Daniel Binns is a media theorist and filmmaker tinkering with the weird edges of technology, storytelling, and screen culture. He is the author of Material Media-Making in the Digital Age and currently writes about posthuman poetics, glitchy machines, and speculative media worlds.
Image generated by Leonardo.Ai, 6 November 2025; prompt by me.
My work on and with generative AI continues apace, but I’m presently in a bit of a reflection and consolidation phase. One of the notions that’s popped up or out or through is that of generativity. Definitely not a dictionary word, but it emerged from — of all places — psychoanalysis. Specifically, it was used by a German-American psychoanalyst and artist named Erik Erikson. Erikson’s primary research focus was psychosocial development, and ‘generativity’ was the term he applied to “the concern in establishing and guiding the next generation” (source: p. 267).
My adoption of the term is in some ways adjacent, in the sense of a property of tools or systems that ‘help’ by generating choices, solutions, or possibilities. In this sense, generativity is also a practice and concept in and of itself. Generative artificial intelligence is, of course, one example of a technology possessing generativity, but I’ve also been thinking a lot about generative art (be it digital/code-based, or driven by analogue tools or naturally occurring randomness), generative design, procedural generation, mathematical/computational models of chance and probability, as well as lo-fi tools and processes: think dice, tarot cards, or roll tables in TTRPGs.
The name I’ve given my repeatable genAI experiments is ‘ritual-technic‘. These are designed specifically as recipes for generativity (one example here). Primarily, this is to allow some kind of exploration or understanding of the technology’s capabilities or limitations. They may also produce content that is useful: research fodder to unpack or analyse, or glitchy outputs that I can remix creatively. But another potential output is a protocol for generativity itself. One the one hand, these protocols can be rich in terms of understanding how LLMs conceive of creativity, human action, and the ‘real’ world. But on the other, they push users off the model, and into a generative mode themselves. These protocols are a kind of genAI costume you can put on, to try out being a generative thing yourself.
Another quality of the ritual-technic is that it will often test not just the machine, but the user. These are rituals, practices, bounded activities, that may occasion some strange feelings: uncertainty, confusion, delight, fear. These feelings shouldn’t be quashed or ignored, they should be observed, marked, noted, and tracked. Our subjective experience of using technology, particularly those like genAI that are opaque, complex, or ideologically-loaded, is the embodiment, the lived and felt experience, of our ethics and values. Many of my experiments have emerged as a way of learning about genAI in a way that feels engaging, relevant, and fun — yes! fun! what a concept! But as I’ve noted elsewhere, the feelings accompanying this work aren’t always comfortable. It’s always a reckoning: with my own creativity, capabilities, limitations, and with my willingness to accept assistance or outsource tasks to the unknown.
For Erikson, generativity was about nurturing the future. I think mine is more about figuring out what future we’re in, or what future I want to shape for myself. Part of this is finding ways to understand the systems that are influencing the world around us, and part of it is deciding when to take control, to accept control, or when to let it go. Generativity is, at least in my definition and understanding, innately about ceding some kind of control. You might be handing one of the reins to a D6 or a card draw, to a writing prompt or a creative recipe, or to a machine. In so doing, you open yourself to chance, to the unexpected, to the chaos, where fun or fear are just a coin flip away.
The AI experiment that turned my ick to 11 (now you can try it too!)
When I sit at the piano I’m struck by a simple paradox: twelve repeating keys are both trivial and limitless. The layout is simple; mastery is not. A single key sets off a chain — lever, hammer, string, soundboard. The keyboard is the interface that controls an intricate deeper mechanism.
The computer keyboard can be just as musical. You can sequence loops, dial patches, sample and resample, fold fragments into new textures, or plug an instrument in and hear it transformed a thousand ways. It’s a different kind of craft, but it’s still craft.
Generative AI has given me more “magic” moments than any other technology I’ve tried: times when the interface fell away and something like intelligence answered my inputs. Images, text, sounds appearing that felt oddly new: the assemblage transcending its parts. Still, my critical brain knows it’s pattern-play: signal in noise.
