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Anticipation and Aesthetics After Automation
Tanya Ravn Ag
Abstract
This paper explores generative technology’s ongoing aesthetic evolution through the lens of anticipation. It proposes anticipation as an alternative perspective to automation to understand the implications of generative technology in relation to aesthetic production and evolution. By focusing on anticipation, the discussion shifts from merely considering AI’s impact on aesthetics as instrumental to creative production to examining the anticipative behaviors and affective responses these technologies develop, engage, and cultivate through our interactions with them. Understanding generative technology as anticipatory highlights how deeply human and cultural expectations influence the design, recursive adaptation, and future anticipations of AI systems. Viewing our engagements with them as anticipative emphasizes the role of affective experience and behavior in shaping evolutionary processes with these technologies. The focus on anticipation introduces a shift in aesthetics research, from analyzing the status and meaning of aesthetic objects and outcomes to examining the anticipatory, processual relations through which outcomes emerge, exist, and evolve. This shift moves aesthetics research towards a deeper engagement with the dynamic and intra-active relationships among humans, technology, and other anticipatory systems, and towards further engagement with the ethical constructs we develop through these relationships.
Key Words
AI aesthetics; algorithm; anticipation; anticipatory system; art; generative technology
1. Introduction: aesthetics after automation
The claim that generative AI technology marks both a quantitative and qualitative shift in aesthetic discourse is beyond doubt. Only two years after the public release of generative AI tools in 2021, the volume of AI-generated image content surpassed the total number of images captured during the first 150 years of photography.[1] While the focus of this article is not the scale of the implications of AI on aesthetic production, this quantitative horizon nonetheless underscores the vast implications these technologies have on aesthetics today. As generative technology unfolds on a global scale to assist creative aesthetic production, AI implicates more than an unprecedented scaling of automated production in aesthetic content. What has been claimed to be a current technological revolution with AI technologies is radically changing cultural practices with technology in aesthetic production. In this article, I propose that the manifestation of generative technology in aesthetic production reveals not only the effects and outcomes of new techniques but more significantly speaks to how our relations with technology are changing.
Since the advancements in deep learning and neural networks in the 2010s, particularly with the development of transformer models like GPT in 2017, generative technology has manifested in tools, systems, and practices at an industrial scale of what Lev Manovich calls ‘cultural AI,’ referring to how AI is being nested in human decisions through various processes of creative production with generative technology.[2] Generative technology largely refers to algorithmic systems driven by advanced AI models for creating new content such as text, images, music, and code by learning patterns from existing data. In aesthetics research, how generative technologies challenge the possibilities of creativity, as commonly understood in terms of an authentic human ability, has particularly been debated. Questions discussed include how we can account for creativity when humans collaborate with machines in the production of digital aesthetic images and objects, and how creativity is even possible when aesthetic production relies on the calculative procedures of generative AI that often result in the remixing and reproduction of existing data. Some researchers have argued that AI means the end of creative originality,[3] while others argue that the boundaries of creativity have expanded with AI.[4] Some researchers have pointed out that the use of artificial intelligence as a medium for creating works of art limits the imagination and creativity of artists.[5] Others insist that although machine learning is changing artistic work, human agency will always persist in the creative process.[6] On the periphery of the discussion on AI and aesthetics, researchers in psychology have shown that artificial intelligence can even help us understand human creativity better.[7]
A particular concern permeating recent research on the topic of AI’s implications for aesthetics is the role of automation in aesthetic production. Namely, the role of automation in changing modes of creativity when the technology being used relies on programmed commands and automatic feedback mechanisms to precisely execute various tasks. Manovich’s unfolding of an AI Aesthetics (2019) focuses on how technological automation affects cultural use and behavior with everyday technology, such as when AI is used in apps for automated aesthetic quality assessment that influences users’ choices and aesthetic preferences in continuous cultural practices.[8] His contribution is rich in providing concrete examples of how AI automates processes of aesthetic creation and choices through technical functions of common programs and platforms.[9] For example, he explains how automated algorithms serve to advance image editing in Adobe Photoshop and streamline the video editing process in Adobe Premiere Pro, how style transfer algorithms automate aesthetic adjustments to images whereby images are modified without manual intervention besides choosing the filter style (for example, “Impressionism” or “Cubism”), and how recommendation algorithms on social media platforms use AI to recommend images and artworks based on users’ preferences and thus automate our exposure to aesthetic content. Also, as we recently have witnessed with the wide availability of large learning models (LLMs) behind automated text and image generation, algorithms are employed to analyze and synthesize data to generate new content, such as images, video, designs, and other digital objects. The discussion on AI and creativity largely accounts for aesthetic images and digital objects that are generated with AI as “results” of autonomous automation processes. The overall concern is about the increasingly limited degree of human decision-making involved in aesthetic production, as processes of calculation seem to limit our options and our results when we choose the prefigured filter styles that we employ, when we assess the personalized recommendations following our search entries, and when we draft our prompts to effectively engage with the calculative and statistical capabilities of the AI assistant.
