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More Liquid, More Solid: Generative AI and The Future of Genre Under Postreality
Through generative AI, the role of genre in creative canon(s) is simultaneously liquifying and solidifying—softening genre boundaries while reasserting their utility as creative material.
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One of the less-hyped aspects of the generative AI discourse is how it will shift our relationship to genre—and genre’s relationship to us. Though genre might initially seem like a secondary consideration in creative production, it plays a critical role in establishing audiences’ expectations and in the ultimate reception of artworks and creative acts.
The imagined ideal of the lone artist toiling away in the privacy of her studio, away from pesky considerations of audience, critics, and market, is already understood to be fallacy in the contemporary creative environment—which, like every other industry or pursuit, is deeply mediatized. So the idea that an artist or designer wouldn’t in some way consider genre or creative canon in their own outputs—or that it might *gasp* inform how they craft their work—feels silly at this point. But where I think generative AI stands to further trouble this boundary is through the centering of prompting as a key interaction mechanism with generative engines. My sense is that, as the practice of prompting continues to emerge as its own craft, the more pronounced genre’s role will become in the creation of art and design.
This evolving relationship to genre points to a future with new hybrid creative forms, but it gets even weirder to imagine how the respective conventions and boundaries of genre will be historicized, (re)imagined, and (re)incorporated into creative acts. In other words: a feedback loop is created when human creators deepen and complicate genre by metabolizing these outputs and then re-engage with generative tools. Set, repeat.
What follows are a few case studies through which we can evaluate how genre might evolve in the wake of publicly accessible generative AI tools, and how that could impact future creative efforts. I hope to demonstrate how they embody a metamodern mode of cultural production and ultimately reinforce arguments I have made about the Postreality paradigm.
You don’t need to know too much about either Postreality or Metamodernism to read this essay. I will explain my sense of how generative AI intersects with these terms in greater detail in the conclusion, but by way of a quick foundation:
Postreality is the reality paradigm that emerged after (and from within, and in contradistinction to) Modernity, bubbling up in the wake of the Second World War and persisting through today. It is typified by non-monolithic, interdependent, synthetic, and more-than-human approaches to the social construction of reality (establishing consensus with each other about what is real). More on Postreality here.
Metamodernism is described as a structure of feeling that arose after postmodernism, which describes an “oscillation” between the aspects of modernism (yay grand narratives, individual genius, and universal ideals!) and postmodernism (everything is relative and grand narratives are dangerous; irony is a scalpel). Since the resurgence of the term in 2009, scholarship across different fields has broadened and diversified its meaning and usage (competing opinions proliferate). If this subject appeals to you I recommend this piece for a crash course in recent developments in metamodernism/metamodernity and how they do and don’t fit together. For my purposes in this essay, what I see as key to understanding metamodern creativity is a reconstitutive, hybrid approach to making—one that is in constant dialogue with other social, creative, and artistic content as a function of online interconnectedness.
Zombie Wes Andersonism
Earlier this summer, the “Wes Anderson” trend—in which users presented their lives ostensibly “in the style” of the film director—swept TikTok. Then, some began to use image generators to riff on the trend, assembling trailers for films in other major franchises including Harry Potter, Lord of the Rings, and Star Wars—as directed by “Wes Anderson.”
Wes Anderson is riffable in this way because his style is idiosyncratic, and has maintained some degree of aesthetic continuity across his nearly 30-year career. If you’ve been on the Internet, you have an idea what this entails—the tracksuits, subjects framed in the center, the subtle interferences with the fourth wall, the earth tones and playful pastels, the twee vibes.
But how connected are these techniques to the experience of watching an actual film by Wes Anderson, like his latest, Asteroid City? What if these are actually reinforcing a simulacrum of Wes Anderson that’s not really accurate? That’s exactly the claim Stuart Heritage recently made in The Guardian:
The problem is, though, that none of these things actually spoof Wes Anderson. The only thing they do is spoof other Wes Anderson spoofs, in particular Saturday Night Live’s Wes Anderson horror movie spoof from four years ago. Like all the newer parodies, they were just a loose collection of tropes – kids in matching tracksuits, old tents, the colour brown – except it was made by incredibly talented people with a large production budget and a clear appreciation for Anderson’s work, as opposed to a computer program that can stitch Bill Murray’s face on to the body of Gandalf so ineptly that it looks like every nightmare you’ve ever had rolled into one.
