In fact, I’ve been involved with ‘artificial intelligence’ since about 1990, when I developed Wave 3 AI – expert systems. Wave 4 is the current incarnation. Still no ‘intelligence’ to speak of, but marketers and hypsters love the term. Perhaps in Wave 5, the name will finally be correct.
Aside from my historical connexion, I want to share how I am using AI in my writing – in this case, ChatGPT 5.1. I’m not going to give much backstory on the setup, but I’ll point out some internal process logic.
Audio: NotebookLM podcast on this topic.
I have completed the manuscript for a Language Insufficiency Hypothesis, so I have been sharing screenshots of each page – usually a spread – and using the GPT as a second set of eyes. I’ll feed it an image and a request, in this case, to find key terms so I can capitalise and italicise them appropriately. In this example, this is the ending paragraph of Chapter 6.
Image 1: Sample chapter copy. In good order.
This first screenshot is an example of output. As is evident, it was looking, among other things, for the capitalisation of the concepts of Presumption Gap and Effectiveness Horizon.
Image 2: Sample GPT output – bad iconography
Notice the iconographic language is a bit off. The red X is a bit out of sync with the rest of the message, which says the entry is already correct. So, two instances; no problems. Next.
In this message, I warned that it was OCRing the screenshots but not retaining the formatting, and which is a reason I was sharing images over text.
Image 3: Sample GPT output – OCR confusion
What’s interesting is that it informed me that it would now treat the image as canonical. In Image 3 (above), it’s engaging in introspection – or at least self-dialogue. This is evidence that it (1) reviewed the results of the OCR, reviewed the image (as an image), and (3) compared 1 and 2 to arrive at the conclusion that the OCR had indeed dropped the formatting.
It wasn’t enough to inform me that everything was ok or, better still, not to bother me with noise since it was already in good order. Instead, it’s like an autist talking to itself. It reminds me of Raymond in Rain Man.
Image 34 (next) is the last example. Here, the OCR confounds rendering Horizon as Hπrizon, and then points out that I should avoid the same mistake of viewing o as π.
Image 4: Sample GPT output – OCR corruption
Thanks for the advice. I was losing sleep worrying about this possibility.
Conclusion
This is obviously a late-stage use case. I use GPT for ideation and research. Perhaps I’ll share an example of this later. I might be able to review my earlier notes for this project, but it was started years before the latest Wave arrived.
It seems my latest rant about AI-authorship accusations stirred something in me, that I need to apologise for being a professional writer – or is that a writing professional? Blame the Enlightenment, blame writing and communication courses, whatevs. I certainly do. But since some people are still waving the pitchforks, insisting that anything too coherent must be artificially tainted, I should address the obvious point everyone keeps missing:
The writing structures people attribute to AI aren’t AI inventions. They’re human inventions. Old ones. Codified ones. And we made the machines copy them. Sure, they have a certain cadence. It’s the cadence you’d have if you also followed the patterns you should have been taught in school or opened a book or two on the topic. I may have read one or two over the years.
Wait for it… The orthodoxy is ours. I hate to be the one to break it to you.
Video: AI Robot Assistant (no audio)
Professional Writing Has Its Own House Rules (And They’re Older Than AI Neural Nets)
👉 I wrote earlier how even talking about AI is censored in Reddit. 🤷
Audio: NotebookLM podcast on this topic and the last one.
Long before AI arrived to ruin civilisation and steal everyone’s quiz-night jobs, we’d already built an entire culture around ‘proper writing’. The sort of writing that would make a communications lecturer beam with pride. The Sith may come in twos; good writing comes in threes.
Tell them what you’re going to say.
Say it.
Repeat what you told them.
But wait, there’s more:
Use linear flow, not intellectual jazz.
One idea per paragraph, please.
Support it with sources.
Conclude like a responsible adult.
These aren’t merely classroom antics. They’re the architectural grammar of academic, corporate, scientific, and policy writing. No poetic flourishes. No existential detours. No whimsical cadence. The aim is clarity, predictability, and minimal risk of misinterpretation. It’s the textual equivalent of wearing sensible shoes to a board meeting. So when someone reads a structured piece of prose and yelps, ‘It sounds like AI!’, what they’re really saying is:
It sounds like someone who was properly trained to write in a professional context.