These perspectives on AI’s implications for creativity tend to view the resulting aesthetic outcome as something of an effect of technology, as if the technology participates with a creative capacity in the aesthetic production. The current discussion on AI and creativity mirrors a discussion with a longer trajectory about AI and intelligence—about whether AI is understood to be intelligent or not.[10] The idea of AI as intelligent reflects the definition by the cognitive computer scientist Marvin Minsky from 1968, who described artificial intelligence as “the science of making machines do things that would require intelligence if done by men.”[11] Minsky’s definition conceives of artificial intelligence in terms of information processing and as intimately connected with our experience with computers. But AI technologies are not intelligent or conscious. They operate through algorithms, which are mathematical instructions that guide data processing, learning, and decision-making. Essentially, algorithms provide the step-by-step rules that enable machines to autonomously analyze information, recognize patterns, and perform tasks. While AI can mimic certain aspects of human intelligence and creativity, it lacks emotional understanding and true creative ability. AI systems follow predefined rules and do not possess human-like emotions. They can identify patterns in existing data to generate new outputs, but they cannot produce content from emotional abstraction and wonder. As such, AI technologies, though advanced, do not possess the will or consciousness that characterizes human intelligence and creativity.
While portraying AI as intelligent or creative may reasonably seem less productive in our understanding of technology, the tendency to mythologize narratives around emerging technologies continues to shape our epistemological habits. The narrative of automation as a normative horizon for AI technology’s influence on aesthetic production persistently runs through aesthetics research today—and seems fitting to characterize art’s conditions with our present technologies. But it might not be.
With the wide adoption of automation processes in technological tools, programs, platforms, and assistants, aesthetics research once again is faced with the challenge of technological change and a concern with the subjection of aesthetic concepts to the production rules and power mechanisms of machines. A former concern with the loss of authenticity has evolved into a renewed anxiety over the loss of creativity. Walter Benjamin’s attention to the loss of the aura and originality of the work of art that became reproducible with production techniques of industrialization creates a critical-thinking architecture whereby the nature of machinic functionality comes to inform an epistemological framework for how we think about aesthetic production as subjected to machines. This framework continues a critical trajectory informed by Descartes, Kant, and Adorno’s idealizing of art as the product of the ingenious human mind that is placed in a dichotomous relation with technology as the manipulative and instrumentalizing other—an “other” that we can try to resist, but that we nonetheless need to accept as conditional. This epistemological framework informs how art research deeply situated in a phenomenological trajectory has studied art as phenomena that we can experience and from which the human mind can reflectively interpret, contextualize, and draw meaning. We see this in recent studies on the role of algorithms in art that focus on how algorithms—or algorithmic culture—give material and matter to art’s representation of technology in critical reflection of these technologies’ (automated) role in society and human culture.[12]
When we adhere to the narrative of automation as the defining framework for understanding the role of algorithmic, generative technology in aesthetic transformation, we confine ourselves to a reductionist perspective. The narrative of automation only suffices to account for generative technology in terms of technical functionalities, for example, how content generation with generative technology creates text, images, music, and code by learning from existing data; how information is analyzed through intricate data processing and pattern recognition to detect and predict trends beyond human capability; and how predictive analytics is used to forecast future outcomes based on historical data. We may be able to explain the technical dimensions of how aesthetic outcomes are produced via machinic functionality with generative technology, as Manovich emphasizes. Aesthetics after automation, then, is understood as conditioned by the technological forces enacted upon it, as we find across the current landscape of research that deals with aesthetic issues on that horizon. However, this narrative also enforces a sense of technological determinism and a mistaking of calculation for creativity, much like the earlier misconceptions about intelligence.
