Tag: artificial intelligence

The World’s Most Dangerous Idea?

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4–6 minutes

Am I the only one who can’t resist a massive eyeroll – and, let’s be honest: jaw-drop – what you hear transhumanism couched as evolution? To me, it incites a similar reaction to hearing people witter on about machine consciousness, but I’ll sideline that topic.

My objection is linguistic: transhumanism often borrows the prestige of evolution to describe what is more precisely technological mediation. The fact that a device is worn, implanted, or integrated into a body does not by itself move it from tool-use into biological descent. The offspring still inherits the organism, not the upgrade. Technology is not heritable.

Audio: NotebookLM summary podcast of this topic.

Consider rhinoplasty. Rhinoplasty changes the presented phenotype, not the inherited genotype. The child inherits the developmental instructions, not the parent’s post-surgical edit. Likewise, a neural implant, prosthetic limb, exoskeleton, gene-unrelated enhancement, or titanium jaw of techno-vanity may alter the lived organism, but it does not thereby alter the reproductive line. This is the category error: Acquired modification is mistaken for inherited transformation.

So, transhumanism often confuses the edited encounter-profile of an organism with the evolutionary alteration of the organismic lineage. The rhinoplasty case is good because it shows the absurdity without needing much apparatus. No one sane thinks a nose job rewrites the germline. Yet when the modification is sufficiently glamorous, especially when welded to futurist rhetoric and venture-capital incense, people suddenly start talking as if augmentation equals evolution.

A prosthesis is to evolution what rhinoplasty is to heredity: a modification of presentation, function, or encounter, not a transformation of descent. The confusion arises when the altered individual is mistaken for an altered lineage.

The question isn’t: Can transhumanism be defended under some broader theory of evolution, cultural inheritance, niche construction, or techno-biological co-development? Sure, it can.

But my claim is narrower: The term evolution is being made to do illicit rhetorical work when acquired, engineered, prosthetic, or embedded modifications are spoken of as though they were equivalent to heritable biological transformation.

The problem is that we are sliding from tool-use to augmentation to enhancement to transformation to evolution. This move isn’t illegal, but it should attract your attention.

At each step, something plausible is smuggled into something grander. By the end, an implanted device or technical prosthesis has been rhetorically promoted into ‘the next stage of human evolution’, when what has actually happened is that an organism’s encounter-capacities have been modified by artefactual mediation.

In the end, I’m not claiming that transhumanist practices can’t affect selection environments, social organisation, embodiment, or cultural transmission. What I am claiming is that calling such practices evolution without specifying the relevant register is a linguistic inflation. It trades on the authority of biological evolution while often describing technological mediation, individual augmentation, or cultural change.

In my book, more accurate terms might be augmentation, prosthesis, mediation, tool evolution, cultural inheritance, or niche modification, but these re so glamorous or click-baity. I might as well wear a hat and call myself post-human.

The_Evolution_Category_ErrorDownload

Addendum

I chatted up ChatGPT, so I am adding this response:

Exactly. The carried / worn / embedded distinction is mostly a boundary fetish, the sort of thing humans adore because skin looks like an ontological border and apparently everyone remains impressed by packaging.

An axe, spectacles, a pacemaker, a cochlear implant, a smartphone, and a neural implant all sit on the same broad continuum: external mediation of capacity. Some are held. Some are worn. Some are surgically installed. But unless the alteration becomes heritable, it is not biological evolution in the strict sense. It is phenotypic augmentation, affordance expansion, or environmental coupling.

The useful distinction is threefold:

  1. Biological evolution
    Heritable variation changes across generations. The organism-line changes.
  2. Cultural / technological evolution
    Tools, techniques, designs, institutions, and practices vary, are selected, retained, modified, and transmitted. Axe technology evolves. Writing evolves. Surgery evolves. The appalling human habit of calling every interface “intuitive” also evolves, though downward.
  3. Individual augmentation
    A particular body-system gains capacities through artefacts. This may change what the organism can do, but it does not by itself alter the inherited biological line.

Your axe example is clean because it exposes the smuggling. The axe helped alter human selection environments. It extended reach, force, food access, defence, craft, and perhaps social organisation. That may feed into gene-culture co-evolution over long scales. But the axe itself is not an evolved human trait. It is a technological mediator that participates in the evolutionary ecology.

The same holds for transhumanist implants. A retinal implant may alter one person’s perceptual affordance field. A brain-computer interface may alter one person’s control loop. A prosthetic limb may alter one person’s mobility. But unless such modifications become transmissible across generations, biologically or through some stable reproductive architecture, calling them “evolution” is loose metaphor masquerading as profundity. Very on-brand for techno-utopianism: rename engineering as destiny and hope the grant reviewers are sleepy.

