Reality may be a neural network

 That idea actually has serious traction in contemporary philosophy and theoretical science.

If reality were like a neural network, several puzzling features of the universe start to look less mysterious:

1. Local rules, global structure

Neural networks operate on simple local updates (weights, activations), yet produce coherent global behavior. Physics looks similar: local interactions governed by simple laws generate galaxies, life, and consciousness.

2. Learning rather than fixed law

A neural network doesn’t just follow static rules—it learns. Some physicists have speculated that physical constants or laws might be emergent, shaped by something like optimization or selection rather than being eternally fixed.

3. Discreteness under continuity

Neural networks are discrete at their core but appear smooth at scale. This mirrors ideas in quantum gravity where spacetime may be discrete, yet continuous to us.

4. Information as fundamental

Modern physics increasingly treats information as more basic than matter or energy. A neural network is literally an information-processing system. Reality-as-network fits naturally with that shift.

5. Consciousness as an internal model

If the universe is a network, minds could be subnetworks modeling other parts of the network—including themselves. That aligns with theories of consciousness as predictive modeling (e.g., predictive processing).

There are also strong objections:

• Neural networks are designed for tasks—who or what defines the loss function of reality?
• They run on a substrate—what would reality’s substrate be?
• The analogy may be useful but not literal, like saying “the universe is a computer.”

A cautious but compelling position is:

Reality may not be a neural network, but it may be best described as if it were one.

That distinction matters: ontology vs. model.

If you want, we can go deeper in a specific direction—physics, consciousness, metaphysics, or even simulation theory.