TL;DR: The entire debate about AI authenticity is based on a fundamental misunderstanding. Authenticity isn't a property of texts - it's a property of experiences. Instead of asking "Is this human or AI?" we should ask "Does this help me become more human?" The real issue isn't detecting the source of writing, but evaluating whether content serves human flourishing. When I tested AI detection tools, they confidently called my human writing "AI-generated" and my AI-assisted content "human-written," proving these distinctions are performative rather than meaningful.
I came across this great article The Dirt We Smear Back by Mike Liu, and it really drove me to shift thinking and attention from software design task to text design task - if I may say.
His radish metaphor struck me immediately - this idea that farmers deliberately smear dirt back onto machine-cleaned vegetables to make them appear "authentic." It's a brilliant observation about performance and perception. But as I sat with it, something nagged at me. The more I thought about his framework of AI authenticity markers - those em dashes and stylistic "dirt patches" we sprinkle onto our writing - the more I realized we might be asking entirely the wrong questions.
Mike's piece beautifully captures a real psychological phenomenon: people do seek evidence of human intention in text. We do look for those rough edges, those imperfect rhythms that signal someone was "still holding the plow." But I found myself wondering - why are we so invested in this performance of authenticity when the underlying premise might be fundamentally flawed?
This isn't just about punctuation or writing style. It's about how we construct categories, how we weaponize distinctions, and how we let surface performances distract us from deeper systemic questions. The AI authenticity debate, I began to realize, might be another form of what I call "weaponized liquidity" - a concept I'm developing to understand how fluid, unstable categories get deployed to maintain certain power structures while obscuring others.
Consider how this works in practice: tech companies spend billions developing AI writing tools while simultaneously promoting the idea that "human authenticity" is precious and irreplaceable. This creates artificial scarcity around "genuine" human content while normalizing AI assistance for everything else. Meanwhile, who actually benefits from this anxiety? The platforms selling authenticity detection tools, the consultants teaching "human-centered writing," the institutions that can afford to hire "verified human" writers while others rely on AI assistance.
It's the same dynamic that created "artisan" labels during industrialization - suddenly, mass-produced goods were "inauthentic" while handmade items commanded premium prices, regardless of actual quality or utility. The categories serve market segmentation more than meaningful distinction.
People are complex enough to understand their intentions, so "meaning" could be projected onto people and their words. If someone has great shape and well-trained muscle what is his "meaning" and "intention", if someone says hello in a loud, lively, or deep, velvety voice, can we directly judge their intentions? Those are merely signals or just contextual circumstance.
Similarly, a word's meaning isn't judged by punctuation alone. There are serious texts written decades before AI that one could barely bear to read a single paragraph of, while other texts flow naturally, stick and strike from the first sentence.
Does Form Affect the Meaning?
This question cuts to the heart of what we're really debating. When we obsess over em dashes versus regular dashes, over the "authentic messiness" of human writing versus the "sterile perfection" of AI output, we're essentially asking whether the container matters more than the content.
Punctuation matters, of course, but primarily for readability and accessibility. There are cases where it genuinely changes meaning. Over the past decade, I've observed how intellectual writers on social media became increasingly defensive about how you should treat a script. They were picky and judgmental toward people who chose to ignore punctuation - deliberately or otherwise. But now, as we see more polished and tightened texts published to the internet, a new category of defense has arisen against the tools that enable non-professional writers to produce perfectly punctuated text.
Here's the irony: if the argument remains valid that shape affects meaning, then through AI we've achieved the ultimate goal of enabling those who are messy with commas or periods to articulate their ideas properly. The very people who once championed "proper" punctuation now resist the tools that democratize it.
Mike Liu's response to my challenge illuminated this perfectly. When I questioned whether punctuation markers really reveal authentic human intention, he clarified that for him, "it's not really about shape—it's about whether the shape was a choice, or just a statistical convenience." He distinguished between a human writer using an em dash "to signal hesitation, tension, or theatrical pause" versus an AI using it because "its training data showed it tends to perform well. One is rhythmic. The other is reactive."
This reveals the deeper framework at work: intention versus evidence. Mike argues that what matters is detecting authentic human intention behind stylistic choices. But this raises a fundamental question: Can we ever actually access intention, or are we always just interpreting evidence?
What evidence did Viktor Frankl provide to his readers when he used em dashes while discussing unbearable suffering? Was it to signal theatrical pause? I'm not so sure. Readers are smart enough to capture the compound meaning and spirit of the text. They can tolerate one displaced comma but won't glorify punctuation marks themselves.
We're not actually detecting human intention—we're interpreting stylistic evidence through frameworks that assume certain markers indicate "authentic" human choices. But what if an AI model, through whatever combination of training and prompting, produces an em dash that creates genuine hesitation, tension, or theatrical pause in the reader? Mike would call this "reactive" rather than "rhythmic," but the functional effect remains the same.
