A concrete suggestion for a buying-time intervention is to develop plans and coordination mechanisms (e.g. assurance contracts) for major AI actors/labs to agree to pay a fixed percentage alignment tax (in terms of compute) conditional on other actors also paying that percentage. I think it’s highly unlikely that this is new to you, but didn’t want to bystander just in case.
A second point is that there is a limited number of supercomputers that are anywhere close to the capacity of top supercomputers. The #10 most powerfwl is 0.005% as powerfwl as the #1. So it could be worth looking into facilitating coordination between them.
Perhaps one major advantage of focusing on supercomputer coordination is that the people who can make the relevant decisions[1] may not actually have any financial incentives to participate in the race for new AI systems. They have financial incentives to let companies use their hardware to train AIs, naturally, but they could be financially indifferent to how those AIs are trained.
In fact, if they can manage to coordinate it via something like assurance contract, they may have a collective incentive to demand that AIs are trained in safer alignment-tax-paying ways, because then companies have to buy more computing time for the same level of AI performance. That’s too much to hope for. The main point is just that their incentives may not have a race dynamic.
Maybe the relevant chain of command goes up to high government in some cases, or maybe there are key individuals or small groups who have relevant power to decide.
A concrete suggestion for a buying-time intervention is to develop plans and coordination mechanisms (e.g. assurance contracts) for major AI actors/labs to agree to pay a fixed percentage alignment tax (in terms of compute) conditional on other actors also paying that percentage. I think it’s highly unlikely that this is new to you, but didn’t want to bystander just in case.
A second point is that there is a limited number of supercomputers that are anywhere close to the capacity of top supercomputers. The #10 most powerfwl is 0.005% as powerfwl as the #1. So it could be worth looking into facilitating coordination between them.
Perhaps one major advantage of focusing on supercomputer coordination is that the people who can make the relevant decisions[1] may not actually have any financial incentives to participate in the race for new AI systems. They have financial incentives to let companies use their hardware to train AIs, naturally, but they could be financially indifferent to how those AIs are trained.
In fact, if they can manage to coordinate it via something like assurance contract, they may have a collective incentive to demand that AIs are trained in safer alignment-tax-paying ways, because then companies have to buy more computing time for the same level of AI performance. That’s too much to hope for. The main point is just that their incentives may not have a race dynamic.
Who knows.
Maybe the relevant chain of command goes up to high government in some cases, or maybe there are key individuals or small groups who have relevant power to decide.