For the official version of record, see here:
Nilsen Ervik, A. (2023). The Work of Art in the Age of Multiverse Meme Generativity. Media Theory, 7(2), 77–102. Retrieved from https://journalcontent.mediatheoryjournal.org/index.php/mt/article/view/586
The Work of Art in the Age of Multiverse Meme Generativity
ANDREAS ERVIK
University of Oslo, NORWAY
Abstract
This article adapts Walter Benjamin’s theory of technological reproducibility to examine how generative AI transforms art. Through analysis of a range of works of art and online popular culture, the article theorizes the aesthetics of generative AI. From the characteristic glitches and recurrent formats, the aesthetics are conceived as muddled confusions of parts, attempts at reinserting what Benjamin terms aura, and as surrealist assemblages of characters, styles and worlds. In Benjamin’s conception, popular culture becomes our collective imaginary, which today feeds into a memetic visual conversation of imitation and variation. AI points toward an age of multiverses where characteristics, current events and cultural artifacts blend together to achieve success in social network attention economies. Following Benjamin, the article examines the politics of these transformative media technologies. More than a risk to the livelihood of artists and deep-faked disinformation, this article argues for the radical potential in freeing visual culture from the property rights of corporations.
Keywords
Generative AI, Memes, Twitter/X, Art, Visual Culture, Walter Benjamin, Media Aesthetics
Figure 1: Image posted to the account Weird Ai Generations, offering the outrageous possibility of the Demogorgon from Stranger Things crossing over to the sit-com Friends. One of the images shows the bloodthirsty beast posing with a disgruntled demeanor, hands on hips, just as a character on Friends might stand. One can almost hear the laugh track responding to the situation. See https://twitter.com/weirddalle and https://twitter.com/weirdaigens (Weird Ai Generations, undated a and undated b, respectively). Accessed 24.08.23.
Generativity, memes and multiverses
Artists Matt Dryhurst and Holly Herndon point out in a blog post that the response to images made with generative AI echoes the discourse surrounding early photography: “The same debates will rage about whether or not prompt based AI imagery can be considered Art, and will just as inevitably be relegated to history once everyone makes use of these tools to better share what is on their mind” (Dryhurst and Herndon, 2022). Instead of asking whether or not photography could be considered art, Walter Benjamin ([1935] 2010) theorized the transformation of art in what he termed an age of technological reproducibility.[1] Written as a reflection with Benjamin, the aim of this article is not so much to give an overview of his work, but to adapt and transpose his theses on reproduction to the contemporary situation of generative AI. The central question for the article thus becomes what kind of transformation generative AI brings for art. This introduction will define the central terms in the proposition here of an age of multiverse meme generativity. Thereafter brief pointers to the following sections of the article will be presented.
Generative AI is a process of machine learning that uses diffusion models. In the learning process, an enormous number of images are gradually turned into noise. After the learning, the process is reversed, as the program generates images out of noise. Running the program, it does not simply reproduce what was in the training material, but lets users create new images by way of written requests for the program, called prompts. Benjamin noted of photography that “since the eye perceives more swiftly than the hand can draw, the process of pictorial reproduction was enormously accelerated, so that it could now keep pace with speech” ([1935] 2010: 13). With image generation it is no longer reproduction but generation that is freed from the eye and from imagination, to now keep pace with writing. The diffusion model is prompted with written descriptions of what one wants it to display. The prompt-image dynamic thus solidifies the relation that Benjamin noted between photography and caption, turning words into what renders the images while also regularly accompanying and indicating what the viewer should see.[2]
Among the central analytic responses to generative AI-images is a special issue in the journal IMAGE devoted to the subject. Generative AI is considered here as generative images as a form of stock photography and a reservoir of styles (Meyer, 2023), an extension of fan culture (Lamerichs, 2023) or in terms of a media collective imaginary (Ervik, 2023). Lev Manovich has argued elsewhere that the image making of AI “return us to common art practices in all traditional cultures”, which he terms “the art of the copy”, as “users constantly refer to various artists, video games, Hollywood films, animation studios in their prompts” all the while “copying and reusing prompts” (2022). An alternate framing for Manovich’s notion of the ‘art of the copy’, which will be pursued in this article, comes by way of general media discourse in which AI-generators have been presented as “the Internet’s Favorite AI Meme Machine” (Knight, 2022). The concept of memes was originally coined by evolutionary biologist Richard Dawkins ([1976] 2006) as the basic unit of cultural transmission, and the term has since devolved into a designator for funny content circulating online. Following Limor Shifman (2014), memes are conceptualized in this article in distinction to a certain type of image (such as image macros) and some fad that spreads quickly only to be forgotten (such as viral posts). Memes are crystallizations of certain identifiable patterns, to be imitated, and varied. Benjamin introduced his exploration as “theses on the developmental tendencies of art under the present conditions of production” ([1935] 2010: 12). This article considers the current conditions as involving software users and social networks in a collectively produced visual culture of memes.
