Grepr CEO Jad Naous：AI 让普通工程师和优秀工程师的差距扩大 10 倍

Jad Naous of @grepr_ai on how AI is widening the gap between okay and great engineers: "Some people...

Jad Naous of @grepr_ai on how AI is widening the gap between okay and great engineers: "Some people were adapting and learning and others weren't as adaptive. And then as we started hiring more people into the team, that disparity between okay engineers and really good engineers just multiplied 10X." "Ultimately you really have to understand the setup. The more complexity that your brain can handle, the more you can break down the tasks and assign them." "My team is still small... In my experience, for the things that we do, that is the case because we build distributed systems and distributed algorithms, and your brain has to handle the complexity because AI doesn't do it." "For the problems that we solve, none of the models can actually think through the things that we do." @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 💬 0 🔄 0 ❤️ 1 👀 378 ⚡