This post is key. The cheaper AI gets, the more opportunity there is for the entire ecosystem - especially including end-customers - to benefit. Everything is bottlenecked by being able to successfully and cost effectively deploy AI in real workloads. Any time we can lower the cost of AI, the total usage goes up. When that happens, the value accrues to all layers of the stack, as noted. The only modification is I think as AI gets more efficient, demand even for the frontier closed models can go up too. You often need the strongest model possible for the orchestration of a task, then you can farm out work to cheaper or more tuned models for the bulk of tokens. This efficiency gain can ironically lead to even more frontier spend in the process because you can cost effectively deploy AI against more tasks. The thing at most risk is margins, which is fair, and in general it seems that intelligence eventually should converge with the margins of the infrastructure stack. Anyway, super fun times to see competition play out to drive down the cost of AI.
这篇帖子很关键。AI 越便宜，整个生态系统——尤其也包括终端客户——就越有机会从中受益。一切都受制于这样一个瓶颈：能否在真实工作负载中成功且具有成本效益地部署 AI。每当我们能够降低 AI 的成本，总使用量就会上升。一旦发生这种情况，正如文中所说，价值就会在整个技术栈的各个层级累积。唯一需要补充的是，我认为随着 AI 变得更高效，即便是前沿的 closed models 需求也可能上升。你经常需要尽可能强的模型来完成一项任务的 orchestration（编排/调度），然后再把大部分 token 的工作分配给更便宜或调校得更好的模型来处理。具有讽刺意味的是，这种效率提升反而可能在流程中带来更多前沿模型支出，因为你可以以具成本效益的方式，把 AI 部署到更多任务上。风险最大的是利润率，这很合理，而且总体来看，intelligence 的利润率最终似乎应该会向基础设施技术栈的利润率收敛。总之，看到竞争展开并推动 AI 成本下降，真是个非常有意思的时代。