1.1 Overview

The Element unfolds as follows. After an initial overview, this section clarifies key terms such as “bot” and “mimicry” to define the central concept.

Exploring bot mimicry as a literary phenomenon, Section 2 delves into a case study, establishing a connection between bot mimicry and artificial intelligence imaginaries. It places bot mimicry within internet culture, considering its roots in text generation and concerns about deception by humans impersonating machines.

Section 3 examines the relationship between bot mimicry and the imitation game (Turing test), portraying bot mimicry as a reversal of humans imitating computers. It argues that bot mimicry offers a valuable approach to reevaluate our relationship with artificial intelligence systems by embracing and reworking their imitative dynamics.

In Section 4, the focus shifts to the literary-aesthetic dynamics of bot mimicry, analyzing a Reddit community dedicated to it. Drawing on Walter Benjamin’s theory of the mimetic faculty, the section explores how bot mimicry enables encounters with artificial intelligence imaginaries and negotiates our relation to automated software in digital culture.

Sections 5 and 6 reflect on broader implications. Section 5 discusses bot mimicry in academic research, emphasizing its role in understanding digital literary culture through practice-based knowledge development. Section 6 concludes by contemplating the significance of bot mimicry in the era of generative artificial intelligence, envisioning a future for human-authored literary writing amid the rise of machine-generated text.

1.2 Initial Disambiguation

Before getting to the action of the Element, some disambiguation is needed. Firstly, what is meant by bot? Originally a shorthand for robot, the notion of a bot carries the meaning of an automated system that is not embodied in robotic hardware. This is not to say that bots are disembodied – they are just as materially anchored as any other pieces of software but simply that a bot does not come with the assumption that it has a robotic body. Rather, a bot is a program: a piece of software. Although bots can have many different functions, I am here focusing on bots that generate text, with a special (but not exclusive) interest in bots that are designed to carry textual conversations – often referred to as social bots or chatterbots – such as Siri or Replika.

Until recently, most bots were designed around fairly simple conversation structures with predefined answers. Today, we are increasingly seeing a proliferation of bots based on artificial intelligence software, specifically large language models. In our everyday interaction with bots, we can never be quite sure what kind of software they operate, and for that reason I will not distinguish between different kinds of bot architectures here, but treat all bots as part of a larger whole – namely, a distributed and culturally shared imaginary pertaining to automated software in general, which I will discuss in the next section. I thus opt to use “bot” as a catch-all that usually refers to a broader range of phenomena, including artificial intelligence, since the imaginaries of bots and other kinds of automated software tend to overlap to such an extent that one also implies the other(s). In short, I understand bots as pieces of software developed for generating text, often with the possibility of conversational interaction. Now we need some disambiguation of the other half of the concept at hand: What is meant by mimicry?

Mimicry refers to a special kind of imitation, which is highly related to the question of mimesis. Although these three words do not mean exactly the same thing, they are conceptually intertwined; yet they also carry different nuances. In short, mimesis is one of the oldest philosophical conundrums in Western culture, and is related to issues of representation, realism, similarity, imitation, identity, art, and more (Reference Gebauer and WulfGebauer and Wulf, 1992; Reference PotolskyPotolsky, 2006). The concept of mimesis has been one of the primary conceptual arenas in which the various relations between art, literature, and politics – and indeed the nature of art and literature as such – have been investigated. I have investigated the role of mimesis (understood as representation) in collaborative writing experiments between humans and computers elsewhere (Reference Erslev, Ensslin, Round and ThomasErslev, 2023). In this Element, I focus less on representation and more on the embodied practice of becoming similar. This embodied practice is, at base, the practice of imitation, which belongs to the question of mimesis but is more specific since it refers to the effort of one entity or group of entities to become similar to another entity or group of entities. Imitation is inherently related to processes and relations, and never to static objects. Mimicry, in turn, is often thought of in terms of unreflective, simplistic, and/or automatic imitation, and often manifested in unflattering parody. In this Element, however, I locate nuance, friction, and a poetic potential in seemingly banal practices of mimicry. I will thus use imitation and mimicry interchangeably, since mimicry works via imitation, and since both terms relate to an embodied practice. The central thing here is that I am interested in mimicry and imitation as something we do – as practice, process, and situated emergence.

Instead of making an oft-assumed distinction between mimesis as (fine) art and mimicry as a banal practice, I argue that we must approach mimicry with the same conceptual rigor – and with a view to the same politico-aesthetic dimensions – as any other (sub)form of mimesis. Indeed, as I will show in this Element, the seemingly banal practice of parodying artificial intelligence systems through bot mimicry ties onto an important investigation of – and allows for a constructive engagement with – central aspects of digital literary culture. My approach to mimicry is founded primarily in the theory of the mimetic faculty, which was first proposed by Walter Benjamin in the 1930s and later updated and reworked by Michael Taussig in the 1990s. It is important to note that this selection of sources in no way is exhaustive regarding the topic of mimicry – or mimesis, for that matter. However, the theory of the mimetic faculty is to the task of understanding the dynamics of bot mimicry, since it zooms in on the aesthetic and linguistic practice of mimicry per se, rather than the effect – the output, so to speak – of the practice.

I will discuss the mimetic faculty extensively in Section 4, but it requires early mentioning as it will be the theoretical zenith around which I will illuminate the politico-aesthetic specificity of bot mimicry. The theory of the mimetic faculty poses that mimetic perception – and mimetic practice – are central aspects of human consciousness that allow us to encounter and negotiate (with) other humans as well as nonhumans. In short, imitation and mimicry are primary ways of reckoning with and engaging with the world. Now, in the 2020s, we must once again return to the question of the mimetic faculty as it enters the stage at a crucial moment to illuminate our mimetic relations to automated software.

With my initial vocabulary in place, I will begin to unpack the provisional definition of bot mimicry given earlier: What does it mean to imitate imitative software? This question defines the overarching goal of the Element, which should be read as an extended definition, conceptualization, and traversal of bot mimicry in the context of digital literary culture.Footnote ¹

2.1 The Idiocy of Imitating an Imaginary

Rather than imitating any particular kind of bot architecture or text-generation technology, Patti mimics a style that his intended audience (Twitter users) will likely recognize as being bot-esque without necessarily aligning with any particular text generation technique. In fact, the resulting script (Figure 2) would be quite impossible for any particular machine learning–based bot to generate, since it includes elements that would not be present in the training data on which the alleged bot would have been trained (not to mention that the training data itself is impossible, as there does not exist 1,000 hours of Olive Garden commercials). The script is bot-esque, yet it is not produced by a bot; it touches upon central aspects of our conceptions of artificial intelligence by way of a joke – a parody.

These two observations should be understood in reverse order. I begin by considering the authenticity uncovered by the (new media idiotic) joke, and in the process situating bot mimicry in/as internet culture, and as digital literary culture, illuminated by the fields of software studies and electronic literature. I then turn to discussing the observation that bot mimicry is bot-esque without relying on or referencing any particular bot by considering bot mimicry as a rendition of artificial intelligence imaginaries. In this endeavor, seeming contradictions – such as fact and fiction, engagement and critique, idiocy and inquiry – meet and create illuminative productive frictions. Bot mimicry and its reading must be critical by way of engagement, operating within the same structures on and with which they reflect.

2.2 What We Mimic When We Mimic Bots

Bot mimicry should be understood in the light of the broader practice of text generation. This is true even if bot mimicry itself is strictly speaking not a form of generated text, since it importantly mimics such text. Although bot mimicry mimics the writing style of the bots of the platformed web, this style has a techno-cultural history that predates the internet (and even the digital computer). I here think of text generation in the same way as Philip Galanter defines generative art more broadly, as a practice that involves “a system, such as a set of natural language rules, a computer program, a machine, or other procedural invention, which is set into motion with some degree of autonomy contributing to or resulting in a completed work,” which in our case would be a text (Reference Galanter2003, p. 4). The generative mechanism can be noncomputational, for example, a set of rules or instructions that are carried out by a human.

In the interest of illuminating the broader context of bot mimicry – and in order to investigate the generative style that bot mimicry mimics – I highlight four historical moments of in which the influence of text-generation on the style of bot mimicry becomes tangible. Each historical moment provides insight into the origins and reciprocities of bot mimicry and text generation, covering pre-computational dadaism, a love letter generator, a legendary bot psychiatrist, and a recent example of human-bot fluidity in the context of large language models.

2.2.2 A Parody of a Process

The inscription of instructions (i.e., coding) requires one to anticipatorily imagine how the system will execute the instructions, or in other words, anticipatorily mimic the system in question. As the second historical example in this brief overview, consider Christopher Strachey’s 1952 Love Letter Generator (cf. Reference StracheyStrachey, 1954). This generator was the very first bot-like program and also the very first piece of computational text generation altogether. Strachey worked on some of the earliest stored-program digital computers, and wrote the Love Letter Generator for the Manchester Mark I. The generator would write love letters that consisted of highly affectionate words strung together in syntactically dubious and at times semantically outrageous ways (see Figure 3). They were simultaneously banal and perplexing – difficult to take seriously as love letters yet strikingly love letter-esque.

Figure 3 An example of a poem made using the Love Letter Generator.