In exploring AI, music, and ethics after the VelvetSundownfallout, a colleague tasked students with building fictional bands: LLMs for lyrics and backstory, image and video generators for faces and promo, Suno for the music. Some students leaned into the paratexts; the musically inclined pulled stems apart and remixed them.
Inspired, I tried it myself. And, wouldn’t you know, the experience produced a pile of Thoughts. And not insignificantly, a handful of Feelings.
Lars Vintershelm, captured for a feature article in Scena Norge, 22 August 2025.
Ritual-Technic: Conjuring a Fictional AI Band
1. Start with the sound
Start with loose stylistic prompts: “lofi synth jazz beats,” “Scandi piano trio,” “psychedelic folk with sitar and strings,” or whatever genre-haunting vibe appeals.
Generate dozens (or hundreds) of tracks. Don’t worry if most are duds — part of the ritual is surfing the slop.
Keep a small handful that spark something: a riff, a texture, an atmosphere.
2. Conjure the band
Imagine who could be behind this sound. A trio? A producer? A rotating collective?
Name them, sketch their backstories, even generate portraits if you like.
The band is a mask: it makes the output feel inhabited, not just spat out by a machine.
3. Add the frame
Every band needs an album, EP, or concept. Pick a title that sets the mood (Just North of Midnight, Spectral Mixtape Vol. 1, Songs for an Abandoned Mall).
Create minimal visuals — a cover, a logo, a fake gig poster. The paratexts do heavy lifting in conjuring coherence.
4. Curate the release
From the pile of generations, select a set that holds together. Think sequencing, flow, contrasts — enough to feel like an album, not a playlist.
Don’t be afraid to include misfires or weird divergences if they tell part of the story.
5. Listen differently
Treat the result as both artefact and experiment. Notice where it feels joyous, uncanny, or empty.
Ask: what is my band teaching me about AI systems, creativity, and culture?
Like many others, I’m sure, it took me a while to really appreciate jazz. For the longest time, for an ear tuned to consistent, unchanging monorhythms, clear structures, and simple chords and melodies, it just sounded like so much noise. It wasn’t until I became a little better at piano, but really until I saw jazz played live, and started following jazz musicians, composers, and theorists online, that I became fascinated by the endless inventiveness and ingenuity of these musicians and this music.
This exploration, rightly, soon expanded into the origins, people, stories, and cultures of this music. This is a music born of pain, trauma, struggle, injustice. It is a music whose pioneers, masters, apprentices, advocates, have been pilloried, targeted, attacked, and abused, because of who they are, and what they were trying to express. Scandinavian jazz, and European jazz in general, is its own special problematic beast. At best, it is a form of cultural appropriation, at worst, it is an offensive cultural colonialism.
Here I was, then, conjuring music from my imaginary Scandi jazz band in Suno, in the full knowledge that even this experiment, this act of play, brushes up against both a fraught musical history, as well as ongoing debates and court cases on creativity, intellectual property, and generative systems.
Play is how I probe the edges of these systems, how I test what they reveal about creativity, culture, and myself. But for the first time, the baseline ‘ickiness’ I feel around the ethics of AI systems became almost emotional, even physiological. I wasn’t just testing outputs, but testing myself: the churn of affect, the strangeness in my body, the sick-fascinated thrill of watching the machine spit out something that felt like an already-loaded form of music, again and again. Addictive, uncanny, grotesque.
It’s addictive, in part, because it’s so fast. You put in a few words, generate or enter some lyrics, and within two minutes you have a functional piece of music that sounds 80 or 90% produced and ready to do whatever you want with. Each generation is wildly different if you want it to be. You might also generate a couple of tracks in a particular style, enable the cover version feature, and hear those same songs in a completely different tone, instrumentation, genre. In the midst of generating songs, it felt like I was playing or using some kind of church organ-cum-starship enterprise-cum-dream materialiser…. the true sensation of non-stop slop.
What perhaps made it more interesting was the vague sense that I was generating something like an album, or something like a body of work within a particular genre and style. That meant that when I got a surprising result, I had to decide whether this divergence from that style was plausible for the spectral composer in my head.
But behind this spectre-led exhilaration: the shadow of a growing unease.
‘Forever’, by Lars Vintersholm & Triple L (ft. Magnus LeClerq), from the album Just North of Midnight.
