Summary
1. The Value of Learning Is Shifting from “Doing” to “Judgment” in the AI Era
As generative AI continues to automate many skills, the value of learning is no longer about simply performing tasks. Instead, it lies in the ability to make informed decisions, guide AI effectively, and evaluate its outputs. In this context, continuous learning is becoming even more important.
2. The Structural Problem: Learning Achievements Are Not Properly Verified
Current systems for certifying education and skills still rely heavily on paper certificates or PDFs, which are not easily standardized or widely recognized. As a result, individuals struggle to clearly demonstrate their learning achievements, limiting the broader adoption of reskilling.
3. NFTs Have the Potential to Redefine Learning Credentials
NFTs offer a new way to securely and globally verify learning histories through their immutability and user ownership. Combined with technologies like Soulbound Tokens (SBTs) and Verifiable Credentials (VCs), they can make personal learning journeys visible and trustworthy—potentially transforming education into a more transparent and decentralized system.
In recent years, many of us have likely come across the phrase, “Transform your life through reskilling.” Since the COVID-19 pandemic, there has been a growing movement of people learning skills such as video editing, web writing, and programming, and using them to earn income through side jobs or freelance work.
However, this landscape is now beginning to change dramatically. With the rapid advancement of generative AI, many skills once considered valuable “practical trades” are becoming increasingly replaceable by AI.
Does this mean that continuing to learn has become meaningless? I don’t believe so. On the contrary, I think now is precisely the time to reexamine the very meaning of learning itself.
AI excels at reproducing existing knowledge and improving efficiency. On the other hand, it is those who have deeply studied a field who can determine “what to ask AI” and “how to evaluate the quality of its output.”
In other words, the value of learning is shifting—from simply being able to perform tasks to being able to make informed judgments. Even to effectively utilize AI, one needs foundational expertise and experience; without them, it is impossible to give appropriate instructions or assess results accurately.
According to Japan’s Ministry of Economy, Trade and Industry, there could be a shortage of up to 790,000 digital professionals by 2030. What is needed here is not merely engineers who can write code, but individuals who can understand technology and design and evaluate solutions to business and societal challenges. The importance of continuing to learn is, if anything, increasing.
Moreover, what one has learned is a crucial element in shaping one’s identity. What fields have you been interested in? What knowledge have you accumulated? What experiences have you chosen?
These are elements that cannot be replaced by AI, no matter how advanced it becomes. They form the depth and trustworthiness of a person. The “trajectory of learning” is a unique and invaluable asset that defines who you are.