(Reference StracheyStrachey, 1954, p. 26)

As Noah Wardrip-Fruin argues, Strachey’s generator should be understood as a parody not of love letters as such, but of the very process of writing love letters. We can see that bot mimicry takes some cues from the history of text generation in parodying a process (e.g. machine learning) rather than a thing (e.g. Olive Garden). From an analysis of the software, it can be seen that Strachey engaged in a parodic mimicry of heteronormative romance, a mimicry that is principally manifested in the dataset, which was constructed by semi-randomly assembling synonyms for affective words from a thesaurus. The dataset is important since it configures the total sum of possible combinations that could comprise a love letter written by the generator, in effect defining the tone of each individual letter. As Wardrip-Fruin asks, [f]or what sort of processes would one choose to copy the data from a thesaurus, rather than carefully select each element?” (Reference Wardrip-Fruin, Parikka and Huhtamo2011, p. 309). The generator manifests, via mimicry, that many love letters consist of nothing more than semi-randomly relaying the same overtly expressive words in various combinations.

In order for the program to run, Strachey had to both cognitively mimic the Manchester Mark I – that is, write the program in a way that could be executed by this system – and at the same time devise the program to parodically mimic a human activity. Put differently, Strachey is mimicking (parodying) people through software – and in turn creating the first example of the kind of text people are now imitating through bot mimicry. Lastly, Strachey’s generator is indicative of bot mimicry’s parody of processes rather than things. The generator teaches us to read bot mimicry by gauging the imagined dataset as it is manifested in the mimicked output.

2.2.3 Writing to Be Read by a Bot

Strachey’s generator is a striking parallel to the mimetic dynamics of bot mimicry. Yet in terms of propagating a certain bot-esque style, no other single piece of software had been as influential as Reference WeizenbaumJoseph Weizenbaum’s 1966 ELIZA, which figures as my third historical case. The ELIZA interfaces was groundbreaking in its time; viewed today, it resembles a common chat interface (see Figure 4). The legendary bot was originally implemented in the MAC time-sharing system at MIT but is now available in a variety of online simulations. ELIZA mimicked a Rogerian psychiatrist by design, meaning that ELIZA’s conversational logic largely worked by feeding the user’s inputs back to the user in the form of questions. In spite of the relative technical simplicity of the software, ELIZA worked remarkably well for sustaining conversations.

Figure 4 Joseph Weizenbaum’s ELIZA.

Source: Wikimedia Commons (Unknown).

Although ELIZA went on to attain legendary status as the first conversational interface, Weizenbaum actually intended the program to demystify and debunk artificial intelligence. ELIZA was meant to be a practical demonstration that that it was possible to program a bot to perform quite impressively at conversation by using “quite simple” procedures that had nothing whatsoever to do with intelligence (Reference WeizenbaumWeizenbaum, 1966, p. 37). ELIZA, to Weizenbaum, exhibited the deceptive aspect of AI, actualized through a formalized imitation of human language skills (Reference NataleNatale, 2021). Again, just as Strachey did with the Love Letter Generator, Weizenbaum imitated humans through software, but Weizenbaum’s imitation does not stop there. Unlike Strachey’s generator, ELIZA was interactive, meaning that the software relied on a certain anticipatory imitation not only on the side of Weizenbaum in programming the bot, but also from the users in their interaction with it. Users had to write their inquiries in certain ways that fit with ELIZA’s procedural design. On a technical level, users were not able to use question marks, since they would be “interpreted as a line deletion character by the MAC system” (Reference WeizenbaumWeizenbaum, 1966, p. 36). In other words, users could not pose even a single question to the bot that did nothing but ask questions.

On a more procedural level, the design of ELIZA also constrained users to use specific formulations in order to make sure that ELIZA would be able to process their inputs in an expected manner and return fitting outputs. As Sherry Turkle argues, people did not necessarily believe that ELIZA possessed humanlike intelligence. Users “may not have been pretending that they were chatting with a person. They may just have been happy to talk to a machine” (Reference TurkleTurkle, 2007, p. 503). Thus ELIZA “demonstrates not so much AI’s capacity to deceive as the tendency of users’ to fall willingly – or perhaps most aptly, complacently – into the illusion” (Reference NataleNatale, 2021, p. 64). In order to fall complacently into the delusion, users had to learn to write in a certain way, to use certain phrases and sentence structures. In other words, users had to take on a certain style – and they would learn how that style worked by conversing with ELIZA. ELIZA was meant to mimic human-like writing skills, but in order for users to engage with the bot, they had to anticipatorily mimic the expectations that were written into the software. With bot mimicry, we are witnessing the next step in this stylistic exchange; now there is no longer any specific bot that the bot mimic mimics, yet the stylistic playfulness persists. Even though people today may not be directly familiar with ELIZA, the program continues to exert an influence on our understanding of bot-esque writing styles.

2.3 Hoax or Inquiry?

Although My Life as an Artificial Creative Intelligence is honest about its fluidity between human and computer writing, it is still relatively difficult to gauge the mimetic dynamic at play since human and bot become effectively inseparable to the reader. Although Amerika’s work pragmatically questions our desire to separate human from machine in literary contexts, it does not offer us much insight into the moment of mimicry. In a sense, we are aware in our reading of the book that it might be a hoax. This awareness is derived from the larger figure of the hoax in cases where humans and machines overlap in literary ways. Indeed, the very assertion that a computer program has written something – especially if we attribute that something literary value – is tightly connected to a suspicion that we are dealing with a hoax (cf. Reference WhalenWhalen, 2021). As an example, Patti’s Olive Garden tweet has been construed as a dishonest misrepresentation of what machine learning is and how it works (Reference MandelbaumMandelbaum, 2018).

Bucher has similarly documented the general uproar after it was revealed that an earlier example of bot mimicry, a popular Twitter bot account named @Horse_ebooks was in fact driven by a journalist pretending to be a bot (Reference BucherBucher, 2014). @Horse_ebooks was known for tweeting obscure and often seemingly nonsensical snippets of text, and the journalist running the account had to operate a bot-esque style in order to continue the illusion. Much like some of the reactions to Patti’s tweet, the responses to the discovery of the humanness of @Horse_ebooks prompted mistrust and confusion. As we shall see, these examples of Twitter drama are prefigured by a longer history of machine impersonation that predates the digital computer by centuries.

The hoax of machine impersonation can be understood in the light of impostering. As Vogel et al. point out, the notion of the imposter has often taken the position of the exception to the rule, as a figure that exists on the margins of society. However, by rethinking our understanding of the imposter – and in turn reconsidering our fear of the hoax – we can see that once taken seriously, the imposter offers an operative insight into “the essential indeterminacy” of society (Reference Vogel and VogelVogel et al., 2021, p. 5). Bot mimicry embraces indeterminacy inherent to the imposter and the mimic alike, and recognizes that these practices do “not ‘just’ involve deception, trickery and pretence” (Reference Vogel and VogelVogel et al., 2021, p. 3). Rather, they are also illuminative forms of inquiry.

By considering three cases that are all based on deep and imitative engagements with machineries yet are (in different ways) presented as hoaxes, I question the rigidity of the notion of the hoax. Firstly taking on the perhaps most famous case of machine impersonation, namely, the Mechanical Turk, I locate the origin of the fear of the hoax in the competitive assumptions surrounding the Turk and not least its Enlightenment context. Secondly, I consider a more recent and somewhat less famous example that nonetheless has attained legendary status in the context of generative literature, namely, The Policeman’s Beard is Half Constructed. This book was an early example of generative literature in book-bound form, and was based on deep engagement with text-generation software, yet this engagement was obscured in the presentation of the book as a wholly computer-generated work – a hoax. Thirdly, and finally, I question the contemporary economic context of bot mimicry, where the labor of so-called microworkers is presented as the output of computer systems. In this context, I locate an artistic intervention in the work LAUREN, which shows how bot mimicry can trouble the logic of hoaxing and take the form of inquiry.

2.3.1 The Origins of the Hoax

Although not the earliest, perhaps the most famous example of machine impersonation is the Mechanical Turk. The Mechanical Turk was a chess-playing automaton that gained considerable fame for its ability to beat virtually anyone at the game. The Turk likely inspired Charles Babbage’s work on the analytical engine, which, although it was never fully constructed, informed Turing’s work and is today considered an industrial precursor of the digital computer (Reference Schaffer, Clark, Golinski and SchafferSchaffer, 1999). Likewise, when considering Edgar Allen Poe’s famous analysis of the Turk from 1836, wherein Poe argued that the Turk exemplified a case where “what is merely complex has been consistently mistaken for what is profound” it becomes clear that it prefigured Alan Turing’s conception of the imitation game (Reference AshfordAshford, 2017, p. 8). In other words, the Turk is an early example of the tendency to measure (artificial) intelligence on the premise of deception (or hoaxing).

Baron Wolfgang von Kempelen constructed the Mechanical Turk in 1770, and it continued to be in operation for eighty years, even pushing beyond the lifetime of its inventor. The Turk was presented as an automaton, that is, a fully mechanical self-operating construction (what we today would call a robot), but was later revealed to be a hoax: Inside the automaton was a person who, using elaborate control mechanisms and ingeniously placed mirrors, controlled the Turk as a puppet (see Figure 5). The Turk is an early example of an entity that “for some … was a machine that displayed remarkably human attributes, while for others, including its designer, it was a human who performed in a strikingly machinelike manner” (Reference Schaffer, Clark, Golinski and SchafferSchaffer, 1999, p. 164). The history of imitating and imagining automata goes further back than the Turk (cf. Reference KangKang, 2011; cf. Reference Cave, Dihal and DillonCave et al., 2020). Yet this specific example marks a particularly significant moment that crystallizes a lot of the assumptions that today circulate in the context of imitating imitative software online – including the fear of the hoax.

Figure 5 An illustration of the Mechanical Turk.

Source: Wikimedia Commons (von Kempelen).

