Human Bottlenecks
Home
❖
Articles
Human Bottlenecks
AI models are very capable and get more capable each year. So naturally people
feel they’re underusing them. There’s a tweet that goes like: your laptop has a
100M USD startup in it, you just have to figure the right sequence of words to
get it out. And beyond money, people imagine AI could boost them in every area
of life. Thus all these perennial ideas: of an AI executive assistant, an AI
tutor, an AI that curates your “digital garden”, an AI that (sigh) writes
flashcards for you.
The general template is: if only I could wire up the right prompts and the right
tools in the right harness, I could have an agent that would boost my
productivity 10x, or fix my problems with therapy, or make me more social, or
more knowledgeable. This was, curiously, the ambition of a lot of early
computing pioneers: Augmenting Human Intellect, Man-Computer
Symbiosis. Engelbart’s lab was called the Augmentation Research
Center! And more recently, people used to complain about how everyone has
the Library of Alexandria in their pocket, and yet, we are not all genius
polymaths.
And these ideas are perennial because they never seem to happen. It’s like the
Solow paradox on an individual level. Why? I think there are two
reasons: first, most people lack what Andy Matuschak calls a “serious
context of use” (AI doesn’t move the needle because there’s no needle to
move); second, most people are bottlenecked by internal factors where AI (or
anything external, for that matter) can’t move the needle.
The Serious Context of Use
I have heard so many people, online and in real life, tell me some variation of:
“I want to [use|build] an app that uses AI to write flashcards”. How many of
those people do you think have ever written a flashcard? How many of them use
Anki every day? Have ever used Anki? The people who want an AI that
writes flashcards for them don’t use flashcards. They have no reason
to. Dually, the people who use flashcards would benefit little from AI
writing their flashcards.
Analogously with “AI tutors”. If you had the ghost of John von Neumann in your
laptop, what would you have him teach you? Let’s be honest. You’d go through
chapter one of some math topic you’re vaguely curious about and then forget
about it. And that would probably be the rational move! Most people are not
autodidacts because most people have no material reason to learn a specific
topic (i.e. their job does not require it) and the problem with learning for the
sake of learning is opportunity cost: there is no a priori reason to learn one
thing over another, so better to do nothing and wait for something to appear
which actually grabs your interest. Again, this is likely rational! Could you
imagine if you found everything interesting? You’d spend years living in a
basement curating a wiki of late Soviet military hardware or something. So, even
if you had John von Neumann in your pocket, it probably wouldn’t move the
needle.
Would an “AI executive assistant” actually boost your productivity? What would
it do, other than tell you to do the things you already know you have to do?
With these ideas that are so attractive in the abstract, the way you deflate
them is you interrogate the concrete, fine-grained details. Take a day at work,
and ask: what exact actions could an AI looking over my shoulder have taken,
that would have made a difference?
Finally there’s the tools-for-thought/notetaking people. God save us. It’s
always the same thing. Your folder with notes—pardon me, your “second
brain”—plus an AI agent that writes, edits, synthesizes information,
answers queries. You could build this in an afternoon, and it won’t move the
needle in your life, for the same reason that building the second brain in the
first place didn’t make a difference.
See, most of us, unless we are students, we really don’t have cause to take
notes on anything. If you’re a student, you take notes from the textbook. I keep
a journal, which is occasionally useful. At some jobs I’ve kept a work journal,
this has also been useful. If I stopped, probably, not much would change.
The notetaking people—and I say this with all the love in the world—are
never, like, a researcher at the cutting edge of their field, building this vast
cathedral of knowledge, note-by-note, so they can derive new insights. Never a
historian who has to read tens of millions of words across thousands of sources
to synthesize the life of some historical person. It’s never someone doing
something hard. It’s always some blogger. Their “digital garden” is about how to
keep a digital garden. It’s very solipsistic: there’s no output, no
deliverables. The deliverable is you take a screenshot of your Obsidian
graph and tweet about it to show off how much it looks like an incomprehensible
ball of twine.
So, what difference is the AI going to make? “It’s going to write my
notes”. About what? “It’s going to read articles for me and summarize them and
add them to the digital garden”. For what purpose? “It’s going to find
connections between my ideas!” What ideas? It’s going to pull an unfinished list
of bulletpoints for an eventual draft of an essay on some inane thing, plus a
bunch of PDFs you haven’t read, and combine them together and make, what?
Another project you’re not going to do? The AI is going to do that?
Again, I say this with all the love in the world. I used to be a
tools-for-thought guy. I hoard PDFs. But we have to be honest with
ourselves. Sometimes, tools don’t move the needle because there’s no needle to
move. Because the “needle” is not a concrete, realizable material need but a
vague, aspirational idea about who we are as people.
Internal Limiting Factors
So, the idea of using computers to augment human capabilities is basically: you
take the human, and you build a scaffold around them, but the human stays the
same. The scaffold can be classical software, or AI, but the human remains a
black box. And the hope is: I just prompt my swarm of AI agents and become 100x
more effective, like Manfred in Accelerando.
Why wouldn’t this work? I think most people are bottlenecked by internal factors
that are difficult to change. Mental energy, motivation, executive function, not
to mention more fundamental traits like intelligence and conscientiousness. So,
external scaffolding, either with classical software or AI, might help somewhat,
but it won’t be transformative.
