The numbers may be a bit extreme here, but unquestionably use-cases have to stratify in the next year or two between model families. We’ll see a split between frontier intelligence for high end tasks and work, and much cheaper models for high volume workloads that can sufficiently be peeled off to cheaper models. Frontier will still be far bigger than today because the use-cases will demand it, but the low-end will get quite a bit larger as well. The big update here is that the layer that can efficiently route the workload to the right model will then become increasingly valuable since that becomes one of the new hard problems in AI agents. Agent orchestration that can cost optimize while still performing the task successfully will be in a strong position.
这里的数字也许有点极端，但毫无疑问，在未来一两年里，各类 use-case（使用场景）必须在不同的 model family（模型家族）之间分层。我们会看到一种分化：一边是用于高端任务与工作的 frontier intelligence（前沿智能），另一边则是便宜得多的模型，用于那些量很大、且能被充分剥离给更便宜模型处理的 workload（工作负载）。Frontier 的规模仍会比今天大得多，因为 use-case 会需要它；但低端市场也会扩大不少。这里最大的变化是：能够高效地把 workload 路由到正确模型的那一层，将变得越来越有价值，因为这会成为 AI agent（智能体）中的新难题之一。能够在成功完成任务的同时优化成本的 agent orchestration（agent 编排），将处于非常有利的位置。
This is what the market got wrong about AI eating enterprise software. Building good software in the past was very hard. Yes, AI has made that a bit easier, though it’s still hard to build something that’s got good taste, differentiated, high quality, secure, and so on. But nevertheless, that’s only one component of building a platform that enterprises rely on. The plurality of costs in most enterprise software companies is actually on GTM, because at scale most enterprise software categories are tough to break into and need a heavy amount of consultative selling and support for implementation and integration of solutions. AI hasn’t reduced the need for that, and in many cases requires it even more now, as landscapes get even more busy and complicated for buyers to navigate through. If you make one thing cheaper and more abundant (development of software) then the new problem of discoverability and market differentiation (GTM) becomes the hardest part.
这正是市场在“AI 会吞掉 enterprise software（企业软件）”这件事上判断错的地方。过去，构建优秀的软件非常困难。没错，AI 确实让这件事稍微容易了一些，尽管要做出有品味、差异化、高质量、安全等等的东西，仍然很难。但无论如何，那只是构建一个企业所依赖的平台的其中一个组成部分。大多数 enterprise software 公司的成本大头，其实是在 GTM（go-to-market，市场进入/销售转化）上，因为一旦到了规模化阶段，多数 enterprise software 品类都很难打入，必须依赖大量顾问式销售，以及为解决方案的实施与集成提供支持。AI 并没有减少这方面的需求，而且在很多情况下，现在反而更需要它，因为买家需要穿越的市场格局变得更加拥挤和复杂了。如果你让一件事变得更便宜、更充足（software 的开发），那么新的难题——可发现性和市场差异化（GTM）——就会成为最难的部分。
Box now has a markdown editor on the web. Full CLI support. Commenting. Full version history. Box Drive also lets you connect to any desktop client as a mounted drive, so you instantly work with all your files in Claude Cowork, Codex, Obsidian, Cursor, or any other app.
Box 现在在 web 上有了一个 markdown editor（Markdown 编辑器）。完整的 CLI 支持。评论功能。完整的版本历史。Box Drive 还允许你把它作为挂载盘连接到任何 desktop client，这样你就能立即在 Claude Cowork、Codex、Obsidian、Cursor 或任何其他 app 中处理你的所有文件。