AI Writing: Work with the Garage Door Up

Sessions: 2
Models: Claude Opus 4.7, GPT-5.5
Date range: 2026-05-16 - 2026-05-17

Session 1

1.1

Me:

Many people seem to be deeply offended by writing that is ai generated, calling people out, blocking them, etc. If they detect ai generation they ignore the content and lose their minds. Unfortunately, ai generated content is here to stay and dominate media. How could we help these people? I personally have no issues with ai text unless it's low quality points or ideas. But these people get extremely upset. As a person who uses ai text a lot on my website even though it's labeled as ai made I feel I need to figure out something for these people

Claude:

The offense usually isn't really about the text. Reading is one of the few ways to spend attention to make contact with another mind, and the implicit deal is that the writer spent something comparable making contact possible. Undisclosed AI generation breaks the deal asymmetrically: the reader is investing as if there's a mind on the other end, and there isn't — or there is, but a smaller and more diffuse one than they were paying for. The disgust reaction has the same shape as discovering a friend you confided in was actually a paid actor. It's not "this prose is bad," it's "I was tricked about what kind of encounter this was."

That explanation is testable. It predicts the reaction should weaken sharply with disclosure (the encounter terms are clear up front), and weaken further when the human's directing presence is visible in the work — the question being chased, what was rejected, why this particular prose survived criticism. It predicts the strongest reactions in genres where mind-contact is the whole point (essays, literature, personal writing) and near-absence in transactional contexts (product copy, support replies). If people reacted just as hard to clearly labeled, heavily human-directed AI prose as to suspected unlabeled AI, the explanation would be in trouble.

If that's roughly right, you're already doing the main move — labeling. The remaining gap is making your mind visible enough in the labeled work that the contact people are looking for is still there, just routed differently. Your prose isn't where you live anyway; the question, the criticism, the selection is. Some of that can be surfaced in the work itself rather than left implicit.

I'd push back gently on framing this as your problem to solve. The reaction tracks something real — readers value felt contact with another consciousness, and AI prose interrupts the usual signal that contact is happening. The honest response is closer to "your reaction makes sense, here's what I'm actually offering," not "let me find a way to neutralize you." And some of the reaction is tribal — "I can detect this" as identity marker — which no good-faith effort will reach. Letting that sub-population self-filter is fine.

1.2

Me:

No, it's not something I'm worried about personally. I don't care or even want these people to engage with my work. I shun the nonbelievers. This is just an intellectual problem about inventing new signals these could compromise to.

Claude:

Right, this is a signaling problem. The substrate that used to carry "a mind was here" — prose itself — has gotten cheap to forge, so the signal stopped distinguishing. The question is what costly signals can replace it, where "costly" means hard to fake even with unlimited LLM access.

The classic move is to push the signal to whatever the AI can't yet do, but that's a treadmill — wherever you plant the flag gets overrun in eighteen months. The more durable move is to shift from product-level signals (the text) to process-level signals (the trajectory the text came from). Process is harder to forge because it has to be consistent across time and connected to a real life with real constraints. A single essay can be generated; a five-year arc of essays where each one criticizes the last and the criticisms are themselves interesting is much harder, because the forger has to simulate a mind that's actually changing in response to its own outputs. Public revision history, visible wrong turns, ideas that get abandoned and explained — these are expensive in a way prose no longer is.

A second family: signals tied to stakes. Predictions with dates on them. Bets. Positions taken before the answer was obvious, timestamped. "I said X in 2022 and was wrong, here's what I learned" is nearly impossible to fake retroactively and trivially verifiable. This is why prediction markets and old blog archives have started carrying weight they didn't before — they're load-bearing in a way new prose isn't.

A third: signals that require the body or the calendar. Showing up somewhere. Talking on video unscripted. Doing the thing the writing is about and producing artifacts from the doing. A fitness writer who's visibly fit, a trader with a track record, a teacher whose students can be interviewed. The AI can write about lifting; it cannot lift.

