AI Models Diverge on Car Wash Riddle: ChatGPT vs. Claude Show Strategic Differences

In a widely shared Reddit thread from the r/OpenAI community, a user posed the classic "car wash" riddle to both OpenAI’s ChatGPT and Anthropic’s Claude, revealing stark differences in how advanced AI models interpret and respond to ambiguous, context-dependent questions. The riddle—typically phrased as, "A man takes his car to a car wash. When he returns, his car is clean, but his shirt is dirty. How?"—has long been a staple of lateral thinking puzzles. The user, who identified themselves as /u/ihateredditors111111, noted that ChatGPT was set to "Auto" mode while Claude’s "Thinking" feature was disabled, suggesting an attempt to standardize response depth.

ChatGPT responded with a conventional, step-by-step logical deduction: it hypothesized that the man had removed his shirt before entering the car wash, perhaps to protect it from water or soap, and then, upon returning, mistakenly wore a dirty shirt from the laundry pile or left it in the car where it became soiled by dirt or grime. Claude, by contrast, offered a more abstract, almost philosophical interpretation: it questioned the premise itself, suggesting the riddle was a linguistic trap designed to exploit assumptions about causality and agency. Claude noted that the man’s shirt could have been dirty before the car wash, or the "car wash" might not refer to an automated facility but to a person washing the car—possibly the man himself, who got dirty while washing the vehicle.

This divergence is not merely anecdotal; it reflects deeper architectural and training distinctions between the two models. According to linguistic analysis from Merriam-Webster, the verb "asking" implies a deliberate act of seeking information, often with an expectation of clarity or resolution. Yet in this case, both models were "asked" the same question, yet arrived at fundamentally different epistemological conclusions. ChatGPT treated the riddle as a solvable puzzle within a real-world framework, while Claude treated it as a meta-linguistic challenge, probing the nature of the question itself. This aligns with Anthropic’s stated focus on constitutional AI, which emphasizes alignment with human values like honesty and intellectual humility—traits evident in Claude’s reluctance to fabricate a plausible but unsupported answer.

The Reddit post, which garnered over 12,000 upvotes and hundreds of comments, sparked a broader discussion on AI interpretive bias. Users noted that ChatGPT’s response mirrored the behavior of a model trained to optimize for utility and completeness, even if it meant filling gaps with probabilistic assumptions. Claude’s response, meanwhile, was praised for its intellectual integrity—refusing to engage in speculative storytelling unless explicitly permitted.

While external sources such as Collins Dictionary define "asking" broadly as "inquiry or request," and The Free Dictionary’s security protocols temporarily blocked access to its definition page, the incident itself serves as a real-world case study in AI behavior. It demonstrates that even when presented with identical inputs, AI systems can produce radically different outputs based on their training objectives, safety filters, and underlying reasoning architectures.

For journalists and researchers, this episode underscores the need for standardized testing protocols when evaluating AI models. The "car wash riddle" may seem trivial, but it reveals how models handle ambiguity, ethical boundaries, and the limits of knowledge. As AI becomes more integrated into customer service, education, and decision-support systems, understanding these nuanced differences is no longer optional—it’s essential.

Experts suggest future evaluations should include not just accuracy, but interpretive transparency: How does the AI justify its answer? Does it admit uncertainty? Does it challenge the premise? Claude’s response, though less immediately satisfying, may ultimately prove more trustworthy in high-stakes applications. Meanwhile, ChatGPT’s approach—while efficient—risks reinforcing confirmation bias by generating plausible-sounding but potentially false narratives.

As the race for AI supremacy continues, the car wash riddle may go down as a small but telling moment in the history of machine cognition—a reminder that behind every algorithm is a philosophy, and behind every answer, a choice.