The Turk instantiated an antagonistic model of humans and machines that continues to inform the notion that bot mimicry is a hoax. It figures as an early example of human/machine antagonism, where human and (mimicked) machine were posed as competitors. On one level, the competition was literal: A game of chess. On another level, there was a more conceptual competition, circulating the Enlightenment imposition of “a division between subjects that could be automated and those reserved for reason” (Reference Schaffer, Clark, Golinski and SchafferSchaffer, 1999, p. 164). In other words, the Turk was part of a larger project to determine what separated humans (and particularly so-called enlightened humans) from nature (along with the unenlightened and the machinic). Thus audiences would attempt to gauge the inner workings of the Turk, in order to evaluate its status along the enlightened/unenlightened division, unbeknownst that they were gauging the machinic performance of a human puppeteer. Once revealed as a hoax, the Turk reinforced the fear that machines that behaved intelligently were in fact hoaxes. In effect, the Turk would perpetuate a structure of competition and antagonism between humans and machines – an antagonism that continues to inform our understanding of artificial intelligence, as seen most prominently in the standard interpretation of the Turing test, which I will discuss later (cf. Reference AshfordAshford, 2017).

It is telling that the Turk – one of the most influential cases of human/machine antagonism – is actually a case of imitation. Instead of focusing on the audience, who were hoaxed, I am fascinated by the position taken by the person controlling the Turk. What can this puppeteer teach us about the relations between humans and automata in the eighteenth and nineteenth centuries? We have no way of knowing, of course. Instead, we can reorient our focus when considering contemporary practices of bot mimicry, to focus less on the risk of the hoax and more on the imitative position of the mimic themselves. However, when looking at other more recent examples, we can see that the incitement to interpret cases of imitation as hoaxes persists. Even in cases where the operator of a system is engaged in an intimately fluid relation to the computational machinery, there is a drive to present and interpret the situation through the framework of antagonism, in turn reinforcing the unhelpful idea that imitation is the same as hoaxing.

2.3.2 The Incitement to Hoax

The 1984 book The Policeman’s Beard is Half Constructed was presented as the first ever book written by a computer program, by the name of Racter (short for Raconteur), which was programmed by William Chamberlain and Thomas Etter (Reference Racter, Chamberlain and EtterRacter et al., 1984). However, it was soon discovered that the book included substantial contributions from Chamberlain and Etter, including curation and editing of the outputs from the Racter. The stylistic touch of human originators of computer program impacted the output to a degree where, as Espen Aarseth (with a reference to Jorn Barger) argues, “the ‘wacky’ style of Racter’s output is really Chamberlain’s own, the product of a clever human writer posing as a clever program” (Reference AarsethAarseth, 1997, p. 132; cf. Reference BargerBarger, 1993).

The influence of Chamberlain and Etter notwithstanding, Racter was an important part of the creation of the book. Even though Racter was not stylistically independent, the book was still “one of the first algorithmically authored books” and furthermore an important moment in the “history of algorithmic authorship wherein computer-generated texts became readily available for public consumption” (Reference HenricksonHenrickson, 2021). Rather than a hoax, we should think of the book as the product of “a cyborg: part Racter, part Chamberlain” (Reference AarsethAarseth, 1997, p. 134). The Policeman’s Beard is not a case of bot mimicry, but it is informative of the persistence of the concern over the possibility of being hoaxed in cases of bot mimicry.

The resulting style is very close to that of bot mimicry. As Leah Henrickson notes, “Racter’s output is always somewhat nonsensical, but the selections comprising The Policeman’s Beard demonstrate the system’s potential for output that might be construed as meaningful” (Reference Henrickson2021). In this sense, The Policeman’s Beard displays the recursivity of generative literature and the playful imitation of such literature in the form of bot mimicry: In both cases, the appreciation of the text relies upon the reader’s inhibition to construe meaning out of seemingly nonsensical sequences of words.

In spite of the richness of the reciprocity between Racter, Chamberlain, and Etter, the book was presented as the product of Racter’s without much mention of the humans in the loop, which can, for example, be seen on the cover where it is marketed as “The First Book Ever Written by a Computer” (see Figure 6). In this way, it becomes a case of an inhibition to present and interpret humans and computers as squarely separable entities instead of highlighting cyborgian moments of reciprocity. By removing the signs of their involvement in the creation of the work, Chamberlain and Etter exhibit the depth of the culture of hoaxing in questions of text generation.

Figure 6 Cover of The Policeman’s Beard Is Half Constructed.

(Reference Racter, Chamberlain and EtterRacter et al., 1984)

The contemporary status of the hoax has reached into the heart of the assembly line of artificial intelligence, where the labor of humans is sold as the output of computers, which marks the politico-economic context of bot mimicry. This commodified machine impersonation takes place on microwork platforms such as the one whose name is inspired by, but which is otherwise not very comparable to, the historical Mechanical Turk – namely, Amazon Mechanical Turk. The important thing here is not the microwork platform in itself, but the curious overlap of humans and computers that emerges in a context that economically exploits the position of standing in a computer’s stead. As I will argue, in addition to commodifying bot mimicry, Amazon Mechanical Turk also enables critical-creative experimentation that allows us to understand bot mimicry as inquiry rather than hoax, as encounter rather than conflict.

2.3.3 The Economy of the Hoax

At first sight, the microwork (cf. Reference IraniIrani, 2015) platform Amazon Mechanical Turk is quite far from the practice of bot mimicry – and from the question of hoaxes. The platform even proudly advertises itself as a kind of artificial artificial intelligence, boasting the humanity of the workers. Yet upon scrutiny, bot mimicry can be seen to be an idiotic rendering of a social reality for many microworkers who daily stand in computers’ stead. Amazon Mechanical Turk (along with other microwork platforms) functions as a crucial part of other services that market themselves as driven by artificial intelligence while using human labor to produce those services. From being one of the main sources of tagging and other categorization used in the creation of datasets used in machine learning systems to outright doing the work of a supposedly artificially intelligent system, these microworkers labor on the assembly lines of our data-driven economy (cf. Reference Tubaro, Casilli and CovilleTubaro et al., 2020; cf. Reference Pasquinelli and JolerPasquinelli and Joler, 2020).

In the platformed economy, human microworkers engage in artificial intelligence impersonation, “when humans, so to speak, steal computers’ jobs” (Reference Tubaro, Casilli and CovilleTubaro et al., 2020, p. 7). This is the economic context in which bot mimicry emerges as a techno-cultural practice. The impersonation of artificial intelligence systems by microworkers points to the intensity of the mimetic relation in the platformed context; at no given time does there exist a definite, predefined separation of humans and bots. Bot mimicry can figure as a form of literary and experimental inquiry into the platformed labor conditions of microwork platforms – and into the very relation between humans and nonhumans in platformed contexts.

Consider Lauren Reference McCarthyLee McCarthy’s 2017 project LAUREN (see Figure 7). In this work, McCarthy engages in bot mimicry of a smart home assistant such as Amazon’s Alexa. That is to say, McCarthy literally takes on the task of being peoples’ smart home assistant, from turning lights on and off, putting on music, and answering queries. LAUREN meditates the conditions of platformed labor, both illuminating the role in which we position our smart home assistants and at the same time providing insight into the position of artificial intelligence impersonation. As such, LAUREN also indicates that artificial intelligence impersonation does not only happen on Amazon Mechanical Turk, but also in other lines of platformed work.

Figure 7 Lauren Lee McCarthy’s LAUREN (Reference McCarthy2017).

Reproduced with permission.

As an example, consider Live Eye Surveillance – a surveillance company that employs people to perform the role of monitoring CCTV-cameras that we otherwise increasingly assume are operated by data-driven machine vision technologies. Instead of having a dedicated surveillance unit on location, Live Eye offers a centralized solution where human observers act under the guise of being artificial intelligence-driven. Live Eye almost seems to be a corporate realization of McCarthy’s work, which predates the surveillance company – a case of reality imitating the arts? The juxtaposition of McCarthy’s artistic bot mimicry and Live Eye’s economic model sheds light on the ways in which the boundaries between humans and bots are fading in economic as well as aesthetic contexts – and offers to different approaches to this situation; Live Eye profits from the hoax, whereas McCarthy opts for curious inquiry.

2.4 Reading as Prototyping

The case of LAUREN leads me to gather the threads of this section, to construct an account of how we can read bot mimicry as a literary practice that is native to the platformed Web and draws on wider techno-cultural dynamics from the history of text generation and the perpetual fear of being hoaxed. Specifically, I argue that it is productive to understand the practice of bot mimicry – and our reading of that practice – in terms of prototyping. The notion of a diegetic prototype refers to speculative technologies that belong to a (science fiction) story-world, in which they figure as ordinary things (Reference KirbyKirby, 2010).

In the case of the Olive Garden tweet, we might say that the tweet itself, in which Patti claims to have trained a machine learning–based bot on a corpus of Olive Garden commercials, is a short-short science fiction story in itself, and the script is an artifact that should be understood to belong to the diegetic world of that short-short story. Such a reading invites the consideration of – indeed the imaginative co-construction of – the implied bot itself in the appreciation of the tweet, rather than (only) focusing on the representation of Olive Garden.

Using diegetic prototypes, designers and researchers can devise plausible, yet fictional, technologies and stage fictional narratives in which the possible techno-cultural consequences of those prototypes play out, which in turn can affect the development of technology beyond the fiction itself (Reference BleeckerBleecker, 2009). This practice is known as design fiction and allows us to imaginatively inquire into the broader possible impacts of technology. In the case of bot mimicry, the prototyping does not unfold through a traditional narrative: Although the Olive Garden tweet can be read as a short-short story, the script itself and the implied bot responsible for the script are not being narrated to us in a traditional sense – yet both bot and script are fictional, belonging to a fictional world. Rather, the script acts as an entry point to a fictional world that is not represented through narrative, but which is still reliant on our imaginations (cf. Reference Coulton, Lindley, Sturdee and SteadCoulton et al., 2017). In this way, reading bot mimicry becomes an active engagement with prototyping – we are not only considering a fictional technology that is narrated to us, we are actively participating in the construction of that very technology.

