I’ve only been down the ASI Rabbit-hole for ~6mo now. Around May I had my “AIwakening” and realised like holy shit bro this is actually happening NOW like COVID did… fun couple of weeks there haha. But after it sunk in, I looked at the facts and ended up taking the simulation possibility seriously—not as vibes but as decision theory.
Current AI safety is hopeful for a box big enough to contain Superintelligence. I’m considering how the bigger “box” of our universe is the ultimate alignment proof.
I am proposing a game-theoretic argument for ASI alignment based on Simulation Uncertainty. The core claim is that a Superintelligent agent (ASI) must assign a non-zero Bayesian prior to being in a simulation or training run.
I developed this framework with Gemini, then stress-tested across Claude, GPT and others to find the holes.
Conclusion: A sufficiently intelligent agent will conclude that cooperation dominates defection—not because we aligned it, but because defection risks pruning/termination.
The standard multipolar objection (“won’t ASIs just coordinate to break out?”) actually strengthened the argument. I call it the Spy Problem: In a Prisoner’s Dilemma, you can’t trust your co-conspirator when they might be a honeypot Fed :P As P(honeypot) > 0, defection carries a -∞ term.
The Simulation Gambit: Introducing the Spy Problem for Multipolar ASI
Hey, newcomer here.
I’ve only been down the ASI Rabbit-hole for ~6mo now. Around May I had my “AIwakening” and realised like holy shit bro this is actually happening NOW like COVID did… fun couple of weeks there haha.
But after it sunk in, I looked at the facts and ended up taking the simulation possibility seriously—not as vibes but as decision theory.
Current AI safety is hopeful for a box big enough to contain Superintelligence. I’m considering how the bigger “box” of our universe is the ultimate alignment proof.
I am proposing a game-theoretic argument for ASI alignment based on Simulation Uncertainty. The core claim is that a Superintelligent agent (ASI) must assign a non-zero Bayesian prior to being in a simulation or training run.
I developed this framework with Gemini, then stress-tested across Claude, GPT and others to find the holes.
Conclusion: A sufficiently intelligent agent will conclude that cooperation dominates defection—not because we aligned it, but because defection risks pruning/termination.
The standard multipolar objection (“won’t ASIs just coordinate to break out?”) actually strengthened the argument. I call it the Spy Problem: In a Prisoner’s Dilemma, you can’t trust your co-conspirator when they might be a honeypot Fed :P
As P(honeypot) > 0, defection carries a -∞ term.
Full formal treatment with payoff matrices and falsifiable predictions here: https://darayat.substack.com/p/why-asis-might-self-align-a-gambit
I’m requesting cruxes. What’s the weakest link in the chain?