AI tools struggle in creative processes because they cannot "see" or have personal preferences. Their output is limited by the user's ability to verbally describe visual inspiration, creating a significant bottleneck. This highlights why human taste and curation remain essential for high-quality creative work.

When using LLMs to judge other models' output, they consistently rate towards the middle of the curve, akin to humans giving a generic "7 out of 10." These AI judges are not "spiky" enough, failing to recognize unique or exceptional qualities that a human evaluator with strong taste would identify.

AI excels at averaging existing data, pushing outputs toward the middle. This creates a premium on genuine human creativity, which is needed for differentiation and to produce standout content. AI isn't replacing creatives; it's increasing the demand for their unique vision and ability to generate extremes.

True creative mastery emerges from an unpredictable human process. AI can generate options quickly but bypasses this journey, losing the potential for inexplicable, last-minute genius that defines truly great work. It optimizes for speed at the cost of brilliance.

AI models produce poor creative writing because they are trained to optimize for superficial proxies for quality, like the number of metaphors. This 'reward hacking' caters to quick judgments from human evaluators on leaderboards, mistaking flashy complexity for genuine literary taste.

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.

A major frontier for AI in science is developing 'taste'—the human ability to discern not just if a research question is solvable, but if it is genuinely interesting and impactful. Models currently struggle to differentiate an exciting result from a boring one.

AI models are trained on vast datasets of existing knowledge. Like a librarian who has read every book, their answers represent an average of what they have 'read.' This makes AI an aggregator of existing ideas, not a generator of truly novel, outlier concepts.

The best AI models are trained on data that reflects deep, subjective qualities—not just simple criteria. This "taste" is a key differentiator, influencing everything from code generation to creative writing, and is shaped by the values of the frontier lab.