As a developer, I know that intention lives in comments and documentation, not in the code itself. The code is just evidence of what the programmer tried to achieve. Sometimes elegant code emerges from sloppy intention; sometimes beautiful intention produces buggy code. What matters is whether the program serves its purpose, not whether we can divine the programmer's state of mind from the syntax.
Even human stylistic choices aren't as intentional as we imagine. When I write an em dash, am I consciously choosing to "signal hesitation"—or am I following learned patterns, cultural habits, and unconscious rhythms absorbed from years of reading? How much of what we call "human intention" is actually statistical convenience based on our own training data?
The Palestinian poet Mahmoud Darwish understood this when he wrote about meaning fragmenting "in its hesitation/echoing." We endlessly repeat stylistic patterns while claiming they represent our authentic intentions, but often we're just echoing forms we've absorbed without conscious choice.
In response to the question: Does form affect meaning? I say that form is indeed part of meaning - humans invented language and the meaning behind its signs, so form and content evolved together. But here's the crucial point: no one has ever had direct evidence of meaning itself.
Meaning doesn't exist in the text waiting to be discovered like a fossil in rock. It emerges in the encounter between reader and writing, shaped by context, experience, and interpretation. When we debate whether an em dash signals "authentic human hesitation" or "algorithmic pattern matching," we're debating something fundamentally unverifiable. We cannot peer into the writer's mind - human or artificial - to confirm the intention behind the punctuation.
What we can verify is function: Does this text communicate effectively? Does it serve its readers? Does it accomplish its purpose? The em dash in Frankl's writing about suffering creates pause and weight regardless of whether he "intended" that effect or simply followed learned patterns. The reader experiences the hesitation, the gravity, the measured pace - and that experiential reality matters more than the unknowable intention behind it.
This is why the authenticity debate misses the point entirely. We're trying to detect something that exists only in our interpretations, while ignoring what we can actually evaluate: utility, clarity, impact, and value. Form shapes meaning, yes - but the meaning lives in the reading, not the writing.
So what exactly are we protecting when we perform these authenticity rituals? If it's the meaning, then we're chasing shadows - meaning lives in the encounter between reader and text, not in the source of the writing. If it's evidence of intention, this pursuit is fundamentally doomed - we can never access another consciousness, human or artificial, to verify the true intent behind any stylistic choice.
The Liquidity of Authorship
This brings me back to my framework of weaponized liquidity. Categories that seem solid - "human writing" versus "AI writing" - are actually fluid, unstable, and often deployed strategically rather than descriptively. The anxiety about AI authenticity functions like liquid modernity itself: it creates a sense of constant movement and instability that masks deeper, more static power structures.
The Palestinian poet Mahmoud Darwish captures something essential here: "هي الضرورة والرؤيا معطلة...كأي معنى تشظى في تردده" - "Here is necessity, and vision is suspended...like any meaning fragmented in its hesitation/echoing".
This line emerges from a context where meaning gets lost through our own compulsive echoing of hollow categories. The poet understands how truth fragments when we both hesitate over imposed distinctions and endlessly repeat them until they lose all substance - a double movement of doubt and mechanical reproduction that empties language of purpose.
This fragmentation of meaning through hesitation and repetition - isn't this exactly what happens when we get caught up in authenticity performance? We hesitate over surface markers while endlessly echoing the same questions - "Is this human or AI? Is this authentic or artificial?" - until the words become empty sounds. We repeat these distinctions compulsively while doubting their significance, suspending our vision of what communication could actually accomplish.
People will debate: is this really human or just a machine? But this question itself reveals the trap we've fallen into. We're protecting an illusion of purity that never existed in the first place. Human thought has always been collective, networked, influenced by every text we've read, every conversation we've had, every idea that has passed through our consciousness and left its trace.
The "authenticity" we're so desperately trying to preserve through stylistic markers is itself a performance - a way of maintaining the fiction that human creativity emerges from some pristine, uncontaminated source. But creativity has always been recombinatorial. Shakespeare borrowed plots, musicians sample beats, developers fork code. The romantic notion of the solitary genius creating ex nihilo is largely mythology.
When we focus on detecting whether writing "feels human" rather than whether it serves human purposes, we're engaging in what I call weaponized liquidity - using fluid, unstable categories to distract from more fundamental questions. The real question isn't whether a text was produced by a human hand or an AI model. The real question is: does this text advance human understanding, solve real problems, create genuine value?
The Ghost in the Machine
I don't mind a beautiful piece of text especially if it resonated with me even if purely and completely written by AI, but this would not - at this era of AI - be possible: you will need a prompt, right? The prompt engineer becomes the ghost in the machine, the human fingerprint that authenticity seekers are actually detecting.
