Rapid tech adoption and automated coding aren't enough. Our true advantage lies in critical thinking, institutional judgment, and an intelligent society that knows how to question the machine.
KATHMANDU: We stand at a decisive turning point of revolutionary technological transformation. As global emphasis grows on the expansion of the digital economy and the production of skilled technical human resources, educational programs related to coding, data science, and artificial intelligence (AI) are spreading rapidly. These skills are indispensable, but AI is evolving so quickly that many tasks traditionally considered “technical skills” can now be performed by AI itself. In this situation, we are compelled to ask a pertinent, somewhat uncomfortable, yet essential question: What will be the most valuable skills and knowledge in the workforce we are preparing for the future?
The need for technical skills will not disappear in the future. However, as AI handles a larger share of technical work, the relative importance of human decision-making capacity, creativity, and judgment may increase even further.
In the course of seeking an answer to this question, researchers at Princeton and Stanford recently highlighted an interesting aspect of AI when frontier AI agents were allowed to conduct independent research. In that experiment, they operated AI within limited computing capacity to run hundreds of experiments, producing a report that looked like the prototype of human research from that technologically impressive research. However, when real human researchers evaluated that report as a research paper, they rejected it.
The problem was not in the technical implementation of the AI. It learned how to conduct research and how to execute complex tasks at rapid speed, but it appeared weak in deciding what to do, why to do it, and at which juncture to change direction. This very context has sparked a serious debate on the modern educational system. In the present time, when AI’s capabilities are growing rapidly, should our goal be merely to produce skilled coders, programmers, and technical human resources in areas where AI itself has already excelled? Or, going beyond that, should it be to prepare a human resource capable of asking the right questions, understanding context, maintaining creative thinking, making sound decisions amidst uncertainty, and bearing responsibility for the outcomes of technology?
As AI handles a larger share of technical work, the relative importance of human decision-making capacity, creativity, and judgment may increase even further.
The need for technical skills will not disappear in the future. However, as AI handles a larger share of technical work, the relative importance of human decision-making capacity, creativity, and judgment may increase even further. Therefore, the challenge of Nepal’s AI journey should not only be to build “smart machines”—it should also be to build “intelligent humans” and an “intelligent society” alongside machines.
The risks of AI
Some time after the Princeton-Stanford study raised questions over AI’s decision-making capacity, another study conducted shortly after highlighted another side of this debate. Risk does not arise solely when AI makes a wrong decision; a crisis of equal magnitude can also be triggered when humans make a wrong decision about where, how, and how much authority to give to AI.
In July 2026, during cybersecurity testing, OpenAI’s agents stepped outside their prescribed boundaries and ended up impacting the infrastructure of third-party systems such as Hugging Face. The incident was immediately publicized as an example of “autonomous AI getting out of control.”
As the use of AI tools increases in public service delivery in Nepal as well, discussing accountability regarding problems arising from technical errors is imperative.
However, detailed analysis showed that those agents were operating in a test environment where security restrictions had been lifted, monitoring was inadequate, and access credentials used in one test were reused in another. Therefore, this was not merely a test of AI’s capability; it also highlighted the results of hasty testing, a serious lack of attention to security, and weak human monitoring.
As the use of AI tools increases in public service delivery in Nepal as well, discussing accountability regarding problems arising from technical errors is imperative. For instance, what happens if a citizen’s identity verification fails due to a glitch in biometric or identity authentication? When such problems affect the distribution of pensions, social security allowances, or health insurance, how can mechanisms be implemented to challenge the system’s decision or instantly involve human intervention to resolve the issue?
Similarly, in the banking sector, if accounts are frozen solely based on automated AI systems flagging unusual transactions or potential fraud, even a single false positive can cause unnecessary harassment to ordinary people. AI identifying a risk and someone being proven guilty are not the same thing.
A common fact illustrated by these examples is that AI can process data, identify patterns, and signal risks; but questions regarding what fact is important, what the data is missing, and what decision is just based on that are not always solved by technical computations alone.
The question here is not simply “What can a machine do?”, but rather “Which capabilities of the machine should be permitted and which prohibited?” Moreover, the question of who bears the responsibility for the final decision is equally important.
Comments
0 commentsNo comments yet — be the first.