I know many AI skeptics who see LLMs as mere lookup tables or "interpolators" - incapable of synthesizing truly novel ideas beyond recombining existing concepts (like composing "pink" with "elephant").

What they miss is that even a "Great Interpolation" with automated minds would be economically and scientifically earth-shattering. Entire research departments and entry-level white-collar professions can be reduced to tasks within the scope of intellectual interpolation. There should be more serious socioeconomic analysis of the consequences of AI that never escapes the convex hull of its training data. I think many AI skeptics would at least grant that interpolative reasoning is something LLMs can do, and I find their dismissiveness incompatible with having thought through the implications.

Whenever someone pushes human knowledge in a new direction, understanding how that insight composes with everything else becomes computationally intractable for individuals. Historically, the interpolative work of integrating a new idea across fields becomes a multi-year research program with many people doing high-throughput exploration. An interpolation-capable AI could make this combinatorial explosion tractable.

For example, when Darwin published natural selection, it took decades for researchers to work out its implications on other research fields. Something like genetic algorithms is absolutely something you could have gotten from high throughput "mere interpolation" of the pink elephant variety, brute force combination of concepts until something emergent pops out. Systemic, massively parallel composition inside the convex hull of existing ideas followed by filtering based on some metric be it measuring surprise, emergence or any other proxy for interestingness.