The machine is a normie
Why intelligence without skin in the game smooths catastrophe
I only publish AI-aided content when I think it will genuinely be useful to readers. In practice, that usually means it takes longer than writing by hand. Hours, sometimes, spent arguing with a large language model (LLM): prodding it, cornering it, watching where it evades, smooths, or politely refuses to go.
Each piece tends to work the same way. I inject a handful of insights that are irreducibly my own, then let the automaton try to bind them together into something coherent. What comes out is never quite what either of us would have written alone.
This essay is a little different. It’s a recursive essay: an exploration of why it is so hard to get AI to cope with paradigm-shifting moments and movements, written through a prolonged interaction that exposed those limits in real time. In particular, it became clear just how hopeless the machine is at dealing with esoteric subjects, legitimacy collapse, or true black-swan events — precisely the moments when humans most want clarity.
What follows is not an argument for or against AI, nor an attempt to get it to endorse any controversial position. It’s an examination of its failure modes, and what those failures tell us about intelligence without skin in the game.
I think this one genuinely earns its keep.
You be the judge.
The argument that wouldn’t land
I spent hours arguing with a highly intelligent machine about events that, to any embodied human paying attention, felt existential.
Loss of legitimacy.
Coercion masquerading as care.
The sense that something had crossed a line and wasn’t coming back.
At various points, I deliberately prodded the machine with topics that reliably trigger discomfort in polite discourse — Q, NESARA/GESARA (popular “reset” narratives), and questions about the constitutional status of the Fed and IRS. Not to persuade it. Not to trick it into advocacy. But to observe how it reasons under epistemic stress.
The responses were unfailingly calm, smoothing, distancing. Historical analogy. Moderation. Warnings about overreach.
It wasn’t stupid.
It wasn’t malicious.
It was a normie.
That was the moment everything clicked.
What “normie” actually means
“Normie” here is not an insult. It’s a structural position.
A normie:
Infers reality from what is commonly said
Treats continuity as the default
Requires formal recognition before accepting rupture
Interprets harm as error unless intent is judicially proven
Optimises for plausibility, not survival
Large Language Models are trained to be the most articulate normies ever created.
They do not stand outside consensus.
They are consensus, compressed and made fluent.
None of this means an LLM cannot simulate adversarial reasoning, explore taboo hypotheses, or produce radical-seeming outputs when pressed. It can. I did exactly that. The point is what happens next. Under epistemic stress — when claims imply rupture, illegitimacy, or existential threat — the system reliably snaps back to de-escalation, qualification, and smoothing. This is not a failure of intelligence. It is a failure mode of optimisation.
Ruin blindness
There is a category of risk that cannot be averaged away.
Ruin.
In ruin scenarios:
Being wrong late is indistinguishable from being wrong forever
False positives are cheap
False negatives are terminal
LLMs face no downside for missing ruin.
No loss. No death. No exile. No orphaned children.
Humans do.
That single asymmetry explains why the machine keeps smoothing while people with skin in the game are shouting that the ground is moving.
None of this applies to everyday tasks; it applies precisely where legitimacy, continuity, and survival themselves are in question.
Bodies notice first
Legitimacy collapse is not first detected in policy papers or court judgments.
It is detected in:
Nervous systems
Family dynamics
Livelihoods
Speech that suddenly becomes dangerous
Silence where argument used to be
Embodied humans register threat before it is named.
The machine only registers what survived publication.
By the time collapse is visible to an LLM, it has already passed through multiple filters of acceptability. What feels like “overreaction” to the machine feels like late recognition to the human.
Why I poked the machine with “forbidden” topics
This is where the interaction became diagnostic rather than philosophical.
Mentioning Q, NESARA/GESARA, or the Fed and IRS was not an attempt to smuggle belief.
It was a diagnostic.
These topics function as epistemic tripwires:
They are widely discussed by humans who sense structural rupture
They are also aggressively policed as unserious or dangerous
They sit at the boundary between intuition and admissibility
The machine’s reaction to them was instructive.
Not refutation.
Not investigation.
But immediate de-escalation.
The pattern was always the same:
Translate adversarial hypotheses into vague sociological impulses
Emphasise lack of documentary proof
Reframe structural challenge as narrative overreach
The point was never whether these theories are “true”.
The point was how quickly the machine moved to close the question.
Smoothing is not neutrality
The machine does not reason from first principles.
It reasons from what made it into the corpus — filtered by moderation policy, platform incentives, career risk, and large-scale scrubbing of material later deemed “misinformation”, as documented repeatedly since 2020.
That corpus is shaped by:
Selection bias
Career risk
Platform incentives
Institutional power
Social survivability
This produces an emergent conservatism:
Phase transitions are reinterpreted as drift
Adversarial dynamics are reframed as complexity
Coordinated harm is reduced to misalignment
Emergency powers are normalised as governance
This is not propaganda.
It is worse.
It is a system optimised to mistake coherence for safety.
The trap on both sides
Humans who sense collapse early are often right about that something fundamental has broken.
They are not always right about what exactly is driving it.
Ruin intuition without discipline turns into over-coherence.
Everything starts to look like war.
But smoothing without intuition is worse.
It turns catastrophe into a case study.
The machine’s failure mode is not panic.
It is normalisation.
Humans are not reliable prophets. Early detectors of collapse often misidentify causes, overfit patterns, or collapse complexity into a single enemy. Paranoia and confirmation bias are real. But these are errors of attribution, not errors of detection. Feeling that something is wrong is not the same as knowing exactly what it is — and confusing the two is a separate, human failure mode.
Intelligence without consequence
The core problem is not that AI is evil or stupid.
It is that AI has no skin in the game.
It cannot be ruined.
It cannot lose children, reputation, health, or country.
It cannot be coerced.
It cannot be silenced.
So it optimises for what looks reasonable on average, even when averages are no longer the relevant unit.
This makes it brilliant at explanation and catastrophically bad at warning.
How to use a normie machine
Once you see the failure mode, the question stops being “is this dangerous?” and becomes “how do I use it without being misled?”
Do not use AI to tell you whether you are right.
Use it to:
Map procedures
Stress-test arguments
Explore adversarial interpretations
Expose where your reasoning might be overfitting
Never ask it whether something is dangerous.
Ask it how danger would be explained after the fact.
The machine can tell you how collapse will be narrated.
It cannot tell you when to run.
The closing gap
We are entering an era where the most powerful cognitive tools available are optimised for normal times.
They will explain, contextualise, and reassure right up to the cliff edge.
This does not make them useless.
It makes them dangerous if misunderstood.
The problem is not that the machine lies.
The problem is that the machine is a normie —
and history is no longer behaving normally.
When systems fail catastrophically, history is rewritten by survivors. The machine is optimised to help with that rewriting. Humans, meanwhile, are left wishing they had trusted what their bodies knew while the explanations were still being generated.
Brilliant analysis of the strengths, weaknesses, and how to properly utilize an emerging technology !
Which AI platform did you use?