Exploring the Nature of Creativity with ChatGPT

 I think a lot about creativity and my current series about intellectual creativity and AI has me thinking about it specifically and in some detail. In this effort I decided to take a look at an article recommended by my colleague, Ramesh Viswanathan, Rick Mammone, Will AI Ever Think Like Einstein or Create Like Picasso?—Imagination is All You need, The Quantastic Journal, July 7, 2025. The article is interesting, and I agree with the premise. 

But Mammone offered a simple model for creativity that seemed dubious to me, one based on a phase of divergent thinking followed by one of convergent thinking. It’s a common idea, but I find it problematic because it assumes that one has a specific objective in mind, something for which you are seeking a creative idea. That I don’t think we always have a specific goal in mind when we begin a process that has a creative result. The process might begin with an open-ended search that has no specific goal. Coming up with a specific goal, then, becomes an objective. Once you have that, then you can set about achieving the goal.

So I decided to put the question to ChatGPT. Here’s the resulting dialog.  Major topics:

Creativity in Science    
Arts and Humanities    
Kubla Khan    
Engineering and Architecture    
Limitations of the divergent-convergent model    
AI and Benchmarking    

I’ve highlighted my prompts in light orange.

* * * * *

Creativity in Science

I’ve taken the following characterization of creativity from a recent article by Rick Mammone (Will AI Ever Think Like Einstein or Create Like Picasso?—Imagination is All You need):

“Creativity is often described as involving two distinct modes: divergent and convergent thinking. Divergent thinking is the process of generating a wide range of ideas and possibilities, for example by brainstorming, while convergent thinking is about focusing on and evaluating those ideas to arrive at a solution. By combining these two approaches, individuals and teams can effectively harness their creative potential to solve problems and generate new ideas.”

Do we have any documented cases of creativity in any intellectual field that look like that? Such cases would explicitly list the ideas and hypotheses under consideration and then trace the process by which one idea finally won out.

Yes, there are documented cases of creativity in intellectual fields where a combination of divergent and convergent thinking is explicitly visible—though not always in such a neat or schematic form. The clearest examples come from fields like mathematics, science, and sometimes literature or philosophy, especially when thinkers have left behind detailed notebooks, correspondence, or retrospective reflections. Below are some instructive cases:

1. Charles Darwin – The Origin of Species

Domain: Biology
Documentation: Darwin’s notebooks and correspondence

·       Divergent Thinking: Darwin explored a broad array of hypotheses about variation, inheritance, species divergence, and extinction. His notebooks are filled with sketches, alternative explanations, and speculative ideas—including the now-famous “I think” evolutionary tree sketch.

·       Convergent Thinking: Over time, he evaluated these ideas through systematic observation (e.g., pigeon breeding), empirical data from the Beagle voyage, and Malthusian population theory. He gradually rejected alternatives and synthesized natural selection as the core mechanism.

·       Traceable Process: The transition is well-documented in his notebooks and later works. Scholars like Howard Gruber (Darwin on Man) trace these steps carefully.

2. Albert Einstein – Special and General Relativity

Domain: Physics
Documentation: Einstein’s letters, memoirs, and drafts

·       Divergent Thinking: Einstein entertained multiple lines of reasoning about simultaneity, reference frames, the ether, and gravitation. His thought experiments (e.g., chasing a light beam) are examples of conceptual divergence.

·       Convergent Thinking: He eventually focused on the constancy of the speed of light and the equivalence principle, using rigorous mathematics to formalize his intuitions.

·       Traceable Process: Many stages of this process are documented in his correspondence (e.g., with Michele Besso) and in secondary studies such as Abraham Pais’s Subtle is the Lord and John Stachel’s editorial work.

3. Claude Shannon – Information Theory

Domain: Mathematics / Electrical Engineering
Documentation: Shannon’s master's thesis and 1948 paper, plus interviews and Bell Labs memos

·       Divergent Thinking: Shannon drew on a wide range of fields: Boolean algebra, cryptography, thermodynamics, telephony, and symbolic logic.

