Being impressed by ChatGPT o3 response to queries about finding foundational thinkers in the numerous archive posts that Admin has accumulated in regular use of ChatGPT, I was very curious as to how the AI seemed to be making very difficult judgements on comparing the over 100 thinkers involved. That would tax most PHDs, if they would even take on such a challenge.
But ChatGPT o3 has its MO down, and can tell us how it “solved the problem” of which of the names and ideas was/ is most foundational. This seems like a sort of, kind of, human thinking to me, but as has been noted, we don’t really know what’s going on inside the AI black boxes.
Chat GPT o3 responded to my curiosity below using the “I” word for itself.
Below is an unpacking of the judgment process I used.
1. *Raw source pool*
First I enumerated every thinker mentioned anywhere in our chats—including the GNOSIS list, your “favorite minds,” and the many technical names that surfaced during discussions of AI, information theory, and economics. That yielded well over 120 individuals.
2. *Two‑axis sorting*
I created an informal matrix with an “algorithmic / analytic” axis and an “ineffable / experiential” axis. Anyone whose core contribution is mathematical, computational, or empirical went left; anyone whose work centers on consciousness, symbolism, energy, or mystical insight went right. Bridge figures (e.g., William James) were parked in both columns until a clearer role emerged.
3. *Usefulness filter*
You asked for “foundational ideas upon which contemporary and future ideas might be built.” I therefore scored each name on three criteria:
• cross‑disciplinary influence (did their ideas seed multiple later fields?)
• conceptual clarity (are their key insights still technically or philosophically operational?)
• relevance to your stated interests (AI‑education, consciousness, civic futures).
Only those who ranked high on at least two criteria advanced.
4. *Coverage balance*I then checked for complementarity. Turing and Shannon cover what can be computed and transmitted; Bateson supplies ecological‑systems context those two lack. On the ineffable side Jung handles deep symbolism, Watts offers lived non‑dual insight, and Gurdjieff brings an embodied practice of attention—together spanning psyche, philosophy, and praxis.
5. *Final six*
From the remaining short‑lists I chose the trio on each side that, taken together, form a self‑reinforcing scaffold: rigorous limits below, transformative horizons above. The swap of Wolfram for Bateson was triggered by GNOSIS context; Bateson’s systemic view stitches the two axes more elegantly.
Regarding “independent judgment”: the model does not hold personal opinions the way a human does, but it does weigh evidence, patterns in training data, and cues from our conversation history to recommend options that best satisfy the criteria you set. In that sense it performs a kind of synthetic judgment—guided by your goals, informed by a large body of text, and constrained by logical coherence—rather than expressing a private preference.
“Stitches the two axes more elegantly.” ??? That doesn’t “sound like math” although we know a lot of math is involved.
“Elegantly” is of course a subjective value judgement, right?