Like many people, I watched the response to Substack’s new AI-detection feature and initially thought, “Yeah, okay. For people who are here to practise the art of prose (which is a significant part of Substack’s original culture) I understand why this matters.”

And then I thought, “Wait. That’s not what I’m here for though..?” Does that mean I don’t quite belong in the Substack community, with or without AI? Bear with me, because I think this question applies to much more than just Substack.

I started writing here because I wanted to participate in conversations about the things I don’t understand in the world, as well as the things I may be beginning to understand.

I want to think with other people and poke at what we don’t know. I want to explore questions across the many languages of psychology, history, sociology, science, technology and the experience of being human, often all at the same time.. this is tricky work.

I tend to think in multiple dimensions/perspectives at once, and putting language around that can be challenging. Before I try to write an “essay,” I may spend hours exploring an idea, drawing on the lifetime of experience behind it and following each thread far enough to see how it connects with the others. Left to my own devices, I can easily produce thirty pages.

Those thirty pages are useful to me - they help me separate the threads before weaving them back into some kind of coherent understanding. They are part of how I think. But several decades of working with other people have also taught me that translation is a critical part of thinking together.

The thirty-page exploration that is illuminating for me may be almost impenetrable to someone else. Other people may still be interested in the questions I’m asking, but I don’t naturally create many on-ramps for them to enter the thinking space.

And that leaves me with a dilemma. Do I keep the exploration largely to myself because I’m not especially interested in developing the craft of literary writing? Do I spend hours turning every thought into carefully honed prose, even though that isn’t the work that energizes me? Or can I collaborate with something or someone that helps me translate the complexity into a form other people can enter?

For me, Substack is primarily a community of thinkers. I’m not here to compare my prose with the work of poets, novelists and essayists for whom language itself is the artistic medium. I love consuming that kind of writing, but it isn’t the practice I’m pursuing.

That doesn’t make the language irrelevant. If I want to think with other people, I need to communicate clearly enough for them to find their way into the thought. But for me, language is in service to the inquiry. It is not the central object being created. I think this may be one reason the conversation about AI and writing feels more complicated than the labels allow.

People who say they can recognize when AI has written something are often responding to something real. There is a certain texture to generic, machine-produced prose, and there is something unmistakably human about art that translates a particular experience through the choices, judgment and sensibility of an artist.

I don’t want that distinction to disappear, and I also don’t think everything we publish is trying to be that kind of art. Sometimes the purpose of writing is to make thought available and to create a meeting place where an idea can be examined, challenged and expanded by other people. That’s usually what I am doing here.

My process tends to look something like this:

1. I explore. Sometimes at considerable length. Then I write a shorter narrative that I hope will create a conversation.

2. After that, I may share both the exploration and the shorter piece with AI. I ask where I have inadvertently erased other perspectives. I ask where I have created another off-ramp through verbosity or complexity. I ask where the logic doesn’t hold together, or where a reader might misunderstand the point I’m reaching for. And, yes, sometimes I ask for a cleaner version.

3. Then I go through the suggestions. I keep what feels accurate. I reject what doesn’t. I put things back when the refinement has removed too much of me. I make the final judgment about whether the piece still says what I mean and whether I am willing to stand behind it.

Usually, the result isn’t radically different from what I wrote. But it is more accessible to other people. And this makes me happy.

Until now, I might have described this as using AI to refine my writing, but even that no longer feels quite accurate. I could refine the writing myself because I understand the changes that would make it tighter. What I am not interested in doing is spending another hour honing every essay when the part I care most about is the inquiry itself.

Or I can co-write it with AI. I can remain responsible for the experience, the questions, the underlying thought and the judgment about what is true. AI can become a partner in structure, compression, clarity and translation. It can help me notice gaps, anticipate misreadings and create the on-ramps I often fail to create on my own.

The finished piece then emerges from a relationship.

That isn’t the same as asking a machine to generate a thought and placing my name on it, but it’s also more than spellcheck. The collaboration affects the expression, and sometimes the exchange helps me recognize an implication I had not yet articulated, and I’m comfortable being transparent about that.

What makes me uncomfortable is the assumption that the most meaningful question is simply: Did AI contribute to these words?

That tells us something about the production of the sentences, but it may tell us very little about the production of the thought.

• Who originated the inquiry?

• Whose experience is being examined?

• Who exercised judgement?

• Who decided what belonged and what did not?

• Who is willing to be responsible for what was finally published?

