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<\/p>\nCreative writing used to be a human prerogative: do it well, do it badly, but either way endorse the consensus that to write about human experience was worth the candle and the coffee. Here was an essential human act, so much so that poetry formed a critical part of the computer pioneer Alan Turing\u2019s original test: to determine whether an unseen respondent to a series of questions was human or a mechanical imposter. The Turing Test is often simplified to denote a single crossing point between two territories, human and machine. Pass the test, and artificial intelligence can stroll on over to our side of the line. Take a look around. Decide what to do with us. But, first, it has to pass.<\/p>\n
In the paper \u2018Computing Machinery and Intelligence\u2019 (1950), published in the journal Mind, Turing set out his objective: \u2018to consider the question, \u201cCan machines think?\u201d\u2019 In true human fashion, he immediately re-phrases the question, at some length, and eventually arrives at the \u2018imitation game\u2019, modelled on a drawing-room entertainment from before the internet, before television. The original game he has in mind involves a guesser in the hotseat who poses questions to a man (X) and a woman (Y), who are out of sight and hearing in a separate room. The guesser has to determine from their written answers which is the man and which the woman. X tries to mislead, and wins if the guesser is wrong; Y wins if the guesser is right. Try it, it\u2019s fun.<\/p>\n
In this context, the first question posed in Turing\u2019s proposed test is less surprising than at first it seems: \u2018Will X please tell me the length of his or her hair?\u2019 Next, Turing asks, equally politely: \u2018Please write me a sonnet on the subject of the Forth Bridge.\u2019 Two questions in, and the contested boundary between human and machine thinking is already looking for answers in literature, in art. Turing\u2019s 1950s version of X \u2013 the participant aiming to mislead \u2013 replies: \u2018Count me out on this one. I never could write poetry.\u2019 To imagine this answer, in the second phase of his game, Turing\u2019s complicated brain is playing the role of a machine playing X, hidden from sight and typing its answers, pretending to be a man (who previously played the game pretending to be a woman). I know, but if the test were easy an air-fryer could pass it.<\/p>\n
Turing isn\u2019t suggesting that a machine can\u2019t write poetry. In the convoluted logic of the imitation game, X calculates that in 1950 ordinary people didn\u2019t write poetry, a commonsense assumption that every computer masquerading as human should know. Among other prejudices from the mid-20th century, Turing\u2019s paper makes incautious references to race, religion and the Constitution of the United States. He likens the inability to see computers as sentient as equivalent to the \u2018Moslem view that women have no souls\u2019. Turing wades in: he doesn\u2019t compute as we would now.<\/p>\n
And neither do the future computers of 2026 that he was trying to envisage. Any of today\u2019s large language models (LLMs), like ChatGPT or Claude, can write an instant sonnet on the subject of the Forth Bridge. I typed in Turing\u2019s test question, and Claude 4 threw up 14 lines of poetry including the abbreviated word \u2018mathemat\u2019cal\u2019, for the scansion. The poem made sense, and was formally a sonnet, and appeared in seconds.<\/p>\n
Whether or not this counts as thinking, Turing intuits that the frontier he\u2019s marking out will be picketed by the arts. In his paper, he picks a fight with an eminent neuroscientist of the time, Sir Geoffrey Jefferson of the Royal Society, who believed that \u2018Not until a machine can write a sonnet or compose a concerto because of thoughts and emotions felt, and not by the chance fall of symbols, could we agree that machine equals brain \u2013 that is, not only write it but know that it had written it.\u2019 For Jefferson, addressing the Royal College of Surgeons in his 1949 Lister Oration, mechanising the efforts of the infinite monkeys on typewriters didn\u2019t really count.<\/p>\n
These days, in the arts, it\u2019s harder to share Jefferson\u2019s confidence. The advances made by AI prick at artistic vanity \u2013 the work of a human artist can\u2019t be all that special if a machine can replicate the results almost instantly. That hurts. A great human artist, we\u2019d like to believe, amplifies and defends the exceptionalist spirit of our species but, in an echo of the anxieties that haunted early photography, a demonised version of AI threatens to steal away our souls. Encroaching on the best of what we can do and make and be, machine art intrudes onto sacred territory. Creative artists are supposed to be special, inimitable.<\/p>\n
Turing\u2019s Imitation Game paper was published 14 years after the first Writers\u2019 Workshop convened at the University of Iowa, in 1936. Turing may not have known, with his grounding in maths at King\u2019s College Cambridge, that elements of machine learning had already evolved across the Atlantic in the apparently unrelated field of creative writing. Before Iowa, the Muse; after Iowa, a method for assembling literary content not dissimilar to the functioning of today\u2019s LLMs.<\/p>\n
First, work out what effective writing looks like. Then, develop a process that walks aspiring writers towards an imitation of the desired output. The premise extensively tested by Iowa \u2013 and every creative writing MFA since \u2013 is that a suite of learnable rules can generate text that, as a bare minimum, resembles passable literary product. Rare is the promising screenwriter unfamiliar with Syd Field\u2019s Three-Act Structure or Christopher Vogler\u2019s Hero\u2019s Journey: cheat codes that promise the optimal sequence for acts, scenes, drama and dialogue. In the same way that an LLM is designed to \u2018think\u2019, these templates are a form of reverse engineering: first study how the mechanics of Jaws or Witness made those movies sing, then identify transferable components for reassembly to achieve similar artistic success further down the line.<\/p>\n
To a computer-programmer, reverse engineering as a machine-learning mechanism is known as back-propagation. In A Brief History of Intelligence (2023), Max S Bennett shows how this methodology has already helped in the development of image recognition, natural language processing, speech recognition, and self-driving cars. Supervising coders work to isolate the required answer in advance, then go back to nudge input responses until the artificial neural network arrives at the pre-set solution.<\/p>\n
