Waymo: AI Projects Ready Only When Evals Are, Not Models

Waymo, the self-driving car company under Alphabet, is taking a firm stance on AI deployment: projects are not ready until their evaluations (evals) are complete, regardless of how well the underlying models perform. This philosophy reflects the extremely high stakes involved in autonomous driving, where even minor errors can have serious physical consequences. In a recent statement, Waymo emphasized that model performance alone is not sufficient to guarantee safety. Instead, the company relies on comprehensive evaluation frameworks that test AI systems under a wide range of conditions, including edge cases and unexpected scenarios. Only after these evaluations are passed does Waymo consider a project ready for deployment. This approach sets a rigorous standard for the entire AI industry, particularly for safety-critical applications. While many companies focus on improving model accuracy and efficiency, Waymo argues that the true measure of readiness lies in how well the system performs in real-world conditions—and how thoroughly those conditions have been tested. The emphasis on evaluations is not just about safety; it's also about trust. Waymo understands that for autonomous vehicles to gain widespread acceptance, the public must have confidence in their reliability. By prioritizing rigorous testing over rapid deployment, the company is building a reputation for caution and responsibility. Waymo's philosophy has broader implications for AI development. As AI systems are increasingly used in healthcare, finance, and other high-stakes domains, the need for thorough evaluation becomes paramount. The company's approach serves as a model for how to balance innovation with safety, ensuring that powerful technologies are deployed only when they are truly ready. In an industry often driven by speed and competition, Waymo's message is clear: when lives are on the line, there are no shortcuts. Evaluations come first, always.