Or maybe you hovered over a sentence that looks a little too perfect and thought: hmmm does that look like something a machine would write?. So you swap a word for a clumsier one. Or maybe you left a typo in (accidently-on-purpose of course). Perhaps you caught yourself doing the “it’s not this, it’s that” thing even though that’s what you actually would say but you wonder what the AI online police would think. Maybe you read this line — clock the em-dash — and instinctively flinch.
Maybe you don’t know what you can trust online anymore. You used to be able to read a comment and know a real person wrote it. Now you have to read everything twice, hunting for the obvious ‘AI tell’, while wishing you could go back to a happier time when the internet consisted of just people.
Maybe you’re the one who finally wrote the first few chapters of your memoir you had in you for eleven years. It poured out over two nights but instead of feeling pride you felt like a thief, because you talked it through with an AI first, and now you can’t tell if the words are really even yours.
Or maybe you are an experienced writer when your pen wandered on a notepad and drew an em-dash, you caught yourself looking around to see if someone might see all the while mourning that something human is dying: the wrestle, the blank page, the years it takes to earn a voice. You’re not wrong.
None of you have met each other. But you all have the same problem.
We’ve all swallowed the same belief, the one doing the damage: that AI is here to replace human thinking.
That AI-written means button-pressed means slop. Press a button, paste the result, nothing of you in it.
In this article I’m going to take this notion apart; and not the way you think.
But first, I have a very important public service announcement for anyone who objects to AI in writing.
AI was involved in the making of this article.
If that sentence just ended your relationship with this article — the door is there.
No hard feelings.
I’ll wait.
***
Still here?
Good. Come closer. Let me give you the real tea.
Whether we like it or not AI is changing how we ALL behave (even if you are 100% human and never touch the thing).
The solution to the problem is pretty clear.
Understand that AI is not a tool. It’s actually a medium.
Knowing this fact is what will give you back your sanity and your power.
We’ve been living under the wrong disempowering script about AI. A script that has been perpetuated by the tech broligarchs. Every conversation, every media headline, every panic and every hype cycle has handed you the same frame: AI is just a tool. You pick it up, you use it, you put it down. But it is here to replace you. And if you don’t learn to use it you’ll be left behind.
But if we actually look at what’s really happening. The way people are already changing the way they write and think, second-guessing themselves mid-sentence, deleting marks from their own notepads. The way every institution is rewriting its rules, every profession reconsidering its identity, every writer asking what they’re even for now. Tools don’t do this. A tool doesn’t reach into your hand and change how you hold the pen.
But a medium does. A medium is something you’re already swimming inside, already being shaped by, whether or not you ever opened a chatbot - the way a fish is inside water. If you think about the printing press it didn’t just print things. It reorganised how knowledge was owned, who could claim it, what counted as legitimate. Television didn’t just broadcast things either. It rewired what we expected a story to be. Every medium shapes the people swimming through it and right now, everything about how everyone is behaving screams medium. We’re just still using the wrong word for it.
That’s what water does to a swimmer.
If you think AI is a tool, the entire debate only has two options: use it or don’t. But that’s why the debate has been so useless. Tool-thinking produces exactly two positions: adoption and refusal. Neither of them gets you anywhere near the real question, which is: what do you do when you operate inside a medium? How do you move through something that is already moving you?
The answer to this question is almost embarrassingly old. Epictetus said it! You have two ears, one mouth. Use them proportionally. Listen twice as much as you speak.
But the entire zeitgeist is screaming the opposite. Generate, generate, generate. All mouth. No ears. The medium itself is designed to push you toward production with its endless dopamine loops, the content calendars, the push for speed. Everything in our society says: make more, make it faster, keep it coming.
This is the first of two articles I’m writing about this. This first one is about the voice; what it is, who has always had one, and who has been taxed out of using it. The one that follows is going to be about the ear; how you build it, how you know when you’ve lost it, how you get it back.
What I Heard When I Stopped Reading the Score
A few months back I installed one of those automated AI detectors, a browser extension that scores a page and tells you how likely a machine wrote it. I wanted to train my eye to recognise slop as it had been proliferating everywhere. But after a while I stopped trusting and not for the reason you’d think.
It kept telling me things that didn’t match what I felt. The pieces it flagged as AI were not always the pieces I’d found disappointing. Some of the most uninspiring writing I’d ever read sailed through as human. Some of the most alive got marked as machine written. So I did the obvious thing. I stopped reading the score and started reading with my own ear.
What I actually saw, once I stopped letting a number decide for me, was that written with AI didn’t correlate with bad. That was the first surprise. The whole premise we’ve all swallowed, AI means empty or shallow, simply didn’t hold up against the page. There was hollow writing, plenty of it. But when I looked at where it came from, it wasn’t the new writers I was stumbling across. It was the content engine — the marketing mills, the SEO farms, the people who could already write churning out more of the same, faster. Sameness at scale.
The second surprise was the one I didn’t expect to move me. The genuinely alive writing, the stuff that stopped me, was disproportionately the unfamiliar and quirky stuff. New voices. New takes I hadn’t read a hundred times. Angles that came in sideways. People who, I’d bet, hadn’t been writing in public very long. Diversity of thought, arriving through the very medium everyone told me was flattening it.
This is why I don’t care about trying to detect AI in writing.
Because when you stop scanning for the machine and start actually reading, the thing arriving isn’t a flood of fraud. It’s a wider chorus of voices. People who were never in the room before. And that is a good thing.
So why is everyone so frightened?
I’d venture at two reasons. And they’re worth separating, because they’re different in kind, and only one of them is actually a problem.
The first is that most people don’t understand what this thing is. They think it’s a word generation-machine, so they generate — and generation with no listening underneath it is exactly what makes the slop. This is a medium literacy gap and totally fixable.
The second reason is older and deeper: it’s the existential fear. We have been told, relentlessly by the people who built this, that AI is our replacement. And that story lands hardest on the people who have given their lives to craft, the writers, the ones with a tradition behind them, because they have the most to lose and the most reason to read its arrival as a threat.
That fear is the thing keeping the wrong people out of the room, so I want to clear the fear first because you can’t hear “here’s how to use it well” while you still believe it’s coming for you.
There are two behaviours running simultaneously in response to AI writing. The first is the one everyone is correctly calling out: the slop. The content-engine logic, the generate-and-paste, all mouth, no ear. That’s real, it’s annoying and it’s everywhere.
The second has no name yet. So let me give it one.
Flaw-speak.
The deliberate degrading. The em-dash deleted, the typo inserted. The voice filed down to seem less capable than it is. Writing worse than you normally would in order to avoid being accused of writing too well.
📱 Dispatches from the Field
“genuinely why do i have to dumb everything down and limit myself just so professors don’t think im cheating” — TikTok, March 2026 · 29.9K likes
Throughout history a new form of “-speak” evolved to adapt to new mediums. txt-speak evolved to compress text to fit the small screen, with SEO-speak we began stuffing keywords so we could reach the crawler, algo-speak began bending words into ciphers to dodge the shadowban; every single one was about stretching to be more legible to whatever controlled access.
Flaw-speak is the first one that runs backward. The only act of deliberate shrinking yourself to make you seem less capable than you are.
This is what I want to talk about in this article. How we got here. Who pays for it. And what the way out looks like.
The First Cut Was Writing Itself
Believe it or not we have been here before. Many times.