The mysterious magic ingredient has been debated in English on the printed page since at least 1580<\/p>\n
If only writing were so simple. According to figures from Data USA, up to 4,000 students graduate each year with creative writing MFAs in the US. No one expects that number of Great American Novels to show for so much studying, despite the fact that many hopeful writing careers start with the prompt mentality invited by Chat GPT: I want to write a bestseller like the one that blew me away last summer. Or, for the more adventurous: something new but relatable, a novel\/memoir hybrid with literary credibility and strong narrative momentum, like a cross between Lee Child and Annie Ernaux. Thank you. I\u2019ll wait. But not very patiently.<\/p>\n
Clearly, when the end result is compared with the original intention, the back-propagation method is fallible for creative writing courses and LLMs alike. To revisit Jefferson, as quoted by Turing, the finished work is undermined when inspired by the wrong \u2018thoughts and emotions\u2019, whether blind ambition in student writers or blind obedience in computers. Something more is required, and the mysterious magic ingredient has been debated in English on the printed page since at least 1580, when Sir Philip Sidney reached for the essence of exemplary creative writing in An Apology for Poetry. When it worked, he concluded, good writing could both teach and delight. It provided a guide to living well in a more accessible form than theology or history or philosophy. Creative writing was special.<\/p>\n
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Sir Philip Sidney\u2019s \u2018Defence of Poesie\u2019, in a 1627 edition of the Arcadia. Courtesy University of Glasgow Library\/Flickr<\/a><\/p>\n So special, in fact, that no one has yet been able to break down the findings of English literature departments \u2013 what makes literature work \u2013 into sufficient granular detail to reformulate as instructions actionable by an LLM. Or by a creative writing student. Nor are the efforts being made in this area by other art-forms particularly encouraging. ArtEmis is a large-scale dataset designed to record and subsequently predict emotional responses to works of visual art. The scheme matches emotional annotations from more than 6,500 participants to textual explanations of what they\u2019re seeing, and from this data ArtEmis hopes to enable the back-propagated creation of artworks that provoke equivalent emotional responses.<\/p>\n The understanding seems to be that if a machine can create a visual image that generates a controlled set of feels, then art will have been successfully created. Which sounds plausible, except human emotional responses are notoriously capricious. The ArtEmis procedure already has an analogue precedent in Hollywood, but if focus groups worked reliably for the arts, then cinemas would be full of bangers. It\u2019s worth remembering that the 2023 strike action by the Writers Guild of America won significant protections against the use of generative AI in screenwriting, specifically disallowing the replacement of human writers by AI. This hasn\u2019t noticeably boosted the production of great art movies. Human writers still make so-so films. Without any intervention from AI, we continue to paint indifferent canvasses and write forgettable novels.<\/p>\n Bad art is something human beings love to do, in vast numbers. It\u2019s part of who we are, and when abandoned by inspiration we trust in the same methods we\u2019ve programmed into LLMs. As predicted by Turing, \u2018digital computers \u2026 can in fact mimic the actions of a human computer very closely\u2019, and for the creation of failed artistic product we\u2019ve taught artificial intelligence all our dodges. Creative writing that falls short, whether originating in a garret or in an Nvidia chip, \u2018writes\u2019 by selecting language units that commonly fit together, as recognised from published material available in the public domain. Familiar word combinations are assembled into almost convincing sentences, a tired use of language formerly called out as clich\u00e9. LLMs are clich\u00e9 machines, trained on a resilient human weakness for generating maximum content with minimum effort.<\/p>\n This explains the headline in June 2025 in the British publishing industry\u2019s leading trade magazine The Bookseller: \u2018AI \u201cLikely\u201d to Produce Bestseller by 2030\u2019. The headline referenced a conference speech by Philip Stone of Nielsen, a company that compiles UK book-sales data. I expect he\u2019s right about that bestseller, because LLMs will come for genre writing first \u2013 police procedurals, spy thrillers, romances \u2013 re-treading identifiable formulas with proven popular appeal. Eager to please (\u2018Hi Rich, how are you today?\u2019), AI also has the advantage, shared by surprisingly few human writers, of being able to churn out derivative product without embarrassment.<\/p>\n Fortunately for everybody else, the endless capacity of an AI to deliver rule-bound and resolution-directed narrative has an unexpected benefit: AI is the tool that will prove not all writing has the same value.<\/p>\n Writing has been reluctant to imagine new ways of reading, despite the vistas opened up by new technologies<\/p>\n To escape the dead man\u2019s handle of clich\u00e9, readers live in hope for organic associations, speculative leaps and surprise inferences. Whereas, to an AI, which is fed the answer before the question, \u2018surprise\u2019 remains an elusive concept. This objection to machine thinking was raised as long ago as 1842 by Ada Lovelace about one of the earliest computers, Charles Babbage\u2019s Analytical Engine (for historical context, Iowa\u2019s first creative writing get-together, though informal, took place in 1897). \u2018The Analytical Engine has no pretensions whatever to originate anything,\u2019 Lovelace observed. \u2018It can do whatever we know how to order it to perform.\u2019 Her italics emphasise the contrast with human thinking where originality, among artists at least, is a cherished value.<\/p>\n![\"Daguerreotype](\"https:\/\/www.newsbeep.com\/us\/wp-content\/uploads\/2026\/01\/adabyron-1834-1000x1200.jpg\"\/)
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