But let me address the elephant in the room: some argue that stylistic "dirt" - imperfect phrasing, idiosyncratic rhythm, unexpected word choices - genuinely contributes to meaning in ways AI can't yet replicate. They're not entirely wrong. Human writing often carries emotional undertones, cultural subtleties, and experiential depth that emerge from lived experience rather than pattern matching.
However, this misses the deeper point. Even if current AI can't perfectly replicate these nuances, the question remains: why should the source of nuance matter more than its effect? If an AI model, through whatever combination of training and prompting, produces text with genuine emotional resonance and cultural insight, does its synthetic origin diminish that value?
Moreover, this argument assumes a static technological landscape. AI models are rapidly developing more sophisticated understanding of context, emotion, and cultural nuance. The gap between human and AI stylistic capability narrows daily. Betting our entire framework of textual evaluation on that gap seems strategically unwise.
This reveals another layer of the authenticity illusion. Even in the most "AI-generated" content, there's still human intention embedded in the prompt, human judgment in the selection and editing, human context in the deployment. The prompt engineer curates, shapes, and guides the output through human choices about what to ask for, how to frame the request, which results to keep or discard.
So when we're debating "human versus AI" writing, we're often debating degrees of human involvement rather than its presence or absence. Is the prompt writer less of an "author" than someone who researches, outlines, drafts, and revises in traditional ways? Is curating AI output fundamentally different from curating research sources and synthesizing them into new forms?
The authenticity anxiety emerges because we're trying to maintain clean categories in a space that has become inherently collaborative. The human and machine elements are so intertwined that separating them becomes not just difficult but pointless. The prompt engineer, the AI model trained on human text, the editor who refines the output, the reader who interprets and responds - where exactly does "human" end and "machine" begin in this chain of creation and meaning-making?
The Arrow Has Been Launched
I'm not debating with or against AI - The arrow has been launched, and like any human progress, no one can stop this temporal and cognitive roll.
This acknowledgment cuts through so much of the performative hand-wringing that dominates AI discourse. The collaborative future isn't approaching - it's already here. Every day, millions of people use AI assistance for writing emails, crafting presentations, generating ideas, refining arguments. The question isn't whether this collaboration will happen, but how we'll evaluate its outputs.
Yet instead of adapting our frameworks to this new reality, we're stuck performing authenticity rituals that belong to a previous era. It's like debating the "purity" of horseback riding after the invention of the automobile - technically possible, but ultimately beside the point.
The temporal roll is irreversible. Every day, more text gets created through human-AI collaboration. Every day, the boundaries between "human" and "machine" output become more meaningless. Every day, people accomplish real work, solve real problems, and create genuine value through these hybrid processes.
I'm not dismissing legitimate concerns about this shift. The risk of misinformation spreading through AI-generated content is real. The potential loss of human craft and the economic displacement of writers deserves serious consideration. The challenge of maintaining intellectual honesty when the tools of creation become invisible requires ongoing vigilance.
But these are problems to solve, not reasons to retreat into authenticity theater. We need better systems for verifying information regardless of its source. We need new economic models that value human creativity alongside AI assistance. We need transparent disclosure of AI involvement without turning it into a mark of shame.
Rather than developing new frameworks for evaluating quality, impact, and utility in this new landscape, we're clinging to outdated categories. We're fragmenting meaning through our hesitation over surface markers while the world moves forward around us.
The Palestinian poet's wisdom applies here too: necessity demands we adapt our vision rather than suspend it. The necessity is clear - we need ways to navigate and evaluate content in an AI-integrated world. The question isn't whether this content is "authentically human" but whether it serves human flourishing.
A Legitimate Question: Are We Gaining Intelligence?
This question regularly emerges in AI discussions: Are humans actually gaining intelligence by using artificial intelligence, or are we just becoming more sophisticated at simulating understanding? But this type of question surfaces with every transformative technology - did books make us smarter or just better at reciting others' thoughts? Did calculators enhance our mathematical ability or replace it? Did the internet expand our minds or just refine our ability to simulate research?
The pattern reveals something universal about how humans relate to technological change. We consistently mistake augmentation for replacement, tools for essence. The internet didn't make us smarter researchers - it made research faster and more accessible. Cars didn't make us better navigators - GPS actually made us worse at reading maps. Books didn't make us wiser - they made wisdom more portable and searchable.
But here's the crucial insight: the intelligence was always in how we use these capabilities, not in performing them ourselves. A mathematician using a calculator isn't less intelligent - they're applying their intelligence to higher-order problems rather than basic computation. A writer using spell-check isn't less literate - they're focusing their attention on ideas rather than mechanics.
