Old in Niches, New at Scale

The first thesis I want to put forward is that algorithmic folklore is old in niches, but new at scale. Let’s begin from the niches: long before algorithms became the structuring logic of digital media, folklore about calculation circulated among mathematicians, philosophers and scientists. As Matteo Pasquinelli has noted, algorithmic thinking existed even before a term to describe algorithms was derived from the name of Islamic polymath Muḥammad ibn Musá al-Khwārizmī (2023: 28). Thus, ‘mathematical folklore’ exists in a variety of genres, spanning authorless and unpublished results believed to be true that are shared informally among specialists to the quirky stories and jokes about scientific fields such as mathematics, chemistry or theoretical physics. Examples of mathematical folklore include recurring narrative archetypes, such as the fabricated stories of Archimedes discovering the laws of buoyancy while having a bath, Isaac Newton discovering gravity via an apple falling on his head and Galileo Galilei experimenting with acceleration by dropping objects from the Leaning Tower of Pisa (Biello, 2006), but also proper folktales, such as the ‘Chessboard of Sissah’ mathematical folktale originating from India, which illustrates the problem of geometric progression using a chess board and grains of rice that are doubled each passing day (Bardi, 2025). Similar forms of folklore persist today: for example, the story about mathematician John Von Neumann solving a complex puzzle at a cocktail party is told as a disciplinary legend (Halmos, 1973), and the tale of scientist Carl Friedrich Gauss discovering an algorithm to sum up a sequence of integers as a schoolboy has been documented to exist in over a hundred versions (Hayes, 2006). In keeping with the small niches where it is shared, this kind of mathematical folklore ‘tends to be quite esoteric and intelligible only to members of the group’ (Renteln and Dundes, 2005: 24).

With the advent of electronic calculators and programming languages, mathematical folklore expanded to new niches such as computer science and software engineering. Several collections document these new folklores: for example, the ‘computer programmer folklore’ studied by the Dartmouth Folklore Archive demonstrates the exclusivity of in-group jokes that work best when presented in written form (Long, Guo and Sylvia, 2017); and the collection of ‘software folklore’ put together by Andreas Zwinkau includes classics such as the ‘Crash Cows’ story about a 1980s freight train routing system that kept failing because its cargo of slightly radioactive cattle flipped random bits in the processor’s memory (Zwinkau, 2023). It is not surprising that specialist domains of computer science such as artificial intelligence and machine learning such as recommended systems also developed their own folklore (Khamitkar et al., 2009). In a widely cited review article published at the onset of the 2010s machine learning boom, Pedro Domingos describes this new and attractive technique as characterized by a troubling amount of uncodified ‘folk knowledge’ and ‘folk wisdom’ that is ‘difficult to come by, but is crucial for success’ (2012). Similarly, computer science professor Michael Woolridge consistently describes how narratives and templates – such as the emotional reactions to Weizenbaum’s 1960 ELIZA program or the LISP rules for animal classification developed by Patrick Winston & Berthold Horn – are retold and reused over the decades until they become part of a veritable ‘folklore of AI’ (Woolridge, 2021: 34, 91) which then keeps informing new developments in the field.

Looking at one prominent example of AI folklore helps illustrate an important shift in scale from expert niches to broader audiences. Here’s a story that researcher Eliezer Yudkowski includes in an AI risk report from the late 2000s:

Once upon a time, the US Army wanted to use neural networks to automatically detect camouflaged enemy tanks. The researchers trained a neural net on 50 photos of camouflaged tanks in trees, and 50 photos of trees without tanks. […] The researchers ran the neural network on the remaining 100 photos, and without further training the neural network classified all remaining photos correctly. Success confirmed! The researchers handed the finished work to the Pentagon, which soon handed it back, complaining that in their own tests the neural network did no better than chance at discriminating photos. It turned out that in the researchers’ dataset, photos of camouflaged tanks had been taken on cloudy days, while photos of plain forest had been taken on sunny days. The neural network had learned to distinguish cloudy days from sunny days, instead of distinguishing camouflaged tanks from empty forest. (Yudkowsky, 2008: 323)