A guiding force in the memetic process of imitation and variation is encapsulated here with the notion of the ‘multiverse’. To introduce this concept, I will point to the framing of one of the foremost and easily-available AI-image generators: DALL-E. This generator is named after both an artist and a fictional character, presenting itself as the answer to the question of what would happen if one combined the surrealist creativity of Salvador Dali with the helpful charm of the Pixar animated waste disposal robot Wall-E. The software is itself the result of both human artistry and technological means, and Dall-E can offer juxtapositions of high and low, appropriating from fine art and popular culture. This process holds the potential for generating multiverses. Not in the strict sense of, say, the many-worlds interpretation of quantum mechanics in physics, but rather in a pop-cultural sense. It is perhaps best exemplified with Barbenheimer, which turns the coincidental same day release of the Barbie movie and Oppenheimer into a meme (and grassroot-driven viral ad campaign, see Know Your Meme, 2023; Gerwig, 2023; Nolan, 2023). Numerous posts have created visual worlds where Barbenheimer takes a singular, paradoxical form, including an AI-generated movie trailer featuring a pink nuclear blast (Curious Refuge, 2023). While artists and corporations can create distinct visual worlds, generative AI seems to be particularly apt for making wildly different visual referents, stylistic registers and motifs come together as diverse and often contradictory crossovers that blend into multiverses.
Early in his reflections Benjamin admits that in principle, the work of art has always been reproducible, by way of replicas and techniques such as woodcuts, engravings and lithography. Likewise, one might trace precursors to generative AI in imaging processes automated with technology. A pre-history of generative AI could for instance include the 1960s computer-generated animations of John Whitney, or the 1970s computer-generated paintings by Harold Cohen.[3] The point, however, is not to trace a genealogy of AI image generators.[4] Instead the following section of this article will examine ways that generative AI has altered artistic production. What is the aesthetics of generativity? The answer will be approached through examples from a range of contexts, including gallery exhibitions, auctions, competitions, and online popular culture.
A second central point for Benjamin is that reproduction allows images to be distributed far and wide, and that “technological reproduction can place the copy of the original in situations to which the original itself cannot attain” (Benjamin, [1935] 2010: 14). Benjamin points to how reproduction meant the possibility of distributing images far and wide. Beyond seeing images and videos made by generative AI, there is today easy access to a range of tools that anyone can try out, including OpenAi, Midjourney, Stable Diffusion, Imagen, Wombo Dream, and Craiyon. What happens when generativity becomes easily accessible for the general population? This will be examined in the third section through focusing on a specific Twitter (now X)-account, Weird Ai Generations. The account is taken as a particularly engaging example of what is currently common usage of generative AI.[5]
Benjamin was a Marxist writer, and his essay starts with reflections on how the conditions of capitalism lead to the exploitation of the proletariat, as well as hold the potential for “creation of conditions which would make it possible for capitalism to abolish itself” (Benjamin, [1935] 2010: 12). What are the political implications of generative AI? In the fourth section a range of potentially negative consequences will be presented, including disinformation and threats to the livelihoods of artists. In the vein of Benjamin, this section will reflect on whether there is nevertheless radical revolutionary potential in AI image generators. Finally, a concluding section summarizes the central points of the article, with regards to how generativity transforms the production, exchange and politics of art.
Works of generative artistic production
Although there are necessary preconditions, the ability to generate images using machine learning emerged in 2014. Ian Goodfellow created what is known as Generative Adversarial Networks (GANs), which consist of two neural networks, one generating and the other discerning. The networks are adversarial in the sense that they are pitted against each other, as the generator produces images while the other scrutinizes them to decide how convincing they appear. Among the artistic experiments with GANs are Trevor Paglen’s Adversarially Evolved Hallucination (2017), Mario Klingenmann’s Memories of Passerby I (2018) and Pierre Huyghe’s Umwelt (2019). Paglen trained a network using images of what he calls “irrational things”, including anything from dream interpretation to folk-wisdom, which made possible generative visualizations of the unseen and imaginary (see Paglen, [2017] 2020). While Paglen presented his work as printed still images, Klingenmann and Huyghe showed the processual streams of generation. Huyghe’s work consists of streams of morphing visualizations produced as AI attempts to recreate what people have looked at based on FMRI-scans of their brain activity. The work of Klingenmann, with its shifting generated faces, has been compared by Fabian Offert (2022) to the contorted features in the paintings of Francis Bacon. Offert goes on to connect this style to both the technology and artistic choices, as it is “typical for the AI art of this era, where the non-photorealism of generative models is the focus of artistic exploration” (2022).
