正文 Markdown
One fun benefit of spending so much time with enterprises is you get to see the array of AI implementation strategies that exist in companies right now. Unlike the early innings of cloud where there were really only a couple deployment patterns available -including being limited by only a couple infra vendors existing- AI actually has a much wider range of approaches in the enterprise. If you ask 10 IT leaders what their coding agent strategy looks like, you get at least 5 different answers. If you ask about their end-user productivity agents, some have standardized on ChatGPT or Claude, others offer a choice of multiple solutions, and many have built their own orchestration layers for employees to use any model. For models, we know the big ones, but increasingly enterprises are experimenting with OSS models, or are at least immensely curious about OSS and would jump at the right offering that felt safe. And increasingly there’s a set of vertical models and agents that equally show up in enterprises for a range of tailored use-cases. For accessing data, some enterprises just have agents take on the role of the user, and others are setting up agent users that have their own identities. Some companies are setting up substantial guardrails on what agents can do, and others are putting responsibility on the user. Given this heterogeneity this early on at the start, it means we’re in for years of landscape changes. Anyone who’s predicting ultimate market outcomes already probably will be wrong as nothing has formally settled in yet. Lots of opportunity ahead.