In a post on LessWrong, a security researcher with a background in infrastructure protection challenges a core assumption in AI safety: that longer AGI timelines uniformly reduce risk. The actual relationship is more complicated. Timeline length redistributes risk rather than eliminating it. In compressed timelines—six months to AGI, say—the dominant threat is accidental misalignment; sophisticated attackers don't have time to upskill and exploit the system. But longer timelines flip the dynamic. The same extensions that give alignment researchers time to iterate and fix problems also enable well-funded adversaries to study frontier AI systems, plan sophisticated attacks, and conduct ongoing cat-and-mouse games with lab infrastructure. The implication: there's no universally "safe" timeline length. Instead, longer timelines shift which threats matter most—from accidental bugs toward deliberate sabotage, model theft, and state-sponsored interference. It's not that more time is inherently better, but that different timeline lengths enable different attack surfaces.

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