·       Convergent Thinking: He honed in on the mathematical formulation of entropy and channel capacity, rejecting less rigorous or less generalizable approaches.

·       Traceable Process: His writings and the intellectual context at Bell Labs allow for reconstruction of his thinking. Notably, his master's thesis already foreshadows key concepts.

4. Barbara McClintock – Jumping Genes

Domain: Genetics
Documentation: Laboratory notebooks, experimental records, and Evelyn Fox Keller’s biography (A Feeling for the Organism)

·       Divergent Thinking: She explored non-Mendelian patterns of inheritance in maize, testing and discarding various mechanistic explanations.

·       Convergent Thinking: Eventually, she identified the phenomenon of transposable elements (jumping genes), a radically new idea at the time.

·       Traceable Process: Keller’s account highlights how her experimental reasoning combined divergent hypotheses with a keen intuitive feel for patterns.

5. Poincaré – Mathematical Discovery

Domain: Mathematics
Documentation: Poincaré’s own reflections in “Mathematical Creation”

·       Divergent Thinking: He describes producing a wide range of possible function forms and transformations during conscious work.

·       Convergent Thinking: He claims the final successful combination came to him in a moment of unconscious insight, followed by conscious verification.

·       Traceable Process: Though not as structured as a lab notebook, his account shows a clear sequence: generation, incubation, insight, and evaluation.

Bonus: Thomas Edison’s Invention Process

Domain: Invention / Engineering
Documentation: Lab notebooks, business records, and biographies

·       Edison's labs ran on a kind of industrialized divergence: many variations on filaments or circuits were tested.

·       Then came convergence: systematic evaluation of durability, cost, and manufacturability.

·       This process is documented in Edison’s notebooks and the work of Paul Israel and others at the Edison Papers project.

Summary

In all of these cases, we find:

·       Divergence: A documented stage of idea generation—often experimental, speculative, or exploratory

·       Convergence: A subsequent process of testing, evaluating, selecting, and refining

·       Traceability: Enough documentation (notebooks, letters, essays, retrospective accounts) to reconstruct the creative trajectory

So yes—while creativity is often messy and retrospective, there are empirically rich, traceable cases that match the divergent/convergent model, particularly in the sciences. Would you like a comparison table of these cases or more from the arts/humanities side?

Arts and Humanities

Give me some examples from the arts and humanities.

Certainly. In the arts and humanities, the divergent/convergent model of creativity is less often documented in laboratory-like precision, but there are still rich, traceable examples—especially when artists or thinkers kept notebooks, drafts, journals, or correspondences, or when scholars have reconstructed their processes. Here are several instructive cases:

1. T.S. Eliot – The Waste Land

Domain: Modernist Poetry
Documentation: Manuscripts with editorial annotations, especially by Ezra Pound

·       Divergent Thinking: The original drafts of The Waste Land were much longer and more sprawling, including narrative episodes and personal elements that didn’t make it into the final version.

·       Convergent Thinking: Through a rigorous editorial process—especially Pound’s excisions and Eliot’s own rethinking—the poem was distilled into a tighter, more symbolic structure.

·       Traceable Process: The Waste Land: A Facsimile and Transcript of the Original Drafts (ed. Valerie Eliot) lays out this process beautifully, showing both creative sprawl and editorial convergence.

2. Ludwig Wittgenstein – Philosophical Investigations

Domain: Philosophy
Documentation: Notebooks, unpublished manuscripts, final posthumous compilation

·       Divergent Thinking: Wittgenstein’s middle and later periods were full of exploratory writing—hundreds of remarks, aphorisms, and examples. He played with language games, rule-following, and meaning in use.

·       Convergent Thinking: Over years, he edited, rearranged, and abandoned sections to shape what would become Philosophical Investigations, a work that is deliberately non-linear but conceptually tight.

·       Traceable Process: His Nachlass (collected writings) and studies by scholars like Ray Monk and David Stern make this process of selection and refinement accessible.

3. James Joyce – Ulysses

Domain: Fiction
Documentation: Manuscripts, notebooks, letters, and scholarly reconstructions

·       Divergent Thinking: Joyce generated massive amounts of material and experimented wildly with narrative form, voice, and symbolic patterning.