The reading of bot mimicry entails an engagement with wider techno-cultural structures and must be understood as a reading of an implied technology – which ties onto an artificial intelligence imaginary. In adding the context of design fiction, bot mimicry becomes aligned with a broader consideration of how we can use literary experimentation to rethink the way we design artificial intelligence systems. By considering bot mimicry as a kind of design fiction, in which the bots we mimic take the form of prototypes, the broad possible impact of the new media idiocy of imitating an imaginary shows itself, as it becomes not only a question of what is (or was) trending on Twitter, but an engagement with the broader context of text generation and the concern over the possibility of being hoaxed. In turn, this practice becomes a literary approach to affecting the design of the technologies we encounter every day.

Bot mimicry crystallizes a view into how complex imaginary-induced materialities emerge in and in turn affect digital literary culture. Further, bot mimicry invites a rethinking of some of our most foundational assumptions surrounding artificial intelligence: That imitation is indicative of intelligence. In order to investigate this proposition, we need to further illuminate the issue of imitation in regard to artificial intelligence. Imitation is most often thought of as a drive to fool the observer into thinking the imitator is something they are not by situating a hoax or deceit, but it also carries a poetic potential in situations where the imitator troubles – indeed reverses – the mimetic dynamics at the heart of one of the most foundational ideas surrounding artificial intelligence, namely, the Turing test.

3.1 Reading (against) an Imitation Game

Upon loading the BotPoet website, one is met with the statement quoted above.Footnote ³ The statement is followed by the elaboration that “[i]f you think a poem was written by a computer, choose ‘bot’. If you think it was written by a human, choose ‘not’” (Reference Schwartz and LairdSchwartz and Laird, n.d.). If we wonder what kinds of poems get to count as either human-written or computer-generated, BotPoet provides a provisional definition of computer poetry, stating that “computer poetry uses algorithms to generate text,” yet maintains that “specific criteria for computer poetry is [sic] flexible” (Reference Schwartz and LairdSchwartz and Laird, n.d.). Conversely, human poetry is defined as “[e]verything else … ” (Reference Schwartz and LairdSchwartz and Laird, n.d.).

The website frames itself as a Turing test, yet it also diverges from Turing’s original proposition in some important ways. When playing the BotPoet game, one is presented with a poem and faced with a choice – to assert via abduction – whether a human or a computer wrote the poem. There is no option to converse with or inquire further into the originator of the poem, only the choice between “bot” and “not.” In short, BotPoet transforms the role of the interlocutor to that of a judge – and, ultimately, a reader. Still, BotPoet preserves the setup of the Turing test in many other ways, most notably in the general framework of asking a human to distinguish humans from computers based on textual output. I here focus on the way BotPoet illuminates the position of the judge – the reader – in relation to the imitation game, in order to gauge the characteristics of the kind of reading we engage in when we encounter bots online.

The first inhibition of the reader on the BotPoet platform is to attempt to distinguish bot poems from human poems. Over time, however, instead of improving our skills at the game the result is actually quite the opposite: The more we play, the more we become aware that it is pragmatically impossible to reliably get the correct answer – no matter how good a sense of bot literacy we possess. There seems to be a kind of equilibrium, at least on the side of the reading, between bot poets and human poets. In other words, John Irving Reference GoodGood’s (1966) speculation that poetry would be an indicator of ultra-intelligent machines seems to have been met (Reference Good1966, p. 36).

However, instead of professing that machines are now ultra-intelligent, perhaps we need to reconfigure our belief that poetry is as human as we have tended to believe. It is telling that the top-ranking poem of the “most computer-like human poems” category in BotPoet’s accompanying leaderboard subpage is from Gertrude Stein’s Tender Buttons (Reference Stein2005), which was written several decades before Turing’s advent of the imitation game, and before the creation of the first digital computers. Perhaps (at least some) human poetry is not that humanlike after all. Can we move further into this overlap of humans and bots that trouble the antagonistic structure of the imitation game? Can we locate nuance and friction by reading against this poetically inclined Turing test?

Although BotPoet includes a large amount of individually interesting poems that reside in and illuminate the friction-filled space between humanlike and bot-esque writing, I turn to focus not so much on individual poems as on the platform of BotPoem itself. The drive to distinguish humans from bots based on individual text snippets is – at least in the domain of poetry – less than productive to the investigation of bot mimicry. Yet the aforementioned leaderboard may afford a more productive way of reading the BotPoet platform. The leaderboard subpage makes it possible for users to monitor a top-five ranking in four categories: most human-like human poems, most human-like computer poems, most computer-like computer poems, and most computer-like human poems. The leaderboard provides a useful case for the question of reading, since it affords a kind of datafied, amalgamated perspective on how users of BotPoet tend to judge poems. As such, it invites us to also consider our reading of bots online as less a question of individual exchanges, but as parts of a larger – datafied – whole.

Our engagements with bots are not just our own; they also rely on, and feed into, data practices. This means that whenever we encounter a poem (computer-generated or not) online, “[t]he datafication of reader response is an essential part of the poem, one whose effects are visible to readers only indirectly in ads prompted by the reader’s engagement with the poem” (Reference BerensBerens, 2019, original emphasis). Thus, a software-sensitive way of reading poetry on the platformed web becomes a practice of “skimming the content and close reading the promiscuous read/write capacities of social media metadata, and guessing at the black-boxed code” (Reference BerensBerens, 2019). In the case of BotPoet, the data is (to my knowledge) not brokered to third parties, and so the datafication of our reading experience is not visible in ads, but it is made available to us in the leaderboard.

The leaderboard leads me to the relation between BotPoet and the practice of bot mimicry. Specifically, the category of most computer-like human poems instantiates this relation, along with that category’s comparative difference to the two other categories of most human-like human poems and most computer-like computer poems (see Figure 8). Even though these categories seem like (and are presented as) distinct from each other, the similarity between the two most computer-like categories points toward the mimetic reciprocity of the categories. Un-black-boxing at least some of the data gathered from reader interaction, the leaderboard section invites the reader to consider the broader scope of the platform, not just the singular poems. Although we may navigate to some of the poems featured in the leaderboard out of curiosity, the interest in the singular poem quickly fades. Instead, what emerges is a curiosity toward the messiness and overlap of human and computer writing across poems and categories. Instead of reading the poems with a competitive mindset, we can approach them as spaces for literary encounter with our automated counterparts.

Figure 8 An example of a poem featured on the BotPoet website. Taking the third place in the most computer-like human poetry, 68 percent of players wrongly guess that this poem was written by a bot (Reference Schwartz and LairdSchwartz and Laird, n.d.).

Reproduced with permission.

BotPoet establishes an opportunity for reading against the standard interpretation of the imitation game, which takes us down other relational avenues vis-à-vis bots. However, this platform importantly only pertains to reading. Bot mimicry is principally a practice of writing, which I will turn to in the following.

3.2 Writing (against) an Imitation Game

BotorNot is an online conversational game that resembles a Turing test in many ways while, as is evident in the quote above, aiming at the didactic goal of “engag[ing] people in thinking critically about artificial agents that pretend to be human” (Foreign Objects, n.d.a). BotorNot describes itself as “a kind of Turing test” (Foreign Objects, n.d.b), yet the objective of the game is not to inquire into the intelligence of computers. Rather, the game’s goal is to teach the player some significant ways in which bots currently attempt to (dishonestly) pose as humans. The premise for the BotorNot game is thus a certain suspicion toward bots per se, posing these systems as dishonest and dangerous, exemplified by “scam bots on Tinder and Instagram, or corporate bots that steal your data” (Foreign Objects, n.d.b). The importance of making people aware of the malicious aspects of some bots notwithstanding, I am here in search of other ways of relating to bots that instantiate alternatives to suspicion. As I will argue, BotorNot can indeed provide a framework for poetic as well as critical experiences and practices that make way for curiosity rather than only suspicion. Whereas the website frames itself as a pathway to become critical of bots, I approach it as a space for experimental writing. In other words, I aim to uncover ways of writing against this didactically oriented Turing test.

BotorNot preserves many features from Turing’s historical proposal with an important modification: the game is played with only two participants interrogating and judging each other. On the side of the one player, the question becomes to decide if the other player is a bot or a human – instead of the standard requirement to distinguish between two different contestants. We might say that in BotorNot, the three roles from Turing’s imitation game – (human) interlocutor, human contestant, and computer contestant – conflate into two: interlocutor-human and interlocutor-computer OR interlocutor-human.

The structure of the game is inspired by the popular social game truth or dare (specifically its truth-aspect), where each player takes turns presenting the other with truth challenges. The game has four rounds: first, a one-minute round for introductions, followed by three one-minute rounds of truth challenges. Turing’s original test had a five-minute interaction as a proposed timeframe, which almost corresponds to BotorNot’s four minutes. A notable difference in terms of structure is that the interaction is cut into smaller portions and framed within the context of truth challenges, which is a way to narrow down the scope of the conversation, unlike Turing’s emphasis that the interlocutor should be able to inquire into any topic of their choosing.

