[THE VIEW]AI Verification, The Invisible Labor

A New Market Created by Generative AI: The Verification Industry
Faster Drafting, But Greater Burden to Secure Trust

We now live in an era where AI writes text, creates drawings, and even composes music. When you ask for a travel itinerary, it instantly provides everything from flight searches and accommodation recommendations to a detailed schedule. Restaurant reservations and shopping lists are also organized within seconds.

However, it is unsettling to trust these answers as they are, since they may contain incorrect information or exaggerated expressions. For example, consider writing a report at a company. In the past, employees would spend several days gathering and organizing materials to draft a report. Now, AI can generate a draft in no time.

But the next step is the real challenge. Humans must meticulously verify whether the figures are accurate, the sources are clear, and the context is appropriate. In reality, while the time required to draft has decreased, the time spent on verification and revision has increased, leading to concerns that overall work efficiency is not as high as expected. Ultimately, people must review and correct the results. In this way, we are now living in the 'AI labeling era,' where humans must recheck the outputs created by AI.

This trend is rapidly creating new roles and industries. In the United States, startups have emerged to verify AI-generated texts and images, with services that assign trust scores to results or trace sources to determine whether something has been fabricated. Interestingly, this is not an unfamiliar development.

We have already experienced this kind of labor in the process of building AI training datasets, known as 'data labeling.' Countless people tagged images and annotated sentences to create large-scale datasets. Now, this process is being repeated, not at the front end, but at the back end. We have entered an era where humans must recheck the texts, images, and voices produced by generative AI to distinguish between what is 'real' and what is 'fake.'

This verification work is simple and repetitive, yet requires intense concentration. A single piece of incorrect information or a manipulated image can cause significant social repercussions. Although it may seem trivial on the surface, in reality, it is far from a light task. Nevertheless, just as data labeling was once called 'invisible labor,' this work also faces the risk of being undervalued. Especially as the burden of verification grows in public sectors such as education, media, and administration, the collective fatigue of society can accumulate even more rapidly.

The importance of verification work, where humans must recheck texts, images, and voices generated by generative AI to distinguish between 'real' and 'fake,' is emerging. Image Getty Images Bank

This irony weighs heavily on both companies and individuals. Companies expected increased efficiency from adopting AI, but in reality, verification and risk management costs are rising. The same is true for individuals. Even if generative AI drafts blog posts, proposals, or emails, people still have to review and refine them. There are even complaints that revising AI-generated writing takes longer than writing from scratch.

The United States sees this phenomenon as a new market opportunity. As fields such as AI security, reliability assessment, and forgery detection grow rapidly, the 'AI verification industry' is becoming a major sector. Investors are attaching a premium to 'trustworthy AI,' and universities and research institutes are strategically nurturing verification technologies. Recently, large tech companies have begun to establish their own AI verification departments or increase investments in external verification startups.

It has become difficult to gain market trust simply by building better AI. Furthermore, institutional changes are following. The Federal Trade Commission and the National Institute of Standards and Technology have issued guidelines on AI transparency and verification procedures, making it clear that companies failing to meet these standards may face regulatory risks. In the United States, AI verification is no longer just a technical issue; it is becoming a core task that determines industrial competitiveness and corporate credibility.

Ultimately, the key competitive edge in the AI era is not just the ability to produce more outputs. What matters is whether those outputs are genuine, trustworthy, and capable of securing social trust. The 'AI labeling era' does not simply signify a new source of fatigue, but signals the rise of new industries and professions based on reliability. What society should focus on is not how quickly AI is adopted, but how robust the verification system is.

In the end, the innovation of generative AI is not complete with smarter machines alone, but is realized in the user experience where people can trust and use the results with confidence.

Son Yoonseok, Professor at the University of Notre Dame, USA