[Reading Science] How Will Labor Be Redesigned After AI in 2026?

Human Work Moves Beyond Execution to a Structure of Judgment

In 2026, the labor market stands at a turning point where the wave of technological restructuring over the past year is directly impacting the value of human labor. If the core of the tech industry in 2025 shifted from algorithm competition to computational infrastructure, then 2026 is the year when this impact fully transitions into the labor market, education, and standards of competency. Technological change is now raising questions not just about industrial efficiency, but about how human work will persist and how it will be transformed.

The year 2026 marks a turning point as the technology landscape, reorganized around computational infrastructure, begins to spread significantly into the labor market, determining how human work will persist and how its value will be redefined. Provided by Pixabay

Generative artificial intelligence (AI) is no longer just a tool that assists with specific tasks. Companies have begun to directly integrate AI into their work structures for major decision-making reports, data analysis, code generation, and document review. This is leading to a shift in the labor market where it is not the 'job' itself but the 'task unit' that is being reorganized. Rather than large-scale restructuring, these changes are being felt first on the ground, in day-to-day operations.

This transformation is not limited to a particular industry or occupation. The very structure of work is changing across office, professional, research, and creative roles. What is happening now is not simply a technological trend, but a process of rewriting the definition of human labor itself.

Technological Change Now Moves to 'Human Work'

The World Economic Forum (WEF) defines the period from 2025 to 2030 as "an era when AI and automation will fundamentally reorganize work structures," and forecasts that the required competencies within professional roles will rapidly increase. Global consulting firms also diagnose that "2026 will be the year when AI proficiency begins to function as a key variable for salary and career advancement."

This is not only because technology has advanced. AI has become stable and affordable enough to be used in actual work, making it a practical alternative for companies to reduce costs and errors. As a result, the center of gravity in technological competition is shifting from "what can be created" to "who works and how." If technological change has shaken up industrial structures, now it is time to redefine the meaning of human labor.

It Is 'Task Units,' Not 'Jobs,' That Are Replaced First by AI

The fastest AI adoption is happening in highly structured tasks. Drafting report outlines, initial contract and terms review, basic coding, data organization, first-line call center responses, and report summarization are all areas where the proportion of AI processing has already increased significantly. The common factor among these tasks is that their criteria for judgment are relatively clear and can be explained through rules and patterns.

Kim Dongkyu, a research fellow at the Future Occupations Research Team of the Korea Employment Information Service, explained, "What disappears due to AI is not the entire job, but the task units that make up that job." According to case studies and field interviews, the most significant changes are occurring in IT development. Content-based roles such as visual and computer graphic design, translation, digital marketing, copywriting, as well as general office work, research, and journalism, are also experiencing rapid changes in work structure.

AI is changing work structures rather than the jobs themselves, starting with IT development and expanding its scope of change to content creation, office work, research, and journalism. Photo by Pixabay

This is why companies refer to this as an "invisible restructuring." Instead of jobs disappearing all at once, it is the tasks that can be judged by rules that are first separated and automated. While this process may appear quiet from the outside, internally, the pace of reorganization is quite rapid.

Human Work Shifts from 'Execution' to 'Judgment'

If AI is responsible for efficiency and speed, humans are moving into the realms of complexity and context. A newly prominent role is the "AI human layer"-those responsible for interpreting, verifying, approving, or adjusting the results produced by AI, rather than simply executing them. These individuals ensure that AI outputs align with human intent and organizational standards, and they handle areas of ethics, emotion, and responsibility that are difficult for AI to address.

AI collaboration designers, AI governance and ethics experts, and human sensing professionals are all engaged not in "producing quick answers," but in "deciding what judgments are appropriate." Judgment-based work is the area most difficult to automate, as it involves contextual changes and the assignment of responsibility. In the age of AI, human work is increasingly being redefined from execution to judgment.

The Rise of AI Agents: Fewer Instructions, Greater Emphasis on Design

This transformation is also changing the office landscape. Nako Sung, Chief Technology Officer at Naver Cloud, observes that as AI evolves from a simple tool into an "agent" capable of achieving goals autonomously, the way people give instructions is changing. Rather than detailing every step of the process as in the past, people now set clear objectives, and AI devises its own plans to achieve them.

However, he emphasizes that "context" is the most crucial factor in utilizing AI. For AI outputs to be usable in actual work, it is necessary to specify what information is new, what should be omitted, and what the organization's criteria for judgment are. While AI accelerates the pace of work, humans remain responsible for designing the direction and meaning of work.

AI Infrastructure: The 'Invisible Foundation' That Redistributes Labor

With the spread of AI, the role of humans is shifting from content creation to system design and operation. Photo by Pixabay

At the core of this change is the rapid expansion of computational infrastructure. Kim Jongwon, Professor at the School of AI Convergence at Gwangju Institute of Science and Technology (GIST), defines AI infrastructure not as a simple IT asset but as "a new kind of factory supporting future industries." As data centers, networks, and AI computational resources combine, the entire industry's decision-making methods and work structures are being transformed.

Professor Kim particularly forecasts, "As AI is increasingly applied in actual industries and workplaces, the human role will shift from content creation to system design and operation." As digital-based design, simulation, and automated operating environments become more widespread, human labor is also being redefined atop AI infrastructure.

Standards for Education and Competency Are Changing Together

With the spread of AI tutors and personalized learning tools, the delivery of knowledge is becoming the domain of AI. The purpose of education is shifting from following predetermined paths to understanding principles and reconstructing problems. While AI performs basic concept explanations and practice problems more efficiently, there is a growing consensus that classrooms and universities should focus on generating questions and connecting meanings.

The concept of "AI literacy" is drawing attention in this process. AI literacy is not merely the ability to use AI tools, but a comprehensive judgment capability that includes understanding AI's limitations and potential errors, interpreting and verifying results, and using them responsibly.

Research fellow Kim Dongkyu defines AI literacy not as a specialized skill for certain jobs, but as a foundational occupational competency required in almost all roles. The goal of education and training is also shifting from skill acquisition to the accumulation of judgment and problem-reconstruction abilities.

Readiness to Work with AI Creates Gaps

If 2025 was the year of competition in computational resources and power infrastructure, then 2026 is the first year that this competition shifts to the labor market. By around 2030, this change is likely to manifest as a clear gap between those who are prepared to work with AI and those who are not.

Technology is not eliminating human work, but redefining it. In 2026, we stand at a crossroads where we must choose whether to rise above AI or fall below it.