The generative AI style can be encapsulated with Gregory Bateson’s notion of “muddle”, as a term for a lack of distinction and outline between things (Bateson [1972] 1987). When image generation glitches, visual distortions reveal the failure of the program to properly configure the prompt it is given in accordance with the categorizations it has been trained with. One particularly well-known example of AI-generated muddles is an online-circulated image that gives an initial impression of displaying familiar things (see Fig. 2). Upon inspection each of the elements in the image turn out to be unrecognizable as anything in particular. While such visual effects may be undesirable from a perspective of caption similarity, it is a digital glitch particular to this form of image-making.
Figure 2: The image has been described as “designed to give the viewer the simulated experience of having a stroke (particularly in the occipital lobe of the cerebral cortex, where visual perception occurs.) Everything looks hauntingly familiar but you just can’t quite recognize anything” (See Know Your Meme, 2019). The description might simply be imaginative fabulations of some internet user, as generators are generally adept at confusing relations and breaking down boundaries between things despite the intentions of the human involved in image generation (see for instance Marcus et al., 2022). Originally posted by Twitter user dumbass ass idiot (@melip0ne) in 2019 with the accompanying sentence encouraging others: “Name one thing in this photo”(https://twitter.com/melip0ne/status/1120503955526750208?fbclid=IwAR2gpJJHAgKHJ5MYWLBC7_jW_V6ve9X4K8_LhfWzIw-rDPPrGzt0kY-bBLI (accessed 24.08.23)).
A central point in Benjamin’s work concerns how technological reproducibility turns the uniqueness of artworks into mass availability. Generativity depends on and deepens this availability, in both process and product. I have elsewhere argued that generative AI does not provide images as captures of something placed in front of a recording apparatus or offer a CGI possibility of viewing a rendered object from any angle, but instead produces the proliferation of “multiple views of nowhere” (Ervik 2023). Another central notion for Benjamin is that of the aura, which terms the non-reproducible connection that artwork has to their unique material past (Benjamin, [1935] 2010: 14). As views of nowhere, generative AI-images lack aura and entering into art markets has involved attempts at re-imbuing the views of nowhere with semblances of aura. An example of this is the “Portrait of Edmond Belamy” by the collective Obvious. The image does not resemble any particular portrait, but is rather an ahistorical emulation of the general vibe of classical portrait painting. This includes generated impressions of brushstrokes, as well as the placing of the image in a golden frame and adding a signature with part of the code that produced it, echoing the traditional convention of a signature as marker of authorship. The portrait was sold in 2018 for over $400k at the auction house Christie’s (2018), who attempted to further provide historical aura: “It may not have been painted by a man in a powdered wig, but it is exactly the kind of artwork we have been selling for 250 years”.
In 2022, the Colorado State Fair Fine Arts Competition awarded grand prize to Jason M. Allen’s image “Théâtre D’opéra Spatial”, made using Midjourney. News reports described the image as “a bright, surreal cross between a Renaissance and steampunk painting”, but also stated that artists “aren’t happy” or alternately “furious” with the AI-generated winner (Roose, 2022; Metz 2022). A poignant formalist critique that relays some issues of generative AI were offered by a Twitter user, commenting that the visual elements of the image are “designed to invoke foreignness”, as “in the orientalist style, they would frequently mix and combine many different styles of [L]evant and [E]gyptian architecture and culture without regards for what it meant or where it came from, just like how AI art will recombine meaning without its context” (Jay Dragon, 2022). This combinatorial process of AI is described by Steven J. Frank as involving the possibility that the generators “can digest the entire cannon of Western art and evaluate synthesized images for dissimilarity or similarity to any or all of the canonical works, producing a novel image as easily as an homage” (2022: 2). While the images by Obvious and Allen are presented as singular, original work, they depend on other art in their training data as references for the process of generation and in modes of presentation that attempt to reinsert aura.
Following Benjamin in turning from photos to film, I will consider some examples of generative video. Among the providers are Runway, offering a text-to-video tool similar to that used in image generation, letting users write prompts to produce short video clips.[6] On the dashboard of the service, example clips are presented, such as “a cinematic shot of a fox in the rain, Ultra-realistic, HD, during golden hour” or “an astronaut tripping through space”. The clips are relatively convincing renderings of a fox-like and an astronaut-like individual, yet their movements have a quality characteristic to generative video. As much as a fox or an astronaut moving through space, what appears to take place is that their position and their form seem to be re-rendered. At least in generally available tools, such as Runway, generative video still seems to lack the proper object retention necessary to convincingly produce unified beings that appear to move through space. Instead, the videos seem to show continuously fluctuating attempts at visualizing what it is prompted with.
Figure 3: Still from “CURSED HEIDI | AI-generated movie trailer”, by Patrick Karpiczenko (top) (see https://www.youtube.com/watch?v=0A2-Af5JEWU&t=1s (accessed 15.08.23)) “Burger Blast Ad 1995 – AI Generated Commercial” (below) by YouTube account AI Lost Media (See https://www.youtube.com/watch?v=0bJ_kO12f-I (accessed 15.08.23). The videos make use of generative video’s tendency towards shapeshifting to create surreal imagery in which humans and other things become muddled together.