AI-generated music used to only survive half-scrutiny: fine as background noise, easy to ignore. They still can be — but with the right prompts and tweaks, the outputs are now more complex, even if not always more musical or artistic.
If all you want is a quick MP3 for a short film or TikTok, they’re perfect. If you’re a musician pulling stems apart for remixing or glitch experiments, they’re interesting too — but the illusion falls apart when you expect clean, studio-ready stems. Instead of crisp, isolated instruments, you hear the model’s best guesses: blobs of sound approximating piano, bass, trumpet. Like overhearing a whole track, snipping out pieces that sound instrument-like, and asking someone else to reassemble them. The seams show. Sometimes the stems are tidy, but when they wobble and smear, you catch a glimpse of how the machine is stitching its music together.
The album Just North of Midnight only exists because I decided to make something out of the bizarre and queasy experience of generating a pile of AI songs. It exists because I needed a persona — an artist, a creative driver, a visionary — to make the tension and the weirdness feel bearable or justified. The composer, the trio, the album art, the biographies: all these extra elements, whether as worldbuilding or texture, lend (and only lend) a sense of legitimacy and authenticity to what is really just an illusion of a coherent, composed artefact.
For me, music is an encounter and an entanglement — of performer and instrument, artist and audience, instrument and space, audience and space, hard notes and soft feel. Film, by contrast (at least for me), is an assemblage — sound and vision cut and layered for an audience. AI images or LLM outputs feel assemblage-like too: data, models, prompts, outputs, contexts stitched together. AI music may be built on the same mechanics, but I experience it differently. That gap — between how it’s made and how it feels — is why AI music strikes me as strange, eerie, magical, uncanny.
‘Seasonal Blend’, by Lars Vintersholm & Triple L, from the album Just North of Midnight.
So what’s at stake here? AI music unsettled me because it plays at entanglement without ever truly achieving it. It mimics encounter while stitching together approximations. And in that gap, I — perhaps properly for the first time — glimpsed the promise and danger of all AI-generated media: a future where culture collapses into an endless assemblage of banal, plausible visuals, sounds, and words. This is a future that becomes more and more likely unless we insist on the messy, embodied entanglements that make art matter: the contexts and struggles it emerges from, the people and stories it carries, the collective acts of making and appreciating that bind histories of pain, joy, resistance, and creativity.
Listen to the album Just North of Midnight in its complete strangeness on SoundCloud.
‘Vapourwave Hall’, generated by me using Leonardo.Ai.
This is a little late, as the article was actually released back in November, but due to swearing off work for a month over December and into the new year, I thought I’d hold off on posting here.
This piece, ‘The Allure of Artificial Worlds‘, is my first small contribution to AI research — specifically, I look here at how the visions conjured by image and video generators might be considered their own kinds of worlds. There is a nod here, as well, to ‘simulative AI’, also known as agentic AI, which many feel may be the successor to generative AI tools operating singularly. We’ll see.
Abstract
With generative AI (genAI) and its outputs, visual and aural cultures are grappling with new practices in storytelling, artistic expression, and meme-farming. Some artists and commentators sit firmly on the critical side of the discourse, citing valid concerns around utility, longevity, and ethics. But more spurious judgements abound, particularly when it comes to quality and artistic value.
This article presents and explores AI-generated audiovisual media and AI-driven simulative systems as worlds: virtual technocultural composites, assemblages of material and meaning. In doing so, this piece seeks to consider how new genAI expressions and applications challenge traditional notions of narrative, immersion, and reality. What ‘worlds’ do these synthetic media hint at or create? And by what processes of visualisation, mediation, and aisthesis do they operate on the viewer? This piece proposes that these AI worlds offer a glimpse of a future aesthetic, where the lines between authentic and artificial are blurred, and the human and the machinic are irrevocably enmeshed across society and culture. Where the uncanny is not the exception, but the rule.
Made by me in, of course, Procreate (27 Aug 2024).
The team behind the powerful and popular iPad app Procreate have been across tech news in recent weeks, spruiking their anti-AI position. “AI is not our future” spans the screen of a special AI page on their website, followed by: “Creativity is made, not generated.”
