a16z 合伙人 Martin Casado 谈 AI 能否替代 VC：分析很强，关系拿不下
Jad Naous of @grepr_ai asks a16z's Martin Casado if AI can do a VC's job: Martin: "My model for VC ...
a16z 的 Martin Casado 自己承认 AI 分析比 VC 强，但 VC 的活是抢 deal 和搞关系，这期聊得挺坦白，还带出 Grepr 砍 90% 日志成本的玩法。
a16z 合伙人 Martin Casado 在与 Grepr 创始人 Jad Naous 的对谈中说，AI 做出的分析不输他合作过的任何天才分析师，但这更暴露了 VC 分析能力的水分。他认为 VC 的核心是拿钱、赢交易和投后增值，这些依赖人际关系，AI 难以替代。对谈还提到 Jad 的 10 人公司把原本 6 个月的数据湖功能压到 10 天完成，团队工程师差距放大 10 倍。Grepr 本身每天可扫描 1PB 日志，把可观测性成本削减约 90%。Casado 认为 AI 运维比写代码更难，每行代码都会翻倍系统行为路径，自动驾驶花了 20 年和 1700 亿美元才接近落地。
Jad Naous of @grepr_ai asks a16z's Martin Casado if AI can do a VC's job: Martin: "My model for VC ...
Jad Naous of @grepr_ai asks a16z's Martin Casado if AI can do a VC's job: Martin: "My model for VC is it's very relationship driven. I just have a belief that markets are actually pretty efficient. It's not about finding the company that nobody else finds. I think people all find the right company." "I think the key is access. It's raising money, it's ability to win deals, and it's ability to add value after that happens." "A lot of that is stuff that startups just can't naturally do. So the way that I think about venture, you kind of have to be a human being to do, because that's the actual job." "That said, I will say I have seen an analysis done by AI that are as good or better than any genius I've ever worked with. So for the analysis piece, they're great." "But I think this is more an indictment on VCs' ability to analyze than it is how powerful the models are. I just think it's all a kabuki show and bullshit." "The markets are very, very efficient. I just feel like my answer is almost a technicality." @jadtnaous @martin_casado Your browser does not support the video tag. 🔗 View on Twitter a16z @a16z Engineering leaders told Grepr founder and CEO Jad Naous they spend 5 to 20% of their infrastructure budget on observability, and it's buying less and less reliability. Jad, a former a16z partner, joins a16z's Martin Casado to discuss whether AI can run production systems as well as it writes code. Grepr sits in a company's log stream, can scan a petabyte of logs a day, cuts observability costs by about 90%, and is building AI agents that use those signals to troubleshoot production issues. How AI is changing engineering at Jad's 10-person company: - A data lake feature that once took one engineer about six months took Jad 10 days - The gap between okay and great engineers on his team "just multiplied 10X" - He runs 3-4 agent sessions at once, with a "tech lead" agent reviewing every PR Why running live systems is a harder problem for AI: - Jad: AI can almost tune databases, but can't yet keep servers running on its own - Martin: every bit of code doubles the ways a program can behave, which is why humans still own systems - The real world is messier than AI's training data, self-driving took 20 years and $170B 0:55 From Stanford to a16z to Grepr 7:00 Why observability spend stopped paying off 11:00 From autocomplete to AI that trains engineers 13:10 Why AI widens the engineer skill gap 14:55 Syntax vs semantics 18:00 Inside Jad's AI workflow 22:55 Six months of work in 10 days 24:50 Can AI reason about how systems really run? 30:10 Why AI SRE tools start in the wrong place 32:30 Can AI run a distributed cluster yet? 37:35 Why engineers buy extra capacity over predicting 39:15 How AI could trace failures in production 43:45 Why AI for ops is harder than for code 49:55 Can AI do a VC's job? 52:05 Why self-driving took 20 years and $170B 57:05 Customers skipped the AI pitch for the 90% savings YouTube: youtu.be/blkIGLlZePg @jadtnaous @grepr_ai @martin_casado Your browser does not support the video tag. 🔗 View on Twitter 🔗 View Quoted Tweet 💬 1 🔄 1 ❤️ 6 👀 2761 📊 1 ⚡
