Category: Writing

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

  July 8, 2025

  

  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.

• Grotesque fascination

  August 18, 2024

  A few weeks back, some colleagues and I were invited to share some new thoughts and ideas on the theme of ‘ecomedia’, as a lovely and unconventional way to launch Simon R. Troon’s newest monograph, Cinematic Encounters with Disaster: Realisms for the Anthropocene. Here’s what I presented; a few scattered scribblings on environmental imaginaries as mediated through AI.

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  Grotesque Fascination:

  Reflections from my weekender in the uncanny valley

  In February 2024 OpenAI announced their video generation tool Sora. In the technical paper that accompanied this announcement, they referred to Sora as a ‘world simulator’. Not just Sora, but also DALL-E or Runway or Midjourney, all of these AI tools further blur and problematise the lines between the real and the virtual. Image and video generation tools re-purpose, re-contextualise, and re-gurgitate how humans perceive their environments and those around them. These tools offer a carnival mirror’s reflection on what we privilege, prioritise, and what we prejudice against in our collective imaginations. In particular today, I want to talk a little bit about how generative AI tools might offer up new ways to relate to nature, and how they might also call into question the ways that we’ve visualized our environment to date.

  AI media generators work from datasets that comprise billions of images, as well as text captions, and sometimes video samples; the model maps all of this information using advanced mathematics in a hyper-dimensional space, sometimes called the latent space or a U-net. A random image of noise is then generated and fed through the model, along with a text prompt from the user. The model uses the text to gradually de-noise the image in a way that the model believes is appropriate to the given prompt.

  In these datasets, there are images of people, of animals, of built and natural environments, of objects and everyday items. These models can generate scenes of the natural world very convincingly. These generations remind me of the open virtual worlds in video games like Skyrim or Horizon: Zero Dawn: there is a real, visceral sense of connection for these worlds as you move through them. In a similar way, when you’re playing with tools like Leonardo or MidJourney, there can often be visceral, embodied reactions to the images or media that they generate: Shane Denson has written about this in terms of “sublime awe” and “abject cringe”. Like video games, too, AI Media Generators allow us to observe worlds that we may never see in person. Indeed, some of the landscapes we generate may be completely alien or biologically impossible, at least on this planet, opening up our eyes to different ecological possibilities or environmental arrangements. Visualising or imagining how ecosystems might develop is one way of potentially increasing awareness of those that are remote, unexplored or endangered; we may also be able to imagine how the real natural world might be impacted by our actions in the distant future. These alien visions might also, I suppose, prepare us for encountering different ecosystems and modes of life and biology on other worlds.

  But it’s worth considering, though, how this re-visualisation, virtualisation, re-constitution of environments, be they realistic or not, might change, evolve or hinder our collective mental image, or our capacity to imagine what constitutes ‘Nature’. This experience of generating ecosystems and environments may increase appreciation for our own very real, very tangible natural world and the impacts that we’re having on it, but like all imagined or technically-mediated processes there is always a risk of disconnecting people from that same very real, very tangible world around them. They may well prefer the illusion; they may prefer some kind of perfection, some kind of banal veneer that they can have no real engagement with or impact on. And it’s easy to ignore the staggering environmental impacts of the technology companies pushing these tools when you’re engrossed in an ecosystem of apps and not of animals.

  In previous work, I proposed the concept of virtual environmental attunement, a kind of hyper-awareness of nature that might be enabled or accelerated by virtual worlds or digital experiences. I’m now tempted to revisit that theory in terms of asking how AI tools problematise that possibility. Can we use these tools to materialise or make perceptible something that is intangible, virtual, immaterial? What do we gain or lose when we conceive or imagine, rather than encounter and experience?

  Machine vision puts into sharp relief the limitations of humanity’s perception of the world. But for me there remains a certain romance and beauty and intrigue — a grotesque fascination, if you like — to living in the uncanny valley at the moment, and it’s somewhere that I do want to stay a little bit longer. This is despite the omnipresent feeling of ickiness and uncertainty when playing with these tools, while the licensing of the datasets that they’re trained on remains unclear. For now, though, I’m trying to figure out how connecting with the machine-mind might give some shape or sensation to a broader feeling of dis-connection.

  How my own ideas and my capacity to imagine might be extended or supplemented by these tools, changing the way I relate to myself and the world around me.

