Last week, I was scrolling through Substack when I noticed it. Three or four pieces, one after another, all saying almost the same thing.
Pangram, detecting AI in content. I hadn’t finished reading the first one before I already knew what the second and third would say.
I wasn’t surprised. I knew something like this was coming. Perhaps we need this, a wake up call.
Platforms want some level of creativity, some level of originality still left in them, and AI is making that harder to find, harder to trust, harder to point at and say, making us question if this one is real.
So now there’s a tool built to catch the tools.
Fighting fire with fire.
Part of me finds that useful. Part of me finds it strange to sit with. A machine now tells me what was written by a machine, and we’re supposed to take its word for it? Although they have make some of the source code open-sourced.
As a technical person like myself, having the how they build it, and able to run on my local desktop it’s bliss. But I know most will use the code to trick the tool. A showcase of the amazing human mind. No matter how many doors they put in front of us, we will be able to open them one by one.
But the detection question isn’t really the one that stayed with me. The one that stayed with me is authorship.
This isn’t like a ghostwriter finishing a book for someone famous, where one human hands their words to another human and the credit gets quietly rearranged.
I’m writing a book myself, having written several non-published books, I know how hard it is.
For some, the guilty pleasure to hand it off to a chatbot to generate a book, is a sin that most deliberately hand it with pleasure. Because “who as time”.
And there’s the rub. Substack does not literally force anyone to post every day. But like most modern platforms, it still rewards regular output and steady engagement aka volume. Readers get used to seeing new posts. Writers feel the need to stay visible in a crowded inbox.
Writing something thoughtful, well researched, and emotionally honest takes time. You often need days or weeks, not hours. Yet the platform environment nudges you towards frequent posts. That creates a gap between depth and volume. Creates the tension, the fear.
To bridge that gap, many writers quietly turn to AI tools. Most do not do this to cheat. They use AI for outlines, drafts, or editing, just to keep up with the pace. In other words, AI is not an outsider sneaking into Substack. It is already part of how many people get the work done.
What we have now is a tool capable of far more than most of us have ever actually asked of it, chatbots, language models, sitting on more range than the fraction we use day to day. What people call AI writing usually isn’t AI on its own. It’s AI instructed by a human to do something specific. The rest of it, the strange mistakes, the moments that catch you by surprise, that’s the machine working through probability, not intention.
Somewhere inside all of that, a real person made real choices. What to ask for. What to keep. What to throw away.
That’s the part I don’t think Pangram, or anything like it, can actually see. This is on us. What level of transparency are we able to disclose. I don’t mean that now we are dishonest due to AI, but here we have a tool that can really do things, for better or worst.
I see amazing stuff made with these GenAI models, but there’s a lot of trash, fast-text(aka fast-food), that only pollutes our mind/body, and consume immense resources, that could be used in more beneficial initiatives.
And the longer I sit with it, the less clean that human-versus-AI line feels. Today, writing is rarely purely one thing or the other. You may brainstorm with AI, rewrite everything in your own voice, then ask AI to check grammar. Is that human or AI writing? I don’t think there is a clear, factual way to slice it.
The tool only sees patterns in the text, not the real process behind it. It has no access to your drafts, your deleted prompts, or the time you spent changing each line. So any score it gives is, at best, an educated guess based on surface clues. Still, once that guess shows up as a number, people treat it like proof. We are asking tech to give a firm answer in a place where a firm answer seems to not exist.
I also wonder who gets hurt.
AI detectors often struggle with very clear, structured, or formal writing, shorter texts, styles that fit certain patterns. That means non-native English speakers, academic writers, or anyone with a strong, consistent style can be at higher risk of being flagged.
The system can end up punishing people who write well or differently. And once scores become visible, they change behaviour.
Writers may feel pushed to add small mistakes on purpose, break up clear sentences, avoid certain rhythms or structures, not because this makes the writing better, but because it makes it look “more human” to the tool. A system that claims to protect human writing can quietly encourage unnatural, performative writing.
What strikes me about Substack’s choice is that it shows they are worried about AI-generated “slop” flooding the platform and damaging trust. They want to show readers they are doing something to keep newsletters real, and they hope an AI score will prove that.
It is a way to signal that subscriptions still buy human effort, not just cheap bulk content.
At the same time, the way the feature was rolled out suggests they did not fully understand how their own community works.
Many writers already use AI in careful, limited ways. Others avoid AI entirely and still feel uneasy about having their work scanned by a tool they did not ask for.
Now, every longer post carries a score that can be misread, weaponised, or turned into a purity test a witch hunt.
Instead of calming people down, the move has made many thoughtful writers more anxious about being judged by a number. It reveals a gap between Substack’s view of the problem and the lived reality on the platform.
Substack seems mainly focused on cleaning up obvious AI spam. Writers are more worried about being mislabelled, losing trust, or being forced into a simple “pure human” versus “AI cheat” story that does not match their actual, mixed process.