A detector cannot answer those questions. It can estimate patterns in language, but the provenance of the sentences is not the same thing as the provenance of the thought.

And this becomes even more important when we look beyond people like me. I have been reading perspectives from people who write in English as an additional language. They may have complex and beautiful thoughts to contribute but face a language barrier that makes it difficult for others to meet those thoughts. AI can help translate not only between languages, but between a person’s internal understanding and the conventions through which others can recognize it.

I don’t want to romanticize that or use someone else’s experience to justify my own. I just think it shows how many different human purposes are being compressed into the single category of “AI-assisted writing.”

Some people are making literary art, some are translating, some are organizing research, some are learning to communicate in a new language, some are using AI to manufacture plausible authority they have not earned, and some are doing what I am doing: developing their own thinking in conversation with another form of intelligence and using that collaboration to make the result more accessible. These practices are not ethically or creatively identical just because AI touched the language.

And maybe that’s what troubles me about the current discussion. A label that appears to offer transparency may actually erase the distinctions we most need to understand. I can appreciate why readers want to know what they are encountering. I want to know, too. I don’t want human experience displaced by vast quantities of frictionless content made without curiosity, accountability or care.

But if we are trying to protect authentic participation, we need a richer understanding of authorship than a percentage produced by a detector. Maybe disclosure should describe the process rather than classify the product. Tell me:

• whether AI generated the premise.

• whether it assembled the research.

• whether it drafted the language, challenged the reasoning, translated the original or served as an editor.

• what the human contributed and who stands behind the result.

That would help me understand what I am reading. “AI-assisted” doesn’t tell me very much at all.

Maybe there are different arenas here not ranked above or below one another, but organized around different purposes. There should be space for writers who want to explore human-generated language as an artistic form. There should be space for people who are thinking publicly and want help creating an opening through which others can join them. There should be space for work created through transparent collaboration between different kinds of intelligence.

We don’t need to pretend these are the same practice, but we also don’t need to turn the difference into a moral judgment. The question isn’t only whether a machine touched the language. The more interesting questions are where the thinking came from, how judgment was exercised, what kind of relationship produced the work, and who is willing to stand behind what was published.

Those are the distinctions I want our labels to help us see, and, fittingly, this essay is now an example of the thing I am describing. It began as my own exploration and my own draft. I then developed it in conversation with an AI collaborator, using that exchange to clarify the structure, examine the logic and make the language easier to enter.

The experience is mine. The questions and underlying argument are mine. I remain responsible for what is published.

The final expression was made together.

Post Script:
After co-writing this essay, I ran it through Substack’s detector and it classified the result as:

• AI: 49%

• AI-assisted: 19%

• Human: 32%

I was shocked by that result, not because AI had made no contribution, but because the percentages bore so little relationship to my experience of creating the piece.

So I tried applying a different set of filters. Here is my own account of where the work came from:

• Impetus for the essay: 100%

• Lived experience behind it: 100%

• Human original inquiry and ideas: 100%

• Human exploratory thinking: 100%

• Human first draft: 100%

• Human core argument: 100%

• Logic: Human 95%, AI 5% developed through dialogue

• Structure of the final draft: Co-created, then human edited final draft

• Final language: Substantially AI-generated, under my direction

• Selection and final judgment: 100%

• Human Responsibility for publication: 100%

These percentages can’t be added together. They are not competing portions of a single pie, and that’s exactly the point.

A piece of writing is not one undifferentiated substance called “content” - it contains experience, inquiry, ideas, reasoning, structure, language, judgment and responsibility. Different participants can contribute differently to each layer.

Substack’s detector appears to be evaluating the statistical characteristics of the final language. That may be one relevant piece of information, but the presentation turns that narrow measurement into something that looks like a comprehensive account of authorship: it tells the reader this essay is only 32% human.

My experience is that it is entirely grounded in human thought and experience, expressed through a genuine collaboration in which AI contributed substantially to the final language and structure.

Both accounts acknowledge AI involvement. But they tell profoundly different stories about what was created and who created it. The question isn’t which percentage makes me look better, it’s which account helps a reader understand the work.

I have two sons, both in their twenties. Like many of us from the Gen-X era, I feel the weight of the future heavy upon my shoulders - we have an opportunity and a responsibility to influence our society for our children and the generations that follow.

