily concentrated in a short list of coastal hubs, with broad hinterlands that lag across all three pillars. This geographic concentration creates a critical vulnerability for Waymo's expansion strategy. A Level 4 vehicle cannot serve only San Francisco; it needs to operate across diverse cities with varying levels of infrastructure maturity.

The implications are clear: Waymo's competitive advantage depends not just on its robotaxi technology, but on whether the cities where it operates invest in the computing networks, digital maps, traffic management systems, and regulatory frameworks that autonomous vehicles require. Without that supporting infrastructure, even the most advanced self-driving system will struggle to scale beyond its current pilot markets.