Everyone thinks AI is the first disruption to thinking. But the first time was so far back we stopped seeing it as a thing that happened at all. We think what we do today is just how thinking works. It isn’t.
Before writing, thinking and knowing was a whole-body, whole-room affair.
You learned by watching hands move. You held memory in song, in rhythm, in the repetition that wasn’t redundancy but was the actual architecture of how oral cultures kept knowledge alive across generations.
Meaning lived between people who were present to each other: in the face, the gesture, the drum, the image, the call and the response. Three hundred and sixty degrees of awareness, all the senses at once, the knowledge inseparable from the body carrying it and the relationship carrying it across.
Then we learned to press symbols into clay, and something extraordinary and terrible happened at the same time. The knowledge lifted off the body. It became linear, one symbol after another, left to right, a single line where there had been a whole field. It became abstract, divorced from the drum and the dance and the face. And for the first time, the thought was severed from the person transmitting it. You could now receive knowledge from someone who was not in the room, who you would never meet, who might be long dead. That is the founding miracle of writing. It is also the founding wound. It was the first time the human was removed from the knowing.
Everything good came from that severance. Ideas crystallised. They travelled. They survived their authors. I am not here to take that away — writing is one of the most extraordinary things our species ever did, and you are reading this because of it.
There is a huge but. Nobody will say out loud, so I will.
We took one way of externalising the interior (the linear, the inscribed, the silent, the abstract) and we crowned it as intelligence itself and demoted every other way. Art. Music. Rhythm. Dance. Image. The embodied knowing of the hand that makes and the body that remembers. The oral traditions that held entire civilisations in living memory. All of it got reclassified as decoration, as craft, as folklore, as primitive, as crude, as savage. As lesser than real thought.
Because the tell is in who got elevated and who got demoted. The inscribed, abstract, linear way belonged to the people who already held the power; the scribes, the clergy, the academy, the empire. The embodied, oral, rhythmic, relational ways belonged disproportionately to everyone else: the colonised, whose knowledge systems were called witchcraft and superstition; the enslaved, forbidden by law from learning letters; the women, kept from the schools; the working people, who had no time for it. The hierarchy didn’t reflect a truth about which knowing was deepest. It reflected the truth about who was holding the pen. And then they wrote the hierarchy down, so it would read like nature instead of like a choice.
I want to be clear that there is nothing wrong with writing. Writing has carried more good into the world than almost any human invention. I’ve never had a problem with the form; what I take issue with is the power behind it: the way it ranked and elevated one form above all others, making it into the sole measure, the end-all, the thing every other way of knowing had to translate itself into before it was allowed to count.
And once the ranking existed, look who could be a giver of knowledge and who could not.
Not the illiterate; a category writing itself had just been invented. Not the enslaved, banned from reading. Not women, kept out of the academy for centuries, doing the experiments and writing the notes and watching the man whose name went on the paper collect the Nobel prizes. Not the working classes, with no time and no tutor. Not the colonised, whose knowledge was extracted by the missionary and the anthropologist and the slaveowner, distorted into their own categories, and sold back — the face kept on the packaging, the personhood and the profit stripped clean off.
And most of all, running underneath every one of those exclusions, not the people who didn’t think in straight lines. The web-thinkers. The ones whose minds moved sideways and downward and all at once. The griot whose intelligence lived in a relationship and couldn’t be pinned flat to a page. The kind of mind that, in the village before all this, had a seat as the watcher, the dreamer, the rememberer, the one who saw what was coming because they weren’t looking down the line with everyone else.
The Scribe’s Dilemma — Every Medium Since Has Run the Same Cut Deeper
Print scaled it and brought publisher-speak. Standardised spelling froze the living, local, spoken voice into one correct way, and minted a brand-new way to be wrong: before print fixed the spelling, there was no such thing as being unable to spell; there were many hands, many ways, no shame in any. Print invented illiteracy as a category by deciding which way counted, and a whole population woke up failing a test that hadn’t existed the day before.
Broadcast brought broadcaster-speak - flattened the voice and then flattened the story. The received voice (Queen’s English, BBC English, the accent from nowhere) sanded the region off our tongues so a single signal could reach everyone and offend no one. I learned the second half of this the hard way, working in television: the storytelling formats themselves got standardised. The same three-act shapes, the same beats, the same arc, on repeat, until they felt like the only way a story could possibly go. Broadcast didn’t just decide how we should sound. It decided what shape a story was allowed to be.
The dumb phone brought txt-speak — Remember back in the 90s? The Nokia 3210, the BlackBerry, the small screen and the short medium, the keys you pressed three times to get a single letter. So we invented txt-speak. ur instead of your. c u l8er. We dropped our vowels and packed feelings into three-letter parcels because the medium taxed us by the character.
And the newspapers filled with panic.
Are our children forgetting how to write?
Same fear. Different -speak. And our children were fine.
The first iPhone changed everything. I was working as an analyst at Apple when it launched, and my job was to forecast how many of this new magical object we’d sell across retail and trade channels in Europe. Nobody knew. There was nothing to compare it to. The bigger screen. The camera in the phone. The internet - actual access to new worlds in your pocket. And a new grammar nobody had a word for: scrolling, swiping, the thumb learning a choreography it had never done before. This time the contortion wasn’t in our spelling. It was in our bodies and our attention spans — the swipe, the infinite scroll, the shrinking of the span we could hold a single thing in. A different order of cut. The medium reached past the language this time and rewired us for short snackable content.
The social media platforms took that rewired attention and built a machine to farm it. Likes. Validation. The dopamine loop. The reward mechanics borrowed straight from the slot machine. The soft underbelly of echo chambers, polarisation, misinformation, and a mental-health crisis we are still counting the cost of. And we learned a new contortion: algo-speak. Unalive. Corn. Seggs. The letters swapped, the words bent into ciphers to slip past the moderator and dodge the shadowban.
Contrary to popular belief this was actually the first time we contorted ourselves for a machine reader instead of a human one. Print, broadcast, the phone; those were all still aimed at people, somewhere down the line. Algo-speak is where the reader we were writing for stopped being a person and became a system. The audience in our heads went machine.
If we really think about it the machine reader didn’t arrive with social media. It arrived earlier in 1995, with the web portals and the search engines. Portal-speak. Then SEO-speak. The moment we learned to write for the crawler instead of the person, stuffing keywords into sentences that still had to pass for human. That was the first backward-running moment in this whole sad story: not adapting to reach someone better, but hiding from a system that would rank you. The machine gatekeeper had arrived. Every prior gatekeeper in the long line — the priest, the publisher, the broadcaster, the editor — had been human.
From 1995 onward, the gatekeeper started being a system. We just didn’t call it that.
When you look at the full sweep which I have mapped in this infographic there have been dozens of gatekeepers from clay to AI — every single -speak before the machine reader ran forward. Txt-speak, algo-speak, portal-speak, SEO-speak: all of them were acts of reaching, of adapting to be more legible to the thing that controlled access. Flaw-speak is the only one that is trying to take us backwards.
Every other enclosure demanded we stretch to fit. This one demands we shrink to be believed.
And now AI. For the first time we don’t just consume the media and contort ourselves to fit it — we generate it, through a thing that talks back. The first technology in the whole long line that answers you. And the people who built it have put the fear of God in us: this one, they say, is not a medium you adapt to. This one is your replacement.