There are examples of AI-generated videos longer than clips. Music videos can often take the form of experimental visual pieces, and it is therefore no surprise to find generative AI-made music videos.[7] Another format common for generative video is the movie trailer, with a particularly successful viral video presenting a trailer for a non-existing Heidi movie (Karpiczenko, 2023). Produced using Runway, the trailer leans into the possibilities for surreal imagery (see Fig. 3). This surrealism can be elucidated with Benjamin’s descriptions of how the actor in cinema performs for the apparatus of film production, being monitored, evaluated and selected ([1935] 2010: 22, 24). With generative videos, a different kind of apparatus is involved, in which humans are no longer performers captured by the camera and assembled through editing, but are objects constantly being re-imagined by AI. Benjamin noted that a film actor (as contrasted with a theatre actor) achieves best performative effect by underplaying. In generative videos, people’s very humanity is underplayed. This can be exemplified with the series of AI-generated advertisement parodies in which enthusiastic crowds eat and drink beer, burgers and pizza, and in the process the people blend into the items consumed.[8] In a piece written by Leif Weatherby (2023) for The Daily Beast, such videos are described with reference to Freud as uncanny, and considered to express our “weirdest fears”. If generative video is uncanny, however, it is because it offers photorealistic glimpses into worlds where humans are not only indistinguishable from anything else, but are constantly shapeshifting components of an everchanging visual muddle.
Industries of memetic desiring-production
A formative insight in Benjamin’s reflections is that human experience is shaped by media technologies and that media “train human beings in those new apperceptions and reactions demanded by interaction with an apparatus whose role in their lives is expanding almost daily” (Benjamin, [1935] 2010: 19). Learning how to interact with generative AI involves developing skills in prompting, and a Dall-E Prompt Book (Parsons, 2022)offers tips on how to optimize prompts to achieve the desired results. AI image-generation is often conceptualized as a tiny loop of an individual user and a program, yet from training data to selection pressure in social networks, generation is a social process. Among the central media apparatus of daily interaction today are social networks, which the images of generative AI feeds into. Describing the effects of reproduction and mass distribution in the popular press, Benjamin points out how “the distinction between author and public is about to lose its fundamental character” since “[a]t any moment, the reader is ready to become a writer” (Benjamin, [1935] 2010: 26). In social networks such distinction has collapsed, as anyone on social networks is at once producer and consumer, prosuming content. Anyone can post their generative AI-creations to social networks, initiating and contributing to visual conversations. What determines the structure of these visual conversations?
Prior to technological reproduction, the value of a painting was individually determined in exchange, whereas the production cost becomes determinant for mass media: “[P]roducing a film is so costly that an individual who could afford to buy a painting, for example, could not afford to buy a film (Benjamin, [1935] 2010: 17). Likewise, generative AI is unimaginably resource-intensive (in its training data and energy consumption), but becomes cheaply available for a global mass of users through paid subscriptions or free simplified versions and derivatives. Generativity and networking continue the process that technological reproduction initiated, in which individual artwork is devaluated. Benjamin describes the classical Greek “whose art depended on the production of eternal values” which led them to consider “the pinnacle of all the arts was the form least capable of improvement” ([1935] 2010: 20). In contrast, to the eternal value perceived in for instance a sculpture, film indicates “the age of the assembled artwork”, characterized by “its capacity for improvement” ([1935] 2010: 20). Today, images are assembled together not only in film but in social network feeds. The images here turn into what Hito Steyerl (2009) terms “poor images”; with often low visual quality they are instances shared to become noticeable and mostly quickly forgotten.
AI generative images within social networks extend and intensify what Benjamin termed exhibition value. He distinguishes this from the ritualistic, which derives value from existing and perhaps even from not being seen, whereas exhibition value is tied to visibility and distribution. Social networks are organized around visibility, as an unseen post might as well not exist. The social network pressure on visibility forms what is regularly termed an attention economy, which was originally conceptualized by Herbert Simon, noting how “a wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the over-abundance of information sources that might consume it” (Simon, 1971: 40-41). The winners of the attention economy are the users that gain traction, followers, likes, but also the memetic formats that prosper in continued imitation and variation.
To consider successful memetics in the social network attention economy, I will focus now on a user that has gained prominence through posting exclusively AI-generated content, under the heading of Weird Ai Generations (WAG). The person behind the account, Matt Laming, and its followers echo the structure of GANs, as Laming acts as a discerner for the hordes of people generating images and posting them to the Reddit subforum r/Weirddale. Yet these users are themselves also discerners, upvoting posts in the subforum. Based on these upvotes, Laming will notice and post some images to the Twitter account as the output of what this population generates.