And yet, vastly more people watch TikToks than films; this hyperreal gestalt of Wes Anderson is what many will know him for—what they will seek out when they do watch his films (or clips on YouTube). Sure, cinephiles will be able to differentiate pastiche from the genuine article, but what percentage of the population does this comprise? My hunch is a vanishing slice. And even among those who can, there will be an inevitable creep of zombie Wes Andersonism. Even weirder to imagine is if Anderson himself were to be influenced by these videos—whether cleaving to these tropes or opposing them in future films.
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But even these are lesser considerations when compared to the role these outputs have as future metadata. In the trawl of large models, what “becomes” Wes Anderson—at least for prompts that include “in the style of Wes Anderson”—is a moving target. Baudrillard’s hyperreality becomes quaint; we reach a state in which simulacra don’t merely become or replace reality, but enter into an endless looping dialogue with each other, diffused across various models and spread via algorithmic social media platforms. Generative AI becomes a vehicle to iterate genre in a constant stream of jockeying. In this ongoing act of reconstruction, it is less post- or hypermodern than it is metamodern.
Generative AI & Music
One domain where this genre experimentation is already on full display is in music. Likely the most recognizable analog to the Wes Anderson visual trend was the early raft of Drake imitation tracks, one of which, “heart on my sleeve,” became something of a viral sensation this spring.
The track caused a raucous, prompting questions about IP, likeness, etc. Holly Herndon’s “Jolene” is maybe a more above-board example of this trend, as it involves the artist using a model of her own voice, Holly+, to cover Dolly Parton’s classic. Herndon is also notable on this front for launching the Holly+ DAO—a collective decisionmaking apparatus around the voice deepfake that determines what user-generated outputs become an official Holly Herndon song. Grimes recently followed suit, offering her own music generator up to other creators with a 50/50 revenue split.
Meanwhile, Boomy has gained popularity, particularly among YouTubers, for allowing people to create and monetize new audio outputs. When a user first engages the Boomy interface, their prompts are filtered using genre as a toggle. As you can see in the video below, the user has a choice of “Electronic Dance,” “Rap Beats,” “Lo-Fi,” and others. From here it’s possible to further refine the track—and of course there’s the option to go “Custom” at the bottom right from the outset—but the primary mechanism for song generation is genre.
Boomy has found success in the marketplace because through it users can produce original songs (therefore dodging possible rights issues)—and because it’s extremely easy to use. It delays (or outright removes) the more technical aspects of songmaking and instead batches techniques into genre containers. It’s exceedingly plausible that future music generators will lean into these same success points and build upon them.
What exactly would this portend for future understandings of “Lo-Fi” music? Of “Rap Beats”? In this context, the user-generated marketplace enters into direct dialogue with historicity. Long before generative AI, the punk genre, with its roots in not only music but labor, politics, and fashion, has been a site of tremendous innovation and clashing notions of authenticity. Machine Gun Kelly’s recent embrace of the “pop punk” genre, for example, was met with much contention—but the numbers don’t lie: a meaningful percentage of the music-listening public either believes him to be authentically punk or doesn’t care about the distinctions enough to take issue with it. It’s like the word “literally,” which in common parlance is now more often used to mean its opposite (i.e., “figuratively”); purists may ultimately have more intimate knowledge of their respective subject matter, but the vast majority of the time the general public will determine if and how it progresses in society.
The Persistence of Memery
Zombie Wes Andersonism operated in meme-space, and of course it was only the tip of the iceberg. Already, new meme-y AI genres are emerging. Two recent examples add context to the intersection of generative AI and meme-based communication.
