The layer that can route to the best AI model for the particular job is going to increase in value substantially. There are at least 3 big reasons: * Cost optimization: there are plenty of use cases where you need frontier intelligence for some tasks and something far cheaper for others. Even in the same task you may use frontier intelligence for planning and review of the work, but an OSS or cheaper model for the bulk of the workload. This is going to be standard across large buckets of work going forward. * Capability maximization: despite the bitter lesson and models generally getting better in the same direction, there are still lots of differences between models. Some are better at tool use, others better at coding, and others again better at certain domains of knowledge work. The ability to route between these at different times is a huge advantage. * Risk mitigation: while the Fable situation is somewhat of a black swan, it’s possible we’re heading toward a regulatory environment where governments may restrict models at different times based on their approval mechanisms or new things they discover. This means you’re going to want flexibility in being able to deploy workloads across different providers as a form of risk mitigation. Ultimately, it’s going to increasingly be a a strategic advantage for the applied AI layer that they can effectively route between models. Will be very interesting to see how this evolves.
那个能够为特定工作路由到最佳 AI model（模型）的层，其价值将会大幅提升。至少有 3 个重要原因：* 成本优化：有很多 use case（用例）里，你会需要在某些任务上使用 frontier intelligence（前沿智能），而在另一些任务上使用便宜得多的模型。即便是在同一个任务里，你也可能会用 frontier intelligence 来做规划和工作审查，但把大部分工作负载交给 OSS 或更便宜的模型。今后这会在大类工作中成为标准做法。* 能力最大化：尽管 bitter lesson（苦涩教训）依然成立，而且 models（模型）总体上是在朝同一方向变强，但模型之间仍然存在大量差异。有些更擅长 tool use（工具使用），有些更擅长 coding（编程），还有些则更擅长某些知识工作领域。在不同时间点在这些模型之间进行路由的能力，是一个巨大的优势。* 风险缓释：虽然 Fable 这次的情况某种程度上算是 black swan（黑天鹅）事件，但我们也有可能正在走向一种监管环境：政府可能会基于自己的审批机制，或基于他们新发现的情况，在不同时间限制不同模型。这意味着，你会希望自己具备灵活性，能够把工作负载部署到不同 provider（提供方）上，把这作为一种风险缓释手段。归根结底，对于 applied AI（应用层 AI）来说，能否有效地在不同模型之间进行路由，将越来越成为一种战略优势。这个方向接下来会如何演化，会非常值得关注。
Everyone thinks this is some kind of 4D chess or conspiracy. But it’s quite standard to try and jailbreak AI models, and by definition they would share that research with the government given that’s whole point. I don’t think Amazon assumed this would be the next move.
每个人都觉得这是什么 4D chess（四维棋）或者阴谋论。但尝试 jailbreak AI models（越狱 AI 模型）其实是相当标准的做法，而且按定义来说，他们既然做这项研究，本来就会把研究结果分享给政府，因为那本来就是整个事情的目的。我不认为 Amazon 预料到这会是下一步动作。
This whole Fable export control situation is actually net positive to regulation discourse. It’s an early peek into what AI regulation would end up looking like at scale when enacted at the model layer instead of the specific application of the AI. The government would have sole discretion over when a model can be released to the to public, based on a bunch of factors that they inherently control. In this case, based on the available reporting, the risk is that the model can be jailbroken to deliver increased cyber exploit capabilities. The issue is that actually you want models to be able to have those capabilities on the defense side of cyber as well, and for all intents and purposes, by Anthropic’s own response, you can execute these capabilities today in other models. So thus the whole challenge will be that you’re debating with the government, over months and months, with every model release, what these models are actually capable of and what their risks are. Inherently, there’s not only a lot of subjectivity in determining those risks, but there’s also many other factors that go into the risks being practical in the first place. The net result is that we would end up with backlog of AI releases, progress in the market inherently would dramatically slow down, and AI would start to look more like any other sclerotic industry. If this paradigm had existed 3 years ago at the start of the current AI wave, we’d likely currently be stuck on GPT-4 level intelligence at this point. This is why, wherever possible, we should be regulating the applied use of AI. We should continue to study and enforce the dangerous use of AI in cyber attacks, financial services risks, fraud, biowarfare, and other spaces. AI safety is incredibly important, but slowing down progress this early in the development of AI I suspect is net harmful.
整个 Fable export control（出口管制）事件，实际上对监管话语来说是净正面的。它让我们提前看到了一眼：如果 AI regulation（AI 监管）是在 model layer（模型层）而不是针对 AI 的具体应用来实施，那么大规模落地后会是什么样子。政府将对一个模型何时可以向公众发布拥有完全裁量权，而依据则是一系列本质上由他们掌控的因素。在这个案例里，根据现有报道，风险在于这个模型可能被 jailbreak（越狱），从而提供更强的 cyber exploit（网络利用/攻击）能力。问题在于，你其实也希望模型在 cyber（网络安全）的防御侧具备这些能力，而且就实际效果而言，按照 Anthropic 自己的回应，今天你已经可以在其他模型上执行这些能力了。所以，真正的挑战将会是：你要在每一次模型发布时，花上数月又数月与政府争论这些模型到底具备什么能力、其风险又是什么。这里面不仅在风险认定上天然存在大量主观性，而且风险之所以会在现实中成立，本身还取决于许多其他因素。最终结果就是，AI 发布会出现积压，市场进展会不可避免地显著放缓，而 AI 行业会开始看起来像其他任何僵化的行业一样。如果这种范式在 3 年前、也就是这一轮 AI 浪潮开始时就已经存在，那么我们现在很可能还卡在 GPT-4 水平的智能上。这就是为什么，只要有可能，我们就应该监管 AI 的应用使用层。我们应该继续研究并执法打击 AI 在 cyber attacks（网络攻击）、financial services risks（金融服务风险）、fraud（欺诈）、biowarfare（生物战）以及其他领域中的危险使用。AI safety（AI 安全）极其重要，但在 AI 发展这么早的阶段就放慢进展，我怀疑总体上是有害的。