Jev 与杰文斯悖论：agent 架构走向分层
作者认为 Jev 用一次性类型化决策处理分类、路由、打分等低熵任务，浏览器类多步工作流提速 100 倍以上，未来是廉价 System 1 模型加前沿 LLM 的分工。
Jev's popularity is Jevons paradox playing out in AI ecosystem. Jev model is an interesting signal for where agentic AI architecture is heading for both consumer and enterprise use cases. A surprising amount of work inside AI agents and solutions like @atomicworkhq AI Coworkers is low entropy tasks like classify, route, score, pick a tool and check a condition. Yet we often use autoregressive LLMs built to generate language for tasks that only need a decision. Jev's architecture flips that model. For the many workloads, one-pass typed decisions can deliver incredibly lower cost and latency, with some multi-step AI workflows like browser-use showing 100x+ improvements. However, the bigger insight is Jevons paradox. Make intelligence 100x cheaper and faster, and we probably won't use 100x less of it. We'll use it 1,000x more. The future of AI agents and AI workforce will not use use just frontier LLM models for doing everything. Cheap System 1 models like Jev for low-entropy decisions. Frontier LLM models for high-entropy reasoning. Incredible launch from @typesafeai and congrats @CompleteSkeptic for reigniting AI build energy on X.💜🔥