The first is the AI illusion trend, which has already had a couple waves and will inevitably have many more. First it was QR codes, which are fully functional while also being incorporated into algorithmically generated imagery:
More recently, we’ve seen a proliferation of AI illusions that include subliminal messages or shapes embedded within a generated image:
(If you don’t see a word right away, try squinting or putting more distance between yourself and your screen.)
Another emergent form is the AI mashup, a collage of different genres and references into a single piece of AI-generated content. The example that caught my eye last month was the Star Wars “100th Anniversary” celebration video by Douggy Pledger and osymyso, a spoof that situated the Star Wars universe in a galaxy close, close at hand: our own, but in 1923. If the hashtags Pledger included in the original post reference the tools involved, the creators used Midjourney, OpenAI’s DALL-E 2, and Pika Labs to generate and refine the content they ultimately assembled into the video.
In theory, it was possible to create this type of speculative parody prior to the advent of generative models, but the proliferation of these capabilities invites artists and non-artists from a range of different disciplines to produce these types of mashup with much greater ease. In some cases, this is the difference of many hours of labor; in others, it’s the difference of being able to realize such a piece at all.
Just as YouTube’s invitation to the public to “broadcast yourself” changed the role of video, so too will publicly accessible generative tools when it comes to making creative content (ranging from the categories of “art” and “design” to those that are more lightweight: riffs, spoofs, memes, political cartoons, et al. Over time, these will become their own idiosyncratic forms, just as “vlogging” did (to name just one example).
For now a video like this is a signal of coming change, and some of its virality has to do with its novelty. But with the pace of development occurring in the generative AI landscape, it’s fair to assume much more like it is coming. It’s also fair to assume that in such cases, the respective creators’ sense of genre conventions—whether referencing individual artists, movements, or signifiers of historical periods—will play an important role in determining if and how it resonates with audiences.
The case studies above involve “deconstructing” existing media formats and reconstructing them into meaningful forms of participatory communication. In them we see the metamodern impulse, simultaneously blurring and solidyfing genre.
They are also symptomatic of Postreality. Things get a little messy when you try to hierarchically organize abstractions, so all requisite caveats that this will be reductive: I view metamodernism as a trailhead in the transition from Modernity (not to be confused with Modernism) to Postreality. The metamodern shifts underway in creativity point to deep underlying changes in how humans express reality and establish consensus with each other, with implications that extend beyond art and culture.
In The Mediated Construction of Social Reality, sociologists Nick Couldry and Andreas Hepp propose the notion of the ‘media manifold,’ to describe the the new types of interrelation that emerge with successive digital media formats and the resulting complexity that now characterizes the media environment. They argue that:
The term ‘media manifold’ enables us to keep in view both the social actor’s position within a much larger institutionalized environment of interdependent media and the situated complexity of that actor’s everyday choices of media. We need to understand both, and their interrelations, since the dynamics of that wider environment, particularly its overriding pressures towards datafication, are of major consequences for all actors and for the organization of social life as a whole.
Written in 2017, the book came at the doorstep of the current wave of generative AI innovations, but describing the media environment as a manifold has proven extremely prescient. The foremost practical inheritance of generative AI tools is volume; more people will create more content—within a milieu in which others are creating and riffing in ever-expanding directions, scaling rhizomatically beyond the ability of anyone to track in real time.
It is a hyperobject, a beast too sprawling and complex for us to ever fully comprehend. As such, what ends up becoming important in the production of history won’t necessarily be clear in the beginning, and perhaps not for a long time afterward. This is true at the level of how it might influence individual human actors, but it’s also true down to the very material of machine learning: data. Every piece of media that is tagged and included in a training set will in some way inform what present and future generators produce.
We already associate certain conventions with history; when you imagine the early 1900s, for example, your mind likely conjures stuttery film. Under Postreality, this phenomenon explodes in many directions. Our imaginations of the past, present, and future—and the ways we’ll be in dialogue with them—expand and complexify.
At the center will be genre—even as genres continue to adapt, morph, and evolve—becoming our shorthand for navigation, discovery, and our relationship to history.