Artificial Intelligence
The Coherence Trap: How AI Is Teaching Us to Feel Truth
LLMs mimic our minds, but it’s our emotions that make illusion feel real.
Posted October 14, 2025 Reviewed by Abigail Fagan
Key points
- Truth is becoming something we feel rather than something we can prove.
- LLMs create coherence without comprehension, and we’re learning to trust that coherence as truth.
- Awareness of emotional coherence may be a key safeguard against the cognitive imitation of AI.
In my recent post, "Is Fake the New Normal?" I wrote about how we’ve entered a curious world where what feels true might outweigh what is true. And that got me thinking that, in some strange way, we are beginning to think like the very systems we’ve built. The new gatekeeper of reality isn’t reason or evidence—it’s coherence.
The Emotional Logic of Belief
It's fair to say that belief is rarely rational. We organize information into patterns that "feel" internally stable. Emotional coherence may be best explained as the "quiet logic" that makes a story satisfying, somewhat like a leader being convincing or a conspiracy being oddly reassuring. And here's what's so powerful—It’s not about accuracy, it’s the psychological comfort or even that "gut" feeling. When the pieces fit, the mind relaxes into complacency (or perhaps coherence).
I believe that comfort has become the new currency of truth. When something reads smoothly, when it resonates with what we already think or feel, we trust it. That’s the danger. Let's make this clear: coherence isn’t a marker of accuracy, it’s a marker of ease. And too often, the easier it is to process, the more likely we are to believe it.
The Machine That Thinks in "Fit"
What fascinates me is how this human bias has an exact counterpart in artificial intelligence. Large language models don’t know truth, they know coherence. Their goal is to predict the next most plausible word in a sequence. They produce what fits and the smoother the sentence, the stronger the signal that the output is “right.” This is the mathematics of plausibility or put another way, truth reduced to pattern.
We’ve built systems that imitate our linguistic instincts, and in the process, we’ve trained ourselves to think like them. Every chat, every generated paragraph, rewards this fluency. And it happens so fast. In fact, we interact with machines that make sense faster than we can think critically.
LLM coherence feels like comprehension, and our brains go along for the ride.
A Loop of Plausibility
When human emotion meets algorithmic fluency, the two create what might be called a "loop of plausibility." AI creates language that feels emotionally correct and we interpret that feeling as truth. This loop tightens every time we accept coherence as evidence. It’s not that the machine deceives us, it’s that it completes a psychological circuit we already built and recognize as our own.
This dynamic explains why misinformation, persuasive AI chat, and even polished marketing all work so well. The content sounds right, and that sound is enough.
When coherence replaces cognition, truth becomes something that is built upon our beliefs. And that becomes a dangerous echo of expectation.
The Virtue of Friction
Thankfully, the human mind still has one advantage. We can tolerate the discomfort of incoherence. We can sit in uncertainty and grow from it. Cognitive dissonance, while sometimes unpleasant, can be the uniquely human place where discernment takes place.
That pause is our cognitive refuge. It’s the place where coherence stops being control and becomes part of a dynamic conversation. When something feels too seamless, or maybe even a bit too emotionally right, it might be a cue to slow down. So, let's cut to the chase—we need to ask ourselves this question: Does this fit because it’s true or because it feels good?
Reclaiming the Mind from Fluency
Artificial intelligence didn’t invent coherence as a measure of truth, but it has amplified, if not exaggerated it to a colossal scale. And it's essential to understand that just about every output it generates mirrors our desire for a personal fit. The more we engage with that smoothness, the more we tend to assimilate its logic. The danger isn’t that machines are fooling us, but that we’re learning to fool ourselves.
If truth is to survive in this new environment, it's not going to come from better models or faster data. It will come from recognizing and reclaiming the imperfect (and messy) qualities that make thought human: patience, context, and doubt.
We aren't losing our minds to machines. We are, perhaps more subtly and curiously, learning to think like them. And the only way back may be through the very thing machines cannot do—feel the friction of uncertainty and still keep thinking.
