VP of Productivity Engineering at ZoomInfo, running DevEx, CI/CD, Quality, and AI in the SDLC across teams in Israel, the US, and India. 20+ years in engineering, including a decade at Philips Healthcare. Serial Founder and Engineering Leader.
AI adoption should strengthen existing productivity goals, not create a parallel strategy. Learn how to evaluate AI tools through productivity metrics, technical debt, team health, pilots, and rollback triggers.
AI token usage, prompts, seats, and AI-generated code can show adoption -but not productivity. Learn why “tokenmaxxing” is a vanity metric and what engineering leaders should measure instead.
Productivity Engineering improves how engineers work by reducing friction, speeding feedback loops, and automating repetitive tasks. Platform Engineering builds the shared platforms, infrastructure, and self-service capabilities that teams rely on at scale.
Faster code generation does not produce cleaner code. It produces more code. Here is the loop we run instead of debt sprints: scheduled scans, agent-generated pull requests, and two numbers that tell you whether it is actually working.
AI hasn't made engineering productivity unmeasurable. It's made the easy metrics dangerous, inflating commits and lines of code automatically, widening the gap between feeling fast and being fast, and hiding real costs downstream. Here's what breaks, why, and what to measure instead.