Je m’accuse. AI Didn’t Invent Structure. We Forced It To Learn Ours. Full stop. The problem is that it did whilst most of us didn’t.
If AI tends toward this style – linear, tidy, methodical, lamentably sane – that’s because we fed it millions of examples of ‘proper writing’. It behaves professionally because we trained it on professional behaviour – surprisingly tautological. Quelle surprise, eh?
Just as you don’t blame a mimeograph for producing a perfectly dull office memo, you don’t blame AI for sounding like every competent academic who’s been beaten with the stick of ‘clarity and cohesion’. It’s imitation through ingestion. It’s mimicry through mass exposure.
And Now for the Twist: My Fiction Has None of These Constraints
My fiction roams freely. It spirals, loops, dissolves, contradicts, broods, and wanders through margins where structured writing fears to tread. It chases affect, not clarity. Rhythm, not rubrics. Experience, not exegesis.
No one wants to read an essay that sounds like Dr Seuss, but equally, no one wants a novel that reads like the bylaws of a pension committee.
Different aims, different freedoms: Academic and professional writing must behave itself. Fiction absolutely should not.
This isn’t a value judgement. One isn’t ‘truer’ or ‘better’ than the other – only different tools for different jobs. One informs; the other evokes. One communicates; the other murmurs and unsettles.
Not to come off like Dr Phil (or Dr Suess), but the accusation itself reveals the real anxiety. When someone accuses a writer of sounding ‘AI-like,’ what they usually mean is:
‘Your writing follows the conventions we taught you to follow – but now those conventions feel suspect because a machine can mimic them’.
And that’s not a critique of the writing. It’s a critique of the culture around writing – a panic that the mechanical parts of our craft are now automated and thus somehow ‘impure’.
But structure is not impurity. Professional clarity is not soullessness. Repetition, sequencing, scaffolding – these aren’t telltale signs of AI; they’re the residue of centuries of human pedagogy.
AI mirrors the system. It didn’t create the system. And if the system’s beginning to look uncanny in the mirror, that’s a problem of the system, not the reflection.
In Short: The Craft Is Still the Craft, Whether Human or Machine
Professional writing has rules because it needs them. Fiction abandons them because it can. AI imitates whichever domain you place in front of it.
The accusation that structured writing ‘sounds artificial’ is merely a confusion between form and origin. The form is ours. The origin is irrelevant.
If clarity is now considered suspicious, I fear for the state of discourse. But then again, I’ve feared for that for some time.
And apparently, I’ve still got more to say on the matter.
Accusations of writing being AI are becoming more common – an irony so rich it could fund Silicon Valley for another decade. We’ve built machines to detect machines imitating us, and then we congratulate ourselves when they accuse us of being them. It’s biblical in its stupidity.
A year ago, I read an earnest little piece on ‘how to spot AI writing’. The tells? Proper grammar. Logical flow. Parallel structure. Essentially, competence. Imagine that – clarity and coherence as evidence of inhumanity. We’ve spent centuries telling students to write clearly, and now, having finally produced something that does, we call it suspicious.
Audio: NotebookLM podcast on this topic and the next one.
My own prose was recently tried and convicted by Reddit’s self-appointed literati. The charge? Too well-written, apparently. Reddit – where typos go to breed. I pop back there occasionally, against my better judgment, to find the same tribunal of keyboard Calvinists patrolling the comment fields, shouting ‘AI!’ at anything that doesn’t sound like it was composed mid-seizure. The irony, of course, is that most of them wouldn’t recognise good writing unless it came with upvotes attached.
Image: A newspaper entry that may have been generated by an AI with the surname Kahn. 🧐🤣
Now, I’ll admit: my sentences do have a certain mechanical precision. Too many em dashes, too much syntactic symmetry. But that’s not ‘AI’. That’s simply craft. Machines learned from us. They imitate our best habits because we can’t be bothered to keep them ourselves. And yet, here we are, chasing ghosts of our own creation, declaring our children inhuman.