Gwern Branwen has extensively documented and analyzed how this story – also known as the ‘neural net tank’ urban legend – has been circulating in dozens of different versions since at least 1992, without ever being properly sourced back to its origin: a speculative question posed by Edward Friedkin at a computer science conference in the early 1960s (2011). As Branwen notes, the neural net tank urban legend is very unlikely to have happened as described in any of its many versions, but its persistence proves its usefulness as a cautionary tale that remains relevant across both specialist disciplinary fields and broader public debates.

The 2020s have witnessed a dramatic sea change: what used to be relatively insular scholarly niches and professional in-groups have become enmeshed in an interdisciplinary field, cross-sectoral industry and public debate coalescing around the term ‘artificial intelligence’. The deployment of algorithmic systems and computational automation at societal scale has not only led to the recognition that tools like Large Language Models (LLMs) can function as cultural technologies (Farrell et al., 2025), but also that previously unrelated and self-contained folklore repertoires – from mathematics and theoretical physics to computer science and machine learning – have now become relevant for a much larger and less specialized audience. Suddenly, a joke about a specific search algorithm or a folktale about a chatbot malfunction are relevant for the public at large. Take the image above (Figure 3) as a case in point: while structurally similar to the neural network tank urban legend, this internet meme does not require much specialist knowledge to be understood. Thanks to extensive societal debates around machine vision technologies and their consistent representation in popular culture, even laypeople can interpret the green bounding box as a computational overlay signifying some kind of algorithmic processing, and connect the incorrect measurement of a very long cow to the failures of machine learning. This trivial example of algorithmic folklore encompasses niches of expert humor ranging from data science to object recognition, and precisely illustrates the thesis that algorithmic folklore is old in niches, but new at scale.

Figure 3: The ‘long cow’ machine vision meme shared on Twitter by computer scientist Dmitri Alexandrov with the caption ‘machine learning has to learn a lot’ (2022).

The Mutual Shaping of Creativities

The second thesis I propose in this article states that the core logic of algorithmic folklore is the mutual shaping of creativities. In order to understand what this means, it is necessary to revisit the connection between folklore and creativity. For decades, folklorists have relied on the concept of ‘folk process’ formalized by musicologist Charles Seeger to explain how folk narratives, performances or practices are transmitted among people and across time. The folk process builds upon three factors identified by folk song collector Cecil Sharp in 1907: continuity, variation, and selection. While continuity is the most visible feature of folklore – granting its perceived stability, timelessness, and resilience – both variation and selection involve ongoing creative efforts by both individuals and communities (Crighton, 2005). For much of human history, the folk process involved creative interventions that storytellers, musicians, craftspeople, other practitioners and their audiences introduced into existing folk repertoires. Without entering into philosophical debates about the precise definition of the term ‘creativity’, it is not controversial to argue that these interventions largely involved human creativity, which has in itself been understood and constructed differently over the centuries (Reckwitz, 2017), often in contrast to computational creativity (Boden, 2009). With the popularization of new media technologies, the folk process has expanded and changed (James, 2010), not only because audiences have now a much more active role in creative processes (Egenes, 2010), but also because the cybernetic logic of computation is impacting each of the three factors (Dorst, 2016). From basic search engine result ranking to the latest development in generative models, algorithms encroach upon aspects of continuity, variation and selection that were traditionally seen as the purchase of human creativity.

As they become implemented across devices and platforms, algorithms play an important role in new folk processes – for example, selecting content to personalize consumer media experiences, or introducing compelling variation to media synthesis. And once these two kinds of creativity – human and computational – operate in the same arena and on the same materials, new kinds of folklore emerge through their interaction (Schellewald, 2022). To see this interaction at play, consider the short ‘Trust me’ video reels that started circulating on Instagram in mid-2023 (Figure 4). These reels are relatively simple, and usually consist of a stock video or still image and a catchy audio clip playing in the background, with an overlaid text that reads something like this:

Trust me, Never get close to 5th person, it will hurt you one day.