It’s a bold position. Adobe has been slowly rolling out AI-driven systems in their suite of apps, to mixed reactions. Tablet maker Wacom was slammed earlier this year for using AI-generated assets in their marketing. And after pocketing AU $47 million in investor funding in December 2023, Aussie AI generation platform Leonardo.Ai was snapped up by fellow local giant Canva in July for just over AU $120 million.
Artist and user reactions to Procreate’s position have been near-universal praise. Procreate has grown steadily over the last decade, emerging as a cornerstone iPad native art app, and only recently evolving towards desktop offerings. Their one-time purchase fee, in direct response to ongoing subscriptions from competitors like Adobe, makes it a tempting choice for creatives.
Tech commentators might say that this is an example of companies choosing sides in the AI ‘war’. But this is, of course, a reductive view of both technology and industries. For mid-size companies like Procreate, it’s not necessarily a case of ‘get on board or get left behind’. They know their audience, as evidenced by the response to their position on AI: “Now this is integrity,” wrote developer and creative Sebastiaan de With.
Consumers are smarter than anyone cares to consider. If they want to try shiny new toys, they will; if they don’t, they won’t. And in today’s creative environment, where there are so many tools, workflows, and options to choose from, maybe they don’t have to pick one approach over another.
Huge tech companies control the conversation around education, culture, and the future of society. That’s a massive problem, because leave your Metas, Alphabets, and OpenAIs to the side, and you find creative, subversive, independent, anarchic, inspiring innovation happening all over the place. Some of these folx are using AI, and some aren’t: the work itself is interesting, rather than the exact tools or apps being used.
Companies ignore technological advancement at their peril. But deliberately opting out? Maybe that’s just good business.
This semester I’m running a Media studio called ‘Augmenting Creativity’. The basic goal is to develop best practices for working with generative AI tools not just in creative workflows, but as part of university assignments, academic research, and in everyday routines. My motivation or philosophy for this studio is that so much attention is being focused on the outputs of tools like Midjourney and Leonardo.Ai (as well as outputs from textbots like ChatGPT); what I guess I’m interested in is exploring more precisely where in workflows, jobs, and daily life that these tools might actually be helpful.
In class last week we held a Leonardo.Ai hackathon, inspired by one of the workshops that was run at the Re/Framing AI event I convened a month or so ago. Leonardo.Ai generously donated some credits for students to play around with the platform. Students were given a brief around what they should try to generate:
an AI Self-Portrait (using text only; no image guidance!)
three images to envision the studio as a whole (one conceptual, a poster, and a social media tile)
three square icons to represent one task in their daily workflow (home, work, or study-related)
For the Hackathon proper, students were only able to adjust the text prompt and the Preset Style; all other controls had to remain unchanged, including the Model (Phoenix), Generation Mode (Fast), Prompt Enhance (off), and all others.
Students were curious and excited, but also faced some challenges straight away with the underlying mechanics of image generators; they had to play around with word choice in prompts to get close to desired results. The biases and constraints of the Phoenix model quickly became apparent as the students tested its limitations. For some students this was more cosmetic, such as requesting that Leonardo.Ai generate a face with no jewelry or facial hair. This produced mixed results, in that sometimes explicitly negative prompts seemed to encourage the model to produce what wasn’t wanted. Other students encountered difficulties around race or gender presentation: the model struggles a lot with nuances in race, e.g. mixed-race or specific racial subsets, and also often depicts sexualised presentations of female-presenting people (male-presenting too, but much less frequently).
This session last week proved a solid test of Leonardo.Ai’s utility and capacity in generating assets and content (we sent some general feedback to Leonardo.Ai on platform useability and potential for improvement), but also was useful for figuring out how and where the students might use the tool in their forthcoming creative projects.
This week we’ve spent a little time on the status of AI imagery as art, some of the ethical considerations around generative AI, and where some of the supposed impacts of these tools may most keenly be felt. In class this morning, the students were challenged to deliver lightning talks on recent AI news, developing their presentation and media analysis skills. From here, we move a little more deeply into where creativity lies in the AI process, and how human/machine collaboration might produce innovative content. The best bit, as always, will be seeing where the students go with these ideas and concepts.
