A conversation with Quentin Anthony, VP of AI Engineering at Zyphra, from AMD's Advancing AI conference covers how frontier model engineers actually use AI coding tools. Key insights: AI models struggle with out-of-distribution tasks like GPU kernels because they lack training data; effective AI use requires deep domain expertise to steer and verify outputs; 'tokenmaxxing' (treating token spend as a proxy for AI progress) is a Goodhart's Law problem. Anthony advocates for verifiable task assignment and active human oversight rather than passive lever-pulling. Supporting data from LinearB shows agentic PRs reach production at 37–79% depending on team quality, with the gap driven by ownership and accountability rather than technical factors.
Table of contents
Why the hardware choice was a software decisionHiring for first principlesWhere the models breakWhat comes next, and why developers stop seeing itThe carpenter and the hammerWhether tokenmaxxing or tokenminimizing, you’re measuring the wrong thingThe scoreboard problemHow LinearB helps Kraken find hidden bottlenecks across thousands of engineers | Nik SudanActivity is not impact969 Impressions1 Comment