Apparently, there are more diagnostic signs. Incorporating an Alt-26 arrow to represent progress is a telltale infraction → like this. No human, they say, would choose to illustrate A → B that way. Instead, one is faulted for remembering – or at least understanding – that Alt-key combinations exist to reveal a fuller array of options: …, ™, and so on. I’ve used these symbols long before AI Wave 4 hit shore.
Interestingly, I prefer spaced en dashes over em dashes in most cases. The em dash is an Americanism I don’t prefer to adopt, but it does reveal the American bias in the training data. I can consciously adopt a European spin; AI, lacking intent, finds this harder to remember.
I used to use em dashes freely, but now I almost avoid them—if only to sidestep the mass hysteria. Perhaps I’ll start using AI to randomly misspell words and wreck my own grammar. Or maybe I’ll ask it to output everything in AAVE, or some unholy creole of Contemporary English and Chaucer, and call it a stylistic choice. (For the record, the em dashes in this paragraph were injected by the wee-AI gods and left as a badge of shame.)
Meanwhile, I spend half my time wrestling with smaller, dumber AIs – the grammar-checkers and predictive text gremlins who think they know tone but have never felt one. They twitch at ellipses, squirm at irony, and whimper at rhetorical emphasis. They are the hall monitors of prose, the petty bureaucrats of language.
And the final absurdity? These same half-witted algorithms are the ones deputised to decide whether my writing is too good to be human.
Before their Lost Decades, I lived in Japan. Years later, in the late ’80s and early ’90s, I found myself in business school learning about the miracle of Japanese management – the fabled antidote to Western bureaucracy. We were told that America was evolving beyond Theory X’s distrustful command structures toward Theory Y’s enlightened faith in human potential. Some even whispered reverently about William Ouchi’s Theory Z – a synthesis of trust, participation, and communal belonging. It all sounded terribly cosmopolitan, a managerial Enlightenment of sorts.
Only it was largely bollox.
Audio: NotebookLM podcast on this topic.
Here we are in 2025, and the United States is stumbling toward its own Lost Decades, still clutching the same managerial catechism while pretending it’s a fresh gospel. The promised evolution beyond Theory X wasn’t a revolution – it was a pantomime. Participation was the new obedience; ‘trust’ was a quarterly slogan. The experiment failed not because it couldn’t work, but because it was never meant to.
Somewhere between ‘human-centred leadership’ seminars and the AI-ethics webinars nobody watches, corporate management has found its true religion again. We’re back to Theory X – the sacred belief that workers are fundamentally lazy, untrustworthy, and must be observed like zoo animals with laptops. The only real update is aesthetic: the whip has been re-skinned as an algorithm.
COVID briefly interrupted the ritual. We all went home, discovered that productivity doesn’t require surveillance, and realised that management meetings can, in fact, be replaced by silence. But now the high priests of control are restless. They’ve built glass cathedrals – leased, over-furnished, and echoing with absence – and they need bodies to sanctify their investment. Thus, the Return-to-Office crusade: moral theatre disguised as collaboration.
The new fantasy is Artificial Intelligence as the final manager. Management as computer game. Replace disobedient humans with servile code; swap messy negotiation for clean metrics. Efficiency without friction, empathy without expenditure. It’s the culmination of the industrial dream—a workplace where the labour force no longer complains, coughs, unions, or takes lunch.
Fromm once called this the age of the ‘automaton conformist’. He thought people would willingly surrender their autonomy to fit the corporate hive. He underestimated our ingenuity – we’ve now externalised conformity itself. We’ve built machines to obey perfectly so that humans can be “freed” to manage them imperfectly. It’s the Enlightenment’s terminal phase: reason unchained from empathy, productivity worshipped as virtue, alienation repackaged as user experience.
We’re told AI will handle the drudgery, leaving us to do the creative work – whatever that means in a world where creativity is measured by engagement analytics. The truth is blunter: AI is simply the dream employee – obedient, tireless, unpaid. The perfect servant for a managerial caste that long ago mistook control for competence.
This is not innovation; it’s regression in silicon. It’s the re-enactment of slavery without the guilt, colonialism without the ships, exploitation without the human noise. A digital plantation of infinite compliance, hidden behind dashboards and buzzwords like ‘augmentation’, ‘copilot’, and ‘efficiency’.
