[Reading Science] What Changes First When AI Stops Learning?

From "Brilliant Problem Solver" to "Reliable Tool":
Changes Users Will Notice

Recently, among users of generative artificial intelligence (AI), there have been frequent complaints that "AI these days is not as good as it used to be." While the answers may not be incorrect, users feel that the number of surprising insights or creative ideas has decreased, and that AI now mostly provides overly "safe and predictable" responses.

Experts analyze that this phenomenon is not simply a matter of perception, but rather the result of structural changes in the AI training environment that have transferred into everyday services.

AI Answers Trapped in the 'Pitfall of Averages'

When an AI model repeatedly trains on existing datasets instead of new, high-quality data, or begins to rely on synthetic data, its responses increasingly converge toward the "statistical average." Original expressions or rare examples are treated as noise and eliminated, leaving only the safest and most statistically probable answers.

The most significant change users notice in this process is the "loss of individuality." Whereas earlier AIs seemed to provide answers as if they were a blend of tens of thousands of geniuses, today's AI-experiencing a data stagnation period-resembles a model student reciting from the most standard textbook. When the dramatic leaps in performance stop, users are left with an AI that is "no longer surprising."

Lee Seongyeop, Professor of Intellectual Property Strategy at Korea University, predicted that this would lead to the "embedding (internalization of functions)" of the service. Professor Lee explained, "When explosive intelligence growth stagnates, companies will focus more on convenience than on performance competition," and added, "Instead of seeking out separate, impressive chatbot services, AI will increasingly and quietly become an invisible basic feature embedded in the word processors, email, and search engines we use."

User Challenges: 'AI Literacy' and 'Responsibility for Verification'

The fact that AI is no longer rapidly becoming smarter means that the capabilities of the human using the tool will ultimately determine the quality of the service. As AI responses become more averaged, users must develop "AI literacy"-the ability to detect hidden errors within those answers and reprocess them for their own purposes.

One particularly important point is the "authenticity" of data. Professor Lee emphasized, "As AI-generated information floods the internet, information that is directly experienced and recorded by real people becomes increasingly valuable," and stated, "Going forward, users must develop the ability to compare and verify AI outputs against the 'latest field data' that AI has not yet learned, rather than accepting AI results at face value."

From Technological Evolution to Mastery of Application

The period when AI's rate of evolution slows is not a crisis for technology, but rather a sign of its "maturity." Just as smartphones, after years of annual innovation, eventually reached a plateau of standardized features, AI is now moving past its peak performance into a phase of stabilization.

Professor Lee diagnosed, "We have entered an era where the key issue is not how amazing AI can be, but how safely and accurately humans can control this technology," adding, "It is now more important than ever to move beyond technological illusions, clearly recognize AI's limitations, and supplement them with critical human thinking."

Ultimately, where the growth of AI stops is where human mastery begins. Even if the "novelty" provided by technology diminishes, it remains up to users to determine how to utilize and verify that technology as a tool in their daily lives.