(NOTE: For those few people who complain that I use AIs to compose text, note that this is an excellent example of how I work.
1) I write a prompt – usually a long one – as if I am talking to a peer. This prevents me from ‘talking as if I’m talking to myself’ which tends to result in text challenging to the reader.
2) GPT Replies with it’s understanding. You’ll note that it’s simply a regurgitation of my argument.
3) Then it offers to write it in accessible prose, and I agree.
4) I edit the output where needed and post it.
This results in text that is more accessible to the reader than had I written in my natural author’s voice, which is closer to the logical structures of programming and math than to ordinary language.
Writing that recent article on how accessible to inaccessible my work is for various audiences was helpful in focusing my efforts on just how challenging learning this written edifice can be.
To some degree you need a cognitively male bias (systematizing), the right personality (low agreeableness), enough IQ (more than expected), and the incentives to study it, and the discipline to study a thing that is so profound but novel.
I sympathize with the audience.
But then, none of the STEM fields is that easily accessible, and most are taught more through repetition to develop an intuition than they are by exposure and immediate understanding.
The difference is of course that (a) this subject is more individually important than any other fields’ to one’s self image and status and confidence in his or her intuitions, and (b) so the individual feels (intuits) that because his or her intuitions in these matters are ‘loud’ compared to the more abstract fields, so while most challenging ‘learning’ requires developing an intuition, this challenging learning requires overcoming an intuition.)
IN REPLY TO:
Unknown author
Why My Work Is Difficult — and Why That’s the Point
A guide for those beginning the study of a universally commensurable system of truth, cooperation, and decidability.
The work you’re about to read is difficult. Not because it is obscure, needlessly abstract, or intentionally inaccessible—but because it makes a trade that almost no other field does: it seeks universal commensurability across all domains of human knowledge, cooperation, and conflict.
This means it doesn’t speak in the idiom of any one discipline. It chooses the most generalizable term from each domain—physics, economics, law, art, psychology—and subjects it to operational reduction until it can be expressed in a common logic of decidability. That means:
The terms used may be unfamiliar even to domain experts.
The concepts may appear deceptively simple—but require re-indexing to multiple domains before their generality becomes intuitive.
The writing may seem dense—not because it is bloated, but because every term is doing maximal semantic work.
A non-obvious consequence of this method is that in disambiguating a term across domains, we expose the implicit assumptions, overloaded meanings, and local constraints that obscured its general form.
In doing so, we often falsify the term’s original definition—not through contradiction, but by revealing its incompleteness when removed from its local context. The result is a redefinition that is more general, more operational, and more commensurable—and often more explanatory than it ever was in its original field.
This is not just synthesis. It is reduction. And that is what makes the work hard—and uniquely valuable.
STEM fields are hard, yes—but they train intuition through repetition. You perform experiments, do problem sets, and the brain adapts. Your evolved intuitions are silent in physics or calculus, so nothing resists the new framework.
This work deals with the most evolved, most defended, and most emotionally loud intuitions we have: those concerning
morality
politics
fairness
agency
status
self-worth
and the justification of belief
These domains were not built for understanding. They were built for social signaling, emotional defense, and moral persuasion.
So the problem is inverted:
Because this is the only framework that:
Provides a system of measurement that unifies the physical, cognitive, cooperative, and institutional sciences under operational laws.
Resolves the epistemological crisis of our age by re-grounding decidability in first principles of existence, action, and reciprocity.
Offers a method of restoring truth, responsibility, and trust in a world dominated by propaganda, rent-seeking, and institutional decay.
Gives individuals a means of mastering their own agency, evaluating their intuitions, and participating in civilization with clarity rather than confusion.
In short:
That’s what this work provides. Nothing less.
This is not a “read it once” project. It is a new grammar. A new system of measurement. A new logic of cooperation.
To learn it, you’ll need:
Cognitive Systematizing – to build nested models and integrate concepts across domains.
Low Agreeableness – to tolerate emotional discomfort when your inherited or learned intuitions are falsified.
High Intellectual Discipline – to work through unfamiliar terms until their meaning clicks.
Incentive – a reason to care: to solve a personal, political, or civilizational problem that no other method can.
If that describes you—or if you want to become that kind of person—you are welcome here.
Expect the unfamiliar.
Expect to be challenged.
Expect that you’ll understand a paragraph only after reading a chapter—and a chapter only after revisiting it once the next one reframes the problem.
Expect that this will take time.
But also expect this:
Most thinkers specialize. They go deep in a field, master its internal grammar, and contribute incrementally to its existing discourse.
That’s not what I’ve done.
I’ve studied physics, engineering, economics, law, art, cognitive science, and philosophy—but not to argue within them. I’ve studied them to extract their first principles, causal relations, and computational regularities, so that they can be expressed in the same operational language:
I studied physics, only to reduce it to engineering: the transformation of invariants into instruments.
I studied economics, only to reduce it to behavioral economics: the measurement of human incentives under constraints.
I studied law, only to reduce it to the organization of behavioral economics: the reciprocal regulation of self-determined cooperation.
I studied art, only to reduce it to the cognitive science of aesthetics: the optimization of perception and intuition for coordination.
I studied cognitive science, only to reduce it to the operational logic of memory, perception, and disambiguation: the algorithmic structure of the brain as an evolved engine of decidability.
I studied philosophy, only to discover what went wrong: why it never completed the reduction from intuition to construction.
So if you’re coming to this work expecting normative argument—what should we believe, what should we do, what would be ideal—you’ll be disoriented. Because this isn’t about argument. It’s about decidability: the capacity to test truth, justify cooperation, and resolve disputes without discretion.
Original post: https://x.com/i/web/status/1914143330893701236