If you’re still using the word hallucination, you’re an amateur.
Not because the word is rude, but because it reveals your mental model: you think the machine is failing at “truth-telling” when, in fact, it’s doing what a prediction engine does—producing an outcome.
The advanced student stops grading AI like a witness and starts grading it like a subconscious: by the quality of the generated reality, and by how much conscious refinement it demands.
The subconscious test: “Show me the world where we win”
Take a legal case.
You load the entire caseload. Every document, every statement, every timeline, every inconsistency, every prior decision, every relevant statute, everything you have.
Then you give the constraint:
“Make my client not guilty. Now show me the complete record of the world in which we win.”
Not advice. Not “help me think.” Not a framework.
Do it all.
Opening statement. Closing statement. Jury selection strategy. Cross-exam. Direct. Deposition scripts. Affidavit drafts. The architecture of the argument. The entire predicted outcome, as if the trial already happened and you’re reviewing the transcript.
And of course you know the depositions didn’t happen. You know the affidavits weren’t actually sworn. You know some “precedent” citations may not exist. You know this because you haven’t done the case in a real court yet.
That isn’t a reason to call it an error.
It’s a reason to call it what it is: a generated reality.
Just like the checkerboard shadow illusion. Your mind isn’t “lying.” It’s predicting a coherent world given dense priors. The illusion is not a recommendation. It’s the outcome your system produces. Persistently.
AI output is the same category of thing: an outcome, predicted into existence under constraints.
So how do we judge “superintelligence” in this model?
We judge it the same way we judge the subconscious: by the impressiveness of the predicted outcome, even while we fully understand it is not “Actual” yet.
The metric is not “did it cite a real case.”
The metric is: “If I hand this completed world to the best human professional on the planet, do they treat it like a cheap draft… or like a roadmap?”
That’s the line.
State-of-the-art today tends to be:
- far above average competence,
- often useful in fragments,
- occasionally brilliant,
- but still visibly “not how the best would do it.”
Superintelligence, in the legal domain, would feel like this:
The best lawyer in United States reads the AI’s predicted win and says, “That’s not just plausible. That’s the play.”
Not because it is “true,” but because it is strategically superior. The human then attempts—within ethical and procedural reality—to instantiate that predicted world as closely as possible.
They will still verify everything. They will still replace fictional citations with real ones. They will still ground every claim in evidence. They will still obey rules of court.
But the sequence, the architecture, the angles, the ordering, the pressure points—the shape of the win—becomes the plan.
That’s how superintelligence announces itself: when the best human stops using it for suggestions and starts using it as the script.
A cleaner scoring rule: “How much attention does it still require?”
Here’s the practical way to think about the ladder:
- A tool that needs constant supervision is not subconscious-like. It’s an assistant.
- A tool that produces a complete, compelling outcome but still needs major conscious rewrites is better, but not yet “the thing.”
- A tool whose outcome-world becomes the default plan for elite humans—where attention shifts from “create strategy” to “verify and instantiate”—is what you’re calling superintelligence.
So the score is not truth. The score is attention.
“How much conscious correction is required before a world-class practitioner says, ‘Yes, that’s the architecture’?”
That’s the subconscious model applied honestly.
Why fabricated details aren’t the point (and why ethics still matter)
In this frame, fictional depositions and fictional affidavits are not “bad behavior.” They are the natural byproduct of asking for a complete predicted world.
But here’s the non-negotiable distinction: you never submit fiction as fact.
A predicted affidavit is not an affidavit.
A predicted deposition is not a deposition.
A predicted precedent citation is not precedent.
Those are placeholders that tell you what reality would need to contain for the win to be achieved.
So the professional move is:
- treat the AI’s outcome-world as a hypothesis-space generator,
- then use conscious attention to convert it into reality-constrained action.
The subconscious gives you the dream. Consciousness checks the receipts.
That is the proper division of labor.
AGI vs superintelligence in this same lens
Now you’re making a second distinction, and it’s a good one:
AGI is breadth.
Superintelligence is depth.
AGI means the same system that can generate a credible case-winning world can also generate a credible, useful world in almost any other domain with minimal special prompting—because its prediction competence generalizes.
So you’d say you have AGI when:
- you can move from law to design to engineering to teaching to operations,
- and the machine still produces outcome-worlds that professionals recognize as competent,
- without you having to micromanage the prompt into a narrow lane.
Superintelligence is when you go narrow—painfully narrow—and it still wins.
Not “law,” but family law.
Not “family law,” but family law in South Carolina.
Not “divorce,” but divorce with children under three, with a very specific fact pattern and a very specific judge and a very specific county culture.
And then the predicted outcome comes back so strong that the best human in that tiny niche says, “That’s the line. That’s the sequence. That’s the pressure point.”
That’s depth.
AGI is when the same entity can then pivot and help a metalworker predict the perfect copper hooded range for a house on Kiawah Island—not as a gimmick, but as a genuinely competent outcome-world the craftsperson respects.
Breadth versus depth.
The advanced student’s conclusion
The amateur asks, “Is it hallucinating?”
The advanced student asks, “How compelling is the generated world, and how much attention does it take to instantiate it?”
That’s how you judge state-of-the-art versus superintelligence under the subconscious model.
Not by whether it always tells the truth.
But by whether its predicted outcomes are so strategically powerful that the best humans adopt them as the default architecture—and use consciousness for what it has always been for:
Verification, veto, refinement, and responsibility.