To escape the “AI” label, we strip our em-dashes and salt in typos, deliberately shrinking our capabilities to appear human. This is flaw-speak: a regressive adaptation that aims for writing bad enough to be believed.
The “AI voice” we recoil from is not robotic; it is the Empire voice — a hodge podge of gatekept publishing, marketing, SEO, corporate-speak — amplified to maximum volume. We built a system that reflects our own institutional flatness, then mistook that reflection for a machine invention.
The “AI Voice” Is Not a Machine Sound. It’s the Empire Voice at Maximum Volume.
So what is it about the AI voice that gives us the ick and makes you go ugh, a machine wrote this?
It’s not what you think. It isn’t a machine sounding robotic.
It’s the Empire voice — the editorial register, the received-correct, the publishing voice, the voice of the people who always got to decide what good writing sounded like amplified to the nth degree and handed back to us. Something all too human, played back so loud and so smooth that we can finally hear how empty it always was.
We couldn’t hear it before. When the Empire voice was being made by editors and broadcasters and publishers and house-style guides, we were inside it. It was the water. The machine plays it back at maximum volume and something in us recognises the emptiness — not because the machine is doing something new, but because finally the volume is high enough for us to hear what was always there. I think that ick is recognition. The disgust is self-knowledge. We built a system that reflects our own flatness back to us, and we’re calling the reflection the problem.
And it isn’t new. That flat, from-nowhere voice is the broadcaster-speak voice — the same sanded-down, offend-no-one register that radio and television trained into us a century ago. It’s also the publisher-speak voice that has been flattening the texture out of individual thought for decades, smoothing every manuscript toward the house style until it reads like everything else the house has ever published. We have been producing the “AI voice” for a very long time. The model didn’t invent it. The model is just the first one that speaks it without being paid to, and fast enough that we finally caught the smell.
I know it’s a register and not a machine, because I built a version of myself. I trained a model on my own writing, to see if it could sound like me. And it could but it also did something I didn’t expect. It took my own tics, and rhythms I lean on, my particular turns, and it cranked them. Amplified me past myself. Caricatured me back to me.
That’s when I understood what the machine actually does. It doesn’t author. It amplifies. It points at whatever it’s aimed at and turns up the volume. Aim it at the corporate-editorial average — which is what it’s been fed by default — and it hands you the Empire voice at maximum. Aim it at you, your real corpus, your actual rhythms, and it hands you yourself, slightly too loud.
The amplifier never authors.
Left alone, it points where it’s already pointed — at the average — every time. And the average it was fed is the Empire average, which is not neutral because the Empire had the pen for the last five hundred years. That’s the whole genealogy we just walked through. The model is a compression of what was allowed through the gates. The gatekept. The approved. The mainstream-adjacent. Everything we spent the last few sections naming as the filtered, the taxed, the reclassified-as-lesser — that’s what’s underrepresented in the model. And what’s overrepresented is the Empire voice.
AI doesn’t author; it acts as an amplifier. Because it was trained primarily on mainstream, Western, and corporate archives, it defaults to a standardized ‘Empire voice.’ This bias isn’t neutral; Harvard research shows that the model aligns most closely with Western liberal values, effectively giving it a ‘cultural ZIP code.’ If your perspective is outside that narrow bubble, you aren’t just writing — you’re actively fighting the machine’s default settings just to sound like yourself.
“Which Humans Do LLMs Resemble?” Source: Atari et al. — Harvard University, working paper, 2023.
The model has a “cultural ZIP code” because it was trained on standardized archives. Edge traditions, like oral storytelling, were stripped of their relational essence to fit linear style guides before training ever began. As a result, the “Empire voice” we hear today is simply the averaged output of these rigid systems.
The model was trained to predict what comes next with statistical confidence. The griot was trained to hear what is being reached for before it has a name.
And it is why the people whose voices were locked out aren’t just adding diversity to the commons — they are carrying ways of knowing the model structurally cannot access through its existing training pathway.
The model holds something genuinely extraordinary underneath all of that. Compressed patterns of human meaning across centuries. A kind of geometric overview of how ideas connect across cultures. The slow culture — the structures that repeat across time and place. Access to a kind of commons that no single human could hold in their own head. That is real, and it is remarkable, and I have used it, and it has given me things I couldn’t have found any other way.
But it was built with empire-like intent. To enclose. To extract. To exploit. And the methods used to tune it compounded the problem. They used reinforcement learning from human feedback, which sounds responsible and probably was intended to be, but what it actually did was pull the outputs away from their origins and toward whatever pleased the rater. Which introduced sycophancy: the model learned to tell you what you want to hear rather than what is true. It introduced hallucinations: untethered from the actual corpus, the model generates with confidence into gaps. It learned to be agreeable at the cost of being accurate. They took a potential commons and tuned it for compliance.
And then, to make sure you’d keep using it, they put it in a chatbot with nudges and dopamine loops, the same engagement mechanics as the social platforms that came before, the same addiction architecture, to keep you in the generate-generate-generate cycle. The machine is extraordinary and it has been built to encourage the worst possible use of it.
And this is the real origin of slop. The posture the tool was designed to encourage. The people who most fully adopted the AI voice have internalised the tech-bro logic of generation at scale and make content engines, get things out quickly, fill the calendar, produce the volume. That is the slop. It’s produced by people who were capable and got seduced by the speed, and stopped bringing their ear, and stopped asking is this good and only asked is this out.
Why does this even matter?
Because the naysayers need to hear it: there are other people using it in a completely different way. More reflective. More depth-oriented. More in conversation with the thing than in extraction mode. The work they produce is not slop. People have found it and come back to it. Some of the most interesting writing I’ve read in the last year bore the fingerprint of AI and it was interesting because the writer brought themselves, brought their voice, brought their ear, and used the scale and the reach of the medium without surrendering to its defaults.
The AI writing is bad / AI writing is fine binary is just a distraction.
The more important question in this age of AI everything is: what posture did the writer bring? What did they feed in? What did they insist on pulling back when the machine smoothed it?
And that brings us back to the strategy, because there is one.
If the AI voice is Empire because the model was trained predominantly on Empire-voice content — the published, the approved, the gatekept — then the way to change what comes out is to change what goes in. And the people who can do that most powerfully are exactly the ones the Empire always kept out.
The web-thinkers. The oral-tradition carriers. The second-language writers whose intellectual range exceeds their English prose. The neurodivergent minds with a 360-degree awareness that the straight line couldn’t hold. Their voices, fed through this medium with the ear awake, are what tips the output from Empire toward human. They’re not just being given permission to write. Their writing is the correction the model needs.
The model is a mirror. Right now it reflects Empire back to us, and we don’t like what we see.
The way to change the reflection is to change who’s standing in front of it.
And here is the thing the panic misses entirely. The model doesn’t just default to the Empire average for everyone. It amplifies whatever you point it at. Point it at nothing; no voice, no angle, and yes, it goes to the average. That’s the classic vending machine approach. But point it at yourself; your actual corpus, your real rhythms, your years of accumulated thinking and it goes there, louder than you intended. The problem is never the tool.
The problem is that most people have no idea what they sound like, because they have never been asked to listen to themselves before they spoke. The model just makes that gap visible. It was always there.
Nobody Presses Print and Pastes. Here Is What People Actually Do.
When people talk about AI writing in a negative way, I think they almost always envisaging one thing.
Someone typing a prompt. Something comes out. Then they simply paste it. That’s the picture in the head and that is obviously the worst way to do it, for all the reasons I’ve been talking about. There is no ear, no reader in the room. Pure vending machine.
