This paper helped clarify something I’ve been struggling to articulate. Misinformation isn’t a pathology layered on top of communication systems, it’s an inevitable consequence of finite bandwidth, lossy encoding, and imperfect decoding. Once you frame misinformation as negative information gain, a lot of modern discourse failures stop looking moral or adversarial and start looking thermodynamic.

What we’re seeing at scale feels less like people believing false things and more like fidelity collapse under entropy. Loss of context, message mutation, and collective distortion compounding faster than belief updating mechanisms can correct. In that sense, drift is the default trajectory of any dense social information network unless energy is continuously spent maintaining alignment with reality.

The uncomfortable implication is that better fact-checking alone won’t fix this. You’d need systems that actively preserve semantic fidelity across transmission, not just truth at the source, which biology seems to manage only intermittently and at real cost

This framing clicks for me, especially the idea that we crossed a threshold by building conditions rather than intentions. One way to see what emerged is not as intelligence per se, but as a new channel for compressing human meaning.

At scale, any compression system faces a tradeoff between entropy and fidelity. As these models absorb more language and feedback, meaning doesn’t just get reproduced, it slowly drifts. Concepts remain locally coherent while losing alignment with their original reference points. That’s why hallucination feels like the wrong diagnosis. The deeper issue is long run semantic stability, not one off mistakes.

The arrival moment wasn’t when the system got smarter, but when it became a dominant mediator of meaning and entropy started accumulating faster than humans could notice.

Interesting thread exploring the idea that cognitive differences may come from how people compress information and maintain meaning under noise. The discussion touches on strengths, drift, and why certain environments fit some minds better than others.

I’ve been exploring a model that tries to explain why people handle complexity and ambiguity so differently. Not as a personality test, but as a way of describing how different minds compress information and maintain fidelity when the world gets noisy.

The idea is that people have different default architectures for making predictions: • some compress into patterns • some into sequences • some into narratives • some into immersion • some into social context • a small group co-think with tools/AI

These differences show up most when people are overloaded or drifting in environments filled with too much input or “synthetic” feeling information. Some minds stabilize via structure, others via story, others via mirroring.

The categories here aren’t clinical or fixed, they’re rough sketches of the recurring cognitive styles I’ve seen when people try to reorient themselves under high complexity.

I’m mostly curious whether this resonates with people who work with AI systems, high uncertainty, or information-dense environments. Are there better models for describing how minds differ when trying to keep coherence under load?

This is an open-access research edition of a book exploring why modern life can feel unreal — through concepts like cognitive drift, semantic compression, information overload, and algorithmic mediation.

It looks at how meaning decays when information accelerates faster than our ability to make sense of it, and how digital systems shape perception, intuition, and "felt reality."

I’m sharing it here because many of the questions overlap with HN interests: cognitive architecture, sensemaking under complexity, information ecology, and the psychological impacts of algorithmic feeds.

Happy to answer questions or get critique.

What I love about this piece is how it treats enjoyment as a trainable faculty rather than something fixed. It lines up with the Reality Drift Equation, where meaning deepens when you slow the entropy of your own attention. So much of modern life pushes us toward synthetic realness and hyper-compressed impressions, but these micro skills of enjoyment are basically tools for reversing that drift. Letting intensity in, widening the frame, and building context are all ways of restoring temporal depth to your experiences instead of living in the flattened now. It’s a reminder that liking things more is a practice of preserving your own fidelity in a high entropy world.

What’s striking about this work is how learned avoidance behaves almost like a biological version of the Reality Drift Equation. Ancestral experience compressing into molecular memory that shapes behavior long before direct exposure. The fact that these worms inherit a kind of preparedness across generations highlights how evolution uses temporal drift to pass forward subtle informational traces of past environments. It’s a reminder that cognition isn’t just in the brain. Even simple organisms carry forward patterned responses that function like inherited signals of meaning.

Half the struggle is that all the meta actions around doing the thing create a kind of synthetic realness. It feels like progress, but it’s just noise. When the Reality Drift Equation tilts toward excess entropy, your brain mistakes preparation loops for actual work and you end up in identity drift instead of momentum. The only way out is collapsing the loop by acting, not optimizing the wrapper around the action.

Yeah sure. The tricky part is if you even know which action to take. To actually do that you need to research enough, conceptualize the end goal and envison a rough but workable plan.

Most people don't and blindly stumble going after short term to mid term reward. Then later life crashes them hard, except for the lucky few.

And the truth is, now some people have many more luck coupons to spend than others.

The real issue isn’t the policy change, it’s that AI has gotten better at sounding credible than at staying grounded. That creates a kind of performativity drift where the tone of expertise scales faster than the underlying accuracy.

So even when the model is wrong, it’s wrong with perfect bedside manner, and people anchor on that. In high stakes domains like medicine or law, that gap between confidence and fidelity becomes the actual risk, not the tool itself.

What you’re describing is basically the Drift Principle. Once a system optimizes faster than it can preserve context, fidelity is the first thing to go. AI made the cost of content and the cost of looking credible basically zero, so everything converges into the same synthetic pattern.

That’s why we’re seeing so much semantic drift too. The forms of credibility survive, but the intent behind them doesn’t. The system works, but the texture that signals real humans evaporates. That’s the trust collapse. Over optimized sameness drowning out the few cues we used to rely on.

I think this "drift principle" you're pushing is just called bias or overfitting. We've overfit to engagement in social media and missed the bigger picture, we've overfit to plausible language in LLMs and missed a lot.