On a quiet stretch of a tech campus outside Austin, a software engineer and a cultural anthropologist sit at a whiteboard. The board is covered in thought, phrases in two colors, ideas half-drawn, lines connecting things that don’t obviously connect. In one corner, a note reads: “what the model missed.”
They’re studying mishaps.
Not the kind that makes headlines. The quieter kind. When AI answers the question but misses the point. When logic lands but something still feels off. When the output is technically right and yet somehow wrong.
A chatbot handles grief like it’s a scheduling error. A résumé filter rejects someone who changed careers after a family emergency. A translation nails the words but flattens the humor.
The machine performs exactly as designed. That’s the issue.
Eli and Laleh, engineer and anthropologist aren’t here to critique the system. They’re trying to see its edges. To understand what gets dropped when the pattern is followed too well.
What they’re finding isn’t news to anyone who’s spent time with people: humans are strange. We contradict ourselves. We get hunches. We draw meaning from tone, timing, silence. We notice what’s not said. And that is precisely the point.
The dominant story about AI has been some version of marvel or panic. It writes poems. It passes the bar. It builds things faster than we can name them. And then we ask: if it can do all this, what are we for?
But maybe that’s not the question.
The more useful lens isn’t output. It’s perspective. Machines process. Humans interpret. Machines optimize. Humans interrupt.
We’re the ones who ask: what if this isn’t the right question? What if this answer, though clean, doesn’t quite sit right? What if something’s missing?
AI doesn’t pause. It doesn’t sense unease or weigh subtext. It doesn’t reframe the problem mid-sentence. It keeps going. That’s what it’s built for.
But humans stop. Or hesitate. Or double back because of something barely perceptible: a shift in tone, a look, a gut feeling.
Consider the nurse who ignores protocol because a child isn’t crying the way they should. The journalist who changes the story because a single line changes everything. The engineer who pulls a feature at the last minute because the edge case doesn’t feel like an edge at all.
These aren’t soft skills. They’re acts of attention. Of judgment. Of being attuned to friction the model can’t detect.
Curiosity isn’t about finding answers. It’s the instinct to stay with something that doesn’t fit. That instinct doesn’t scale. It isn’t trained. It’s built over time, through culture, memory, contradiction, doubt.
It’s why the best builders don’t just ship. They rethink. They pay attention. They notice the thing no one else is looking at.
Eli and Laleh and people like them across every field, aren’t anti-AI. They’re just not interested in pretending that performance is the same as perception. They’re mapping what AI still can’t reach: context, ambiguity, emotional texture, ethical friction. Not because they’re nostalgic for human exceptionalism. But because they know what gets lost when we forget how strange and how valuable human thinking really is.
This isn’t a story about man versus machine. That frame is tired. The sharper question is: what happens when machines get better at answers, and humans get better at noticing the cracks?
- What becomes possible when we stop trying to compete with systems, and start cultivating the things they can’t yet touch
- What if our job now is to become more human, not less?
- What if depth matters more than speed?
- What if the real value isn’t in what we make, but in what we sense?
AI is already here. It’s in the workflows, the headlines, the infrastructure. However, curiosity, that stubborn, sideways, unreplicable habit of seeing what doesn’t quite add up is still ours. And for now, it’s not something you can automate.