What makes AI-generated work feel like slop?

Chris Williamson says people use “taste” and “slop” frequently without explaining what they mean. He is interested in how someone develops the ability to judge work, rather than using taste as a marker of status.

Williamson describes an exercise from Billy Oppenheimer: save examples of things you like and explain why. He added the reverse, keeping a record of things he dislikes and the reasons, creating a collection to revisit when making his own work.

His conversation partner introduces Ira Glass’s account of the gap between a beginner’s taste and the quality of their early work. They summarize the advice: keep producing work on a regular schedule, since closing that gap takes time and practice.

The speakers discuss procrastination and the value of putting work out. If you release only things you already know will succeed, they argue, you get fewer opportunities to learn from a varied set of attempts.

Williamson’s conversation partner defines taste as the ability to tell good work from bad. He suggests that people often mean a lack of intention when they call something slop: a powerful tool can produce recognizable defaults if the creator does not guide it.

Williamson agrees that people can criticize poor work but asks them to say why. Simply applying the label without explaining it can itself be unoriginal, the pair suggest.

They discuss an AI-generated shadowboxing character that initially impressed viewers. Williamson wonders how learning that a clip was generated changes people’s reactions to an otherwise identical result.

He notes that some documentary channels emphasize that humans researched their work. In his view, such statements can signal the effort behind an output when the finished product alone makes its production process difficult to judge.

The speakers look at an AI-made comedy account, Gossip Goblin. They see it as an example of visible craft and intentional production applied to a medium that many viewers associate with low effort.

After watching the clip, they discuss its use of recognizable British stereotypes and ask whether audiences will credit effort in AI-made work. Williamson thinks many viewers currently have a strong aversion to AI regardless of the craftsmanship.

His conversation partner expects human-made work may increasingly be labeled and valued in the way consumers distinguish handmade goods. They also discuss the difficulty of communicating model quality through complicated evaluations.

The pair watch a second AI parody that recasts a scene as a competition over pickles. Their reaction returns to the central question: the tool may be the same, but creators make choices about the joke, presentation and execution.