OpenAI Says Its LLM Can Write Creatively

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Sam Altman recently tweeted that OpenAI has trained a model that’s good at creative writing, asserting that it was the first time he’d been “really struck by something written by AI.” While the unnamed model isn’t publicly available yet, Altman gave us a glimpse of its potential by sharing a prompt—“please write a metafictional literary short story about AI and grief”—alongside the 1,172-word narrative it generated.

Reactions to Altman’s tweet were mixed—some were deeply moved by the AI’s story, while others dismissed it as trash. But I think debating the literary merit of the piece misses the point. The model’s demo begs a deeper  question: Are large language models capable of writing creatively? 

When we judge whether AI can write creatively, we’re really expressing our own beliefs about what creativity is—not something many of us spend much time thinking about. We may think we know it when we see it, but putting “it” into words is surprisingly difficult. Is originality an illusion, a deft trick of taking in data about the world and parsing and rearranging it? Or is it rooted in some ineffable aspect of human experience? Or is it something else entirely: a subjective judgment that’s open to interpretation by whomever is interacting with the creative work? 

As I tried to get to the bottom of these questions, I found a bunch of fascinating ideas about how creativity might work in machines. One thing I did not find is a black-or-white answer to the question of whether LLMs are our next great literary talent. It turns out it depends a lot on how we, the humans in this story, look at things. 

Machines and theories of creativity 

More than two decades before Altman’s tweet, cognitive scientist Margaret Boden published a paper on creativity and artificial intelligence. Boden theorized that creativity came in three broad types: “combinational” creativity, improbably combining familiar ideas (a chef who prepares dishes that are a fusion of Spanish and Thai cuisines); “exploratory” creativity, discovering new ideas within a familiar conceptual space (a chef at the cutting edge of contemporary Spanish food); and “transformational” creativity, changing some dimension of a familiar space so that new structures can arise (a chef reimagining what constitutes food). 

Boden cited an early instance of AI demonstrating combinational creativity: a computer program called the Joke Analysis and Production Engine that was programmed to generate riddles with puns like: “What kind of murderer has fiber? A cereal killer,” and “What do you call a depressed train? A low-comotive.” However, Boden said that computer programs of that era had been most successful in exploratory creativity, and that the transformational kind remained a distant dream. 

Somewhat amazingly, research published just last month that used Boden’s criteria to evaluate the current generation of LLMs reached a similar conclusion. The authors conclude that at best, LLMs are capable of exploratory and combinational creativity. Transformational creativity, according to them, remains out of reach because of the models’ autoregressive nature—in other words, the property of LLMs to generate text one token at a time, with each new token being informed by all previously generated tokens. In case you’re wondering what transformational creativity in writing looks like when humans do it, one prominent example is James Joyce’s Ulysses. In the book, Joyce pioneered a stream-of-consciousness style that broke traditional narrative structures and redefined what a novel could be.

The idea of transformational creativity—fundamentally shifting a contemporary way of thinking—lines up well with the standard set by the creators of the Lovelace Test. Proposed by a trio of computer scientists in 2001, the Lovelace Test was designed as an alternative to the Turing Test, intended to measure creativity in machines. A program passes the Lovelace Test if it generates an output that its creator cannot explain based on the program’s design, algorithms, or knowledge base. It must genuinely surprise the creator in a way that suggests the system has originated something novel, rather than just recombined or repeated existing patterns. By this standard, current LLMs still fall short of exhibiting the kind of autonomous, unexplained creativity—in writing or otherwise—that the Lovelace Test demands.

A uniquely human struggle(?)

Writing has long been thought of as a mysterious thing that’s peculiar to humans, an alchemy of one’s thoughts, experiences, emotions, and observations that moves people who read it. It’s usually talked about in terms of being a struggle. Virginia Woolf, who to put it mildly thought a lot about the writing process, said, “The main thing in beginning a novel is to feel, not that you can write it, but that it exists on the far side of a gulf, which words can't cross: that it's to be pulled through only in breathless anguish.”

Most in the trade agree that writing is indeed a struggle, and one intimately linked to the larger struggle of living. It is, as singer-songwriter Nick Cave put it, “a blood and guts business, here at my desk.” “ChatGPT’s melancholy role is that it is destined to imitate and can never have an authentic human experience,” Cave continued, while responding to a fan who sent him a song that ChatGPT had generated in his style.

Another part of creativity where LLMs fall short has to do with our unconscious. One of the first modern theories of creativity, put forward by Graham Wallas in 1926, includes a step of “incubation,” or deliberately putting your work aside and doing other things. Wallas drew from the experiences of mathematician Henri Poincaré, who meticulously documented his thought process leading up to two major discoveries. To the point of incubation, Poincaré described how a breakthrough occurred to him unexpectedly as he stepped onto a bus that was ferrying him to a geological excursion. 

People who believe that creativity is rooted in real human experience (as opposed to records of it in training data) reject that non-sentient LLMs can ever truly write creatively. In other words: AI can be prompted to describe, in remarkable detail, how sour a lemon tastes, but it’s never actually recoiled from the sharpness of its juice—and, to them, that makes all the difference. 

In the eye of the beholder

In the 1890s, when Helen Keller was still a child, she was accused of plagiarizing a short story she’d penned. Though she was eventually cleared, the incident seemed to have made a mark on her friend Mark Twain. A decade later, Twain wrote in a letter to her: “Oh, dear me, how unspeakably funny and owlishly idiotic and grotesque was that ‘plagiarism’ farce! As if there was much of anything in any human utterance, oral or written, except plagiarism… For substantially all ideas are secondhand, consciously and unconsciously drawn from a million outside sources.” Twain, in his delightfully evocative prose, was pre-empting the popular belief that originality is an illusion—that all creativity is, at its core, derivative. His skepticism toward originality foreshadowed ideas of postmodern theorists, who questioned not only the notion of unique authorship but also the source of meaning in a text.

Traditionally in literature, the author is celebrated as a unique creative genius, the sole originator of a work’s meaning. Post-modernist thinkers challenged this view—arguing that the meaning of a text comes from the reader’s interpretation, not the writer’s intent​. In his prescient 1986 essay "Cybernetics and Ghosts," surrealist writer Italo Calvino dreams of machines capable of writing, and in that eventuality, describes how literature will abound: “Once we have dismantled and reassembled the process of literary composition, the decisive moment of literary life will be that of reading. In this sense, even though entrusted to machines, literature will continue to be a ‘place’ of privilege within the human consciousness… The work will continue to be born, to be judged, to be destroyed or constantly renewed on contact with the eye of the reader.” 

Per this school of thought, creativity doesn’t reside in the writer’s hand—human or AI—but in the reader’s willingness to make meaning.

What does this tell us about ourselves, and LLMs?

I started research on this piece with the hopes of answering what I thought was a binary question: Either LLMs could write creatively, or they couldn’t. But my honest conclusion is that creativity exists on a spectrum. Like many things that intrigue and beguile us—love, consciousness, intelligence—it defies straightforward definitions. Language models are creative, and have been for many decades—if you see creativity in the processes of combination and exploration, or if you believe meaning, ultimately, resides in the reader's interpretation. But if you believe that true creativity requires lived human experience, unconscious incubation, or a fundamentally new way of thinking, LLMs still have ways to go. As we question whether an AI can truly write, reason, or create, it seems that we’re compelled to define our own abilities more precisely than ever before. 

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