A few thoughts on what we will see in AI structurally for the foreseeable future: * Frontier intelligence continues unabated and pushes the industry forward continuously. The top labs will continue to buy the best and the most data, build the most compute, be at the forefront of improved training breakthroughs, and so on. A few different approaches stratify the market on pricing and capability, but overall competitive pressure brings down pricing on a per task basis. That said, we just ask more from the models over time - as one thing gets cheaper, we just use more - so frontier spend and use remains robust. * Open weights rapidly absorbs frontier breakthroughs (and drives other breakthrough directions given the constraints), offering both lower cost intelligence and the ability to be post trained for specific workflows and domains. This creates a healthy counter balance to the frontier as you can run models “at cost” on a hyperscaler at any time, and tune models just for your tasks. * The Applied AI layer has a huge opportunity to combine frontier intelligence with open or cheap closed models to orchestrate workflows in any given domain. Due to evals, deep domain context, being trusted with enterprise data and workflows, this layer can maximize performance and cost combination. The applied AI layer will also often have their own RLed models especially for high volume, predictable tasks in their systems. * Individual enterprises will generally focus on their enterprise context, making sure they can get any AI system the right data and information to work with, in a continuously improving way. Some will go off and train their own models for specific areas of work (large banks, pharma, etc.) where they can get real alpha from doing so given the many tradeoffs, but most will spend energy on making sure they can get all of the gains from AI breakthroughs on their data and workflows. Net net: even though some of this gets framed as zero sum, there’s just a ton of opportunity for all layers of the stack and approaches.
关于在可预见的未来，我们会在 AI 领域看到哪些结构性变化，我有几点看法：* 前沿智能（frontier intelligence）会持续不停地发展，并不断推动整个行业向前。顶级实验室会继续购买最好的、最多的数据，构建最强的 compute（算力），站在训练改进突破的最前沿，等等。少数几种不同的方法会在定价和能力上让市场分层，但总体来说，竞争压力会把单位任务的价格拉低。话虽如此，随着时间推移，我们对模型的要求也会越来越多——某件事一旦变便宜了，我们就会用得更多——因此前沿层的投入和使用仍会保持强劲。* 开放权重（open weights）会迅速吸收前沿突破（同时也会在这些约束下推动其他方向的突破），既提供更低成本的智能，也提供针对特定 workflow（工作流）和领域进行 post-train（后训练）的能力。这会对前沿层形成一种健康的制衡，因为你随时都可以在 hyperscaler（超大规模云厂商）上以“成本价”运行模型，并且只为你的任务去调优模型。* Applied AI（应用层 AI）这一层有巨大的机会，把前沿智能与开放模型或廉价闭源模型结合起来，在任意领域中编排 workflow。由于有 evals（评测）、深度领域上下文、以及对企业数据和 workflow 的可信接入能力，这一层可以把性能与成本的组合优化到最大。Applied AI 层通常也会拥有自己的 RLed models，尤其是用于其系统中高频、可预测的任务。* 单个企业通常会聚焦于自身的企业上下文，确保它们能以持续改进的方式，为任何 AI 系统提供正确的数据和信息来使用。有些企业会进一步为特定工作领域训练自己的模型（大型银行、pharma 等），因为在权衡多种取舍之后，这样做能带来真实 alpha（超额收益）；但大多数企业会把精力花在确保自己能把 AI 突破带来的收益，充分应用到自己的数据和 workflow 上。归根结底：虽然其中一些事情常被描述成零和竞争，但实际上，栈中的各个层次和不同路径都存在大量机会。
Here’s a great post on driving down costs, while maintaining high performance, with frontier intelligence as a manager and lower cost models for the workhorse tasks. This will be the template for what model routing looks like in the future. “We started this experiment expecting to measure how much Fable’s 2x premium would increase cost. We were surprised to find that Fable’s effective delegation actually decreased cost overall. It specified constraints and outcomes instead of spelling out the implementation, gave feedback instead of making fixes itself, and in most cases never touched the code at all. These are the habits of a good manager.” The industry is increasingly figuring out what it looks like to mix models together to be able to get targeted performance levels and optimal cost structures. Of course, the only way to get this is to have a deep understanding of the business problem you’re trying to solve and how to effectively route work to different models. If you’re in the applied layer - whether it’s customer support, legal, finance, or coding - this is how your harness will become a core area of differentiation.
这里有一篇很棒的文章，讲的是如何在保持高性能的同时降低成本：用前沿智能担任 manager（管理者），把具体的重活交给低成本模型。这会成为未来 model routing（模型路由）的模板。 “我们一开始做这个实验，是想衡量 Fable 的 2x 溢价会把成本提高多少。结果我们惊讶地发现，Fable 有效的 delegation（委派）实际上反而降低了整体成本。它规定的是约束和结果，而不是把实现细节逐条写出来；它提供反馈，而不是亲自去修补问题；而且在大多数情况下，它根本不碰代码。这些就是一个好 manager 的习惯。” 整个行业正越来越清楚，如何把不同模型混合起来，以获得目标性能水平和最优成本结构。当然，想做到这一点，唯一的方法就是对你要解决的业务问题有深刻理解，并知道如何把工作有效路由给不同模型。如果你处在应用层——无论是 customer support、legal、finance，还是 coding——这就是你的 harness（编排/执行框架）会成为核心差异化领域的方式。
The biggest challenge right now with the topic of every enterprise having their own model is that your most valuable information and insights are not only always changing, but they’re often your most sensitive information. Your most sensitive information can’t be packed into a model usually because it contains data that not everyone gets to have access to, and you can’t keep your security layer inside the model or an agent. I think there are going to be 100X more use cases for custom trained models, especially inside of domain-focused products, but training a model per enterprise is going to be a lot harder than it looks.
现在谈“每家企业都有自己的模型”这个话题时，最大的挑战在于：你最有价值的信息和洞察不仅一直在变化，而且它们往往也是你最敏感的信息。你最敏感的信息通常无法被打包进一个模型里，因为其中包含并非所有人都能访问的数据，而且你也无法把你的 security layer（安全层）保留在模型或 agent 里面。我认为，custom trained models（定制训练模型）的用例会多出 100 倍，尤其是在聚焦特定领域的产品内部；但为每一家企业单独训练一个模型，会比看上去难得多。