Everything is temporary but 3rd person in your share list is permanent

Trust me, Third person is dreaming to get married to you

Figure 4: Three ‘Trust me’ reels from Instagram (Mysticdhruv.. 2024; _callme__ifuuu_, 2024; ubaid_sayss, 2024). Screenshots by the author.

The videos rely on Instagram’s dynamic ranking of a user’s top contacts to entice them to tap on the ‘Share’ button and, ideally, send the content to the one in the position indicated by the reel text, who – according to this rudimentary form of algorithmic divination – will hurt them, or who secretly loves them. But this is not the only algorithm at play here: once the user shares the video with someone else, the interaction itself will become a new constellation of data points that Instagram’s recommender system will ingest to further refine its delivery of that piece of content, and perhaps of similar ones. In short, two specific algorithmic decision-making functions implemented by Instagram shape the human creative decisions behind a genre of content that exploits them to achieve broader circulation.

Following the developments in natural language processing (NLP) and media synthesis, algorithmic systems are increasingly involved not only in the circulation of content but also in its production. This complicates how human and computational creativity interact, as exemplified by the ‘glorbo’ experiment. On July 20, 2023, Reddit user kaefer_kriegerin created a post titled ‘I’m so excited they finally introduced Glorbo!!!’ in one of the major World of Warcraft discussion boards (r/WoW). In the post, the user praises the introduction of a new in-game feature reportedly called ‘Glorbo’, before admitting that ‘I just really want some major bot operated news websites to publish an article about this’ (kaefer_kriegerin, 2023). Created in response to widely shared suspicions that various gaming news websites were using automated systems to scrape Reddit and publish AI-generated articles, this post was meant to function as bait that could be easily tracked, as the word ‘glorbo’ did not exist before. Surely enough, on the same day, Z League’s gaming content website The Portal published an article titled ‘World of Warcraft (WoW) Players Excited for Glorbo’s Introduction’, which reported about how ‘the highly anticipated new feature has sparked a wave of positive sentiment among players, who are eagerly awaiting its impact on the game’ (Reed, 2023). After extensive media coverage about how Reddit users had succeeded by ‘enhancing the algorithmic profile of the Glorbo post and making it more attractive for bot harvesting’ (Edwards, 2023), The Portal took the news article down. But the experiment was successful: by injecting a made-up word into their interactions, Reddit users were able to exploit the computational creativity of an algorithmic system to reveal traces of its own operation.

The ‘trust me’ Instagram reels and the Glorbo experiment are two examples of how the pervasive implementation of algorithms forces new kinds of encounters between human and computational creativity. Examining these encounters through the lens of the folk process can illuminate how different forms of creativity interact in the production and circulation of algorithmic folklore. In the case of the ‘trust me’ Instagram reels, algorithmic decisions such as contact ranking and content recommendation (selection) determine the human creation of new versions according to a fixed textual template (variation) which in turn ensure the circulation and reproduction of the genre (continuity). In the Glorbo example, a non-existing word is coined and developed into lore by humans (variation) in order to reveal the algorithmic scraping of community data (selection) and trick AI-powered bots into creating content about it (continuity). The folk process itself does not radically change, but humans and algorithms respond to, influence or challenge the creative decisions that are taken or delegated at different stages. Sometimes, it is algorithms influencing human creativity, other times it is humans exploiting algorithmic creativity. Through these interactions, two non-commensurable kinds of creativity affect one another, expanding and constraining the range of possible narratives, performances and practices. In STS terms, this is an example of ‘mutual shaping’, which has traditionally been applied to the analysis of media artifacts to capture the fluctuating balance of social and technological forces in driving their transformations (Boczkowski, 2004). When it comes to algorithmic folklore, examining how the folk process expands and changes supports the thesis that its core logic is the mutual shaping of creativities – human and computational ones.