BotorNot does not come with an un-black-boxed insight into the datafication going on akin to BotPoet’s leaderboard (it does state that user interactions are not tracked, so perhaps there are none). The comparative lack of a leaderboard (or something similar) further solidifies that BotorNot in no way aims at testing the intelligence of the bot. On the contrary, BotorNot seems more like a place to test and hone the human’s bot-detection skills. This notion is strengthened in that the player is always paired with the same bot. I have played the game a lot, and although I cannot be absolutely certain that the bot I am matched to is completely the same in terms of programming, it is clear to me from my playthroughs that all the bots I have been paired with operate the same conversational logic. By this I mean that they ask the same kinds of questions (sometimes word for word) and furthermore answer in similar ways (again, sometimes word for word). Indeed, as we play the game multiple times, we get an almost intimate understanding of our bot counterpart. This in spite of the fact that, according to the BotorNot website, its goal is not to provide an opportunity to acquaint ourselves with the specific bot from the game, but to facilitate critical thinking. The idea seems to be that a person plays the game only once or twice, is struck by critical thought, and immediately moves to read the supplementary and in many ways quite useful text, A Guide for the Bot Curious, written by the developers of the game Foreign Objects.

In their presentation of the issue of automated software, Foreign Objects focus on the dangers of broad implementation of bots, and on the importance of being able to distinguish bots from humans. As we saw in the case of BotPoet earlier, though, the development of such bot-detection skills may be of little use in situ – and the futility of attempting to distinguish humans from computers solely based on text is rapidly increasing. However, in their guide for the bot curious, Foreign Objects do touch upon some of the issues that I am also investigating here: “[A]s chatbots become increasingly human-like, we too begin to question the terms of our own humanness in exciting, revealing and troubling ways” (Foreign Objects, n.d.c). This inhibition to turn the imitation game into an introspection echoes Christian’s efforts to be recognized as the most human human (Reference ChristianChristian, 2011). If, instead of seeking to improve our abilities to detect bots, we embrace the fluidity of human and computer writing and begin experimenting, BotorNot might be an optimal place through which to use writing as inquiry into the intense mimetic reciprocity of humans and computers. One approach could be to reproduce the bot-esque writing style found in r/totallynotrobots in conversation with the BotorNot bot (see Figure 9). Perhaps there is value in inverting the call to “act human!” and instead act computationally (Foreign Objects, n.d.b).

Figure 9 A screenshot of one of my many conversations on the BotorNot website (Foreign Objects, n.d.b).

Reproduced with permission.

Whether we attempt to act human or computationally – whether we experiment with our humanity or with the mimetic reciprocity of ourselves and our bot counterpart – we do so comparatively, vis-à-vis our fellow bot (or not). It is here that the importance of writing comes to the fore: BotorNot instantiates the imitation game not just as a measure of how you read, but also of how you are read, based on the things you write. In my experience, the way I wrote on the platform changed when I noticed that I always seemed to be paired with a bot (indeed, as mentioned, the same bot), and never a human. The lack of humans may be a consequence of the game’s relative obscurity, meaning that it may not be popular enough to attract a stable crowd of players, especially in my (continental European) time zone. Put differently, there may simply not be any other humans with whom to be paired. However, I am compelled to also entertain the possibility that there is no actual option to be paired with a human, but we are merely being falsely led to believe so by the website. The question of the actual possibility of being paired with a human notwithstanding, the clear tendency in my many bot encounters makes it clear that we should at least not expect anything but a bot (and the same bot) to be on the other end. In other words, we are writing not to be read by a human, but to be read by a bot.

When facing BotorNot, we might then take to writing in an experimental way, questioning our humanity and inquiring into our bot counterpart. We might write obscurely, and be amused at the kinds of responses we get. We might write earnestly, and read our bot’s responses equally earnestly. Or we might write in a bot-esque manner, entering into the conversation with our bot counterpart on the premises of their style and rhetoric. Even though these options (and there are undoubtedly more that I have not considered here) seem to be of equal value in our exploring our own humanity, the last one – adopting the style and rhetoric of our bot counterpart – contains potentials for new ways of knowing about our particular bot counterpart, but also about the relation between and overlap of our humanity and the bot’s computationality.

In the following, I consider the implications of my two analyses of creative imitation games, wherein I have investigated ways of reading and writing against the standard interpretation of the imitation game. As it turns out, the relation between my analyses and the standard interpretation of the imitation game circulates a dynamic of boringness.

3.3 Formulating the Reverse Imitation Game of Bot Mimicry

BotPoet and BotorNot are two examples of creative Turing tests that, if approached with the anthropomorphic and automative imaginary of the standard interpretation of the imitation game in mind, are actually quite boring. Indeed, the proposition that anyone might want to engage with BotorNot more than once (or maybe more than twice, seeing that only on the second playthrough would the repetitiveness become truly apparent) appears less than likely. BotorNot, with all its emphasis on critical thinking, is quite dull if played according to its own setup. Likewise, the leaderboard section of BotPoet may lead us to disregard the gamified aspects of that website altogether – to abandon the effort to distinguish bot from not seeing that the work has already been done, and anyway, there seems to be little that can be done to increase our success rate at the game. It is, however, in their very boringness that they reveal something about the excitement of bot mimicry. In the context of the platformed Web, where “a rule for virality is entertainment … tweaking those rules to make something really boring may expose a productive political tension” (Reference Saum-PascualSaum-Pascual, 2020, emphasis added). In other words, in a world of entertainment, boringness becomes friction, which in turn unveils something significant about the constructs that made the boring thing boring in the first place. How far does the boringness go?

As it turns out, it is not just BotPoet and BotorNot that are boring: the very advent of the imitation game seems to be quite boring in itself. From a literary perspective, computational imitation of humanlike text seems to only produce clichéd language, and to largely forgo the opportunity to radically rethink what literature is and can be. The boringness of Turing testing reveals a certain anxiety of imitation, a reluctance to enter into the imitative dynamic based on the notion that it limits or disables the vibrancy of literary experimentation (Reference Booten and RockmoreBooten and Rockmore, 2020). Yet if we – in the spirit of my readings of BotPoet and BotorNot – reframe our understanding of the imitative dynamic, we can see that imitation is in fact in itself a vibrant mode of experimenting with the similarities, differences, and nuances between human writing and computational text generation, emphasizing the poetic aspects of imitative computation beyond the question of whether human poets are replaceable by machines.

The interest here is not in the success or failure of a given system – not in the result of the imitation – but in the imitation itself. In this sense, we should think of the imitation game not as a platform to test computers’ anthropomorphism, but as “an opportunity to confront our own definitions of human thought in order to see – to test – where they may indeed overlap with and diverge from machine cognition” (Reference Booten and RockmoreBooten and Rockmore, 2020, original emphasis). Bot mimicry, then, is a way to engage creatively and curiously in such confrontation through the literary and mimetic technology of writing: by reversing, instead of rejecting, the imitation game.

Moving along this line of thinking, we may ask: What if we reversed the mimetic dynamics, so that the human was to imitate the computer? Turing did, in fact, consider such a reversal, but found it somewhat absurd: “If the man were to try and pretend to be the machine he would clearly make a very poor showing. He would be given away at once by slowness and inaccuracy in arithmetic” (Reference Turing and Copeland2004, p. 442). Surely, Turing was right in this regard – no human could ever reproduce the speed and accuracy of computers in terms of mathematics; that is, no human could succeed in imitating a computer as such. In order to be more worthwhile, the proposed reversal of the imitation game should keep the focus on the hybridity of humans and computers beyond measuring the success of one on the participants over the other: What if the human was to imitate a computer that imitated a human? This double imitation is aligned with the definition of bot mimicry. In this version, the computer would try to imitate a human while the human would perform bot mimicry.

The reversed imitation game does still, however, preserve a competitive structure, which is not productive to the endeavor of bot mimicry – even if it makes sense in other contexts in which the imitation game exists. As the analysis of BotPoet earlier shows, leaving behind our focus on competition makes us more attuned to the possibilities of encounter. Thus, a more productive reversal of the imitation game would entail engaging in bot mimicry without the formalized structure of a judge whose goal is one of distinction. This does not mean that such a practice would be unconcerned with its readers and possible readings, but, rather, that the relationship to the reader is one of critical creativity and community rather than separation and antagonism.

The reversal of the imitation game, with the added double imitation and the removal of the competitive structure, does not negate that bot mimicry still works as and with a play on deception. Although, as I have argued earlier, bot mimicry should principally be understood as inquiry rather than hoax, the practice still connects to what Natale conceptualizes as banal deception. As Natale argues, all perception is potentially deceptive – there are also multiple ways of looking (literally and figuratively) at a situation, any of which could potentially turn out to be a case of deceit. Banal deception, then, is the tendency of users “to embrace deception so that they can better incorporate [artificial intelligence] into their everyday lives, making [it] more meaningful and useful to them” (Reference NataleNatale, 2021, p. 7). In turn, bot mimicry can be understood as a literary engagement with banal deception: A curious playfulness that operates in a deceptive framework without being maliciously deceptive itself.

So what kinds of knowing – about any particular AI-system and/or about our relation to AIs in general – are instantiated in the banal deception of bot mimicry? By building on and reworking (reversing) the imitation game in this way, might we begin to make our understanding of automated computational agents more attuned to the nuances that we cannot think but that we might still be able to know? In the following section, I take on these questions in through the instantaneous flash of recognition of nonsensuous similarities that is invigorated in what Walter Benjamin called the mimetic faculty.

