A New Observer Paradox

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. That’s 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 know there’s a cup, so you reach for it and drink. No surprises.

But this act—seeing, understanding, and trusting the scene—relies on layers upon layers of processing. From the retinas in your eyes distinguishing wavelengths of light to the visual cortex detecting edges and building shapes, your mind is performing immense pattern recognition. Yet you aren’t aware of any of this. You see the world as if it’s “out there,” even though all of this happens inside your skull.

This brings us to a deep insight: the world we experience is not the world itself, but a representation of it. A representation filtered, compressed, and re-presented to us as observers. This isn’t far off from Plato’s allegory of the cave. The shadows we see are interpretations of reality, not reality itself. Whether or not they are “true” is less important than the fact that they are useful.

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. But here’s where things get strange: patterns don’t fully exist until they are observed.

The word “pattern” originates from the Latin patron, meaning protector or guide. A pattern guides us—away from uncertainty, toward predictability. Patterns repeat. They are regularities in the world that can be described more simply than listing all of their elements. Instead of saying “tree, tree, tree, tree,” you say, “a forest.” Instead of plotting each position of a bouncing ball, you describe its trajectory with Newton’s laws. Patterns simplify, compress, and guide action.

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 this raises a question: who or what creates representations?

Patterns require observers. Without an observer—something capable of detecting regularities—the world would be pure chaos. Observers filter and compress raw information into meaningful patterns. This is why being an “observer” requires something critical: computational bounds.

An observer must process only a subset of the world’s information. Why? Because 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.”

This is the paradox: observers must be computationally smaller than the systems they observe. They can only interact with a limited slice of reality, so they must filter and compress. This act of filtering creates patterns.

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

This dynamic is mirrored in quantum mechanics’ Observer Effect, where the act of measurement affects the outcome. While quantum mechanics is its own rabbit hole, the lesson is profound: the act of observing is never passive. An observer is always engaged in selecting, compressing, and abstracting information.

But this is not just about quantum particles. It’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.

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

Here’s where it gets even stranger: 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. AI systems identify faces because they’ve been trained on patterns of data.

This creates a kind of 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?