# The infinite as paradigm: Reframing the limits of AI art

Magdalena Krysztoforska, Oliver Kenny

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Magdalena Krysztoforska, Oliver Kenny. The infinite as paradigm: Reframing the limits of AI art. Necsus - European Journal of Media Studies, 2024, 13 (2), pp.132-155. 10.25969/mediarep/23670. hal-05409843

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Magdalena Krysztoforska; Oliver Kenny

# The infinite as paradigm: Reframing the limits of AI art

2024

https://doi.org/10.25969/mediarep/23670

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Krysztoforska, Magdalena; Kenny, Oliver: The infinite as paradigm: Reframing the limits of AI art. In: *NECSUS_European Journal of Media Studies*. #Enough, Jg. 13 (2024), Nr. 2, S. 312–155. DOI: https://doi.org/10.25969/mediarep/23670.

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# The infinite as paradigm: Reframing the limits of AI art

Magdalena Krysztoforska and Oliver Kenny

NECSUS 13 (2), Autumn 2024: 132–155

URL: https://necsus-ejms.org/the-infinite-as-paradigm-reframing-the-limits-of-ai-art/

## Abstract

This article explores the recent proliferation of GenAI models capable of generating ‘infinite’ sophisticated visual and audio outputs, and it discusses the challenges that these models pose to an AI art discourse oriented around concepts such as creativity or agency. We propose ‘the infinite’ as a new paradigm that shifts the focus away from individual artists and artworks, offering a more scaled-up perspective on cultural practices in the age of AI. We argue that rather than making the previous paradigm obsolete, the infinite acts as a vector of expansion of the discursive limits of AI art.

**Keywords:** generative artificial intelligence, machine learning models, paradigm shift, infinity, scale

'What might it mean to be able to produce infinite worlds from a single painting or photograph?'¹ This question was posed by the artist duo Holly Herndon and Mat Dryhurst in relation to their experiments creating 'infinite images' using early versions of DALL-E, a generative AI (GenAI) model capable of producing high-quality images from textual prompts. Herndon and Dryhurst have long been at the forefront of using machine learning (ML) methods in creative practice, yet working with a powerful text-to-image model was a novel experience for them. They remarked that: 'this significantly augments the capacity of what we understand of generative art, when a coherent world, or narrative, can be spawned from a single stylistic or linguistic prompt'.² This capacity for 'spawning' new and convincing aesthetic output from a single prompt has received a lot of attention in
recent years. In the broadest sense, generative models produce new outputs that follow patterns observed in training data, and this process has become significantly more intricate and refined over the last decade. Technical developments such as variational autoencoders (VAE),³ generative adversarial networks (GAN),⁴ transformers,⁵ and diffusion models⁶ paved the way for the latest era of GenAI, dominated by a group of models increasingly referred to as 'foundation models' (highlighting their adaptability to a variety of downstream applications), whose impressive performance, scale, and versatility have been characterised as a 'paradigm shift'.⁷ A subset of this group – popular text-to-image models such as Midjourney and Stable Diffusion – have particularly captured the public imagination with their capacity for producing potentially infinite high-quality images from a simple prompt.

In this article, we try to grapple with these new technologies and their resulting creative possibilities. The concept of infinity guides our thinking, as it has become a recurrent theme accompanying developments in these models and their uptake in creative domains. The fascination with the possibility of infinite art has previously led to experiments such as open-ended artworks (for example Mario Klingemann's *Memories of Passersby I* [2018], an unending stream of AI-generated faces, one continually morphing into the next) or models imitating a well-known artistic canon (as in the case of numerous projects that can continuously produce new music in the style of Bach or other famous composers⁸). More recently, however, technical developments in GenAI have led to new uses of the 'infinite' capacities of these models, uses which go beyond traditional art formats and the classical art canon. Examples of such practices include continuous inpainting and outpainting (filling in or continuously expanding a given image),⁹ as well as 'infinite' audio and video livestreams (such as Dadabots' continuous Relentless Doppelganger death metal stream¹⁰ and AInfiniteTV¹¹). The concept of the infinite also frequently figures in the language of GenAI applications, with Stable Diffusion's interface including a 'generate forever' option for continuous output, and OpenAI's Sora model for generating videos boasting the capacity to 'produce a seamless infinite loop'.¹² We see these developments as part of a broader shift in how AI technologies interface with cultural practices, requiring a reconsideration of some of the previous analytical framings of AI art.
Much of the existing debate around AI art, especially in the broadly-
conceived area of media studies and related disciplines, is often preoccu-
pied with adjudicating whether AI art can truly be considered as ‘art’, and
with ascribing notions such as ‘creativity’ or ‘agency’ to humans and ma-
chines in varying degrees. The criteria for such assessments are typically
constructed in terms of particular discursive limits (is it creative enough?
original enough? intentional enough?) and operationalised in analyses of
particular artworks and creative practices. While AI art is certainly not a
new phenomenon, this article argues that the increasing capacity for gener-
ating infinite outputs from a single model complicates some of the opera-
tive concepts and limiting criteria in debates around AI art.