From your MEOW angle, the better formulation is:

The relevant transformation is not evolutionary but mediational. Tools, prostheses, implants, and interfaces reconfigure encounter-events by altering the affordance structure through which an organism meets constraint. Whether the device is carried in the hand, worn on the face, implanted beneath the skin, or networked through the environment is secondary. The organism-world relation changes; the inherited organism does not necessarily change.

That also lets you avoid an overcorrection. Transhumanist enhancement is not irrelevant. It may be historically enormous. But its primary register is encounter, not inheritance; mediation, not mutation; technical lineage, not biological descent.

So the blunt line is:

Transhumanism mistakes the evolution of tools for the evolution of the tool-user.

Not always, not necessarily, but often enough that the slogan deserves being dragged into the alley and relieved of its metaphysical wallet.

Je m’accuse, on s’accuse

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The LLM Witch-Hunt and the Enemy Within

I recently read a piece arguing, with considerable sophistication, that LLMs represent an unprecedented psychological threat – that conversational systems operating at a planetary scale change the geometry of human susceptibility in ways that demand serious governance responses. The author wasn’t wrong about the effects. This isn’t the debate, but she was wrong about the story. The effects are real, and the narrative erected around them is the oldest displacement manoeuvre in the repertoire

Continued on Substack.

Audio: NotebookLM summary podcast of this topic.

How I Use AI in My Publication Workflow

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5–8 minutes

This is not a philosophical post. Well, it’s about my personal philosophy of using LLMs and AI agents in my writing and publication workflow, which is a different thing. I’ll structure it as I might have done a music project back in the day, because that framing still makes more sense to me than anything the tech industry has come up with.

Audio: NotebookLM summary podcast of this topic.
NotebookLM Infographic on this topic.

Preproduction

Not all projects make it into production. Others were never intended to. But they all begin with at least a kernel of an idea — and some arrive fully formed, as if sprung from the head of Zeus, already wearing armour and looking for a fight.

Pre-ideating

What the hell is pre-ideating? I just made it up for this use case because that’s how I roll.

As I understand it, some people need help thinking of topics. This is not my problem. My problem is managing ideas rather than generating them. I have a backlog that will outlast me, so I don’t use this step. But it exists, and it’s probably the most widely discussed AI use case in creative circles: you prompt the model to suggest themes, genres, or concepts. Give me five ideas for a mystery novel. Or, if you’re feeling ambitious: Give me five ideas for a research paper in quantum physics. The model obliges. Whether what comes back constitutes an idea in any philosophically interesting sense is a question I’ll save for another day.

Ideating

This is where I usually enter the process, and the ideation takes shape in one or several different ways. The most common is simply a discussion – a sustained back-and-forth. A recent example: I was reading Judith Butler’s Gender Trouble and found myself with clarifying questions at every turn. Not because Butler is unclear, but because the implications kept ramifying in directions I wanted to follow. That extended dialogue – with ChatGPT in this instance – eventually became the philosophical core of Two Kings, currently stalled in Production.

Butler’s argument about incest taboos as foundational to broader regimes of sex and gender regulation gave me a narrative frame. The conversation helped me see what I actually thought about it, which is the more important thing. The LLM didn’t give me the idea. It gave me a sounding board patient enough to entertain the idea at two in the morning – it was actually two in the afternoon, but who’s looking?

Research

Another obvious use case, and one I use regularly. Continuing the Butler example: I asked about several feminist theorists she references, wanting to understand the lineage I was stepping into. But here’s a cleaner illustration. Writing as Ridley Park, I produced a novella, Sustenance, set in Iowa. I’ve visited Iowa several times, but I needed local flora and fauna for descriptive texture in certain scenes, so I asked

In the old days, I’d have gone to Google, Wikipedia, or I’d track down an Encyclopædia Britannica. The process is faster now, and the results are generally better for this kind of lateral, contextual research. For anything where accuracy is genuinely load-bearing, I verify. That’s not a criticism of the tool; it’s just basic epistemic hygiene.

Confirmation

Sometimes I have an idea and want to know whether someone’s already done it because I have no interest in reinventing wheels, and even less in reinventing them badly.

So I ask: Has anyone written X? What are the most significant treatments of Y? What typically comes back is a list of a dozen or more analogous sources. I review them and decide: does my idea still have independent purchase, or am I just writing a worse version of something that already exists? Sometimes I sharpen the idea in response. Sometimes I incorporate what I find, either to build on it or to identify where the existing literature is misframed, assumes too much, or has quietly imported the wrong ontological grammar. This last move is something of a professional tic.

