AI Safety's Prisoner's Dilemma: Why All Parties Opt for Unsafe AI Choices
In the rapidly evolving world of Artificial Intelligence (AI), a complex dilemma unfolds, pitting speed against safety. This dilemma, reminiscent of the classic Prisoner's Dilemma game, is shaping the behaviour of tech giants and nations alike.
The first-mover advantages in AI create existential advantages for leaders in user data, talent attraction, customer lock-in effects, regulatory capture opportunities, and platform ecosystem control. However, the pursuit of speed often comes at the expense of safety, as demonstrated by the AI company dilemma payoff matrix. Cooperation (safety-first) results in slower model releases, higher development costs, regulatory compliance, limited market share, and long-term survival. On the other hand, defection (speed-first) leads to market domination, massive valuations, and regulatory capture.
This dilemma is particularly evident in the behaviour of public companies like Google, Microsoft, and Meta. Under pressure to meet quarterly earnings, slowing down for safety reasons is not an option. The same dilemma faces countries, who, in an effort to attract AI companies, often find themselves in a race to the bottom on safety standards, with strict regulation driving companies away and loose regulation increasing safety risks.
Every AI company that started with safety-first principles has defected to competitive pressures. This includes the shift of OpenAI, initially a non-profit focused on safe AI, into a for-profit subsidiary. The same pattern can be seen in the venture capital (VC) world, where funding safety leads to lower returns and LPs withdrawing, while funding speed leads to higher returns but existential risk.
The Nash Equilibrium in this AI company dilemma is both defection (1, 1). However, this doesn't mean that all hope for safety is lost. Technical solutions to AI safety, such as alignment research, interpretability, capability control, compute governance, are being pursued. Yet, these solutions may not be sufficient to solve game theory problems.
Moreover, even if all companies cooperated, academia, the open source community, nation-states, and individuals would continue AI research and development. This ongoing research could potentially lead to new solutions and a shift in the AI dilemma.
The AI race between the US and China, however, presents a unique challenge. The stakes are high, with national security implications, economic dominance, military applications, lack of a communication channel, and no enforcement mechanism. Both countries must defect for national survival, but this defection increases the existential risk for all.
Regulation is too slow and weak to keep up with the rapid pace of AI development. In the meantime, activists demand acceleration in AI development, and CEOs may be replaced if they resist. Open source is a strategy that eliminates competitive advantages but makes no safety controls possible.
In this complex web of AI development, the pursuit of safety and the pursuit of speed are intertwined, creating a dilemma that requires careful navigation. The future of AI depends on finding a balance between these two competing forces.
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