• All the King’s horses

  March 18, 2024

  

  I’ve written previously about the apps I use. When it comes to actual productivity methods, though, I’m usually in one of (what I hope are only) two modes: Complicate Mode (CM) or Simplify Mode (SM).

  CM can be fun because it’s not always about a feeling of overwhelm, or over-complicating things. In its healthier form it might be learning about new modes and methods, discovering new ways I could optimise, satiating my manic monkey brain with lots of shiny new tools, and generally wilfully being in the weeds of it all.

  However CM can also really suck, because it absolutely can feel overwhelming, and it can absolutely feel like I’m lost in the weeds, stuck in the mud, too distracted by the new systems and tools and not actually doing anything. CM can also feel like a plateau, like nothing is working, like the wheels are spinning and I don’t know how to get traction again.

  By contrast, SM usually arrives just after one of these stuck-in-the-mud periods, when I’m just tired and over it. I liken it to a certain point on a long flight. I’m a fairly anxious flyer. Never so much that it’s stopped me travelling, but it’s never an A1 top-tier experience for me. However, on a long-haul flight, usually around 3-5 hours in, it feels like I just ‘run out’ of stress. I know this isn’t what’s actually happening, but it seems like I worked myself up too much, and my body just calms itself enough to be resigned to its situation. And then I’m basically just tired and bored for the remainder of the trip.

  So when I’ve had a period of overwhelm, a period of not getting things done, this usually coincides with CM. I say to myself, “If I can just find the right system, tool, method, app, hack, I’ll get out of this rut.” This is bad CM. Not-healthy CM. Once I’m out of that, though (which, for future self-reference, is never as a result of a Shiny New Thing), I’ll usually slide into SM, when I want to ease out of that mode, take care of myself a bit, be realistic, and strip things back to basics. This is usually not just in terms of productivity/work, but usually extends to overall wellbeing, relationships, creativity, lifestyle, fun: all the non-work stuff, basically.

  The first sign I’m heading into SM is that I’ll unsubscribe from a bunch of app subscriptions (and reading/watching subscriptions too), go back through my bank history to make sure I’m not being charged for anything I’m not into or actively using right now, and note down some simple short-term lifestyle goals (e.g. try to get to the gym in the next few days, meditate every other day, go touch grass or look at a body of water once a week etc). In terms of work, it’s equally simple: try to pick a couple of simple tasks to achieve each day (usually not very brain-heavy) and one large task for the next week/fortnight that I spend a little time on each workday as one of those simple smaller tasks. For instance, I might be working on a journal article; so spending a little time on this during SM might not be writing, per se, but maybe consolidating references, or doing a little reading and note-taking for references I already have but haven’t utilised, or even just a spell-check of what I’ve done so far.

  Phase 1 of SM is usually the above, which I tend to do unconsciously after weeks of stressing myself out and running myself ragged and somehow still doing the essentials of life and work, despite shaving hours, if not days, off my life. Basically, Phase 1 of SM constitutes a bunch of exceptionally good and healthy things to do that I probably should do more regularly to cut off stressful times at the pass; thanks self-preservation brain!

  In terms of strictly productivity, though, SM has previously meant chucking it all in and going back to pen and paper, or chucking in pen and paper and going all in on digital tools (or just one digital tool, which has never worked bro so stop trying it). An even worse thing to do is to go all in on a single new productivity system. This usually takes up a whole day (sometimes two) where I could be either doing shit, or trying to spend quality time figuring out more accurately why shit isn’t getting done, or — probably more to the point — putting everything to one side and giving myself an actual break.

  I’ve had one or two moments of utter desperation, when nothing at all seems like it’s working, when I’ve tried CM and SM and every-other-M to no avail; I’ve even tried taking a bit of a break, but needs must when it comes to somehow just pushing on for whatever reason (personal, financial, professional, psychological, etc). In these moments I’ve had to do a pretty serious and comprehensive life audit. Basically, it’s either whatever note-taking app I see first on my phone, or piece of paper (preferably larger than A4/letter and a bunch of textas, or even just whole bunch of post-it’s and a dream. Make a hot beverage or fill up that water bottle, sit down at desk, dining table, lie in bed or on the floor, and go for it.