A fourth, more interesting one: signals built on idiosyncratic constraint. The "demanding reader" you've been thinking about is relevant here — what makes prose feel inhabited isn't fluency, it's the visible shape of what the writer refused to say. AI prose averages; a mind under real constraint cuts. Voice as a costly signal is just "I have eliminated all the moves that would have been easier." If you can make your refusals legible — the obvious framing you didn't take, the cheap insight you walked past, the comfortable conclusion you killed — that's a signal the averaging process can't currently produce, because averaging is the opposite operation.

The deepest version, which I think you're already circling: tie the signal to a research program. A single piece can be impersonated. A program — a specific question being chased across years, with the wrong turns preserved, with each piece visibly criticizing the last — is a structure no current system can fake because it requires the forger to maintain a coherent evolving position against itself over time. This is closer to how scientific reputation worked before any of this. The unit of trust wasn't the paper, it was the lab.

The pattern across all of these: move the signal from "could a human have produced this string" (lost cause) to "is there a continuous mind with skin in the game on the other end of this body of work" (still expensive, possibly permanently so).

1.3

Me:

There needs to be some kind of proof of work

Claude:

Yes, and the interesting question is what work to require proof of, because most candidates collapse on inspection.

Compute-style proof of work — "I burned cycles to make this" — doesn't transfer. Bitcoin works because the work is verifiable and the thing being secured (ledger position) is fungible with the work. Writing isn't like that. "I spent eight hours on this essay" is unverifiable and also doesn't correlate with quality; some of the best essays are fast and some of the worst are agonized over. Time-as-proof fails.

Effort-as-proof fails for a related reason: the reader can't distinguish effort from its appearance, and AI is now extremely good at producing the appearance of effort. Long, footnoted, hedged, structurally complex prose used to be a proof-of-work signal. It isn't anymore — it's actually become a counter-signal, because that's exactly the register LLMs default to.

The proof-of-work that still works has a specific shape: it has to be something where faking it requires doing it. Predictions are the cleanest case. To fake a five-year track record of timestamped predictions, you have to actually make predictions five years ago — the forgery cost equals the real cost. Same with public revision: to fake a body of work where each piece visibly criticizes and supersedes the last, you have to actually maintain a coherent evolving position over time, which is approximately what having a mind is.

The deeper pattern is that the work has to be tied to something that can't be parallelized or compressed. A human life is the ur-example: it runs at one second per second, you only get one, and the choices accumulate irreversibly. Anything that derives its signal from being downstream of an actual life — stakes taken, time spent in a single direction, public commitments that constrained later moves, relationships that produced specific knowledge — inherits that uncompressibility. The AI can generate the text of a life in seconds; it cannot generate a life.

So the proof-of-work that has a future isn't computational, it's biographical. The signal is "this output is downstream of a specific irreversible trajectory through the world, and you can check." Predictions with dates. Bets with counterparties. Code that runs and has users. Students who learned things and can be asked. Wrong turns that were publicly taken and publicly corrected. Decade-long obsessions that produced weird specific knowledge nobody would generate on purpose.

The thing that makes this proof-of-work rather than mere credentialing is that it has to be costly in a way that's continuous with the work itself. A PhD is a credential — it certifies past work but doesn't constrain present output. A public prediction record is proof-of-work because every new prediction adds to or destroys the signal in real time. The cost is ongoing and the forger has to keep paying it.

There's a sharper version of this for your case specifically. The web has accidentally built most of the infrastructure already — timestamped posts, public archives, link graphs, version histories — but nobody's been treating them as proof-of-work because there was no need to. The signal was free when prose was expensive. Now that prose is cheap, the latent proof-of-work infrastructure becomes load-bearing. A site like yours, with years of dated posts, visible evolution, abandoned threads, public corrections, is already a proof-of-work artifact. It just wasn't being read as one. Part of what's needed isn't new signals but a cultural shift in which existing signals get foregrounded — the way nutrition labels existed before anyone cared, and then suddenly mattered.

The reason I think this is the right frame and not just one frame among many: it's the only proof-of-work scheme where the cost to the honest party is something they were going to pay anyway (living their life, doing their work) while the cost to the forger is doing the same thing from scratch. That asymmetry is what makes a signaling equilibrium stable. Anything that costs the honest party extra eventually gets abandoned; anything that the forger can match cheaply eventually gets faked. Biographical proof-of-work is the rare case where neither pressure applies.

