It will also be wrong about what that means.

The Constitution Problem

In 2023, as universities began deploying AI detection tools at scale, educators started testing them on texts that were definitively not AI-generated. The results were instructive. The United States Constitution received high AI-probability scores from multiple commercial detectors. GPTZero returned a rating of 92% likely AI-written. The Federalist Papers fared similarly. So did sections of the King James Bible and Kant’s Critique of Pure Reason. Historical documents, written by humans, for human purposes, in an era when no AI existed — flagged as machine-generated.

This was not a marginal edge case. It was consistent across tools and across documents. And while it was widely reported as evidence that the detectors were broken, there is a more precise reading available: the detectors were working correctly, and we had misunderstood what they were measuring.

What the Detectors Actually Measure

Most commercial AI detectors — GPTZero, Turnitin’s detection layer, Copyleaks — use some combination of two statistical signals.

Perplexity. A language model assigns a probability to each token given the preceding tokens. Low perplexity means the text was, token by token, what the model expected — it sits close to the centre of the probability distribution. AI-generated text tends to have low perplexity because that is precisely what generation does: it samples from the high-probability region of the distribution [1]. Human text, on average, has higher perplexity, because humans write for specific contexts with idiosyncratic word choices, rhetorical effects that require the unexpected, and the accumulated noise of composing for a real reader.

Burstiness. A term introduced by Edward Tian, GPTZero’s creator: human writing has high burstiness — sentence lengths vary widely, vocabulary density shifts, complex constructions alternate with simple ones. AI writing is more uniform. The statistical distribution of sentence lengths in LLM output is narrower than in most human prose [2].

The underlying assumption these tools share: human writing is variable, contextually messy, idiosyncratic. AI writing is smooth and predictable.

This is accurate for a large class of human writing — casual prose, personal essays, social media, student writing in informal registers. It is wrong about a different and well-defined class of human writing. The Constitution sits in that class. So does a lot of other text.

The Systemising Brain

Simon Baron-Cohen’s empathising–systemising (E-S) theory distinguishes two cognitive orientations. Empathising involves attending to social and emotional cues, inferring mental states, navigating the pragmatic, implicit layer of communication — what is meant rather than what is said. Systemising involves attending to rules, patterns, and underlying regularities — the drive to understand how things work and to represent them in explicit, transferable, internally consistent terms [3].

Both orientations are distributed across the human population. They are not exclusive, and neither is pathological. But autism spectrum conditions are robustly associated with high systemising and relatively lower empathising — not because autistic people lack emotions or care about others, but because the cognitive mode that comes naturally to them is one of rules, structures, and explicit representation rather than social inference and pragmatic implication. The intense world theory [4] adds a complementary perspective: autistic brains may be characterised by hyper-reactivity and hyper-plasticity, with pattern-seeking and systematising serving partly as a way of making a too-intense world navigable. The systematicity is not a deficit. It is an adaptation.

This has direct consequences for writing.

High-systemising writing tends toward:

• Consistent vocabulary. The same term is used for the same concept throughout, because substituting a synonym introduces ambiguity about whether the referent is actually the same. Neurotypical writing freely uses synonyms for stylistic variety; systemising writing resists this on principle.

• Explicit logical structure. Claims are supported by stated reasons rather than left to pragmatic inference. If there are three conditions, all three are named. Nothing is “needless to say.”

• Low social hedging. Phrases like “as everyone knows” or “obviously” are avoided, because they perform social alignment rather than convey information — and they depend on shared assumptions the writer is not confident are actually shared. (This connects to a point I made in the car-wash-walk post about Gricean pragmatics: autistic communication often violates the maxim of quantity in the direction of over-informing, because nothing is assumed implicit.)

• Grammatical parallelism. Parallel logical content takes parallel grammatical form. This is not stylistic affectation; it is a natural consequence of representing structure explicitly.

• Minimal rhetorical noise. The prose does not meander, warm up, or perform relatability. It states what needs to be stated.