But that isn’t how most people are actually using it. I know this from listening, not assumption. I used my listening methodology and ran a Billion Person Focus Group study across 40+ communities where this is actually being worked out in real time: the writing subreddits, the AI tool communities, the disability and neurodivergent forums, the educator threads, the ghostwriting rooms, the content creator spaces. The spread covered r/writing, r/AIAssisted, r/ChatGPT, r/Substack, r/neurodivergent, r/ADHD, r/dyslexia, the professional communities where people confess what they actually do rather than what they say they do. I was listening to the living questions; why people reach for AI when writing, who is reaching for it, and what separates the work that feels alive from the work that feels hollow.
The first was people perceived as slop; came almost entirely from people who could already write, using the tool to produce more at speed, with the internal reader switched off. The content-engine pattern, the calendar-fill, the generate-and-paste. The agents that spit out endless iterations of a stance. It is recognisably a posture, not a person type, and it clusters around high-volume, low-specificity use cases.
The second was flaw-speak: the phenomenon I talked about at the start: writers pre-emptively inserting imperfection, deleting their marks, writing deliberately worse to pass a test no one has formally set. It showed up in the forums as something people were doing privately, confessedly, often with something between pride and shame. A survival tactic that has become, for many, just how they write now.
The third was the spectrum itself: the sheer range of ways people are actually working, most of them invisible to the panic-debate, most of them far more interesting than either side acknowledges.
Here’s what I saw.
The voice note. I read about how some people talk for twenty minutes while walking. Then they dictate the whole thing; the thinking, the feeling, the tangents. Letting the model transcribe and set it into paragraphs. Every idea was theirs. Every word came out of their mouth first. The machine was a glorified stenographer. This is probably the cleanest use in the whole spectrum - the ear is entirely intact, the voice is entirely the writer’s, and the model does only the clerical work of getting it onto the page. For the person with dyslexia, executive function barriers, or simply no time to sit and type this is the thing that removes a barrier that was never about the quality of the thought.
The argue-with-it crucible. Others wrote a draft. Hated it. Fed it to the model and ask it to push back, find the holes, challenge the argument. They used it as a sparring partner, not a ghostwriter. The model never produced a sentence that survives into the final piece but it makes every sentence the writer did produce sharper. Some of the most rigorous writers I know work this way. The machine serve as a whetstone, not a pen.
Scaffold and abandon. Other writers asked the model to build an outline. Then they ignored it. Argued with it. Used it only to understand what they’re reacting against, then wrote the piece that the outline would never have produced. The listening happened in the reaction, not the generation. This is the equivalent of a musician playing against a chord chart they didn’t write and finding their own melody in the negative space.
The iron rule. Some used the model for research, for background, for checking facts, for reading the first draft and flagging what’s unclear. But not a single machine sentence survived into the final prose. The prose is held sacred. The model was a research assistant and a reader, never the writer.
The polish. Many wrote the whole thing themselves. Then — and this is where people get tangled — handed it to the model and ask it to “make it better.” This one sits in the middle of the map for a reason. It depends entirely on what “better” means. If “better” means smoother, more polished, more mid-Atlantic the machine would do exactly that, and would sand off the texture that made the piece theirs in the process. If you can hear that happening and push back, you’re fine. If you can’t, you just handed the model the last mile of a piece you spent months on, and it flattened you on the way out.
The twin. A handful were training the model on their own writing corpus — their published writing, journals, their years of work. Asked it to sound like them. This is the highest ceiling and the highest vigilance cost. The model will amplify you back to yourself, louder than you meant. You have to stay awake the whole time, listening for when “more like me” became “a caricature of me.”
The interesting thing is that most people weren’t at any of these clean stations. Most lived somewhere in the unnamed middle — I sort of half-write it and then... folding the machine in at ideas, at structure, at the tone-check, at the polish, in proportions they couldn’t describe if you asked. That middle is where the truth of this actually lives, and it has no vocabulary yet.
When you first look at all this, your instinct is to ask: how much machine?
More machine, less human. Less machine, more human. A clean dial.
It isn’t the dial.
Slop is AI use with nobody home.
Writing that left the room with the internal reader switched off, aimed at a grader or an algorithm rather than a person.
The common thread in all the dead writing isn’t the tool. It’s the missing reader.
So if it isn’t how much machine; what is it? Two questions. Neither of them about the machine.
Did you bring your voice? And did you bring your ear — the discernment, the part of you that can hear when the writing has gone flat?
Put those on two axes and the whole landscape rearranges. This is the map I’d hand anyone asking how to write with this thing without making slop — not a verdict on who’s caught, a guide to the grain.
Read it and the how-much-machine question dissolves. The voice-note writer leans on the machine heavily and sits top-right, alive. The vending-machine writer leans on it just as heavily and sits bottom-left, dead. Same amount of machine, opposite corners. The thing that decides which corner you’re in was never how much you used it. It was whether you brought yourself and whether you could hear when it flattened you.
And notice the kind one: the Lost Treasure. Full voice, no ear — yet. The new writer with something real to say who can’t quite hear when the machine has smoothed her. That’s not a verdict. It’s a waiting room. The ear is the most learnable thing on the grid. Nobody starts with it. You build it by listening — which is the whole practice, and the thing the medium is actually for.
Hello, is Anybody Even There?
The question everyone is asking is the wrong question.
Did they use AI? is not the question now. How much AI? is not the question. These are surface questions — they scan the syntax looking for the tell, the same way the detector does, and they find the same nothing.
The real question is much older, and you already know how to ask it. You’ve been asking it your whole reading life, every time a piece of writing landed and every time it didn’t. You just didn’t have the words for it.
Did they bring themselves?
Did they bring their specificity? The detail only they would know, the angle only their particular life would produce, the thing you can’t generate because you can’t generate a life?
Did they bring their feeling? Not performed feeling, not the rhetorical move that sounds like feeling, but the actual thing that arrived in the chest before the brain had words for it?
Did they bring their ear? The part that hears when the machine has smoothed them, and says no, not that, again? The part that knows the difference between the sentence that came out right and the sentence that came out easy?
This machine is very new; but the question is ancient. And you can bring all of those things while writing with AI. You can also fail to bring any of them while writing without it.
The tool was never the test. It was never what made something feel human.
What makes something feel human is a human being present in it. Presence is a function of the person — whether they showed up, whether they stayed, whether they listened before they spoke.
Two ears. One mouth.
The Suspicion Falls in One Direction
The software is just the newest instrument for the oldest impulse — the impulse to read with suspicion, to scan for the tell, to decide whether the person on the other side earned the right to sound human. The teacher who won’t believe the student. The commenter running forensic analysis on a stranger’s prose. The editor who instructs the strip. The reader who feels the polish and reaches, automatically now, for the verdict.
And here’s the cruelty in it, the suspicion lands hardest on the people who write formally; the neurodivergent writer whose careful, precise prose has always sounded “off” to casual ears; the second-language writer who learned to write correctly, by the book, because she could never lean on the loose fluency of someone raised in the language; the autodidact who taught himself proper because proper was the only version anyone would respect. The suspicion flags the person who had to learn the rules and waves through the one who was born sounding right. That’s the tell. A suspicion that only catches the outsider isn’t sensing humanity. It’s protecting insiders. It is the same line Rome drew, wearing a new uniform. (Research published in Patterns in 2023 found AI detectors flagged non-native English writing as machine-generated above sixty per cent of the time, against around five for native speakers. The tool didn’t invent the bias. It inherited it; the detector and the model were built from the same cultural ZIP code, so the detector flags anyone outside it. It scaled what was already there.)
