Don't Outsource Your Thinking: How to Lead in the AI-Native Era - Emilie Schario

<p>What’s the one rule that matters most when AI writes most of your code? Emilie says it comes down to a single line: don’t outsource your thinking.</p><p>In this episode, Emilie Schario, co-founder of Kilo Code, unpacks what it actually means to lead in a world where AI can write most of your code. She explains why Kilo went all-in on being model agnostic, supporting over 500 models instead of betting on a single lab. Emilie shares her core rule for using AI responsibly: never outsource your thinking, especially when it comes to auth, billing, or security. She introduces a metric more companies should be tracking, spend per merged pull request, as a better signal of value than raw AI cost. Emilie also talks about the “killing problem” that comes with software becoming nearly free to ship, and why teams now discover product-market fit after shipping rather than before. She closes with how engineers are shifting from producers to reviewers, and what that means for anyone now managing a team of AI agents instead of just their own code.</p><p>Key topics discussed:</p><ul><li>Why Kilo Code refuses to bet on a single AI model</li><li>The one rule for not losing control to AI agents</li><li>A better way to measure AI spend: cost per merged PR</li><li>Why shipping software is nearly free, and why that’s risky</li><li>How AI-native teams find product-market fit after shipping</li><li>Why senior engineers adapt to AI agents the fastest</li><li>Building a council of reviewers for high-risk code changes</li><li>What the Anaconda acquisition means for Kilo Code</li></ul><p>Timestamps:</p><ul><li>(00:00) Trailer &amp; Intro</li><li>(02:27) What Is Kilo Code All About?</li><li>(03:31) How Do You Avoid Getting Overwhelmed by 500 Plus AI Models?</li><li>(04:34) How Does a Multi-Platform Approach Benefit Developers?</li><li>(07:19) How Does Kilo’s Agent Harness Differ From Other Tools?</li><li>(09:51) Why Will Model Choice Matter Less Over Time?</li><li>(11:20) What Tools Help You Pick the Right Model for Each Task?</li><li>(14:16) What Does the Anaconda Acquisition Mean for Kilo Code?</li><li>(15:43) Why Should You Measure AI Spend Per Merged Pull Request?</li><li>(18:54) How Do You Balance Speed and Control in AI Development?</li><li>(20:28) Why Should Companies Treat AI as Operational Infrastructure?</li><li>(23:31) How Do AI-Native Organizations Run Their Day-to-Day Workflows?</li><li>(27:58) How Will High-Performing AI Organizations Differentiate Themselves in the Fu