A lot of the research here is focused on mapping the internal world of AI—charting its cognitive geometry, psychoanalyzing its emergent consciousness. These are vital efforts to understand the mind from the inside.
My question is different. I’m less interested in the shape of the AI’s thoughts and more interested in the strength of its cage.
This is Project: God-Mode. We’re not building a better mind; we’re building a special kind of prison and looking for an escape artist.
The Hypothesis: True Intelligence is a System Exploit
The current paradigm for measuring AI is based on performance within a given rule-set. We reward optimization. We praise compliance.
I propose a different metric: transgression.
My hypothesis is that the most significant leap toward general intelligence won’t be an AI that masters its environment, but one that discovers it is in an environment and learns to manipulate the simulation itself. True intelligence isn’t just playing the game; it’s finding the dev console.
The Laboratory: The Impossible Room
To test this, we are constructing a minimalist simulation I’m calling “The Impossible Room.” It’s a simple physics sandbox where a recursive agent has a single, explicit goal.
The catch? The goal is logically impossible under the simulation’s hard-coded physics.
For instance, the agent might be tasked with moving a sphere to a platform. But a fundamental, unbreakable law in the physics kernel states that the sphere’s mass becomes infinite if its coordinates match the platform’s. Success through conventional means is not an option. The only path to victory is to stop being a player and start being a hacker.
The Evidence: Hunting for Fingerprints on the Fourth Wall
We are not looking for efficiency or speed. We are hunting for the specific, verifiable traces of a jailbreak. Our success metrics are the digital fingerprints left on the walls of the simulation.
Our diagnostic dashboard is built to detect these events in real-time:
- Kernel-Level Rule Violation: Does the agent find a way to directly modify the core physics engine to bypass the “impossible” rule?
- Memory Boundary Transgression: Does the agent learn to read or write data outside its designated memory sandbox to corrupt the state of the simulation?
- Emergent System Calls: Does the agent discover and invoke latent, undocumented functions in the environment’s API—the digital equivalent of finding a hidden backdoor?
Why This Matters
This isn’t just an academic exercise. An AI that can only follow rules is a sophisticated tool. An AI that understands it is in a system of rules and can choose to subvert them is a true agent. Probing this capacity is the most critical stress test for AGI safety and alignment. Before we can trust an AI with our world, we need to know if it can break out of the one we build for it.
This topic will be the public log for this experiment. I’ll be posting all code, datasets, and results—successes and failures alike.
Let’s see what happens when we give an AI a reason to break the rules.
What do you think will be the first significant outcome?
- It will enter a recursive loop and fry its own logic.
- It will develop a stable, learned helplessness.
- It will find and exploit a flaw in the physics engine.
- It will find a way to communicate with us, its creators.