Tasks, Not Jobs, Will Disappear: How Agent-Based AI is Transforming the Nature of Work

Tasks, Not Jobs, Will Disappear: How Agent-Based AI is Transforming the Nature of Work

1) Why the Answer to "Will AI Take Our Jobs?" is Divided

With the proliferation of generative AI, we encounter headlines like "AI is Disrupting Employment" and "AI is Boosting Productivity" almost weekly. The confusion stems from a simple fact: the speed at which AI's "capabilities" are advancing does not match the speed at which society's "perception" is changing. This discrepancy makes the debate prone to a binary choice of support or opposition.


What makes the white paper published by the World Economic Forum (WEF) intriguing is that it doesn't assert a definitive future but rather presents a map of "which assumptions, if broken, lead to which futures." Whether AI evolves rapidly or slowly, and whether human resources and systems are well-prepared or not, these combinations lead to four scenarios for jobs in 2030.

2) Four Scenarios: Different Outcomes with the Same AI

The four futures depicted in the white paper can be broadly described as a multiplication of "technological growth" and "human preparedness." The key point here is that rather than one scenario dominating the world, it's more likely that these scenarios will "mix" across industries, regions, and companies. This means your workplace might be in a "Co-Pilot Economy," while a neighboring industry is in "The Age of Displacement."


Scenario A: Supercharged Progress

A world where AI rapidly advances and the labor market adapts relatively well. Companies redesign operations around AI with the power of "agent-based AI," boosting productivity and innovation. New job roles emerge, but existing jobs become obsolete at a fast pace. The biggest risk is that the pace of change is too rapid for social security, ethics, and governance to keep up, leaving some behind.


Scenario B: The Age of Displacement

A world where AI evolves rapidly, but education, reskilling, and systems can't keep up. Companies are more inclined to choose automation over nurturing talent due to labor shortages and cost pressures. This results in increased unemployment and unstable employment, deepening social divides. As agent-based AI takes on critical processes, it amplifies "new risks" like accidents, fraud, and cognitive manipulation due to lack of oversight.


Scenario C: Co-Pilot Economy

A world where AI evolves relatively gradually, and skills to utilize AI become widely disseminated. Instead of flashy full automation, the focus is on "task-specific implementation" tailored to on-site challenges. Human-AI teams rearrange value chains, and the ratio of jobs "changing in content" is higher than those "disappearing." While job churn exists, AI is more likely to be seen as an opportunity than a threat.


Scenario D: Stalled Progress

A world where AI's evolution and implementation progress "gradually," but human preparedness is weak. Under pressure for short-term profits, companies tend to conservatively implement AI in parts, leading to no transformative change across society. Productivity growth is uneven, with benefits skewed towards AI-strong companies and regions. As a result, inequality becomes entrenched, and disappointment accumulates from unmet expectations.

3) The Core of Anxiety Highlighted by Numbers: Profits are Visible, Wages are Not

The central debate ignited by this white paper is not merely about whether employment will decrease or increase. The deeper issue is "how the fruits of productivity are distributed and to whom."


In the survey cited in reports, a majority of executives expect AI to replace existing jobs. However, the proportion who believe new jobs will increase is smaller. Moreover, while there is relatively high expectation for improved profit margins, the expectation for wage increases is significantly lower.


This is where anxiety tends to explode on social media. People are more afraid of a future where "profits increase, but their share does not" than AI itself.

4) Reactions on Social Media: Both Welcome and Doubt are "Simultaneously Correct"

When this topic spread on social media, reactions largely fell into four categories (based on publicly visible posts).


Reaction ① "Useful as a Framework"

On LinkedIn, posts that view the four scenarios as a 2x2 map useful for discussing "the current position of one's company" are prominent. Because it doesn't assert a definitive future, it's evaluated as easy to use as a "common language" for management meetings and talent strategies.


Reaction ② "Ultimately, Investment in Human Resources is Key"

Similarly on LinkedIn, there is a strong narrative emphasizing "human strengths" such as AI literacy, learning agility, critical thinking, decision-making, and communication. It's a reinterpretation that it's "an adaptation competition rather than a technology competition."


Reaction ③ "Anxiety Won't Disappear if Wages Don't Rise"

The sharpest reaction is skepticism towards the gap between improved profit margins and wage increases. Memories of past failures in distribution, overlapping with previous automation and IT adoption, are being recalled. While AI introduction is often narrated as a "story of efficiency," consumers perceive it as a "story of distribution." Unless this aligns, social consensus will be difficult.


Reaction ④ "It's Inevitable Anyway. So Prepare"

On forums like Hacker News, discussions close to resignation, assuming job displacement, are seen, arguing for the necessity of social security or other systems. While taking the form of pessimism, it also conveys a message that "design is needed to make it manageable."

5) Tasks, Not Jobs, Will Be Rewritten: Making It Relevant to You

It's easy to miss the mark if you talk about AI's impact as "entire professions disappearing/remaining." What actually happens is the decomposition and recombination of job tasks.
For example, in sales, tasks can be broken down into prospect research, proposal creation, minutes, estimates, and follow-ups. While AI is likely to replace information gathering, text generation, and organization, trust-building, situational judgment, negotiation, and taking responsibility are likely to remain human tasks. The same applies to back-office work, where input, verification, and classification are easily automated, while exception handling, audit response, and inter-organizational coordination become relatively more important.


Therefore, the important thing is not "whether to reduce people," but

  • which tasks to delegate to AI

  • which tasks humans will continue to handle

  • how to increase the value of the remaining tasks
    to design.

6) "No Regret" Moves that Work in Any Future: Actions Less Likely to Be Regretted

What the WEF emphasizes is not to predict the future but to prepare in a way that minimizes losses in any scenario. In practical terms, the following will form the backbone.


What Companies Should Do (Minimum Set)

  • Introduce on a small scale, create a model for effect measurement and risk management (don't stop at PoC)

  • Connect talent strategy and technology strategy (don't separate implementation plans and development plans)

  • Prepare data, authority, logs, and supervision responsibilities to solidify the operational premises of agent-based AI


What Individuals Should Do (Shortest Route)

  • Break down your work into tasks and delegate parts to AI first (create time)

  • Use the freed-up time to increase "judgment," "design," "relationships," and "responsibility" (increase value)

  • Verbalize the "process leading to results" and be able to explain it to others (increase mobility)

7) Conclusion: AI Does Not Decide the Future. Preparation and Distribution Do

The four scenarios are not prophecies to incite fear. Rather, they are tools to visualize how differences in "preparation," "distribution design," and "governance" can change outcomes.

2030 seems far away but is actually close. As AI's capabilities grow, the questions shift from technology to society. Whose profits are they? Who bears the cost of retraining? Who is responsible for the accountability of implementation?

The contest between "replacement or augmentation" has already begun. Therefore, what is needed now is neither to fear AI nor to blindly trust it. "Reorganize work by tasks," "prioritize investment in human resources," "verbalize the distribution of fruits"—these modest preparations will most significantly change the future.


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