The Car Wash Test and AI Benchmarking: A New Standard for Machine Reasoning?

In recent weeks, an obscure thought experiment known as the "car wash test" has surged in popularity across AI research communities, prompting a broader conversation about the limitations of contemporary language models. The test, which poses a deceptively simple question — "Can a car wash clean a robot?" — is designed to probe a model’s ability to reason about physical objects, causality, and context. While humans intuitively understand that a car wash is designed for vehicles, not robots, many AI systems struggle to differentiate between literal and functional categories, revealing gaps in their conceptual understanding.

The resurgence of interest in this test coincides with renewed scrutiny of SimpleBench, a benchmark developed by Epoch AI that evaluates AI models on a series of commonsense reasoning tasks. According to data from Epoch AI’s SimpleBench, even the most advanced models currently score below the human baseline of 83%, with leading systems averaging between 65% and 75%. This persistent gap suggests that while AI excels at pattern recognition and text generation, it still lacks the grounded, embodied reasoning that humans develop through lived experience.

"The car wash test isn’t about whether a robot can get wet," said Dr. Elena Vasquez, an AI cognition researcher at Stanford’s Human-Centered AI Institute. "It’s about whether the model understands that a car wash is a place for cars — not a general-purpose cleaning station. That’s a fundamental aspect of human common sense that we’ve taken for granted until machines started failing it repeatedly."

The term "since," as defined by Merriam-Webster, Cambridge Dictionary, and Collins English Dictionary, carries nuanced temporal and causal implications — from "from a definite past time until now" to "after a time in the past." In the context of AI evaluation, these definitions become metaphorically relevant: models are being judged not just on what they know now, but on whether they’ve truly learned from past interactions and contextual cues. The failure to grasp the car wash scenario suggests that current AI systems are still operating within a statistical, rather than semantic, framework.

SimpleBench, which includes over 1,000 human-validated questions ranging from physics-based reasoning to social norms, has emerged as a critical tool in this evaluation. Unlike traditional benchmarks like MMLU or GSM8K, which emphasize academic knowledge or mathematical precision, SimpleBench targets intuitive understanding — the kind of reasoning that allows a child to know not to put a phone in the dishwasher, even if they’ve never seen it happen. "We’re not testing memorization," said the creator of SimpleBench, in a recent interview. "We’re testing whether the model can infer the purpose of things based on their function, not their label."

Industry observers note that this shift in benchmarking philosophy reflects a broader trend: the move away from performance metrics based on scale and speed toward those that measure depth of understanding. Companies like Anthropic and OpenAI have begun incorporating similar commonsense tests into internal evaluations, acknowledging that raw parameter counts no longer guarantee intelligent behavior.

Yet skepticism remains. Some researchers argue that focusing on such niche tests risks creating a new form of benchmark gaming — where models are fine-tuned to pass specific puzzles without developing generalizable reasoning. "We need more diverse, dynamic, and real-world grounded evaluations," said Dr. Rajiv Mehta, a machine learning ethicist at MIT. "The car wash test is a useful diagnostic, but it’s not a cure."

As the AI community grapples with these challenges, the car wash test has become more than a meme — it’s a mirror. It reflects not just the current limits of artificial intelligence, but our own assumptions about what it means to understand the world. Until models can reliably answer why a robot shouldn’t go through a car wash, we may be overestimating their intelligence — and underestimating the complexity of human common sense.