AI models are trained to find the most probable answer, reflecting the average of their data. Truly great, tasteful work is often unique and statistically unlikely, a quality that current models, which regress to the mean, struggle to produce. They can solve PhD-level math but fail at creative tasks like writing a good tweet.

The negative reaction to "AI slop" isn't because the writing is poor. AI often produces above-average content using effective patterns. The problem is that these patterns are now so accessible and widely used that they've become saturated and generic, making the content easy to spot.

As CGI becomes photorealistic, spotting fake hardware demos is harder. An unexpected giveaway has emerged: the use of generic, AI-generated captions and descriptions. This stilted language, intended to sound professional, can ironically serve as a watermark of inauthenticity, undermining the credibility of the visuals it accompanies.

AI makes it easy to generate mediocre content, shrinking the gap between bad and passable. However, the effort required to create truly good, differentiating content has increased, widening the gap between what is passable and what is excellent, making true differentiation more difficult.

A horseshoe theory applies to writing quality. Truly great writing requires taking creative risks that can easily result in terrible writing. AI, trained on the vast corpus of average text, produces 'good' but not 'great' output because it is optimized to avoid risk and stay near the mean.

Luis von Ahn highlights a critical flaw in AI: it generates impressive one-off examples but struggles with quality consistency at production scale. Generating 1,000 stories, for example, reveals a high percentage of "pure slump," requiring intense human oversight to maintain brand quality.

LLMs conform to the average of their training data. When used for creative tasks like writing, they act as "memetic conformity machines," sanding off originality and producing work that sounds like everything else—the literal definition of mediocre.

VP of Growth Eoin Clancy identifies three signs of low-quality AI content: it offers no new "information gain," fails to match the brand's voice, and uses robotic tells like em dashes or formal words like "utilize."

As AI makes content creation ubiquitous, the internet is flooded with shallow, generic "AI slop." Consumers are adept at spotting it, with 59% saying it damages their trust in a brand. This creates a premium for human-crafted, authentic stories.