Become an Epic Product EngineerThe Future of the GitHub Product
If AI handed you back hours of implementation time and you're not sure where to spend it, this episode is for you. Kent talks with Jay Parikh - Executive Vice President of CoreAI at Microsoft - about putting that time into customers, ideas, and outcomes instead of more output. They cover where the line between engineer and product manager is (and isn't), why small teams win the learning loop, activity metrics vs outcome metrics, tracking focus time and toil, capacity planning at GitHub scale, and why Copilot Fusion means you shouldn't have to pick a model. (00:00) - Meet Jay Parikh and Microsoft CoreAI (01:16) - Where software engineers fit now (03:33) - Spend saved time on outcomes and customers (05:12) - Product engineer vs product manager (06:47) - Small teams, faster learning loops (08:30) - Capacity planning at GitHub scale (11:41) - Activity metrics vs outcome metrics (14:38) - Tracking focus time and toil (15:44) - Copilot Fusion: stop picking models (18:32) - Agents that manage the project (19:00) - Homework: raise your ambition, measure outcomes Jay Parikh leads CoreAI at Microsoft, the organization building the tools, platform, and infrastructure for AI-native developers, including GitHub. Kent caught up with him the day of his GitHub Universe keynote. The through-line is outcomes over output. When agents take on more of the implementation and verification, Jay wants that time to go into ideation, creation, collaboration, and "picking right" - and then into checking whether the feature actually moved the business. The line between software engineer and product manager is situational, not fixed, but getting closer to the customer is all upside for engineers. They talk about small teams that traverse a faster learning loop than competitors, why activity metrics (lines of code, PR counts) were useful for getting people moving but aren't the goal, dividing PR velocity by incident rate, measuring focus time vs unfocused time and toil across CoreAI, capacity planning when volume per person keeps climbing, and Copilot Fusion and Copilot projects: express your intent and budget, and let the system orchestrate the models and agents. Homework: whatever level of ambition you have, raise it. Then define explicit outcome metrics (leading and lagging) and wire them into your dashboards and your agents. Homework Whatever level of ambition you have with these tools, raise it: try something bigger, bolder, and faster than you think you're ready for. Try different tools and different ways of doing the work to find the edge. Define explicit outcome metrics for what you're building, and know which are leading and which are lagging. Wire those outcome metrics into your dashboards and your agents so the whole learning loop runs faster. Resources GitHub Universe 2026: Evolving every layer of the stack (Jay Parikh) Copilot Fusion docs Copilot projects docs Jay on GitHub Jay on LinkedIn Guest: Jay Parikh Company: Microsoft GitHub: @jayparikh Host: Kent C. Dodds Website: kentcdodds.com 𝕏: @kentcdodds GitHub: @kentcdodds YouTube: kentcdodds-plus Podcast: epicproduct.engineer