When we rely on a normative narrative of automation, we conflate automation as a technical process with an epistemological framework for understanding aesthetic evolution. This conflation obscures what I see as the most significant qualitative impact of generative technology on aesthetics: that our cultural practices of producing aesthetics are shifting along with our changing anticipative dynamics with these technologies.
2. Generative technology as anticipatory
In the following, I will propose an alternative perspective on generative technology, moving beyond understanding it merely as a tool for automation. Instead, I will consider its anticipative capabilities, which shifts our focus from automation as an instrumental narrative to the level of human intra-action with these technologies. I use the term ‘intra-action,’ as opposed to ‘interaction,’ with reference to Karen Barad’s theory, which considers the dynamic and productive nature of human engagement with technologies, emphasizing how both humans and technologies co-constitute each other in an ongoing process of mutual influence and transformation.[13]
Firstly, we need to acknowledge that generative technologies of today are qualitatively different from previous technologies—such as print reproduction, photography, and video, which were primarily designed to record data and document the past for future reference, as Mark Hansen has characterized twentieth-century media.[14] Scholars in media studies have explicitly explored the cultural implications of generative technology in terms of how everyday media operate by sensing, selecting, and feeding forward data into systems that drive our anticipations of and actions towards the future.[15] They have examined how media technologies are intertwined with anticipatory and predictive life processes,[16] how habitual media shape human expectations and interactions with the future,[17] and how digital media open new paths of interaction that construct and circulate future knowledge and create anticipatory visions.[18]
The attention to the anticipatory nature of generative technology is not a new idea, but has been explored in the field of anticipation studies since the early 1990s.[19] Michael Rovatsos, who has significantly contributed to this field of inquiry, has elaborated on the role of anticipation in artificial intelligence through the conceptualization of the anticipatory system. With this concept, he refers to a system that behaves in an anticipatory way, making decisions in the present according to anticipations about something that may eventually happen in the future.[20] Rovatsos’ notion of a technological system as anticipatory is based on Robert Rosen’s anticipatory systems theory from 1985, which defines the anticipatory system as “a system containing a predictive model of itself and/or its environment which allows the system to change state at one instant in accord with the model’s predictions pertaining to a later instant.”[21] As an anticipatory system, generative technologies operate and evolve based on anticipatory functionality built into them in response to our expectations of their outputs and results. For example, generative algorithms act as anticipatory mechanisms for such systems when they predict and create outcomes based on learned patterns and historical data. They learn the patterns and structures of their training data and generate new data with similar characteristics in response to our prompts or requests. Designed to autonomously perform tasks and produce outcomes, these algorithms adjust to meet the expectations they are programmed to fulfill.[22] The anticipatory capabilities of algorithms are central in applications such as the aforementioned image filters, and also in text-to-image generation, where the system generates responses that align with our instructions and anticipates our desired outcomes.
Rovatsos emphasizes that much of the predictive work of AI is performed by the system after the human anticipatory process has been considered. A key capability of AI systems is to apply known properties to new data, deducing information about hidden or future aspects of the world, much like human, commonsense reasoning.[23] Thus, the algorithm operates not only through anticipatory calculation but also through the design of the system’s behavior to represent the anticipatory processes inherent in our human world. Anticipation is not merely a default mode of these technologies. They incorporate human worldviews, assumptions, ideas, emotions, and aspirations as conditions for their functionalities and capabilities. Cultural anticipation is embedded in the basic design of algorithms, much like it has been integrated into aesthetic expressions that reflect how societies have perceived, adapted to, and shaped their worldviews since ancient times. We can, for example, think of ancient Islamic art, where intricate geometric patterns, symbolizing the infinite and the divine, reveal anticipations of a harmonious connection between the material and spiritual worlds. Similarly, geometric motifs in Native American pottery or textiles embody cultural anticipation of balance and continuity through representations of cosmic cycles, seasons, and natural rhythms.