The account presents a central strategy for its success in its framing, as the AI-generations posted can be considered ‘weird’. This weirdness can come as the aforementioned muddle. An example of this is found in a post with a caption indicating the prompt to simply have been “ok hand”. Its nine different images show, however, various failures at generating coherent hands. The images introduce joints bending impossibly, too many fingers or contorted, finger-like structures. Despite its caption, the hand is anything but okay. The post shows that in the social network attention economy, caption similarity and photorealism are only partially desirable traits. Instead, the AI’s failure in properly understanding and rendering a prompt is embraced for potentially humorous results. Another kind of weirdness is comparable with the art coming out of the Surrealist movement of the 1920s and 1930s. These images can be characterized with Gilles Deleuze and Félix Guattari’s term assemblages. The images feature juxtapositions of seemingly random bits into “partial objects” which do not seek being “glued back together to create a unity that is precisely the same as the original unity” (Deleuze and Guattari, [1972] 2000: 42). The viewer can discern with relative certainty the identity of the individual parts, but will struggle to understand the meaning behind their connections. As an example of such surrealist assemblages, I will take a closer look at several entries to WAG featuring Darth Vader (see Fig. 4).
Figure 4: A selection of Darth Vader images posted to Twitter by Weird Ai Generations, adapting and varying the popular cultural icon into everyday situations, from being in awkward family photos, being rendered as a coffee maker, to doing home chores and having a tea party with Kim Kardashian. See https://twitter.com/weirddalle (accessed 24.08.23).
Posts with Darth Vader regularly place the character in what is at once unfamiliar and familial settings. As with memes more generally (see Shifman, 2014), the attraction of these images comes from the humorous quality that juxtapositions can produce. It is funny to see Darth Vader placed in the meme format of ‘awkward family photos’, pushing a cart while shopping, at a playground with younglings or mowing the lawn. As a grand villain of a wildly popular franchise, apart from the recognizable outfit the most iconic aspect of Darth Vader is his line stating that he is the father of the hero. The images could thus be seen as turning Darth Vader into a more considerate father figure, doing everyday chores while visibly remaining the iconic villain in attire that is inappropriate and cumbersome for the situations. Yet there are posts in which the character is placed in a Soviet space propaganda poster, a cooking show, shown riding a unicycle, meeting an animated penguin, demonstrating at a BLM rally, turned ancient Egypt pharaoh, or presented in an MRI-scan. These posts push beyond the interpretation of the images as attempting to return the character to the family.
Deleuze and Guattari’s understanding of desire provides an alternate perspective on the images. In their formulation, assemblages are ever-expansive: “The productive synthesis, the production of production, is inherently connective in nature: ‘and …’ ‘and then …’” (Deleueze and Guattari, [1972] 2000: 5). The duo further contrasts a psychoanalytical understanding of the unconscious as a theater which unfolds according to a predetermined script with how desiring-production is machinic, a factory generating novelty. The connection between objects is thereby not a way of uncovering some hidden prefigured relations between them as much as producing novel juxtapositions. With Deleuze and Guattatri, the memetic desiring-production can happen upon the humorous potential of Darth Vader conforming to family relations, but it can potentially include any other elements in its connective generation of novelty.
Darth Vader is an instance of a broader, recurrent format in the WAG: popular cultural characters. For Benjamin ([1935] 2010: 22-23), film was considered as disrupting tradition through resurrecting heroes of the past and of myths. This constituted at once a destructive rupture and a fervent renewal, which is echoed in the popular usage of generative AI. The images of WAG align with Benjamin’s reflection on popular cultural icons: “The ancient truth expressed by Heraclitus, that those who are awake have a world in common while each sleeper has a world of his own, has been invalidated by film and to be sure, less by depicting the dream world itself than by creating figures of collective dream, such as the globe-encircling Mickey Mouse” (Benjamin, [1935] 2010: 31). Previously, I have conceptualized generative AI, with reference to Benjamin, as an extension and acquisition of a collective imaginary, which “exists today in a feedback mechanism with media, which act at once as reservoirs and prompts for it” (Ervik, 2023). Characters that are visually identifiable across permutations are more likely to become part of the memetic collective imaginary, as patterns to be repeated and varied in desiring-production. Darth Vader is still identifiably the same character when turned into a Nespresso machine, just as another post shows the Xenomorph alien remaining visually coherent imagined in the form of a stapler. The juxtapositions reveal a drive to stretch the characters out of bounds, to imagine them out of the familiar and attach them into novel situations. The AI-generated images offer glimpses of meme multiverses, in which distinctly different fictional characters and universes muddle together.
