I found that I cannot use AI to learn/solve philosophical thinking.
Professionals can easily solve problems that I couldn’t resolve after years of thinking and reading.
Reflecting on myself, perhaps the AI I use is only searching within “my” limited vector space.

This led me to wonder, what kind of people truly “know how to use” AI?

Older people use new tools to solve existing problems, such as work issues; while children/students use AI to address academic problems.

Often, we assume that compared to “adult” problems like work, the academic challenges faced by younger people are simpler and more systematic. However, in reality, younger people don’t have time to learn the meta-problem of “how to learn”—here, the “meta-problem” refers to their lack of opportunity to develop the habit of following the “traditional path.” Consequently, they have ample motivation to seek their preferred “shortest path,” which is inevitably validated afterward by assessments like “exams.”

The distinct characteristics of academics are:

1. Short timeframes for each stage.
2. Verifiability.

We have specific scores, and the entire system design maximizes the cycle of these two characteristics.

In contrast, most adults facing so-called “adult problems” often “know” and have the ability to quickly search for existing solutions. Thus, AI is seen more as a “fast tool” or a potential shortcut.

We will then observe a common pattern: adults encountering problems will try AI. If it’s not within their expertise, two possibilities arise:

1. Inability to use AI correctly to solve the encountered problem. This doesn’t necessarily mean they can’t utilize AI but often points to giving up halfway.
2. Obtaining incorrect solutions through AI, as most adult problems cannot be scored and are more experiential in nature.

In summary, we arrive at a very interesting and widely applicable explanation for the fundamental reason why younger individuals are more likely to master new tools effectively.

Because children have academics as a “playground,” and they lack ready-made tools and experience, they are compelled to master new tools to find the shortest path—essentially utilizing the broadest and deepest search.

It’s crucial to emphasize that the variable truly at play here is not “age” but “whether there is still a traditional path to fall back on.” Students’ advantage stems from being institutionally deprived of the old path—not because they are young, but because path dependence hasn’t yet formed. Career changers, novice practitioners, and frontier researchers among adults are structurally similar to students; conversely, if the old path (rote memorization, copying answers) remains effective for students, they will also revert to it. Therefore, a more accurate statement is: AI doesn’t favor the young; it favors those with no way back. Academics happen to be an institutionalized form of “no way back.”

This incidentally addresses another question: how long it takes for a new thing to be widely adopted or disproven.

Essentially, it’s the students who have fully mastered and can flexibly apply the tool in the “final stage,” transitioning from the student phase to joining society and becoming productive contributors.

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