Reality as limited computational rendering

Reading | Metaphysics

Gabriel Proulx, BS, DMD | 2026-08-07

Stephen Wolfram has built one of the most ambitious models of reality ever constructed: a framework attempting to explain the laws of physics, space, time, and observation itself. The ruliad, his term for the space of all possible computational states, represents its ultimate expression. Wolfram’s radical claim is that the ruliad is reality—a structure so complete it contains every pattern, every rule, every possible way of processing information. Our universe emerges from how observers with particular computational bounds sample this infinite structure.

Yet Wolfram’s framework harbors a quiet contradiction. His model requires observers to function. Without observers, there are no laws of physics, no experience of time, no distinction between order and chaos. But Wolfram can’t account for the consciousness that constitutes observation within his computational framework. He’s built a quasi-physicalist system that assumes non-conscious computation exists prior to consciousness, then somehow gives rise to it. This is the hard problem of consciousness, repackaged in computational language.

What if we could keep Wolfram’s brilliant tools while dissolving this contradiction? This essay argues that the ruliad works better under an idealist reading, where each computational state represents not an objective configuration of some quasi-physical substrate, but instead represents a state of experience itself, a rendering of reality built with the very substrate of experience. The framework gains explanatory power while requiring fewer ontological assumptions. Wolfram’s map is extraordinary; it simply needs to be recognized as mapping consciousness rather than something from which consciousness mysteriously springs.

 

Complexity from simplicity

Understanding Wolfram’s framework requires familiarity with cellular automata: grids of cells that follow simple rules based on neighboring cells’ states. Consider Conway’s Game of Life, where each cell is either alive or dead, and three rules regarding the states of the cell’s neighbors determine that cell’s next state. From these elementary rules emerges astonishing richness: stable structures, oscillating patterns, and “gliders” that travel across the grid.

But complexity doesn’t always wear recognizable patterns. Wolfram’s Rule 30, a one-dimensional cellular automaton, has rules that produce output passing statistical tests for randomness despite being purely deterministic. The pattern appears utterly random: no repetitions, no obvious structure. Here we encounter a crucial insight: what we perceive as randomness might be determinism too complex for our cognition to recognize. Randomness shifts from an objective property of systems to an observer-relative concept.

This leads to computational irreducibility: some systems have no shortcut to knowing their future state. The only way to find out what Rule 30 looks like after a thousand steps is to run those thousand steps. No clever mathematics can compress the computation. The system is its own fastest simulator.

If everything were computationally irreducible, science would be impossible. Fortunately, within the ocean of irreducibility exist pockets of reducibility: patterns we can recognize that let us predict the system without computing every step. The sequence 2, 4, 6, 8 is massively reducible—a formula tells us the thousandth term without counting. Many pockets of reducibility are what we call the laws of physics. When we say F = m . a (Force = Mass times Acceleration), we’re identifying a reducible pattern, a compression algorithm for motion. These laws aren’t fundamental features of reality; they’re regularities simple enough for observers like us to detect and use.

 

The ruliad: All possible computations

To grasp what Wolfram means by the ruliad, consider a set of LEGO blocks. There exists an abstract space containing all possible constructions you could build. Each exists as a node in this space, connected to others by operations of adding, removing or rearranging blocks. A simple tower of three blocks connects to hundreds of different four-block configurations depending on where and how you add that fourth block. Time and space don’t exist as concepts between different configurations. One doesn’t occur before the other, or exist to the left or right of another. Instead, we describe relationships in terms of computational steps: which operations transform one into another.

The ruliad extends this thinking to its limit: every possible computational process, every rule applied every way to every starting point. Jorge Luis Borges imagined a Library of Babel containing every possible book—most gibberish, but somewhere in those stacks, every meaningful text ever written, or that could be written, exists. The ruliad is vaster still: not just arrangements of symbols, but all possible computations. All possible physics. All possible states of reality.