A Literacy Vacuum

The third thesis argues that algorithmic folklore emerges to fill a literacy vacuum. Like other kinds of technological innovations such as networked communications and digital media, algorithms transform existing literacies and demand the development of new ones (Livingstone, 2004). Technology and literacy do not necessarily develop at the same pace, and given the accelerated diffusion of algorithmic media and systems across social and cultural contexts, it is not surprising that literacy struggles to keep up (Hargittai et al., 2020). This opens a sort of ‘literacy vacuum’ – a gap in which algorithmic technologies (from search optimization and recommender systems to generative models and self-driving cars) become highly relevant while structured and widely shared ways of making sense of them lag behind (Cotter and Reisdorf, 2020). As this vacuum expands, it is filled by lay forms of knowledge that people develop in response to these complex and opaque technologies: urban legends, folk theories, vernacular practices, and so on. David Barton and Carmen Lee call these forms of knowledge ‘vernacular literacies’, emphasizing their key feature of being ‘voluntary and self-generated, rather than being framed and valued by the needs of social institutions’ (2012: 283). Vernacular literacies also do not appear fully formed in this vacuum – instead, they slowly consolidate around the shared experiences and recurring challenges that people encounter as they engage with a new media technology. In the case of algorithms, they begin as algorithmic folklore.

To illustrate how this formation unfolds, let’s look back at the inception of diffusion models, which became the preferred approach for text-to-image generation in 2021. As more and more people started experimenting with generative diffusion models, bewilderment about the photorealism of their outputs was accompanied by skepticism about its consistency. For example, a widely circulated tweet by Miles Zimmerman showcasing surprisingly convincing images of a party created by Midjourney was followed by extensive discussions in which other Twitter users zoomed into deformed details like mangled hands, smudged tattoos and asymmetric neck lines (Dixit, 2023). In a 2023 essay, Kyle Chayka identifies the failure at generating hands as symptomatic of the limitations of generative models:

[…] tools such as Midjourney, Stable Diffusion, and DALL-E are able to render a photorealistic landscape, copy a celebrity’s face, remix an image in any artist’s style, and seamlessly replace image backgrounds […]. But when confronted with a request to draw hands the tools have spat out a range of nightmarish appendages: hands with a dozen fingers, hands with two thumbs, hands with more hands sprouting from them like some botanical mutant. (2023)

Just as poorly aligned teeth were identified as a tell-tale sign for the previous generation of image synthesis technology (GANs, or Generative Adversarial Networks), hands emerged as the reality check for diffusion-based tools. Initially, this observation circulated informally among people experimenting with these models and sharing their observations on social media. Then, it became encoded in humorous images and copy-pasted texts mocking the current hype around AI replacing creative labor or correlating these failures to folk stratagems for identifying the supernatural (Figure 5).

Figure 5: Two popular memes about ‘diffusion hands’ (MattsIdeaShop, 2023; Erkhyan, 2022).

Once the literacy vacuum around diffusion models was filled by algorithmic folklore about malformed hands, this nugget of folk knowledge started to circulate beyond communities of early adopters and critics with unpredictable effects. Around early 2023, an image of a realistic rubber finger with an attached ring captioned as ‘Ring-Finger-Ring’ started circulating online, accompanied by a short narrative explanation: ‘Criminals will start wearing extra prosthetic fingers to make surveillance footage look like it’s AI generated and thus inadmissible as evidence’. From tweets to Instagram posts to TikTok videos and LinkedIn stories, this narrative consolidated from a speculative scenario to an actual phenomenon supposedly impacting law courts. This would be a compelling story, were it not for the fact that the wearable prosthesis is in fact a work by artist Nadja Buttendorf titled FINGERring. Created in 2016, well before the advent of generative models, the artwork had nothing to do with criminal intentions – if anything, the opposite, as in the work description Buttendorf suggests how ‘robots can wear the humanlike accessoire to feel a little bit human’ (2016). Similarly, in March 2024, when the Princess of Wales Kate Middleton shared a Mother’s Day photo of herself with her three children, observers highlighted how several details in the image suggested it had been doctored – including ‘distortion in Prince George’s right fingers, a body part that A.I. notoriously has trouble rendering’ (Boucher, 2024). Following an official admission that the image had been minimally manipulated by Kensington Palace, multiple news agencies removed it in accordance with their policies.