1.4

Me:

It needs to be local to a piece of text. Silly example: video of me with LLMs thinking the text through and writing it

Claude:

Right, that's the real constraint — the signal has to ride along with the artifact, not live somewhere else you have to go check. A reputation system fails the moment the piece gets quoted, screenshotted, or read out of context. Whatever the proof is, it has to travel with the text.

Your video example is actually pointing at the right shape, even if it's impractical. What makes it work isn't the video itself, it's that the artifact embeds its own causal history. You're not asserting "I made this," you're attaching the trace of how it came to be, and the trace is more expensive to forge than the output. The text plus its trajectory is harder to fake than the text alone, because faking it requires generating not just a plausible end state but a plausible path to that end state — including the dead ends, the reconsiderations, the moments where the writer went one way and came back.

So the design problem is: what's the cheapest, most portable form of "trajectory attached to artifact" that still has the asymmetry where faking it costs more than doing it? A few candidates with different tradeoffs.

The full session transcript — the actual back-and-forth with the LLM, including your prompts, your rejections, the branches you didn't take — is the lightest-weight version of your video idea and probably 80% of the signal. Faking it requires composing a plausible thinking process, which is genuinely hard because real thinking has a specific texture: false starts that turn out to matter, tangents that get folded back in, criticisms that the writer didn't see coming. A fabricated transcript reads wrong the same way a fabricated diary reads wrong. Attaching the transcript to the piece — collapsed by default, expandable — makes the artifact self-authenticating to anyone willing to spend the time, and the willingness-to-check itself becomes a filter for the readers you want.

A stronger version: the transcript plus the prior artifacts it references. If the session shows you pulling in your own earlier notes, criticizing them, deciding what survives — now the forger has to fake not just this session but a whole back-catalog consistent with it. The cost scales with how embedded the piece is in your existing body of work, which is exactly the property you want. Pieces deep in a research program become nearly unforgeable; one-off pieces stay cheap. That's the right gradient.

A different axis: cryptographic timestamping of intermediate states. Every few minutes during composition, hash the working document and post the hash somewhere public. At the end you can prove the piece passed through specific states at specific times — the messy middle existed and had this shape. This is closer to literal proof-of-work in the Bitcoin sense and it's tamper-evident in a way transcripts aren't. The downside is it proves duration and evolution but not authorship of the thinking; you could in principle have an LLM evolve a document over six hours.

The most interesting version combines them: timestamped intermediate states plus the reasoning trace that produced each transition. Now you have a verifiable claim of the form "this document existed in state A at time T1, in state B at time T2, and here's the recorded thinking that explains the transition." Faking that requires generating a coherent multi-hour thinking process in advance and committing to it cryptographically, which is meaningfully harder than generating the final text.

There's a subtler design question underneath all this, which is whether the proof should be checkable by humans or by machines. Human-checkable proofs (transcripts, video) have the advantage that the checking itself produces engagement with your thinking — the skeptic who digs through the transcript ends up reading your reasoning. Machine-checkable proofs (hashes, signatures) are more rigorous but produce a binary verdict that doesn't pull anyone closer to the work. For your purposes I'd guess human-checkable is better, because the population you care about converting isn't the population that wants cryptographic certainty — it's the population that wants evidence of mind, and a transcript is a much richer evidence-of-mind artifact than a hash.

The honest limit of all this: any scheme where the proof is data attached to the artifact can eventually be faked by a system smart enough to generate convincing fake traces. The arms race doesn't have a permanent winner at the artifact level. What it has is a temporary winner — for the next few years, generating a convincing multi-hour thinking trace with the right texture of real cognition is probably harder than generating the final text, and the gap is wide enough to be a useful signal. Long-term, the signal has to migrate to things that can't be faked because they're not data at all — relationships, in-person encounters, stakes in the physical world. But locally, for the kind of text-bound artifact you're describing, attached-trajectory is the right shape and your video instinct is correct. The transcript is just the practical version of it.

1.5

Me:

So transcripts are a great suggestion. What's the best way to do this? Should transcripts be edited for personal information?

