One of the key architectural questions of the 21st century in business will be how you maximize your corporate IP in the form of decisions, insights, workflow patterns, and best practices in a world where so much intelligence is packed into AI models. One might think these questions could just get bitter lessoned out of existence, but in reality they become even more germane as intelligence becomes more powerful. In a world where any firm also has access to frontier intelligence, understanding how you leverage it uniquely becomes a critical question. That’s why so much value is left to be created between the enterprise and the underlying AI itself. Having evals for your workflows, ensuring that you can route models from different tiers of intelligence, capturing traces in a way that improve your own workflows, and making sure the value of your information compounds as AI gets better all become critical considerations. Which is also why there’s so much opportunity right now in the applied AI layer. The companies that help figure this out for other enterprises will be in the best position to win the next enterprise workloads.
在商业领域，21 世纪一个关键的架构性问题将是：在如此多 intelligence（智能）被封装进 AI models（AI 模型）的世界里，你如何最大化企业自身的 IP，具体体现为决策、洞察、工作流模式和最佳实践。人们可能会以为，这些问题最终会被 bitter lesson 式地“淘汰”掉，但现实是，随着 intelligence 变得更强大，它们反而变得更加切题。在一个任何公司都能获得 frontier intelligence（前沿智能）的世界里，理解你如何以独特方式加以利用，就成了一个关键问题。这也正是为什么，在 enterprise（企业）与底层 AI 本身之间，仍有大量价值有待创造。为你的 workflows（工作流）建立 evals（评测），确保你能够在不同 intelligence 层级的 models（模型）之间进行路由，以能够改进自身 workflows 的方式捕获 traces（追踪记录），并确保随着 AI 变得更强，你的信息价值也能持续复利增长——这些都会成为关键考量。这也是为什么，applied AI（应用层 AI）现在存在如此多的机会。那些能够帮助其他企业解决这些问题的公司，将最有可能赢得下一波 enterprise workloads（企业工作负载）。