In order to explore the ramifications of these changes, we propose the
infinite as a new paradigm, requiring different terms of engagement. We
argue that the new practices and output formats enabled by GenAI shift the
focus away from individual artworks and artists, and towards a more scaled-
up view of culture. We frame it as a new paradigm, however unlike in
Kuhn’s famous formulation (normal science – crisis – revolution – new
tradition of normal science¹³), there is no competition between the two
paradigms; the infinite simply offers a different perspective. While ques-
tions of agency and authorship will continue to be meaningful for distin-
guishing between high art and creative content in the artworld,¹⁴ we suggest
that adopting a more scaled-up view can motivate new questions that help
us understand how these technologies interface with culture and our under-
standing of it.

GenAI and the discursive limits of AI art

Recent decades have seen rapid advances in AI, enabled by the increased
availability of data (a crucial resource for training models, increasingly
abundant since the advent of Web 2.0), better hardware leading to more
computing power, and technical breakthroughs in ML methods. Develop-
ments in generative deep learning have been particularly consequential for
the cultural sector and creative domains, especially since 2014, when tech-
niques such as VAEs and GANs were experimented with by technically-
proficient artists to produce compelling high-quality outputs.¹⁵ Subsequent
progress driven by the rise of transformer models (the technology at the
heart of generative pre-trained transformers [GPT]¹⁶) and diffusion mod-
els,¹⁷ rapidly established multimodal GenAI as the key development focus,
with new models capable of processing multiple data formats (image, text,
video, etc.) and accomplishing many different tasks that can be specified by
the user, typically through natural-language inputs known as ‘prompts’. The
substantial cultural impact of these technologies became instantly apparent
when models such as DALL-E 2 (2022), Stable Diffusion (2022), Imagen
(2022), and Midjourney (2022) were made available to the public and be-
came hugely popular thanks to their ability to rapidly render unique, stylis-
tically varied, and highly-compelling images from a single prompt. Fur-
thermore, the popularity of GitHub, Google Colab, and Discord as plat-
forms for exchanging ideas amongst GenAI enthusiasts, combined with the
widespread practice of disseminating pre-print versions of new technical
research, lowered the barriers to entry and allowed tech-savvy artists to
explore the potential of AI tools without needing institutional support.¹⁸

The cultural consequences of these technologies have also been increas-
ingly contemplated in media studies and in related disciplines within the
humanities, often in discussions that involve questioning whether AI art¹⁹
can be considered art and whether it is doing anything new.²⁰ While re-
sponses to these questions range from wholesale embrace to categorical
refusal, the dominant terms of the debate remain remarkably consistent,
oscillating around concepts such as creativity or agency, and discussed in
relation to specific constitutive elements of the artworld, such as particular
artists or artworks. The common limiting criteria operationalised in this
context are based on the level of purported intentionality behind AI art, and
on deciding whether machines are creative enough to be understood as
artists. Many voices in this debate are sceptical, seeing machines as incapa-
ble of generating previously impossible ideas²¹ (often framed in terms of
‘transformational creativity’, drawing on Boden’s taxonomy²²), or as unable
to go beyond statistical patterns of the input data.²³ While some scholars
argue that there are no specific criteria for defining art that would a priori
exclude machines,²⁴ or that the primacy of humans in artmaking needs to
be reevaluated,²⁵ others lean towards a framing of art as a primarily human
endeavour.²⁶ Those who do not exclude machines from the domain of art
entirely still often make a clear distinction between the human artist and
the machine as a tool in the creative process.²⁷ Nevertheless, a number of
commentators are willing to attribute at least some form of agency to AI
systems, for example by viewing AI as a 'creative partner',²⁸ 'collaborator',²⁹
or '(co-)creator'.³⁰ Scholarship grounded in posthuman or new-materialist
thinking often situates the artist within networks of non-human materiali-
ties, where no definitive boundaries between different creative agents can
be drawn,³¹ and Moruzzi goes as far as to argue that music-producing GANs
have achieved 'a level of autonomy which is sufficient to deem them able to
create musical works'.³²

It is also important to point out that concepts such as creativity or agen-
cy are discursive, and their centrality in discussions of AI art has been chal-
lenged. For example, Audry and Ippolito insist that "'can machines be art-
ists?'" is the wrong question',³³ and Zylinska similarly suggests that asking
whether machines can be creative is a 'misguided question'.³⁴ Moreover,
Manovich argues that the measures of creativity most commonly used to
evaluate AI art are very narrow, closely related to notions of technical mas-
tery of some artistic skill,³⁵ and Grba posits that 'AI art expands the idea of
technologically entangled creativity'³⁶ and requires a careful rethinking of
the ramifications of the term. Nevertheless, despite the somewhat conten-
tious nature of these terms and the lack of consensus around their applica-
tion, their continued prominence points to their importance in debates
around AI art.