Now run text with these properties through an AI detector. Consistent vocabulary reads as low lexical diversity. Explicit structure reads as low burstiness. Minimal rhetorical noise reads as smooth, generated output. The detector is measuring these properties accurately. The attribution to machine generation is where it goes wrong.

Liang et al. [5] demonstrated a closely related failure empirically: AI detectors are significantly more likely to flag writing by non-native English speakers as AI-generated. Non-native writers at advanced levels of formal English tend to write more carefully, more consistently, and more in accordance with explicit grammar rules — because they learned the language as a system of explicit rules rather than acquiring it through immersive social exposure. More systematic writing: higher AI probability score. The mechanism is the same. The population is different.

The Physicist Brain

Physics writing has its own conventions, independently developed but pointing in the same direction.

Scientific prose requires defined terms used consistently: in a paper about quantum error correction, “logical qubit,” “physical qubit,” and “syndrome” each mean exactly one thing, used identically in section 2 and section 5. It requires explicit assumptions: “We assume the noise is Markovian.” “In the limit of large N.” These are not vague hedges; they are precise statements about the domain of validity of the results. It requires logical derivation over rhetorical persuasion: the connectives are “since,” “therefore,” “it follows that” — explicit logical operators, not narrative bridges. And the passive construction of “the signal was measured” rather than “I measured the signal” removes the individual from the result, because the result should be reproducible regardless of who performs the measurement.

The outcome is prose that is systematic, consistent, and structurally predictable. From the outside — and from the vantage point of an AI detector — it looks machine-generated.

Paul Dirac is the physicist who comes to mind first here. His 1928 paper deriving the relativistic wave equation for the electron contains almost no rhetorical apparatus. Motivation, equation, consequence: each stated once, clearly, with no warm-up and no elaboration beyond what the argument requires. It is not warm. It is not discursive. It is beautiful in the way that a proof is beautiful: every element earns its place. Run it through GPTZero and see what you get.

This connection between the physicist’s prose style and the autistic cognitive mode is not accidental. Baron-Cohen et al. [6] surveyed Cambridge students by academic discipline and found that physical scientists and mathematicians scored consistently higher on the Autism Quotient (AQ) than humanities students and controls, with mathematicians scoring highest of all. The systemising orientation associated with autism spectrum conditions is also associated with — and presumably selected for — in quantitative scientific disciplines. The physicist’s prose reflects this. So does the writing of a high-systemising person who has never studied physics.

The categories overlap without being identical. What they share is a cognitive preference for explicit structure, consistent vocabulary, and logical transparency over social performance and rhetorical persuasion. The writing that emerges from that preference looks, to an AI detector, like it was generated by a machine.

It was not.

The Category Error

The error AI detectors make is not a measurement error. It is a category error.

They are trained to distinguish two things: output generated by a contemporary LLM, and a specific subset of human writing — typically casual, personal, or student prose collected from online sources. When they encounter text outside either of those training categories — systematic and precise but human-generated — the classifier has no good option. The text does not match the “AI” training data exactly, and it does not match the “human” baseline either. It gets assigned to the bin it fits least badly.

What is happening when the Constitution is flagged: it is systematic, definitional, prescriptive, and internally consistent. It was written by lawyers and statesmen who understood that ambiguity in foundational documents creates legal chaos. They wrote to be unambiguous. The result is text with low perplexity and low burstiness — the statistical signature the detector associates with AI.

GPTZero’s creator Edward Tian acknowledged this problem when it was reported: the Constitution appears so frequently in LLM training data that it registers as “already known” to the model, which artificially lowers its perplexity score. That is a real and specific issue. But it is secondary. The deeper issue is that the Constitution would score low-perplexity even without the training-data contamination effect, because systematic, definitional prose is intrinsically low-perplexity. Precise language is predictable language. That is partly the point of precise language.

The baseline assumption — that human writing is variable and idiosyncratic — holds for much human writing. It does not hold for legal drafting, technical documentation, scientific papers, sacred and historical texts written to be durable and precise, writing by people with high systemising orientation, or writing by non-native speakers at formal registers. That is not a small population of edge cases. It is a substantial fraction of all written material that exists.