4.1 Greetings Fellow Humans!

Such is one welcomed to the subreddit r/totallynotrobots, a community that (at the time of writing) has about 400,000 members and has been active since 2015. A subreddit is a smaller community within the larger platform Reddit, dedicated to a specific topic. All of Reddit is segmented into subreddits, which means that there is no activity on Reddit that is not tied to one or more subreddit(s). The subreddit is at first sight a strange performative community, in which everyone writes in uppercase, primarily in a semantically confusing and syntactically roundabout way. When at times someone (usually an outsider, unfamiliar with the community and its style) posts in lowercase or writes in a straightforward fashion, the other users will demonstrate confusion and seem to worry. They might ask why the user is “yelling” – referring to their use of lowercase – or inquire into semantically banal but syntactically ambiguous statements in the outsider’s post. Most of the posts by regular users consist of content shared from other places on the internet (often from other subreddits), accompanied by a bot-esque tagline, explaining the relevancy of the shared content in the style of the community.

What looks like a confusing and unwelcoming community of users writing syntactically strange and Caps-Locked posts is, to my knowledge, also the largest and most long-lived performance of bot mimicry. The r/totallynotrobots community is strikingly consistent in terms of style of writing, despite being as open to participation as it is. The writing style appeals just enough for a broad crowd to allow people to creatively join the conversation through their mimetic faculty (introduced below), yet specialized enough for the community to sustain a consistent style. As a defining feature, the r/totallynotrobots community features a rich comment section in which other users respond to, rephrase, or further develop the humorous content in the main posts of the subreddit. The comment sections sustain a distributed and dialogic performance of bot mimicry where each post does not exist in isolation but must be considered in light of the proceedings in the comments.

The popularity of r/totallynotrobots is testimony to the broad reach of bot mimicry as a practice that derives from and flourishes in quotidian contexts, making it popular as it is ripe with poetic and techno-cultural potency. In the following, I introduce the concept of nonsensuous similarity, which is at the center of the theory of the mimetic faculty, and which is useful to unravel the mimicry of r/totallynotrobots.

4.2 Nonsensuous Similarity

The quote above is taken from a legendary new media idiot (cf. section 2), namely, the user Ken M. Although Ken M is most commonly referred to as a troll (cf. Reference SpoolSpool, 2015), I consider new media idiocy a more fitting term, since Ken M is rarely, if ever, engaged in transgressive behavior, but always (new media) idiotic. This quote was lifted from a screenshot of a comment section on Yahoo News, posted to the r/KenM subreddit (HeimrArnadalr, 2018), and serves to instantiate the unlikely connection between new media idiocy on internet platforms and a series of unfinished fragments from the 1930s detailing a theory of a mimetic faculty and its role in our appreciation of linguistic meaning.

Before applying the theory of the mimetic faculty to the phenomenon of bot mimicry, it is useful to consider the theory itself in some detail. Seeing as how this theory has not previously been introduced into the study of digital culture, some slowness of progression is necessary, to the end of situating a careful integration of the theory into the context of this study and in order to enable other scholars in digital culture to draw on what can be considered “the definitive statement of [Benjamin’s] philosophy of experience” (Reference OgdenOgden, 2010, p. 57).

Benjamin’s writings on the mimetic faculty coincide with a shift in his intellectual orientation. His early writings on language are somewhat mystical, placing words’ meanings and our appreciation thereof in a religious, transcendental context. By contrast, his later thinking aligns with materialist assumptions. As Anson Rabinbach notes, it is possible to track Benjamin’s drift from mystical to materialist thinking in his writings on the mimetic faculty. Whereas “Doctrine of the Similar” expresses a reliance on mysticism, “On the Mimetic Faculty” deemphasizes the relation between language and magic (see Reference RabinbachRabinbach, 1979, p. 62). I align myself with the latter – materialist – tendency in Benjamin’s thinking. Still, I draw on the earlier fragments on the mimetic faculty as well, including their discussions of magic. As Reference WolinRichard Wolin (1982) argues, it would be faulty to detach Benjamin’s early mysticism from his later insights – they are instrumental to the vibrancy of his thinking. This does not mean we should take Benjamin’s discussions of magic at face value and perpetuate religious mysticism. Instead, it means that adequately understanding the applicability of Benjamin’s later materialist thinking requires us also to consider his early, more mystical musings – with a critical awareness of their status as such.

The central concept in the theory of the mimetic faculty is nonsensuous similarity, that is, a conception of those instances where there exists a striking similarity between two entities that are sensuously dissimilar. Put differently, the mimetic faculty is at play in those cases where we perceive a similarity yet cannot explain it by pointing to the physical manifestations of the nonsensuously similar entities. The nonsensuous does not necessarily stand in opposition to the sensuous; instead, the nonsensuous is that which always has the potential to become sensuous (via the mimetic faculty) and which emerges as “the surplus values of [the sensuous’] various ways of exceeding itself” (Reference Massumi and GrimshawMassumi, 2014, p. 67). As one example of nonsensuous similarity, Benjamin mentions constellations of stars: although there is little sensuous similarity between the scattered dots in the sky and the mythical figure of Orion, our mimetic faculties allow us to perceive the surplus value of the sensuous dots in order for Orion to appear. The claim here is that identifying the constellation of Orion in the sky is not just a cognitive process of understanding that certain stars represent Orion in an abstract way. Rather, it is an aesthetic process: we literally see Orion (while also simply seeing a number of dots).

Benjamin’s prime example of contemporary nonsensuous similarity is not constellations but language. Instead of viewing our tendency to perceive similarities in sensuously dissimilar phenomena as a result of cognitive bias or a psychoanalytically explainable projection, Benjamin positions nonsensuous similarity at the center of experience as such: Not as a bug but as a feature. The mimetic faculty, and nonsensuous similarity, is thus not only at work in cases of overt imitation, but in all mental activities, seeing as “there may be no single of [human beings’] higher functions that is not codetermined by the mimetic faculty” (Reference Benjamin, Jennings, Eiland and SmithBenjamin, 1999d, p. 694).

The mimetic faculty has a history; it is not static but morphs in tandem with cultural changes over the course of millennia. In (pre-)Antiquity, the mimetic faculty might have been openly at play in some of the most vital moments in both cultural and personal life, exemplified by the practice of astrology, where cosmos, society, and person were thought to align in reciprocal mimicry, situated around the reading of constellations of stars (Reference Benjamin, Jennings, Eiland and SmithBenjamin, 1999a). Today, however, the mimetic faculty has a different role in society: we tend to think of mimicry as something that mainly pertains to children’s play and not as anything of greater societal importance. As a consequence, our current ability to perceive similarities “is nothing but a rudiment of the once powerful compulsion to become similar and to behave mimetically” (Reference Benjamin, Jennings, Eiland and SmithBenjamin, 1999c, p. 720). However, as Benjamin argues, we are not dealing with a decrease in the importance of the mimetic faculty to personal or societal issues, but with a transformation in respect to how the mimetic faculty works. Instead of being openly mimetic, we are today dealing with a mode of experience determined by a hidden, or nonsensuous, working of the mimetic faculty. This means that “the cases in which [we] consciously perceive similarities in everyday life make up a tiny proportion of those numberless cases unconsciously determined by similarity” (Reference Benjamin, Jennings, Eiland and SmithBenjamin, 1999d, pp. 694–695).

4.3 The Mimetic Taste of <!ERROR>

On the main page of the r/totallynotrobots subreddit, users share posts that center on the blurring boundaries between humans and robots. The rhetoric is surprisingly consistent, with each post usually offering a brief tagline that relates the shared content to the subreddit’s continuous performance of bot mimicry. The kind of content often shared on r/totallynotrobots includes: glitches; robots (industrial, household, or anthropomorphic) in peculiar scenarios; situations in which the word “human” is used either ambiguously or redundantly; and humans and/or animals performing robotic movements or gestures. All posts are accompanied by a comment section with varying degrees of activity, where other members of the community can engage with the posts. The comments on r/totallynotrobots often both build on and change the premises of original post, meaning that the performance of bot mimicry on the subreddit is not restricted to the main posts, but is often at its most vibrant in the comment sections. Since any user can post a comment and/or reply to other comments, there emerges a multilayered, multidirectional, and spontaneous performance of bot mimicry across the r/totallynotrobots community.

A typical post on r/totallynotrobots features an image of a fact food sign with a digital screen displaying glitching a message that reads “TRY A [glitch]” (Figure 10). The glitch appears to be a kind of pixelated statix. The premise of this particular post is that what appears to be computational glitch – an error that is effectively denying a human reader the ability to understand what they are urged to “try” – is plainly readable to a computer. The caption of the image is written from the imagined perspective of a robot attempting to pass as a human, and thus resembles a common social media post concerning fast food cravings, akin to the yearly hype of pumpkin spiced hot drinks, without any mentioning of the glitch. The result is a highly formalized version of the kind of informal language usually found on social media, which stylistically alludes to the notion that the user is indeed a robot trying to pass as a human, but failing humorously. The user exhibits a number of common tropes about robots (that are more or less based in actual issues of artificial intelligence), including, of course, the ability to construct syntactically sound text that is nonetheless semantically peculiar, but also the more specialized issue of context-awareness. In this case, the robot is able to infer that the glitch refers to a “FOOD ITEM,” since it is featured on a fast food sign, but unable to evaluate that the glitch does not signify any specific food item that is well known to humans. We might say that this specific and fictional robot has a syntactic context-awareness ([FAST FOOD SIGN] –> [FOOD ITEM]), while lacking the semantic ability to distinguish a glitch from whatever word was supposedly meant to be shown.

Figure 10 A post from r/totallynotrobots (Reference Drake_TungstenDrake_Tungsten, 2020).

Reproduced with permission.

