AI Safety Learning Path

Motivations for AI safety

Motivations for AI safety come from the need to prevent both near-term harms and extreme, long-horizon risks from increasingly capable AI systems. Scholars discuss risks such as critical system failures, bias, and AI-enabled surveillance, along with emerging misuse risks including weaponization, automated cyberattacks, manipulation of public opinion, and even bioterrorism. They also consider speculative scenarios where advanced AI systems could be difficult to control—such as losing control of future AGI agents or enabling perpetually stable authoritarian regimes. A major motivation is “existential safety,” the concern that advanced AI could pose catastrophic outcomes (including human extinction). While experts disagree on the severity and primary sources of AI risk, surveys indicate that many researchers take high-consequence risks seriously, even if they remain relatively low-probability. This motivates both technical work (e.g., robustness, monitoring, and alignment) and broader governance efforts (e.g., norms, policies, and regulation) to reduce the chance of accidents, misuse, and loss of control as AI capabilities advance.

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Existential safety and AGI risk

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