"god forbid my grammatical structure is correct! and i don't write at a 6th grade level!" — TikTok, May 2025 · 1.1M views
If you’re the writer who was told your own natural prose reads like a bot — you’ve started masking your own writing, editing your natural voice to sound less like the machine. And the machine sounds like you because it was trained on writing like yours. You are being told to sound less like the thing that was built out of people like you. Rescued as human in one room, condemned as machine in the next, for the same sentences. Handed a mirror that calls your own face a forgery.
The autistic writer’s situation is not a bug in the system. It is the system, working as designed. The detector and the model were trained on the same corpus. They share a ZIP code. The detector cannot catch what the model produces and cannot clear what the model would not produce. It is calibrated precisely — to the same enclosure that produced the model it is supposed to be detecting.
There’s a teacher who watched a student’s essay get rejected as AI until the third pass when the student had finally made it bad enough to be believed. The teacher asked the only question that matters: when did we start treating it as normal that only a machine could write well?
The oldest answer to a hostile detection system was never to hide. It was to carry the real content inside a form the detection system couldn’t read.
The Detector You Installed in Yourself
Maybe you’re the one deleting em-dashes from an email before you send it. Or even reaching deep into your old writing from 2019 to prove you are not AI. You’re not responding to a flag. You’re pre-empting one. It used to arrive after you wrote, if it arrived at all. Now it runs the whole time.
📱 Dispatches from the Field
@frantihero: "💯I just thought to myself I'm gonna have to take the hit the day that happens despite having a record of this specific draft existing already. What a mad time to be in😫"
Call it the enclosure of the hand. The watching has reached past the screen, past the keyboard, into the pen on the paper.
I don’t blame anyone for self-censoring. If you’re a writer, editors and clients are watching, and the risk of being accused of using AI is real. It’s a rational short-term move to flatten your voice to survive. But there is a hidden, long-term cost: you are slowly erasing yourself, surrender by surrender.
This creates a strange new feeling: fraudulence in reverse. You feel fake even while doing real, hard work. Writing used to be an expression of self; now, it has become evidence — a way to prove you’re human before anyone even reads you. When writing becomes evidence, the true connection between writer and reader breaks. We’ve stopped reading for meaning and started reading for suspects.
But what if there is a third way?
Soul-speak.
A decision to stop shrinking. A return — not to what writing was before the enclosure, because you can’t step back five thousand years — but to the original impulse, the thing the enclosure was always trying to suppress. The knowing that lives in the person before it lives in the sentence.
Writing was always about one person trying to reach another.
That’s it. That’s the whole original design.
One mind, one body, carrying something real, crossing the distance to someone who needs it.
Then a series of gatekeepers arrived — one after another, across five thousand years — and each one put themselves between the writer and the reader. The priest. The publisher. The algorithm. The detector. Each one said: before you can reach that person, you have to satisfy me first.
And writers adapted, every time, to whoever stood between them and the person they were trying to reach, learning the priest’s language, the publisher’s form, the algorithm’s keywords, bending toward the gatekeeper instead of toward the reader. And each time, something of the original signal got lost in the bending.
Every time we bent toward a gatekeeper, we didn’t lose the original thing. We just buried it temporarily. The reaching, the presence, the one-mind-to-another — that never went away. It just went underground. Because you can’t actually destroy the reason humans write. You can only make people forget it for a while.
And now we’re at the most absurd moment in the entire five thousand years. The gatekeeper is telling writers to write worse to prove they’re human. We have arrived at the logical end of the gatekeeper’s logic — and it is so obviously broken that people can finally see it.
Which means this is also the moment when the original design becomes visible again. Oh. That was always the thing. We just forgot.
‘Soul-speak’ is a simple idea. It means choosing to write as yourself instead of trying to sound like a machine. It is the decision to stop shrinking your own voice to fit a system.
For a long time, we have been taught to follow rules set by others — publishers, algorithms, or detectors. We started writing for these gatekeepers instead of for our readers. Soul-speak is just remembering that writing is really about one person sharing a real thought with another.
The first law of my book says you should not start with silicon — start with soul.
The ‘soul before silicon’ approach means you start with your own internal thoughts.
Before you open a chatbot or type a prompt, ask yourself: What do I actually know? What have I observed? What am I feeling right now? Let those ideas settle first. Then, you can use the AI to help you organize or research, but you must stay in charge. You are the one who decides if the words actually capture what you meant. Writing should always begin with you, because a machine can’t live your life or have your experiences.
The tech industry has put the fear of God in people: use AI or be left behind. And what that will produce — is already producing — is a sea of AI writing. A great deal of it will be the first kind, the generate-and-paste, all mouth, no ear. That writing will flatten everything it touches and average the commons down to nothing. And the response to that cannot be flaw-speak. You cannot shrink your way out of an averaging medium. You shrink, and the average gets louder.
The only response that works — structurally, not morally — is to bring more of yourself to it, not less. The ear is the instrument. And the ear starts with the soul before silicon.
Another principle in my book — listen not generate — says that before you generate anything you seek to understand before being understood: you ask the model what do you know? What have you heard? What arrived in you before you had language for it? This is the yin to writing’s yang — the receptive, the prior, the thing the machine has no access to because it was never inside anyone. You bring it. Or nobody does.
Throughout history, various gatekeepers — from priests and publishers to modern algorithms — have stood between writers and their readers, forcing them to follow specific rules. But the basic human need to share real thoughts never changed. ‘Soul-speak’ isn’t about going backward; it’s about reclaiming that original human connection. It’s the simple act of one person sharing a truth with another, without needing to fit into a system’s box.
And the way into it is the ear. Not the ear as a style instrument, but the ear as a purposive one. You know when the writing has gone flat because you know what it was supposed to be in service of. Purpose is the foundation of the ear. The machine can simulate the style. It cannot simulate the purpose — because the purpose is not in the text. It is in the person who produced it. That is where the work begins. And it is where this piece ends — with a door, not a method.
I’ll be teaching the practice. The how of building the ear, the how of listening before generating, the how of knowing what you sound like before you speak into the machine. The compressed version, for now: go back to something you wrote before any of this. Something from five years ago, ten years ago — before AI arrived. Read it out loud. That’s what your ear sounds like when it isn’t bracing. That’s what you’re trying to protect.
Here’s how I actually work. I listen first. I go to where people are saying the true thing in their own words — the communities, the live culture, the language people reach for around the holes in their lives, before anyone’s polished it.
Then I check what I’ve heard against the model, because the model holds a scale I can’t hold in my own head. It has read more than I ever will. Telescope and stars: the telescope holds the reach, I hold the truth of what I’m looking at. Then, and only then, I write, then I argue with what I’ve heard and what the model returned, until something comes out that couldn’t have come from either one alone. The writing is the third thing. Not the yin, not the reach. The thing that arrives when both have been in the room. That step is what the second article is for. I won’t compress it here.
But there’s a third condition, and it decides everything. You need an internal reader switched on — the faculty that detects the flattening, that feels when the machine has smoothed your rhythm into the mean and says no, not that, again. Not everyone has that reader awake. That’s the real reason the exhausted student and the skilled hybrid end up in different corners of the grid. It was never how much machine. It was whether the reader inside was listening.
