How should the world slow down AI progress if it ever decides it needs to? If you ever see substantial evidence of catastrophic risk emerging, social instability caused by mass unemployment occurring, or a software intelligence explosion (SIE) beginning that causes progress to outpace our ability to adapt, you could decide that it’s prudent to slow down progress to have more time to prepare and adapt to coming capabilities.
While there has been a lot of attention devoted to the question of whether you should slow down, thus far not a lot of attention has been devoted to the question of how you would slow down, and the actual instruments that you have available to cause a slowdown. Some commonly discussed mechanisms, such as token taxes, datacenter moratoriums, and 6-month training run pauses would all have significant downsides. This makes them, by themselves, unattractive as instruments to slow down AI progress and address societal or political concerns about AI.
Instead, if you are forced to slow down, the most effective and least harmful approach would be twofold. First, to address catastrophic risks or a SIE, I’ll recommend a layered set of restrictions to slow down the rate of algorithmic progress by limiting the amount of compute that AI companies can pour into R&D internally. The first restriction would be a hard cap at a certain threshold of total R&D compute. The second would be a progressive tax below that threshold. And finally these two restrictions would be accompanied by a backstop in the form of a cap on training compute for individual training runs, to provide extra assurance against evasion. The hard cap on R&D and training compute would be targeted at risks that could arise more suddenly, such as misalignment and catastrophic misuse risk. And the progressive tax would be targeted towards risks that rise more smoothly with respect to capabilities (such as broader societal harms that require time to adapt to).
Second, to address mass unemployment concerns specifically, I’ll propose a capability-gated tax on AI deployment, as the intensity of deployment of powerful AI systems would be tied to displacement, and so metering inference should allow you to control the velocity of economic displacement. Both of these approaches should be designed as dynamic, conditional instruments that are able to be updated in the face of new evidence.
In this post I’ll sketch out some problems and desiderata for slowdowns, particularly through the lens of a critical window of capabilities where your risk-reduction efforts are most leveraged, as well as the concept of an overhang. I’ll argue that slowdown mechanisms should move us slowly through the critical windows of capabilities, avoid being overly blunt, and be dynamic so they can be tuned as evidence emerges. Then, I’ll present a taxonomy of policy levers and targets in the AI tech stack, and explain why the mechanism proposals need to be targeted at the layer of the tech stack that corresponds to where the risk comes from, and use the correct lever to match the harm structure of the risk you’re attempting to address. Finally, I’ll explain how these considerations motivate the above proposal, and briefly touch on what trigger mechanisms could be used as tripwires for when slowdown mechanisms should be implemented.
To be clear, I’m uncertain that slowdowns currently are or will ever be desirable, yet I think it’s an important question to interrogate because there may be scenarios where the risk is large enough to merit slowing down, or where progress will grow so fast that it will truly outpace our ability to adapt and prepare. One specific objection worth touching on: what about China?
Is it even worth thinking about slowing down if China won’t slow down?
Yes, for two reasons. First, the Overton window may shift in the future sufficiently to allow for an international agreement to be formed. Second, the gap between the US and China may widen enough to allow for the US to unilaterally implement these instruments.
To be precise about what a slowdown could get you, you need to think about how leveraged your actions are at different levels of capabilities.