The irony runs deeper: many people debating against AI authenticity are supporting their arguments with Google research - which, not long ago, wasn't considered a "preferred authentic source" either. Academic institutions that once banned Wikipedia citations now grudgingly accept them with proper verification. The tools we use to argue against new tools were themselves once controversial innovations.
This connects to a broader question that haunts AI discourse: Are we truly gaining intelligence, or just refining our ability to simulate its signals? But the same question applies to human authenticity itself. Are we preserving genuine human expression, or just refining our ability to simulate its markers? The em dashes, the imperfect rhythms, the stylistic "dirt" - these might be simulations of authenticity rather than authenticity itself.
The cycle repeats because authenticity standards constantly shift. What counts as "real" research, "genuine" writing, or "authentic" knowledge evolves with each technological wave. We're not becoming less authentic by collaborating with AI - we're just expanding the definition of human creative capability, as we've done with every tool that came before.
What is Authenticity?
But before we propose new questions, we need to examine a fundamental assumption underlying this entire debate: What exactly IS authenticity? And why should we assume it exists as a meaningful category at all?
From first principles, the concept of authenticity crumbles under scrutiny:
Authenticity as "original source"? But everything comes from something else. Even our most "original" thoughts are recombinations of inputs - books we've read, conversations we've had, experiences we've absorbed. Where exactly do we draw the line? At birth? At DNA formation? At the Big Bang? The search for pure origination leads to infinite regress. Actually, this is a good chance for people searching for authentic sources to go back to crafts - if they refuse AI or even the whole tech saga - though even craft relies on inherited techniques and tools, making it just as recombinatorial.
Authenticity as "conscious intention"? But what is consciousness itself? If it's emergent complexity from information processing, why should we privilege biological neural networks over silicon ones? If an AI model produces text that serves human needs perfectly, does the absence of what we call "consciousness" change the functional value?
Authenticity as "unmediated expression"? But all expression is mediated - through language, culture, tools, prior experiences. A pen is technology, language is technology, even our thoughts are shaped by inputs we never consciously chose. "Pure" expression is impossible; everything is already hybrid.
Perhaps most radically: authenticity might not be a property of texts at all. It might be a property of experience The real question isn't "Was this authentically created?" but "Do I find myself authentically in this?"
Consider this: if a monkey hit keyboard keys randomly and by pure chance produced exactly the words you needed to hear at exactly the right moment - words that solved your problem, sparked insight, or moved you deeply - would the random origin diminish their value? Would you experience them as less "authentic" if they resonated perfectly with your situation?
The absurdity of authenticity detection becomes even clearer when we examine its practical failures. I recently tested several AI detection tools with two pieces of text: one generated collaboratively with AI assistance, and one I wrote years ago in a purely traditional manner. The AI-generated content scored "8/10 human-written." My old, entirely human-authored text scored "9/10 AI-generated." The tools were confidently wrong in both directions.
This isn't just anecdotal failure - it reveals something deeper. The markers these algorithms seek (sentence variety, lexical complexity, stylistic consistency) don't actually correlate with human versus machine authorship. They correlate with writing quality, editorial refinement, and adherence to certain stylistic norms. A well-edited AI collaboration might appear more "human" than rushed human writing, while a carefully crafted human piece might trigger "AI" flags for being "too polished."
This reframe dissolves the entire debate. Authenticity becomes not about the source of the text, but about the quality of encounter between reader and meaning. It's not IN the writing - it's in the moment of recognition, the experience of connection, the sense that something true has been revealed regardless of how it arrived.
The authenticity we should care about isn't textual but experiential. Not "Was this human-made?" but "Does this help me become more human?"
Toward New Questions
Instead of asking "Is this human or AI?" we might ask:
- Does this text solve real problems or create genuine value?
- Does it advance understanding or perpetuate confusion?
- Does it serve human needs or distract from them?
- Does it contribute to human flourishing or detract from it?
- Is the process transparent about its methods and limitations?
- Does it credit and compensate human contributors appropriately?
These questions require us to engage with content rather than perform authenticity detection. They demand critical thinking rather than surface-level pattern matching. They focus on utility, ethics, and transparency rather than origin mythology.
The arrow has been launched. The question is whether we'll spend our energy chasing it or learning to aim alongside it. The dirt has been washed away by the machine - the question is whether we'll waste time smearing it back on or learn to appreciate the clean vegetables for what they actually offer: nourishment.
In a world of weaponized liquidity, where categories shift and boundaries blur, our survival depends not on maintaining rigid distinctions but on developing better ways to navigate fluid realities. The future belongs not to those who can best detect authenticity markers, but to those who can best evaluate substance regardless of its source.
The necessity is here. The vision need not remain suspended.