Look around the room right now. What do you see? Patterns of light resolve into objects: your screen, a cup, a book, a desk. You don’t think much about it because you don’t have to, it's automatic. That’s really the whole the point of perception: to distill the chaos of the world into something actionable. You don’t need to analyze every photon or angle of light. You just need to know there’s a cup, so you can reach for it and drink. No surprises.

This act of seeing, how we go about trusting the scene in our mind's eye, depends on layers upon layers of pattern processing of which we are blissfully unaware. From the retinas in our eyes distinguishing wavelengths of light to the visual cortex detecting edges and building shapes, our minds are performing immense pattern recognition and pulling it into a scene. Our minds do it so well that we just see the world as if it’s “out there,” even though all of this happens between our ears. Close your eyes, it mostly disappears.

It's crazy that this isn't more apparent, but the world we experience is not the world itself, it is a representation of it in our minds. We experience a world "out there" even though what is "out there" is inside, filtered, compressed, and re-presented to us as observers. Eastern mystics may say the world is an "illusion" and it sounds rightly profound, yet it isn't an illusion so much as a really good representation based on these filters. Like Plato's cave, the shadows we see are interpretations of reality, not reality itself.

We arrived htere because whether or not these representations are “true” is far less important than the fact that they are useful. Our systems evolved for survival, not accuracy. Survival isn't helped when we treat the world as illusion. Survival dictates we treat the vision as real. If it sounds it looks like a lion in the bushes, best to be on the side of caution, even it its just a rustling branch.

Today, as we teach machines to “see” and act in the world, we are beginning to understand just how layered and complex this process of perception really is, just how much our representations determine not only what we see, but what we see in ourselves. We have an intimate relationship between ourselves and our representations. But it goes much deeper.

It is really impossible to define a pattern without an observer to the point we can't even be sure one exists. Can we say an observation exists without an observer? The tree in the forest may not, in fact, make a sound unless there is someone to hear it. This may seem like semantics, but this basic idea is starting to have repercussions across science beyond just quantum mechanics.

The word “pattern” originates from the Latin patron, meaning protector or guide. Why? A patron is a model of society to emulate, similarly a pattern is a model, but more of the sense of an abstraction, a core set of similarity across individual occurrences.

We might say a pattern guides us away from uncertainty and toward predictability. A pattern is many ways is a prediction. Patterns repeat and thus have some level of predictability to be useful. They are regularities in the world that can be described more simply than listing all of their elements, from a model of society to an equation that predicts the trajectory of a ball in flight. Patterns simplify, compress, and guide action that can be quick when a pattern becomes more recognizable.

The Greeks had a word for this too: idea (ἰδέα), meaning form or pattern. Plato’s ideal forms were thought to be the purest patterns—mental representations of perfect objects. Once again, patterns link to representation. But here lies a chicken and egg problem, which came first, the pattern or the pattern recognizer (aka the observer)?

Patterns require observers. Without an observer, something capable of detecting regularities, the world contains far too much information to be valuable. There are a couple of related concepts of combinatorial explosion and computational irreducibility. Essentially, any slide of the world you try to take, you'll have way more information than it is possible to analyze, so we must take only abstractions or compressions. Observers filter and compress raw information into meaningful patterns. If an observer could process everything, it would be as large and complex as the world itself. It would cease to be a distinct “observer.” Observers, as they are by definition a part of the universe and a subset of it, are not just constrained by, but are defined by computational bounds.

This is the paradox: observers must be computationally smaller than the systems they observe, yet try to make sense of a world much more complex and vast than they are. They can only interact with a limited slice of reality, so they must filter. This act of filtering is what creates patterns. Without a filter, there is no pattern defined.

Without compression, there is no observation. Without observation, there is no pattern.

Most know about the Observer Effect, where the act of measurement affects the outcome in quantum mechanics. Yet science is just coming around to the idea that the act of observing is never passive. An observer is always engaged in selecting, compressing, and abstracting information.

IIt’s true of all observation:

1. The eye detects light waves and compresses them into shapes.

2. The mind abstracts those shapes into objects.

3. We act on those objects, trusting that the patterns we’ve detected are reliable.

4. We find idealized shapes based on abstracted similarities, like the Platonic Solids.

It’s so seamless we forget we’re doing it. But this process of 'observation, compression, action' is the foundation of how any bounded system interacts with its environment.

So it may be starting to become obvious, but patterns can only be detected by other patterns. To observe a regularity, the observer itself must exhibit a kind of regularity, a structure tuned to recognize specific inputs. Your retina detects patterns in light because it evolved as a biological pattern recognizer. A neuron fires because it “sees” a particular input as significant. Facial recognition systems identify faces because they’ve been trained on patterns of data.

Which brings us back to the chicken-and-egg problem: where did the first patterns come from? How did the first “observers” emerge to detect anything at all?

To answer this, we must look for the simplest possible observers, systems so basic they border on the definition of existence itself. Stephen Wolfram suggests that simple nodes and rules in his computational universe, akin to cellular automata, might be the first pattern recognizers. For instance:

• Node A gives rise to Node B.

• Node B gives rise back to Node A.

This simple alternation is a pattern. It doesn’t require an advanced observer to “see” it—existence itself generates regularity. From such primitive processes, more complex patterns and observers can emerge. Observers detect patterns, compress them, and validate them. Trust in those patterns allows them to expand their scope—their “context windows”—and recognize more sophisticated patterns. For more on Wolfram and Observer Theory from a computational perspective, check out Wolfram Physics and his article on Observer Theory.

Interestingly, even time itself may emerge from this process. Time, as we perceive it, is not an independent reality but a byproduct of observing causal patterns. Without patterns to observe—no change, no sequence—there would be no perception of time.

Observers are necessarily finite. They cannot perceive all interactions at once, so they observe sequences instead. The “arrow of time” may simply reflect the order in which a bounded observer detects patterns in its environment.

As we build artificial systems to recognize patterns, we see these principles in action. Modern AI models like Large Language Models (LLMs) or knowledge graphs are designed to compress and abstract vast amounts of data into representations.

• LLMs compress linguistic patterns to generate coherent text.

• Knowledge graphs link concepts and relationships to create high-level representations of information.

Yet, AI is still a bounded observer. Its ability to detect patterns is limited by computational resources, training data, and design constraints. Like us, it sees a subset of the world, compresses what it can, and trusts the patterns it has learned.

This brings us full circle: patterns require observers, and observers are bounded systems navigating a chaotic world through the act of compression.

The Observer Paradox reveals something deep about existence itself: the ability to see, know, and act depends on the ability to compress reality into manageable patterns. Without computational bounds, there would be no observation, no pattern, no time—no anything as we know it.

We are participants in this recursive process, much like AI systems and the simplest rules governing particles or nodes. The patterns we see are shaped not just by the world but by who and what we are as observers.

As we explore this further—across physics, biology, cognition, and artificial intelligence—we will find that the act of recognizing patterns is not just a human feat. It is a universal principle of existence, repeated at every scale.

The question then becomes: How far can this recursion go?