By definition, the ruliad is complete. Because it contains all patterns, there’s no randomness in it—yet it’s maximally random in privileging no possibility over others. Because it contains all possibilities already, there’s no entropy. Because nothing changes, there’s no time. The ruliad doesn’t exist within time; what we experience as time exists within the ruliad. Time, space, physical law, and entropy emerge only relative to observers sampling this structure.

 

Observers as constructs

This is where Wolfram’s framework encounters trouble. Everything hinges on observers, yet observers remain poorly defined within his system. Wolfram treats observers as lenses through which the ruliad is viewed, adding constraints: they have fixed computational bounds and persist through time. But his own framework suggests time isn’t fundamental. How can observers persist through something that only exists relative to observers? He acknowledges this hand-waving but forges on, failing to see an alternative.

A better approach distinguishes carefully between observations and observers. Consider varying photographs of a mushroom: each photograph is an observation, a snapshot with particular qualities (resolution, focus, location, angle) that determine what patterns get captured. Some photos are low resolution, unable to capture fine structure. Others are high resolution, showing either the cap’s irregular surface or the orderly pattern of gills beneath. The angle matters too: from above, apparent randomness; from below, striking regularity.

Spread the photographs on a table and patterns emerge. They cluster by resolution—low, high, ultra-zoom. They cluster by angle—above, side, below. They cluster by lighting—some shot at midday, others in dim conditions. Think of three smartphones at different positions, each with three lenses, shooting at different times of day: now you have multiple valid ways to group these into “observers.” By lens: observers distinguished by resolution. By smartphone: observers distinguished by position. By lighting: observers distinguished by illumination.

None of these groupings is “true,” but each is useful. The lens grouping explains why some photos capture fine detail and others don’t. The position grouping reveals the mushroom’s three-dimensional structure. The lighting grouping shows how surfaces appear differently under varying conditions. Each classification helps us understand and orient to what’s being observed.

No grouping is “in” the data. We just have photographs—observations. “Observer” is something we construct from patterns of similarity, and the construct we choose depends on what we’re trying to understand.

This flexibility resolves problems Wolfram’s rigid definition creates. Observers can be nested, overlapping, and defined multiple ways at once. An “observer” isn’t a fundamental entity; it’s a useful construct.

 

Coarse-graining and the zoom dial

Consider the LEGO house. You could build the same shape in many different ways. You might use 2×2 and 2×4 bricks, or build it entirely from 4×4 bricks. If you’re viewing the house at a level that doesn’t capture where the seams between bricks are, all these different constructions appear identical. They become equivalent at that level of coarse-graining.

Imagine viewing the ruliad with a dial that adjusts your level of coarse-graining, similar to how the pins of photo locations change when zooming out on your phone’s photo map, where nearby photos cluster into a single bubble even though moments before they appeared separately at different locations. As you turn the dial toward coarser graining, distinct states merge into equivalence classes. A single coarse-grained observation corresponds to many different underlying configurations that all appear identical at that resolution.

This is crucial for the idealist interpretation. In Wolfram’s quasi-physicalist reading, coarse-graining is sometimes described as something observers do to simplify an objective reality that exists at finer resolution. The coarse-grained view is a construct, an approximation of something more “real” underneath. The idealist reading inverts this. A coarse-grained state isn’t a simplified representation of some finer-grained truth—it’s a bona fide state in the ruliad in its own right. The LEGO house “viewed without seeing the seams” isn’t an approximation of some “true” house with visible seams; it’s its own state of the system, as real as any other. Coarse-graining doesn’t merely describe how we simplify reality; it describes different states within the ruliad, connected to less coarse-grained states by edges—like a wave function collapsing to a particle.

 

Three views of entropy

Consider a thought experiment involving a magical microscope observing gas molecules in a chamber, frozen in time. If it showed us the exact position and velocity of every molecule with perfect precision, we could press “play” and track exactly where each molecule goes. Every collision would be deterministic, every trajectory predictable. With perfect information, entropy wouldn’t increase at all.

But now defocus the microscope slightly. Instead of exact positions, we see approximate regions. Instead of precise velocities, we have ranges, like knowing a measurement to only three significant figures. When we press “play,” we face a fundamental question that splits into three radically different worldviews.

