Nodes All the Way Down

When I first encountered object-oriented programming in the late 80s, I felt a peculiar sense of vertigo. The concept of "objects" and "methods" interacting with each other represented a mind-blowing paradigm shift from the "top-down" programming techniques I'd learned in my first FORTRAN class. Suddenly, code wasn't just a series of sequential instructions but a complex ecosystem of interacting entities. This revelation exposed a fundamental truth I've spent decades exploring: everything connects.

The Information Substrate

Information theory doesn't announce itself with the gravitas of physics or the historical weight of literature, yet it may be the most fundamental framework we have for understanding... well, everything. From the dance of quarks to the spread of memes on social media, the universe presents itself as a vast interconnected graph—nodes and edges all the way down.

Consider what's happening when you read these words. Photons bounce off your screen, triggering cascades of electrochemical signals along your optic nerve. These signals propagate through networks of neurons, activating patterns of thought shaped by your previous experiences with language. My ideas travel across space and time to trigger new neural patterns in your mind. Information flows through graphs within graphs.

The Computational Universe

Shannon saw this first, recognizing that information isn't just something we communicate—it's a measurable, physical quantity. His information theory provided the mathematical foundation for coding, compression, and communication. But it went deeper, suggesting that information might be as fundamental to reality as energy or matter.

Stephen Wolfram's computational universe hypothesis takes this further, positing that computation isn't something we invented but rather discovered—an intrinsic property of reality itself. Simple rules, applied recursively, generate astonishing complexity. The universe doesn't just contain information; in some sense, it might be information.

Networks Within Networks

The networks we can perceive directly exist at human scale: social networks, transportation systems, organizational hierarchies. But identical patterns emerge at scales both smaller and larger than our direct perception. The chemical reactions in a single cell form a complex graph of molecular interactions. Your microbiome—the ecological community of microorganisms that shares your body—forms another network with patterns strikingly similar to social networks among humans.

This pattern recognition extends outward as well. Galaxies cluster along filaments of dark matter, forming cosmic webs that mirror the neural networks in our brains and the social networks in our societies. When researchers map the large-scale structure of the universe, it looks remarkably like a neural connectome.

Universal Network Mathematics

What makes this particularly fascinating is that these networks—regardless of scale or domain—often follow similar mathematical principles. Power laws govern the distribution of connections in social networks, neural networks, and cosmic webs alike. Small-world network properties emerge across domains, from Kevin Bacon's six degrees of separation to the "small-world" property of neural networks in C. elegans.

The rise of artificial intelligence only strengthens these connections. Neural networks—inspired by biological brains but implemented in silicon—now achieve feats of pattern recognition and generation that blur the line between human and machine cognition. Large language models trained on human writing can produce text indistinguishable from human authorship. The boundary between "artificial" and "natural" intelligence becomes as fuzzy as the boundary between "my computer" and "the network" felt decades ago.

Fractals of Information

This fractal nature of networks—similar patterns repeating at different scales—might help explain why Bayesian inference works so well for reasoning under uncertainty. Bayes' theorem gives us a formal way to update our beliefs given new evidence, but it also describes something deeper: how information propagates through networks, whether neural, social, or computational.

Richard Feynman's path integral formulation of quantum mechanics describes particles exploring all possible paths through spacetime simultaneously. This can be visualized as a branching graph of possibilities, with amplitudes flowing along edges to determine probabilities. Even at the quantum level, nature seems to process information through network structures.

The Geometry of Everything

Einstein showed us that spacetime itself isn't just a static backdrop but a dynamic, relational entity. General relativity describes gravity not as a force but as the curvature of spacetime—a relationship between points that can be modeled as a graph.

The universe we inhabit might have more dimensions than the four we directly perceive. String theory suggests additional dimensions curled up so tightly we can't detect them directly. Yet they shape the behavior of fundamental particles—nodes in an even more complex multidimensional graph than we can visualize.

Star Patterns

When I think about the device on my wrist—a smartwatch containing billions of transistors switching states trillions of times per second, powered by energy born in fusion reactions at the core of a star billions of years ago—I'm reminded that we exist across multiple scales simultaneously. Those transistors embody patterns of information flow, which themselves mirror the patterns in neural networks, which in turn resemble the patterns of galaxies.

Carl Sagan's observation that "we are star stuff" captures a profound truth about our material composition. But perhaps equally profound is the realization that our consciousness, the patterns of information flow that constitute our thoughts and awareness, mirror patterns found throughout the cosmos.

We are not just star stuff; we are star patterns—constellations of information organized into networks within networks, graphs within graphs, nodes connected by edges all the way down. And all the way up.

Those early programming paradigms have evolved dramatically, but that sense of vertigo remains—the glimpse of an infinite regression of patterns, each level echoing others above and below. Information theory isn't just another scholarly subject; it might be the skeleton key that unlocks our understanding of everything.