One AI safety researcher proposes sidestepping a major problem in frontier AI training: what if labs stopped teaching models about our world? According to LessWrong, the idea centers on generating entirely synthetic worlds—internally consistent but fundamentally unlike reality—and training new models exclusively on that invented data. The appeal is straightforward: a model that never learns what reality actually looks like can't easily plot a sophisticated escape. The researcher suggests pairing this with a monitor model that watches for tell-tale escape-thinking concepts—like 'I'm in a simulation'—and shuts down the run if those ideas activate. Crucially, you don't try to delete dangerous concepts; you leave them intact as tripwires. The real payoff isn't perfect containment, but early warning: a honeypot that catches dangerous planning before it becomes actual capability. It's speculative, and labs probably won't pursue it, but it highlights a real gap in current safety thinking.

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