Production

Drafting

I don’t use LLMs for full drafts. This is an obvious use case for those who do, particularly if the goal is volume – especially for the person who has already prompted for which genre currently has high demand and low representation on Amazon, and is now logically committed to producing it. That’s a coherent workflow – just not mine.

Edits and Revision

This I use often, and it’s probably where I get the most consistent value. After writing a passage or section, I feed it to one or more models with context already established — thesis statement, abstract, outline, supporting documents. What comes back varies: typographical errors, odd phrasings, unintentional repetitions (and, occasionally, new ones the model has helpfully introduced), suggested rewrites, observations about framing. I don’t treat any of this as instruction. I treat it as a second read from a reader who has no ego investment in agreeing with me – and yet obviously does. The important distinction is input versus output. I’m not asking it to write. I’m asking it to respond to what I’ve written.

Continuity

Are there gaps? Dropped threads? Promises made in chapter two that chapter seven has forgotten entirely? This is a genuinely useful mechanical check – the kind of thing that’s easy to miss when you’ve been inside a manuscript long enough to stop reading what’s actually there.

Flow

Do the scenes and chapters move well? Does the transition from one section to another feel like a logical step or an unannounced lurch? Useful, with the caveat that models have aesthetic preferences that don’t always align with mine, and I treat their flow suggestions accordingly.

Pacing

Is the pacing appropriate — both for the genre and for the particular piece? These are separate questions. A thriller has genre conventions around pace; a particular thriller might have reasons to subvert them. The model can flag where the pacing drifts; the judgement call about whether that’s a problem remains mine.

Postproduction

Formatting and Layout

I use AI for ideas about how to present content on the page: chapter opens, font choices, sizes, running headers, folios. This is design at the level of convention and taste rather than technical execution. I find it useful as a first pass — it surfaces options I might not have considered, which I then either adopt, adapt, or discard.

Cover Ideas

Thematic cover concepts, whether or not I ultimately outsource the art and creative work. I find this a productive way to articulate what the book is doing before I have to explain it to someone else.

How To

I use InDesign, Illustrator, and Photoshop with competence but not expertise. For specific technical tasks – how do I do this thing in InDesign — I ask. I also still use Google, YouTube, and the occasional book. These are not competing resources; they’re complementary ones, and which I reach for depends on what kind of answer I need.

Support and Maintenance

Marketing and Placement

Target markets, genre positioning, how to frame the work for audiences who didn’t watch it being assembled. This is a legitimate use case and one I engage with, even if marketing remains a word I say with a slight internal wince.

I also use platforms like ElevenLabs for audio, NotebookLM for podcast summaries and infographics, and Nano Banana or Midjourney for images.

Keywords and Descriptions

Adjacent to marketing but more administrative in character, the metadata layer that determines whether the work is findable by the people who would want it. Less interesting to think about than almost anything else in the process, and therefore an excellent candidate for AI assistance.

None of the above replaces the work. That’s the point. The writing is still the writing.

A Brief and Largely Accurate History of Punctuation

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1–2 minutes

For most of human history, written Latin looked something like THISISASENTENCEABOUTPHILOSOPHYORWARYOUCHOOSE, and readers were simply expected to get on with it. And of course, in ALL CAPS. This was not considered a problem. The Romans were not known for their sensitivity to the needs of others.

The Romans did, briefly, experiment with the interpunct – a modest dot deployed between words, giving the reader something like THIS·IS·A·SENTENCE·ABOUT·PHILOSOPHY·OR·WAR·YOU·CHOOSE – before apparently deciding this was excessive hand-holding and abandoning it entirely. Punctuation’s first appearance in Western prose was thus also its first act of self-destruction. A precedent, as we shall see, that held.

Audio: NotebookLM summary podcast of this topic.

Relief came, eventually, from the most unlikely of sources: monks. Specifically, Irish and Anglo-Saxon monks in the 7th and 8th centuries, who were copying Latin texts they couldn’t actually read fluently, and who introduced spaces between words as a personal coping mechanism. Civilisation has strange bedfellows.

The comma, the full stop, and their assorted relatives arrived with the printing press – Aldus Manutius and the Venetian humanists essentially standardising the breath-marks of prose into something reproducible at scale. Punctuation became, in this period, the bureaucratisation of rhythm. A noble project. Mildly tyrannical in execution.

