In the AI era, intention matters more than answers

Every transformative technology reshapes not only what humans can do, but how we think. The smartphone, for example, compressed the world into the palm of our hands. It expanded access to information while quietly cultivating new forms of dependence.

Artificial intelligence follows the same pattern — only at a much faster pace and with far deeper cognitive consequences.

AI has entered daily life with sweeping speed. Large language models are attracting users on a historic scale, creating the impression that almost any problem can be solved instantly.

Yet I have long held a simple belief: there are no real shortcuts. Every apparent shortcut carries a cost that is easy to overlook.

When AI generates answers effortlessly, our instinct to verify begins to weaken. As it produces text and code at superhuman speed, we often skip analyzing its logic, structure, and intent. Instead of thinking independently, we find ourselves reviewing AI outputs. Over time, this creates what might be called "cognitive debt".

In exchange for short-term efficiency, we risk sacrificing long-term intellectual capacity — critical thinking, memory, and creative autonomy. For young people whose intellectual foundations are still forming, this trade-off is especially consequential.

Discomfort, friction, and sustained effort are not obstacles to learning; they are its training ground. When difficulty is removed too early, we also remove the very conditions under which independent thought develops.

If AI is to remain a tool rather than become a crutch, the user's role must be clearly defined. Before turning to AI, we should first ask ourselves two questions: What am I trying to accomplish? Why does it matter? Only then should we ask: How can AI help me do this better?

AI excels at answering "how", but it cannot replace our responsibility to define "what" and "why". When we outsource those questions from the start — asking AI to write an article or build an app without first clarifying our intent — we often end up with shallow results, frustration, and endless debugging. What needs protection is not execution, but intention.

This is why my approach to AI always begins with my own thinking and needs. When a task arises, I first clarify what I want to accomplish and how I intend to approach it. Only then do I consider how AI might improve efficiency. It supports my decisions; it does not make them for me.

In my own work with AI, I prioritize questioning over delegation. I often ask it to analyze my dialogue history and identify patterns or blind spots in my thinking — especially the ones that are difficult to recognize on my own. In this way, AI functions less as a problem-solving tool and more as a medium for structured reflection.

Another practice I value is journaling. I keep simple, factual daily records and use a local AI tool to track patterns, milestones, and shifts in my mental state over time. What matters most is that the thinking remains mine. AI can summarize and observe, but it cannot — and should not — replace judgment. When the record-keeping is honest and frictionless, AI becomes a lens for self-understanding rather than a substitute for it.

This approach has gradually led me to a deeper question: what truly distinguishes human thinking from AI? One essential difference lies in memory and emotion — in our ability to selectively remember and forget. This selectivity shapes our identity, values, and character. AI, by contrast, can accumulate everything, yet it owns nothing.

As AI grows more capable, the most important skill for young people may not be mastering tools but remaining the authors of their own intentions. Efficiency without reflection carries hidden costs. In a world overflowing with answers, the ability to ask "why" is our most irreplaceable strength.

Written by Kai Yijing, senior product operations manager for Qwen Code at Tongyi Lab and a graduate of Beijing Forestry University. She specializes in driving AI product adoption and developer community growth, with a keen focus on integrating personal knowledge management methodologies into cutting-edge AI workflows to enhance productivity and innovation.

Released at the MEET2026 Intelligent Future Conference in December 2025, the "Top 10 AI Trends Report for 2025" marked a symbolic turning point in the evolution of technology and society. It signaled that artificial intelligence is no longer merely a passive tool awaiting instructions but is steadily emerging as an active partner in work, learning, and decision-making.

As AI becomes embedded in everyday scenarios — from classrooms to offices to creative studios — a pressing question arises, especially for young people: how can we embrace its capabilities while still safeguarding independent thinking and intellectual autonomy?