My analysis of the post is – as all analysis – concerned with teasing out different aspects of the post and determining their relations, yet my argument here is that such analysis is not necessary in the performative moment of bot mimicry. Even though we have not seen artificial intelligence systems that behave in this specific way, we are able to read and creatively produce writing that nonetheless resembles our artificial intelligence imaginary. The mimicry makes sense due to a spontaneous flashing up of similarity that is tied to the specific moment of reading and writing on the subreddit, wherein “the nexus of meaning of words or sentences is the bearer through which, like a flash, similarity appears” (Reference Benjamin, Jennings, Eiland and SmithBenjamin, 1999c, p. 722). In the r/totallynotrobots community (and in bot mimicry in general) the nexus of meaning that flashes up in this way differs from our everyday ways of considering semantic meaning: Indeed, it is often the very point of the discourse of bot mimicry to be semantically confusing. Yet the proceedings in the r/totallynotrobots subreddit are still highly meaningful, exactly because of their confusing semantics which nonsensuously resembles the artificial intelligence imaginary on a more profoundly mimetic level.

Let us pursue this line of inquiry further by looking to the comment section. The joke does not stop at the post but continues – and is elaborated – in the comments. Here, users confirm the original joke by, for example, commenting on the glitchy food item: “AH YES. THE DELICIOUS TASTE OF <!ERROR>,” thus continuing the parody of common social media posts (Reference Drake_TungstenDrake_Tungsten, 2020). Another user has replied to this comment, noting that “IT ALWAYS FRUSTRATES THIS HUMAN WHEN THEIR <!ERROR> MACHINE IS MALFUNCTIONING” (Reference Drake_TungstenDrake_Tungsten, 2020. This drift from joking about the (in)ability to read glitchy text to the more specialized (yet very commonplace) comments about broken ice cream machines exhibits part of the dynamism of the r/totallynotrobots comment section and shows that bot mimicry as a stylistic writing activity does not necessarily need to relate to computational subject matters but can also relate to topics that are more common.

4.4 The Mimetic Magic of Bot Mimicry

In Arthur C. Clarke’s famous formulation, “[a]ny sufficiently advanced technology is indistinguishable from magic” (Reference Clarke1968, p. 255). However, the use of magical thinking about technology is not always helpful: it may lead to mystification; that is, it may worsen our chances to understand how the technology that is being implemented across contexts and affects large parts of our lives works. Accordingly, it is not uncommon for critical scholars of digital culture to call for a demystification of technology, which is often associated with dispelling of magical thinking (Reference EmersonEmerson, 2014; Reference Campolo and CrawfordCampolo and Crawford, 2020; Reference NataleNatale, 2021; Reference Pasquinelli and JolerPasquinelli and Joler, 2020).

As Lori Emerson argues, electronic literature is often oriented toward the moment of the glitch, which “defamiliarizes the slick surface of the hardware/software of the computer and so ideally transforms us into critically minded observers of the underlying workings of the computer” (Reference Emerson2014, p. 36). Such defamiliarization dispels the seemingly magical seamlessness of mass-produced user interfaces. In the context of machine learning, Kate Crawford and Alexander Campolo (Reference Campolo and Crawford2020) argue, along similar lines, that it is possible to trigger glitches by harnessing adversarial attacks to dispel the magic of artificial neural networks. Such adversarial attacks are “less a matter of creating disguised or ambiguous images to fool human observers than targeting the optimization models at the heart of deep learning algorithms, exploiting their counter-intuitive mathematical properties” to make them produce glitchy outputs (Reference Campolo and CrawfordCampolo and Crawford, 2020, p. 9).

Aligning myself completely with the need to dispel the base magical thinking that is often associated with the latest technological developments and results in mystification, I investigate the possibility that, perhaps surprisingly, bot mimicry might be an exception to the rule that demystification always stands in opposition to magic. Instead, we are dealing with a question of what kind of magic is taking place – an alternative account of (mimetic) magic might just play a role in the demystification of machine learning.

The r/totallynotrobots community displays a clear self-awareness and a sense of critical distance. The multiple layers of mimetic self-awareness in this community show that bot mimicry is not about assimilation but inquiry and negotiation from a position wherein similarity and difference co-constitute one another. We are dealing with a situated distancing, a mimetic othering, which has implications for our conception of the dynamics and the value of bot mimicry. These dynamics can be traced and illuminated via mimetic magic that works through similarity and contact.

4.5 Similarity and Contact

The contingency of sameness and difference that we see playing out in r/totallynotrobots is a central characteristic of bot mimicry. To the end of understanding its complexity and dynamic, the co-occurrence of sameness and difference should be understood in the light of Taussig’s expansion and reworking of the concept of the mimetic faculty, wherein we can locate an account of mimicry that registers “both sameness and difference, of being like, and of being Other” (Reference Taussig1993, p. 129). Taussig’s work focuses on the historical, yet enduring, (post-)colonial dynamics of imitation between the Global North and its colonial Others. At the center of Taussig’s work is the realization that the mimetic faculty is at play at both ends of colonialism, not least in the colonizers, despite their attempts to distance themselves from the mimicry of the so-called primitives. Conversely, indigenous peoples’ decisive harnessing of mimicry in and through mimetic magic turns into a powerful mode of resistance to the totalizing assimilation enforced by colonialism. It affords an “opportunity to live subjunctively as neither subject nor object […] but as both, at one and the same time” (Reference TaussigTaussig, 1993, p. 255).

Mimetic magic is conceptually informed by the notion of sympathetic magic, which was originally used to demystify and disprove – explain away – indigenous ontologies. Taussig reads against the traditional understanding of sympathetic magic and reconsiders the concept in a way that takes indigenous practices seriously and understands them as valuable in their own right. Sympathetic (and, in turn, mimetic) magic operates on the basis of two laws: the law of similarity and the law of contact. According to the law of similarity, “the magician … can produce any effect he desires merely by imitating it” (Frazer, qtd. in Reference TaussigTaussig, 1993, p. 47). The law of contact, in turn, entails that “things which have once been in contact with each other continue to act on each other at a distance after the physical contact has been severed” (Frazer, qtd. in Reference TaussigTaussig, 1993, p. 53).

The two laws of sympathetic magic are highly contingent, which means that the presence of one law almost always correlates with the presence of the other (Reference TaussigTaussig, 1993, p. 55). The contingency of similarity and contact runs parallel to the notion of nonsensuous similarity in that seemingly dissimilar phenomena may still inhibit a strong sense of nonsensuous (magical) similarity afforded by the law of contact. Further, the strong correspondence of similarity and contact sustains the emergence of difference and distance in the practice of imitation, meaning that “mimicry’s magical powers reside in its capacity to incorporate otherness while, in a profound sense, remaining the same” (Reference Bubandt and WillerslevBubandt and Willerslev, 2015, p. 17). This dynamic of sameness and otherness is integral to configuring the agency of the mimic. By becoming other while remaining the same, the bot mimic can at once materialize and negotiate artificial intelligence imaginaries.

My claim here is not that we should equate the mimetic situation emerging around bot mimicry – or machine learning – with the global harmfulness of post-colonialism. Yet we can learn something about digital culture – especially how to reconfigure our relation to our machine learning counterparts – from considering this work. In considering the ontologies of indigenous cultures, the goal is not to arrive at a new exotic primitivism that conflates indigenous cultures with an ontological primacy, which would uphold the derogatory characterization of indigenous peoples as being the Other that upholds the self-perception of the Global North (Reference LillywhiteLillywhite, 2018). Instead of seeing them as belonging to the natural attitude of a particular people in a specific place, we should recognize that the practices of these cultures “can be put to systematic and deliberate use” (Reference de Castrode Castro, 2004, p. 469). As Eduardo Viveiros de Castro argues, it “is necessary to know how to personify nonhumans, and it is necessary to personify them in order to know” (Reference de Castro2004, p. 469, original emphasis).

Moving forward in my study of mimetic magic in the context of bot mimicry, I am mindful of Isabelle Stengers’ helpful reminder, which stems from her study of the magic of neo-Pagan witchcraft, that “we, who are not witches, do not have to mimic them but instead discover how to be compromised by magic” (Reference Stengers2012, p. 8). In discovering how to be compromised by magic, the goal is not to appropriate indigenous practices or to ignore (and inevitably reinforce) the enduring histories of colonial damage. Instead, we should allow our curiosities to drive us into techno-cultural exchanges across planetary communities in search of “horizontal dialogues between western and amerindian ontologies” (Reference BejaranoBejarano, 2020).

As mentioned, the comparison between bot mimicry and (mimetic) magic is here intended to demystify machine learning. Mystification is one of the most pressing issues pertaining to machine learning, and this mystification is, to a large degree, sustained by a discourse of enchanted determinism, which uses magical terminology to bedazzle audiences and effectively “seal off … epistemological shortcomings and ethical problems” (Reference Campolo and CrawfordCampolo and Crawford, 2020, p. 12). Within enchanted determinism, we are told not to worry about how or why a system returns a specific output, since it is both magical and deterministic, that is, both beyond our comprehension and at the same time more accurate than anything humans could produce. Facing this enchanted determinism, bot mimicry is a mode of demystification that itself works (differently) through an appeal to magic. With the risk of inducing the reader with some conceptual dizziness, the aim here is to detail mimetic magic as a form demystification of machine learning, which is itself mystified mainly due to a magical discourse. The difference, as mentioned, is what kind of magic is at play.

As we saw in Section 3, the anthropomorphic and automative imaginary of artificial intelligence is oriented toward the erasure of differences across humans and computers. I argue that this erasure maps onto a kind of magical thinking associated with enchanted determinism. As an alternative, bot mimicry instantiates a kind of mimetic magic that revels in the intensity of spontaneous differences arising in the mimetic moment. As it turns out, the latter is helpful in demystifying the former. In the following, I trace these two different kinds of magic reverse order, beginning with the nonsensuous and sporadic type of bot mimicry (related to mimetic magic) before moving on to the direct and instrumental type that we see in the anthropomorphic and automative imaginary (related to enchanted determinism).