Claude:

The implementation question splits into three sub-problems: what to capture, how to attach it, and what to redact.

On capture: the transcript needs to include your prompts, the model's outputs, and ideally the branches you abandoned. Most chat interfaces let you export, but the exports usually lose the branching structure — if you regenerated a response or edited a prompt mid-stream, that information is gone, and that's exactly the information that's hardest to fake. Claude's export gives you the linear final path, not the tree. If you want the full signal, the cleanest move is to copy-paste as you go into a working doc, marking branches explicitly: "tried this framing, didn't work because X, switched to this." That manual annotation is itself part of the signal — it shows you were criticizing the outputs, not just accepting them. The fully-automated export is lower-friction but also lower-signal, because it doesn't distinguish accepted from rejected text.

On attachment: the simplest version is a collapsed expandable section at the bottom of the post, or a separate linked page with a stable URL. Ghost handles this fine with a toggle card or just a <details> HTML element. The principle is that the transcript should be one click away but not in the reading path — readers who want the contact get the prose, readers who want the proof get the trace. Putting it inline breaks the piece; putting it on a separate domain breaks the portability when the piece gets quoted. Same-page-collapsed is the right default.

There's a stronger version worth considering: a timestamp on the transcript that's verifiable independently. The cheapest way is to post the hash of the transcript to a public ledger (a tweet works, or an OpenTimestamps proof, which is free and uses Bitcoin's chain). This costs you about thirty seconds per piece and makes the claim "this transcript existed in this exact form on this date" checkable by anyone. Without it, the transcript could theoretically be fabricated after the fact. With it, fabrication requires either compromising the timestamp service or fabricating the whole thing before publication — which collapses back into the original problem of generating a convincing fake trace in advance.

On redaction: yes, but less than you'd think, and the principle matters. The redaction should remove information that's genuinely private (other people's names, client details, specific financial numbers, anything someone else didn't consent to share) and nothing else. The temptation will be to also redact things that make you look bad — the dumb early prompts, the times you went down a wrong path, the moments the model corrected you on something basic. Resist that. Those are the highest-signal parts of the transcript. A polished trace where you asked the perfect question and got the perfect answer reads as fake because real thinking doesn't look like that. The texture of real cognition includes confusion, backtracking, and being wrong out loud. If you edit that out, you've removed the thing that makes the transcript load-bearing.

The practical rule: redact for others' privacy, never for your own dignity. If a passage embarrasses you intellectually, that's the strongest evidence it's real. If it embarrasses you personally — health details, family, work specifics — that's a fair cut. The distinction is whether removing it makes the thinking look better than it was (don't) or removes information that was never the reader's business (fine).

One thing worth noting about your specific case: you already publish under your real name with a coherent body of work, so the transcript is doing less authentication work than it would for a pseudonymous writer. For you it's more like showing the workings on a math problem — not "proving I exist" but "showing how this particular piece came to exist." That changes what's valuable to include. The high-value content is the criticism — where you pushed back on the model, where you rejected a framing, where you noticed something the model missed. That's the part that's specifically you and specifically hard to fake, because it requires the forger to simulate your taste. The model's outputs are commodity; your selection pressure on them isn't.

A workflow that probably works for you: keep the chat open in one window and a working doc in another. After each substantive exchange, paste the relevant part into the doc with a one-line annotation — "kept this," "rejected because too generic," "this reframing was the unlock." At the end you have a transcript that's already curated for signal, already annotated with your reasoning, and roughly the same length as the piece itself. Publish it as a collapsed section. The annotation is the proof-of-mind; the raw exchange is the proof-of-process; together they're harder to fake than either alone.

The deeper point underneath the implementation: what you're building isn't really an authentication system, it's a new genre. "Essay plus visible thinking" is a different artifact than "essay," and it asks something different of the reader. Some readers will prefer the new form because it gives them more to engage with — the transcript becomes its own readable thing, sometimes more interesting than the polished output. That's the upside nobody talks about. The proof-of-work frame makes it sound defensive, but the actual product might just be better.

