"How I Made This" Statement
... so you don't have to click on the AI detector button
Substack’s new Pangram AI detector is not a tool that promotes trust ... or even works. Here’s the actual eight-step process behind my writing, and why provenance, not detection, is the question that actually matters.
As many know already, Substack turned on an AI detector last week. I am disabling this tool for all my Substack posts, but not for the reason you might think.
Built with a company called Pangram, this tool lets anyone scan a post more easily and get back a percentage of how much it thinks a machine wrote. It’s on by default, live across the platform now.
I will be turning it off on everything I publish.
Not because I’m worried about what it would find. No one should be surprised that I use AI hard, and I’m not interested in hiding that. I turned it off because I disagree with the premise these detectors are built on (and, honestly, it’s a waste of tokens).
I don’t necessarily disagree with Chris Best’s idea of building trust. I just don’t think this tool feeds into that purpose.
You don’t build a detector for people you trust.
I do find the “how I make this” statement an interesting addition, but am disappointed that it’s attached to an AI detector ... making it more of a “defense” than an honest share of workflows.
If you don’t know, this statement is a place to explain your process instead of just submitting to a score. It’s not supposed to be a policing mechanism or some kind of confession, but that’s how it will be perceived when attached to an AI detector.
While I can’t imagine anyone wants to make a habit of reading these, I did think the idea deserved a real post, so I have a single place to point people whenever it comes up.
Then I can get back to what actually interests me ... understanding what happens between writers and machines, not proving I’m on the right side of a percentage that really doesn’t mean much.
For example, I ran a completely AI-generated draft through Pangram’s scanner. It came back 100% human. Then I ran a piece I’d actually written, edited by hand over several passes, sentences rewritten three or four times. It came back 22% AI.
That’s the tool now sitting on every post on Substack.
That said, even a detector that worked perfectly would still be asking the wrong question.
But you should know that it doesn’t really work, anyway.
Why disable Pangram
Here’s the actual mechanism.
On the Publish page of any draft, there’s now a toggle: “Disable AI detection.” Click it, and readers see “AI detection unavailable” instead of a Pangram score when they try to scan the post.
I’ll be flipping that toggle on everything going forward, because I don’t want to support a system built on distrust.
Every scan Pangram runs on Substack almost certainly costs Substack something — a per-check API call to a third-party detection company maybe. Some of that cost gets covered by subscription revenue, mine or my readers’.
I don’t know Substack’s exact arrangement with Pangram, and they haven’t published it, but I don’t want a fraction of a cent of what someone pays to read me routed that way.
For many writing instructors at the university, this is not a new problem.
Turnitin is built on the same premise (even before it rolled out AI). We assume that students are cheating one way or the other and this poisons our interactions around writing with distrust on both sides.
You don’t build a detector for people you trust.
Substack’s value proposition was to build a social space based on human relationship, not algorithms — a network built on people choosing to read each other, not a machine-curated feed.
A detector doesn’t fit into that idea. It quietly replaces it with suspicion as the default posture, whether or not anyone ever gets flagged.
Humans should be the filter, not AI.
How I actually use AI
A detector starts from the assumption that something’s being hidden, and the job is to find it.
I never used Turnitin for plagiarism either, for the same reason. The tool assumes the student cheated and hands you a report to confirm or deny.
That posture doesn’t build trust.
So here’s my “how I make this” statement, offered to you freely, instead of some defense against AI detection.
It’s not really a formula, because my workflows change post to post depending on what I’m writing and how formed the thought already was.
But here’s roughly how this post happened, as an example:
I talked out my position on AI detectors into Pocket — a voice-journaling app walking while I thought.
That recording became a structured note in my own markdown knowledge base, organized by what kind of thing each part was: an argument, a fact, a story, etc.
I developed the thinking further by writing three separate LinkedIn posts responding to Substack’s rollout: one on the platform decision itself, one on the wrong question detectors ask, and one testing the detector on my own writing. Working through those made it clear the idea deserved more room than a microessay, so I decided to compose this post.
I had Claude pull up everything I’d already written about AI detectors, back to 2023, so this post could build on arguments I’d already made instead of reinventing them.
We built an outline together. Once I approved it, we drafted section by section — each one written, shown to me, and revised before it got committed to a new Markdown file. Nothing moved forward without my sign-off.
I ran the draft against a running note I keep of my own stylistic habits when I revise AI text, for example where I break a sentence, when I go conversational versus direct, which constructions I reach for and which ones I’ve banned myself from using too much.
(If you want to see the related documentation, they now live in my skill library.)I edited the final wording by hand.
Before it goes anywhere near Substack, I run it through a set of finalizing passes: checking for links back to my own previous posts and outside sources worth citing, reading it through the eyes of audiences who might drift or feel left out, and writing the search- and AI-search-facing summary and description that sit at the top of the post.
In nearly every instance I’ve checked, a piece that went through this whole process comes back 100% human anyway.
The real question
The real problem is that we conflate text generation with provenance or authorship. AI slop isn’t slop because there is AI involved, it’s because there isn’t any real authorship.
This is content posted just to post and manipulate the algorithm, not because someone worked through an idea. That’s a human problem that we created long before generative AI existed.
Provenance is what a detector can’t see. It can’t trace words back to a specific person, at a specific time, making a specific decision to say this or that.
Medium’s Partner Program is a good example. It pays out on views regardless of how those views got earned. That’s created a real AI slop problem.
But nearly every online environment runs on some version of that same incentive: post more, post faster, optimize for the number instead of the thought.
AI just made it easier to do at scale.
Honestly, Substack has been an exception to this … and I think still is (but the AI detector doesn’t really help in any way I can see.)
Provenance is what a detector can’t see. It can’t trace words back to a specific person, at a specific time, making a specific decision to say this or that.
A quote has provenance when you can find the primary source.
A claim has provenance when someone can check it against what actually happened.
Writing has provenance when the person who published it can walk you back through where every piece of it came from, in their own record, dated before the piece existed.
That’s a testable property. A detector’s percentage isn’t.
A piece built for volume has no provenance, whether or not AI touched it.
Nobody wrote it down first, thought about it, decided it mattered enough to say. It was generated to fill a slot, and the slot doesn’t care where the words came from.
Ask me where any claim in this piece came from, and I can point to the dated note it started as in a markdown file I control.
Detecting “AI” measures the wrong variable.
Two pieces of writing can contain an identical percentage of machine-generated text and differ completely on whether either one can be traced back to a person who thought, decided, and stood behind those words.
AI makes this problem more difficult, but detectors won’t solve it.
How I make content (short version)
If you scan this post, or any post of mine, you’ll see it says detection is unavailable.
That’s on purpose.
Here’s the short version of everything above, written to stand on it’s own if this is the only section you read.
I use AI heavily — full drafts, structured outlines, revision passes built to catch my own patterns. It’s a full on agentic writing workflow.
Every idea I publish traces back to a dated note in a knowledge base, so I can show you where any claim in this post came from, not just tell you a machine didn’t write it.
I turned off Substack’s detector because I don’t want subscriber revenue funding a tool built on the same logic as the plagiarism detectors I’ve refused to use in my classroom for a decade.
I have nothing to hide. I just don’t want to fund a tool built for people you don’t trust, and that’s not how I want to relate to the people who read me.
If you want to know how something specific got made, ask. I’ll tell you exactly.
If any of this made you curious about the actual mechanics (not just the argument, but how the workflow itself works) paid subscribers can take my course, Writing with Machines, free for a limited time.
Or go straight to the source: my live structured skills library documents the specific tools and workflows behind everything described above, updated as I revise them ... because no workflow is static.