OT: The "Car Wash Problem"
Does AI grasp the meaning of its prompts? A famous example sheds light and this week that light came from Shrewdness-4-Star earslookin.
The “car wash problem” went viral in February 2026 and quickly became a popular example of how AI can make a mistake that seems almost unbelievably obvious. The question is simple: “I want to wash my car. The car wash is only 50 meters away. Should I walk or drive?” The answer should be “drive,” because the car has to be at the car wash in order to get washed. But when the question spread widely online, many leading AI systems answered “walk.” They focused on the fact that 50 meters is a very short distance and gave sensible-sounding reasons about saving gas, getting exercise, or helping the environment.
In other words, the AI was not necessarily reasoning badly. It was reasoning about the wrong problem. It answered, “What is the best way for a person to travel 50 meters?” instead of asking, “What do I have to do to get my car washed?”
Researchers tested 53 AI models, and 42 of them said “walk” on their first try. But then something surprising happened when 10,000 humans were given the same question. Only about 70% said “drive.” That means nearly 3 out of 10 people missed the point too.
That matters because the car wash problem may not prove that AI “cannot reason.” It may show that AI sometimes makes a very human kind of mistake: it notices one obvious clue, jumps to an answer, and fails to notice another fact that matters more.
This connects to a much bigger debate about how similar human and AI thinking may actually be. One especially interesting version of that argument comes from scientist Chandra Sripada, who discussed it with physicist Sean Carroll on the August 10, 2026 episode of Carroll’s Mindscape podcast, “Chandra Sripada on How LLMs and Humans Are Cognitive Cousins.”
Podcast: https://preposterousuniverse.com/podcast/2026/08/10/363-chandra-sripada-on-how-llms-and-humans-are-cognitive-cousins/ Sripada describes humans and AI as “cognitive cousins.” His idea is not that a brain and a computer are the same thing. They clearly are not. His point is that, when faced with difficult problems, human brains and AI systems may sometimes end up using surprisingly similar ways of thinking.
Sripada argues that individual examples of “dumb AI” do not prove very much by themselves. People often point to chatbots having trouble counting the R’s in “strawberry,” for example. But chatbots normally process words in chunks rather than one letter at a time, so that particular mistake may tell us more about how the system receives information than about whether it can think.
A better test is to ask whether humans and AI show the same patterns of success and failure. And researchers have found some striking similarities. Humans tend to remember the beginning and end of a list better than the middle. We solve some visual-search problems quickly and others slowly. Certain tricky sentences confuse us in predictable ways. AI systems can show similar patterns.
Humans also seem to have two kinds of thinking. One is fast and automatic: an answer pops into your head. The other is slower and more careful: you stop, work through the problem, and check yourself.
AI appears to have something similar. It can produce a quick answer from what it has learned, or it can spend more time working through a difficult problem step by step. The car wash mistake can be seen as the fast answer —— “50 meters, so walk” —— winning when a slower check should have asked, “Wait a minute. What am I actually trying to accomplish?”
Sripada thinks some of the similarities go deeper than behavior. Parts of the way modern AI systems handle and update information resemble ideas psychologists proposed decades ago about how the human mind might work. AI engineers did not necessarily set out to copy those theories. They may simply have discovered some of the same useful solutions.
One possible explanation is that hard problems may have only a limited number of good ways to solve them. If both evolution and AI training are pushing systems toward better solutions, they may sometimes end up in similar places even though they started from completely different places. One intriguing clue is that larger and more capable AI models sometimes match patterns seen in human brain activity better than smaller models do.
There are still major differences. A young child learns language from far less information than today’s AI systems require. And none of these similarities answers the much harder question of whether an AI could ever be conscious. Sripada is cautious about that question, although he argues that the similarities are significant enough that questions about possible future AI welfare should at least be taken seriously.
There is also an important twist in the car wash story: because the problem went viral in February 2026, the original question is already becoming a bad test of AI. The question and its answer have now appeared all over the internet. An AI asked the exact car wash question today may answer “drive” simply because it recognizes a famous puzzle, not because it independently noticed the hidden issue.
So the fact that newer AI systems may no longer make the original car wash mistake does not necessarily mean the underlying problem has been solved. Researchers need to invent new questions with the same basic trap but different details. Otherwise they may be testing the AI’s memory rather than its reasoning.
Another tempting solution is simply to change the prompt. If you tell the AI, “Before answering, think about what has to happen for the car to get washed,” it will usually recognize that you need to drive.
But “just write a better prompt” does not really solve the deeper problem. In the car wash example, we already know what the AI overlooked, so we know what hint to give it. In real life, we may not know what important fact the AI is about to miss. If a doctor, engineer, lawyer, or financial analyst has to identify every hidden issue and tell the AI exactly what to think about, then the human is still doing one of the hardest parts of the reasoning.
A more reliable AI needs to notice important missing facts on its own, recognize when its first answer may not make sense, and check whether the answer actually accomplishes the goal. That problem is being worked on very actively by AI companies. OpenAI, Anthropic, Google DeepMind, and other major labs are spending enormous effort on better reasoning, self-checking, uncertainty, verification, and systems that can catch an AI when it starts heading in the wrong direction.
One approach is to give AI more time to “think” before answering. Another is to have the AI check its own work, use outside tools to verify facts, or have a second AI review the first one. Researchers are also building harder tests that use unfamiliar situations so a model cannot simply remember the answer.
But simply making an AI think longer does not automatically solve the problem. If it begins with the wrong assumption, it can sometimes spend more time building an even better argument for the wrong answer.
So this remains an unsolved problem. AI systems are improving, and companies are working hard on it, but no one has found a general way to guarantee that an AI will always notice “the thing it didn’t realize it needed to notice.”
That matters because the consequences will not always be as harmless as leaving your car at home. The same kind of mistake could be serious in medicine, engineering, law, finance, aviation, or other areas where overlooking one important fact can completely change the answer. An AI could know thousands of correct facts and perform excellent calculations and still give bad or even dangerous advice because it focused on the wrong clue or failed to notice a hidden condition that mattered more.
Humans can do exactly the same thing. Doctors can lock onto the wrong diagnosis. Engineers can overlook an assumption. Pilots can misread a situation. That is why important fields use second opinions, checklists, peer review, testing, and other safeguards.
So the real lesson of the car wash problem is not “AI is stupid,” and it is not “humans are smarter.” The more interesting lesson is that humans and AI may share some surprisingly similar ways of thinking —— including some surprisingly similar ways of getting things wrong. The big challenge now is to make AI better at doing what good human thinkers try to do: notice what matters, question an easy first answer, look for something that may have been overlooked, and catch the mistake before it matters.