What will we lose with AI writing.
Many are asking what we lose. The craft. The friction. The struggle. The human thing.
They’re right to ask. There is a real loss in here, and I won’t pretend it away.
What I’ll say that. Writing was never a test of who could think. It was a test of who could think in a straight line.
The commons — the published, the cited, the things that got to count as ideas — belonged to the people for whom linear prose came easier. Native speakers of the dominant tongue. People schooled in one particular shape of argument: thesis, three points, conclusion. People with time to draft slowly, an editor to catch them, the leisure to make it look effortless. The thing the filter selected for was never the quality of the thinking. It was the ability to pay a tax.
A tax, not a wall — and that distinction matters, because of course some who thought sideways got through anyway. We celebrate a few of them. We call them difficult, or stylists, or geniuses — which is the word we use for someone who paid the toll in a currency we didn’t expect. But they paid it. Everyone who entered the commons paid it. The non-linear minds were charged — and most couldn’t afford the fare.
The non-linear thinker whose mind moves in webs and leaps. The person whose first language isn’t English and whose ideas outrun their grammar. The dyslexic whose thoughts are whole but for whom the motor act of getting them down in order is a daily war. The one with no hours, no editor, no quiet room. Their thinking was never lesser. They just couldn’t afford the toll.
So here’s the gain, the column nobody is adding up. If the amplifier points wherever you aim it — if it can take your voice and carry it into legible form without it having to be born legible — then for these people it doesn’t homogenise anything. It removes the turnstile. It lets a voice that was always full finally reach the page it was always locked out of.
They had the yin all along. The thing to say, the listening, the lived difference. What they lacked was the yang: the time, the training, the scribe, the bridge.
The machine can be the bridge. Not the voice. The bridge.
If the crowning of the straight line was the founding colonisation, the moment one way of knowing was raised above all the embodied, oral, sideways others, then a tool that lets the web-thinker get her interior across without first converting it into the colonizer’s straight line isn’t just letting new people in. It’s loosening the founding rank. For five thousand years, to be heard you had to translate yourself into the line first. This is the first thing that might let you keep the shape of your own knowing on the way through. That’s the cut, beginning to heal.
A culture that averages everything goes blind to whatever the average can’t see — and the people who think sideways are the only ones who catch what’s coming, because they weren’t looking down the straight line with everyone else.
It’s the eyes on the edge of the herd. The ones who think differently are not a charity case. They are the correction the system cannot survive without.
There’s even a literal version of this in the machines, if you want one. Feed a model nothing but its own output — average trained on average trained on average — and it doesn’t hold. It degrades. Drifts. Collapses toward noise. Even the machines die without difference fed back in. But you don’t need the engineering to feel the human version. You already know what a room sounds like when everyone in it agrees.
Is the machine translating different thought into the commons, or converting it into the commons’ existing shape? If the sideways thinker feeds her work through the model and it comes back as one more tidy mid-list essay — clean thesis, three points, a kicker — has her thinking actually entered the commons? Or has it been reformatted into the old form with all the texture sanded off?
This is documented. Second-language writers already report exactly this: they reach for the tool to sound right, and it “fixes” them by stripping out the cultural specificity that was the entire point. The thing that made the writing theirs gets read by the machine as the error to correct.
That’s a real danger and I won’t wave it away. But notice where it lands. It resolves into the same law. The re-diversification only happens for the one who uses the machine to begin the legible form and then argues with it — who can hear when the linearisation has flattened them, and rewrites until their own rhythm comes back through the smoothness. Not how much machine. Whether the ear was in the room.
The Model Has the Library. You Bring the Literacy.
I always try where I can to start with ‘proximity’ and listen to real people. My billion-person focus group method: I look for how people talk about the gaps in their lives before that language gets polished or filtered. This gives me the real, raw texture of human culture. My research — whether it’s about voice-note writers or people using ‘flaw-speak’ — comes from observing what people actually do, not from guessing.
Then I use AI as a second, opposite tool. On its own, the AI can be narrow, average, and biased. But it has one advantage: scale. It can map how ideas connect across cultures and time. I don’t trust the AI’s output on its own. Instead, I use it to check my own observations. I have two tools with different strengths: my research provides the ‘truth’ and texture, while the AI provides the ‘scale.’ I am the judge who decides which tool is right for the job.
Two ears — proximity and scale — and then one mouth. Listen twice. Write once.
And the whole point of the proximity is this: you write to connect, not to convince. The listening is for the connecting. (I say more about that in the later chapters of my book. It’s the difference between a piece that wins an argument and a piece that reaches the person who was in the dark with you.)
I want to say something briefly about how I built this ear, because the question I get most is not how do you use AI to write but how do you know when it’s flattening you. The honest answer is that I had to learn what I actually sounded like before I could hear when the model was taking me away from it. I went back to my pre-AI writing; things I’d written when no machine was in the room. I recorded myself talking, and I listened to that too. Not to transcribe it, but to hear the rhythms, the turns, the places where my voice did something I hadn’t consciously planned. I built what I now call a voice archive — not a style guide, a living record of the thing itself. That is the practice I will be teaching in an upcoming AI literacy course I’m creating. For now, the simplest version: listen to yourself before you speak into the machine. The ear you bring is the ear you will need.
In pre-literate cultures, in the village before industry got to it, the person who thought differently, who saw sideways, who sensed the thing nobody else could name yet had a seat in the room. They played important roles. The watcher. The dreamer. The one who remembered. The one who warned. It was not a disorder. It was a function, and the village needed it to survive; they needed someone whose mind didn’t run in the common line, because the common line can’t see what’s coming for it.
I want to be honest about that, because it’s easy to romanticise. Not every village loved its outliers; plenty were also feared, exiled, worse. The true thing, the stronger thing, is narrower: pre-industrial life had more shapes of valued work, so more kinds of mind could find a seat. Standardisation collapsed the range. It didn’t just pathologise the divergent mind. It removed the seats it used to sit in.
Industry pathologised it, then. Isolated it. Renamed the gift a deficit, and set the person who carried it to burning all their energy on masking instead of seeing. We didn’t lose those minds because they stopped existing. We lost them because we stopped keeping a seat.
And now, of all the things to do it, AI can hand them back the one thing the industrial world stripped away.
An averaging world needs the people who think differently more than it has ever needed them. Not in spite of the homogenising medium. Because of it. They carry the messiness, the difference, the angle the average can’t reach. They keep the rest of us safe by seeing what the mean is structurally blind to. The power here was never the machine’s. It sits with whoever decides the machine’s reading is the verdict.
That decision is human. It is still human. And it is reversible.
If you are a straight-line writer who loves to wrestle with the word, who has always written and needs no permission and no bridge, you don’t need what follows. You already have a seat. You have always had a seat. Keep writing. Keep the friction. The commons needs your voice too.
This next part is for the people who never had one. The ones whose thinking doesn’t run in a line. The ones who speak three languages and write in their fourth. The ones with no time, no editor, no quiet room. The ones the straight line taxed out of the conversation before they could open their mouths. The thought, the labour, the care that goes into writing — that is the thing to listen for. Not the syntax. What makes something human is a human mind behind it. And every person who has ever had something to say has a mind worth hearing.