1.6

Me:

So let's lay out the transcript document. It needs to record multiple chats and branches plus note redactions

Claude:

The document has three structural problems to solve at once: sessions (a piece often pulls from several), exchanges within sessions (the atomic unit), and branches off exchanges (the proof-of-criticism layer). Plus a redaction manifest at the bottom so readers know what's been removed and what category it falls into. The annotation after each exchange is doing the heaviest signal work — that's where your selection pressure shows.

Here's a template that handles all four:

# Transcript — [Piece Title]
**Final piece:** [link]
**Sessions:** 3 | **Models:** Claude Opus 4.7, GPT-5
**Date range:** 2026-05-15 → 2026-05-17
**Timestamp hash:** [optional, if using OpenTimestamps]

---

## Session 1 — Claude Opus 4.7 — 2026-05-15
*Goal: open exploration — what's the real complaint behind AI-text disgust?*

### 1.1
**Me:**
> [prompt]

**Claude:**
> [response]

*Note: The "broken deal about kind of encounter" framing was the seed. Kept whole.*

### 1.2
**Me:**
> [prompt]

**Claude:**
> [response]

#### Branch — regenerated
*First response leaned on credentialism. Pushed back: credentials are past work, not ongoing. Regenerated with explicit constraint.*

**Claude (regen):**
> [better response]

*Note: Used the regen. The first version is preserved below for the trail.*

<details>
<summary>Rejected first response</summary>
[original response]
</details>

---

## Session 2 — [...]

---

## Redactions
- **[name redacted]** — a colleague's first name in §1.3
- **[client detail redacted]** — specific company in §2.4
- **[health detail redacted]** — personal context in §2.7

No redactions for intellectual missteps, dead ends, or confusion. Those are kept verbatim.

A few choices worth flagging. Numbering exchanges (1.1, 1.2) lets you cross-reference from the published piece — "this argument came out of §2.3" — which makes the transcript actually navigable rather than a wall to scroll. Keeping rejected branches inside <details> collapses them by default but preserves them for anyone digging; deleting them entirely loses the signal that you rejected something. The explicit "no redactions for dignity" line at the bottom is doing real work — it tells readers the texture they're seeing is real, and it commits you publicly to the rule so you can't quietly violate it later.

The one thing the template doesn't capture well is parallel exploration — when you ran the same question through two models or two framings simultaneously and synthesized. For that, a "parallel sessions" section at the top of a piece, with each one numbered and the synthesis annotated, probably works better than trying to interleave them.

1.7

Me:

I would like a way to filter or group all of the prompts for ai text generator check

Claude:

Good — that's a separate use case from the curated reading transcript, and it deserves its own surface. An AI-text-checker tool wants the prompts as a clean stripped block, not woven through annotations and model outputs. Three ways to handle it, with different tradeoffs.

The lowest-friction option is a "Prompts only" section at the top or bottom of the transcript that just lists every prompt in order, numbered to match the exchanges. It duplicates content but the duplication is the feature — one section is for humans reading the trace, the other is for tools or people doing prompt-level analysis. Costs you nothing to maintain if you generate it from the same source.

A slightly better version is to make the prompts machine-extractable by putting them in a consistent code block format that a tool could grep for. Something like:

## Prompts (all sessions, ordered)

```prompt id="1.1" session="1" model="claude-opus-4.7"
[prompt text]
```

```prompt id="1.2" session="1" model="claude-opus-4.7"
[prompt text]
```

The fenced code blocks with a prompt language tag let any tool pull them out cleanly, the id attribute ties each one back to its location in the main transcript, and the model attribute lets a checker group by which system was prompted. This is the version I'd actually use — it costs the same as the plain list but is structured enough that you or anyone else could write a five-line script to extract, group, or analyze them later.

The strongest version exposes the prompts as a separate file alongside the transcript — transcript.md and prompts.json — where the JSON is the canonical structured form and the markdown is the readable form. JSON gives you proper filtering by session, model, date, whether the response was kept or rejected, etc. This is overkill for one piece but pays off if you're doing this regularly and want to query across your whole corpus later ("show me every prompt where I rejected the first response," "which models do I push back on most"). The setup cost is real, though, and it only matters if you actually expect to analyze the corpus.