Likewise, the anticipatory functionalities of AI technology are designed based on specific worldviews that determine the needs and expectations that the calculative tasks are intended to meet. Therefore, human assumptions significantly influence how anticipatory systems operate.[24] In text-to-image generation, for instance, the system’s abilities are constrained by its training data, algorithms, and computational resources. These elements define the variety of images it can produce from a given text input and exclude factors not represented in the training data. Different text-to-image models create images in distinct ways based on how they are designed to interpret and convert textual descriptions into visual content. The anticipatory system thus incorporates anticipation differently based on how it is designed to encode its objectives.
Human anticipation not only factors into the initial design and coding of anticipatory systems. Algorithms are designed to understand our questions and respond with relevant, new content. Over time, the algorithm begins to recognize patterns and trends, which allows it to anticipate and predict future inputs and requests. Through this process, the AI system changes. If we, for example, consistently select images with certain styles or subjects when we engage with text-to-image large language models (LLMs), the algorithm will learn to prioritize these features in its outputs. This process of continuous engagement and feedback teaches algorithms what to expect and prioritize in their responses to us and makes them more attuned to our human anticipations over time. They are dynamic and adaptive, recursively designed to evolve through our prompts, interactions, and feedback.[25] Particularly those algorithms used in machine learning and AI are designed to adapt and learn from our prompts or inputs, shaping themselves according to our intra-actions. With reference to Gilbert Simondon’s theory of individuation, which informs my understanding of the role of anticipation in aesthetic evolution (or invention) with technology, our intra-actions derive not only from reflective engagement but also from motor anticipations that prepare us for engagement with the world as adaptive responses to environmental stimuli prior to when our cognitive systems are engaged. At stake is not passive, pre-set, and conscious expectations, but an active, dynamic process that shapes and is shaped through our intra-actions.
AI-generated filters, effects, and features don’t end with the outputs of instructions, selections, and prompts. They also affect how we view, use, and engage with aesthetic content, and how we create meaning from it and construct our world with it. For example, style transfer algorithms not only influence aesthetic decision-making by offering various options but also shape our expectations of the aesthetic characteristics of different artistic movements. Lotte Philipsen’s research at the Centre for Aesthetics of AI Images (AIIM) at Aarhus University explores this aspect of AI aesthetics, focusing on how ultra-realistic, AI-generated images, such as those created by the text-to-video generative AI model Sora, alter our future expectations of how our surroundings should appear.[26] We can thus consider how automated video editing of raw footage into short films in different styles, for example, impacts our aesthetic experience of video content and our expectations for future content. As generative technology is being designed to meet, assist, and stimulate our anticipations, it conditions how we imagine and produce aesthetics while changing the sensory and emotional dimensions of human experience. As such, our technological intra-actions and experiences shape how we continue to engage with technology in future anticipative situations of aesthetic production and engagement.
Understanding generative technology as anticipatory highlights how human and cultural anticipation is built into the anticipatory design of algorithms, how it conditions their recursive adaptation and use, and how future anticipative perception, imagination, and behavior transform. While these dimensions of considering generative technology as anticipatory are not exhaustive, I emphasize them here—leaning on anticipation studies—to illustrate how cultural norms, uses, and imaginations significantly influence the evolving nature of these technologies and shape aesthetic outcomes.
Shifting our understanding of generative technology from a focus on automation to a focus on anticipation encourages us to reflect on our own anticipations towards these technologies. It also highlights how our anticipatory emotions and habits evolve both the technologies and their cultural uses as well as the future aesthetic outcomes that emerge from these uses. In other words, this perspective shifts our focus from merely considering the impact of AI on aesthetics in terms of the automation of creative production, to examining the anticipative behaviors and affective responses that these technologies engage, develop from, and cultivate through our intra-actions with them. As human understanding, emotion, affective reactions, and cultural expectations play a crucial role in the development of the capabilities characterizing generative technologies, humanities research has a task to explore, in terms of how these conditions shape future aesthetic production and discourse. These dynamics are, however, complex and challenging to analyze through traditional aesthetics research, which often relies on interpretation, contextualization, and categorization.