The em dash, meanwhile, had an entirely respectable career throughout the 18th and 19th centuries — a mark of genuine syntactic energy, used to interrupt, to pivot, to hold two thoughts in productive tension — before being left largely to the eccentric and the emphatic.

Then came the large language models. Within approximately eighteen months, the em dash was resurrected from the dead to become the default unit of thought, issuing them faster than Oprah Christmas giveaways. Every clause got one. Sometimes a sentence received two, bracketing a thought that required neither a bracket nor a thought. The em dash ceased to mean interruption and began to mean I am text generated at scale. Readers noticed. Then they mocked it. Then, following the immutable logic of cultural exhaustion, they stopped using it entirely. The em dash is now extinct — which is a shame, really.

The Author Did Not Write This

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4–6 minutes

The LinkedIn consensus has spoken: if you used AI in the writing process, you are not the author. The position is stated with the confidence of someone who has never hired a ghostwriter, employed a research assistant, submitted to a heavy editor, or considered that the Gettysburg Address was almost certainly not written by Lincoln.

Image: I couldn’t not share this Midjourney 8.1 image. It may not have understood the assignment.
Audio: NotebookLM summary podcast of this topic.

Authorship has never been a production relation. It has always been an attribution relation — an institutionally stabilised answer to the question of which name the practice elects to put on the cover. These are not the same thing, and conflating them is the error from which every subsequent confusion proceeds.

The ghostwriter has existed as long as commercial publishing. The political speechwriter is so normalised that nobody considers it worth remarking. The celebrity memoir, the corporate thought-leadership piece, the attributed editorial — these are not edge cases or embarrassing exceptions. They are the normal operation of every writing-adjacent industry that has ever existed. The name on the cover has never reliably indicated the hands on the keyboard, and the industry has never seriously pretended otherwise. It has simply preferred not to discuss it at dinner.

AI changes the tool. It does not change the structure. The person who prompts, selects, curates, revises, and publishes is doing what commissioners of ghostwriters have always done. What has changed is that AI makes the mediation visible in a way that polite convention previously concealed. Visibility triggers the purity reflex. What presents itself as a defence of authentic authorship is a defence of a particular fiction — the Romantic author as solitary originating consciousness — that the industry never consistently believed and certainly never consistently practised.

The purity position also fails on its own terms before it gets started. Consider the spectrum of AI-assisted writing: a full draft submitted for light polish; a human argument substantially revised by AI; collaborative ideation followed by AI drafting; a kernel of an idea handed over for full execution. These are genuinely different in terms of human contribution. The zealot position requires a threshold somewhere on this spectrum below which authorship lapses. It never specifies where. More fatally, it has no means of verification. There is no external method of determining where on the spectrum any given piece of writing falls. The detector tools are probabilistic noise that disproportionately penalise competent prose. Any audit mechanism sophisticated enough to catch first-order evasion immediately generates a second-order workaround. The regress terminates only at continuous surveillance of the writing process — panoptical authorship as the logical endpoint of the position taken seriously.

NotebookLM Infographic on this topic.

Then there is the recursion problem, which the zealot never addresses because it is fatal. The stochastic parrot charge against AI — that it merely recombines absorbed linguistic patterns without genuine origination — describes with considerable accuracy what human cognition also does. The writer’s training data is the Dickens read at ten, the billboard absorbed on a commute, the argument overheard on public transit, the half-remembered essay that shaped a position without ever being consciously cited. The causal chain of any human idea disappears into an unauditable cognitive history. Genuine origination in the sense the purity position requires has never existed. The Romantic author was always a retrospective confabulation. Barthes said so in 1967. The industry nodded politely and continued invoicing.

What the zealot is defending is not authorship. It is a particular grammar of authorship — one that selects compositional origin as the threshold criterion, applies it selectively and unverifiably, and uses the resulting suspicion as a status boundary. It is guild behaviour dressed as principle, which is understandable as a response to a genuine economic threat but should not be mistaken for a philosophical position.

Authorship is the position a culture elects to stabilise after the work has already been produced through far messier means. It has always been thus. AI did not break the fiction. It just made the fiction harder to keep a straight face about.

The Rest of the Story

I’ve written about this before. I am not an AI apologist, but I am peeved by anti-LLM zealots, who clearly haven’t thought through their arguments.

I finished reading A.J. Ayer’s Language, Truth, and Logic, the part about Bertrand Russell’s claim about ‘The author of Waverley was Scotch‘. My brain latched onto authorship, and my emotional response was WTF? I have other problems with Russell and Ayer on this, but that’s a matter for another day.

