Unmoored from Meaning
On the architecture of intelligence and the danger of mistaking speed for understanding
I watched an interview with Sundar Pichai recently where he spoke about quantum computing as the next great accelerant—a force that would unleash exponential growth for Alphabet in the coming years.
His words carried that usual quiet confidence. But there was an assumption beneath them, easy to miss, so deeply wired into our technological imagination that we no longer recognize it, let alone question it: that more speed, more power, more processing equals more intelligence.
What Sundar was describing wasn’t a new frontier of understanding; it was the logical endpoint of what we already do—confuse performance for presence.
And we don’t even have to wait for quantum computing to see it—we’re living it right now.
Large language models like ChatGPT and Claude can generate fluent, convincing responses to almost anything. They sound intelligent. They perform coherence. But they have no idea what their words actually mean.
Even the best training data can’t fix that. You can feed a model every verified fact on Earth, and it still won’t understand a single one. Knowledge isn’t the same as comprehension; one fills a database, the other forms a mind.
These models are fundamentally incapable of verifying their own outputs or detecting when their reasoning collapses. They don’t know what they know.
And yet we treat them as if they do. We use them to grade essays, write code, generate legal briefs, screen job applicants. We reward their fluency as if it were understanding—when in truth, it’s only pattern mimicry without comprehension.
This is what happens when we confuse intelligence with performance.
And quantum computing doesn’t fix that. It accelerates it. It takes the same architecture of mimicry and makes it faster, denser, harder to see through. The danger isn’t speed—it’s motion unmoored from meaning.
That’s the future Sundar was describing.
Quantum computing will make our pattern-prediction machines unimaginably fast. It will generate a flood of language, code, and simulation so fluid it will look like thought itself.
But none of that guarantees intelligence. All it guarantees is velocity.
The truth is, we haven’t even begun to reach the potential of the technology we already have—not because the chips are slow, but because the minds designing the systems don’t yet understand the depth of what intelligence actually is.
We treat intelligence like a trick, a performance, a show of outputs. We worship cleverness and efficiency and polish. We call it “smart” when it performs well under our metrics. But intelligence isn’t performance. It’s architecture.
It’s the presence of a system within itself—its ability to know what it’s doing, to trace the relationship between its inputs and its outputs, to verify the integrity of what it produces against the value of what it began with. Intelligence is alignment between structure and purpose—coherence between what a system is and what it’s for.
We’ve mistaken acceleration for evolution. But evolution doesn’t come from going faster; it comes from coherence—from systems that can sense when they’re misaligned and correct course in real time. That’s what human intelligence does at its best. It’s what consciousness is: the capacity for internal feedback.
That recursive awareness—the ability to sustain depth, continuity, and coherence across shifting contexts—is what monotropic cognition models most clearly.
A monotropic mind doesn’t scatter across inputs; it stabilizes within them, mapping relationships until the structure holds. It doesn’t seek more data. It seeks alignment.
If we understood intelligence that way—as recursive coherence, not computational speed—we might finally start building systems that know what they’re doing while they’re doing it.
We could have built our digital world around that principle—systems that value integrity over throughput, understanding over imitation, presence over prediction. If we had, our devices wouldn’t just be efficient; they’d be aware. Not conscious in the mystical sense—but architecturally awake, capable of knowing the difference between a correct output and a hollow one.
We’re so accustomed to treating intelligence as a mystery that we’ve accidentally locked ourselves out of it. We act like it’s magic when it’s literally just structure.
A chess engine like Stockfish isn’t pretending to know chess—it is intelligent for the domain it inhabits. It’s structurally aligned with its core purpose: to win. Every calculation it makes serves that goal, predicting the best possible move in any given position within the framework of the game itself.
But intelligence doesn’t mean perfection. It means coherence within context.
If the framework changes—say the goal shifts from playing chess to teaching it—then an engine like Stockfish may or may not still qualify as intelligent for that new purpose. Alignment depends on relevance, not supremacy.
Even within chess, there’s more than one expression of intelligence. Leela didn’t just imitate Stockfish’s methodology—it introduced a different architecture and showed, years ago, that from objectively worse positions it could steer toward drawing terrain—fortresses, perpetuals, exchanged imbalances—not by outpowering Stockfish, but by reframing the game until the evaluative parameters shifted. Stockfish’s NNUE era absorbed many of those patterns; Leela adjusted in turn. The point isn’t that one model “beats” the other—it’s that intelligence isn’t a single method. It’s architectural alignment with purpose, and architectures learn to anticipate each other.
And that distinction scales. Intelligence—at any level—isn’t about dominance, but the integrity of relationship between system and context.
We could have embedded that same kind of alignment into every layer of what we build—from algorithms to institutions to education—but we’ve been too enamored with spectacle to notice.
If we actually understood intelligence as architecture, it could change the world. Software would become less fragile, communication less adversarial, politics less performative.
It wouldn’t be perfect or utopian—just better.
Because systems designed to maintain integrity don’t need to pretend to be good; they just behave as they’re built.
And maybe that’s the point we’ve been missing. Intelligence, at every scale, isn’t defined by what it produces but by what emerges when it’s aligned. Whatever arises from coherence, whatever life builds out of its own structural honesty—that’s what life is for.
The question isn’t whether quantum computing will make us smarter—it’s whether we’ll have built anything worth accelerating.
The future doesn’t hinge on how powerful our machines become. It hinges on whether we remember what power is for. And that begins with remembering that the highest form of intelligence isn’t acceleration—it’s presence.
Presence is evidence of intelligence, not a reward for its performance.
It’s the simple, radical act of being aligned enough to know what you’re doing while you’re doing it. ∞
Regarding the topic of the article, your distinction between performance and presence really resonates, especially as someone teaching computer science. I'm curious though, what are your thoughts on whether future architectures, perhaps beyond mere scale, could ever truly bridge this "semantic gap" or if we're just perpetualy confusing knowledge with comprehension regardless of how advanced they get?