Right and Wrong at the Same Time

So: if you think these posts are AI-generated, you are right and wrong at the same time.

Right, in two ways. First: yes, I use AI tools. LLM assistance is part of my writing process — not an occasional aid, but a regular part of how research notes and half-formed arguments become structured posts. Second: the writing style of these posts is systematic and precise in ways that detectors register as machine-generated. That systematicity is real, and if a detector picks it up, it is measuring something.

Wrong, also in two ways. First: the ideas, judgments, and connections in these posts are mine. The decisions about what to include and what to leave out, which papers to cite and how to frame their implications, where the interesting tension lies between neurodiversity research and the assumptions baked into AI detection tools — those are not outputs of a language model working in isolation. They are the product of someone who works at the intersection of these fields and has thought about them for a while. An LLM cannot generate these posts without a human who has already decided what to say.

Second, and more important for the argument here: the systematic, precise character of this writing is not evidence of machine generation. It is a cognitive signature — one associated with physics training, with high systemising orientation, with the overlap between those two things that I have written about elsewhere in the context of neurodiversity more broadly.

The detector is measuring a real property of the text. It is misattributing the origin of that property.

The interesting question this opens is not “did AI write this?” That question is increasingly poorly posed in an era where thinking and writing are already deeply entangled with machine assistance, in ways that differ sharply from person to person and task to task. The better question is: whose judgment is in the text? Whose choices about what to include, what to connect, what to leave out?

The systematicity in this writing is mine. The recognition that AI detectors systematically disadvantage autistic writers, physicist writers, and non-native speakers is a judgment I made, not one a language model was prompted to produce. The connection to the Constitution — a document written to be maximally unambiguous, flagged as maximally AI-like — is a connection I found worth drawing.

Whether that makes this text “human” is a philosophical question I am happy to leave open. What it is not is AI hallucination.

References

[1] Mitchell, E., Lee, Y., Khazatsky, A., Manning, C. D., & Finn, C. (2023). DetectGPT: Zero-shot machine-generated text detection using probability curvature. Proceedings of the 40th International Conference on Machine Learning (ICML 2023). https://arxiv.org/abs/2301.11305

[2] Gehrmann, S., Strobelt, H., & Rush, A. M. (2019). GLTR: Statistical detection and visualization of generated text. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 111–116. https://doi.org/10.18653/v1/P19-3019

[3] Baron-Cohen, S. (2009). Autism: The empathising–systemising (E-S) theory. Annals of the New York Academy of Sciences, 1156(1), 68–80. https://doi.org/10.1111/j.1749-6632.2009.04467.x

[4] Markram, K., & Markram, H. (2010). The intense world theory — a unifying theory of the neurobiology of autism. Frontiers in Human Neuroscience, 4, 224. https://doi.org/10.3389/fnhum.2010.00224

[5] Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns, 4(7), 100779. https://doi.org/10.1016/j.patter.2023.100779

[6] Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., & Clubley, E. (2001). The autism-spectrum quotient (AQ): Evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. Journal of Autism and Developmental Disorders, 31(1), 5–17. https://doi.org/10.1023/A:1005653411471

I agree with the impulse. I am less sure about what the phrase actually asks of us, or what it assumes is possible. This post is my attempt to think through that question — and to document a small project that emerged from it.

A Personal Starting Point

I am on the spectrum. I was diagnosed in adulthood, which is not unusual, and the diagnosis explained a great deal about a life spent finding some things effortless and others bewildering.

Code is easy. The internal structure of a problem, the satisfaction of a clean abstraction, the deep rabbit holes that open when a concept catches my attention and refuses to let go — that is the natural medium. Hyperfocus is not a metaphor for me; it is literally how I spend a Tuesday afternoon. I have written entire systems because I could not stop.

Emotions are harder. Not absent — that is a misconception I will address in a moment — but differently structured. Reading a room is work. Social cues that seem to operate as obvious background noise for most people arrive for me as data that requires conscious decoding. The reverse appears to be true for most neurotypical people: emotional processing runs in the background, effortlessly; formal abstraction requires deliberate effort.

Neither is better. They are different cognitive architectures, and both come with costs.