The case of ‘diffusion hands’ exemplifies how a literacy vacuum is filled by forms of knowledge that progressively consolidate into an emerging vernacular literacy. In the beginning, the technological innovation at the core of generative text-to-image models like DALL-E, Stable Diffusion and Midjourney opens up a vacuum of visual literacy: a new degree of photorealism unsettles existing heuristics to determine if an image is AI-generated or not. Then, initial observations circulate among early adopters: this new technology fails at generating convincing hands, and mangled fingers become a reliable detail to verify the synthetic nature of images. Eventually, this folk knowledge is encoded in humorous memes and cautionary tales, through which it reaches a broader public. The farther this vernacular knowledge reaches from first-hand observations, the stranger its effects become: an artwork created long before the advent of diffusion models is repurposed to anchor an urban legend about criminals exploiting their failures; an official image of Kate Middleton is coaxed into broader conspiracy theories about the royal family via the suspicion of being AI-generated. As existing visual literacy fails to grapple with a technological innovation, a precise observation of a technical failure rapidly morphs and expands into a vague bit of wisdom, with unexpected consequences; this progressive movement from lack of knowledge to overreliance on a lay theory demonstrates the thesis that algorithmic folklore emerges to fill a literacy vacuum.

The Boundaries of Expertise

With this fourth thesis, I propose that algorithmic folklore renders the boundaries of expertise more porous. As algorithms become both a technical and rhetorical resource for the development of products, platforms and systems, knowledge about their operation is carefully contained within professional and commercial silos (Pasquale, 2015). Examples of this containment abound in both industry and research: for the former, think of platform companies like Netflix or Google protecting the technical details of their recommender systems through black boxing practices, or powerful organizations like OpenAI keeping their models and training datasets behind closed walls (Burrell, 2016). As for the latter, how researchers deploy the vocabulary of myth and magic to describe the non-interpretability of deep learning (Campolo and Crawford, 2020), or how the career of an ‘AI expert’ is predicated on the assumption that machine learning is too complex and opaque for laypeople to grasp (Avnoon & Eyal, 2025). At first glance, the notion of ‘expertise’ itself might seem diametrically opposed to that of folklore, with technocratic approaches to knowledge dismissing folk theories as unscientific, unsystematic, and relativistic (Kinsella, 2002). But growing evidence across domains shows that folk theories are instrumental to mediating between different domains of expertise and, occasionally, between experts and non-experts (DeVito, 2021). In this contested discursive arena, algorithmic folklore plays an important role as it allows the production of knowledge about algorithms to not only circulate among both experts and non-experts, but also to move across domains alongside informal, unpredictable pathways (Bishop, 2020).

To understand this movement, let’s follow it alongside a couple of those pathways. The first begins with a rumor about the undesired behavior of an algorithmic system: targeted advertising. This rumor has circulated in many versions since at least the mid-2010s, and likely emerged independently from the personal experiences of people around the globe who, around the same time, started suspecting that Facebook somehow ‘listened’ to their everyday life through their smartphones and served them ads for products they mentioned in casual conversations (BBC, 2017). News stories about the rumor were so widely discussed that in 2016 the company itself felt the need to dispel it by stating that ‘Facebook does not use your phone’s microphone to inform ads or to change what you see in News Feed’ (Facebook, 2016). Through extensive interviews conducted in Finland, Minna Ruckenstein has identified this rumor as a representative example of everyday understandings of algorithms, demonstrating how ‘personal algorithm stories can treat eavesdropping as a fact’ (2023: 54) even if expert knowledge about targeted advertisement dismisses it as implausible:

the conviction that phones listen to one’s conversations becomes incorporated into algorithmic folklore, informing personal algorithm literacies. These literacies might be based on erroneous or misguided perceptions, but they are algorithm literacies all the same; people read the digital environment and use their observations and gut reactions as pedagogical guides. (2023: 55)