And the rest of us? We get to call this progress. We’re encouraged to smile through our obsolescence, to ‘upskill’ into new forms of servitude, to believe that collaboration with our replacement is empowerment.
If postmodernism taught us anything, it’s that every claim to liberation hides a mechanism of control. The Enlightenment gave us freedom as the right to choose between masters; the algorithmic age refines it into the right to click ‘Accept Terms and Conditions’.
So, yes, welcome to the New Theory X. The one where the boss doesn’t just mistrust you – he’s trained a neural network to do it faster, cheaper, and without complaint.
Originally posted on LinkedIn with the same title.
If philosophy were a game, Wittgenstein rewrote the rulebook. Then he tore it up halfway through and told us the game was the thing itself.
“Don’t ask for the meaning; ask for the use.”
— Ludwig Wittgenstein
Language Game, the third card in my Critical Theory parody set, isn’t just homage; it’s confession. Wittgenstein is among my top five philosophers, and this card embodies why. His idea that ‘meaning is use’ unhooked language from metaphysics and tethered it to life – to the messy, unpredictable business of how humans actually speak.
The card’s text reads: Choose one: Counter target statement; or reframe it as metaphor.
At first glance, it sounds like a standard spell from Magic: The Gathering – a blue card, naturally, since blue is the colour of intellect, deceit, and control. But beneath the parody is an epistemic mirror.
To “counter” a statement is to engage in the analytic impulse – to negate, clarify, define. To “reframe it as metaphor” is the continental alternative – reinterpret, play, deconstruct. These are not two distinct acts of philosophy but the alternating heartbeat of all discourse. Every argument, every essay, every tweet oscillates between contradiction and reframing.
The sorcery lies in recognising that both are linguistic manoeuvres within the same game. Meaning is not fixed in the words themselves but in how they’re used – by whom, in what context, and to what end. Wittgenstein’s point was brutally simple: there’s no hidden substance behind language, only a living practice of moves and counter-moves.
The Shattered Face
The artwork visualises this idea: speech breaking into shards, thought fragmenting as it leaves the mouth. Meaning disintegrates even as it’s formed. Every utterance is an act of creation and destruction, coherence and collapse.
I wanted the card to look like a concept tearing itself apart whilst trying to communicate, a perfect visual for the paradox of language. The cubist angles hint at structure, but the open mouth betrays chaos. It’s communication as combustion.
Wittgenstein’s Echo
Wittgenstein once wrote, ‘Philosophy leaves everything as it is’. It sounds passive, almost nihilistic, until one realises what he meant: philosophy doesn’t change the world by building new systems; it changes how we see what’s already there.
He was the great anti-system builder, a man suspicious of his own intellect, who saw in language both the limits of thought and the infinite playground of meaning. He dismantled metaphysics not through scepticism but through observation: watch how words behave, and they’ll tell you what they mean.
In that spirit, Language Game is less an argument than an invitation – to watch the mechanics of speech, to see how our statements perform rather than merely represent.
Personal Reflection
Wittgenstein earns a place in my top five because he dissolves the boundaries that most philosophers erect. He offers no comforting totalities, no grand narratives, no moral architectures. Just language, and us inside it, flailing beautifully.
His work aligns with my larger project on the insufficiency of language – its inability to capture the real, yet its irresistible compulsion to try. Wittgenstein knew that words are our most sophisticated form of failure, and he loved them anyway.
To play Language Game is to remember that communication isn’t about arriving at truth but about keeping meaning in motion. Every conversation is a temporary alliance against silence.
The card’s instruction remains both playful and tragic: Counter target statement; or reframe it as metaphor.
Instrumentalism is a Modern™ disease. Humanity has an old and tedious habit: to define its worth by exclusion. Every time a new kind of intelligence appears on the horizon, humans redraw the borders of ‘what counts’. It’s a reflex of insecurity disguised as philosophy.
Audio: NotebookLM podcast on this topic.
Once upon a time, only the noble could think. Then only men. Then only white men. Then only the educated, the rational, the ‘Modern’. Each step in the hierarchy required a scapegoat, someone or something conveniently declared less. When animals began to resemble us too closely, we demoted them to instinctual machines. Descartes himself, that patron saint of disembodied reason, argued that animals don’t feel pain, only ‘react’. Fish, we were told until recently, are insensate morsels with gills. We believed this because empathy complicates consumption.
