Recently, a seemingly trivial question has sparked a heated global discussion: Are there more wheels or doors in the world? Although this question might appear insignificant at first glance, it actually provides an excellent opportunity to explore the capabilities of generative artificial intelligence (AI).
This topic unexpectedly went viral in 2022 when a simple question on Twitter ignited countless debates among netizens, and it later gained widespread attention on TikTok. People began passionately debating whether there are more wheels or doors. However, answering this question is not straightforward, especially if we only consider our everyday perspective. Counting the wheels and doors around us feels like an endless math problem—impossible to accurately complete.
If you see a car, the wheels and doors seem obvious and might be easy to count. But in reality, the definitions of wheels and doors extend far beyond that. For instance, the engine in a car might contain many wheels, and the concept of "doors" can be expanded to include places like the trunk and glove compartment. The debate split into two factions: the "door side" and the "wheel side," with supporters of each passionately defending their points and even sparking controversies on social media.
Image source: Picture generated by AI, authorized service provider Midjourney
As the topic's popularity continued to rise, many companies seized the opportunity for publicity. Door companies and wheel manufacturers纷纷 voiced their opinions, and some brands even launched promotional activities like "thinking about this question while drinking coffee." Over time, the topic gradually faded from public view, but the question remains unresolved.
With the rise of generative AI, people began to wonder if this technology could solve the mystery. For example, ChatGPT, a popular AI tool known for its rapid response, became the subject of discussions.
When I asked ChatGPT, "Are there more wheels or doors in the world?" it responded, "This question does not have a clear answer. We can break down the question to discuss it." It then listed many possible sources of wheels and doors, including cars, motorcycles, furniture, etc. Although this response did not provide a conclusion, it effectively explained the complexity of the issue and demonstrated the generative AI's thought process.
Not satisfied with such a vague answer, I continued to challenge ChatGPT: "Your previous response avoided the issue. Why not choose one, are there more wheels or doors?" ChatGPT leaned towards "more wheels" and provided some reasons, such as the widespread application of wheels in vehicles, toys, luggage, etc. This made me wonder if AI's easy choice could be misunderstood as the answer.
Finally, I tried to guide ChatGPT to think deeper by suggesting a comparison of wheels and doors through annual production statistics. At this point, ChatGPT began to provide some data, discussing global car production and related wheel and door manufacturing, showing the potential of this technology in handling complex issues.
