For a long time, abilities like that lived at the edges of the economy—useful, sometimes critical, but not central—because the center was built on repetition, structure, and control. That’s what we trained for, and that’s what we rewarded. As machines absorb more of that work, the center doesn’t disappear, but it hollows out, and the value begins to migrate toward what remains difficult to automate: judgment, synthesis, anomaly detection, and the ability to work with information that doesn’t resolve cleanly.
This is where the reframing of neurodivergence begins to matter in a practical sense. The underlying traits haven’t changed, but the environment they operate in has, and with it the balance between cost and contribution. ADHD, in a system built on low-stimulation, delayed-reward tasks, looks like a deficit. Research by Nora Volkow at the National Institute on Drug Abuse shows that those brains don’t engage as strongly with that kind of work. In environments where signals change quickly and decisions carry immediate consequences, that same sensitivity can become an asset—not universally, and not without cost, but in ways that are increasingly relevant.
Autism shifts the lens again. The difficulty is often with ambiguity and shifting social expectations, but the strength lies in systems—seeing structure, tracing logic, finding where something breaks. Researchers like Simon Baron-Cohen at University of Cambridge have documented that profile for years, and it becomes more valuable as systems grow more complex and less transparent.
Dyslexia follows a similar pattern in a different direction. Reading may be slower, but pattern recognition across space and structure is often stronger. Work summarized from Maryanne Wolf and observations from NASA point to the same trade-off.
Different wiring.
Different payoff.
But this is where the clean version of the story breaks.
The system doesn’t want this shift.
Standardization isn’t just efficient—it’s enforceable. It makes performance legible, outcomes predictable, and people interchangeable enough to manage at scale. Schools, corporations, and bureaucracies aren’t neutral observers of this transition; they are built on the logic that’s being disrupted, and that logic still works well enough to defend itself.
So the result is not a broad revaluation of cognitive difference. It is something narrower and more uneven. The traits that map cleanly to high-value roles—pattern recognition in AI oversight, system analysis, edge-case detection—get pulled upward, often into specialized or elite positions, while the rest remain embedded in systems that still reward predictability and compliance.
That’s not inclusion.
That’s selection under new rules.
What emerges isn’t the end of sorting. It’s a reshuffling—one that elevates certain forms of difference while leaving the underlying structure intact. The system learns to extract value from variance without needing to accommodate it broadly.
Some people adapt to that shift. Others are filtered out faster as the margin for mismatch narrows.