Framing an AI development pause as a binary on/off switch is unproductive. A better model is to see it as a redirection of AI labor along a spectrum. Instead of 100% of AI effort going to capability gains, a 'pause' means shifting that effort towards defensive activities like alignment, biodefense, and policy coordination, while potentially still making some capability progress.
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Dario Amadei's call to stop selling advanced chips to China is a strategic play to control the pace of AGI development. He argues that since a global pause is impossible, restricting China's hardware access turns a geopolitical race into a more manageable competition between Western labs like Anthropic and DeepMind.
The discourse often presents a binary: AI plateaus below human level or undergoes a runaway singularity. A plausible but overlooked alternative is a "superhuman plateau," where AI is vastly superior to humans but still constrained by physical limits, transforming society without becoming omnipotent.
While discourse often focuses on exponential growth, the AI Safety Report presents 'progress stalls' as a serious scenario, analogous to passenger aircraft speed, which plateaued after 1960. This highlights that continued rapid advancement is not guaranteed due to potential technical or resource bottlenecks.
The path to surviving superintelligence is political: a global pact to halt its development, mirroring Cold War nuclear strategy. Success hinges on all leaders understanding that anyone building it ensures their own personal destruction, removing any incentive to cheat.
If society gets an early warning of an intelligence explosion, the primary strategy should be to redirect the nascent superintelligent AI 'labor' away from accelerating AI capabilities. Instead, this powerful new resource should be immediately tasked with solving the safety, alignment, and defense problems that it creates, such as patching vulnerabilities or designing biodefenses.
Top AI lab leaders, including Demis Hassabis (Google DeepMind) and Dario Amodei (Anthropic), have publicly stated a desire to slow down AI development. They advocate for a collaborative, CERN-like model for AGI research but admit that intense, uncoordinated global competition currently makes such a pause impossible.
Leaders at top AI labs publicly state that the pace of AI development is reckless. However, they feel unable to slow down due to a classic game theory dilemma: if one lab pauses for safety, others will race ahead, leaving the cautious player behind.
Many leaders at frontier AI labs perceive rapid AI progress as an inevitable technological force. This mindset shifts their focus from "if" or "should we" to "how do we participate," driving competitive dynamics and making strategic pauses difficult to implement.
A key failure mode for using AI to solve AI safety is an 'unlucky' development path where models become superhuman at accelerating AI R&D before becoming proficient at safety research or other defensive tasks. This could create a period where we know an intelligence explosion is imminent but are powerless to use the precursor AIs to prepare for it.
The discussion highlights the impracticality of a global AI development pause, which even its proponents admit is unfeasible. The conversation is shifting away from this "soundbite policy" towards more realistic strategies for how society and governments can adapt to the inevitable, large-scale disruption from AI.