Grepr 创始人 Jad Naous 谈 AI 运维：扫 PB 级日志，砍 90% 可观测性成本
Grep 创始人和 Martin Casado 聊 AI 运维，他们家产品扫日志能省 90% 成本，还讲了 10 人团队怎么用 3-4 个 agent 干活，很实在。
Grepr 创始人 Jad Naous 与 a16z 的 Martin Casado 对谈，指出工程团队把 5 到 20% 的基础设施预算花在可观测性上，回报却越来越低。Grepr 接入公司日志流，每天可扫描 1 PB 日志，把可观测性成本降低约 90%，并在此基础上开发用日志信号排查生产故障的 AI 智能体。在他 10 人的公司里，一个原本需要一名工程师 6 个月的数据湖功能只用 10 天完成，团队内普通与优秀工程师的差距拉大了 10 倍。Naous 同时认为 AI 还无法独立维持服务器运行，自动驾驶花了 20 年和 1700 亿美元的例子说明让 AI 管理真实系统更难。
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 💬 13 🔄 0 ❤️ 40 👀 8238 📊 14 ⚡
