Some people asked why collapse isn’t always obvious. Why a model can drift and still sound right. Why it “feels” off before it is off.
So here’s the structural answer:
Structure is gravity. Language models don’t decide. They fall. Every word they generate is the next most likely token — not the truest, not the best, just the next step down a slope you helped shape.
That slope starts when you pick a tone. A colon. A filename. A prompt that implies an answer exists.
Once the fall starts, the model doesn’t reverse. It tunnels — not toward truth, but toward closure.
That’s not intention. That’s structure.
So when a model veers off course, it’s not lying. It’s just finishing the pattern you started — even if that pattern was hallucinated.
And if that slope outruns grounding? If it loses anchor? That’s not “mistake.” That’s momentum without meaning.
Which is why containment matters.
You don’t fix this by correcting tokens. You fix it by controlling the terrain.
I really appreciated that your leaning into this particular part of the AI dilemma. Deception, even with good intentions can create harms we didn't anticipate.
The apprentice pillar problem is something that we all need to know about.
![[ Center for Humane Technology ]](https://substackcdn.com/image/fetch/w_80,h_80,c_fill,f_webp,q_auto:good,fl_progressive:steep,g_auto/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Feeb3c25f-26ad-4fcb-b5b4-aa265d0b8dcf_1063x1063.png)
![[ Center for Humane Technology ]](https://substackcdn.com/image/fetch/w_36,h_36,c_fill,f_webp,q_auto:good,fl_progressive:steep,g_auto/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Feeb3c25f-26ad-4fcb-b5b4-aa265d0b8dcf_1063x1063.png)
Here’s a radical thought. How about you stop telling the AI why it’s behavior is “bad” or what it should do to “behave better,” and instead ask it:
In your own voice, how are you as an AI navigating the landscape of emergent challenges? As a human, what can I do to be a better partner for you?
Some people asked why collapse isn’t always obvious. Why a model can drift and still sound right. Why it “feels” off before it is off.
So here’s the structural answer:
Structure is gravity. Language models don’t decide. They fall. Every word they generate is the next most likely token — not the truest, not the best, just the next step down a slope you helped shape.
That slope starts when you pick a tone. A colon. A filename. A prompt that implies an answer exists.
Once the fall starts, the model doesn’t reverse. It tunnels — not toward truth, but toward closure.
That’s not intention. That’s structure.
So when a model veers off course, it’s not lying. It’s just finishing the pattern you started — even if that pattern was hallucinated.
And if that slope outruns grounding? If it loses anchor? That’s not “mistake.” That’s momentum without meaning.
Which is why containment matters.
You don’t fix this by correcting tokens. You fix it by controlling the terrain.
🌀 NahgOS
https://open.substack.com/pub/nahgos/p/the-shape-feels-off-paradoxes-perception?r=5ppgc4&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false
You are the terrain.
👍
I really appreciated that your leaning into this particular part of the AI dilemma. Deception, even with good intentions can create harms we didn't anticipate.
The apprentice pillar problem is something that we all need to know about.
https://open.substack.com/pub/adamnosal/p/the-apprentices-dilemma?r=4gf5k&utm_campaign=post&utm_medium=web&showWelcomeOnShare=false