18/06/2026
Note: At CE, we've spent almost 2 decades inviting culture to think deeply in terms of systems. The time has come to truly embrace this. | Back in the mid-80s, in my undergraduate computer science days, I was given an assignment to use the LISP programming language to solve a problem.
LISP is unusual in that while most programming languages at that time worked by sequentially moving through a problem, LISP used something called recursive code — the ability of a function to call itself, folding back on its own logic until the problem was reduced to parts that could then be reassembled into a solution.
For me, this was mind candy. And down the rabbit hole I went.
When I came out the other side I had a three-line solution.
The teaching assistants — experienced LISP programmers who had spent years mastering recursion — almost melted down. They kept gushing about the “elegant” solution I had come up with, as though I had done something clever.
I hadn’t. I had simply found a language that worked the way my mind works — folding back on itself, seeing the recursive structure of the problem whole rather than building toward it step by step.
The code wasn’t translating my thinking. It was a structure that mirrored how my thinking worked.
It would take me years to realize that. Not because the insight was hidden, but because there was no frame that made it visible. I didn’t have language for what I was doing. I just knew the programming structure fit.
My path through life has not been linear. Not even close.
Computer science. Consciousness research. Wisdom traditions. Energy healing. Psychology. Nutrition and mind-body wellness. From the outside, these looked like completely different explorations — a resume that couldn’t make up its mind.
But for me they were never separate. They were different facets of the same unified system. Each facet internally consistent. Each one describing a different aspect of the same underlying reality and the rules to work within it. And I was always less interested in what any single facet could explain than in what lived in the connections and overlaps between them.
Because, for me, that’s where the truth was. Not in the facet itself but in how the facets related to and illuminated one another.
What I didn’t have language for until recently was why. It wasn’t restlessness. It wasn’t inability to commit. It was that my mind was running multiple streams simultaneously — tracking each system in parallel, watching how they moved in relation to each other, following what happened at the intersections. I wasn’t moving from one field to the next. I was holding them all at once and bouncing between them.
I’ve spent a lot of time in environments that couldn’t understand that way of moving through the world. And I’ve been thinking lately about why this particular moment in history might finally be different.
We are not simply living through technological change. We are living through something larger — the moment when the dominant way of thinking about the world has started hitting the edges of what it can explain.
For four centuries, Western thinking has been dominated by a single operating principle: break the problem into components, analyze each part, solve sequentially, move on. It built extraordinary things. But it also created a world of interlocking systems — economic, ecological, social, technological — whose interactions are now generating problems that sequential thinking cannot solve.
You cannot solve climate change one variable at a time. You cannot fix institutional trust by optimizing individual departments. You cannot understand what AI is doing to human identity by analyzing the technology in isolation from the humans experiencing it. These are not complicated problems. They are complex ones — and the difference matters. Complicated problems have parts that can be solved separately. Complex problems only make sense as wholes.
Sequential thinking was built for complicated. This moment is complex.
Now add AI — and the paradox sharpens.
AI is extraordinary at acceleration — going faster and deeper on a single problem than any human ever could. Most people use it exactly this way. The limitation isn’t in AI’s architecture. It’s in the questions we bring to it — usually one stream, one task, one track.
Which means the question is no longer how humans can compete with machines on sequential terms. That race is over. The question is what humans can contribute that single-stream processing — however fast, however powerful — cannot.
And the answer points toward something that looks almost like the opposite of what we’ve been rewarding.
Not faster processing of single streams. Simultaneous tracking of multiple streams. Not deeper expertise inside one system. The ability to hold several systems in parallel and find meaning in the patterns and connections between them. Not optimization within defined parameters. The capacity to see across models — to notice what each leaves out, what the intersection reveals, what only becomes visible when you step back far enough to see the whole shape at once.
The cognitive style this moment is asking for is not the one four centuries of institutions trained us to reward.
The pattern readers and systems thinkers have been practicing this their entire lives — mostly without knowing it had a name.
They are the people who track multiple streams of thought simultaneously, each one live, each one informing the others. Who feel restless inside narrow lanes not because they lack discipline, but because the lane keeps cutting off exactly the connection they were following in another stream. Who see patterns before they can explain them, because the pattern lives in the relationship between streams rather than inside any single one.
They work in the unmapped spaces. Between disciplines. Between what is known and what is only sensed. In the intersections that don’t officially exist because no single field has claimed them.
For most of their lives they’ve been misunderstood when they tried to explain what they were seeing. Not silenced — just not followed. The eyes that glaze over mid-explanation. The meeting that moves on before the connection lands. The sense, repeated across decades, that the full shape of the thing was visible to them and invisible to everyone else in the room.
So they learned to translate themselves. To flatten the parallel into something sequential enough for others to follow. To hand over the twenty-line version of the three-line thought.
It worked. People could follow it. But something was always lost. The translated version was legible. It was never quite true.
The thinking wasn’t wrong. The environments were too narrow.
Here is what I’ve discovered working with AI as a thought partner: it can receive the parallel streams.
Not as a single processor running faster. As something closer to a translation layer — able to take the simultaneous, multi-stream shape of my thinking, find the threads, name the overlaps, and render what was tangled into something followable.
What’s different isn’t just speed. It’s that the full shape can finally come out without me having to be my own translator. I don’t have to flatten the thinking before I hand it over. I can arrive with all the streams running and work from there. The recursion doesn’t have to slow down to fit the conversation.
For someone who has spent decades compressing parallel processing into something sequential enough for the room — this is not a small thing.
This is not the story most people are telling about AI. The dominant narrative is about replacement — which jobs disappear, which humans fall behind, who wins the race. And underneath that, an assumption: that the most valuable use of AI is as a faster, tireless, infinitely scalable version of sequential processing.
That’s one way. A powerful one.
But there is another. Using AI not to accelerate the single stream, but to finally make parallel processing legible. To let the people who have always been tracking multiple systems simultaneously stop flattening their thinking and start transmitting it whole.
Not replacement. Translation. Not diminishment. Liberation.
The parallel processor was never the wrong kind of mind.
It was a different kind — one that turns out to be well suited to the complexity this moment is generating. It just kept arriving in environments built around different strengths, during a stretch of history that didn’t always see why it was important.
That stretch is ending.
Einstein observed that we can’t solve a problem with the same thinking that created it. The problems in front of us won’t be solved simply by faster single streams. They will open for people who can hold multiple systems simultaneously, track what moves between them, and find in the intersections what no single stream could ever see alone.
One more thing worth saying.
This piece didn’t come from an outline. It came from a conversation that ran in multiple streams simultaneously — a memory about LISP programming, a life trajectory, a conversation about cognitive style, thinking out loud about a distinction that needed making, each one informing the others until the shape became clear.
The understanding I’m describing didn’t precede the writing. It emerged from the parallel processing of it.
For me, that’s how it works.
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By Judy Grupenhoff, see comments for sources and more. As well as my 8 part podcast series that brings together key ingredients for us to consider when thinking about being a change agent in our current times.