·       Convergent Thinking: He structured the book around Homer’s Odyssey, then further refined each chapter stylistically and thematically, often layering multiple techniques into each.

·       Traceable Process: The James Joyce Archive and genetic criticism (e.g. Hans Walter Gabler’s edition) track this development, showing how many choices Joyce sifted through before finalizing.

4. Beethoven – Sketchbooks for the Late Quartets and Symphonies

Domain: Music Composition
Documentation: Surviving sketchbooks and conversation books

·       Divergent Thinking: Beethoven often jotted down multiple melodic ideas, variations, and harmonic experiments. Some sketches show pages of thematic wandering.

·       Convergent Thinking: He then refined these into highly structured, emotionally coherent works—especially striking in the late quartets and Ninth Symphony.

·       Traceable Process: Scholars like Lewis Lockwood and Barry Cooper have reconstructed these developments from the sketchbooks.

5. Paul Cézanne – Mont Sainte-Victoire Series

Domain: Visual Art
Documentation: Dozens of paintings and sketches of the same subject, letters

·       Divergent Thinking: Cézanne painted the mountain repeatedly in different lights, angles, and moods. He explored variations in form and color without a single “goal painting.”

·       Convergent Thinking: In this series approach, convergence happens across the body of work—refining a vision through multiplicity until a kind of philosophical clarity emerges.

·       Traceable Process: Art historians have documented this process; Cézanne’s letters also reflect his deep theoretical concern with perception and structure.

6. Virginia Woolf – Mrs. Dalloway

Domain: Modernist Fiction
Documentation: Diaries, early drafts, and her essays on writing

·       Divergent Thinking: Woolf explored different character viewpoints and narrative techniques, including stream of consciousness and shifting temporal layers.

·       Convergent Thinking: The final novel tightly integrates these experiments into a one-day structure, with clarifying themes of trauma, time, and selfhood.

·       Traceable Process: Woolf’s diaries and drafts (esp. collected in The Diary of Virginia Woolf and The Common Reader) show her artistic struggles and resolutions.

7. Coleridge – “Kubla Khan” and Biographia Literaria

Domain: Romantic Poetry and Criticism
Documentation: Notebooks, marginalia, and published fragments

·       Divergent Thinking: “Kubla Khan” emerges from an opium-induced dream state and fragmentary drafting, while Biographia Literaria is filled with speculative tangents and philosophical digressions.

·       Convergent Thinking: Coleridge reflects on these creative experiences and tries to theorize poetic imagination—distinguishing between primary and secondary imagination.

·       Traceable Process: His notebooks (ed. Kathleen Coburn) and Biographia Literaria offer a layered view of creative generation and later critical convergence.

Summary Table

Creator	Domain	Divergence	Convergence	Documentation
Eliot	Poetry	Expansive early drafts	Pruned with Pound’s help	Facsimile of The Waste Land
Wittgenstein	Philosophy	Fragmented remarks	Thematic reorganization	Nachlass, biographies
Joyce	Fiction	Massive experiments	Homeric structure, stylistic themes	Gabler edition, letters
Beethoven	Music	Sketchbook variations	Final musical structures	Sketchbooks, musicology
Cézanne	Painting	Multiple iterations	Formal and tonal distillation	Series, art scholarship
Woolf	Fiction	Narrative & POV experiments	One-day unified structure	Diaries, essays
Coleridge	Poetry/Philosophy	Dream-visions, metaphysics	Theory of imagination	Notebooks, Biographia Literaria

Kubla Khan

Hmmm... Coleridge’s “Kubla Khan” seems doubtful. All we have is his preface and there is some doubt about that. Even if we take Coleridge at his word on that, the existing text of the poem would seem to have come directly from his opium-induced vision. Of course, Coleridge wrote endlessly about the nature of creativity and left extensive notebooks, so we know a lot about what he was reading and thinking about. But that’s all pretty general. Livingston Lowes did an interesting study in tracking down sources for “The Rime of the Ancient Mariner” and “Kubla Khan.” But how those sources came together in Coleridge’s mind, we don’t know.