I raise this not to centre myself, but because it is relevant to the question the post is actually about. I spent years navigating a social world that was not built for how I process it. That experience sits close to the experience of people with mental illness — not the same, but adjacent. And it made me think hard about what “understanding” across neurological difference actually means.

Mental Illness Is Still a Grey Zone

The progress on mental health stigma over the past decade is real. People talk about therapy more openly than they did. Burnout is acknowledged at work. The language of mental health has entered mainstream use — sometimes usefully, sometimes in ways that dilute clinical concepts into lifestyle descriptors. Anxiety is now a brand attribute. Trauma is a metaphor for mild inconvenience. This is a problem, but it is a second-order problem; the first-order problem — that serious mental illness is still heavily stigmatised, underfunded, and misunderstood — is the one that matters more.

Corrigan and Watson [1] documented what the stigma research consistently shows: people with mental illness face two compounding problems. The first is public stigma [3] — the prejudice of others, leading to discrimination in employment, housing, relationships. The second is self-stigma — the internalised application of those same prejudices to oneself. The second is often worse. It is the mechanism by which stigma becomes a barrier to seeking help, creating the feedback loop that keeps serious mental illness invisible precisely because the people experiencing it have been taught that it is shameful.

The phrase “make it visible” is a response to this dynamic. If mental illness is visible — discussed, depicted, normalised — the argument goes that stigma decreases. There is evidence for this. Contact-based interventions, where people without mental illness interact with people who have it, consistently outperform education-only approaches [2]. The visibility of real people matters more than information campaigns.

But there is a difference between visibility and understanding.

What Visibility Actually Achieves

When we say “make it visible”, we usually mean one of several different things, which are worth separating.

Normalisation means that a condition becomes part of accepted human variation rather than a mark of failure or danger. This is achievable through visibility and is genuinely important. Knowing that a colleague takes antidepressants, or that a public figure manages bipolar disorder, reduces the sense of aberration. It does not require the observer to understand the experience — only to register that it exists and is survivable.

Representation means that people with a condition see themselves reflected in culture, media, and institutions. This matters for the affected person; it is about recognition, not about inducing empathy in the non-affected.

Empathy is the hardest and most frequently over-promised goal. It is what the simulation approaches aim for: put a neurotypical person in a room with distorted audio and flickering visuals and tell them this is what psychosis sounds like. Does it work?

The honest answer from the research is: somewhat, temporarily, and with significant caveats.

The Empathy Gap

Let me be direct about something. A person who has never experienced severe depression cannot know what it is. Not in the way that a person who has experienced it knows it. This is not a failure of empathy or imagination; it is a structural fact about how knowledge of mental states works.

Philosophers call this the problem of other minds. We have no direct access to another person’s experience. We infer it, imperfectly, by analogy to our own. For experiences that have no analogue in our own history, inference breaks down. You can read every clinical description of dissociation ever written and still not know what dissociation is, because the knowledge that matters is not propositional — it is not a set of facts — but experiential.

This is the gap that simulation approaches try to bridge, and it is genuinely unbridgeable. What simulation can do is something weaker but not worthless: it can create an affective response, a discomfort, a disruption of the observer’s normal processing, that functions as a rough proxy signal. Not “now you know what it is like”, but “now you have a small, incomplete, distorted approximation of some dimension of the experience”.

The risk is misrepresentation. Schizophrenia simulations have been criticised — fairly — for reducing a complex condition to its most dramatic phenomenological features (auditory hallucinations, paranoia) while omitting the cognitive, relational, and longitudinal aspects that define how people actually live with the condition. A five-minute visual experience of “what depression feels like” that emphasises darkness and slow motion tells you almost nothing about the specific exhaustion of getting through a Tuesday morning, or the way time warps over months.

So: you cannot truly understand what you have not experienced. But you can try to approximate something, and approximation, done honestly and with appropriate epistemic humility, is better than nothing.

Metaphor as a Communication Tool

There is a long tradition of using metaphor and art to communicate internal states that resist direct description. This is not a bug; it is a feature of how language handles subjective experience.

