a16z 合伙人 Martin Casado 谈 Jev:实验室在做会说话的存在,软件需要的是会做选择的模型
Martin Casado on why the labs missed Jev: they're building beings that speak, and software needed a ...
a16z 的 Martin Casado 聊了个有意思的观点:实验室都在造会聊天的存在,但软件其实需要一个从选项里挑答案的模型,Jev 就火了。
a16z 合伙人 Martin Casado 在与 Box CEO Aaron Levie、Steven Sinofsky 的对谈中解释为何各大实验室错过了 Jev 这类模型。他指出 LLM 是文本进文本出,源自聊天场景,过去几年把这种生成文本的模型硬塞进传统程序一直很别扭。Jev 的思路是让模型从给定选项中做选择,速度更快、成本更低,且因为可以针对性训练而准确率更高,Casado 称这可能是自 ChatGPT 以来采用速度最快的 AI 模型。三人还讨论了 2028 年 AI 选举话题、智能体对权限与安全体系的冲击,以及 GDPR 式 AI 监管的风险。
Martin Casado on why the labs missed Jev: they're building beings that speak, and software needed a ...
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 Your browser does not support the video tag. 🔗 View on Twitter a16z @a16z Box CEO Aaron Levie, Steven Sinofsky, and Martin Casado join Erik Torenberg to discuss "We Must Pace the Frontier," the upcoming AI election in 2028, and Jev: They argue that most of today's AI regulation debate is happening before anyone has defined the risks being regulated. Every prior wave, from computer viruses to aviation, built its safety standards after learning how the technology actually failed. Then it gets concrete. Agents don't get tired, run at enormous scale, and probe systems in ways employees never could, which may mean rethinking permissions, authentication, and the security stack itself. They close on why AI innovation may increasingly happen outside the frontier labs, in the software built around the models. 00:50 "We Must Pace the Frontier" 03:50 Do the labs believe their own x-risk talk? 06:49 If it's existential, nationalize it 11:32 "You're asking us to regulate you?" 12:08 2028 as the AI election 18:50 Tech never learned to navigate regulation 25:50 The law that came from one 1983 hack 28:39 Noam Brown's heat exfiltration idea 32:30 Cold War covert channel stories 34:35 Agent swarms look like a DoS attack 41:25 Sinofsky's fear: GDPR for AI 46:20 No jets if the FAA started in 1910 48:38 Jev: decision engines vs. chatbots 53:01 Labs build beings, software needs tools YouTube: youtu.be/TLJNJDf2XGo @levie @stevesi @martin_casado @eriktorenberg Your browser does not support the video tag. 🔗 View on Twitter 🔗 View Quoted Tweet 💬 5 🔄 4 ❤️ 16 👀 6050 📊 5 ⚡