There are two ways of experiencing WAG: scrolling through an archive of posts of this particular account, or finding its posts as individual instances in a feed of a wide range of posts by different users. The former lends the posts an exhausting and obsessive quality, while the latter allows them to be precisely weird, as baffling instances. This sensation can be explicated through Benjamin’s reflections on painting and distraction. Benjamin introduced the notion of “[r]eception in distraction”, by which he means“the sort of reception which is increasingly noticeable in all areas of art and is a symptom of profound changes in perception” (Benjamin, [1935] 2010: 33, 34, original in italics). For Benjamin the central examples were architecture, conceived as a traditional artform experienced fleetingly, and film, as juxtaposing different scenes and situations together to create a flow of tactically structured distraction. With Benjamin, the architecture of social networks can be described as likewise initiating “habitual states of distraction” ([1935] 2010: 34). Benjamin further noted how Chaplin’s movies offered avant-garde tactics appreciable to the general public. Through a form of populist avant-gardism, the WAG posts have a similar potential, as they jolt the distracted viewer out of their habitual mode of sleep-scrolling with something experienced as unexpected and inexplicable.[9] Combining the identifiable with the paradoxical more likely triggers sleep-scrollers’ attention, and the posts can turn into patterns to be repeated and varied, to hold intrigue across variations (before they lose allure and fade into oblivion, becoming replaced by new patterns).
The politics of deepfakes and memes
The emergence of photography coincides with socialism, which occasions Benjamin to present it as “the first truly revolutionary means of reproduction” ([1935] 2010: 16). Recording and distribution meant the possibility of reaching mass audiences. This had the potential for radical renewal and desolation: “The crisis of democracies can be understood as a crisis in the conditions governing the exhibition of politicians” ([1935] 2010: 25, original in italics). Benjamin identified that “mass movements, and above all war, are a form of human behavior especially suited to the apparatus” ([1935] 2010: 35). The media development was thus interconnected with the rise of fascism, as “a new form of selection – selection before an apparatus – from which the champion, the star, and the dictator emerge as victors” (Benjamin, [1935] 2010: 26). It is pertinent to ask what kind of politics generative AI engenders. What are its crises, and what is its potential for a positively radical renewal?
Attention-grabbing generative images can be used to spread disinformation. This can be relatively benign, such as a rendered image circulating of Pope Francis in a puffer coat, but also tied more directly to political events such as images that supposedly show the arrest of former president Trump, and images supposedly showing an attack on Pentagon (see for instance Lu, 2023). More believable than stills, yet remaining currently too crude to be convincing still, are so called deepfake videos. Deepfakes use generative AI to superimpose someone’s facial features and voice onto someone else, making it possible to generate videos that appear as convincing recordings of well-known people saying and doing something they have not. There are a number of easily available tools to create deepfakes today, including Deepfakes web, DeepFaceLab and REFACE. Evident to such tools becoming increasingly accessible are a series of short videos on WAG, offering impressions of US-presidents Trump, Obama and Biden engaged in rude banter while playing videogames. TikTok is also filled with deepfake celebrities, among them a highly convincing AI-rendering of Tom Cruise shown goofing off in various situations for millions of views (see for instance Cover, 2022). One of the most publicized examples of deepfakes is a rendered Obama stating: “We’re entering an era in which our enemies can make it look like anyone is saying anything at any point in time. Even if they would never say those things. So, for instance they could have me say things like … Trump is a total and complete dipshit” (BuzzfeedVideo,2018). The video then reveals its façade and makes a claim for maintaining credible media outlets and assessing critically the validity of sources. More than actually misleading someone, deepfakes have thus far mostly been used for trivial entertainment and as warnings. The war in Ukraine has involved social network distributed deepfakes of Russian President Putin declaring peace and Ukrainian President Zelensky surrendering, yet the videos have been unconvincing and quickly removed (see for instance Wakefield, 2022).
As opposed to concerns raised by deepfakes, the images explored in the previous section would seem apolitical in their obsession with franchises, characters and celebrities. Yet is there not a political prospect to such multiverse meme generativity? I want to point here to how several posts to WAG are outrageous. There are for instance a multitude of images showing toy versions of anything from hand grenade to bullet proof vest, crack pipe, gun and bong, a daycare guillotine. There are images displaying renderings of a public execution at Disney World, Teletubbies in Chernobyl, or Mario Kart in Germany 1945. There are also posts offering satirical responses to current topics, such as the potential arrest of Trump, or Musk’s rebranding of Twitter (see Fig. 5).[10] These are not attempts at deepfakes and disinformation, but memes that combine generation with mischievous trolling. Such images could be described with Benjamin’s conceptualization of Dadaist poetry, as “word-salads” that contain “obscene exclamations and every imaginable kind of linguistic refuse” ([1935] 2010: 32). A perspective on the radical potential that such images contain is offered in Benjamin’s identification of the scandal as an avant-garde tactic: “The form of social behavior provoked by dada is: to take offense” ([1935] 2010: 32). Benjamin furthermore considered the outrageous and shocking as a mechanism that acted as a social safeguard: “Collective laughter is one such preemptive and healing outbreak of mass psychosis” (Benjamin, [1935] 2010: 31). Films showing “sadistic fantasies or masochistic delusions” were according to Benjamin something that “can prevent their natural and dangerous maturation in the masses” ([1935] 2010: 31). Against violence and warfare, let people play with crude and sometimes distasteful media.
