These Researchers Made AI Drive a Toyota Corolla to Get In-N-Out
Three engineers tested GPT, Claude, and Grok as autonomous driving agents in a Toyota Corolla on a real-world fast-food run. Only one model completed the task successfully.
Key Takeaways
- Key Highlight：Three engineers tested GPT, Claude, and Grok as autonomous driving agents in a Toyota Corolla on a real-world fast-food run. Only one model completed the task successfully.
- Innovation & Tech：Highlights advancements in GPT, Claude, Grok, demonstrating rapid progress in model capabilities.
- Industry Impact：Reported via Wired, offering actionable signals for developers and technology leaders.
Engineers mounted an experimental system in a Toyota Corolla, letting GPT, Claude, and Grok take turns controlling the vehicle on a trip to an In-N-Out Burger. The test explored whether general-purpose LLMs can serve as real-time driving agents.
The experiment highlights growing interest in using large language models beyond text generation, applying them to physical-world control tasks like autonomous driving. It also exposes the gap between conversational competence and the spatial reasoning, latency, and safety margins that driving demands.
Only one of the three models managed to complete the drive, underscoring that off-the-shelf LLMs are not yet reliable enough for unsupervised vehicle control. The result is a useful data point for teams weighing foundation models versus purpose-built autonomous stacks.
For the AI industry, the stunt is less about consumer autos and more about benchmarking agent capabilities in high-stakes, multimodal environments. Expect more such real-world LLM agent tests as researchers probe the limits of foundation models in embodied control.
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Industry Insights & Analysis
As artificial intelligence rapidly evolves, breakthroughs surrounding GPT, Claude, Grok, These are shifting toward scalable, robust real-world implementations.
Driven by both open-source ecosystems and proprietary model architectures, the integration between compute optimization, data engineering, and agentic workflows is accelerating. This development provides a strategic benchmark for upcoming AI tooling and developer workflows.