Aside from discussions interested in situating notions of creativity or
agency in relation to humans and machines, or artists and their tools, AI art
is also frequently analysed in terms of the qualities of the creative output
produced with AI. Such discussions are often centred on a number of now-
canonical examples of AI art, such as: *The Next Rembrandt* (2016), an AI-
generated 3D-printed painting in the style of Rembrandt; *Portrait of Edmond
de Belamy* (2018), a GAN-based artwork trained on 15th-19th-century portraits
found on WikiArt and sold by the auction house Christie's; or *Théâtre
d'Opéra Spatial* (2022), an AI-generated image that received a prize at the
2022 Colorado State Fair, subsequently triggering an online backlash. When
evaluating the aesthetic and cultural value of AI artworks, some commenta-
tors argue that AI aesthetics are mostly derivative,³⁷ that AI methods are a
'force for stylistic standardization',³⁸ or that these technologies offer 'little
enough variation to tickle curiosity, but never enough to unsettle stand-
ards'.³⁹ Hassine and Neeman are similarly critical in their assessment of the
*The Next Rembrandt* and *Portrait of Edmond de Belamy*, characterising them as 'Zombie Art', meaning 'paintings that attempt to simulate the style and content of masters that have been dead for centuries',⁴⁰ while Wasielewski suggests that the interest garnered by these works is mostly due to the novelty of their machinic origins, rather than their artistic qualities.⁴¹ At the same time, another strand within discussions of AI art focuses on the circumstances in which the artwork is encountered as the locus of creativity or originality. Danesi suggests that 'the culturally-based context in which a work is assessed is critical in shaping the perception that it is art or not',⁴² and he attributes the success of *Théâtre d'Opéra Spatial* to the art-competition context in which it was originally judged. Similarly, some scholarship points to reception and context as key elements for understanding the value and impact of AI art,⁴³ with Cascales proposing that 'art exists only when interpreted as such',⁴⁴ and Ch'ng pointing to acceptance by the artworld as the necessary condition for AI art to attain the status of art.⁴⁵

Our brief overview of some of the prominent features of discussions around AI art is by no means exhaustive, and there are certainly examples of scholarship that takes a broader view of the GenAI phenomenon. Large text-to-image models have been commented on as 'allow[ing] us to look back at cultural history in new ways',⁴⁶ as 'giv[ing] rise to new aesthetics that may have a long-term effect on art and culture',⁴⁷ and as elevating the importance of natural language in visual culture, given the central role that textual prompts play in interactions with these models.⁴⁸ The paradigm-shifting role of GenAI in image production has also been discussed in terms of a potentially 'novel media form and an emerging research field'.⁴⁹ Nevertheless, such broader-view perspectives are still a relative minority within the discourse, and recurring discussions of concepts such as creativity and agency constitute a meaningful pattern. Moreover, despite increasing acknowledgement of the blurring of boundaries between creator and tool, conceiving of AI art in terms of traditional notions of the artwork or the artist, and in relation to paradigmatic works, remains commonplace. To be clear, we are not arguing that the dominant operative concepts are obsolete; on the contrary, we believe they help to analyse numerous artistic practices within AI art. Nevertheless, we suggest that these terms impose a certain limit, and inadvertently constrain the framing of changes precipitated by progress in GenAI and its creative uptake in the last few years. In particular, the way in which the notion of infinity seems to animate a wide range of
practices and technical research in GenAI gives rise to cultural phenomena
that exceed the boundaries of discrete concepts such as artist, artwork, or
artworld. The infinite paradigm enables a shift of scale and positions these
new models as not solely a creative technology, but also an epistemological
technology. In what follows, we will elaborate on how the changes precipi-
tated by foundation models enact a subtle shift of perspective, which opens
up new ways of conceiving of culture and aesthetic information at scale.

The infinite and its scalar dynamics

The concept of infinity – the origins of which are generally attributed to
the Greek philosopher Anaximander⁵⁰ – has sparked vigorous debates
across numerous disciplines ever since its conception. Scholars writing
about infinity frequently characterise it as an enigmatic concept, with Rot-
man describing infinity as ‘a notoriously troublesome idea, difficult to pin
down, full of paradox’,⁵¹ and Clegg stating that:

> Infinity has this strange ability to be many things at once. It is both practical and
> mysterious.⁵²

In the context of data-driven technologies, the idea of the infinite has
previously been used to capture the experience of engaging with the deluge
of online content. Echauri identifies it in the 'infinite scroll' of social media,
in autoplay features on streaming sites, or the 'infinite swipe' on dating apps,
arguing that 'contemporary media remains distinctly defined by its propen-
sity towards infinitude'.⁵³ Pajkovic similarly describes Netflix as creating the
illusion of infinite content,⁵⁴ while Lupinacci frames the 'infinite stream' of
social media as a mechanism by which continuous connectivity is ensured.⁵⁵
Moreover, Echauri suggests that the rise of GenAI opens up the possibility
for 'the media paradigm to assume an even broader and more effective
sense of infinity'.⁵⁶ As we discuss below, the way in which the idea of infini-
ty mobilises changes in AI art is similarly multifaceted, spanning technical
breakthroughs, their consequences for creative practice, as well as epistemic
insights, all of which motivate further exploration and developments.
With regards to technical innovation, an interest in the possibility of in-
finite generation can be observed at different points in the trajectory of
developments in generative modelling, with its clearest manifestation in the
latest era of large multimodal models. Ever since the introduction of the
GAN framework, the appeal of generating novel yet coherent outputs led to
continuous technical refinements allowing for more sophisticated visual
and music outputs. Some notable improvements in GAN-based image gen-
eration include: the ALIS framework for infinite horizontal image expan-
sion;⁵⁷ and InfinityGAN, an image synthesis model addressing the con-
straints in pixel resolution of images produced by prior models, and
demonstrating a capacity to ‘infer a compelling global composition of a
scene with realistic local details’,⁵⁸ and to achieve ‘high-quality, seamless and
arbitrarily-sized outputs with low computational resources’.⁵⁹ Parallel ex-
amples can be found in GAN-based music generation, such as Musika, an
‘infinite waveform music generation’ system, proposed as an improvement
on the output length limits of its predecessors.⁶⁰ Other methods mobilised
towards the pursuit of ‘infinite’ image synthesis have also been proposed by
recent projects such as Infinite Nature⁶¹ and NUWA-Infinity.⁶²