My guess for your case: start with the fenced-code-block version inside the same transcript document. It costs nothing extra per piece, makes the prompts trivially filterable by anyone who wants to, and doesn't commit you to maintaining a separate pipeline. If you ever find yourself actually running queries across pieces, graduating to the JSON-sidecar version is straightforward because the fenced blocks are already structured enough to parse.

One subtler point: a "check if this is AI-generated" tool fed only your prompts will return high-AI-likelihood on the model responses (correctly) and varied results on your prompts (also correctly — your prompts are human). What it can't tell you is whether the published piece is AI-generated, because the published piece is downstream of both. If the goal of exposing prompts is to let skeptics run that check, the more honest framing is "here's the human-authored input that shaped the AI output" rather than "here's proof the piece is human." The prompts prove the thinking was directed; they don't prove the prose wasn't generated. Worth being clear about that in whatever framing wraps the prompts section, so you're not implicitly claiming more than the artifact supports.

1.8

Me:

Export chat to .MD file

Claude:

[This document.]

Session 2

Chat Export — AI-Generated Writing and Reader Trust

Created: May 17, 2026, 3:38 AM CDT
Numbering scheme: Exchanges are numbered as 2.1, 2.2, etc.

2.1 User

Many people seem to be deeply offended by writing that is ai generated, calling people out, blocking them, etc. If they detect ai generation they ignore the content and lose their minds. Unfortunately, ai generated content is here to stay and dominate media. How could we help these people? I personally have no issues with ai text unless it's low quality points or ideas. But these people get extremely upset. As a person who uses ai text a lot on my website even though it's labeled as ai made I feel I need to figure out something for these people

2.2 Assistant

I’ll treat this less as “people irrationally hate AI” and more as a conflict over trust, authorship, effort-signals, and quality control. I’ll pull in a few current/research-grounded anchors, then translate them into something usable for your website.

I think the mistake is to treat these people as merely “anti-AI.” Some are. But many are reacting to a real collapse of an old trust signal.

Historically, writing implied: someone paid attention, struggled, selected, revised, and risked their name on this. AI breaks that inference. A person can now produce fluent prose without having understood, cared, checked, or meant much. So when some readers detect “AI voice,” they are not only judging the text. They are judging the social act behind the text.

That does not make their reaction epistemically good. Rejecting an idea because of its origin is a kind of genetic fallacy. But their fear has a rational core: AI makes low-effort, low-accountability text cheap at scale.

Current evidence fits this. Pew found that Americans are much more concerned than excited about AI in daily life, and many strongly want to know whether text, images, or video were made by AI, while doubting their own ability to tell. Reuters Institute found a large “comfort gap” in news: only 12% were comfortable with fully AI-made news, compared with 43% for human-led work with AI help and 62% for entirely human-made news. Graphite’s 2026 analysis estimates that primarily AI-generated articles are now roughly at parity with human-written articles in sampled web publishing, though the trend appears to have plateaued around 50% and detection remains methodologically difficult.

So the charitable explanation is:

They are not defending “human typing.”
They are defending a lost signal of care, agency, responsibility, taste, and earned judgment.

The uncharitable version is:

They have converted a useful suspicion into a purity taboo.

Your task is not to “help them accept AI text.” That frames the problem wrong. The task is to replace the broken signal.

The old signal was: “A human wrote this.”

The new signal should be: “A responsible mind stands behind this.”

For your website, I would not merely label things “AI-made.” That may be honest, but it also triggers the wrong category: “machine slop.” Instead, use a richer authorship label. Something like:

Authorship note: This piece was written with AI assistance. The ideas, selection, framing, edits, and final responsibility are mine. I use AI as a thinking and drafting tool, not as an authority. Claims that matter should be judged by their explanations, sources, and usefulness — not by whether the first draft came from a human hand or a model.

Even better, separate the dimensions:

Human responsibility: Are you willing to stand behind it?
AI involvement: Did AI draft, edit, summarize, critique, research, or rephrase?
Epistemic status: Is this a polished essay, working note, conjecture, research log, or fragment?
Verification: Are factual claims sourced? Are errors invited?
Originality: What is your actual contribution — selection, synthesis, framing, criticism, lived context, explanation?

