We feared displacement, and were humiliated to find the machine hollowed out our value and just moved on.
There’s a specific kind of unease I’ve seen spreading through rooms filled with smart, experienced people. It’s not panic. It’s a kind of tentativeness that appears when a faculty you’ve trusted for years still seems to work, but no longer produces quite the same reaction around you.
But let me step back a minute.
I’ve been doing the work of designing digital experiences for nearly thirty years, since the internet was a novelty and “digital strategy” sounded like an oxymoron. Long enough to watch one set of standards replace another, then another, then another; long enough to build a career, like most of my peers, on being reasonably good at navigating those shifts as the dotcom boom gave way to the interactive web, the social web, the mobile web, the algorithmic web, the ambient web, and now the agentic web. These labels blur, and they overlap, but the progression is real enough. With each shift, the rules of what counts as “good,” and what it means to be effective, changed too. I’ve been around long enough to recognize, with continued discomfort, that something is shifting again in what it means to be effective. And this one feels closer to the heart of the expertise many experienced people, myself included, have spent a career building.
Here’s what I think is happening.
When Knowing Lost Its Premium
Sometime in the latter half of the last century, a particular kind of expertise began to lose some of its premium. It’s funny to talk about this in 2026, but there was a time when knowing things — holding facts, processes, institutional knowledge in one’s head — made people indispensable, in part because they controlled access to it. I can remember these people in manager and leader positions during high school and college internships, though looking back I think I was catching the tail end of that regime. Information retrieval started getting cheaper. Fast. The encyclopedia gave way to the database. The database gave way to the search engine. Knowing things didn’t stop mattering, but being the person who knew things others didn’t stopped being such an enduringly valuable position.
Not cleanly, and not all at once, but what rose up was a generation of pattern-finders: quants, framework-builders, systems thinkers, algorithm architects. The people who could look at complexity and derive structure from it, who could see the underlying logic of a market, a workflow, a system, and make it legible and actionable. For roughly the past forty years, maybe a little longer, that capacity has commanded an extraordinary organizational and economic premium. And yes - I know I’m compressing a lot of nuanced professional history here.
Naturally, the people who built careers on knowing things didn’t go quietly. Most argued, reasonably enough, that what they did – what they knew – contained irreducible human depth no framework or machine could replicate, or that they would remain a necessary complement to the pattern-finders. The market seemed to have been less sentimental about that distinction than they were. Information possession is rarely enough to confer authority anymore.
I think pattern-finders may be next.
The Recycling of Patterns
Here’s the more uncomfortable version of what I’ve been turning over in my head: most of what I thought of as pattern-finding as a skill wasn’t exactly that. It was something closer to pattern-collection, then pattern-recycling. The genuine pattern-finders — the people who can consistently look at situations and produce something structurally new — have always been rare. What I think most pattern-finding expertise actually consisted of was people building well-curated libraries and practiced judgment about which item in their catalog fit the current situation well enough.
This is hard to say, partly because I’ve spent my career in rooms full of people I respect who were doing exactly this, and partly because I was, and am, doing it too. The thing is, it’s genuinely hard to distinguish between someone who is retrieving a sophisticated pattern and someone who is creating one. Both can look like originality in the moment. Both may even feel like originality to the person doing it.
What I see AI doing, with increasing comprehensiveness, is showing how much of this work is a kind of sophisticated recycling. Not because LLMs are smarter than the best human pattern-identifiers, but because it turns out that what most professional pattern-finding actually is maps disturbingly well to what large language models do natively: aggregate, interpolate, recombine, and generate fit judgments at scale, cheaply and continuously. Now, I don’t know how much “most” is, and that may be the question AI is actually forcing into view. But it’s hard to argue that a catalog of patterns and a working sense of which one plausibly fits can be pulled, unnervingly well, from the statistical relationships encoded in these systems.
And that somehow feels violating to the kind of expertise that has been treated as critical in organizations for my entire career. Not just because a machine can do it, but because watching a machine do it reveals something about what the expertise was all along.
Organizations weren’t irrational to value this. They rewarded (and for the moment continue to reward) something genuinely valuable and rare for the problems organizations face. Companies were dealing with increasingly intricate challenges: global supply chains, matrixed organizations, fragmenting markets, accelerating product cycles. The people who could recognize “we’ve seen this before; it’s really X” became extraordinarily valuable. Management consulting exploded. Frameworks proliferated. The consultant who could walk into a struggling retail chain and say, in effect, “this is how you’re in a classic innovator’s dilemma problem,” or the designer who could recognize “what you need here is this jobs-to-be-done lens,” commanded a premium because they could make complexity tractable by fitting it into known patterns.
I suspect the people elevated by those mechanisms (including many of the people currently in positions of authority to respond to this moment) have developed deep investments in a world that is now shifting underneath them. I don’t think of this as cynical self-protection, at least not mostly. I think it is more structural blindness. Or maybe ordinary institutional caution, since I am certain that people who have lived through enough supposed revolutions acquire good heuristics not to respond to every one. But here’s the pattern I see: a perceptual apparatus formed by decades of experience in one epoch becomes, at the moment of epoch change, the very thing that makes it harder to see clearly. There’s a tragedy there the Greeks would have relished.
