How should you slow down AI progress if it becomes necessary?

Summary and Introduction 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