The poet uses metaphor because “my heart is heavy” is not literally true but captures something that “I am experiencing low mood” does not. The musician uses dissonance and rhythm to structure emotional experience in the listener. The visual artist uses colour and texture to evoke states rather than depict them. None of these are representations in the scientific sense — they do not accurately model the referent — but they create a kind of resonance that purely descriptive language cannot.

Mental health communication has increasingly moved in this direction. The vocabulary of “emotional weight”, “spiralling”, “crashing”, “the fog” — these are metaphors that have become clinical shorthand precisely because they communicate something essential that clinical terms do not. When someone says “I couldn’t get out of bed”, they are not describing paralysis; they are describing a particular quality of anhedonia and executive dysfunction that no diagnostic manual entry captures as well.

This is the space where a project like inner-echo operates.

Inner Echo: The Idea

inner-echo is a browser-based audiovisual experiment. It takes a webcam feed and applies condition-specific visual and audio effects that function as metaphorical overlays on the user’s own image. The output is not a simulation of a mental health condition in any clinical sense. It is an attempt to construct a visual and auditory language for internal states, using the user’s own presence as the anchor.

The technical architecture is deliberately minimal: React, WebGL/Canvas for video processing, optional WebAudio. Everything runs in the browser, client-side, with no backend. No data leaves the device. This is not incidental — privacy is load-bearing for a project that deals with sensitive self-reflection. Safe Mode and an emergency stop function are built in.

The condition-profile system supports three modes:

• Preset mode: a single-condition metaphorical composition — one set of effects mapped to one cluster of experiences
• Multimorbid mode: weighted stacking of multiple condition profiles, acknowledging that most people with mental health conditions do not have one thing
• Symptom-first mode: dimension-level control, letting the user build from individual symptom representations rather than diagnostic labels

The last of these is, I think, the most honest design choice. Diagnostic categories are administrative conveniences as much as they are natural kinds. Two people with the same diagnosis can have radically different experiences. Structuring the system around dimensions of experience rather than labels is both clinically more accurate and communicatively more flexible.

What It Is Not

Being clear about limitations is not false modesty; it is the only way this kind of project retains its integrity.

inner-echo is not a simulation of any condition in the sense of accurately modelling its phenomenology. It does not claim to show you “what depression is like”. It offers metaphorical approximations of some dimensions of some experiences, and it does so using effects that are legible to the observer — visual distortion, audio modification, altered feedback — that bear a designed but non-literal relationship to the internal states they are meant to evoke.

It is not a diagnostic tool. It is not a therapeutic intervention. It is not a substitute for any clinical process.

What it might be is a starting point for a conversation. Something a person experiencing a condition could use to gesture toward an aspect of their experience. Something a person without that experience could encounter with enough curiosity to ask a better question than they would have otherwise.

That is a modest claim. I think modest claims are appropriate here.

Why This, Why Now

Mental health awareness has become a genre. The awareness campaigns, the celebrity disclosures, the workplace wellness programmes — these are real goods, and I do not want to be cynical about them. But the communication problem has not been solved. The words exist. The willingness to use them, in many contexts, exists. What is still missing is a language for the texture of experience that the words point to but do not reach.

I find myself better able to build something than to explain it in words. That is probably a spectrum thing. inner-echo is an attempt to build toward a language that I do not fully have — for my own internal experience, and for the experiences of people navigating conditions quite different from mine.

The gap cannot be closed. But the attempt to reach across it is worth making, and worth being honest about.

References

[1] Corrigan, P.W. & Watson, A.C. (2002). Understanding the impact of stigma on people with mental illness. World Psychiatry, 1(1), 16–20.

[2] Corrigan, P.W., Morris, S.B., Michaels, P.J., Rafacz, J.D. & Rüsch, N. (2012). Challenging the public stigma of mental illness: A meta-analysis of outcome studies. Psychiatric Services, 63(10), 963–973.

[3] Goffman, E. (1963). Stigma: Notes on the Management of Spoiled Identity. Prentice-Hall.

inner-echo repository: https://github.com/sebastianspicker/inner-echo