3. Anticipation and affect
Ever since Baumgarten’s foundational work in aesthetics, research in this field has aimed to deepen our understanding of the sensory and emotional dimensions of human experience. Although anticipation has been a durational theme in philosophy, namely in various contributions to understanding the future by twentieth-century philosophers such as Henri Bergson, Edmund Husserl, George Herbert Mead, Alfred North Whitehead, Charles Peirce, Martin Heidegger, Gilles Deleuze, and Ernst Bloch, among others, anticipation has only scarcely been dealt with in the domain of aesthetics research.[27] Marcus Bussey notes in his interesting chapter on anticipatory aesthetics in the Handbook of Anticipation (2017), that the linking of anticipation with aesthetics in the philosophy of aesthetics has concerned a human need to make meaning and see patterns and relationships in and between the physical and social elements in the world. He explores how anticipation plays a crucial role in the creation and experience of art by emphasizing the importance of the senses—“future senses”—in understanding and creating art and being central to knowledge work on aesthetics.[28] But Bussey’s work on an anticipatory aesthetics does not reflect on the implications of technology on anticipation in aesthetic experience. This I believe brings along radically changing conditions for how we experience aesthetic at all, which is something I have explored in more depth elsewhere.[29]
The algorithms running our technologies today permeate aesthetic experiences through applications, programs, and digital platforms, nurturing anticipation as a temporal structure of affective experience in the digital organization of culture. Anthropologists have long studied anticipation as an essential analytical lens for understanding how humans construct their cultural futures, led by scholars such as Arjun Appadurai and Jane Guyer.[30] More recently, anticipation has been taken up in response to a contemporary situation characterized by technoscience as an affective condition of temporal orientation and positioning, shaping a regime in which anticipation is a defining structure of our current moment.[31] In a recent contribution to research on anticipation in anthropology, Christopher Stephan and Devin Flaherty’s introduction to a special issue of The Cambridge Journal of Anthropology, titled “Experiencing Anticipation” (2019), emphasizes an understanding of anticipation as an affective state. They situate anticipative experience in embodied competencies and perceptual habits, where individual experiences connect with the broader social and cultural world as an assumptive background.[32] With the conception of anticipation in terms of affectivity, we see a changing understanding of the role of anticipation in how we culturally evolve with technology. From being used to describe the act of emotional preparation for future situations or events, or taking measures in advance to prepare for something, like in the understanding of anticipatory assumptions informing the design of a technical system to prepare the possibilities of its solutions (associated with the term ‘anticipatory’), the concept of anticipation is increasingly being engaged to emphasize the process of anticipation itself. This shift concerns less the preparation (in the past) and more the process of becoming, or invention, like engaging with the future through sensory and emotional experiences and behaviors that produce and participate in shaping the future (associated with the term ‘anticipative’).
We see this shift in the concept of anticipation towards the affective in recent research that has explicitly attended to anticipation as a central experience of digital culture—for example, in André Jansson’s exploration of premediated anticipation of the “circulated self” in transmedia textures of social media,[33] or Katrin Döveling, Anu A. Harju, and Denise Sommer’s insights into the broader landscape of affect in digital contexts when discussing how digital affect cultures traverse digital terrains and construct culture-specific communities of affective practice.[34] Ludmilla Lupinacci discusses a constant state of anticipation that arises from continuous connection and a state of liveness as a central aspect of digital culture, proposing that the reorganization of our world through digital, algorithmic, and data-driven platforms and infrastructures has prompted a permanent state of anticipation as a technocultural modus of experiencing.[35] Anu Koivunen et al. have studied how anticipation creates a temporal structure of experiencing and feeling that permeates user experience on digital platforms as an affective structuring of intimacies, socialities, and relationships, through temporalities of foreboding, prospecting, and speculating about one’s own and others’ social media presence and actions.[36] This is along the lines of which Raymond Williams used the notion of feeling to describe how a new formation of thought may emerge at any one time in history as a popular response to official discourse and its appropriation in cultural texts.[37] These studies help us to understand the role of anticipation as an affective aspect of human engagement with technology.
