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.

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.

Key topics discussed:

• Why Kilo Code refuses to bet on a single AI model
• The one rule for not losing control to AI agents
• A better way to measure AI spend: cost per merged PR
• Why shipping software is nearly free, and why that’s risky
• How AI-native teams find product-market fit after shipping
• Why senior engineers adapt to AI agents the fastest
• Building a council of reviewers for high-risk code changes
• What the Anaconda acquisition means for Kilo Code

Timestamps:

• (00:00) Trailer & Intro
• (02:27) What Is Kilo Code All About?
• (03:31) How Do You Avoid Getting Overwhelmed by 500 Plus AI Models?
• (04:34) How Does a Multi-Platform Approach Benefit Developers?
• (07:19) How Does Kilo’s Agent Harness Differ From Other Tools?
• (09:51) Why Will Model Choice Matter Less Over Time?
• (11:20) What Tools Help You Pick the Right Model for Each Task?
• (14:16) What Does the Anaconda Acquisition Mean for Kilo Code?
• (15:43) Why Should You Measure AI Spend Per Merged Pull Request?
• (18:54) How Do You Balance Speed and Control in AI Development?
• (20:28) Why Should Companies Treat AI as Operational Infrastructure?
• (23:31) How Do AI-Native Organizations Run Their Day-to-Day Workflows?
• (27:58) How Will High-Performing AI Organizations Differentiate Themselves in the Future?
• (30:11) What Is the “Killing Problem” Now That Shipping Software Is Free?
• (32:40) How Do You Shift to a Metrics-First Mindset?
• (34:24) How Is the Product Manager’s Role Changing in AI-Native Teams?
• (37:06) How Do You Balance Rapid Feature Delivery With Product Quality?
• (41:48) How Is the Software Engineer’s Role Evolving in the Age of AI?
• (43:57) What Is the Best Advice for Junior Engineers Entering an AI-Native World?
• (45:51) How Can Developers Handle the Cognitive Load of Code Reviews?
• (49:11) How Should Docs and Code Attribution Evolve With AI Agents?
• (53:06) How Can AI Improve Your Life Outside of Work?
• (55:04) 3 Tech Lead Wisdom

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Emilie Schario’s Bio
Emilie is VP, Engineering at Anaconda and the former cofounder of Kilo (acquired by Anaconda), where she focuses on turning AI investment into real engineering output. She operates at the intersection of AI tooling, engineering org design, and the day-to-day realities of running high-performing teams, helping organizations translate innovation into measurable impact.

Follow Emilie:

• LinkedIn – linkedin.com/in/emilieschario
• Playbooks and Priority newsletter – emilie.substack.com
• Kilo Code – kilo.ai/

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