Consider executive function. My own experience of managing ADHD is the
external scaffolding helps (todo lists, calendars, timers, a million little ways
to trick myself into working) gets me from zero to “kind of functional”. But it
saturates there. Stimulants fix the original, internal bottleneck, which is my
neurochemistry. And then I can accomplish my goals (c.f. Liebig’s law of the
minimum). All the pomodoros in the world are as nothing to a little
molecule diffusing through my brain tissue, binding to NET and
DAT. And what scaffolding is useful is just classical software:
Todoist and calendars. Is an agent going to match the effectiveness
of methylphenidate in ADHD? I doubt it.
Consider intelligence. Can AI augment human intelligence? What does that look
like? Consider how AI agents only became useful when the models crossed a
particular capability threshold, i.e., you can’t put GPT-2 into a harness and
get GPT-5 outcomes out of it. Can you put a human in an AI scaffold and give
them +30 effective IQ? I doubt it, unless the AI is doing all the thinking, at
which point, what use is the human? The limiting factor in the human-AI
centaur is the human! So intelligence is fixed until we get very
advanced biotechnology.
Knowledge is another limiting factor. I find that even very educated people tend
to underrate the importance of knowledge. A lot of people have this attitude
that you can just Google everything just-in-time as it comes up. Like Babbage, I
can’t rightly apprehend the confusion of ideas that would lead someone
to think this. Maybe it’s downstream of the lack of a serious context of
use. Everything you do, every action and idle thought, draws on this vast
(implicit, unseen) trove of knowledge. Claude Shannon invented digital computing
because he remembered this then-obscure branch of mathematical logic called
Boolean algebra and saw that it could be realized in hardware. A trillion-dollar
industry, conjured out of some old tomes.
The reason knowledge is still a bottleneck, in the AI era, is not: “if you don’t
have the knowledge, you can’t write the prompt”. Rather: if you don’t have the
knowledge, you don’t understand the question, or why it matters, or how to judge
the answers, and you won’t ever think to ask. You’re in a completely different
continent from “writing the prompt”. And because long-term memory is private and
internal, AI can’t boost it. It can, maybe, with judicious use, help in the
acquisition of new knowledge.
So: executive function, intelligence, and knowledge are huge bottlenecks to what
you can do, and because they are internal to the brain, AI can’t touch them
until we have far more advanced biotechnology. Corollary: contrary to the
popular view of human capital is becoming worthless, the returns to
education are now higher, because intelligent, educated people with working
reward circuitry stand to gain more from AI.
Published
18 May, 2026
Previous
The Applicability of Spaced Repetition
Next
None
Home
—
About
—
Portfolio
—
Articles
—
Fiction
—
Consulting
—
RSS
© 2014 – 2026 Fernando Borretti |
The desire to augment human intellect through artificial intelligence stems from the long-standing ambition of combining human thought with computation, an idea which has driven early computing pioneers. This ambition often posits that by correctly arranging prompts and tools, an agent could achieve tenfold increases in productivity or resolve complex issues. However, this aspiration frequently fails to materialize, resembling the Solow paradox on an individual level, which the author attributes to several limiting factors.
The primary barrier to successful AI augmentation is a lack of a serious context of use. This context is critical because the utility of AI is entirely dependent on a concrete need or goal; for instance, an individual wishing for an AI to write flashcards will not necessarily benefit from that AI, as they do not engage with flashcards themselves. Similarly, in the context of AI tutors, learning is hindered by a lack of material reason to learn a specific topic, as the opportunity cost of learning is often greater than the perceived benefit. This difficulty extends to tools focused on knowledge management; the creation of a "digital garden" or comprehensive note-taking systems does not inherently yield new insights unless the user is engaged in a research process that demands tangible deliverables. The failure often lies in attempting to outsource tasks without defining the necessary output or applying them to a functional need.
Beyond the lack of context, human capabilities are fundamentally bottlenecked by internal factors that external scaffolding, whether classical software or AI, cannot overcome. These limiting factors include mental energy, motivation, executive function, intelligence, and knowledge. While external support can provide superficial functional improvements, it cannot transform core cognitive capacities. For executive function, external aids like calendars and to do lists can help individuals with conditions like ADHD achieve a baseline level of functionality, but they cannot substitute for addressing the underlying neurochemistry. Similarly, augmenting intelligence is doubtful unless the AI is performing all the necessary thinking; the human remains the crucial bottleneck.
Knowledge presents another significant constraint. The lack of deep, contextual knowledge prevents effective prompting because understanding the question, its relevance, and how to evaluate answers precedes the ability to articulate a prompt. Because long-term memory is internal and private, AI cannot directly augment it, although it may assist in the acquisition of new knowledge. The problem is not one of access to information, but a lack of understanding that prevents meaningful inquiry.
Ultimately, these internal limitations—executive function, intelligence, and knowledge—are fixed limitations to what individuals can achieve, as they reside within the brain, largely inaccessible to current AI technology unless transformative biotechnological advancements occur. Consequently, rather than viewing human capital as becoming worthless, the author suggests that the returns on education are actually higher, as intelligent, educated individuals with functioning reward circuitry stand to gain significantly more from the integration of AI tools. |