The conversation around AI tends to focus on outputs. This post is about something upstream from that.
I was in a room with a small group of engineers and product people. We had a standing rule for these sessions: no constraints. Not on money, not on time, not on what the law currently allowed. Laws change, and if we only invented inside existing rules we’d never get ahead of them. The goal was to ask questions nobody had thought to ask yet.
One of those sessions started with a conversation between me and a colleague. His father had Parkinson’s. My son had ataxia, a neurological condition affecting coordination and balance that causes involuntary movement. We were talking about tremors, how they made it hard for devices to understand intentional gestures, how the technology kept misreading the signal. Somewhere in that conversation someone said: what if we thought about this the way noise-cancelling headphones work? Not filtering out the gesture, but filtering out the tremor. Separating the signal from the noise at the body level.
That question became a patent.
The patent room is an extreme version of something that happens every day in less formal settings. The standard for a patent is legal novelty, something that hasn’t existed before in that form. But the same upstream act, two experiences colliding into a question worth asking, happens whenever someone brings their specific life to a problem and asks it differently than anyone else would have. The question doesn’t have to be patentable to matter. It just has to come from somewhere a model can’t go.
I’ve thought about that moment a lot since AI became the dominant conversation in technology. Because that question could not have come from a model. Not because the model isn’t capable of sophisticated reasoning. It is. But because the question didn’t come from reasoning. It came from two people’s lives colliding in an unconstrained space.
That’s where creativity actually lives. Not in the execution. In the question.
What the Room Was Actually Doing
Those sessions had a specific energy. You removed the practical objections that normally narrow thinking before it has a chance to go anywhere interesting. No one said “that’s too expensive” or “we can’t do that yet.” You created space for people to bring their actual experience, not their professional expertise, but their lives. Their parents. Their kids. Their frustrations. Their observations from places that had nothing to do with the problem on the table.
And then you let those things collide.
We did a session focused on aging in place, how to help elderly people live independently longer, especially when their families were far away. Every person in that room had a version of that worry. A parent in another state. A grandparent who had fallen. The anxiety of not knowing. The ideas that came out of that session didn’t come from market research or competitive analysis. They came from people who were living the problem and had been given permission to imagine solutions without limits.
That combination of unconstrained thinking plus personal stakes is what produces the questions worth asking. Remove the constraints and you get speculation. Add the personal stakes and you get invention.
The cochlear implant came from a researcher whose child was deaf. He wasn’t analyzing the hearing aid market. He was watching his daughter navigate a world that hadn’t been designed for her and asking whether it had to be that way. The sticky note came from a choir singer who had been frustrated by a bookmark falling out of his hymnal for years before he connected that frustration to an adhesive a colleague had invented that nobody wanted. Neither of those connections was the product of a logical process. They were the product of a life that had been paying attention to a problem long enough to recognize an answer when it appeared from an unexpected direction.
Personal stakes aren’t the only path to the right question. Plenty of inventors have no direct connection to the problem they’re solving. But stakes create a kind of attention that’s hard to replicate any other way. You notice differently when you care.
The question came first. The execution came after.
What AI Actually Does
AI is extraordinarily good at execution. Given a well-formed question, it can explore the solution space faster and more thoroughly than any human team. It can find patterns across domains that no individual would have the bandwidth to survey. It can synthesize, generate, iterate, and refine at a scale that would have seemed impossible five years ago.
I use it this way every day. And the back-and-forth of working with a model, the “that’s close but not quite it,” the steering toward something you can sense but not yet fully articulate, has its own creative quality. It reminds me of those patent sessions. The iterative energy of a room where ideas are being shaped in real time, where one person’s response moves another person’s thinking, where the answer emerges through the conversation rather than arriving fully formed.
But there’s a difference. In that room, every person brought something the others didn’t have. Their specific experience, their specific frustration, their specific version of the problem. The model brings what it was trained on. Which is vast, more than any person in any room could hold, but it is still bounded by what humans have already thought, written, and recorded. It can recombine brilliantly. It cannot bring something from outside its training the way a person brings something from outside their expertise.
The Constraint That Isn’t a Constraint
We told those rooms: imagine no constraints. And it worked, because removing the practical filters let thinking go somewhere it couldn’t go otherwise.
But you could give an AI the same instruction. “Imagine no constraints on money, time, or law. Solve this problem.” And it would produce output. Sophisticated output. Options and combinations and lateral connections across domains it has been trained on. It might even produce something that looks creative.
What it can’t do is bring something from outside its training into that unconstrained space. The model has no constraints to remove because it has no constraints in the first place, and also no life experience pressing against those constraints, no accumulated frustration waiting for permission to become a question. The removal of constraints only matters if something was being constrained. In a human, what gets constrained is everything they’ve lived and observed and felt and noticed. Remove the filter and that floods in.
The model has no equivalent. It can go wide within what it knows. It cannot go to the place where my colleague’s father’s tremor and my son’s tremor became the same problem.
The Most Important Skill
There’s a version of the AI conversation that focuses on prompting. How to write better prompts. How to get better output. How to work with the tools more effectively. That’s useful, and I’m not dismissing it.
But I think it understates where the leverage actually is.
The prompt is downstream of the question. And the question is downstream of the specific collision: your son’s tremor meeting your colleague’s father’s tremor, a choir singer’s bookmark meeting a chemist’s unwanted adhesive, a researcher’s deaf daughter meeting a question about whether the world had to be that way. That’s not experience in the abstract. It’s experience that has been building pressure against a specific problem long enough to recognize an answer when it comes from an unexpected direction.
That capacity lives in you, not in the tools.
What the Model Will Never Have
The model has no son with ataxia. It has no colleague’s father with Parkinson’s. It has no parent far away whose silence worries it. It has no memory of a room where someone said “what if we” and everything shifted. It has no frustration that has been accumulating for years, waiting for the right collision.
It has no stakes. It notices nothing differently because it cares about nothing. It brings no life to the question because it has no life to bring.
Those aren’t limitations that will be engineered away. They’re not gaps in the training data or constraints on the context window. They’re the definition of what the tool is.
And they’re the definition of what you are.
The question comes from you. The creativity is yours. The execution, extraordinary, useful, genuinely remarkable, is something you can share.
Don’t confuse the two.