It’s truly wild that we’re getting this level of performance from open models. Congrats to Kimi team on this. Every time we lower the cost of frontier intelligence, the use-cases that enterprises can take on just go up. There’s a tremendous amount of workflows that enterprises would love to deploy that are only gated by the cost of tokens. Importantly for the startup ecosystem, the combined breakthroughs from open and closed labs enable a ton of value to accrue to the layer, which can leverage a variety of models to complete full tasks for customers. This diversity of models and approaches means that the applied AI layer can tune models to their workflows and route intelligence appropriately. Huge win for all.
我们竟然能从 open models 获得这种级别的性能，真的非常惊人。祝贺 Kimi team 做到这一点。每次我们降低 frontier intelligence（前沿智能）的成本，企业能够承接的 use-case（用例）就会随之增加。有大量企业非常想部署的 workflow（工作流），目前唯一的限制其实就是 token 的成本。尤其对 startup ecosystem（初创生态）来说，open labs 和 closed labs 的联合突破，让大量价值能够沉淀到这一层：它可以利用多种不同模型，为客户完成完整任务。这种模型与方法的多样性，意味着 applied AI layer（应用型 AI 层）可以针对自身 workflow 调优模型，并把智能合理路由到合适的位置。对所有人来说，这都是一次巨大的胜利。
Here’s another awesome use-case for what we can now do with our unstructured data because of AI agents. Box now works with Databricks so you can take structured data from enterprise content (like contracts, financial document, supply chain data) and connect that data into Databricks. This means that I can now query large document datasets without moving or reprocessing that content. And you can connect the data with any other system, like your ERP data, CRM, or product analytics. This opens up a ton of new use-cases for enterprise content. All possible because of headless software and agents.
这是另一个很棒的用例，展示了因为 AI agents（AI 智能体），我们现在能够如何利用非结构化数据。Box 现在已经与 Databricks 打通，因此你可以从 enterprise content（企业内容）中提取 structured data（结构化数据）——比如合同、财务文件、供应链数据——并把这些数据连接到 Databricks。这意味着我现在可以在不迁移或重新处理这些内容的情况下，直接查询大型文档数据集。而且你还可以把这些数据连接到任何其他系统，比如你的 ERP 数据、CRM，或产品分析系统。这为 enterprise content 打开了大量新的 use-case。所有这些之所以成为可能，都要归功于 headless software 和 agents。