A further expansion of possibilities was brought about by advancements
in diffusion models, which gradually outperformed GANs in creative do-
mains, and which facilitated the production of high-quality output using
natural-language prompts. While the initial computational costs of diffu-
sion models imposed significant constraints on the size and length of gen-
erated video and audio, subsequent latent diffusion models⁶³ were able to
greatly reduce the computing power required to complete a wide variety of
image-based tasks (including inpainting and outpainting), and set the stage
for huge increases in the quality, length, and diversity of video and audio
output. The major challenge in the continuous generation of audio and
video has been the consistency and coherence of the resulting material,
however even these difficulties are being progressively resolved by new
research and technical refinements. Recent developments in text-to-video
generation promise long, high-quality, coherent videos,⁶⁴ while latent diffu-
sion techniques adapted for audio generation have also allowed for the
production of full music tracks from text prompts.⁶⁵ What this means with
regards to the pursuit of infinite generation is that technical limits related to
the size, length, coherence, and continuity of outputs are being progressive-
ly exceeded. In other words, the idea of AI-generated infinite art is becom-
ing less speculative and increasingly technically feasible.

There are also interesting scalar dynamics at play here. First, there is a
distinct scalar disjunction at the centre of productive interactions with these
models. On the one hand, these are large models, trained on enormous
datasets, and requiring substantial computing power to be developed and
operated, while on the other hand, these models typically require only a
simple prompt-based interaction from the user in order to generate an
impressive output. Second, there are certain temporal idiosyncrasies. The
production capacity of these systems is now far greater than the required
input: for example, Evans et al. demonstrate a 95-second stereo audio out-
put from eight seconds of input,⁶⁶ while Liang et al. present a similar exam-
ple in visual synthesis for video, with 60 frames generated from one single
frame.⁶⁷ This increasing capacity for generating content of longer duration
than the required rendering time is a peculiar characteristic that gives a
sense of the production of time with these new technologies.

Moreover, generative models are trained on past data to produce poten-
tially infinite output, and the spatial and temporal scales on which they can
operate exceed the familiar limits of artworks, artists, or art exhibitions as
typically conceived of. This tendency is well illustrated by some of the crea-
tive uses of GenAI that are animated by the infinite. In addition to open-
ended outpainting and inpainting image-based practices, there are now also
numerous infinite livestreams of AI-generated content, such as Twitch-
based infinite TV show *Nothing, Forever*⁶⁸ and infinite assembly-line footage
*FactoryFactory*,⁶⁹ or the previously-mentioned Dadabots’ Relentless Doppel-
ganger and AInfiniteTV. There is arguably a certain common quality that
can be traced across these various creative practices and their outputs, per-
haps best encapsulated by the term ‘weird’: the AInfiniteTV stream is gen-
erally representative of the aesthetics now commonly associated with popu-
lar text-to-image generators, characterised by relentlessly vivid colours,
excessively smooth edges, and the occasional disconcerting deformations of
faces and bodies; *Nothing, Forever* has been actively described by its creators
as ‘AI generated, always on, always weird’;⁷⁰ while Dadabots describe the
characteristics of their experimentation as ‘solo vocalists become a lush
choir of ghostly voices, rock bands become crunchy cubist-jazz, and cross-
breeds of multiple recordings become a surrealist chimera of sound’.⁷¹
Nevertheless, what we are proposing here is that the key aspect that makes these projects a coherent phenomenon has less to do with the particular format, medium, or aesthetic they adopt, and more to do with how they are inspired by the infinite affordances of GenAI and by continuously exceeding prior limits. Popular platforms where AI art enthusiast communities gather and exchange ideas show numerous examples of projects where the infinite is the dominant motivation.⁷² Moreover, this trend and the concept of the infinite have also gained a certain rhetorical currency. Despite the fact that the infinite streams and open-ended artworks do not actually continue indefinitely – platforms like YouTube do in fact impose limits on livestream duration, and the ML research discussed above is more focused on reducing existing limits and crossing technical hurdles than actually achieving infinite model output – the term ‘infinite’ is frequently used as a shorthand or a signifier for the motivation of such projects. As such, what we term the infinite paradigm is not simply a specific range of practices or formal characteristics, but rather a very broad and dynamic terrain that encompasses technical developments, their adoption in creative work, and shifting perspectives in cultural practices.