4. Perspectives for aesthetics research
The shift I propose here—from understanding the implications of generative technology on aesthetics on an epistemological horizon of anticipation rather than automation—moves aesthetics research towards seeking to better understand, and critically discuss, the dynamic and intra-active relations between humans and technology. This is a topic with a long trajectory across academic disciplines and one we need to revisit and explore with new methodologies in the domain of the humanities. The focus on anticipation redirects aesthetics research by shifting the emphasis from analyzing the content, representation, meaning, and status of objects and outcomes as somewhat effects of technology to examining the anticipative, processual relations through which these outcomes emerge, exist, and evolve through emotional responses, prompts, and other practices. Analytically, we attend less to our interpretation of the aesthetic object and more to the processes whereby aesthetic production and invention come about. We can then study aesthetic objects as constructs of anticipative dynamic relations among humans, machines, and also other systems of life forms, and hence as symptoms of an aesthetic evolution with technology. We attend to the intra-actions between human anticipative emotion and behavior and anticipative dynamics of technological systems, in addition to other systems. We then can seek to understand the relationship between generative technology and aesthetic production as open to intentional and potentially (hopefully) ethical inference, rather than deterministically defined by technological processes and their inscribed worldviews and intentional aims.
Here we may recall computer scientist and philosopher Mihai Nadin’s more holistic understanding of the “anticipatory system” in his seminal 2009 article, “Anticipation and the Artificial: Aesthetics, Ethics, and Synthetic Life,” in which he regards anticipation as a precursor to intelligence and as a distinguishing feature of all living systems. Nadin understands anticipation as a fundamental characteristic of life and an underlying factor that enables evolutionary (aesthetic) processes, positioning anticipation within the realm of aesthetics as manifesting in aesthetic forms and ethical values. Hereby our abilities to anticipate become central. Our abilities to anticipate influence our aesthetic judgments and ethical decisions in aesthetic processes.[38] These abilities we need to cultivate and stay reflective about, which is perhaps a central task for aesthetics research not least after the proliferation of GenAI.
This attention to anticipative human-systems relations (machine and other systems) also opens new understandings of aesthetic productive relations beyond the dynamics of concrete human engagement with technological capabilities such as design instructions and prompts. While anticipation plays a crucial role in the evolutionary dynamics between anticipatory systems and human anticipative emotions and behaviors of designing, prompting, and engaging with technological systems and assistants at the micro level of, for example, producing images with generative AI, these intra-active situations also occur beyond the interface of the machinic system. We can think of the selfie spot at an art exhibition, which is designed based on anticipative aims, assumptions, and worldviews and that invites anticipative “prompts” by art-goers, encouraging new anticipative behaviors that entail sharing and externalizing the aesthetic experience in digital environments. Aided by the algorithmic design of anticipatory systems built into social media platforms, this situation inspires future anticipative behaviors (and habits) towards aesthetic experiences in the contexts of art. The emphasis on the role of anticipation in aesthetic production thus applies to any kind of aesthetic phenomena, situation, and cultural path. Through this lens, we can examine aesthetic production as conditioned by anticipative emotion and affective engagement, which co-evolves with technological culture.
Through the lens of anticipation, we might also try to understand the formation of aesthetic cultures by focusing on the anticipative dynamics from which they evolve. A recent thematic interest in the future, across exhibition themes and titles worldwide,[39] might mirror how art today evolves through exchanges with ideas, concepts, technologies, and expertise at innovation laboratories, artist residency labs, and technology incubators such as CERN, Bell Labs, S+T+ARTS Residencies, New Inc., Rhizome’s 7×7, and the Ars Electronica Future Lab, and how funding programs like Horizon Europe—the EU’s key funding initiative for research and innovation—explicitly call for explorations into new roles for art in technological innovation in an anticipative account of art’s closer relationships with technology. If we stay with a narrative of automation, these conditions of much artmaking and invention today could be understood in terms of art’s subjection to the technocultural forces of global capitalism. In that perspective, art can either submit or resist. But these dynamics might be better understood and examined on the basis of a trajectory of anticipative relations that have long connected artmaking and technology, which we might be familiar with through cybernetic experiments with control and communication in machines and living organisms in art since the 1940s that resulted in programmed objects, interactive environments, and self-organizing installations. This trajectory, which an anticipatory perspective opens up, concerns not a historical narrative of technological innovation told through art as an experimental lab but a trajectory of artistic experimentation with forms of dialogue, negotiations, and exchanges between human and machine and other systems (of nature, organisms, the universe, poetry, and so on) that should concern our ethical attention and provide reasons for cultivating our critical abilities to anticipate.
