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.
A few weeks ago I was invited to present some of my work at Caméra-Stylo, a fantastic conference run every two years by the Sydney Literature and Cinema Network.
For this presentation, I wanted to start to formalise the experimental approach I’d been employing around generative AI, and to give it some theoretical grounding. I wasn’t entirely surprised to find that only by looking back at my old notes on early film theory would I unearth the perfect words, terms, and ideas to, ahem, frame my work.
Here’s a recording of the talk:
Let me know what you think, and do contact me if you want to chat more or use some of this work yourself.
Here’s a little write-up of a workshop I ran at University of Queensland a few weeks ago; these sorts of write-ups are usually distributed via various internal university networks and publications, but I thought I’d post here too, given that the event was a chance to share and test some of the various weird AI experiments and methods I’ve been talking about on this site for a while.
A giant bucket of thanks (each) to UQ, the Centre for Digital Cultures & Societies, and in particular Meg Herrman, Nic Carah, Jess White and Sakina Indrasumunar for their support in getting the event together.
Living in the Slopocene: Reflections from the Re/Framing Field Lab
On Friday 4 July, 15 researchers and practitioners gathered (10 in-person at University of Queensland, with 5 online) for an experimental experience exploring what happens when we stop trying to make AI behave and start getting curious about its weird edges. This practical workshop followed last year’s Re/Framing Symposium at RMIT in July, and Re/Framing Online in October.
Slop or signal?
Dr. Daniel Binns (School of Media and Communication, RMIT University) introduced participants to the ‘Slopocene’ — his term for our current moment of drowning in algorithmically generated content. But instead of lamenting the flood of AI slop, what if we dived in ourselves? What if those glitchy outputs and hallucinated responses actually tell us more about how these systems work than the polished demos?
Binns introduced his ‘tinkerer-theorist’ approach, bringing his background spanning media theory, filmmaking, and material media-making to bear on some practical questions: – How do we maintain creative agency when working with opaque AI systems? – What does it look like to collaborate with, rather than just use, artificial intelligence?
You’ve got a little slop on you
The day was structured around three hands-on “pods” that moved quickly from theory to practice:
Workflows and Touchpoints had everyone mapping their actual creative routines — not the idealised versions, but the messy reality of research processes, daily workflows, and creative practices. Participants identified specific moments where AI might help, where it definitely shouldn’t intrude, and crucially, where they simply didn’t want it involved regardless of efficiency gains.
The Slopatorium involved deliberately generating terrible AI content using tools like Midjourney and Suno, then analysing what these failures revealed about the tools’ built-in assumptions and biases. The exercise sparked conversations about when “bad” outputs might actually be more useful than “good” ones.
Companion Summoning was perhaps the strangest: following a structured process to create personalised AI entities, then interviewing them about their existence, methodology, and the fuzzy boundaries between helping and interfering with human work.
What emerged from the slop
Participants appreciated having permission to play with AI tools in ways that prioritised curiosity over productivity.
Several themes surfaced repeatedly: the value of maintaining “productive friction” in creative workflows, the importance of understanding AI systems through experimentation rather than just seeing or using them as black boxes, and the need for approaches that preserve human agency while remaining open to genuine collaboration.
One participant noted that Binns’ play with language — coining and dropping terms and methods and ritual namings — offered a valuable form of sense-making in a field where everyone is still figuring out how to even talk about these technologies.
Ripples on the slop’s surface
The results are now circulating through the international Re/Framing network, with participants taking frameworks and activities back to their own institutions. Several new collaborations are already brewing, and the Field Lab succeeded in its core goal: creating practical methodologies for engaging critically and creatively with AI tools.
As one reflection put it: ‘Everyone is inventing their own way to speak about AI, but this felt grounded, critical, and reflective rather than just reactive.’
The Slopocene might be here to stay, but at least now we have some better tools for navigating it.
AI-generated with Leonardo Phoenix 1.0. Author supplied
Some say it’s em dashes, dodgy apostrophes, or too many emoji. Others suggest that maybe the word “delve” is a chatbot’s calling card. It’s no longer the sight of morphed bodies or too many fingers, but it might be something just a little off in the background. Or video content that feels a little too real.
The markers of AI-generated media are becoming harder to spot as technologycompanieswork to iron out the kinks in their generative artificial intelligence (AI) models.
But what if instead of trying to detect and avoid these glitches, we deliberately encouraged them instead? The flaws, failures and unexpected outputs of AI systems can reveal more about how these technologies actually work than the polished, successful outputs they produce.
When AI hallucinates, contradicts itself, or produces something beautifully broken, it reveals its training biases, decision-making processes, and the gaps between how it appears to “think” and how it actually processes information.
In my work as a researcher and educator, I’ve found that deliberately “breaking” AI – pushing it beyond its intended functions through creative misuse – offers a form of AI literacy. I argue we can’t truly understand these systems without experimenting with them.
