Category: Research

• Understanding the ‘Slopocene’: how the failures of AI can reveal its inner workings

  

  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 technology companies work 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.

  

  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.

  Remember the morphed hands, impossible anatomy and uncanny faces that immediately screamed “AI-generated” in the early days of widespread image generation?

  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.

  

  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.

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  This article was originally published on The Conversation on 1 July, 2025. Read the article here.

• New research published: The Allure of Artificial Worlds

  

  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.

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  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.

• This algorithmic moment

  

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  So much of what I’m being fed at the moment concerns the recent wave of AI. While we are seeing something of a plateauing of the hype cycle, I think (/hope), it’s still very present as an issue, a question, an opportunity, a hope, a fear, a concept. I’ll resist my usual impulse to historicise this last year or two of innovation within the contexts of AI research, which for decades was popularly mocked and institutionally underfunded; I’ll also resist the even stronger impulse to look at AI within the even broader milieu of technology, history, media, and society, which is, apparently, my actual day job.

  What I’ll do instead is drop the phrase algorithmic moment, which is what I’ve been trying to explore, define, and work through over the last 18 months. I’m heading back to work next week after an extended period of leave, so this seems as good a way of any as getting my head back into some of the research I left to one side for a while.

  The algorithmic moment is what we’re in at the moment. It’s the current AI bubble, hype cycle, growth spurt, whatever you define this wave as (some have dubbed it the AI spring or boom, to distinguish it from various AI winters over the last century¹). In trying to bracket it off with concrete times, I’ve settled more or less on the emergence of the GPT-3 Beta in 2020. Of course OpenAI and other AI innovations predated this, but it was GPT-3 and its children ChatGPT and DALL-E 2 that really propelled discussions of AI and its possibilities and challenges into the mainstream.

  This also means that much of this moment is swept up with the COVID pandemic. While online life had bled into the real world in interesting ways pre-2020, it was really that year, during urban lockdowns, family zooms, working from home, and a deeply felt global trauma, that online and off felt one and the same. AI innovators capitalised on the moment, seizing capital (financial and cultural) in order to promise a remote revolution built on AI and its now-shunned sibling in discourse, web3 and NFTs.

  How AI plugs into the web as a system is a further consideration — prior to this current boom, AI datasets in research were often closed. But OpenAI and its contemporaries used the internet itself as their dataset. All of humanity’s knowledge, writing, ideas, artistic output, fears, hopes, dreams, scraped and plugged into an algorithm, to then be analysed, searched, filtered, reworked at will by anyone.

  The downfall of FTX and the trial of Sam Bankman-Fried more or less marked the death knell of NFTs as the Next Big Thing, if not web3 as a broader notion to be deployed across open-source, federated applications. And as NFTs slowly left the tech conversation, as that hype cycle started falling, the AI boom filled the void, such that one can hardly log on to a tech news site or half of the most popular Subs-stack without seeing a diatribe or puff piece (not unlike this very blog post) about the latest development.

  ChatGPT has become a hit productivity tool, as well as a boon to students, authors, copy writers and content creators the world over. AI is a headache for many teachers and academics, many of whom fail not only to grasp its actual power and operations, but also how to usefully and constructively implement the technology in class activities and assessment. DALL-E, Midjourney and the like remain controversial phenomena in art and creative communities, where some hail them as invaluable aids, and others debate their ethics and value.

  As with all previous revolutions, the dust will settle on that of AI. The research and innovation will continue as it always has, but out of the limelight and away from the headlines. It feels currently like we cannot keep up, that it’s all happening too fast, that if only we slowed down and thought about things, we could try and understand how we’ll be impacted, how everything might change. At the risk of historicising, exactly like I said I wouldn’t, people thought the same of the printing press, the aeroplane, and the computer. In 2002, Andrew Murphie and John Potts were trying to capture the flux and flow and tension and release of culture and technology. They were grappling in particular with the widespread adoption of the internet, and how to bring that into line with other systems and theories of community and communication. Jean-Francois Lyotard had said that new communications networks functioned largely on “language games” between machines and humans. Building on this idea, Murphie and Potts suggested that the information economy “needs us to make unexpected ‘moves’ in these games or it will wind down through a kind of natural attrition. [The information economy] feeds on new patterns and in the process sets up a kind of freedom of movement within it in order to gain access to the new.”²

  The information economy has given way, now, to the platform economy. It might be easy, then, to think that the internet is dead and decaying or, at least, kind of withering or atrophying. Similarly, it can be even easier to think that in this locked-down, walled-off, platform- and app-based existence where online and offline are more or less congruent, we are without control. I’ve been dropping breadcrumbs over these last few posts as to how we might resist in some small way, if not to the detriment of the system, then at least to the benefit of our own mental states; and I hope to keep doing this in future posts (and over on Mastodon).

  For me, the above thoughts have been gestating for a long time, but they remain immature, unpolished; unfiltered which, in its own way, is a form of resistance to the popular image of the opaque black box of algorithmic systems. I am still trying to figure out what to do with them; whether to develop them further into a series of academic articles or a monograph, to just keep posting random bits and bobs here on this site, or to seed them into a creative piece, be it a film, book, or something else entirely. Maybe a little of everything, but I’m in no rush.

  As a postscript, I’m also publishing this here to resist another system, that of academic publishing, which is monolithic, glacial, frustrating, and usually hidden behind a paywall for a privileged few. Anyway, I’m not expecting anyone to read this, much less use or cite it in their work, but better it be here if someone needs it than reserved for a privileged few.