  Life Audit Part 1: Commitments and needs/wants

  What are your primary commitments? Your main stressors right now? What are your other stressors? Who are you accountable to/for, or responsible for right now? What do you need to be doing (but actually really need, not just think you need) in only the short-term? What do you want to be doing? What are you paying for right now, obviously financially, but what about physically? Psychologically?

  Life Audit Part 2: Sit Rep

  As it stands right now, how are you answering all the questions from Part 1? Are you kinda lying to yourself about what’s most important? How on earth did you get to the place where you think X is more important than Y? What can you remove from this map to simplify things right now? (Don’t actually remove them, just note down somewhere what you could remove.)

  Life Audit Part 3: Tweak and Adjust

  What tools, systems, methods — if any — do you have in place to cope with any of the foregoing? If you have a method/methods, are they really working? What might you tweak/change/add/remove to streamline or improve this system? If you don’t have any systems right now, what simple approach could you try as a light touch in the coming days or weeks? This could be as simple as blocking out your work time and personal time as work time and personal time, and setting a calendar reminder to try and keep to those times. If you struggle to rest or to give time to important people in your life; why? If your audit is richly developed or super-connected around personal development or lifestyle, or around professional commitments, maybe you need to carve out some time (or not even time, just some headspace) to note down how you can reorient yourself.

  The life audit might be refreshing or energising for some folx, and that’s awesome. For me, though, doing this was taxing. Exhausting. Sometimes debilitating. Maybe doing it more regularly would help, but it really surfaced patterns of thinking and behaviour that had cost me greatly in terms of well-being, welfare, health, time, money, and more besides. So take this as a bit of a disclaimer or warning. It might be good to raise this idea with a loved one or health-type person (GP, psych, religious advisor, etc) before attempting.

  Similarly, maybe a bit of a further disclaimer here. I have read a lot about productivity methods, modes, approaches, gurus, culture, media, and more. I think productivity is something of a myth, and it can also be toxic and dangerous. My personal journey in productivity media and culture has been both a professional interest and a personal interest (at times, obsession). My system probably won’t work for you or anyone really. I’ve learned to tweak, to leave to one side, to adjust and change when needed, and to just drop any pretense of being ‘productive’ if it just ain’t happening.

  Productivity and self-optimisation and their attendant culture are by-products of a capitalist system¹. When we buy into it — psychologically, professionally, or financially — we propagate and perpetuate that system, with its prejudices, its injustices, its biases, and its genuine harms. We might kid ourselves that it’s just for us, it’s just the tonic we need to get going, to be a better employee, partner, friend, or whatever; but when it all boils down to it, we’re human. We’re animals. We’re fallible. There are no hacks, there are no shortcuts, and honestly, when it boils down to it, you just have to do the work. And that work is often hard and/or boring and/or time-consuming. I am finally acknowledging and owning this for myself after several years of ignorance. It’s the least any of us can do if we care.

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  This post is a line in the sand with my personal journey. To end a chapter. Turn a page. To think through what I’ve tried at various times; to try and give little names and labels to approaches and little recovery methods that I think have been most effective, so that I can just pick them up in future as a little package, a little pill to quickly swallow, rather than inefficiently stumbling my way back to the same solutions via Stress Alley and Burnout Junction.

  Moving forward, I also want to linger a little longer in the last couple of paragraphs. But for real this time. It’s easy to say that I believe in slowing down, in valuing life and whatever it brings me, to just spend time: not doing anything necessarily, but certainly not worrying about whether or not I’m being productive or doing the right thing.

  I want to have a simple system that facilitates my being the kind of employee I want to be; the kind of colleague I want to be; the partner I want to be; the immediate family member (e.g. child, parent, grandchild etc) I want to be; the citizen, human I want to be. This isn’t some lofty ambition talking. I’m realistic about how much space in the world I am taking up: it’s both more than I ever have, but also far from as much as those people (you know who I mean). I want time and space to work on being all of these people, while also — hopefully — making some changes to leave things in a slightly better way than I found them.

  How’s that for a system?

  Notes

  1. For an outstanding breakdown of what I mean by this, please read Melissa Gregg’s excellent monograph Counterproductive: Time Management in the Knowledge Economy. ↩︎

• This algorithmic moment

  February 1, 2024

  

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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

  January 22, 2024

  

  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.