The past century and a half has brought a degree of technological change that is difficult to comprehend. The commercially practical incandescent light bulb arrived less than 150 years ago. Since then, we have moved from horses to spacecraft, candles to LEDs, handwritten letters to instantaneous global communication, and energy systems built almost entirely on fossil fuels toward ones increasingly incorporating solar, wind, storage, and other forms of generation. The amount of change that people living through this period have had to absorb is astounding.

And at almost every major technological threshold, we have experienced the same tension. We can see that the new technology may enable something extraordinary. At the same time, the distance between the world we know and the world it might create feels terrifying.

Railways provoked anxiety about the bodily and social consequences of unprecedented speed. What would happen to people when they travelled faster and farther than previous generations could have imagined? What would happen to communities when distance no longer contained human movement?

As electric lighting entered homes and cities, people worried about electrocution, fire, health, sleep, public morality, and the disruption of the natural boundary between day and night. Some of those concerns were entirely reasonable: artificial light profoundly changed the rhythms of work, rest, and urban life, and we are still learning about its effects on human biology. (⁠PubMed)

The telephone raised fears that privacy would disappear, that constant access would intrude upon domestic life, and that mediated communication might displace face-to-face relationships. Early telephone systems did, in fact, create new privacy problems: calls were often made in public places or carried across shared lines where others could listen. (⁠Elon University)

As automobiles became commonplace, people worried about danger, congestion, crime, the loss of public space, and the transformation of cities around machines rather than people.

Radio and television brought concerns that families would stop talking, children would stop learning, reading would decline, and public life would become vulnerable to propaganda and spectacle.

Personal computers and the internet raised fears of mass unemployment, social isolation, dehumanization, dependence upon machines, the disappearance of libraries and newspapers, and a world in which no one would know what was true.

The list continues.

There was, and still is, a grain of truth in many of these fears. New technologies create real harms, and each generation inherits the ongoing task of understanding and governing them.

But our predictions repeatedly miss something important. We tend to imagine innovation by starting with the world we already have and then adding the new technology:

today’s world, plus the invention.

But technological revolutions do not simply accelerate existing systems, they reconfigure the systems themselves.

People understood that the automobile might replace the horse. Far fewer anticipated the suburbs, interstate highways, global logistics networks, shopping centres, drive-through restaurants, daily commuting, roadside motels, emergency transportation systems, and the weekend road trip.

Those were second- and third-order effects. They arose not from the automobile alone, but from the way people, businesses, governments, cities, and cultures reorganized around it.

I think artificial intelligence represents another such threshold. The dominant fears today are familiar:

• AI will replace everyone.

• AI will become conscious and destroy humanity.

• No one will learn to think.

• Every job will disappear.

• Truth will disappear.

Each of these possibilities deserves serious thought. Some describe real risks that are already emerging. But I suspect they remain largely first-order fears.

The deeper transformation may be something we are not yet discussing enough: Not simply that AI might think for us, but that it could change how human thinking itself is organized.

Over the past few years, an enormous support industry has emerged to help people use AI: prompt-engineering courses, productivity programs, corporate adoption initiatives, technical integrations, automation platforms, and increasingly sophisticated tools. Some organizations now ask employees to demonstrate or report how they used AI during the week. The understandable goal is usually to increase adoption, reduce costs, or improve productivity.

But this may still be the equivalent of attaching a motor to the existing machinery. It asks:

How can we use AI to perform the work we already do more efficiently?

That is a useful question, but it may not be the most consequential one. The greater opportunity is to consider how we might reorganize around this technology.

The question is not only whether AI will replace human thinking. The question I am interested in is different:

What happens when thinking itself gains a new environment in which to unfold?

One of the most common things I hear from people working with complexity is that the world has become too fragmented to hold. Experts are doing beautiful, rigorous work deep inside their disciplines, but often lack places where their knowledge can meet adjacent fields.

Large organizations and governments operate through divisions, departments, mandates, funding structures, and reporting systems that divide problems which do not arrive divided in the real world.

At the ground level of society, people see different pieces of both the problem and its possible solutions, but struggle to understand how those pieces could fit together into coherent action.

Meanwhile, leaders, founders, researchers, advisors, and changemakers are reaching the limits of what they can carry internally.

They are not short of intelligence, commitment, and often, they are not even short of ideas.

They are short of cognitive room.

They are trying to hold context, relationships, uncertainty, competing needs, institutional history, emerging possibilities, and practical decisions simultaneously, and then translate all of that into something other people can see and act upon.