And Pangram itself presents as a very accurate AI detector. It claims high success rates and very low error. It is sold as a way to help people “see” AI content and restore faith in what they read. On the surface, it looks like a clear, modern fix.
But its promise is stronger than what any tool can honestly guarantee. Like all detectors, Pangram still has false positives and false negatives. It cannot see context or intent. It cannot tell if AI was used for a first messy draft, for a single paragraph, or not at all. It can only respond to the final text that is put in front of it.
This makes Pangram a blunt instrument. In some cases it may spot obvious, low-effort AI spam. In others, it will quietly cast doubt on careful, human-led work that simply happens to share certain patterns.
Yet the label or score often looks cleaner and more confident than the reality behind it. Pangram is built around a simple story about what AI detection can do. The real world of writing is far messier. The risk is that its neat story wins over its messy limits, and people forget how much it cannot know.
All of this feels familiar, in a way, especially to me. It shines a light on the wider mindset in tech.
Many companies act as if every problem can and should be solved with another layer of technology. If there is fear, build a tool. If there is doubt, add a score. If there is conflict, create a new feature.
But the key tensions here are human, not technical. They are about what we count as honest work, how we deal with mixed human-AI processes, how much we trust each other, and how we live with uncertainty. These are not bugs in a system. They are questions that need open talk, shared norms, and, in some cases, acceptance that there is no neat fix.
By reaching for a detection tool, Substack is acting in a very “tech-first” way. It is treating a moral and social issue, authorship and trust, as if it were a simple product flaw. That is the deeper worry, not that the tool exists, but that it is being treated as the obvious answer.
So if detection cannot solve this, what can?
I keep coming back to the idea that we should stop trying to perfectly police AI and start building more honest, flexible norms. Accept that there will be human-only, AI-assisted, and fully AI-generated writing. Encourage clear labelling or simple statements of process. Give readers control over what they want to see and support. Let the community quietly ignore low-quality content over time, instead of hunting for it with tools.
The real issue is not whether AI is involved at all. It is whether people feel misled. If readers know what they are getting, and writers can explain their process when asked, trust becomes easier to rebuild.
Then there are the writers who openly say AI should handle most or all of the work. They see writing as something that can simply be automated. For them, this backlash is a direct challenge.
In this new climate, “I automate everything” is no longer heard as a neutral productivity choice. It is heard as a claim about what you think human effort is worth. When people are angry about “AI slop”, those who loudly push full automation are the first to be blamed, even if they care about craft.
This does not mean they must stop using AI. But it does mean they need to be much clearer and more open about how they use it. Saying “AI did it” is not enough. They will have to show how they stay in control, what checks they do, and why readers should still trust the outcome. If they want a future where heavy AI use is accepted, they will need to help build a culture of transparency, not just defend automation.
In the end, I think this whole storm is not mainly about one company or one tool.
It is about a larger clash between how we actually work now and the simple stories we still tell ourselves. We live in a time where most creative work is under economic pressure to move faster, AI is quietly woven into many workflows, tools promise certainty where there is none, and trust is fragile and easily shaken.
The Pangram roll-out did not create these tensions. It simply made them visible. The real crisis is not that technology is broken, but that our ideas about authorship, effort, and honesty have not caught up with the messy, hybrid reality we are already living in.
What stuck with me wasn’t just the detection story though. It was the pattern itself, three or four writers, none of them coordinating, all reaching for the same headline the same week. I recognised it because it’s the exact thing I worry about most, not that AI writes badly, but that everything starts arriving in the same shape.
It remind me of the 80s and 90s where we all see almost the same thing on TV and we would spend weeks talking about that particular topic, extrapolating, inventing, collaborate together. But today since our feeds are personalised, we hardly know what our friends know about.
But that morning, scrolling, I watched it happen to human writers reacting to real news, with no AI involved at all, so I think. Sameness doesn’t need a machine to spread. It just needs enough people looking at the same thing at the same time and reaching for the same words. Showing us different ways of seeing the situation.
I don’t have a tidy answer for what that means yet. But perhaps we got use to AI, in our strategy, creating our content, some more than others.
Which is quite a feat, look how we adapt so quickly to it.
I think all this feels odd, the last years we have being push to use it for almost anything, and now we could be penalised for using it. Are we able to reduce the use of GenAI, or not use it at all in some platforms? Not sure if that’s feasible any more. Especially we have come to accept GenAI as a competitive must have tool.
Quite a catch-22.
And again, I noticed it happening, in real time, in my own feed, before I’d even had my coffee.
Perhaps Substack algorithm did toggle it a bit so we writers and readers knew what is coming, and obtain from us the necessary feedback they need for their next batch of features...we are after all using their platform.
About the Author
Tino Almeida is a tech leader, coach, and writer reshaping how we think about leadership in a burnout-driven world. With over 20 years at the intersection of engineering, DevOps, and team culture, he helps humans lead consciously from the inside out. When he’s not challenging outdated norms, he’s plotting how to make work more human, one verb at a time.