I read Don Norman’s Beyond Human Categories and had the annoying feeling that he said something I have been trying to say, except much better.
Don’s post is about why generative AI breaks many of our old categories. One part that stuck with me is his framing of AI as both specialist and generalist. Humans usually have to choose a shape. You can go deep in a narrow area, or you can become broad and shallow across many areas. Most of us are somewhere in between.
AI makes this distinction weird. It can appear broad like a generalist and deep like a specialist, while still being naive, error-prone, and strangely eager to please.
That helped me connect two things I have been thinking about: research taste and distributed cognition.
Maybe the question is not “should I become a specialist or a generalist?” Maybe the better question is:
What kind of cognitive system am I learning to build around myself?
Don’s point, as I understood it
Don’s argument is not just that AI is powerful. It is that AI does not fit cleanly into categories built for humans.
We keep asking questions like: is AI conscious? Is it intelligent? Is it creative? Does it have empathy? Does it have morals?
Those questions are tempting because they are familiar. But Don’s point, as I read it, is that they may be the wrong questions. We barely understand those concepts in humans, so using them as the default yardstick for AI can make us feel philosophical without making us much more precise.
The more useful move, at least for me, is to treat AI as part of a larger cognitive system. Not a mind like ours. Not a tool like a hammer. Something stranger: a node in a network of people, models, writing, institutions, workflows, interfaces, habits, and artifacts.
That framing immediately felt useful.
Specialist vs. generalist is an individualist frame
The specialist/generalist distinction makes sense when the unit is one person’s mind and career.
A PhD makes this tension especially painful. You are supposed to become very good at a small thing, but good research often requires borrowing from many places: design, cognitive science, systems, methods, writing, teaching, and the actual messy world.
Specialists go deep. Generalists connect. Both matter.
But AI changes the cost of moving between domains. I can ask for summaries, examples, counterarguments, code sketches, paper trails, and analogies across fields. That does not make me an expert in those fields. It does not remove the need to read, verify, or develop taste. But it does change the rhythm of thinking.
So maybe the scarce skill is not knowing everything.
Maybe the scarce skill is knowing how to coordinate partial knowledge.
AI makes breadth cheap, but judgment expensive
This connects to something I wrote in prototyping to understand humans. I was trying to understand what makes HCI research valuable when AI makes building plausible systems much easier.
If AI can help us generate prototypes, write code, summarize literature, produce design alternatives, and simulate parts of the research workflow, then “I built a thing” becomes less impressive by itself.
The more important question becomes: what did the artifact help us understand?
AI can increase the surface area of exploration. It can help me move faster across unfamiliar material. It can make more ideas reachable. But it can also make weak connections feel profound and shallow summaries feel like understanding.
That is why judgment becomes more important, not less.
The work is not just asking AI for more ideas. The work is deciding which ideas deserve friction.
Distributed cognition, but also distributed responsibility
I like the distributed cognition framing because it moves the unit of analysis away from the individual mind. That feels right for HCI. People do not think alone. We think with notebooks, diagrams, search engines, software, collaborators, papers, deadlines, institutions, and now models.
It also connects back to an afternoon with don norman, where the most interesting part of his DLab talk, at least for me, was not just “AI in education” in the generic sense. It was the idea that learning should help people connect across problems, disciplines, and forms of support.
But there is a danger here too.
Distributed cognition can become distributed irresponsibility.
If the model suggests, the paper says, the prototype shows, the user study implies, and the team agrees, then who is actually responsible for the claim?
This is the part I want to keep making uncomfortable for myself. AI as distributed cognition is not just a nice metaphor for augmentation. It is also a demand for better accountability. We need to know what to ask, what to trust, what to verify, what to preserve as human difficulty, and what kind of knowledge should survive after the tool changes.
That last part feels especially important for HCI research in the age of AI. If we are studying human-AI systems, then “the system” is not only the interface. It is also the researcher, the model, the prompts, the documentation, the study protocol, the evaluation frame, the institution, and the incentives around the claim. Research taste is partly the ability to notice where responsibility is hiding.
A reading note I want to keep
Why I’m saving this: Don gave me a cleaner frame for something I have been circling around. Maybe “specialist vs. generalist” is not the right unit anymore. Maybe the more interesting unit is the cognitive system: person plus model plus tools plus representations plus collaborators plus institutions.
My TL;DR: Don argues that generative AI breaks the old distinction between specialists and generalists. It is not very useful to judge AI only with categories built for biological humans, like consciousness, morality, or empathy. A better frame is to treat AI as one node in a distributed cognition system.
What it changes in my thinking: I have been wondering whether a researcher should become more specialist, more generalist, or some strange hybrid. Don’s framing makes me think the answer may not be an individual identity at all. The more important question is what kind of thinking system I am learning to assemble around myself.
What I am still unsure about: Distributed cognition is powerful, but it can also blur responsibility. If cognition is distributed across people, models, tools, and institutions, then accountability has to be designed too. Otherwise everyone can point somewhere else when the system produces bad knowledge, bad design, or bad decisions.
Go read the original: Don says this better than my tiny summary, and I really do think the original is worth reading: Beyond Human Categories.
Related notes
This connects back to my older note, an afternoon with don norman, where I wrote about Don’s DLab talk and his thoughts on education in the age of AI.
It also extends my note on prototyping to understand humans. There I was asking what makes HCI research worth doing when building tools becomes easier. This post is one possible answer: maybe good research is not just about making artifacts, but about learning how to arrange cognition so that better questions, claims, and responsibilities become possible.