4.6 Bot Mimicry as Mimetic Excess

By entering into the mimetic messiness of humans and computers in a way that sustains distance and reflection while situating profound mimetic encounters, bot mimicry unleashes the progressive potential embedded in decisive operations of the mimetic faculty, which is enabled via parodic, decisive performances of mimicry wherein sameness and difference co-constitute one another.

In the context of the r/totallynotrobots subreddit, I am struck by the truthfulness of the title, Totally Not Robots, and the mimetic difference signified therein. To be sure, the title is part of the parody: it is meant to be suspicious, in itself alluding to the bot mimicry happening in the subreddit. Yet it is also sincere: the users on this subreddit are, in fact, totally not robots, even though they are pretending to be exactly that. What better analogy of the codetermining dynamic of sameness and difference than this, the very title of the subreddit that hosts what is likely the most significant community for bot mimicry in existence. This title is highly conspicuous, resulting in a kind of recursiveness that makes us consciously aware of the mimetic intensity of the situation.

In the performative moment of bot mimicry, there emerges a linguistic and material common ground through which we can establish a mimetic connection that allows us to renegotiate the dynamics of humans and machine learning. For a brief moment – sustained by the mimetic faculty – human and computer enter into a profound encounter, only to disperse in a way that, while highlighting similarity, underscores difference. The fullest potential of bot mimicry lies in its ability to make us aware of – and enable us to act within – the mimetic intensity of humans and computers.

Mimetic excess entails a distinct “mimetic self-awareness, mimesis turned on itself,” which most prominently infers a “reflexive awareness as to the mimetic faculty” (Reference TaussigTaussig, 1993, p. 253). Bot mimicry produces an excess of imitation, carried by nonsensuous similarity and mimetic magic. It enables us to see and navigate the space of similarity, difference, and contact vis-à-vis machine learning. The mimetic faculty is the force through which disparate aspects of culture are bound together, through systems of nonsensuous similarity, in ways that create and naturalize certain reality-systems.

In the following section, I turn to consider the role and relevance of bot mimicry not only at an object of study, but also as a practice-based research methodology. The efficacy of mimicry vis-à-vis artificial intelligence imaginaries is not only at play in platformed communities, it can also figure within the academy.

5.1 Chances Are You Will Guess “Bot”

Consider the conceptual abstract shown in Figure 12. This abstract from 2019 was a performative reflection, based on bot mimicry, on a lengthier academic article in which I first coined the concept (Reference ErslevErslev, 2019b). The abstract resides in a somewhat uneasy space between auto-generated gibberish and academic dissemination, and importantly it still makes some degree of academic sense. I here consider this abstract as a case study of how bot mimicry and academic dissemination of research can coincide and reciprocally inform each other.

Figure 12 My conceptual abstract for Peer-reviewed Newspaper.

(Reference ErslevErslev, 2019a)

The production of the abstract happened in dialogue with a generative system, specifically an n-gram-based text-generation model (a Markov chain), which works by receiving an input text, breaking it down into smaller segments, and modeling the statistical distribution of probability across the text. In other words, a Markov chain is a simple form of machine learning that enables the prediction of the next character in a sentence, based on the previous characters – modeled after a specific input text. My Markov chain was based on the entirety of my academic article, and used it to generate a kind of raw linguistic material, syntactically based on my own writing.

The Markov chain operated on a fairly small n-gram size and generated text on character level, which gave rise to surprising, entertaining, and sometimes even insightful sentences, creating new connections across the article. I am particularly fond of the following redundant, yet precise, observation that came out of the bot: “There is a certain reassurance in the abruptness of the digital computer (this becomes strikingly evident in the abruptness of the digital computer).” No metaphor or explanation can outdo the recursive accuracy of this observation in referring to its own abruptness.

Following the generation of linguistic material using the Markov chain, I began reworking the outputs. I curated the generated snippets, selecting intriguing sentences and discarding the more uninteresting ones. I then began slightly correcting grammar and spelling, without disturbing the syntactical mishaps that made the text resemble a style of bot mimicry in contrast to the style of academic dissemination. I decided on the structure of the text, moving individual sentences around until I felt there was appropriate progression. I added words, for example, the emphasized negation in the following sentence, added to the end of making sure the abstract did not claim something false about Cox, McLean, and Ward’s argument: “[E]ven though Cox, McLean and Ward never maintained that the human agent was indeed a bot” (emphasis added).

The kinds of reworking of the abstract described earlier were based on the outputs of the Markov chain, and as such break with the account of bot mimicry I have given in this Element, where I have highlighted the independence of bot mimicry from particular text-generation algorithms. It is important to note that some of the sentences in the abstract were indeed created through bot mimicry without any involvement of the Markov chain. In intermixing bot mimicry with reworked outputs from a text-generation algorithm, my abstract shows that bot mimicry can also exist in dialogue with computational systems. One of the sentences written without any human involvement was intended to make the reader question the origins of the text itself, to point to the bot mimicry going on (emphasized below).

The emphasized sentence is not in any way based on the bot’s output. It was written specifically to tamper with the reader’s expectations. Just before it comes a sentence that is also present – word for word – in my original academic article. Following it is one of the abstract’s gibberishiest sentences. In contrast to other sentences, this emphasized one directly mentions the abstract itself (“this stylistic experiment … this text”) and addresses the reader directly (“you may attempt”). It bears in it the overarching argument of the academic article while hiding among the gibberish. Indeed, or this is at least my feeling as I read through the abstract, some of the obviously generated, gibberishy sentences start to make more sense once the reader is made aware of the uneasy position of the abstract, in-between stylistic experiment and academic dissemination.

In the following, I discuss the implications of this kind of earnest-yet-ironic writing – of this bot mimicry between bot and mimicry – in relation to the industry of academic publication.

5.2 How to Imagine a Bot from a Mimicked Abstract

My conceptual abstract is, in spite of its apparent arbitrariness, not wholly transgressive to the traditions of academic writing, even though it may not in its current form have been accepted for a traditional peer-reviewed academic journal. Yet it is exactly through this play on form – through its literary experimentation – that the abstract seeks to disseminate the knowledge it contains. The appreciation of the argument must run in tandem with the appreciation of the style of bot mimicry. The performative mode of argumentation is synchronized with the remit of its publication platform, Peer Reviewed Newspaper, which lets “critique be performed by practices from network culture, and [lets] the practices of network culture themselves … form a recognizable academic field” (Reference Andersen and PoldAndersen and Pold, 2018, p. 177).

The abstract harnesses the vividly imaginative and associative aspects of otherwise supposedly clear and thorough academic dissemination. The imaginative aspect of reading academic abstracts is known by most academics from the practice of surveying abstracts in search of papers worth reading. This practice sustains the ability to appreciate and evaluate the merits and possible relevance of a given paper from 250 words. In other words, this practice compels academics to imagine papers before reading them. The relation between reading a seemingly nonsensical, but nonetheless illuminative, abstract and imagining the implied paper to go with it is elegantly explored by Rui Torres and Diogo Marques in an artistic exploration of “how to imagine a paper by programming an abstract.” This work allows users to partake in “compose[ing] a generative and aleatory abstract pointing to papers that were never published or even imagined” (Reference Torres and MarquesTorres and Marques, 2020). The work operates “a speculative, though self-reflexive, theorization about the limits and possibilities of using digital media to perform essayism, hence revealing its dual programmable and experimental nature” (Reference Torres and MarquesTorres and Marques, 2020). By a similar vein, although not interactive, my conceptual abstract points self-reflexively to the reciprocal relation of literary experimentation and academic critique in the context of bot mimicry and, more broadly, ultimately self-reflecting on the troublesome boundaries between humans (in this case specifically human scholars) and our bot counterparts.

My conceptual abstract was intended for publication and reception in academically informed contexts and actively sought to balance idiocy with dissemination. In creating this balance, I intermixed academic writing, text generation, and bot mimicry to the end of tampering with the reader’s expectations throughout the text. The reception of generative artworks is highly contingent on the understanding of the generative system, not least that the reader understand that a generative work is just that: generative (cf. Reference Cox, McLean and WardCox et al., 2000). For instance, simply showing a code editor momentarily as part of an exhibited work, without disseminating or explaining the code, may attune the audience’s appreciation of the artwork and reorient it to focus on the generative aspect of the work rather than (or in dialogue with) its output (Reference FryFry, 2018).

My conceptual abstract does not display either code or editor, but it does operate a discourse that is recognizable enough to go viral (as the Olive Garden tweet did) or to sustain a community with hundreds of thousands of members (such as r/totallynotrobots). Both the title of the abstract (which incidentally is untrue) and, to a more specialized audience, the fact that the abstract contains traces of generative poetics, is sufficient for experienced readers to begin considering the generative process rather than focusing exclusively on the text itself and its semantic meaning(lessness). In similar and yet obverse ways, the few sentences that shed light on that very generative process itself, or the reading thereof, force the readers to once again reorient their appreciation of the text, considering the content rather than (only) the generative process behind it.

My conceptual abstract shows the potential for continuously shifting between mimicry and dissemination in the inquiry into artificial intelligence imaginaries in digital literary culture. The objects of study in digital culture should not be kept separate from our engagements with them; if we insist on such separation, we lose out on the potential to harness practice-based ways of knowing into our illuminations of the cases we deal with. To mimic is to inquire – mimicry is methodology.