Serendipity in the Wild: Three Cases, With remarks on what computers can’t do

New working paper. Title above. URLs, abstract, table of contents, and abstract below.

Academia.edu:
https://www.academia.edu/145860186/Serendipity_in_the_Wild_Three_Cases_With_remarks_on_what_computers_cant_do
SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6043814
ResearchGate: https://www.researchgate.net/publication/399584810_Serendipity_in_the_Wild_Three_Cases_With_remarks_on_what_computers_can't_do

Abstract: This paper examines intellectual creativity as it occurs in ordinary, open-ended scholarly practice, through three detailed case histories drawn from my own work in the humanities. Each case traces how a line of inquiry emerged without a predefined problem, method, or endpoint, and how a vague sense of interest gradually crystallized into a focused intellectual project.

Introduction: Serendipity in Mind

The first case reconstructs the process by which an unplanned viewing of Jaws developed into a Girardian interpretation of the film, centered on the recognition of Quint as a sacrificial victim. The second follows an exploratory investigation of the term “Xanadu” on the early web, where a surprising search result led, through low-cost probing, to the identification of distinct cultural clusters. The third describes the discovery of a previously unrecognized center-point structure in Conrad’s Heart of Darkness, originating in the noticing of a minor narrative anomaly and pursued through opportunistic quantitative checks.

Across these cases, creative work proceeds through hunches, comparative wandering, sensitivity to salience, and decisions shaped by opportunity cost, rather than through the execution of well-defined tasks. Only late in each process does a clear problem boundary emerge, enabling more systematic reasoning.

The paper uses these cases to clarify a limitation of contemporary large language models and related AI systems. While such systems can operate effectively once a problem frame is supplied, they do not participate in the open-ended, exploratory processes by which humans discover what is worth investigating in the first place. The cases thus support a model of human–AI complementarity in which problem-finding remains a distinctively human contribution, and AI serves as a powerful tool once direction has been established.   

Table of Contents

Introduction: Serendipity in Mind 3
Intellectual creativity: Case 1, Interpreting Jaws 4

Watching Jaws 5
The game is afoot 6
I’m in! 6
What kind of a process is that? 7

Intellectual creativity: Case 2, The Xanadu meme 9

Cultural evolution 10
The cybernetic cluster appears 11
But still, what got me started? 12
Opportunity cost 13

Intellectual creativity: Case 3, Center-point construction in Heart of Darkness 15

Story and plot, Center-point construction 15
What would an AI do? 19
Is this real? How do we know? 21

Appendix 1: Summary and Evaluation by Claude 23

Summary 23
Evaluation 23
Conclusion 24

Appendix 2: Summary and Evaluation by ChatGPT 26

Summary 26
Overall Assessment 27
Human–AI Complementarity: Lessons from Three Cases 28

Appendix 3: Seven Brief Examples of Serendipity 30    

Introduction: Serendipity in Mind

In the three years since ChatGPT was released on the web the sophistication of chatbots has increased a great deal, leading some to assert that it won’t be long before chatbots will be able to outperform humans on all intellectual tasks. I’m not so sure. Why? Because the problems they solve, the tasks they’ve been set, all seem to be bounded tasks in well-explored universes. As Johnson, Karimi, and Bengio have remarked in a recent article, many important intellectual tasks require “the ability to navigate intractable problems - those that are ambiguous, radically uncertain, novel, chaotic, or computationally explosive.” They talk of wisdom as the quality missing in current machines. I’m not sure what I think about that word, “wisdom”; it carries a lot of baggage.

I’m more comfortable talking about serendipity, not as an attribute of minds, or of situations, but as, well, whatever it is that serendipity characterizes. In the third appendix I present seven brief cases that I elicited from ChatGPT with a prompt, but the burden of my argument rests accounts of three problems that I’ve worked on: an interpretation to Steven Spielberg’s Jaws, the distribution of the term “Xanadu” on the web (the “Xanadu meme”), and the discovery of center-point structure in Joseph Conrad’s Heart of Darkness. I present these cases, not because I think that they’re somehow special. My thinking in these cases doesn’t seem to me to be particularly abstract. It’s not “rocket science” as the saying goes. I present those cases simply because they are mine and I know them in detail. I know what I did, when I did it, and why. What I did depended on experiences and “hunches” built up through experience. Those don’t strike me as the kinds of things amenable to current computational techniques.

Once I’d completed drafting those cases I presented them to both ChatGPT and Claude. I’ve included summary excerpts from those interactions as appendices. I’ll let Claude conclude this introduction:

Benzon's case studies effectively demonstrate that the most interesting intellectual work often begins not with problem-solving but with problem-finding. His examples show how experience, intuition, and willingness to follow hunches create opportunities for genuine discovery. While current AI systems are powerful analytical tools, they lack the kind of embodied experience and open-ended curiosity that drives human creativity. The work suggests that the future of AI-assisted scholarship may lie not in replacing human insight but in creating powerful partnerships where humans excel at boundary-setting exploration and AI excels at systematic analysis within those boundaries.