Interestingly, over the years, Facebook’s repeated denials have corresponded to several investigations seeking to prove that Facebook was, indeed, listening to people through snippets of audio (Nichols, 2018), using contractors to transcribe them (Hern, 2019) and collaborating with a partner offering an ad targeting service based on microphone (Cox, 2024). In short, what began as a rumor and consolidated into a lay theory dismissed by experts, was eventually confirmed to be actually factual.

Knowledge about algorithmic systems also moves in the other direction – that is, from experts to laypeople. This pathway is evident in the widespread phenomenon of ‘engagement bait’, the practice through which creators seek to maximize interaction (both human and algorithmic) with their content. In 2024, Instagram users noticed that an increasing number of reels included very long descriptions unrelated to the video content, which read like ‘The Tesla Cybertruck is an all-electric, battery-powered light-duty truck unveiled by Tesla, Inc. Here’s a comprehensive overview of its key features and specifications:’, or ‘No problem! Here’s the information about the Mercedes CLR GTR:’, followed by several paragraphs of technical facts about these cars. These long descriptions, which were likely generated by ChatGPT or other LLM chatbots, captured the viewers’ attention for a few seconds, enough to deceive the Instagram recommender system into classifying the content as educational, thereby recommending it to more people (Know Your Meme contributors, 2024). Similarly, in 2020, TikTok creators started utilizing the #xyzbca hashtag because an urban legend spread about it making the videos more visible (Travis, 2020). When compared with the long Instagram descriptions, this trick seems to be much less grounded in the actual functioning of recommender systems, as TikTok would have no reason to boost a meaningless hashtag. And yet, the more the urban legend spread, and the more people utilized the hashtag, the more TikTok is likely to have recommended these to other users or display them in their Explore or For You page. In both of these cases, non-expert social media users glean something about the functioning of content recommendation, creating tricks and myths that exploit algorithmic logics to turn their beliefs into reality.

The rumors, tricks and urban legends described in this section demonstrate how algorithmic folklore mediates the circulation of knowledge between experts and non-experts through strange, circuitous paths that involve both humans and algorithms. In the case of the Facebook targeted advertising rumor, a common user experience results in a widely discussed suspicion about corporate misbehavior, which experts dismiss on technical grounds arguing that the company does not need to use smartphones to snoop on people’s everyday lives. Eventually, the rumor is validated by repeated findings of actual cases in which Facebook has collected and analyzed audio data without consent: what started as a lay theory becomes recognized as expertise. In the case of engagement bait, expert knowledge about recommender systems is reverse-engineered by creators who devise description formats and meaningless hashtags to enroll algorithms into boosting the visibility of their content. While it is unclear how much these tricks succeed in manipulating engagement, the fact that at the time of writing there are around 30 million TikTok videos tagged with #xyzbca speaks for itself: expert knowledge about algorithms trickles down to lay people, who repurpose it for their own needs. The movement of knowledge about algorithms goes both ways, mediating between experts (computer scientists, product managers, marketers, consultants) and non-experts (content creators, users, audiences). Sometimes, experts look at the intuitions that non-experts share and find them to be correct; other times, non-experts generate knowledge that experts seek to gatekeep through opacity and non-disclosure. Both of these pathways have the combined effect of blurring the distinction between experts and non-experts, questioning the legitimacy of claims to expert knowledge production, and support the thesis that algorithmic folklore renders the boundaries of expertise more porous.