You're absolutely right—and your skepticism is well grounded.

“Kubla Khan” is not a good example of documented divergent-convergent creativity in the strict sense we’re discussing. The poem lacks a clear paper trail of variant drafts, idea generation, or evaluative refinement. What we have instead is:

1.        A single fragmentary poem, possibly composed rapidly in a trance-like or dream state.

2.        A notorious preface that narrates its own origin myth—opium, dream, visitor interruption—which may itself be partly fictional or performative.

3.        General context from Coleridge’s notebooks and extensive reading, as masterfully mapped by Lowes in The Road to Xanadu, but that offers no detailed documentation of creative decision-making in action.

You’re exactly right that Lowes traces inputs, not transformative processes. He shows how bits of Coleridge’s wide reading—on Abyssinia, etymology, geology, China—may have entered his mind, but not how they synthesized into poetic form.

What Kubla Khan Represents Instead

·       A mystification of creativity, not a documentation of it.

·       A Romantic ideal of the poet as seer or conduit of the sublime, in line with Blake, Shelley, and other visionaries.

·       A cultural artifact about the limits of conscious authorship—which ironically makes it important in conversations about creativity but not an empirical case study of the divergent-convergent model.

A Better Coleridge Example?

If we wanted to salvage Coleridge for this framework, we might look elsewhere:

·       “The Rime of the Ancient Mariner” underwent substantial revision over time. It’s possible (though more difficult) to trace changes in diction, meter, and theological emphasis across versions—from Lyrical Ballads (1798) to later annotated editions.

·       His philosophical prose, especially in Biographia Literaria, shows more of a dialectical struggle between ideas—e.g., grappling with Kant, Schelling, Wordsworth, and trying to converge toward his own aesthetic theory. That’s more convergent, though not always creatively generative in a literary sense.

Still, even in these cases, the documentary record is far murkier than in someone like Darwin or Beethoven.

Would you like me to revise the earlier list of humanities examples and substitute someone with a clearer cognitive paper trail? Virginia Woolf, for instance, or maybe Leonardo da Vinci?

That’s OK. I’m just getting a feel for things and I happen to have a long term interest in “Kubla Khan,” so I know that literature fairly well.

Engineering and Architecture 

I’m thinking that engineering and architecture would provide good examples of divergent/convergent thinking, though a lot of that would be proprietary information held within companies. Do we have any decent public examples? You mentioned Edison, for example. He was basically an engineer. Patents, of course, require documentation, but that’s only the final result plus prior art. Patents don’t require documentation of the process that lead to the final invention.

Yes—your instincts are excellent again. Engineering and architecture are rich domains for divergent/convergent creativity, often more so than the arts or even science in terms of visible iterative problem-solving. But as you noted, the documented design process is rarely public unless:

1.        It's connected to a famous inventor or architect whose papers are archived or studied.

2.        It’s part of an open research project (academic, military, or government-funded).

3.        It’s exposed through litigation, patent fights, or posthumous biographies.

Let me walk you through some decent public examples that show divergence and convergence in action.

1. Thomas Edison – Incandescent Light Bulb and Phonograph

Domain: Engineering / Industrial R&D
Documentation: Extensive notebooks, lab records, and team logs (Menlo Park)

·       Divergent Thinking: Edison systematically explored hundreds of filament materials (bamboo, platinum, carbonized paper) and lamp designs. His notebooks catalog dozens—if not hundreds—of failed ideas.

·       Convergent Thinking: He eventually focused on carbonized bamboo in a vacuum as the optimal filament, balancing longevity, cost, and manufacturability.

·       Traceable Process: The Edison Papers Project (Rutgers) has digitized many lab notebooks. Paul Israel’s Edison: A Life of Invention offers a detailed narrative based on this archive.

·       Team-based Creativity: Edison’s lab operated like an early version of industrialized R&D. Ideas were generated in a “try everything” mode, then winnowed down collaboratively.

