[Gangnam Perspective] The End of Credentials

"Dig one well. Then you can make a living for the rest of your life." This is what the parents’ generation used to tell their children. There was a time when a bank clerk could stick to banking, an accountant to accounting, and a developer to coding, do the same work for life, and then retire on a severance package and a pension. Then at some point, things changed. "Dig two wells." We entered an era in which you had to build deep expertise in one field and add another specialty on top of it just to make a living.
With the arrival of ChatGPT in 2023, even this formula is being shaken. Artificial Intelligence (AI) now does coding, design, data analysis, and even strategic planning. No matter how many wells humans dig, it no longer seems to matter.
Under the bombardment of AI, both educational institutions and corporate organizations have fallen into what can only be called a state of shock. University officials preparing for the new academic year in 2026 are heaving deep sighs these days. They have rushed to create AI utilization courses to keep up with the times, yet they look perplexed about what they should actually be teaching. Companies have drastically cut back on hiring new employees altogether. Rather than recruiting new staff who cannot compete with AI, it is more efficient to train existing employees to make better use of AI.
In the field of education, models of future talent suited to the AI era are adrift, while companies are turning away from entry-level hires. An era of structural unemployment and hardship for young people, in which education has lost its way and the path into society is blocked, has begun. Do the older generations even understand why young people are so angry? A mass unemployment crisis among the young is right before our eyes, yet our society is as insensitive as a frog in a laboratory beaker, oblivious to the water that is slowly heating up.
The competency models built around knowledge, skills, and attitudes that emerged in the 1970s still stand as the standard textbook for recruiting and managing talent. In the 2000s, alternative models such as T-shaped talent, focused on depth plus breadth, and pi-shaped talent, emphasizing multiple areas of expertise and integration, spread and have been used as substitutes for traditional competency models.
However, in the AI era these models have lost their power. Existing talent models are centered on functions or credentials. At a time when AI is replacing human functions through automation and speed, we cling only to these old functionalist models. In terms of physics, we are obsessed with the scalar quantity. Just as we measure only magnitude with expressions like "temperature 30 degrees" or "speed 100 kilometers per hour," talent management and evaluation still focus solely on "competency score 90," "10 years of experience," and "five certifications." Meanwhile, we have no alternative that both leverages AI and preserves uniquely human competitiveness. If the "age of scalars" is ending, we should be searching for the "age of the vector," yet we remain stuck in place.
At the moment when AI is devouring human jobs, the only real alternative being proposed is the human capacity to design, judge, and make decisions independently. We must cultivate our uniquely human strengths in experience, judgment, and a sense of responsibility if we are to stand up to AI at all. To extend the vector metaphor, the urgent task is to build knowledge and technical competencies that make effective use of AI. Then we need the ability to ask "why" so that we can break free from blind dependence on AI and determine the right direction to move in. On top of that, we need decision-making capabilities that apply our own knowledge and judgment, enhanced by AI, to the right place at the right time. We also cannot leave out integrative capacity: the ability to bring together our various competencies and maximize synergy.
Compared with the era when it was enough to dig just one well, the world has become truly exhausting. More frightening than the exhaustion, however, is the sense of total darkness ahead—the fact that people do not know what they should be preparing for. The older generations offer only vague platitudes about using AI well and asking good questions.
AI is shaking the very definition of the intelligence-based meritocracy we had grown accustomed to. If the 20th century was an era that emphasized "how much, how fast, how accurately," then in the AI era you survive by mastering "why, where, and how." The scalar age is coming to an end. Are we truly sensing the paradigm shift in labor? If we cling to past credentialism and fail to set new coordinates, there will be no future for individuals, organizations, or society as a whole.
jjack3@fnnews.com Reporter