Figure 5: Stills from a video posted to Weird Ai Generations with the caption “Elon eating the Twitter logo”. The video is an example of posts that ridicule Elon Musk for his purchase of Twitter and for rebranding it to X. The format of ‘someone well-known eating something weird’ could itself be considered a meme, as a recurrent pattern in the images and videos shared by the account. See: https://twitter.com/weirddalle (accessed 24.08.23).
Playing with the controversy surrounding J. K. Rowling’s views on transgender issues (see for instance Shaffi, 2023), a post on WAG displays the author holding a trans flag. This post points more generally to a form of inclusivity at play in the multiverse meme generativity, in which the only reasons for non-inclusivity would be the protection of the borders of intellectual property rights and public decency – both of which can be humorously transgressed. Weird generations are encouraged, as liberating the closed boundaries of fiction, of visual reality, to combine and construct endless alternative multiverses. The results play with expectations about where something properly belongs to create a sense of unfamiliarity. More crucially, it represents radical effort, working to free characters and universes from corporate ownership. Instead of creative borders, be they between franchises or individual artists, multiverse meme generativity indicates a radical collective culture. No character, style, or visual world belongs to anyone, but are collective visual conversations.
At a certain point, the account WAG started including ads for the particular image-generator WOMBO. And as of Autumn 2023, the name Weird Ai Generations has been given over to an alternate account, which is presented as “Brought to you by @WOMBO” (the account formerly known as WAG is instead renamed as no context memes).[11] Seemingly, the generative AI-company Wombo realized the potential for gaining users through associating itself with the wild and weird generative posts by the original account. The account owner, on the other hand, has turned its collection of playful subversiveness into marketing. And even if it had not, multiverse meme generativity could be considered as less valuable a political project than working against the exploitation of artists. Generative AI may lead to the loss of assignments for illustrators, designers and actors, because their styles, likenesses and voices have been used as part of the training material (likely without their knowledge and agreement) and generation offers lower-cost alternatives to hiring artists to do the job. Fascism, according to Benjamin, involves “granting expression to the masses – but on no account granting them rights” ([1935] 2010: 34). A select small group of property-owners were the ones making money of the cultural products discussed by Benjamin, and it remains this way, as beyond individual users finding marketing opportunities it is the platform owners of generative tools and social networks that exploit the work of artists.
Would it be possible to conceptualize an embrace of the devaluation of artworks and image production that AI image generation could entail? Aaron Bastani (2019) has proposed a ‘fully automated luxury communism’, that rather than oppose automation, champions it. From labor to energy, resources and food, Bastani makes the case that the removal of human effort is good, as long as its rewards are for the common good rather than for shareholder profits. Might this also be the case for artistic production? Is it conceivable that part of such fully automated luxury communism might be the luxury to create images of worlds as one pleases? Artistic concerns over livelihood will continue to make sense in an economic system in which artwork needs to be commodified and turned into income in order to pay for basic subsistence. This does not mean that such concerns are the only viable option.
Fully automated visual communication
Whether AI is capable of creating art is itself a question of machinic categorization, attempting to define the category of art in order to place something within or outside of it. Instead of categorization, this article has examined how generative AI transforms artistic work, and how its current conditions of production in the attention economy shape it, and how it has the potential to disrupt the current political regime. To conclude then, what characterizes the transformations of art in an age of multiverse meme generators?
The images of generative AI have been described herein as muddled confusions of parts and assemblages of celebrities, characters, styles and worlds. Generative AI will likely continue to improve in terms of caption similarity, photorealism and signifiers of media and styles. Some of the characteristics might thereby swiftly become markers of specific periods rather than signal that the images are created by AI. While some of the earliest examples of generative art presented in this text were intriguing partly due to their uniqueness, only a few years after they were shown, tools allowing greatly improved visual recognizability have become commonplace. The improvements have reached a point where AI generated images (simulating for instance paintings or photography), at least when viewed on screens, may be indistinguishable from images produced by other means. Video currently remains less convincing, with objects blending together and visual fields fluctuating rather than giving the appearance of solid beings and objects moving and interacting. At the same time, part of both the artistic experimentation and the collective enthusiasm for generative AI seem to come as much from its mistakes as from its ability to render something successfully.
More than resembling or replacing the work of designers, illustrators, actors, animators or spreading disinformation, generative AI has so far made possible image-making without expertise or effort, which has proved particularly successful in generating viral content. I have framed this process as memetic, because of how the formats, styles and motifs become repeated and renewed. Generative AI is itself also formed from the remains of technological reproduction, as the databases of images and videos act as training material for AI to imitate and vary, to turn into memes. Generative AI is an apparatus that extends reproductive capture and turns it into a re-interpretation and recombination of visual culture.