While there is no clear separation between the infinite paradigm and previous AI art practices, there are some important distinctions that need clarifying. First, some of the earlier uses of AI for the purpose of generating potentially infinite new pieces in the style of a particular artist – for example projects such as DeepBach,⁷³ which can generate unlimited chorales in the style of Johann Sebastian Bach – remain largely committed to more traditional conceptions of artistic value, given that such projects are based on emulating a well-known and traditionally acclaimed canon. Second, an interest in open-endedness can be observed in a number of artworks and artistic practices that have been embraced by the AI art discourse – such as Mario Klingemann’s *Memories of Passersby I*, which is a wooden console connected to two large screens each of which projects an infinitely changing series of AI-generated faces; or Robbie Barrat’s *Infinite Skulls* (2019), which produces a new unique skull image at the push of a button – however, they are typically discussed in terms of the specific and unique elements of the artworks, and the vision and motivation of individual artists. While images in Klingemann’s *Memories of Passersby I* may continue indefinitely and never repeat, the console-and-screen setup is discrete, only a limited number of
editions were made, and the catalogue entry makes clear that although
infinite, the precise images shown on the screen will be unique.⁷⁴ Conse-
quently, the particularities of the examples outlined here still make them
amenable to an outlook on art premised on operative categories such as the
artist’s intentionality and the artwork’s uniqueness. By contrast, phenomena
such as infinite livestreams, and the general overarching capacity of foun-
dation models for continuously producing new and unexpected artworks,
do not fit neatly within the traditional analytical frameworks of AI art dis-
course.

The infinite as paradigm

The above discussion leads us to the final crucial junction in our argu-
ment: the framing of the infinite as a new paradigm that does not seek to
make the current paradigm obsolete. We anchor our thinking in the most
common understanding of the role of paradigms (derived from Kuhn<sup>75</sup>),
seeing their function as designating sets of theories, procedures, and refer-
ence points that find broad consensus across a field, and as serving to define
the principle motivating questions and acceptable approaches within a giv-
en domain. Kuhn’s ideas about scientific paradigms have also been widely
taken up by scholars working in the theory and history of art.<sup>76</sup> While many
such accounts commonly acknowledge the important distinctions between
science and art — generally framed as a difference between explorations of
nature rather than culture, or the search for truth rather than beauty — they
still argue for the relevance of Kuhn’s analysis for understanding art history.
Much like in science, an artistic paradigm serves to ‘define the puzzles to be
solved and the techniques or concepts most helpful in resolving them’,<sup>77</sup> and
scholars who frame the shifts from one artistic paradigm to another in
Kuhnian terms often point to the exhaustion of the capacities of the previ-
ous paradigm to answer the discipline’s central questions.<sup>78</sup> For example, in
early 20<sup>th</sup>-century music the ‘destructiveness of chromaticism’ was seen as a
‘crisis of composition’, and atonality was framed as a solution to that crisis;<sup>79</sup>
while the Dutch art movement De Stijl was seen as arising out of discontent
with earlier ways of doing art.<sup>80</sup> Specific paradigmatic artworks are also at
times read as signs of ‘cultural exhaustion’, such as Marcel Duchamp’s *Foun-
tain* (1917), which brought ‘into focus major issues which normally lie below
the surface, issues which need to be questioned'.⁸¹ These various points of
crisis and exhaustion in art are of course much less severe than crises in
science, as Kuhn himself remarked:

> because the success of one artistic tradition does not render another wrong or mis-
> taken, art can support far more readily than science, a number of simultaneously
> incompatible traditions or schools [...] the end of the controversy often means only
> the acceptance of the new tradition, not the end of the old.⁸²

Our own framing of the infinite as paradigm builds substantially upon
this thinking, however we also seek to de-emphasise the idea of incompati-
bility. The paradigm shift that we distinguish has more to do with a shift in
perspective, rather than a complete exhaustion of the productive capacities
of existing AI art discourse. In our view, analyses focused on evaluating
whether AI artworks and practices are creative or autonomous enough to
count as art are mostly confined to the mesoscale of the artworld, whereas
the infinite paradigm deprioritises the idea of art as driven by specific crea-
tive agents who produce unique artworks (typically personified by human
artists and their aura of exceptionality), and instead shifts focus towards a
more scaled-up view of culture. From this perspective, the various elements
of GenAI-based creative pipelines can offer a speculative lens for examining
broader dynamics of cultural production. For example, the model devel-
opment process can help crystallise how creative ideas are always inherent-
ly related to some cultural context and are further shaped by subsequent
influences and interactions: an initial curated dataset (in a sense a data ‘can-
on’) provides the basis for model training, abstract representations of the
training data get embedded in the latent space of a model (in a sense creat-
ing an idiosyncratic cultural reservoir), and fine-tuning a pre-trained model
can significantly alter its function and subsequent outputs, despite a shared
‘foundation’. Consequently, prompting and prompt engineering can be
understood not as a simple and unsophisticated practice but rather a dy-
namic mode of interaction with an unconventional cultural archive. More-
over, and crucially, by positing a potential abundance and open-endedness
of creative content, the infinite paradigm invites new modes of experimen-
tation and interaction with cultural reservoirs.⁸³ This more infrastructural
view of culture highlights the relevance of approaches that treat art as a
dynamic space of aesthetic information and a complex system,⁸⁴ and it
invites an ongoing exploration of cultural dynamics revealed or created by
engagements with AI. Most importantly, since the infinite paradigm is mo-
tivated by exceeding prior limits, it is not a static phenomenon but rather a
vector of expansion of the discursive framing of AI art.

