Pulling things together about creativity & LLMs

Consider these recent posts about AI:

Two Ways to Use AI: Homo Economicus vs. Homo Ludens (2.27.26) 

Chatbots have increased my sense of intellectual agency such that being an intellectual “outsider” becomes a superpower. (2.17.26) 

Three mathematicians are not impressed with the ability of AI to do professional math (2.16.26)

What do they have to do with one another? They are about creativity, interdisciplinary work and, ultimately, about the division of labor between humans and AI, currently represented by LLM-based chatbots. The NYTimes article about mathematicians makes the point that the creative work in mathematics involves creating frameworks in which to present and solve problems. AIs cannot do that currently. They’re better suited to working on well-defined problems.

That’s certainly consistent with observations I’ve been making for a while. All those benchmarks involve well-defined problems. The real problem, in contrast, is to frame such a problem in the first place. I’ve got a working paper where I present three case studies from my own work, cases where I started out with nothing in particular in mind and ended up doing a bit of focused research: Serendipity in the Wild: Three Cases, With remarks on what computers can't do.

The post about my “superpower” is based on the fact that the LLMs are trained on materials published to the web and so “gravitates” toward those ideas. But what if you take up a stance outside the current intellectual ecosystem, but nonetheless are conversant with it and can ground your work in it, at least partially? That’s been my situation since my early work on “Kubla Khan.” In 1978 I filed a doctoral dissertation entitled, “Cognitive Science and Literary Theory.” As far as I know, I was pretty much alone in working that territory. Here’s how I characterized cognitive science (somewhat idiosyncratically):

The basic problem of cognitive science is establishing a five way correspondence between the following:

1. Brain Geometry: Neuroanatomy is the study of the geometry of the brain. Comparative neuroanatomy is the study of the correspondence between brains of different species.
2. Computation: Different types of computers can perform different classes of computations and the nature of the computations depends on the geometry of the computer.
3. Behavior: Much of psychology is the study of the behavior of organisms. The behavior an organism exhibits is determined by the class of computations which its brain can perform which in turn depends on the geometry of the brain.
4. Phylogeny: Animals at different evolutionary grades have different brain geometries. The brain geometry must be capable of performing the class of computations necessary for survival in the animal’s particular ecological niche. But what is the relationship between moving to a new niche and the emergence of a new brain geometry?
5. Ontogeny: As a child matures different brain structures develop and permit new classes of computation sustaining new types of behavior. But how does phylogeny exploit differential maturation rates to create a new class of computer?

Those are five distinct intellectual domains. My dissertation was strongest on behavior, literary texts, and computation, cognitive network semantics, but I touched on neuroanatomy and phylogeny (there’s a section on mirror recognition in humans and apes). Offhand I don’t recall anything about childhood develop.

A decade later, however, David Hays and I published “The Principles and Development of Natural Intelligence” (1988). The principles themselves were computational. We identified neural structures associated with each, gave examples of behaviors enabled by each principle, and placed them in both phylogenetic and ontogenetic contexts. That’s a large part of the framework I’ve been working with my entire career – there’s also culture and cultural evolution. Am I an expert across that entire domain? Of course not. But I do have a high degree of expertise in some areas, particularly literary and textual analysis and semantic structures, and I’ve read in the technical literatures across that whole range. I’ve got a “feel” for the material, enough so that I can prompt chatbots (Claude and ChatGPT) across the whole range and follow it when it fills in details that I don’t myself command.

That most recent post (2.27.26) contrasts the way I use chatbots (Homo Ludens) versus more conventional usage (Homo Economicus). You might also look at the still more recent post, Why Gemini 3.1 is so good [long chains of reasoning, across disciplinary boundaries]. That title tells half the story. Somewhere in the video Jones makes the point that, while Gemini 3.1 Pro can construct long trains of reasoning that humans cannot, often crossing disciplinary boundaries, humans can very those chains. That’s my situation. I can check the reasoning for ontological consistency. That’s not the same as truth, but it’s a pre-requisite for it.