When the Work Doesn't Fit Inside Your Head
A six-week practice in using AI to think, build, and make sense of complex work.
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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.