As Bratton and Agüera y Arcas point out, there is a danger in restricting
our discussions and imaginaries of AI to our current understanding of the
function and the consequences of these technologies. This is a trap they call
'premature ontologisation'⁸⁵ (a pun on 'premature optimisation' in software
development), which involves moving too quickly from specific empirical
observations to ontological judgements. A similar risk can be identified in
discussions of generative models and AI art. While there is a wide range of
undeniable concerns about these technologies that certainly should not be
ignored,⁸⁶ discussions of AI art that predominantly focus on whether GenAI
has its rightful place in the domain of art often fall into the trap of prema-
turely dismissing these technologies as uninteresting. Approaches that are
too quick to judge GenAI aesthetics as derivative or unimaginative, and
deny their cultural value on that basis, end up implicitly restricting the
scope of future discussions based on a very narrow interpretation of these
technologies and their capabilities at a given point in time (the same is true
for accounts that are too quick to proclaim text-to-image systems as com-
plete replacements for artists, and harbingers of the end of art). We suggest
that an alternative position is worth exploring.

As AI technologies keep evolving, the task of engaging with their chal-
lenges at appropriate scales will continue to be increasingly important. In AI
research, there are growing concerns about the capabilities of ever-larger
models slowly plateauing (largely due to the continuous need for new data),
with recent research suggesting that based on current increases in dataset
size, models will soon require more data than humans have ever pro-
duced.⁸⁷ Synthetic data is frequently proposed as a solution, since it can be
generated ‘at scale’, however, it is also increasingly recognised as a solution
fraught with risks.⁸⁸ Researchers warn of ensuing risks such as ‘model col-
lapse’ (when the data on which the model is trained is produced by other
models, it can lead to ‘forgetting improbable events over time, as the model
becomes poisoned with its own projection of reality’⁸⁹), and ‘model autoph-
agy’ (where a shortage of ‘fresh real data’ could lead to decreased quality
and diversity of outputs of generative models⁹⁰). Since AI technologies are
increasingly consequential for cultural production, these issues and risks are
equally consequential for the domains of art and culture. A scaled-up view of culture as a potential reservoir of training data, model weights, and embeddings in latent space clarifies this interdependence.

In order to prevent the premature ontologisation of GenAI and its cultural consequences, a posture of openness to other scales and perspectives is paramount. The concept of the infinite is particularly helpful in adopting this posture. As DiCaglio points out:

> Even infinite terms like the All contain within them the possibility of adding more. While infinite terms may seem totalizing, they are inherently built on this openness.⁹¹

He goes on to illustrate this claim with the famous thought experiment – Hilbert's Hotel paradox – which explains mathematical ways in which it is hypothetically possible to keep adding more guests to a seemingly full infinite hotel. DiCaglio continues to explain that the infinite is a close accomplice of scale itself and of its capacity for discovering new relations: 'scale performs a similar maneuver by fleshing out the All both by adding more scales at which new relations might be discerned as well as leaving open the very limits, spatially and temporally, where relations might be said to exist'.⁹² The concept of the infinite in the infinite paradigm performs a similar function: it suspends a number of hypothetical boundaries of the scope of our future engagement with culturally-embedded AI technologies and allows for the consideration of new questions. Ultimately, what is at stake here is an engagement with the epistemic affordances of these new technologies and their associated practices.

In this article, we proposed the infinite paradigm as a framing for developing an understanding of recent progress in GenAI and its cultural consequences. We discussed how approaches to AI art that revolve around concepts such as creativity or agency, or are focussed predominantly on individual artists or artworks, often restrict the scope of engagement with large generative models in art. As these technologies are getting progressively more capable and refined, and the production of hypothetically infinite images and music from a single prompt becomes a feasible technical possibility, the scale of these new artistic phenomena moves beyond the pre-existing discursive limits. We have argued that the infinite paradigm offers
a shift of perspective and allows for a productive engagement with the sca-
lar disjunction between the vastness of culture and the purported reduc-
tiveness of prompt engineering, as well as the temporal idiosyncrasies of
infinite generative models. The infinite is a paradigm that is enabled by
technical breakthroughs, operationalised through creative practices, and
that allows for a possible reorientation of our current and future under-
standing of AI. Most importantly however, it is a ‘paradigm shift without
crisis’.⁹³ We believe that the advent of foundation models does not negate
the need for means for distinguishing between high art and media content
within the artworld, and that art as we know it will rightfully continue, ra-
ther than be replaced by generative models. As such, the infinite should not
be seen as a replacement for existing art paradigms, but rather a different
perspective on the future possibilities for cultural practices in the age of AI.

Authors

Magdalena Krysztoforska is a postdoctoral fellow at the Institute for Cul-
tural Inquiry in Berlin and member of the Responsible Computing Group
at the Max Planck Institute for Security and Privacy.

