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Using a model in which agents compete to develop a potentially dangerous newtechnology (AI), we study how changes in the pricing of factors of production(computational resources) affect agents' strategies, particularly theirspending on safety meant to reduce the danger from the new technology. In themodel, agents split spending between safety and performance, with safetydetermining the probability of a ``disaster" outcome, and performancedetermining the agents' competitiveness relative to their peers. For givenparameterizations, we determine the theoretically optimal spending strategiesby numerically computing Nash equilibria. Using this approach we find that (1)in symmetric scenarios, compute price increases are safety-promoting if andonly if the production of performance scales faster than the production ofsafety; (2) the probability of a disaster can be made arbitrarily low byproviding a sufficiently large subsidy to a single agent; (3) when agentsdiffer in productivity, providing a subsidy to the more productive agent isoften better for aggregate safety than providing the same subsidy to otheragent(s) (with some qualifications, which we discuss); (4) when one agent ismuch more safety-conscious, in the sense of believing that safety is moredifficult to achieve, relative to his competitors, subsidizing that agent istypically better for aggregate safety than subsidizing its competitors;however, subsidizing an agent that is only somewhat more safety-conscious oftendecreases safety. Thus, although subsidizing a much more safety-conscious, orproductive, agent often improves safety as intuition suggests, subsidizing asomewhat more safety-conscious or productive agent can often be harmful.

Industrial Policy for Advanced AI: Compute Pricing and the Safety Tax