Tanya Ravn Ag
tanyaravnag@gmail.com
Tanya Ravn Ag is an Assistant Professor at the Department of Arts and Cultural Studies, University of Copenhagen. Her research focuses on how art changes with technology and digital culture, which she currently explores in the research project, “Algorithms in Art: Displacements with Algorithmic Culture in Danish Art since 1990,” funded by the Novo Nordisk Foundation (2024-2025). She frequently serves as an art jury member and is currently a board member of ISEA International. She is the editor of Digital Dynamics in Nordic Contemporary Art (Bristol: Intellect, 2019).
The research for this article is supported by the Novo Nordisk Foundation – Project Grants for Research in Art History.
Published on July 14, 2025.
Cite this article: Tanya Ravn Ag, “Anticipation and Aesthetics After Automation,” Contemporary Aesthetics, Special Volume 13 (2025), accessed date.
Endnotes
[1] Alina Valyaeva, “AI Has Already Created As Many Images As Photographers Have Taken in 150 Years. Statistics for 2023,” Everypixel Journal, posted August 15, 2023. Please change period to comma after the author’s name for all the notes.
[2] Lev Manovich, AI Aesthetics (Moscow: Strelka Press, 2019), https://manovich.net/index.php/projects/ai-aesthetics.
[3] Elijah Clark, “The End Of Originality: Is AI Replacing Real Artists?” Forbes, December 23, 2023, https://www.forbes.com/sites/elijahclark/2023/12/23/the-end-of-originality-is-ai-replacing-real-artists/.
[4] Then et al, “Aesthetics and Artificial Intelligence: Impact and Criticism of Art,” Education Achievement Journal of Science and Research, December 2023.
[5] Anne Ploin; Rebecca Eynon, Isis Hjorth, and Michael A. Osborne, “AI and the Arts: How Machine Learning Is Changing Artistic Work,” Oxford Internet Institute, March 4, 2022, https://www.oii.ox.ac.uk/wp-content/uploads/2022/03/040222-AI-and-the-Arts_FINAL.pdf.
[6] Fernand Gobet and Giovanni Sala, “How Artificial Intelligence Can Help Us Understand Human Creativity,” Frontiers in Psychology 10 (2019): 1401.
[7] Manovich, AI Aesthetics.
[8] Manovich, AI Aesthetics, 4.
[9] Manovich, AI Aesthetics, 3.
[10] John Johnston, The Allure of Machinic Life: Cybernetics, Artificial Life, and the New AI (Cambridge, MA: MIT Press, 2008).
[11] Tom Stonier, “The Evolution of Machine Intelligence,” In Beyond Information, 107-133 (Berlin: Springer, 1992).
[12] Patrivia de Vries, Algorithmic Anxiety in Contemporary Art: A Kierkegaardian Inquiry into the Imaginary of Possibility (Amsterdam: Institute of Network Cultures, 2019).
[13] Karen Barad, Meeting the Universe Halfway: Quantum Physics and the Entanglement of Matter and Meaning (Durham, NC: Duke University Press, 2007).
[14] Mark B. N. Hansen, Feed-Forward: On the Future of Twenty-First-Century Media (University of Chicago Press, 2015).
[15] Hansen, Feed-Forward.
[16] Sarah Kember and Zylinska Joanna, Life after New Media: Mediation as a Vital Process (Cambridge, MA: MIT Press, 2012).
[17] Wendy Hui Kyong Chun, Updating to Remain the Same: Habitual New Media (MIT Press, 2017).
