Why Creativity Cannot Be Interpolated

AI systems can produce novel outputs, but genuine creativity requires respect for constraints—the accumulated structure of prior discoveries. Current AI systems lack this capacity because their training often follows greedy paths that restrict the necessary representations. Understanding is presented as the cognitive ability to navigate and integrate across constrained perspectives. While LLMs are coherent within a single frame, they lack an independent trajectory, requiring human supervision for grounding. The text argues that human-AI co-creativity is the most promising path forward. Furthermore, intelligence requires creativity, where understanding constrains the search space, distinguishing genuine exploration from mere pattern matching. True creativity involves respecting the phylogeny of ideas, which leads to well-structured representations, contrasting with the fractured internal states often seen in purely data-driven training methods.

The core tension explored is between mere interpolation and transformational creativity, which necessitates understanding structure to achieve genuine novelty. The piece posits that AI’s current success stems from powerful pattern matching (System 1) rather than true reasoning or open-ended exploration (System 2). This distinction extends to the nature of intelligence: optimization for objectives yields competent skills but inhibits the divergent thinking necessary for unknown unknowns, suggesting goal-directed agency acts as a "false compass" when applied without creative freedom. The analysis deepens into the role of phylogeny, arguing that true creativity emerges not from random generation or mere imitation, but from respecting the evolutionary path—the 'why' behind the structure of ideas. The contrast between derivative work (laundering ideas) and inspiration (deliberately extending lineages) suggests that authenticity requires an internal process of understanding, even if intrinsic motivation remains elusive in current systems. The shift towards viewing creativity as a function of respecting pre-existing constraints—whether physical, concrete, or cognitive—challenges the purely objective optimization paradigm prevalent in much of modern AI research by foregrounding the necessity of structured exploration over blind goal pursuit.