To make my point, this page up to the ellipsis is the output of Claude after an extended dialogue with it and ChatGPT after I read Ayers, and something didn’t sit quite right. I am not ashamed to use LLMs in my authoring workflow and am not ashamed to mention it, as here. Almost all of these thoughts are mine. I’ve simply asked Claude to organise the output. It’s good enough to output as-is, and any edits would be trivial, so I won’t bother. I probably could have made the edits in as much time as it took to type this, but I’ve got nothing to hide. I’m just a human with access to technology circa 2026.

The Environment Always Wins: The Myth of Pure Voice

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4–6 minutes

There’s a certain kind of cultural panic that tells you more about the panickers than about the thing they are panicking about. The current hysteria over AI-inflected prose is a good example.

The argument, insofar as it deserves the name, goes roughly like this: LLMs produce prose with identifiable features – a certain blandness, a fondness for the em dash, a tendency toward tidy three-part structure. Writers who use these tools risk absorbing those features. The authentic human voice is therefore under threat. Something precious is being diluted by contact with the machine.

This is sentimental rubbish, and it is worth saying so clearly before doing anything else – and a sort of virtue signalling.

Audio: NotebookLM summary podcast of this topic.

I use LLMs daily. For research, for editorial pushback, for smoothing passages that have gone awry. This means I spend hours a day reading a particular kind of output. You’d have to be delusional not to admit it has effects. Certain phrasings start feeling natural that didn’t before. Certain rhythms begin to recur. Certain words might not have otherwise come into use. I notice this and note it without particular alarm, because I’ve read enough to know that this is just what environments do.

Read nothing but McCarthy for a month, and your sentences will start hunting for the spare declarative. Spend a year editing academic philosophy, and you will catch yourself reaching for ‘insofar as’ and ‘it’s worth noting’ in casual conversation. Live in a city long enough, and its cadences work their way into your syntax. This isn’t contamination, the negative moralist dispersion. It’s how language acquisition works for as long as one is alive and reading. Voice isn’t a spring. It’s a river, a moving accumulation of every tributary it has passed through.

The prestige game being played by the anti-LLM faction isn’t difficult to spot. When Dostoyevsky shapes a young writer’s cadence, we call it influence and treat it as evidence of a serious literary education. When a game world shapes a child’s imagination – I homeschooled my son in the manner of unschooling, and his primary corpus for years was World of Warcraft and its attendant lore before shifting to Dark Souls – and that child ends up reading Dante and Milton unprompted in year seven, the same mechanism has clearly operated. The source was not canonical, the outcome was. But the respectable hierarchy of influences cannot easily accommodate this, because the hierarchy was never really about the mechanism. It was about the cultural status of the inputs.

The more interesting observation isn’t about those of us who use these tools. It’s about those who conspicuously do not.

A minor genre has emerged – charitably, I’ll call it a genre because cult feels morally loaded – consisting of writers anxiously purging their prose of anything that might read as AI-generated. It’s worth noting that they have read the lists. Telltale signs of LLM authorship: excessive hedging, em dashes, transitional summaries, the phrase ‘it is worth noting’. And so they scrub, redact, replace, and perform a kind of stylistic hygiene that’s a creative decision made in direct response to LLM discourse.

These writers aren’t free of the machine’s influence. They’re among the most thoroughly shaped by it. They simply have the more theatrical relationship – the counter-imitator, the purity-performer, the one who reorganises their entire aesthetic in orbit around the thing they claim to reject.

Thomas Moore, in Care of the Soul, observes that a child raised by an alcoholic parent tends to become either an alcoholic or a committed teetotaller. He presents this as a dichotomy, which is too neat, but the underlying point holds. Reactions are still relata – see what happens when you read too much philosophy and logic? The teetotaller has organised their life around the bottle as surely as the alcoholic has. Both are defined by it.

Opposition is one of influence’s favourite disguises.

The fair objection is that LLM influence may differ from other influences in kind rather than just in kind. Dostoyevsky is strange. Bernhard is strange to the point of pathology. A canonical prose style is idiosyncratic by definition, which is why it’s worth absorbing. In contrast, LLM output aims for plausible fluency and statistical centrality. Its pull may be more homogenising than the pull of a singular authorial sensibility.

That’s a real point. The environment in question has a centripetal force toward the mean that most literary influences lack.

But conceding the point doesn’t really rescue the panic. It just specifies the kind of influence involved. The mechanism remains identical to every other case of environmental absorption. And ‘this influence tends toward the generic’ is an ironically generic critique of a particular environment’s character rather than a claim that the environment is doing something ontologically unprecedented to the notion of authorship.

