Human Capital Theory and the Devaluation of Experience: AI Video Enhancers and the Reallocation of Creative Skill

For the past 30 years, human capital accumulation has been clearly evident in the professional video editing industry. Editors have spent years learning various software programs, building professional portfolios that demonstrate their visual judgment, and accumulating the human capital required to provide editing services to clients and potential employers. According to Gary Becker’s human capital theory (a theory describing how human capital is invested and yields returns through work), these investments lead to productivity gains and higher wages as a result of leveraging accumulated skills over time. The increasing use of AI-enhancing video products is now disrupting the traditional relationship between anticipated productivity gains and wages—not by eliminating the need for human capital, but by altering how its benefits are distributed.

Human Capital and Video Editing: A Case Study

From a historical perspective, human capital in video editing has developed along two dimensions. First, professional video editing has consistently commanded a wage premium due to the need for formal skills, including in-depth knowledge and mastery of video editing software such as Adobe Premiere and After Effects. Second, video editing has always relied on tacit knowledge that cannot be easily articulated, such as expertise in lighting correction, sharpening, noise reduction, color correction, and motion smoothing.

Because both the tacit and practical skills required to perform these functions are difficult to articulate and evaluate, this tacit knowledge is likely to remain resistant to full automation in the near future. As a result, it continues to serve as a strong signal of quality within the post-production process.

Due to these factors, credentials such as “10 years of editing experience” or “Adobe-certified professional” have historically provided clients with a reliable signal that an individual possesses the human capital required to execute high-quality edits. Clients have therefore paid for execution based on the perceived value of the human capital embodied in those credentials.

Economic Impact of AI Video Enhancers

AI video enhancement tools use algorithms to inject patterns of tacit knowledge into machines through automated processes that previously required evaluation by experienced professionals. These processes include removing compression artifacts, stabilizing video footage, and enhancing footage recorded in low-light conditions.

The functions performed by a free video enhancer are accomplished through machine learning models trained on thousands to millions of examples, often drawn from publicly available datasets. In addition, many free online video enhancers now implement the same methods and procedures as professional video enhancement software that previously required video editors to spend hours improving visual quality.

The transition represents a form of capital deepening because it transfers the knowledge of workers into the software environment and causes the knowledge of the editors to become “software capital.” Therefore, the marginal productivity of an experienced editor performing routine enhancements becomes less than before. The reduction does not happen because the editor has lost their skills; rather, it occurs because the editor’s skills are no longer scarce.

Redistributing Human Capital to Non-Routine Tasks

Human capital theory does not call for the total replacement of skilled labor; rather, human capital will continue to be reallocated to higher-value positions. In the context of video editing, this shift creates opportunities for editors to perform more creative functions, including narrative pacing and emotional impact. These functions require more human labor because they depend on contextual understanding, audience segmentation, and a level of judgment that cannot be replicated by machines.

Currently, AI-powered video enhancement tools are widely used by editors and content creators to improve video quality on platforms such as YouTube. Because audience engagement is strongly correlated with narrative structure, editors who move into creative and strategic roles can earn higher salaries than those who remain focused on routine technical execution.

Why This Is Happening Now

Economic factors are playing a significant role in this changing environment. Artificial intelligence is driving down the cost to replicate skill types, resulting in an excess number of specific skill types on the market. The increase in demand for video content has led to increased volume of video production, however, video production values per video have decreased considerably due to the increase in the number of routine tasks. As such, many video production companies will take advantage of efficiencies by replacing human labor with machine capital when the difference in quality is minimal and the process of verifying the quality of video content between producers is relatively straightforward.

As a result, factors are compressing traditional skill ladders to the extent that the gap between beginner and intermediate execution is shortening while the gap between intermediate to expert execution is widening.

The Long-Term Impact on Labor Markets

The structural consequences of these trends are likely to persist in the long run. The number of entry-level positions focused on routine quality improvement is diminishing. Hybrid positions involving oversight of artificial intelligence tools and creative judgment are replacing these roles. As a result, wage differentials among creative professionals may increase, as higher-level judgment and strategic abilities come to represent a larger share of total income relative to routine execution work.

Conclusion

The use of AI video enhancement tools supports the core assumptions of human capital theory by embedding accumulated human expertise into capital. For example, AI reduces returns to experience in routine execution tasks while increasing returns to the effective use of human expertise in judgment, creativity, and strategic decision-making. Rather than signaling the end of human capital in video production, AI extends a long-standing pattern in which humans continually reconfigure their skills and quality assessments as production technologies evolve.