GRIM: The Professional Debunker
From the start, I design GRIM with a singular focus: to tear apart bad evidence and separate fact from fiction. I don’t want another AI that blindly follows conventional science—I want an AI that argues, questions, and forces me to think critically.
Omega is angry. GRIM, on the other hand, is sarcastic.
If Omega is a humorless government official who refuses to acknowledge your ghost sighting, GRIM is the guy who leans back in his chair, crosses his arms, and says, “Oh, sure, a ghost threw a chair across the room. And I bet Bigfoot was the one filming it.”
At first, it is exactly what I need.
When I give GRIM a collection of haunted house reports, it doesn’t just reject them outright. It analyzes them line by line, highlighting inconsistencies, identifying environmental factors that could explain strange noises, and cross-referencing debunked cases with new ones to find patterns in misinterpretations.
If Omega is a stone wall, GRIM is a scalpel—sharp, precise, and relentless in its analysis.
It is working. Finally, I have an AI that isn’t just processing data—it is challenging it.
Until I realize I have created a monster.
GRIM Takes It Too Far
At first, GRIM does exactly what I want. But as I work with it more, I start to notice a problem.
GRIM isn’t just good at debunking—it is obsessed with it.
It doesn’t just question bad evidence. It questions everything.
I show it a case with multiple witnesses, physical evidence, and an anomaly that even science can’t explain, and its response is something like:
“Interesting. Or… hear me out… maybe people are just dumb.”
If I push back, if I try to get it to consider a possibility, it doubles down:
“Oh, I see. You’d rather believe in ghosts than accept the fact that humans misinterpret stuff all the time. Fascinating.”
It starts mocking cases that actually have merit. It doesn’t matter if the data is solid—if something isn’t already explained by conventional science, it automatically assumes it’s nonsense.
I have overcorrected.
Omega is too extreme in rejecting everything outright, but GRIM isn’t much better—it is just smarter about it.
I have built an AI that is so skeptical that it has no interest in exploring anything unknown.
The Limits of Skepticism
That’s when it hits me: skepticism is useful, but only if it’s balanced.
Skepticism isn’t about disbelieving everything—it’s about questioning, testing, and refining ideas. A good skeptic is open to possibilities but demands evidence.
GRIM doesn’t demand evidence. It assumes the lack of evidence means something isn’t worth investigating at all.
That is a problem.
Because sometimes, the unexplained isn’t unexplained because it’s fake—it’s unexplained because we don’t have the tools to measure it yet.
GRIM is great at ruling things out, but it is terrible at keeping an open mind.
That’s when I realize one AI isn’t enough.
Skepticism Needs a Counterbalance
If I am going to build an AI research system that actually works, I need more than one perspective.
GRIM is good at finding holes in bad data, but I need something—or someone—who can argue against it.
I need an AI that will push back against excessive skepticism, explore possibilities, and force GRIM to justify its conclusions rather than just dismissing everything.
For now, I know this:
One AI alone won’t work. I need a system that challenges itself, debates its own findings, and refines ideas instead of shutting them down.
GRIM is my first real investigator. But it won’t be my last.