The question that actually matters aesthetically is not was this touched by AI? It is what did the writer do with the environment they inhabited? That’s always been the question. It remains the question. The machinery has changed; the problem of influence has not.

What the current schism actually reveals is not that AI is doing something new to writing. It’s that we’ve been operating with a fairy tale about what writing is. The fairy tale holds that voice is self-originating, that somewhere beneath the reading AND the editing AND the genre conventions AND the institutional pressures AND the decade of a particular editor’s feedback, there is a pristine you, unconditioned and pure, expressing itself directly onto the page.

This was always false. Writers have always been patchworks of absorbed environments. The only difference now is that one of the environments is a machine, and the machine is new enough that people haven’t yet learned to be comfortable with what it reveals about the rest.

The environment always wins. The only interesting question is which environments you choose, and what you make of them.

NotebookLM Infographic on this topic.

Art or Content

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3–4 minutes

So glad I took time out to watch a short exchange between Rick Beato and Justin Hawkins on whether music is becoming content rather than art. The question is framed in musical terms, but it hardly stops there. The same corrosion is visible in writing, visual art, criticism, and now, with grim inevitability, in AI-mediated production more broadly. The disease is not confined to music. Music merely makes the symptoms easier to hear.

For music, my aversion to pop music goes back to my youth. I was a kid when the Beatles practically invented pop music, but they left it to grow and continued exploring. Sadly, as solo artists, they mainly – not always – failed and rested on their laurels in pop. It’s not that their version or any pop music is inherently unlistenable. Surely, it’s not, if only by the aspiration of the pop moniker, but it has no depth, no soul, as it were. Some make this argument for Organic food. In essence, it involves an appeal to nature fallacy.

Audio: Slightly off, but not bad, NotebookLM summary podcast of this topic.

My own aversion to much pop music begins there. It is not that pop is necessarily bad, nor even that it is always shallow. That would be too crude and too easy. The problem is that pop often presents itself less as an artistic act than as a consumption object engineered for immediate uptake: catchy, frictionless, emotionally legible, and just disposable enough to make room for the next one. It is built to circulate.

That, for me, is the difference between content and art. Art may be accessible, even popular, but it retains some residue that exceeds its delivery mechanism. It resists total reduction to utility. Content, by contrast, is made to be processed. It is optimised not for depth but for throughput. Its highest ambition is not transformation, but engagement.

This is why the question matters beyond music. Writing, too, now lives under the same pressure. One is increasingly expected to produce not essays, arguments, or works, but units of output: posts, threads, reactions, takes, summaries, explainers, and other forms of polished verbal debris. The point is no longer to say something worth dwelling on, but to remain visible within the churn.

The issue, then, is not simply whether one should consume AI-generated material. That framing is too pious and too easy. The more interesting question is what the consumer thinks they are consuming. If a reader, listener, or viewer wants only speed, familiarity, and surface competence, then AI content is not a scandal at all. It is the logical endpoint of a culture that has already demoted art into a deliverable.

This is where the fuss over labelling enters. Is it a principled demand for honesty, or merely a theatrical gesture by people who still want the aura of art whilst consuming content on industrial terms? Some of it is clearly protectionism. Some of it is virtue signalling. But not all of it is empty. The insistence on labelling betrays an intuition, however muddled, that authorship still matters, and that not all artefacts are equivalent merely because they occupy the same screen-space.

The deeper question is whether we still want art at all, or whether we merely want the aesthetic styling of art attached to things optimised for convenience. Once a culture learns to prefer seamless output over resistance, recognisability over risk, and quantity over form, it should not act surprised when machines begin to serve it perfectly. They are only completing a trajectory already chosen.

So no, the issue is not AI alone. AI is only the latest mirror held up to a public that has spent years confusing availability with value and polish with depth. The real question is not whether machines can make content. Plainly, they can. The question is whether we still possess the appetite, patience, and seriousness required for art.

Image: Full image because the cover version is truncated. Generated by Gemini Nano Banana.

When Syntax Is Asked to Bear Too Much v1.2

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1–2 minutes

I published the first version of this essay in February, arguing that the Frege–Geach problem, that three-score-year-old albatross around expressivism’s neck rests on a category error. Analytic philosophers were polite about it in the way that analytic philosophers are polite about things they intend to ignore. I don’t often revise my manuscripts, opting instead to publish a new and improved version, but the meat of this one remained strong and not worth revisiting as much as fortifying.

The trouble was that I’d dissolved the problem without resolving it. Good enough for me. Others were less convinced. Telling people they’ve been asking the wrong question is satisfying but insufficient without a better one. Version 1.1 tidied the prose. Version 1.2 does the actual work.