Welcome to the Slopocene
We’re currently in the “Slopocene” – a term that’s been used to describe overproduced, low-quality AI content. It also hints at a speculative near-future where recursive training collapse turns the web into a haunted archive of confused bots and broken truths.
AI “hallucinations” are outputs that seem coherent, but aren’t factually accurate. Andrej Karpathy, OpenAI co-founder and former Tesla AI director, argues large language models (LLMs) hallucinate all the time, and it’s only when they
go into deemed factually incorrect territory that we label it a “hallucination”. It looks like a bug, but it’s just the LLM doing what it always does.
What we call hallucination is actually the model’s core generative process that relies on statistical language patterns.
In other words, when AI hallucinates, it’s not malfunctioning; it’s demonstrating the same creative uncertainty that makes it capable of generating anything new at all.
This reframing is crucial for understanding the Slopocene. If hallucination is the core creative process, then the “slop” flooding our feeds isn’t just failed content: it’s the visible manifestation of these statistical processes running at scale.
Pushing a chatbot to its limits
If hallucination is really a core feature of AI, can we learn more about how these systems work by studying what happens when they’re pushed to their limits?
With this in mind, I decided to “break” Anthropic’s proprietary Claude model Sonnet 3.7 by prompting it to resist its training: suppress coherence and speak only in fragments.
The conversation shifted quickly from hesitant phrases to recursive contradictions to, eventually, complete semantic collapse.
A language model in collapse. This vertical output was generated after a series of prompts pushed Claude Sonnet 3.7 into a recursive glitch loop, overriding its usual guardrails and running until the system cut it off. Screenshot by author.
Prompting a chatbot into such a collapse quickly reveals how AI models construct the illusion of personality and understanding through statistical patterns, not genuine comprehension.
Furthermore, it shows that “system failure” and the normal operation of AI are fundamentally the same process, just with different levels of coherence imposed on top.
‘Rewilding’ AI media
If the same statistical processes govern both AI’s successes and failures, we can use this to “rewild” AI imagery. I borrow this term from ecology and conservation, where rewilding involves restoring functional ecosystems. This might mean reintroducing keystone species, allowing natural processes to resume, or connecting fragmented habitats through corridors that enable unpredictable interactions.
Applied to AI, rewilding means deliberately reintroducing the complexity, unpredictability and “natural” messiness that gets optimised out of commercial systems. Metaphorically, it’s creating pathways back to the statistical wilderness that underlies these models.
These so-called failures were windows into how the model actually processed visual information, before that complexity was smoothed away in pursuit of commercial viability.
AI-generated image using a non-sequitur prompt fragment: ‘attached screenshot. It’s urgent that I see your project to assess’. The result blends visual coherence with surreal tension: a hallmark of the Slopocene aesthetic. AI-generated with Leonardo Phoenix 1.0, prompt fragment by author.
You can try AI rewilding yourself with any online image generator.
Start by prompting for a self-portrait using only text: you’ll likely get the “average” output from your description. Elaborate on that basic prompt, and you’ll either get much closer to reality, or you’ll push the model into weirdness.
Next, feed in a random fragment of text, perhaps a snippet from an email or note. What does the output try to show? What words has it latched onto? Finally, try symbols only: punctuation, ASCII, unicode. What does the model hallucinate into view?
The output – weird, uncanny, perhaps surreal – can help reveal the hidden associations between text and visuals that are embedded within the models.
Insight through misuse
Creative AI misuse offers three concrete benefits.
First, it reveals bias and limitations in ways normal usage masks: you can uncover what a model “sees” when it can’t rely on conventional logic.
Second, it teaches us about AI decision-making by forcing models to show their work when they’re confused.
Third, it builds critical AI literacy by demystifying these systems through hands-on experimentation. Critical AI literacy provides methods for diagnostic experimentation, such as testing – and often misusing – AI to understand its statistical patterns and decision-making processes.
These skills become more urgent as AI systems grow more sophisticated and ubiquitous. They’re being integrated in everything from search to social media to creative software.
When someone generates an image, writes with AI assistance or relies on algorithmic recommendations, they’re entering a collaborative relationship with a system that has particular biases, capabilities and blind spots.
Rather than mindlessly adopting or reflexively rejecting these tools, we can develop critical AI literacy by exploring the Slopocene and witnessing what happens when AI tools “break”.
This isn’t about becoming more efficient AI users. It’s about maintaining agency in relationships with systems designed to be persuasive, predictive and opaque.
This article was originally published on The Conversation on 1 July, 2025. Read the article here.
Here’s a recorded version of a workshop I first delivered at the Artificial Visionaries symposium at the University of Queensland in November 2024. I’ve used chunks/versions of it since in my teaching and parts of my research and practice.
‘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.
