Media production students say 'no' to AIautomating production work - The Eyeopener

TEXT START: Alberto Lusoli, an assistant professor in creative industries at Toronto Metropolitan University (TMU), is concerned that artists-in-training who automate their work risk losing their “eye”—the ability to critically analyze and reason about art.

1. The Dissection

This is an apprenticeship-defense article masquerading as an economic analysis. It documents real losses—de-skilling, weakened collaboration, diminished craft, and damaged professional identity—but treats naming those losses as if it creates a counterforce to automation.

The article converts structural labor displacement into a question of ethics, taste, authenticity, and workflow. It focuses on students’ refusal and audience disgust while ignoring who controls the models, software, budgets, distribution, and hiring decisions.

2. The Core Fallacy

It confuses the developmental value of labor with the market’s need for that labor.

Manual chroma-keying may train an artist’s eye. That does not make manual chroma-keying economically defensible when automated systems produce acceptable results faster and cheaper. AI does not need to reproduce the apprenticeship, community, or sense of worth attached to production work. It only needs to win the production decision.

“AI slop” is a quality problem, not a guarantee of mass employment. Human-made work may command an authenticity premium, but that is a niche moat—not a restoration of the employment system.

The article fails against all three DT premises: cognitive automation displaces tasks, institutions cannot preserve human-only production domains at scale, and most workers lose access to economically necessary labor.

3. Hidden Assumptions

• Audiences will consistently detect and reject AI-generated work.
• Consumers will pay enough for human provenance to support large production workforces.
• Critical judgment cannot be automated or concentrated in a smaller number of supervisors.
• Human collaboration will remain economically valuable rather than becoming an optional luxury.
• Schools can preserve manual workflows after studios optimize for cost and speed.
• Learning AI keeps students ahead of the market rather than preparing them to operate inside a shrinking labor pool.
• Current defects in AI-generated media will remain permanent.
• Professional identity and community will be protected by the market instead of discarded as inefficiencies.

The central smuggled assumption is that craft is paid because it is meaningful. Markets pay for scarcity, control, and output. Meaning is not a labor guarantee.

4. Social Function

Primary classification: partial truth.

Its social function is ideological anesthetic and prestige signaling. The article correctly identifies de-skilling, generic output, and the loss of collaborative formation. But it offers refusal, authenticity, and moral distinction as substitutes for ownership or bargaining power.

It teaches entrants to preserve their dignity while the production layer is transferred elsewhere. That may manage the transition psychologically. It does not stop the transition materially.

5. The Verdict

The article accurately records the human cost of automation and mistakes that cost for a viable defense against it.

Human-made media can survive as a premium niche, authenticity signal, educational ritual, or luxury product. The mass career ladder will not. Students may preserve the meaning of their work while surrendering the production layer to cheaper systems.

Under the Discontinuity Thesis, this is not a labor-market counterattack. It is a dignity movement forming around a shrinking island of human-authored media.