The Clockwork Penguin

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.

Tag: AI creativity

  • On generativity, ritual-technics, and the genAI ick

    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.

  • Cinema Disrupted

    K1no looks… friendly.
    Image generated by Leonardo.Ai, 14 October 2025; prompt by me.

    Notes from a GenAI Filmmaking Sprint

    AI video swarms the internet. It’s been around for nearly as long as AI-generated images, however its recent leaps and bounds in terms of realism, efficiency, and continuity have made it a desirable medium for content farmers, slop-slingers, and experimentalists. That said, there are those who are deploying the newer tools to hint at new forms of media, narrative, and experience.

    I was recently approached by the Disrupt AI Film Festival, which will run in Melbourne in November. As well as micro and short works (up to 3 mins and 3-15 mins respectively), they also have a student category in need of submissions. So over the last few weeks I organised a GenAI filmmaking Sprint at RMIT University last Friday. Leonardo.Ai was generous enough to donate a bunch of credits for us to play with, and also beamed in to give us a masterclass in how to prompt to generate AI video for storytelling — rather than just social media slurry.

    Movie magic? Participants during the GenAI Filmmaking Sprint at RMIT University, 10 October 2025.

    I also shared some thoughts from my research in terms of what kinds of stories or experiences work well for AI video, and also some practical insights on how to develop and ‘write’ AI films. The core of the workshop as a whole was to propose a structured approach: move from story ideas/fragments to logline, then to beat sheet, then shot list. The shot list, then, can be adapted slightly into the parlance of whatever tool you’re using to generate your images — you then end up with start frames for the AI video generator to use.

    This structure from traditional filmmaking functions as a constraint. But with tools that can, in theory, make anything, constraints are needed more than ever. The results were glimpses of shots that embraced both the impossible, fantastical nature of AI video, while anchoring it with characters, direction, or a particular aesthetic.

    In the workshop, I remembered moments in my studio Augmenting Creativity where students were tasked with using AI tools: particularly in the silences. Working with AI — even when it is dynamic, interesting, generative, fruitful, fun — is a solitary endeavour. AI filmmaking, too, in a sense, is a stark contrast to the hectic, chaotic, challenging, but highly dynamic and collaborative nature of real-life production. This was a reminder, and a timely one, that in teaching AI (as with any technology or tool), we must remember three turns that students must make: turn to the tool, turn to each other, turn to the class. These turns — and the attendant reflection, synthesis, and translation required with each — is where the learning and the magic happens.

    This structured approach helpfully supported and reiterated some of my thoughts on the nature of AI collaboration itself. I’ve suggested previously that collaborating with AI means embracing various dynamics — agency, hallucination, recursion, fracture, ambience. This workshop moved away — notably, for me and my predilections — from glitch, from fracture or breakage and recursion. Instead, the workflow suggested a more stable, more structured, more intentional approach, with much more agency on the part of the human in the process. The ambience, too, was notable, in how much time is required for the labour of both human and machine: the former in planning, prompting, managing shots and downloaded generations; the latter in processing the prompts, generating the outputs.

    Video generated for my AI micro-film The Technician (2024).

    What remains with me after this experience is a glimpse into creative genAI workflows that are more pragmatic, and integrated with other media and processes. Rather than, at best, unstructured open-ended ideation or, at worst, endless streams of slop, the tools produce what we require, and we use them to that end, and nothing beyond that. This might not be the radical revelation I’d hoped for, but it’s perhaps a more honest account of where AI filmmaking currently sits — somewhere between tool and medium, between constraint and possibility.