Comparison: Car wash logic by different AIs

On May 1st, someone ran what looked like a trivial experiment: feed the same prompt — explain the logic of a car wash — to multiple AI systems and compare what came back. The choice of problem deserves credit before anything else. A car wash is not a trick question, but it is a complete system. It has an arrival state, a queue, a mechanical sequence with strict ordering, exception handling (the forgotten antenna, the cracked convertible top, the oversized truck that shouldn't have entered at all), a payment logic that usually precedes the service, and a clean exit state. Most adults could navigate one blindfolded. Almost none could write the decision tree from scratch. That gap between intuitive understanding and articulable logic is precisely where AI systems tend to reveal themselves — and precisely why the comparison, reported across both Singularity and ChatGPT community channels on the same date, attracted enough attention to register as a tracked story. The early signals don't detail which systems were included or what the divergences looked like, but the experiment's design implies the interesting question isn't which AI got it "right." It's which ones spotted the edge cases unprompted, which ones produced clean procedural logic versus loose narrative description, and which ones confidently omitted the complications that make the problem interesting.

If confirmed as a rigorous comparison, what this means is straightforward and a little uncomfortable for the field. Mundane reasoning tasks — the kind with real mechanical and social logic but no prestige — are arguably better benchmarks than the abstract puzzles AI labs tend to reach for. A system that handles the car wash problem well has demonstrated sequential reasoning, exception awareness, and the ability to model a process it likely hasn't been explicitly trained on as a formal system. A system that handles it poorly, but fluently, is demonstrating something the industry should pay more attention to: confident incompleteness. The second-order effect is that comparisons like this, if they spread, shift evaluation culture away from leaderboard performance on curated benchmarks toward messier, more human tests. That shift favors the people building AI tools over the people selling capability claims.

Watch for follow-up posts that name the specific models tested and show side-by-side outputs — that's what turns this from an interesting methodology into an actual finding worth acting on.

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