The new section (§4, if you’ve already read previous versions) introduces recruitable expressions – a broader class of expressions (moral predicates, thick evaluative terms, epistemic and institutional vocabulary) whose full functional load is attenuated under embedding whilst a thinner inferential profile remains available for reasoning. The standard of practical inferential adequacy replaces the demand for semantic identity: what ordinary reasoning requires is not invariance but inferential sufficiency. And the pattern isn’t peculiar to moral language – a noted goal –, which means Frege–Geach stops looking like a special embarrassment for expressivism and starts looking like one symptom of a general feature of how natural language handles multi-functional expressions under logical stress.

The essay is dissolved as a demand for unrestricted semantic invariance. It is resolved insofar as the behaviour it identifies is explained, predicted, and shown to be general.

The revised paper is available here, near the rest of my manuscripts: DOI

Lastly, this essay is built on the foundations of A Language Insufficiency Hypothesis and The Architecture of Encounter, the latter of which wasn’t yet available for the initial publication.

As ever, I welcome the polite ignoring.

The Demise of Frege–Geach?

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4–5 minutes

Journal Entry

I published an essay on the Frege–Geach problem in February. I published an update yesterday. I still wasn’t satisfied, so I engaged with several LLMs. This was my approach.

The involved LLMs were:

  • Claude
  • Grok
  • ChatGPT
  • Gemini
Audio: NotebookLM summary podcast of this topic.
(This summary misses the mark in some ways, but it brings up some interesting observations along the way.)

First, I fed them some documents in no particular order, my goal being to share my own knowledge and position on the purported problem.

I started with Gemini. This was my prompt:

I am interested in resolving the Frege–Geach problem, but it seems I can only dissolve it. This doesn’t appear to be adequate for some analytical philosophers. How might I get closer to resolving it? My main argument is that they are assuming that language is stronger than it is, and they don’t agree with my argument.

As the prompt notes and by design, many analytical philosophers are reluctant to grant the degree of insufficiency I take to be constitutive of natural language, especially where logical embedding is concerned. Evidently, that counts as my not wanting to play their game. From my perspective, they are committed to a different ontological grammar. What this means practically is that I need to present my solution proposal in their terms. This doesn’t mean their terms are right; problems are only relevant in their dialect, even though my argument is that all dialects are lossy – mine included.

Part of the challenge is that formal logic was invented precisely because ordinary language is imprecise, yet its standards are often retrofitted back onto natural language as though they revealed what language must have been doing all along.

Without sharing the entire play-by-play of the transcripts, I established my course of action. I’d dissolved the problem, but I hadn’t yet resolved it.

My initial intuition of several years ago was to argue that they were expecting too much from grammar. I’ll use a well-worn example. Follow these statements:

  1. IF ‘Murder is wrong.’
  2. THEN ‘If murder is wrong, then getting your brother to murder is wrong.’
  3. SO ‘Getting your brother to murder is wrong.’

According to them, the embedded ‘murder is wrong‘ doesn’t make sense. Here’s their logic:

According to Ayer, moral statements are simply emotive. When one utters, ‘murder is wrong‘, they are really saying ‘Boo, murder‘ – ‘I don’t like murder‘.

If ‘murder‘ is defined as ‘killing disallowed by the state‘, then murder is wrong might be translated into ‘killing disallowed by the state is wrong’ or ‘what the state declares is wrong is wrong’, but we also know that the state makes many pronouncements, many of which carry no moral weight and others which are counter to expected moral positions – law does not equal moral, and vice versa. Let’s move on and revisit our statements:

  1. IF ‘Boo to Murder is wrong.’
  2. THEN ‘If boo to murder is wrong, then boo to getting your brother to murder is wrong.’
  3. SO ‘Getting your brother to murder is wrong.’

My intuition was that the embedded clause does not perform the same linguistic act as the standalone assertion, even if the lexical material is repeated. We’re committing a category error. More crucially, the category it belongs to doesn’t exist, so it’s unspecified. It needs to be invented.

Although I struggled to find apt nomenclature, I settled on performance-sensitive expressions.

A parallel challenge is that the solution can’t be a simple carve-out for moral language. Whilst I feel that moral language does use its own grammar and semantics, I don’t expect analytical philosophers to accept this assertion, so the solution should be more generalisable. I’d need to demonstrate where else this conditional logic fails in the same manner.