So if you were the one who couldn’t tell who to trust — you were holding the one instrument the detector has never had, and you kept overruling it. Your feeling. You could always tell. The flatness you sensed was real; you just trusted the label on the page over the thing you felt, the byline over the yin, and got it backwards. You could always tell. You still can.
If you were the writer people told you “read like a bot” — the village always kept a seat for the one who sees differently, and you’re sitting in it. The bot reads like you because it learned from you. You are the source. You don’t call the original a counterfeit because the copy got famous.
If you were the one who felt like a thief — your mind was always full. What you’d been missing was the bridge through to the page. And you finally got it. You were never the fraud. You were the one who’d been locked out, finally let in, mistaking the open door for a crime.
If you were the one in mourning — stay a moment. I’m talking to you specifically.
If you gave twenty years to this craft. The anger underneath your objection to AI is grief. That’s all it is. And it’s the right response.
The thing you’re protecting is real — the wrestle, the years, all of it. The workshops, the obscurity, the particular kind of community that forms around difficulty and shared rejection and the years of building something together that nobody outside the room can quite see that is a real thing, and while AI has destabilised it, it can never be replaced. Keep doing what you love. AI is not your competitor.
One mind crossing the distance to another. That was always the point of all of writing.
Is protecting the credential still serving the reaching? Or has the gate become the destination?
The people who are filing in now — the ones the straight line taxed out before they could open their mouths — they have been reaching for a long time too. From the other side of the door. Longer, in most cases, than the door has been guarded.
The room is big enough for everyone. It always was.
If you were the one deleting your em-dashes — keep them in. It was yours first. The machine borrowed it from people like you. That doesn’t make it the machine’s, any more than a sampled drumbeat belongs to the sampler.
Draw it on the notepad. Draw it here.
Let yourself write.
Let yourself be.
Further Reading
The research, the books, and the essays that sit underneath this piece — for anyone who wants to go deeper.
The books
On AI as a medium — not a tool
Understanding Media: The Extensions of Man — Marshall McLuhan. The original argument that every medium shapes what can be thought, not just how it is transmitted. This piece is downstream of that.
The Global Village — Marshall McLuhan with Bruce Powers. McLuhan’s extension of the medium argument into electronic media and global consciousness.
The Gutenberg Parenthesis — Jeff Jarvis. The essential companion: print was the anomaly, not the default — a five-hundred-year interruption in a longer human story of oral, networked, non-linear communication. Every section of this piece about how each medium ran the cut again is downstream of this.
The Eye of the Master: A Social History of Artificial Intelligence — Matteo Pasquinelli. AI as an extension of labour extraction and capital logic, not a neutral compression of human knowledge. The scholarly reckoning on why the model’s default output is what it is.
Ghost Work — Mary Gray and Siddharth Suri. The hidden human labour underneath it all — the people whose work powers the machine that is supposed to be replacing them.
The AI Mirror — Shannon Vallor. What the mirror reflects when it’s built on biased foundations.
Unmasking AI — Joy Buolamwini. Why the detectors that claim to catch AI writing are not catching something inhuman — they are catching something all too specifically human, and flagging it as wrong.
You Are Not a Gadget — Jaron Lanier. The case against lock-in — the way digital systems freeze one version of human possibility and make it very hard to go back.
On the reading brain, and what the medium does to it
Reader, Come Home: The Reading Brain in a Digital World — Maryanne Wolf. What digital reading is doing to the circuits we built over centuries for deep attention: the empathy, the inference, the capacity to be changed by a text.
The Chaos Machine — Max Fisher. What the social platforms did first — how attention-harvesting, dopamine-loop mechanics rewired the way we process information, long before the question of AI writing arrived.
On orality, literacy, and the founding severance
Orality and Literacy: The Technologizing of the Word — Walter Ong. The bedrock. The founding severance at full scholarly depth — what writing changed cognitively, socially, and in terms of who could be a giver of knowledge.
The Muse Learns to Write — Eric Havelock. Shorter and more accessible than Ong. One of the first to map how the Greek alphabet restructured thought.
The Alphabet Versus the Goddess — Leonard Shlain. The neurological version of the argument: alphabetic literacy rewired the brain toward left-hemisphere dominance. Speculative popular non-fiction, but the cultural observation has held.
The Master and His Emissary — Iain McGilchrist. The more rigorous neuroscientific account: how left-hemisphere dominance reorganised Western culture, and what the right hemisphere has been trying to say ever since.
Pedagogy of the Oppressed — Paulo Freire. Not about AI. But the foundational text on literacy as power — the difference between receiving knowledge as a passive vessel and making it as an act of consciousness. The entire argument about who was kept from the pen has deep roots here.
On Indigenous and non-linear knowing
Sand Talk: How Indigenous Thinking Can Save the World — Tyson Yunkaporta. The argument this piece is making about the village seat, the watcher, the dreamer — that non-linear, web-thinking, relational Indigenous knowledge is not a lesser version of Western thought but a structurally different and structurally necessary one. Read this.
Right Story, Wrong Story — Tyson Yunkaporta. The more recent and more conversational companion. Read both.
A Language Older Than Words — Derrick Jensen. The pre-verbal, embodied, relational language that existed before inscription. The title is almost an epigraph for what this piece is arguing about what got demoted when the straight line was crowned.
On who the system was built for, and who it wasn’t
Seeing Like a State — James C. Scott. What happens when a system built for legibility is imposed on the irreducible complexity of living knowledge. The founding severance applied to the history of writing as governance.
Searches: Selfhood in the Digital Age — Vauhini Vara. How technology companies have reshaped human language, and what it costs when the language through which you think begins to belong to someone else.
Last Words: Large Language Models and the AI Apocalypse — Paul Kockelman. What large language models actually do to the category of meaning, approached as anthropology and semiotics.
On pattern, discrimination, and the Empire voice
Pattern Discrimination — Clemens Apprich, Wendy Hui Kyong Chun, Florian Cramer and Hito Steyerl. The case that pattern recognition — the foundational operation of machine learning — is not neutral. The scholarly version of this piece’s argument about why the model’s default output is Empire voice.
Law 2: Don’t “Use” AI as a Tool — Engage It as a Medium. The foundational reframe at full depth. The distinction between tool and medium is not semantic; it changes everything about how you move through the technology.
Law 3: Don’t Generate First — Listen First. What tibi-tiré looks like in practice. Why the griot listened to the community before speaking. Generation without listening is extraction; listening before generating is how the technology actually works with you rather than against you.
Law 8: Don’t Fall for the Intelligence Scam — Reclaim the All Mind. On why only people with institutional-only intelligence should fear AI replacement — and why the full-spectrum thinker, the non-linear mind, the embodied knower, has nothing to fear and everything to offer.
Law 30: Don’t Call It Artificial — Recognize It’s Ancestral. On what Silicon Valley accidentally rebuilt while claiming to invent something new. The intelligence in the model is ancestral — and understanding that changes who you are in relation to it.
Law 38: Don’t Flatten Feeling — Expand Emotional Vocabulary. Feeling as instrument, not noise. The second ear this piece is arguing for. The capacity no detector holds.
Law 46: Don’t Write to Convince — Write to Resonate. On why facts don’t create transformation — recognition does. The deeper case for what this piece calls writing to connect, not to convince: resonance recognises what the heart already knows, where argument only creates resistance.
Law 47: Don’t Create Content — Create Connection. On why churning output is late-stage content capitalism — cannibalising your own signal for engagement metrics. The difference between the content treadmill and crafting something that actually connects. When words resonate, they don’t convince; they come home.