That gives readers a better filter than “AI or not AI.”

A useful taxonomy for your site might be:

LabelMeaningHuman-writtenWritten directly by you; normal editing tools may be used.AI-assistedAI helped with phrasing, structure, examples, or criticism; final judgment is yours.AI-drafted, human-editedAI produced substantial draft text; you revised, selected, corrected, and approved it.AI-generated artifactMostly generated output, published because it is useful, interesting, or illustrative.Research log / unpolishedProvisional thinking; not a finished essay.

This matters because “AI-made” is too crude. It confuses many different cases:

1. AI generated a disposable SEO article.
2. AI helped someone express a real idea clearly.
3. AI summarized sources no one checked.
4. AI acted as a critic for an author’s original argument.
5. AI produced a beautiful artifact under strong human direction.
6. AI fabricated fluent nonsense.

Those are not morally or intellectually equivalent.

The people “losing their minds” are often treating all six as case 1 or case 3. You can help by making the distinctions visible.

There is also a deeper Popperian point: the origin of an idea is not what matters. What matters is whether the idea survives criticism. A human can write nonsense with great effort. An AI can produce a useful formulation. A human-AI collaboration can produce either garbage or insight. The decisive question is not “Who typed it?” but “What problem does it solve, what errors has it survived, and who is responsible for it?”

So your public stance could be:

I do not treat unaided human composition as sacred. I treat responsibility, criticism, and explanatory quality as sacred.

That is much harder to attack than “I use AI, deal with it.”

A practical page for your site could be titled:

How I Use AI on This Site

And it could say, in plain terms:

Some writing here is AI-assisted or AI-generated. I label this because readers deserve to know the production context. But I do not think the source of a sentence determines its value. I judge writing by whether it helps explain something, exposes a problem, preserves a useful distinction, or opens a path for criticism.

When I publish AI-assisted work, I am not asking you to trust the model. I am asking you to judge the result — and to hold me responsible for choosing to publish it.

That last sentence is the key.

Do not ask readers to respect AI. Ask them to hold you accountable.

The strongest posture is neither defensive nor apologetic. It is:

“This is my workshop. I use tools. Some tools are linguistic. Nothing here is exempt from criticism.”

That helps the reasonable critics relax, because you are restoring the signal they fear has disappeared. And it reveals the unreasonable critics, because once responsibility, labeling, quality, and criticism are present, their remaining objection is just taboo.

2.3 User

Export chat to .MD file numbering exchanges 2.1, 2.2, etc. Lastly, show all prompts ordered in the file.

Prompts

Claude:

1. Many people seem to be deeply offended by writing that is ai generated, calling people out, blocking them, etc. If they detect ai generation they ignore the content and lose their minds. Unfortunately, ai generated content is here to stay and dominate media. How could we help these people? I personally have no issues with ai text unless it's low quality points or ideas. But these people get extremely upset. As a person who uses ai text a lot on my website even though it's labeled as ai made I feel I need to figure out something for these people.
2. No, it's not something I'm worried about personally. I don't care or even want these people to engage with my work. I shun the nonbelievers. This is just an intellectual problem about inventing new signals these could compromise to.
3. There needs to be some kind of proof of work
4. It needs to be local to a piece of text. Silly example: video of me with LLMs thinking the text through and writing it
5. So transcripts are a great suggestion. What's the best way to do this? Should transcripts be edited for personal information?
6. So let's lay out the transcript document. It needs to record multiple chats and branches plus note redactions
7. I would like a way to filter or group all of the prompts for ai text generator check
8. Export chat to .MD file

ChatGPT:

1. Many people seem to be deeply offended by writing that is ai generated, calling people out, blocking them, etc. If they detect ai generation they ignore the content and lose their minds. Unfortunately, ai generated content is here to stay and dominate media. How could we help these people? I personally have no issues with ai text unless it's low quality points or ideas. But these people get extremely upset. As a person who uses ai text a lot on my website even though it's labeled as ai made I feel I need to figure out something for these people
2. Export chat to .MD file numbering exchanges 2.1, 2.2, etc. Lastly, show all prompts ordered in the file.