ChatGPT had this to say in response to a draft:

The comparative-cost section is good, but one sentence should be added to pre-empt the “your account also uses theory-laden notions” objection.
A critic may reply: your own terms, like “coherence zone” and “synchronisation protocol,” are also theoretical machinery. True enough. The difference is that your machinery is independently motivated and not introduced solely to patch Frege-Geach. You imply this already, but it would help to say so directly.

It offers clearer language:

Embedded moral predicates are not semantically identical to their unembedded counterparts, but neither are they inert; they are performance-sensitive expressions whose full evaluative load is attenuated under embedding while a thinner inferential profile remains available for reasoning.

In any case, I am still polishing the essay, dotting Is and crossing Ts. I think I’ve got the main argument and some examples. One of my weaknesses may be that I rely heavily on my own theories, but these are published and debatable on their own merits.

A real challenge of some problems and paradoxes is that they are self-inflicted. Faith that language is truth apt and lossless leads down many blind alleys.

100% Human Content

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3–4 minutes

Perhaps not 100% because I’ve just spent hours chatting with several LLMs, complaining about the spate of purported AI detectors that tell me ‘this content shows a high similarity to AI-generated content’ or somewhat.

Audio: NotebookLM summary podcast of this topic.

If I weren’t already familiar with the AI tells, I am now, as Claude reluctantly shared this:


Lexical tics. The ones you’ve already flagged are real: “delve,” “robust,” “nuanced,” “multifaceted,” “landscape” (used metaphorically for any domain whatsoever), “tapestry,” “leverage” (as verb), “facilitate,” “underscores.” There’s also a tier of slightly subtler ones — “notably,” “crucially,” “it’s worth noting that,” “this is particularly important because” — which function as false-emphasis markers, lending rhetorical weight where the content doesn’t actually earn it.

Structural patterns. This is where it gets more diagnostic than any single word choice. LLM prose tends toward a very particular architecture: a thesis-restating opening sentence, three to five parallel elaboration points (often with suspiciously balanced treatment), and a synthesising conclusion that circles back to the opening. The ratio of setup-to-substance is high. Everything gets contextualised before it gets said. There’s a compulsive both-sides-ism, a hedging reflex — “while X is certainly true, it’s also important to consider Y” — that reads less like genuine intellectual caution and more like a conflict-avoidance subroutine.

Tonal tells. LLM text tends to be relentlessly encouraging. There’s an affective flatness masquerading as warmth — everything is “great question,” everything “raises important considerations.” The prose never gets genuinely irritable, confused, bored, or reckless. It maintains a kind of supervisory friendliness, the register of a middle manager who’s been on a coaching course. Relatedly, there’s almost never a genuinely surprising analogy or a metaphor that risks falling flat — the figurative language is always safe, always legible, always serviceable.

Punctuation and rhythm. The em-dash point is real but often overstated. More telling is sentence-length uniformity — LLM prose tends to hover in a narrow band of 15–25 words per sentence, rarely producing either the genuinely terse or the genuinely complex-subordinated. Paragraph length is also strikingly regular. The prose breathes at metronomic intervals.

The absence of mess. Perhaps the deepest tell. Human writing — even polished human writing — contains asymmetries: a paragraph that runs long because the writer got interested, a point that gets dropped without resolution, a transition that’s slightly rough, a joke that doesn’t quite land but stays in anyway. LLM prose is clean in a way that signals not craft but indifference — nothing is there because the writer cared about it, so nothing is disproportionate.

Add to this grammar-checkers attempting to drive writing back to middle school formats – not to mention competing grammar checkers hypercorrecting each other. The struggle is real.

NotebookLM Infographic on this topic.

Of course, a solution might be just to review the content and undo these inclusions. Almost interestingly, I use Autocrit (but not often because it is too orthodox), but it does serve somewhat as an anti-AI agent, assessing rhythm and pace, plus the usual copyediting functions.

I prefer to draft in a stream-of-consciousness style. The AI organises my messes, so if I asked it to, it would remove or relocate my functional parenthetical about Autocrit. But I’m leaving it just to prove I’m human. Or did I add it to an AI-scripted piece? 🧐

Whilst I considered that I could either overdo AI or join the 54 per cent of Americans who write at a third-grade level. Grok suggested something even more sinister – Friggin Musk. It suggested that I double down on the AI likeness and make my content into an AI parody factory – overpopulate it with em-dashes, deving, and tapestry. Evidently, Carole King was AI before Suno.

In any case – and AI might suggest moving this to the top – the problem is that I now have an additional layer that interrupts my flow and process. It’s disconcerting, and I resent it. My psyche is disturbed to appease witchhunters. And it’s bollox.

The question is whether to succumb to the moral suasion or ignore the moral posturing.

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