And I include myself in this. I’m literally using pattern-finding to analyze the disruption of pattern-finding. That recursion does not fully resolve.
What Comes After Seeing?
There’s something I’ve been circling around in conversations with people who seem to be less disoriented by this moment. I don’t have a clean label for it yet, which I think is itself meaningful. It’s not knowledge-holding. It’s not exactly pattern-finding either. It may be something more like a formed sensitivity to where current explanations stop working in ways that are interesting rather than merely wrong.
Let me try to be a little more precise, while admitting that this is the part I understand least clearly.
Every applied pattern has edges: places where the algorithm almost works but doesn’t quite, where the framework generates confident but subtly wrong readings, where the map and the territory don’t fully align. Most people, most of the time, smooth over those edges. The pattern is good enough. Decide and move on.
Sometimes people are arrested by these edges. They find the breakage more interesting than the fit. They drag their frameworks into unfamiliar territory not so much to apply them, as to find where they fail. And when they fail in ways that feel charged with interesting follow-up questions, something fires. A sense that there is something here that the explanation doesn’t account for. That the current framing is insufficient, and that a deeper pattern might be available if someone goes looking.
This isn’t intuition in the mystical sense. It’s perception bent, over time, by deep and consequential experience: by having been wrong in ways that required genuine reconstruction rather than incremental repair, by having cared about outcomes in ways that left … residue? I’ve been thinking of it as something like what people mean when they say an experienced clinician knows something is wrong before the test comes back, or when a developer catches a code smell before tracing the dependencies. The perception fires before deliberate cognition does, but it is not pre-theoretical. It is saturated with theory, memory, consequence, and scar tissue.
I suspect this is one of the places where AI’s limitation may actually matter … at least for now. This kind of perception seems to require having been somewhere, wanted something, been wrong about something, and cared about the difference. It has been interesting to think about how that sort of pre-cognitive perception relates to LLM capabilities. LLMs can respond impressively within frames. What I have not seen them do is originate that felt compulsion that a way of looking at a problem is failing in an interesting way. Maybe that, too, will prove more tractable than I think. I can’t rule that out. But for now it still seems meaningfully different from what these systems are best at.
Because what this faculty can do, if it is a real and developable one, is to tell when and how to invoke the machine. Once you can sense where understanding breaks down productively, you are no longer trying to out-pattern the machine. You are doing something the machine still appears to need but cannot reliably generate for itself: identifying which breakages are worth investigating.
At the Edges of the Pattern
There’s a phrase that crystallized this whole rabbit hole for me, and I haven’t been able to shake it. The first professional cognitive identity was: I am someone who knows things others don’t. That was displaced by: I am someone who sees what others can’t. What I think may be displacing that now … what the people navigating this moment most usefully seem to embody … is something I’m still looking for a label to describe.
The phrase: I am someone who senses where understanding breaks down.
Admittedly, a convenient progression.
The Harder Part
I do have to admit this argument has a self-serving structure I can’t fully escape. Every displaced cognitive class has argued they possessed some irreducible human depth that couldn’t be systematized or transferred. The fact-holders said it. The pattern-finders say it. The pattern repeating doesn’t validate the claim, but it definitely makes it seductive.
If I’m even directionally right, the implications are unresolved and probably not comfortable. If the genuinely valuable human contribution increasingly resides in those rarer moments of noticing where something doesn’t fit, the organizational and compensation consequences are profound. A sparse role can be high-leverage without being well-rewarded. Institutions are often better at pricing legible, repeatable contribution than intermittent but catalytic ones. The person who occasionally notices the anomaly that matters may create enormous leverage and still look, on paper, less productive than the person who can generate competent structure and momentum all day long. If that is true, then many organizations may be structurally inclined to undervalue precisely the form of judgment they claim to be prioritizing in this rush to AI.
That, in turn, makes the previous section harder to romanticize. Even if this emergent faculty is real, it may not map cleanly to prestige, compensation, or stable professional identity. It may be rarer. It may be harder to credential. It may be more intermittent than institutions know how to organize around. It may also be a miserable basis on which to try to build a knowledge career. I don’t know.
And the people most unsettled by this shift have every reason to seize on a description of what remains valuable and recognize themselves in it. I’ve tried to write against that pull, but I can’t be sure I’ve succeeded.
What I’m more confident about is this: I don’t think the discomfort in rooms full of smart, experienced people is primarily economic. It is the sense that the thing that they … we … (I?) … have understood to make us value is being hollowed out. The cognitive identity that organized entire careers — I am someone who sees what others can’t — feels like it’s being destabilized at exactly the moment when it is most dominant.
John Henry won the battle. The tragedy wasn’t just losing his life. It was that the victory still proved the role could be defined well enough to be replaced. Everyone watching him fight the machine understood that immediately, even if they couldn’t fully articulate it.
Most of the people going the way of John Henry right now think they are John Henry. What they may be missing is that he at least knew exactly what the machine was doing.
The people who find their way through I suspect are not the ones who become the best prompters, or who bolt AI onto their existing toolkit and call it transformation. They are more likely to be the ones who can tolerate some reconstruction of professional self-conception rather than simply attaching new tools to the old one.