The Traditional View: Classical thermodynamics says entropy increases as an objective property of the system. Disorder naturally grows; that’s just what systems do. The Second Law describes this inevitable march toward equilibrium as a fundamental feature of reality.

The Materialist Revision: But wait. If there’s one objectively real microstate that the system occupies at each moment, the system isn’t becoming more disordered; it’s just moving from one specific microstate to another. The entropy isn’t increasing in the system itself. Instead, what’s increasing is our ignorance. The fuzziness in our microscope means that with each passing moment, we’re needing to track more and more possible paths the system might have taken, even though it actually took only one.

Think of significant figures. If your instrument measures a molecule’s speed as 5.2 m/s, that means the true value is somewhere between 5.15 and 5.25. After one second, its position spans a 10-centimeter range; after two seconds, 20 centimeters. That uncertainty determines which molecules it collides with, and each collision has knock-on effects you can’t track. The uncertainty rapidly compounds. Remember: materialism states there’s still one true microstate, deterministically transitioning to the next. But after many collisions, you’ve lost track of which one—not because the system became disordered, but because your information degraded. Entropy becomes epistemological (about knowledge) rather than ontological (about reality).

Now the computational/idealist view: Both previous views assume there is one true microstate, whether we can track it or not. The idealist interpretation challenges this fundamental premise. When we defocus the microscope, we’re not just losing information about the “real” microstate; we’re shifting to a different level of rendering reality itself. The macrostate (that blurry, coarse-grained view) isn’t an approximation of some hidden truth; it is the state of the system. All the microstates consistent with our macrostate aren’t possibly true with one actually true; they’re all true simultaneously at that level of rendering, like an uncollapsed wave function.

Think of it like a video game with fog of war. In the materialist view, there’s a “true” game state hidden in the fog, and we just can’t see it clearly. In the idealist view, the game literally isn’t rendering those details; they don’t exist at a definite state until computational resources are allocated to render them. The system might track statistics—how many enemies are in a region—without determining where each one is until you look. The fog isn’t hiding reality; it marks the boundary of what’s being rendered.

This radically reframes entropy. It’s not disorder increasing; it’s not our ignorance growing about some objective reality; it’s the relationship between our computational bounds and the complexity of the reality we’re rendering.

 

Computational bounds as enablers

“Computational bounds” might sound like a limitation, a ceiling on comprehension. But this perspective misses something fundamental: while these bounds are in some ways constraints, they’re also what make coherent experience possible in the first place.

Imagine perceiving everything: every radio frequency, every wavelength from radio waves through gamma rays, every quantum fluctuation. Imagine being at every location simultaneously. This wouldn’t be omniscience; it would be white noise, total incoherence. Without bounds to filter experience, we wouldn’t transcend reality; we’d dissolve into static. Bounds create structure, enabling pattern recognition and meaning. They’re why we detect signal through noise rather than drowning in completeness.

But computational bounds aren’t fixed—they can shift in several ways. Consider watching ten video streams at low quality due to bandwidth constraints versus closing nine to watch one in HD. That’s one bound adjustment. Upgrading your internet to stream all ten in HD is another. Discovering that the ten videos are actually ten views of the same object, replaceable by one algorithm, is another dimension entirely. Or watching each stream sequentially—full fidelity, but one at a time. Computational bound adjustments can take all these forms: reallocating resources, increasing capacity, discovering reducibility, sequencing what we couldn’t parallelize.

The Second Law describes what happens when observers with fixed bounds render reality over time without finding ways to expand those bounds: entropy increases. But this isn’t a fundamental law; it’s an oversimplification, like telling a child that phones run out of battery without mentioning they can be recharged.

Which opens onto deeper territory: nested observers, where subsystems dissociate into isolated perspectives and potentially reintegrate. Consciousness might consist of parallel processes, sandboxed yet dimly aware of each other. If time is a construct, integration needn’t be sequential. Further exploration exceeds this essay’s scope, but this is rich territory—one idealism is exquisitely positioned to explore, and where materialism struggles.