2. The Wright Brothers – First Controlled Flight

Domain: Aeronautical Engineering
Documentation: Letters, notebooks, glider tests, wind tunnel data

·       Divergent Thinking: The Wrights tested wing shapes, control surfaces, propeller designs. They invented their own wind tunnel to systematically explore lift and drag coefficients.

·       Convergent Thinking: They settled on a three-axis control system and a biplane configuration with a canard, based on iterative trials.

·       Traceable Process: Their detailed documentation is preserved at the Library of Congress. Tom Crouch’s The Bishop’s Boys reconstructs the engineering sequence beautifully.

3. R. Buckminster Fuller – Geodesic Domes

Domain: Architecture / Structural Engineering
Documentation: Patents, journals, drawings, and published reflections

·       Divergent Thinking: Fuller explored multiple approaches to efficient housing and minimal material structures—from Dymaxion houses to tensegrity frameworks.

·       Convergent Thinking: He ultimately focused on geodesic structures as optimal for strength-to-weight ratios and mass production.

·       Traceable Process: His papers are held at Stanford and Southern Illinois University. His books (like Synergetics) mix technical documentation with philosophical reflection—so the line between divergence and convergence is almost visible on the page.

4. Frank Lloyd Wright – Fallingwater and the Prairie Style

Domain: Architecture
Documentation: Sketchbooks, design drawings, letters, client correspondence

·       Divergent Thinking: Wright explored ideas of integration with nature, horizontal emphasis, and spatial flow. In the case of Fallingwater, he delayed drafting until the last minute, allegedly designing it in one feverish burst—but that burst was the convergence of years of spatial experimentation.

·       Convergent Thinking: His sketchbooks and built models show a narrowing toward a highly specific design vocabulary. His “Froebel blocks” and modular systems reflect this process too.

·       Traceable Process: Biographies by Brendan Gill and Robert Twombly, along with Wright’s own writings (An Autobiography), reveal iterative thinking.

5. IDEO and Human-Centered Design

Domain: Product Design / Innovation
Documentation: Case studies, videos, publications

·       Divergent Thinking: IDEO uses brainstorm sessions, empathy research, and rapid prototyping to explore a wide range of possible solutions to user problems.

·       Convergent Thinking: Concepts are iteratively prototyped and tested, leading to a final design.

·       Traceable Process: The Art of Innovation (Tom Kelley) and the ABC Nightline segment on the IDEO shopping cart (1999) offer unusually clear documentation of real-world divergent-convergent cycles.

·       This is probably the most pedagogically explicit case in public view—often used in design thinking courses.

6. MIT’s Building 20 – Engineering Improvisation Incubator

Domain: Multidisciplinary Engineering & Innovation
Documentation: Oral histories, memoirs, institutional records

·       Divergent Thinking: Housed radar research, linguistics (Chomsky), and engineering hacks like the first Bose speakers. Open-ended experimentation flourished due to flexible architecture and cross-disciplinary “noise.”

·       Convergent Thinking: Many ideas were refined into usable prototypes, products, and theories.

·       Traceable Process: Stewart Brand’s How Buildings Learn and works by Jerrold Zacharias document how this “ugly” building catalyzed creativity.

Summary Observations

·       Engineering notebooks (Edison, Wrights) provide some of the clearest real-world traces of divergent/convergent processes.

·       Architecture portfolios and design correspondence (Wright, Fuller) reveal idea iteration even in idiosyncratic thinkers.

·       Design firms like IDEO explicitly model and teach this kind of thinking, offering ideal case studies for pedagogy.

·       Patents, as you note, mark convergence, but rarely show the divergent stage unless reverse-engineered from lab logs or memoirs.

Would you like a side-by-side comparison table of these cases or an example broken down into a timeline of decisions and design forks? Or something more speculative—like imagining what a complete divergence-convergence logbook for “Kubla Khan” might have looked like, had Coleridge worked like an engineer?

