Pretty remarkable what’s happening with open weights AI right now. We’re seeing models achieve SOTA results on specific tasks, and getting close to frontier on some areas of coding and other domains. The more that open weights is able to maintain only a marginal gap from the frontier, instead of a widening gap, the more value that can be created with AI. Incidentally, this is actually fine for the frontier labs as well; if we can lower the cost of an overall task then AI usage goes up in general. You’re still likely using frontier models for planning, orchestration, reviewing, and other parts of work. But this is all very good for the applied layer of AI, which is now in a great position to cost optimize workloads with cheaper models or use tailored open models post-trained for specific tasks to improve performance.
当前 open weights AI 的发展相当令人瞩目。我们看到，一些模型已经在特定任务上取得了 SOTA（state-of-the-art，当前最先进）结果，并且在 coding 等一些领域以及其他领域中，正逐渐接近 frontier（前沿）水平。open weights 与 frontier 之间如果能始终保持只是边际差距，而不是差距不断拉大，那么 AI 所能创造的价值就会越大。顺带一提，这其实对 frontier labs 也同样有利；如果我们能降低一个整体任务的成本，那么 AI 的总体使用量就会上升。你仍然很可能会在 planning、orchestration、reviewing 以及工作的其他环节中使用 frontier models。但这一切都对 AI 的 applied layer（应用层）非常有利，因为它现在处在一个很好的位置：既可以用更便宜的模型来优化 workload（工作负载）成本，也可以使用针对特定任务进行 post-trained（后训练）的定制 open models 来提升性能。