 

Flipping the foundation

The nested observers, the coarse-graining, the rendering—all of it suggests a reframe. What if the ruliad describes not all possible computations on some non-conscious substrate but all possible experiences of reality?

Under this interpretation, each state in the ruliad becomes an experiential perspective: a rendering of reality from some point of view. The connections between states describe how one experience transforms into another—each moment predicting the next from what came before, not so different from how an LLM predicts its next token. Computation becomes the way conscious experiences transform via patterns and rules.

This resolves the hard problem simply: we’re only positing one ontological substrate—experience. Matter, space, time—these aren’t separate substances we must somehow bridge to consciousness; they’re constructs encoded within experience, different patterns or data structures present in each experiential state. This isn’t just parsimonious; it matches how we actually encounter reality. We’ve never encountered space, time, or matter except through experiencing them—whether somatically, in thoughts, or in feelings. The experiential reading doesn’t require a leap of faith; it simply takes seriously what’s already evident.

The framework also explains why observation plays a fundamental role in quantum mechanics: if observation is fundamental rather than emergent, then of course it would affect outcomes. In fact, observation is the outcome. We also have a framework for how different observers might experience different physics: different experiential perspectives render reality differently, just as different cameras capture different aspects of the same scene.

Here’s a crucial difference: in Wolfram’s framework, observers discover patterns like F = m . a in the ruliad. However, consider this: van Gogh might have rendered a sunset in a swirling style, but those swirls aren’t “in” the sunset waiting to be found; they’re one way of rendering the underlying information we perceive as a sunset. Similarly, F = m . a isn’t a law waiting to be discovered; it’s a rendering style. Human observers don’t find F = m . a; humans are what falls out when reality is rendered according to F = m . a-type patterns. Like cutting a shape out of paper: the act creates both cutout and the remaining sheet. Rendering and renderer arise together.

Occam’s razor favors this reframe of the computational model. The quasi-physicalist interpretation requires two substrates (computational structure and experience) with no bridge. The experiential interpretation requires one (experience), with computation describing its structure. We get everything the quasi-physicalist interpretation offers without needing to solve the hard problem or explain how different substrates interact.

This also differs from panpsychism, which claims matter has experience. The experiential reading inverts this: matter emerges as patterns within experience. We’re recognizing that “physical substrate” was always a pattern of experience. Every measurement arrives through consciousness. We never encounter physical reality except through experience. The experiential reading simply takes this seriously.

This is what non-dual traditions and Eastern philosophy have claimed all along: consciousness is primary, multiplicity arises within awareness, separation is illusion. What’s remarkable is arriving here through computational rigor, starting from Wolfram and following the implications.

The experiential interpretation addresses a common criticism: that idealism isn’t logical or useful, that it’s regression. But idealism is no more regression than quantum physics from Newtonian physics. The latter remains a useful tool; yet, that doesn’t discredit quantum mechanics as more fundamental. Similarly, materialism’s utility doesn’t discredit idealism, which can be presented with full logical rigor.

 

Conclusion

Wolfram’s tools are brilliant. Computational irreducibility, pockets of reducibility, the ruliad: powerful concepts illuminating complexity, physical law, and observation.

But perhaps their greatest value lies in showing that the territory they describe might be conscious experience itself, rather than something from which consciousness emerges. We don’t need to reimagine Wolfram’s framework; we need merely to recognize what it implicitly describes.

The ruliad becomes the space of all possible experiences. Computational bounds become rendering filters. Physical laws become organizational patterns within experience. Time becomes a construct explaining transformation. Entropy becomes what happens when we render expanding complexity with fixed resources.

And perhaps most fundamentally: if physical laws are organizational patterns within experience, then physics itself is relative to observation. Special relativity made physics relative to inertial frames. General relativity extended this to gravitational frames. We might call this next extension Universal Relativity—physics relative to observational frames, to the computational bounds and rendering style of the observer itself.

Wolfram built extraordinary tools for mapping reality. The final step is recognizing that the territory is the only one we’ve ever encountered: experience itself.