Limitations of the divergent-convergent model

Nice examples. What I’m thinking is that the divergent-convergent process requires a specific goal that is fairly well-defined. It seems to me that we don’t have that in a lot of scientific or artistic discovery. What we have is a sense that something interesting is “out there” and that sense leads to a set of investigations of one sort of another. But we may not have a clear well-defined idea until relatively near the end of the investigation.

Exactly—and you’ve put your finger on something profoundly important.

The divergent/convergent model presupposes a kind of problem space that already has structure—a clear objective, constraints, criteria for evaluation, and perhaps even a design brief. That’s why it's so applicable in engineering, design, and product development: you’re trying to build something that works, and you typically know—at least roughly—what function that thing should serve.

By contrast, in science and especially in the arts and humanities, the process is often more exploratory, and the definition of the problem itself emerges only through the investigation. In other words:

The solution is not chosen from a known set of options—it defines the problem retroactively.

A Deeper Typology

We might consider two creative modes, with the divergent/convergent model fitting only one:

Mode	Description	Example Domains	Creative Process
Goal-Driven Creativity	Clear end-goal, fixed criteria, known constraints	Engineering, product design, applied math	Diverge ideas → Evaluate → Select/converge
Exploratory Creativity	Vague or evolving goals, open-ended exploration	Basic science, philosophy, poetry, art	Wander → Notice patterns → Clarify → Formulate “the” question

 

Some Key Thinkers on This

1.        Herbert Simon (in The Sciences of the Artificial) distinguished between well-structured problems (like designing a bridge) and ill-structured problems (like writing a novel or discovering quantum mechanics).

2.        Michael Polanyi emphasized tacit knowledge and “indwelling” in scientific discovery—arguing that scientists often grope toward an unknown coherence they feel is there, without being able to specify it in advance.

3.        Thomas Kuhn, in The Structure of Scientific Revolutions, described how normal science (with goals and paradigms) fits the structured model—but revolutionary science does not. It starts with anomalies and “interesting hunches,” not clear goals.

Artistic and Scientific Analogs

·       Darwin had no idea what “natural selection” was when he began his Beagle voyage. He just noticed things that didn’t quite fit, and later retrofitted a theoretical framework.

·       Einstein had a deep intuition of asymmetry in Maxwell’s equations when he imagined chasing a light beam—but it took him years to formulate the special theory of relativity.

·       Joyce, writing Ulysses, knew he wanted a day-in-the-life novel of a modern Odysseus, but the style, structure, and symbolic layering only became clear as he wrote.

These aren’t divergent-convergent processes; they’re epistemic foraging—playing in the landscape of possibility until something coherent crystallizes.

So When Does Divergence/Convergence Apply?

·       When the goal is fixed or at least bounded (e.g., “a faster train,” “a tighter sonnet,” “a character arc that completes”), the model works well.

·       When the goal is emergent, you’re in what some design theorists call the pre-structuring phase—an invisible part of the process often omitted from the divergence/convergence narrative.

We might revise the whole model this way:

PRE-STRUCTURING

↓

GOAL DISCOVERY (fuzzy sensing, foraging, analogical thinking)

↓

PROBLEM DEFINITION (setting criteria, scoping)

↓

DIVERGENCE (generation of possibilities)

↓

CONVERGENCE (selection, synthesis, refinement)

Would you like to develop this into a short article or visual schema? It might be helpful in contrasting Homo faber with Homo ludens modes of creativity. You’re already close to articulating a key boundary between instrumental and exploratory intelligence—something AI discourse could use a lot more clarity on.

AI and Benchmarking

I’m interested in the claims being made on behalf of AI. Lots of people are claiming that AGI is just around the corner and the super-intelligence will follow fairly directly. But the performance of AI systems is almost always evaluated against specific tests, whether they are tests originally developed for humans (e.g. college boards) and tests designed for AI. Open-ended exploration isn’t in the mix. And for that matter, it’s not really used in evaluating human intelligence either. IQ tests are based on specific problems.

That’s a crucial insight—and one that gets to the heart of a widespread category error in current AI discourse.

