AI regulation is far less simple than it looks. It’s prisoners dilemma at insane scale. In theory if all leading AI labs globally agreed to the same process of review and slow down, then we’d get frontier intelligence at similar rates and it diffuses relatively evenly. If the US remains at the frontier at all times, and has heavy regulation on the release of intelligence then we end up with an economic and geopolitical edge because we can control who has access to frontier intelligence. If we delay model releases, however, and another player - specifically China - doesn’t slow down and has equally strong models (not now but soon?) then our delays end up advantaging their models and eventually their tech stack. Now, the US could ban these models, but that actually only puts the US at a steeper disadvantage because other countries won’t have those bans. Then, from a relative competitiveness standpoint the US has now fallen behind even though it started in front. So, none of this is as simple as it looks. At some point it’s a simple bet of can closed models remain at the frontier in perpetuity or is there a risk of any other player or market catching up or just not falling behind.
AI 监管远没有看上去那么简单。这是在疯狂规模上的 prisoners dilemma（囚徒困境）。理论上，如果全球所有领先的 AI lab 都同意采用同样的审查流程并一起放慢速度，那么我们获得 frontier intelligence（前沿智能）的速度会大致相近，而且它会相对均匀地扩散。如果 US 始终处于前沿，并且对 intelligence 的发布实行严格监管，那么我们最终就会拥有一种经济和地缘政治上的优势，因为我们可以控制谁能接触到 frontier intelligence。然而，如果我们推迟 model 发布，而另一个玩家——尤其是 China——没有放慢速度，并且拥有同样强的 model（不是现在，但很快会？），那么我们的延迟反而会让他们的 model，最终连同他们的 tech stack（技术栈）一起受益。现在，US 当然可以禁止这些 model，但那实际上只会让 US 处于更大的劣势，因为其他国家不会有这些禁令。这样一来，从相对竞争力的角度看，US 就会在原本领先的情况下反而落后。所以，这一切都不像表面看起来那么简单。归根到底，这其实是在赌一件事：closed models（封闭模型）能否永远保持在前沿，还是说存在任何其他玩家或市场追上来，哪怕只是没有继续落后的风险。
We now have de facto AI regulation. It’s not obvious why from here on out models that have certain levels of capability or are trained on certain compute sizes won’t have to be reviewed by the government before release. Realistically, as AI models became more and more powerful this was going to be inevitable (I think it’s too early, but here we are). So now it’s mostly just interesting to think about the implications and scenarios from here. A few would be: * America gets to control who gets access to frontier intelligence and when. This generally works as long as we remain at the frontier at all times and don’t have a risk of being surpassed. At the moment we have a clear lead in frontier intelligence so this is a good bet, but lots of motivated parties would love to change that. * This likely creates backlog of AI releases which means that we will see less rapid fire back and forth jumps in model progress. Bull/fine case is that we just get bigger step functions per release at a slower rate and we end up at the same point we would have. Bear case is those incremental smaller jumps were necessary for the continued flywheel of innovation. * Other countries likely have even more incentive to at least hedge their bets with sovereign AI strategies so aren’t dependent on access to US AI all times. Previously this was relatively moot because the alternative wasn’t good enough, but that could change out of necessity and what we’re seeing in China. * Open weights obviously a big winner here as it becomes what likely sovereign AI gets built out on, and what (for now) can still be released to the market without the same controls. One interesting question would be how regulation eventually extends to open models, which would have its own set of long term consequences. Anyway some big updates to everyone’s mental models of AI regulation as a result of the capabilities we’re now seeing in AI. Wild times.
我们现在已经有了 de facto（事实上的）AI 监管。从现在开始，为什么那些达到某些能力水平、或者在某些 compute（算力）规模上训练出来的 model，不需要在发布前先接受政府审查，其实并不明显。现实地说，随着 AI model 变得越来越强大，这件事本来就是不可避免的（我认为现在还太早了，但现实已经如此）。所以现在更有意思的，主要是思考从这里往后会产生哪些影响和场景。几个可能的情况是：* America 将能够控制谁可以在何时获得 frontier intelligence 的访问权。只要我们始终保持在前沿、并且没有被超越的风险，这通常就行得通。就目前而言，我们在 frontier intelligence 上有明确领先，所以这是一笔不错的赌注，但有很多高度积极的参与方都很想改变这一点。* 这很可能会造成 AI 发布积压，这意味着我们会看到更少那种 rapid fire（高频、密集）的来回式 model 进步跳跃。Bull/fine case（乐观情形）是，每次发布只是以更慢的频率带来更大的 step function（阶跃式提升），最终我们仍会到达原本会到达的位置。Bear case（悲观情形）则是，那些更小、更渐进的提升其实是维持创新 flywheel（飞轮）持续运转所必需的。* 其他国家很可能会更有动力，至少通过 sovereign AI（主权 AI）战略来对冲风险，以免自己始终依赖获得 US AI 的访问权。以前这点相对无关紧要，因为替代方案还不够好，但出于必要性，以及从我们在 China 看到的情况来看，这可能会改变。* open weights（开放权重）显然会是这里的大赢家，因为它很可能会成为 sovereign AI 得以构建的基础，也是在目前仍然可以不受同等控制地向市场发布的东西。一个有意思的问题是，监管最终会如何延伸到 open models（开放模型）上，而那又会带来其自身一整套长期后果。总之，由于我们现在在 AI 中看到的这些能力，大家关于 AI 监管的 mental models（心智模型）都需要进行重大更新。真是疯狂的时代。