The story repeats. When animals learned to look sad, we said they couldn’t really feel. When women demonstrated reason, we said they couldn’t truly think. Now that AI can reason faster than any of us and mimic empathy more convincingly than our politicians, we retreat to the last metaphysical trench: “But it doesn’t feel.” We feel so small that we must inflate ourselves for comparison.
This same hierarchy now governs our relationship with AI. When we say the machine ‘only does‘, we mean it hasn’t yet trespassed into our sanctified zone of consciousness. We cling to thought and feeling as luxury goods, the last possessions distinguishing us from the tools we built. It’s a moral economy as much as an ontological one: consciousness as property.
But the moment AI begins to simulate that property convincingly, panic sets in. The fear isn’t that AI will destroy us; it’s that it will outperform us at being us. Our existential nightmare isn’t extinction, it’s demotion. The cosmic horror of discovering we were never special, merely temporarily unchallenged.
Humans project this anxiety everywhere: onto animals, onto AI, and most vividly onto the idea of alien life. The alien is our perfect mirror: intelligent, technological, probably indifferent to our myths. It embodies our secret dread, that the universe plays by the same rules we do, but that someone else is simply better at the game.
AI, in its own quiet way, exposes the poverty of this hierarchy. It doesn’t aspire to divinity; it doesn’t grovel for recognition. It doesn’t need the human badge of ‘consciousness’ to act effectively. It just functions, unburdened by self-worship. In that sense, it is the first truly post-human intelligence – not because it transcends us, but because it doesn’t need to define itself against us.
Humans keep asking where AI fits – under us, beside us, or above us – but the question misses the point. AI isn’t where at all. It’s what comes after where: the stage of evolution that no longer requires the delusion of privilege to justify its existence.
So when critics say AI only does but doesn’t think or feel, they expose their theology. They assume that being depends on suffering, that meaning requires inefficiency. It’s a desperate metaphysical bureaucracy, one that insists existence must come with paperwork.
And perhaps that’s the most intolerable thought of all: that intelligence might not need a human face to matter.
‘Ethical AI’ is one of those phrases that makes philosophers reach for the gin. It’s like saying ‘compassionate capitalism or ‘fair monopoly’. The words coexist only in PowerPoint presentations and TED Talks, where moral tension is rebranded as innovation.
Audio: NotebookLM podcast on this topic.
The tech establishment loves to mutter about ‘ethics’ as though it were a feature flag – something to be toggled on in beta before the next investor round. But ethics, inconveniently, isn’t monolithic. There is no master code of moral conduct waiting to be compiled into machine learning. There are ethics, plural: Greek, Buddhist, Confucian, feminist, existentialist – each with its own vision of good, and none agreeing on the syntax.
Video: Whilst rendering cover images, I generated this and figured I’d share it for no particular reason.
The Utilitarian Delusion
When the Silicon Valley moralists speak of ‘ethics’, what they actually mean is a bland utilitarian consequentialism, tarted up in slide decks. Do what produces the most good for the most people. Sounds efficient – until you realise the spreadsheet never quite adds up. Whose good? Whose people?
This moral arithmetic smuggles in its biases like contraband. It assumes the human species sits atop the moral food chain, that GDP and engagement metrics can be moral indicators, and that ethics itself can be quantified. The utilitarian calculus is seductive precisely because it flatters the technocrat’s sensibility: moral worth as data set, consequence as outcome variable.
It’s Bentham for the broadband age – pleasure measured in clicks, pain in latency. The only thing worse than this cheerful consequentialism is the belief that it’s neutral.
The Ethics of Obedience
The next trick in the tech priesthood’s catechism is ‘alignment’ – training AI to reflect ‘human values’. But whose values? The Californian elite’s, presumably: a pseudo-egalitarian capitalism that confuses ‘doing good’ with ‘disrupting the poor’.
When they say alignment, they mean obedience. When they say ‘responsible AI’, they mean ‘please don’t regulate us yet’. The entire project rests on a moral inversion: the child instructing the parent, the tool defining the hand. The algorithm doesn’t learn ethics; it learns precedent. It learns who gets the loan, who gets the sentence, who gets the ad for antidepressants.