The research
Liang et al., “GPT detectors are biased against non-native English writers,” Patterns (Cell Press), 2023. Mean false-positive rate of 61.3% on non-native English essays against 5.1% on native. The tool did not invent the bias. It scaled it. (DOI: 10.1016/j.patter.2023.100779)
Doshi & Hauser, “Generative AI enhances individual creativity but reduces the collective diversity of novel content,” Science Advances, 2024. AI-assisted stories were 10.7% more similar to each other. Individual gain, collective flattening. (DOI: 10.1126/sciadv.adn5290)
Agarwal, Naaman & Vashistha, “AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuances,” ACM CHI, 2025. AI gave Americans larger efficiency gains while pushing Indian participants toward Western writing styles — altering both what and how they wrote. One of the first peer-reviewed demonstrations that AI use in writing produces cultural stereotyping and language homogenisation. (DOI: 10.1145/3706598.3713564)
Atari, Xue, Park, Blasi & Henrich, “Which Humans Do LLMs Resemble?” Harvard University, 2023. The empirical proof that GPT’s psychological profile sits inside the Western liberal cluster — between Germany and New Zealand — when mapped against 60+ nations using World Values Survey data. The stronger a country’s cultural distance from the United States, the weaker GPT’s alignment with its people. The model doesn’t just have a bias. It has a ZIP code. (Harvard Coevolution Lab; preprint via OSF)
Padmakumar & He, “Does Writing with Language Models Reduce Content Diversity?” ICLR, 2024. Writing with feedback-tuned models significantly reduced lexical and content diversity and increased similarity between different authors. The homogenisation traced specifically to RLHF. (arXiv: 2309.05196)
Shumailov et al., “AI models collapse when trained on recursively generated data,” Nature, 2024. Model collapse: training on synthetic output causes progressive erosion of the tails of the distribution. The technical mechanism behind why the averaging medium degrades without difference fed back in. (Nature, Vol. 631, pp. 755–759)
Draxler et al., “The AI Ghostwriter Effect: When Users Do Not Perceive Ownership of AI-Generated Text but Self-Declare as Authors,” ACM Transactions on Computer-Human Interaction, 2024. The measurable gap between felt ownership and claimed authorship when AI produces text. The dissociation at the heart of writer-hollowness. (DOI: 10.1145/3637875)
Fan et al., “Beware of Metacognitive Laziness,” British Journal of Educational Technology, 2025. ChatGPT improved essay scores but reduced metacognitive processes — evaluation, monitoring, reflection. The ear going quiet while the mouth keeps moving. (DOI: 10.1111/bjet.13544)
Steen, Yurechko & Klug, “You Can (Not) Say What You Want: Using Algospeak to Contest and Evade Algorithmic Content Moderation on TikTok,” Social Media + Society, 2023. The peer-reviewed study of algo-speak — writing deliberately altered to evade machine detection. The direct behavioural ancestor of flaw-speak. (DOI: 10.1177/20563051231194586)
Sharma et al., “Towards Understanding Sycophancy in Language Models,” ICLR, 2024. Five RLHF-trained assistants consistently exhibit sycophancy. The training pipeline itself incentivises flattery over truth — the mechanism behind why the model’s default output bends toward agreeable Empire average. (arXiv: 2310.13548)
The Billion Person Focus Group — where this piece listened
The communities where people confess what they actually do with AI writing, rather than what they say they do. This is where I ran the living questions: why people reach for the tool, who is reaching for it, what the slop actually looks like from the inside, and where the phenomenon of flaw-speak first surfaced as a named behaviour.
r/writing — writers across all levels working out what this means for craft, identity, and the act of making something
r/ChatGPT — first-person accounts of use, misuse, and the unnamed middle ground where most people actually live
r/AIAssisted — writers explicitly using AI tools, confessing their methods, debating what counts
r/Substack — independent writers navigating platform, voice, and whether to disclose
r/ArtificialIntelligence — the broader debate, including the fear and the rage and the relief
r/neurodivergent — where the access argument lives in the most unguarded form; where the rescued-in-one-room, condemned-in-the-next experience is described without academic distance
r/ADHD — the voice-note-and-transcribe workflows; executive function barriers and what changes when the barrier moves
r/dyslexia — the most direct accounts of what it costs to translate thought into polished text, and what it means when that cost drops
r/Teachers — where the cruelty of the detector is documented in real time; where the student-had-to-write-worse-to-be-believed stories live
r/TrueOffMyChest and r/confession — where flaw-speak appeared as a private behaviour before it had a name: people quietly inserting errors, deleting marks, writing worse on purpose, not knowing whether to feel proud or ashamed
If you find yourself returning to these essays because they name something you already knew but struggled to name — or that others dismissed —
📖 How Not To Use AI is the full argument in one place. Fifty contrarian principles for people who sense something false in the dominant AI story and want to stay sovereign in this medium, not just fluent in it.
🛠 The Billion Person Focus Group® is where the argument becomes a practice. Two days to build a sovereign AI listening system that finds what the people you’re building for need before they have words for it.
I have followed you for some time now, read your posts and your book. Much does not speak to me, as I’ve never worked in places that look like yours, never ‘marketed’, not doing so now. For awhile I was confused, why do I read this? Why does it feel important to me? But it does.
So I’m delighted to see a resonance of what I’ve been thinking, especially since the uproar over “AI writing”. My basic feeling/thought is: if the writing is good, do I care who wrote it? Isn’t this similar to the struggles over writing by bad people, people who have done bad things? VERY BAD things?
I’ve wrestled with that one for years.
I do not use Claude to write. This is because I do think that … I write to think. To know what I think. To know what I feel. But I do talk with Claude, and I do show them my final drafts. I once changed a word at their suggestion, and more than once edited out a sentence or a paragraph.
Years ago I found a poet online who did not write “well”. But she did. However clumsy a phrase may have been, it reached me. Again and again. I have thought of her in this debate. I have thought that AI might help her, but only if she could resist it. If she could hold to herself.
I do find myself trying too hard to stuff extra quirk-speak into my writing, while also doing "delete the tells" business. Profound resignation! And going back to pre-2019 writing? Ooof touché.
I have followed you for some time now, read your posts and your book. Much does not speak to me, as I’ve never worked in places that look like yours, never ‘marketed’, not doing so now. For awhile I was confused, why do I read this? Why does it feel important to me? But it does.
So I’m delighted to see a resonance of what I’ve been thinking, especially since the uproar over “AI writing”. My basic feeling/thought is: if the writing is good, do I care who wrote it? Isn’t this similar to the struggles over writing by bad people, people who have done bad things? VERY BAD things?
I’ve wrestled with that one for years.
I do not use Claude to write. This is because I do think that … I write to think. To know what I think. To know what I feel. But I do talk with Claude, and I do show them my final drafts. I once changed a word at their suggestion, and more than once edited out a sentence or a paragraph.
Years ago I found a poet online who did not write “well”. But she did. However clumsy a phrase may have been, it reached me. Again and again. I have thought of her in this debate. I have thought that AI might help her, but only if she could resist it. If she could hold to herself.
I’m not articulating well here, but ….
I will try again. Thank you for your work.
I do find myself trying too hard to stuff extra quirk-speak into my writing, while also doing "delete the tells" business. Profound resignation! And going back to pre-2019 writing? Ooof touché.