[18] Christian Pentzold, Cornelia Brantner, and Lena Fölsche, “Imagining Big Data: Illustrations of ‘Big Data’ in US News Articles, 2010-2016,” New Media & Society 21, no. 1 (2019): 139-167.
[19] Mihai Nadin, “Anticipation and the artificial: aesthetics, ethics, and synthetic life,” in AI & Society: Knowledge, Culture and Communication 25 (2010), 103-118; Michael Rovatsos, “Anticipatory Artificial Intelligence,” in Handbook of Anticipation: Theoretical and Applied Aspects of the Use of Future in Decision Making, ed. Roberto Poli (Berlin: Springer, 2017); Riel Miller (ed.), Transforming the Future: Anticipation in the 21st Century (Paris: UNESCO and Routledge, 2018).
[20] Rovatsos, “Anticipatory Artificial Intelligence,” 2017.
[21] David Rosen, “Anticipation and the Future: A Philosophical Perspective,” in Handbook of Anticipation, edited by Roberto Poli, 1-20 (Berlin: Springer, 2017) (originally published in 1985), 8.
[22] Brian Winston, Media Technology and Society: A History from the Telegraph to the Internet (Routledge, 2024).
[23] Rovatsos, “Anticipatory Artificial Intelligence,” 11.
[24] Rovatsos, “Anticipatory Artificial Intelligence,” 2.
[25] Yuk Hui, Recursivity and Contingency (London: Rowman & Littlefield International, 2019).
[26] Lotte Philipsen, Interviewed for Videnskab.dk, February 19, 2024, https://videnskab.dk/teknologi/tre-forskere-om-sora-saadan-kan-nye-ultra-realistiske-ai-videoer-aendre-din-verden/.
[27] Roberto Poli, “Anticipation in Philosophy,” in Handbook of Anticipation, edited by Roberto Poli (Berlin: Springer, 2017).
[28] Marcus Bussey, “Anticipatory Aesthetics: New Identities and Future Senses,” in Handbook of Anticipation, edited by Roberto Poli, 50-65 (Berlin: Springer, 2017), 50.
[29] Tanya Ravn Ag (Tanya Søndergaard Toft), Images of Urgency: A Curatorial Inquiry With Contemporary Urban Media Art (Ph.D. diss., University of Copenhagen, 2017).
[30] Jane Guyer, “Prophecy and the Near Future: Thoughts on Macroeconomic, Evangelical, and Punctuated Time,” American Ethnologist 34, no. 3 (2007): 409-421; Arjun Appadurai, The Future as Cultural Fact: Essays on the Global Condition (London: Verso, 2013).
[31] Vincanne Adams, Michelle Murphy, and Adele E. Clarke. “Anticipation: Technoscience, Life, Affect, Temporality,” Subjectivity 28, no. 1 (2009): 246-265.
[32] Christopher Stephan and Devin Flaherty, “Anticipatory Practices: The Temporal Politics of ‘Making Time’.” Subjectivity 12, no. 1 (2019): 1-20, 11.
[33] A. Jansson, “Mediatization and Social Space: Reconstructing Mediatisation for the Transmedia Age,” Communication Theory 23 (2013): 279-96.
[34] Katrin Döveling, Anu A. Harju, and Denise Sommer, “From Mediatized Emotion to Digital Affect Cultures: New Technologies and Global Flows of Emotion,” Social Media + Society 4, no. 1 (2018): 1-11.
[35] L. Lupinacci, “Absentmindedly Scrolling Through Nothing’: Liveness and Compulsory Continuous Connectedness in Social Media,” Media Culture & Society 43 (2) (2021): 273-90.
[36] Anu Koivunen et al, “Affective Economies in the Age of AI,” Media, Culture & Society 46, no. 1 (2024): 1-20, 1.
[37] Raymond Williams, Marxism and Literature (Oxford: Oxford University Press, 1977).
[38] Nadin, “Anticipation and the artificial,” 2010. Please provide the full information.
[39] Tanya Ravn Ag, “Art’s Intratemporal Relations to the Future,” Conference Proceedings for ISEA 2023: Symbiosis, Paris, France, May 16-21, 2023.