  As a bookend for the AI-generated image that opened the post, I asked Bard for “a cool sign-off for my blog posts about technology, history, and culture” and it offered the following, so here you go…

  Signing off before the robots take over. (Just kidding… maybe.)

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  Notes

  1. For an excellent history of AI up to around 1990, I can’t recommend enough AI: The Tumultuous History of the Search for Artificial Intelligence by Daniel Crevier. Crevier has made the book available for download via ResearchGate. ↩︎
  2. Murphie, Andrew, and John Potts. 2003. Culture and Technology. London: Macmillan Education UK, p. 208. https://doi.org/10.1007/978-1-137-08938-0. ↩︎

• Critics and creation

  

  I started reading this interview this morning, between Anne Helen Peterson and Betsy Gaines Quammen. I still haven’t finished reading, despite being utterly fascinated, but even before I got to the guts of the interview, I was struck by a thought:

  In the algorithmised world, the creator is the critic.

  This thought is not necessarily happening in isolation; I’ve been thinking about ‘algorithmic culture’ for a couple of years, trying to order these thoughts into academic writing, or even creative writing. But this thought feels like a step in the right direction, even if I’ve no idea what the final output should or will be. Let’s scribble out some notes…

  If there’s someone whose work we enjoy, they’ll probably have an online presence — a blog or social media feed we can follow — where they’ll share what they like.

  It’s an organic kind of culture — but it’s one where the art and vocation of the critic continues to be minimised.

  This — and associated phenomena — is the subject of a whole bunch of recent and upcoming books (including this one, which is at the top of my to-read pile for the next month): a kind of culture where the all-powerful algorithm becomes the sole arbiter of taste, but I also think there is pressure on creatives to be their own kind of critical and cultural hub.

  On the inverse, what we may traditionally have called critics — so modern-day social media commentators, influencers, your Booktubers or Booktokkers, your video essayists and their ilk — now also feel pressure to create. This pressure will come from their followers and acolytes, but also from random people who encounter them online, who will say something like “if you know so much why don’t you just do it yourself” etc etc…

  Some critics will leap at the opportunity and they absolutely should — we are hearing from diverse voices that wouldn’t otherwise have thought to try.

  But some should leave the creation to others — not because they’re not worth hearing from, they absolutely are — but because their value, their creativity, their strength, lies in how they shape language, images, metaphor, around the work of others. They don’t realise — as I didn’t for a long time — that being a critic is a vocation, a life’s work, a real skill. Look at any longer-form piece in the London Review of Books or The New Inquiry and it becomes very clear how valuable this work is.

  I’ve always loved the term critic, particularly cultural critic, or commentator, or essayist… they always seemed like wonderful archaic terms that don’t belong in the modern, fragmented, divided, confused world. But to call oneself a critic or essayist, to own that, and only that, is to defy the norms of culture; to refuse the ‘pillars’ of novel, film, press/journalism, and to stand to one side, giving much-needed perspective to how these archaic forms define, reflect, and challenge society.

• What makes good academic writing?

  

  I’m often asked by students for samples of writing that align with what’s required for assessment tasks. This semester is no different, so I actually spent some time digging through old courses and studios I’ve run, finding a few good examples that I can share with the students.

  Very often my feedback on student reflections tends towards hoping they’ll integrate or synthesise research, ideas, and thoughts on their making. I usually find myself saying ‘take a position and argue it’, by which I mean that reflective writing — at least in an academic context — shouldn’t be about a summary of everything achieved, every decision made. Rather, choose a single point — be it a creative choice, or a quote from a journal article, or something watched — and then unpack that single point to make connections to other researchers and scholars, other makers, other reflections/insights the student generated in the class.

  This is difficult to achieve, even for seasoned researchers. Add to this that the accepted conventions of academic writing — the vast majority of it in many fields — are so restrictive in terms of expression as to be incomprehensible. This means that students become terrified of approaching any academic writing. It’s seen as boring, or dense, or difficult. This greatly stifles their curiosity, or their interest in finding the connections I try to encourage.

  If only, I hear them say or imply, academic writing was easier to engage with. Which reminds me that there are some truly wonderful, writerly, scholars out there. You just have to look. This is far from an exhaustive bibliography, but here are a handful of scholars that I read for the joy of experiencing good writing as much as for research.

  • Ingold, Tim. 2011. “The Textility of Making.” In Being Alive, 219–28. Milton Park: Routledge. https://doi.org/10.4324/9780203818336-28.
  • Jagoda, Patrick. 2016. Network Aesthetics. Chicago: University of Chicago Press. http://ebookcentral.proquest.com/lib/rmit/detail.action?docID=4427890.
  • Miles, Adrian, Bruno Lessard, Hannah Brasier, and Franziska Weidle. 2018. “From Critical Distance to Critical Intimacy: Interactive Documentary and Relational Media.” In Critical Distance in Documentary Media, edited by Gerda Cammaer, Blake Fitzpatrick, and Bruno Lessard, 301–19. Cham: Springer International Publishing.
  • Murray, Janet Horowitz. 2017. Hamlet on the Holodeck: The Future of Narrative in Cyberspace. Updated edition. Cambridge, Massachusetts: The MIT Press.
  • Peters, John Durham. 2015. The Marvelous Clouds: Toward a Philosophy of Elemental Media. University of Chicago Press. https://doi.org/10.7208/chicago/9780226253978.001.0001.
  • Pomerance, Murray. 2008. The Horse Who Drank the Sky: Film Experience beyond Narrative and Theory. New Brunswick: Rutgers University Press.
  • Stewart, Kathleen. 2011. “Atmospheric Attunements.” Environment and Planning D: Society and Space 29 (3): 445–53. https://doi.org/10.1068/d9109.