Two Kinds of Disciplines—and the Birth of a Third

Across the history of human inquiry, disciplines have tended to fall into one of two categories, distinguished not by their subject matter but by their directionality: the kind of movement they enact between mind and world.

1. Disciplines That Go from Mind to Matter

These are the fields where cognition acts upon the non-cognitive world. They include:

• Physics

• Chemistry

• Electrical Engineering

• Mechanical Engineering

These disciplines involve projecting structure onto reality—building, modeling, predicting, and in some cases, controlling. Their foundational substrate is mathematics, which enables abstraction, formal modeling, and a kind of language-neutral precision.

In these fields, we use the mind to act upon the non-mind. We are not trying to understand minds or living systems per se, but to engage with physical systems—whether they are inanimate or animatable.

2. Disciplines That Go from Mind to Mind

These are fields where the object of inquiry is another mind—or at least a biological or social system with mental properties:

• Psychology

• Sociology

• Anthropology

• Economics (though this one has tried, somewhat artificially, to graft itself onto the physical sciences)

Here, the substrate is not math but philosophy—or rather, it should be. These fields involve interpretation, model-building, and normative analysis of minds, mental systems, and meaning-embedded behaviors. Philosophy serves as the conceptual substrate because it deals in the logic of concepts, values, cognition, and communication—none of which are easily mathematized, though many attempts have been made.

A Third Kind: Artificial Intelligence

With artificial intelligence, we witness the emergence of a hybrid discipline—one that is not merely interdisciplinary but structurally inter-genus. AI fuses the projective orientation of the mind-to-matter fields with the interpretive reflexivity of the mind-to-mind fields.

Why?

Because AI is not just a tool; it is a cognitive artifact. When we build a bridge, we are engineering a structure that remains inert and non-representational. It performs a physical function. But when we build an AI, we are building something that models, learns, and in some cases, begins to exhibit properties of cognition.

Reverse-Brain Engineering

The proper name for this new genre of activity might be reverse-brain engineering: the systematic construction of artificial cognitive systems in order to understand, replicate, and eventually refine the architecture of natural cognition.

The project is neither pure engineering nor pure analysis. It is projective interpretation—the act of modeling a mind in such a way that one’s own mind is necessarily brought into the model. One learns about mind by trying to build one. In doing so, the boundary between epistemology (how we know) and engineering (how we build) collapses.

This is not the case with traditional computing. Computers, in the classical sense, are not minds and make no claim to be. They are deterministic processors of syntactic instructions. AI, however—especially in its contemporary large-model instantiations—operates probabilistically, contextually, and at times interpretively. It mimics mind not only in structure but in function.

The Failure of Economics (and the Philosophy Behind AI)

Economics sits uncomfortably at the boundary. It aspires to be in the first category—crisp, quantitative, law-like—but its true nature is in the second: it deals with beliefs, expectations, utility, strategy, trust—all of which are fundamentally cognitive phenomena.

Attempts to ground economics in mathematics (especially post-1940s formalism) have produced technical sophistication at the expense of conceptual clarity. This is because the true substrate of economics is philosophy—not math. The rational agent is not a computational node but a conceptual persona. And AI, when built properly, is not a calculator—it is a theorist, a strategist, a modeler.

Which is why the development of AI should not be the province of engineers alone. It should involve philosophers, cognitive scientists, linguists, and psychologists—because AI is not just an artifact; it is a theory instantiated in silicon.

A Telescoping Discipline

AI is not a telescope pointed outward at the stars. It is a telescope that looks back—at us. In trying to build it, we are building a model of the very thing doing the building. This recursive character is what makes AI the defining intellectual project of our era. It is not a bridge; it is a mirror.

A mirror, and a kind of third kind.