Engineers Put Three Top AIs Behind the Wheel; Only One Reaches In‑N‑Out

In a practical trial that combined state‑of‑the‑art language models with ordinary car hardware, a trio of engineers assigned three well‑known AIs—OpenAI’s GPT, Anthropic’s Claude, and xAI’s Grok—to drive a Toyota Corolla to a local In‑N‑Out outlet. The goal was to determine if conversational systems, usually limited to text, could convert their reasoning into safe, real‑time vehicle operation.

The Corolla was fitted with a conventional array of sensors—cameras and lidar—and each AI was linked to a bespoke interface that transmitted perception data while receiving steering, throttle, and brake commands. Although the models were not built for motor control, the engineers supplied a concise onboard instruction set explaining how to read sensor inputs and generate driving actions.

Throughout the test, GPT was able to traverse the streets yet faltered with moving obstacles, pausing at intersections and sometimes producing conflicting commands that needed manual correction. Claude took a more guarded stance, keeping within its lane but unable to advance toward the goal, essentially looping indecisively at traffic lights. By contrast, Grok completed the full journey unaided, navigating turns, merges, and the final stop at the fast‑food venue with ease.

Observers point out that the varied results underscore how each model’s training focus shapes real‑world performance. GPT excels at broad language generation, a capability that does not inherently convert to the split‑second judgments needed for driving. Claude’s architecture emphasizes safety and interpretability, which can cause overly cautious actions in dynamic traffic. Grok, constructed with a more unified multimodal strategy, seems better equipped to turn textual reasoning into concrete motor commands.

The trial highlights both the potential and the present constraints of adapting large language models for embodied AI applications. Grok’s achievement hints at a route to more proficient autonomous platforms, yet robust sensor fusion, real‑time processing, and stringent safety checks remain essential. The engineers intend to improve the interface, broaden tests to diverse road scenarios, and investigate hybrid models that could merge GPT’s linguistic depth with Grok’s operational dependability.