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a16z 播客：Martin Casado 谈实验室为何错过 Jev——LLM 生成文本，软件需要的是“选择”
a16z 发布的一段播客片段中，Martin Casado 讨论 Jev 与 LLM 的差异：LLM 是文本进、文本出，过去几年业界试图把这种生成文本的模型塞进传统程序，效果“非常别扭”；而 Jev 的思路是给定一组选项、由模型选出最优解，速度快、成本低，并可针对该任务专门训练以提升准确度。他称这可能是 ChatGPT 以来采用最快的 AI 模型。Casado 还表示，前沿实验室在造“会说话的存在”，而传统软件需要的是工具。同帖另含 AI 监管、2028 大选、Agent 规模化带来的权限与安全栈重构等讨论，均为观点性内容。
AI推荐理由 a16z 播客片段，Martin Casado 提出一个具体观点：LLM 是文本进文本出，而传统软件需要的是“选择”而非“生成”，因此 Jev 这类决策引擎模型在成本、速度和准确度上更契合软件集成，并称其采用速度可能是 ChatGPT 以来最快。信息密度尚可，但属于个人论断与播客转述，无数据或可验证证据支撑“最快采用”的说法；同帖还涉及 AI 监管、Agent 安全等话题，但均为观点性讨论，非官方发布或研究结论。
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正文
Martin Casado on why the labs missed Jev: they're building beings that speak, and software needed a model that chooses.
"LLMs were text in, text out. They generate text, and they came from chat... We've spent the last few years trying to take this thing that spits out text and cram it into a traditional program... It's just been super janky."
"Jev basically said, 'Generating text as output is very expensive, but it's also more complicated than you need... If you give us a set of options, we'll choose the best option. We can do that incredibly fast, incredibly cheaply, but also with much more accuracy because we can train just for this.'"
"This has probably been the fastest adoption of an AI model since ChatGPT. It's been remarkable because we were all primed for this."
"[The labs] are trying to create beings, and beings speak. If you're trying to create God, God speaks in natural languages. This is really about something that's for traditional software."
@martin_casado