AI payoff depends on human adoption

Lee Soo-hwa

The author is a research professor of Big Data Innovation Convergence College at Seoul National University and the head of the AI Center at DLG Law Corporation.

The most expensive productivity experiment in history has begun. Four major U.S. technology companies are expected to spend a combined $695 billion to $725 billion on capital investment this year. Wall Street is asking a simple question: When will all that spending on steel, electricity and silicon turn into cash flow? The answer to that query will not be found in how many sentences chatbots produce but in how many real tasks AI completes.

This distinction matters because the scale of investment alone does not guarantee productivity. Data centers can be built quickly, but institutions change slowly. Unless students and workers learn to restructure daily processes around AI, expensive computing capacity may remain underused or devoted to low-value conversation.

If conversational AI is tourism, then autonomous AI agents are immigration. A tourist arrives at a place, explores and asks questions but ultimately leaves. An immigrant, on the other hand, settles down. When a procurement AI agent can analyze orders, inventory and delivery schedules; propose corrective action; and complete the approval process, the technology is no longer a toy. At that point, it has become an employee.

The question “Why learn coding when AI can do it?” resembles asking why people should study mathematics when calculators exist. The point is not to memorize programming syntax but to learn how to break a problem into parts, define inputs and outputs, anticipate exceptions and verify what AI produces. Someone must still judge whether AI’s result is correct and accept responsibility when it is not.

Korea is already filled with robots and smart factories. But workers often cannot freely use the data generated by their own factories. Software and data remain locked behind a fence built by vendors. Employees bear the blame when experiments fail but receive little authority, recognition or reward when they succeed. Under such conditions, no one wants to be the first to cross the divide.

Software instruction has expanded, but information technology and coding will still not be an independent section of the College Scholastic Ability Test in 2028. Subjects excluded from the entrance exam are easily pushed to the margins at school. China, meanwhile, has taken a different path. It placed AI and coding education within its national strategy in 2017, then introduced related high school textbooks and experimental schools the following year. When the state sets a direction, classrooms tend to follow.

Coding should become a given subject, comparable to Korean, English and mathematics. But it must not be taught through multiple choice questions. Students should be evaluated based on whether they can clean and structure data, build an environment in which AI can work and interpret and verify AI-generated analysis and visualization.

Companies must make the same transition. They should give employees access to data, application programming interfaces, experimental spaces and rewards, then gradually expand the workers’ authority. Employees cannot be expected to innovate while every useful tool remains locked away and every mistake carries a penalty.

Investors also need better measures. Subscriber totals and login counts reveal little about productivity. The number of paid tasks completed, revenue per task, inference costs and the time and expenses saved by customers are more useful indicators. A taxi driving in circles can keep its meter running, but it is not taking its passenger anywhere.

As AI handles more work, demand for AI-specific memory chips and high-performance storage will rise in tandem. But one line of AI-generated code does not immediately result in a semiconductor order. For memory demand to grow exponentially, AI must process a rapidly expanding volume of meaningful work.

That requires classrooms and workplaces to change first. Korea’s AI competitiveness will come alive only when everyone can assign real work to AI, examine the results and take responsibility for them. When that transformation spreads in schools, offices and factories, it will ultimately appear in the order books of the country’s semiconductor plants.