Waymo has stepped up its criticism of camera-only autonomous driving, explaining that fully driverless vehicles need a combination of sensors to operate safely at scale, an apparent challenge to Tesla’s strategy just days before the expected unveiling of its purpose-built Cybercab.
The Alphabet-owned autonomous driving company said in a blog post last week that cameras alone are insufficient for full autonomy and warned that so-called pure end-to-end artificial intelligence systems can produce unpredictable failures. Although Waymo did not name Tesla, the comments directly target the approach used by Elon Musk’s electric vehicle maker, which has rejected lidar and relies primarily on cameras and AI.
The timing has intensified the rivalry between the companies. Tesla is expected to formally introduce the two-seat Cybercab at an event on September 3, while Waymo announced three additional markets on Tuesday as it expands a commercial robotaxi operation that now serves customers in more than a dozen U.S. cities.
What was once largely a technical debate over competing approaches to autonomous driving has become a commercial contest. Both companies are betting on a market that could eventually generate hundreds of billions of dollars, but they are pursuing radically different paths to reach it.
“Cameras are incredible, but they aren’t enough,” Srikanth Thirumalai, a Waymo vice president overseeing driving software, wrote in the company’s blog.
“Now, after more than 200 million real-world miles, the data is clear: safe, fully autonomous operations at scale require more,” he added, noting that combining cameras, lidar and radar gives the vehicle a redundant view of its surroundings that a single sensor type cannot provide.
Waymo’s argument rests heavily on operational experience. The company says it has accumulated more than 200 million real-world autonomous miles and operates roughly 4,000 robotaxis across 14 U.S. cities, providing about 500,000 paid trips a week.
Tesla, by comparison, has only recently begun expanding its own robotaxi service beyond limited trials. Its network has so far operated in a small number of Texas and Florida markets using modified Model Y vehicles, with the company saying it is prioritizing safety over rapid expansion.
The technological disagreement is fundamental.
Waymo uses a sensor suite combining cameras, lidar and radar, alongside sophisticated AI and mapping systems. The approach is designed around redundancy: if one sensor has difficulty interpreting an object or environment, other sensors can provide additional information.
Tesla has taken the opposite route. Musk has repeatedly dismissed lidar as a “crutch” and has argued that cameras, neural networks and sufficient computing power can provide everything required for autonomous driving.
Waymo now argues that this AI-first strategy carries a potentially dangerous weakness.
Thirumalai said a pure end-to-end system that takes raw camera images and directly produces steering commands could be vulnerable to “black box failures,” where the system’s decision-making becomes difficult to understand or predict.
“Even the best AI models with trillions of parameters still hallucinate,” he told Axios. “There is no click reboot or reload or refresh [in] physical AI. You have to deal with the consequences of it.”
The argument has been largely supported because while an error in a chatbot can generally be corrected with another response, an error by an autonomous vehicle can cause a collision within seconds. That prompts the question about whether AI can drive reliably enough across millions of journeys and unpredictable real-world conditions.
Tesla’s upcoming Cybercab provides a particularly important test of that proposition. The vehicle is a purpose-built two-seater designed from the outset for autonomous operation. It has no conventional steering wheel or pedals and is expected to have a relatively small battery compared with Tesla’s existing vehicles.
Tesla has also signaled ambitions for large-scale production. A recent filing indicates a target of more than 125,000 Cybercabs annually, although the company’s ability to reach that level will depend on regulatory approval, manufacturing readiness and the performance of its autonomous software.
Tesla has considerable ground to make up. Musk previously predicted that Tesla would have 1 million robotaxis operating by 2020, a target that was not achieved. The company has instead spent the past year conducting limited commercial trials and has only recently begun removing safety monitors from most of those vehicles.
Tesla has also begun registering Cybercabs with the Texas Department of Motor Vehicles ahead of Thursday’s event. Dozens of vehicles have reportedly been spotted in parking areas around the United States, suggesting the company is preparing for a broader deployment, although the timing and scale remain uncertain.
The stakes for Tesla extend beyond the launch itself. If its camera-only system can deliver reliable autonomous driving at commercial scale, the company could demonstrate that a less hardware-intensive approach can outperform or undercut Waymo’s more sensor-heavy model.
That is where the economics of the technology become as important as the engineering.
Waymo’s system is expensive. Lidar, radar, and additional computing hardware add to vehicle costs, while the company generally deploys its autonomous technology on vehicles manufactured by third parties. Waymo therefore has to purchase those vehicles and then retrofit them with its autonomous-driving hardware and software.
Tesla has a structural advantage if its AI approach works. It manufactures its own vehicles, controls much of the vehicle software and hardware stack, and could potentially integrate autonomous-driving technology during production rather than retrofit cars afterward.
That could give Tesla substantially lower costs per robotaxi.
The company is effectively making a high-risk technological bet: invest heavily in AI and computing to eliminate expensive sensors and simplify the hardware required for autonomous driving.
Waymo is making the opposite calculation. It is accepting higher hardware costs in exchange for additional sensing capability and redundancy.
Neither strategy has yet definitively won the argument.
Tesla still has to demonstrate that its autonomous system can operate reliably across a much wider range of conditions, including severe weather, unusual road layouts, emergency vehicles, pedestrians, construction zones and school areas. These are precisely the types of situations Waymo encounters as it operates a larger commercial fleet.
Waymo’s advantage is therefore its accumulated operational data and experience. Tesla’s potential advantage is scale and cost.
The confrontation also explains the increasingly pointed rhetoric between the two camps. Pierre Ferragu, an analyst and managing partner at New Street Research who covers Tesla, accused Waymo of falling into the rhetoric of an incumbent whose technology could eventually be made obsolete by advances in AI.
Waymo spokesperson Ethan Teicher responded by emphasizing the company’s accumulated driverless mileage, AI monitoring, and multi-sensor architecture.
The debate is ultimately about more than whether lidar is necessary or whether end-to-end AI is superior. It is about which architecture can deliver autonomous transportation at the lowest cost while maintaining an acceptable level of safety.
If Waymo is right, Tesla’s decision to abandon lidar could leave it exposed when autonomous systems encounter situations that cameras and AI struggle to interpret. If Tesla succeeds, however, the cost advantage of its vertically integrated manufacturing model could become a major competitive threat to Waymo and other robotaxi operators.
The September 3 Cybercab event will therefore be watched not simply as another Tesla product launch, but as a test of two competing visions for the future of autonomous transportation.