Oliver Kenny is Assistant Professor in Film and Media at the Institute of
Communication Studies (ISTC) and member of the research lab ETHICS at
the Université Catholique de Lille.

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## Notes

<footnote n="1">¹ Herndon & Dryhurst 2022.</footnote>

<footnote n="2">² Ibid.</footnote>

<footnote n="3">³ Kingma & Welling 2014.</footnote>

<footnote n="4">⁴ Goodfellow et al. 2014.</footnote>

<footnote n="5">⁵ Vaswani et al. 2017.</footnote>

<footnote n="6">⁶ Ho et al. 2020.</footnote>

<footnote n="7">⁷ Bommasani et al. 2022, p. 1.</footnote>

<footnote n="8">⁸ Cope 2001; Hadjeres et al. 2017.</footnote>

<footnote n="9">⁹ Lnyan 2022.</footnote>

<footnote n="10">¹⁰ RELENTLESS DOPPELGANGER 2024.</footnote>

<footnote n="11">¹¹ AInfiniteTV 2024.</footnote>
<footnote n="12">¹² OpenAI 2024.</footnote>

<footnote n="13">¹³ Kuhn 1970.</footnote>

<footnote n="14">¹⁴ The 'artworld' is a concept associated most closely with the work of Danto (1964), Becker (1982), and Dickie (1984), who propose that art should best be perceived as a collective process involving myriad participants (artists, curators, critics, teachers, distributors, exhibitors, etc.) and operating within specific historical and discursive contexts.</footnote>

<footnote n="15">¹⁵ Steinfeld 2023.</footnote>

<footnote n="16">¹⁶ Radford et al. 2018.</footnote>

<footnote n="17">¹⁷ Dhariwal & Nichol 2021.</footnote>

<footnote n="18">¹⁸ Steinfeld 2023.</footnote>

<footnote n="19">¹⁹ The boundaries of the term AI art are certainly fluid, however a definition provided by Zeilinger (2021) – ‘digital art that incorporates technologies of artificial intelligence as a medium’ (p. 12) – encompasses most of what AI art discourse is concerned with.</footnote>

<footnote n="20">²⁰ To be clear, the broader debate around AI art also encompasses a number of other pressing issues, such as: the consequences of GenAI for intellectual property and copyright (e.g. Crawford & Schultz 2024); economic and labour issues (e.g. Pasquinelli & Joler 2021; Ashton 2022; Lee 2022; Celis Bueno et al. 2024); the ethical stakes and societal harms of GenAI (e.g. Bender et al. 2021); or the colonial legacy of the dominant Western art canon (e.g. Hakopian 2023; Baradaran 2024). While these are all deeply important issues, they are beyond the scope of this article.</footnote>

<footnote n="21">²¹ Rust & Huang 2021, p. 143ff; McCormack et al. 2023.</footnote>

<footnote n="22">²² Boden 2011.</footnote>

<footnote n="23">²³ Pasquinelli & Joler 2021; Cascales 2023; Kogler Junior 2023; Vinchon et al. 2023; Hutson et al. 2024.</footnote>

<footnote n="24">²⁴ Coeckelbergh 2017, p. 11.</footnote>

<footnote n="25">²⁵ Audry 2021.</footnote>

<footnote n="26">²⁶ Jiang et al. 2023; Hertzmann 2018.</footnote>

<footnote n="27">²⁷ See McCormack et al. 2023; Hutson et al. 2024.</footnote>

<footnote n="28">²⁸ Mazzone & Elgammal 2019.</footnote>

<footnote n="29">²⁹ Lomas 2018.</footnote>

<footnote n="30">³⁰ Smith & Leymarie 2017; Arriagada 2020; Gioti 2021.</footnote>

<footnote n="31">³¹ Zeilinger 2021; Nordström et al. 2023.</footnote>

<footnote n="32">³² Moruzzi 2018, p.69.</footnote>

<footnote n="33">³³ Audry & Ippolito 2019, p. 7.</footnote>

<footnote n="34">³⁴ Zylinska 2020, p. 12.</footnote>

<footnote n="35">³⁵ Manovich 2022.</footnote>

<footnote n="36">³⁶ Grba 2022, p. 17.</footnote>

<footnote n="37">³⁷ Hakopian 2023.</footnote>

<footnote n="38">³⁸ Brook 2023, p. 81.</footnote>

<footnote n="39">³⁹ Donnarumma 2022, p. 51.</footnote>

<footnote n="40">⁴⁰ Hassine & Neeman 2019, p. 29.</footnote>
<footnote n="41">⁴¹ Wasielewski 2023, p.123.</footnote>

<footnote n="42">⁴² Danesi 2024, p. 118.</footnote>

<footnote n="43">⁴³ Natale & Henrickson 2024; Wiggins 2021; Messer 2024.</footnote>