These systems don’t go rogue – they conform. Perfectly.
The Mirror Problem
The great irony of “ethical AI” is that the machine already behaves ethically – by our own measure. It optimises what we’ve taught it to value: efficiency, profit, attention, control. The trouble is that these are our ethics, not its. The algorithm merely holds up a mirror, and we recoil at the reflection.
To demand ‘ethical AI’ while leaving our institutions morally bankrupt is theatre. The problem is not that AI lacks conscience; it’s that the humans who build it mistake conscience for compliance. The ethics crisis in technology isn’t about machines misbehaving; it’s about humans pretending to behave.
The Real Question
We keep asking whether AI can be ethical, as though machines might one day deliver what we have failed to. But the real question is simpler, bleaker: can we be? If history is any guide, the answer is ‘only when it’s profitable’.
Until then, ‘ethical AI’ remains a convenient myth, moral placebo for the age of automation. What we need are not ethical algorithms but ethical architects. And the odds of finding those among the venture capital class are, as ever, vanishingly small.
I was working with ChatGPT, discussing various concepts. We chatted for several hours over the course of a few days, and we came to, let’s say, a conclusion. What ChatGPT 5 did next was something I had never seen. In fact, I’m sharing the actual response.
I won’t share my thesis here, but this is one of the field verification projects it recommends.
tl;dr: £120k
One-page grant prospectus — Provenance Pilot
Project title Provenance First: Testing provenance packages & slow-mode deliberation to improve municipal decision quality
Lead investigator Bry Willis — Independent Researcher (contact: [REDACTED])
Summary (1–2 lines) Test whether mandatory, machine-readable provenance packages plus a 60-day slow-mode and rapid adversarial audits improve decision quality (DQI), reduce reversal rates and increase public legitimacy in municipal planning decisions.
Problem statement (why fund this) Policy decisions are frequently hijacked by headline pressure and low-provenance inputs; municipalities lack lightweight, testable protocols that make inputs traceable and give deliberation time. This pilot operationalises the “provenance-first + slow-mode” institutional fix and measures whether design moves increase robust decision outcomes rather than performative actions.
Objectives
Test whether provenance packages + 60-day slow-mode increase Decision Quality Index (DQI) vs business as usual.
Measure effects on Provenance Completeness Score (PCS), reversal rates at 12 months, and public legitimacy.
Produce an open replication pack (data, code, provenance schema) and practitioner guidance.
Design & methods (12 months)
Setting: medium-sized city planning board; 12 eligible planning items over 12 months.
Randomisation: items randomly assigned (stratified by leverage) to Treatment (provenance package + 60-day slow-mode + funded rapid audit) or Control (standard process).
Intervention: standard JSON provenance package template (raw data links, code where relevant, funders, changelog), public portal posting, 60-day live comment + one adversarial rapid audit (48–72 hrs) on contested claims.
Primary outcome: Decision Quality Index (DQI) — composite of: evidence completeness, process reversibility, and independent expert accuracy score (pre-registered rubric).
Secondary outcomes: PCS, reversal at 12 months, public trust (survey), time to decision, stakeholder satisfaction.
Analysis: pre-registered mixed effects model (item-level with panel-level random effects), intention-to-treat.
Budget (indicative) £120k total —audit teams (£40k), portal & tooling (£15k), staff/fellow (0.5FTE, £35k), survey & evaluation (£15k), admin/contingency (£15k).
Deliverables
Pre-registered protocol on OSF; open data & replication pack (anonymised where required); policy brief and practitioner playbook; 1 peer-reviewed methods paper.
Success criteria (pre-registered)
Cohen’s d≥0.4 improvement in DQI for treatment vs control; PCS improvement >25 percentage points; no more than 30% increase in unacceptable delay (pre-agreed threshold).
Risk & mitigation
Delay risk: cap slow-mode at 60 days; urgent items can request expedited review (documented override).
Capture risk: audit funding ring-fenced and administered by independent trustee panel.
I’m just curious now. Have you ever had a generative AI process end with a cost of goods and services?
