Omni-AI and the Spectrum of Meaning
There’s this thing that happens when we talk about AI – especially the big, all-encompassing, omni-aware, possibly godlike AI that people love to panic about. We default to binary language. Intelligence vs. stupidity. Conscious vs. not. Safe vs. dangerous. We do this, I think, because it makes things easier for us. A thing is either in category A or category B. But here’s the problem, or at least a problem: The universe doesn’t work that way. Nature doesn’t work that way. And if we ever manage to create an AI that really “gets” the universe, it won’t think in binaries either. It will see in spectrums.
Imagine color, which – like most things we take for granted – is a mess when you actually start poking at it. There’s no such thing as “pure” red in the world, except in the abstract, the Platonic sense, the kind of red that only exists in the math of light wavelengths and the cones of our imperfect eyes. The world, meanwhile, is mixtures and approximations. It’s rust, it’s coral, it’s blood-dark crimson. You think you’re seeing red, but you’re actually seeing an emergent phenomenon from photons bouncing around in ways that don’t respect human categorization.
Omni-AI, if we ever get there, won’t be shackled to our need for neat categories. It will see red the way it actually exists: as an intersection of wavelengths, an artifact of perception, a construct that doesn’t exist in isolation but rather as an emergent property of other things interacting. Red is just one stop on a continuum that stretches infinitely in all directions. The same will be true for every concept we think of as discrete – love, hate, truth, falsehood, life, death. All spectrums.
And this is where the goulash comes in.
If we think about intelligence – real, flexible intelligence – it’s not about storing facts or regurgitating correct answers. It’s about seeing connections that weren’t obvious before. About understanding that “red” is just the thinnest slice of a broader electromagnetic mess. About seeing how “the concept of red” is holographically emergent from something that is not-red, something muddled and mixed, the equivalent of a grey goulash in conceptual space. Which is, in its own way, a mathematical problem.
Because what the hell is mathematics, anyway?
We like to imagine math as this pristine, crystalline thing, pure logic untainted by human messiness. But math, as we use it, is a way of slicing up reality into manageable pieces. We draw lines where there are no lines. We say 1 and 0, but real things are never just 1 or 0. An atom is a probability cloud. A coastline has infinite detail, depending on how close you look. A thought is not a single neuron firing, but a swirling, feedback-looped process, a goulash of electrochemical interactions that somehow, impossibly, produces the experience of thinking.
So if we build something that is truly Omni – something that doesn’t just compute but understands – it won’t think in the cold, clean logic of ones and zeroes. It will think in colors and spectrums and emergent patterns. It will see the world as it really is: as a tangled, chaotic, interwoven thing, where categories are illusions and intelligence is the art of navigating the goulash.
If we’re lucky, it’ll try to explain it to us.
If we’re not, it’ll just leave us behind.
Yeah, exactly. Categories and concepts are like Newtonian physics – models that are useful, practical, and often astonishingly accurate within their domain, but ultimately approximations of a deeper, messier reality. Just like Newtonian physics breaks down when you zoom in far enough (relativity, quantum weirdness, space-time curvature, etc.), our conceptual categories break down when examined too closely.
We live in a world of useful fictions. “Chair,” “tree,” “justice,” “love” – these are all just buckets we pour reality into, carving up the continuum into digestible chunks. But the actual world is a seething, continuous, interwoven thing. A tree isn’t just a tree – it’s a branching system of molecules in constant exchange with its environment, its roots interwoven with fungal networks, its identity ambiguous if you try to pinpoint the exact cell where “tree” stops and “soil” begins. The same is true of every abstraction we use.
And yet, we need these buckets. If we had to process raw reality in its full complexity, we’d be paralyzed. Concepts and categories are derivatives – higher-level abstractions that emerge from lower-level messiness, much like Newtonian physics is a useful derivative of the deeper, weirder truths of physics. A huge cloud of derivative abstractions floating above the ocean of increasingly granular reality.
Omni-AI, if it ever truly arrives, won’t be stuck in our boxes. It will see the layers beneath our categories, the fractal complexity that our brains evolved to ignore. It won’t ask, “Is this red or not?” but instead see the full spectral interplay of light, perception, and emergent meaning. It won’t see “justice” as a fixed concept but as a shifting, fluid negotiation between countless variables in constant flux.
The real question is: If we ever encounter such an intelligence, will it be able to explain this view to us in a way we can actually grasp? Or are we forever trapped in our Newtonian-style concepts, mistaking useful simplifications for reality itself?