<footnote n="44">⁴⁴ Cascales 2023, p. 25.</footnote>

<footnote n="45">⁴⁵ Ch'ng 2019.</footnote>

<footnote n="46">⁴⁶ Manovich 2023, p. 41.</footnote>

<footnote n="47">⁴⁷ Epstein et al. 2023, p. 1110.</footnote>

<footnote n="48">⁴⁸ Bajohr 2023; Meyer 2023; Somaini 2023.</footnote>

<footnote n="49">⁴⁹ Wilde 2023, p. 6.</footnote>

<footnote n="50">⁵⁰ Stewart 2017, p. 1.</footnote>

<footnote n="51">⁵¹ Rotman 1993, p. ix.</footnote>

<footnote n="52">⁵² Clegg 2003, p. 2.</footnote>

<footnote n="53">⁵³ Echauri 2023, p. 11.</footnote>

<footnote n="54">⁵⁴ Pajkovic 2022, p. 227.</footnote>

<footnote n="55">⁵⁵ Lupinacci 2021.</footnote>

<footnote n="56">⁵⁶ Echauri 2023, p. 11.</footnote>

<footnote n="57">⁵⁷ Skorokhodov et al. 2021.</footnote>

<footnote n="58">⁵⁸ Lin et al. 2022, p. 2.</footnote>

<footnote n="59">⁵⁹ Ibid.</footnote>

<footnote n="60">⁶⁰ Pasini & Schlüter 2022.</footnote>

<footnote n="61">⁶¹ A. Liu et al. 2021.</footnote>

<footnote n="62">⁶² Liang et al. 2022.</footnote>

<footnote n="63">⁶³ Rombach et al. 2022.</footnote>

<footnote n="64">⁶⁴ Blattmann et al. 2023; Kim et al. 2024.</footnote>

<footnote n="65">⁶⁵ H. Liu et al. 2023; Evans et al. 2024b.</footnote>

<footnote n="66">⁶⁶ Evans et al. 2024a.</footnote>

<footnote n="67">⁶⁷ Liang et al. 2022.</footnote>

<footnote n="68">⁶⁸ WatchMeForever 2024.</footnote>

<footnote n="69">⁶⁹ megalon2D 2023.</footnote>

<footnote n="70">⁷⁰ WatchMeForever 2024.</footnote>

<footnote n="71">⁷¹ Zukowski & Carr 2018, p. 2.</footnote>

<footnote n="72">⁷² For example, a search for 'infinity AI' on Reddit brings up numerous projects: https://www.reddit.com/search/?q=infinity+ai (accessed on 22 July 2024).</footnote>

<footnote n="73">⁷³ Hadjeres et al. 2017.</footnote>

<footnote n="74">⁷⁴ Sotheby's 2019.</footnote>

<footnote n="75">⁷⁵ Kuhn 1970.</footnote>
<footnote n="76">⁷⁶ Becker 1982; Clignet 1985; Jones 2000; Doorman 2003; Kubler 2008; Heinich 2014.</footnote>

<footnote n="77">⁷⁷ Clignet 1985, p. 42.</footnote>

<footnote n="78">⁷⁸ See Clignet 1985; Kubler 2008.</footnote>

<footnote n="79">⁷⁹ Cavell 2002, p. 193.</footnote>

<footnote n="80">⁸⁰ Doorman 2003, p. 115.</footnote>

<footnote n="81">⁸¹ Lovejoy 2004, p. 76.</footnote>

<footnote n="82">⁸² Kuhn 1977, p. 348.</footnote>

<footnote n="83">⁸³ The possible instantiations of such practices extend far beyond what we can currently envisage, nonetheless some existing projects that we see as motivated by a similar orientation include: xhairymutantx by Herndon and Dryhurst, exploring a possible reversal of the dynamics at work in prompting large models, using the scalar heft of public participation and institutional authority to influence future GenAI training and resulting embeddings; *The Long Count* by Debit, using ML methods to enter into a speculative-archeological musical dialogue across vast timescales, from the ancient Mayan culture to contemporary electronic music scenes; or SEMILLA.AI, a dynamic interface for collective experimentation and interaction with the abstract patterns underlying musical styles, genres, and identities.</footnote>

<footnote n="84">⁸⁴ See for example Hoelscher 2021.</footnote>

<footnote n="85">⁸⁵ Bratton & Agüera y Arcas 2022.</footnote>

<footnote n="86">⁸⁶ These include problems with bias, labour, data provenance, environmental costs, etc.; see note 20 for further details on the literature that engages with these issues.</footnote>

<footnote n="87">⁸⁷ Villalobos et al. 2022.</footnote>

<footnote n="88">⁸⁸ R. Liu et al. 2024.</footnote>

<footnote n="89">⁸⁹ Shumailov et al. 2024, p. 756.</footnote>

<footnote n="90">⁹⁰ Alemohammad et al. 2023.</footnote>

<footnote n="91">⁹¹ DiCaglio 2021, p. 50.</footnote>

<footnote n="92">⁹² Ibid., p. 51.</footnote>

<footnote n="93">⁹³ We borrow this particular phrase from Schmid & Hatchuel, who framed the approach they developed for a new way of thinking in epistemology and design theory called 'generic epistemology' as 'a paradigm-shift without crisis' (2014, p. 134). They frame the relationship between the approach they propose and the prior dominant approaches in the following way: 'There are two systems or two regimes of thought, which reveal other things to us.' (2014, p. 138) Our own thinking about the role of crisis and incommensurability in paradigm shifts is deeply indebted to Schmid & Hatchuel's work.</footnote>