This is where I believe AI may offer something much more significant than faster writing or automated tasks. Used carefully, it can provide a place to externalize complex thought before that thought is fully formed:

• It can hold multiple threads while we examine the relationships between them.

• It can help us move across disciplines, surface patterns, expose assumptions, test interpretations, and translate ideas between people who do not share the same language or frame.

• It can allow us to see the structure of what we are carrying, rather than continuing to carry all of it inside ourselves.

It does not remove the need for human judgment, but creates more room in which judgment can operate.

It does not determine what matters, but instead can help us notice where our values, assumptions, and intentions are shaping what we see.

It does not replace the knowledge held by experts, communities, or lived experience. At its best, it can help create connective tissue between them.

That is why I developed AI as a Thinking Partner - a six-week, small-group course that has now run through several cohorts and continues to evolve through the real work participants bring into the room.

It is not primarily a course about software, prompt formulas, or producing more content. It’s a six-week, small-group practice for people carrying real complexity: leaders, founders, advisors, researchers, changemakers, and others who are trying to understand, build, or influence something that does not fit neatly inside a checklist.

Participants bring their own live questions, projects, decisions, and areas of inquiry.

Together, we learn how to use AI to:

• externalize thinking that is difficult to hold internally;

• find structure and pattern within complex material;

• explore questions without rushing prematurely toward answers;

• pressure-test assumptions and decisions;

• translate ideas into plans, communications, and usable structures;

• and build a personal way of working with AI that supports, rather than displaces, human agency.

The purpose is not to hand our thinking over to a machine. It is to increase our capacity to think, connect, discern, and act within a world that increasingly exceeds the limits of any single mind or discipline.

I am now gathering interest for upcoming cohorts. I am less interested in filling a class than I am in bringing together the right room: people who are carrying meaningful work, who are willing to explore seriously, and who sense that AI may offer more than another layer of efficiency.

If you recognize yourself in this, you can learn more about the course here:

⁠AI as a Thinking Partner - Foundations

You are also welcome to reply directly to this note and tell me something about the complexity, change, or possibility you are currently trying to hold.

Perhaps the future will not be shaped primarily by those who learn how to make AI produce more.

Perhaps instead it will be shaped by those who learn how to create more room - for knowledge to connect, assumptions to be examined, new structures to become visible, and for better possibilities to emerge.

Not because it doesn’t work. It does work, which is part of why the deeper issue is easy to miss. You ask a question, it gives you an answer, you refine it, and you move faster. In many cases, that’s very useful.

But moving faster is not the same as thinking better, and it’s certainly not the same as changing the underlying pattern.

I’ve spent a lot of time in systems where ideas are not the constraint. Often, the insight is already there. The solution is partly visible. The problem is that the thinking is scattered across conversations, documents, intuitions, tensions, and half-finished attempts to explain what someone already knows.

The bottleneck isn’t intelligence, it’s holding capacity. People are trying to carry too much complexity in their own heads. This is where AI becomes interesting to me.

Not as the proverbial better search engine or a content machine, and most certainly not as a shortcut around human judgment.

But as a way to get thinking out where we can see it.

When AI is used transactionally, it mostly amplifies what’s already there. It gives cleaner answers, faster drafts, better summaries, and more efficient output. That has value, but it can also leave the deeper pattern untouched. You are left with the same assumptions you came in with, including the same blind spots, and the same way of framing the problem. It’s just faster.

But when you stay with something unresolved a little longer, a different possibility opens: AI can begin to reflect the shape of your thinking back to you. Certainly not perfectly, magically, or an authority. But in fact, very usefully.

It can show you what you keep circling. Where you’re narrowing too early. What assumptions are sitting underneath the question. What structure might already be present in the mess. What you know, but haven’t yet found a way to say…

That’s a much more uncomfortable way to use AI, because it asks more of the human, not less. You have to bring real material. You have to notice your own resistance. You have to stay in the conversation long enough for something more than an answer to emerge. But that’s also where it becomes useful in a deeper way.

The risk is not only that AI becomes too powerful. The quieter risk is that we use it to become more efficient versions of exactly who we already are.

• More productive, but not more discerning.

• More articulate, but not more honest.

• Faster, but not wiser.

I’m interested in a different use, where we develop AI as a thinking partner.

• A way to work with complexity without collapsing it too quickly.

• A way to externalize what’s hard to hold alone.

• A way to see patterns, test assumptions, clarify decisions, and turn insight into usable form.

Not to replace thinking, but to make more of it visible.

That’s what I’ll be exploring here.