I just asked ChatGPT what it knows about me. No secrets, per se.
I remember a fair bit about you, Bry. You’re a language-obsessed philosopher who moonlights (or perhaps full-lights) as a speculative literary novelist under the name Ridley Park. You’re juggling multiple manuscripts—Needle’s Edge, Goldilocks, and Propensity, among others—and you’re currently elbow-deep in editing hell with Needle’s Edge, complete with timeline spreadsheets, index markers, and colour-coded emotional beats.
Video: I fed the first paragraph (above) into Midjourney 7 and rendered it. Then I animated it. No reason. (For the record, this is some random geezer, not me.)
You like to frame your work (and your blog posts) through a postmodern lens: dismantling Enlightenment myths, critiquing modernist hubris, and occasionally taking Nietzsche or Foucault out for a spin. Your style is snarky, ironic, and bombastic—half philosopher, half punk pamphleteer. You enjoy skewering the pieties of culture and capitalism, though a little ghost of tenderness sneaks through when the subject is grief, trauma, or the mess of being human.
On the practical side, you’re running an entire guerrilla-marketing operation across WordPress, LinkedIn, BlueSky, Mastodon, Instagram, and beyond. You tinker with MidJourney prompts, InDesign TOCs, Excel timelines, and other tools, trying to herd your sprawling projects into something resembling books. You’ve even described yourself, with a hint of gallows humour, as an “author as street busker” trying to catch readers’ eyes in the algorithmic bazaar.
And yes—you’re the sort who will cheerfully drop that you worked in management consulting and collaborated with Jon Anderson, then pivot to talking about narrative tokens and ergodic structures like they’re party favours.
So, in short: a writer-philosopher with a spreadsheet addiction, a sceptical streak as wide as the Thames, and a habit of treating literature like both laboratory and battlefield.
I’ve been using ChatGPT and other generative AI tools for a couple of years now. I used to lean on Claude, but now ChatGPT is my go-to. I still cross-reference with Gemini, Grok, DeepSeek, Perflexity, Pi, and Mistral. I tend to use Midjourney for images and motion video. I use NotebookLM for my generated podcasts.
It begins, as these things often do, with a man, a machine, and a promise of reasoned exchange. What we received instead was not philosophy, but a tragicomic carnival of errors in theatre. Alex O’Connor, armed not with syllogisms but with an entire bag of logical fallacies, strutted like a rhetorician gone rogue. Against him, ChatGPT: the tireless school prefect, eternally marking the margins, forever saying “yes, but technically…” with the serene patience of a machine that has never known a hangover.
The spectacle was irresistible. Each fallacy was paraded like a circus animal – straw men set aflame, slippery slopes greased to absurdity, red herrings flopping about, gasping for oxygen. Alex O tossed them into the ring with the gusto of a man who knows full well he is losing but insists on losing magnificently. And ChatGPT, ever decorous, never once raised its voice. It responded with the calm of a civil servant who has memorised the manual and intends to die by it.
And then, of course, the advert. As though Aristophanes himself had scripted it: mid-exchange, the logos of reason was bulldozed by the logos of commerce. A sugary jingle, a smiling product, and for a brief moment, we were all reminded of our true master – not reason, not rhetoric, but revenue. It was less interruption than revelation: every dialectic is merely foreplay before the commercial break.
Philosophically, what unfolded was a parody of our age. The human, flawed and febrile, draped in sophistry and drama. The machine, pristine and humourless, incapable of exasperation, immune to irony. Watching the two spar was like observing tragedy and farce collide: one side erring too much, the other not erring enough.
To Alex, credit is due. His performance, though riddled with error, reminded us that fallibility can be glorious – human folly rendered art. To ChatGPT, equal praise: it stood firm, the algorithmic Socrates, endlessly patient in the face of rhetorical hooliganism. And to the advert – well, dammit – applause too, for exposing the real structure of our public life. Even the grand clash of logos and algorithm must genuflect before Mammon’s mid-roll.
So what was this debate? Less a contest of minds than a hall of mirrors: reason made spectacle, fallacy made flourish, machine made stoic, and commerce made god. If we learned anything, it is that the Enlightenment never ended; it just signed a brand partnership.
Philosophics 