Benajmin makes a distinction between the optical unconscious and the instinctual unconscious of psychoanalysis. The former is evident in the specific technological properties of image production, the aspects that “lie outside the normal spectrum of sense impressions” (Benjamin, [1935] 2010: 31). While for Benjamin photography and slow motion give glimpses of the optical unconscious, generative AI turns its training material into views of no-one and nowhere. The instinctual unconscious has been framed here, with Deleuze and Guattari’s reorientation, as less a psychoanalytic theatre and more a factory of generative possibilities. Benjamin points to how film explodes the rooms that modernity has enclosed us in, “so that now we can set off calmly on journeys of adventure among its far-flung debris” (Benjamin, [1935] 2010: 30). AI expands and adds to an already-existing collective imaginary characterized not only by styles of artists but by icons, celebrities, brands, franchise universes and characters that can be mixed and mismatched. Multiverse meme generativity explodes the enclosures of intellectual property rights, in which characters are allowed to exist only in brand-sanctioned worlds and forms.
Generative AI points to an age beyond reproduction of reality, in which distinctions between the truthful and disinformation becomes a muddle. In this age, neither images nor videos can necessarily be reliable indicators of anything beyond providing glimpses into visual multiverses. In the multiverses any characteristics, current events and cultural artifacts can be rendered into something that could once have been or could become. It could have been or could become something more than an image, a trailer, an ad, a fad. But more likely than extending and solidifying into long-form or formats of longer cultural standing is its memetic repetitive variability being strategically employed to provide jolting shocks to the habitually distracted sleep-scrollers of social network feeds.
Generative AI produces a situation comparable to that anticipated by Paul Valéry in which “[j]ust as water, gas, and electricity are brought into our houses from far off to satisfy our needs in response to a minimal effort, so we shall be supplied with visual or auditory images, which will appear and disappear at a simple movement of the hand, hardly more than a sign” (Valéry, quoted in Benjamin, [1935] 2007: 3). Where the images come from is a question of how they are produced, having previously required photographic capture, brushstrokes, animation techniques – in short, effort beyond the current possibility of simply writing prompts for AI to generate them. It is also a question of who creates them. Anyone can today engage in communities online by simply letting AI turn the flow of one’s fingertips or voice commands into visual conversations. Yet another question is who owns these images, and whether they should be individual or collective. A tension remains between the loss of artistic professional control, and the democratizing potential of fully automated image communication. It is easy to make the case for these tools as exploiting art workers, but they also hold a radical potential, of liberating visual culture from corporate ownership.
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Notes
[1] The title is translated from German into English as “The Work of Art in the Age of Mechanical Reproduction” and alternately to “The Work of Art in the Age of its Technological Reproducibility”. I am not the first to make the connection between Benjamin’s article and AI image generators. Steven J. Frank (2022) connects Benjamin’s essay to generative art and is mainly focused on Benjamin’s notion of aura and how it ties into questions of value and intention in works of art.
[2] This comparison was originally made in Ervik, 2023; see also Benjamin, [1935] 2010: 19.
[3] See for instance Tancred, 2022 and Holmes, 2012.
[4] For an account of the last ten years of development of generative AI, see Offert, 2022.
[5] Autumn 2023, the account changed its name from Weird Ai Generations into no Context Memes, presenting itself as “memes| fka weirddalle”. Another account has been renamed Weird Ai Generations, and seems affiliated in some way as it is frequently reposted by no context memes. For the article, I will continue to refer to Weird Ai Generations (WAG) despite the change in name and focus.
[6] Several companies advertise text-to-video tools similar to generative images. These include Make-A-Video by Meta AI, Imagen Video by Google and Phenaki, neither of which are operational for users by summer 2023, with websites displaying short clips as proof of concept.
[7] See for instance the video to Magdalena Bay’s song “Dreamcatching” (directed by Felix Kreis with AI artist Max Kreis, published 2022) and André Bratten’s album Picture Music (made by Birk Nygaard, published 2022).
[8] See for instance, Sava, 2023, Ai Lost Media, 2023, and Pizza Later, 2023.
[9] Sampson develops a theory of social media sleepwalkers (Sampson, 2020), see also Ervik, 2022.
[10] While prompts that involve specific celebrities, as well as words related to violence and nudity are disallowed and may lead to banning from using Dall-E, others, such as Craiyon and Stable Diffusion, pose less restrictions.
[11] See https://twitter.com/weirddalle (accessed 24.08.23) and https://twitter.com/WeirdAiGens (accessed 24.08.23).
Dr. Ervik is a senior lecturer in media studies at the University of Oslo, Norway. He is the author of Becoming Human Amid Diversions: Playful, Stupid, Cute and Funny Evolution (Palgrave Macmillan, 2022). The book develops a philosophy of the predominant yet obtrusive aspects of digital culture, arguing that what seems like insignificant distractions of digital technology – such as video games, mindless browsing, cute animal imagery, political memes, and trolling – are actually keyed into fundamental aspects of evolution. Ervik is also an artist, with a practice focused on networks, ecosystems and post-anthropocene luxury. See andreaservik.com.
Email: a.n.ervik@media.uio.no
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