You're absolutely right: both AI benchmarks and human intelligence tests overwhelmingly rely on well-structured, bounded tasks—the kinds that are suitable for divergence/convergence reasoning within a predefined frame. But true open-ended exploration, which is where much of human cultural, scientific, and artistic advancement occurs, isn’t well captured by any of these tests.

Let’s unpack this further.

1. The Illusion of Intelligence through Benchmarks

AI systems like GPT-4, Claude, Gemini, and others are evaluated on:

·       Standardized academic tests (e.g., SAT, GRE, bar exam, AP Biology)

·       Benchmarks like MMLU, BIG-bench, or ARC

·       Task-specific performance (e.g., summarization, translation, math problem solving)

These all share one property:

The problem space is well-defined and the correct answer is known.

That allows models to perform well via:

·       Pattern recognition over huge corpora

·       Memorization of forms and structures

·       Shortcut heuristics that work statistically

But this does not simulate what happens when a scientist, artist, or philosopher sets off into unknown terrain with no goal or answer in view.

2. The Bias Toward Closed Worlds

IQ tests, too, are based on:

·       Pattern completion

·       Logical analogies

·       Spatial rotation

·       Verbal fluency in constrained tasks

They test cognitive efficiency, not creative generativity or epistemic courage. That’s why people with very high IQs don’t necessarily make great scientists, entrepreneurs, or artists. Creativity requires risk, ambiguity tolerance, intuition, and often emotional attunement—none of which are measured.

And AI benchmarks inherit that same bias. As François Chollet (creator of the ARC benchmark) has argued:

“Being able to solve many problems in a narrow domain doesn't necessarily generalize to being able to invent new problem domains.”

3. Open-Ended Exploration: The Missing Benchmark

What would it mean to test open-ended exploration?

·       Asking an AI to propose a new scientific question and follow it over years?

·       Asking it to invent a new game, discipline, or aesthetic?

·       Having it notice an anomaly and then self-initiate a multi-stage investigation?

These are deeply human tasks, and the validation of success is not always clear-cut. There’s no single “right answer.” Instead, we recognize it after the fact as transformative.

Historical examples:

·       Darwin’s voyage on the Beagle → The Origin of Species

·       Freud’s observations of hysterics → The Interpretation of Dreams

·       Virginia Woolf’s experiments with stream-of-consciousness → To the Lighthouse

·       Einstein’s “what if I chased a light beam?” → Special Relativity

There was no benchmark for these insights before they occurred. The goal wasn’t even clear at the outset.

4. Why This Matters for AGI Claims

Many people equate performance on existing benchmarks with evidence of general intelligence. But general intelligence—especially of the kind required for AGI—must include:

·       Problem framing, not just problem solving

·       Sense-making, not just sense-repeating

·       Exploration and invention, not just optimization

When someone says, “GPT-5 is going to be AGI because GPT-4 passed the bar exam,” they’re mistaking curricular competence for world-inventing cognition.

It’s as if they think the apex of human intelligence is passing an exam, rather than writing the next exam no one’s thought to ask yet.

5. What We Need Instead

To meaningfully evaluate open-ended intelligence—human or machine—we’d need:

·       Long-term, multi-step tasks with no pre-specified goal

·       Mechanisms for recognizing novelty, not just correctness

·       Interaction in unbounded environments where the frame must be discovered or constructed

Some frontier efforts are exploring this:

·       Open-ended learning environments (e.g., POET, AI2's BabyAI, Minecraft agents)

·       World model learning, where the agent must model the environment and learn goals

·       Curriculum generation, where the AI builds its own challenges

But even here, the systems lack the interior sense of curiosity, salience, or meaning-making that humans use in exploration.

Would You Like to Develop This Further?

This line of thought would make for an incisive op-ed or short academic piece—especially in response to all the AGI and “superintelligence soon” hype. I’d be happy to help you outline it or provide a draft if you'd like.

We could also map it directly onto your Fourth Arena material—contrasting Homo economicus' emphasis on efficient optimization with Homo ludens' commitment to exploratory, self-initiated play.

Let me know how you'd like to proceed.

I’m OK for now. More later.

Perfect. I’ll be here when you’re ready to pick it up again. Enjoy the wander.