Join us in the AI Product Kitchen this week as we talk to Nan Yu, Head of Product at Linear. In this episode, we talk about how Linear is transforming product development by turning AI agents into first-class team members and revolutionising how the highest-performing tech teams organise their work. Nan leads product at Linear, the modern issue tracking platform trusted by companies like OpenAI, Scale AI, and Ramp.

Learn about Linear's unique approach to AI product development, from replacing manual taxonomies with intelligent systems to deploying synthetic actors that participate in software workflows just like human colleagues.

In this episode, we dive into:

• Agents as First-Class Team Members: How Linear is building AI agents that can be assigned tasks, review code, and communicate through the platform like human teammates
• The Death of Manual Taxonomy: Why AI will make current approaches to organising backlogs, labelling issues, and categorising product ideas feel "obviously archaic" within five years.
• Diligence on Tap: How AI unlocks true product management by handling the consistent, repetitive work that currently requires large teams, freeing humans to focus on strategy and product taste rather than mundane tasks.
• Low-Cost AI Experimentation: Linear's bottom-up approach to AI development, running multiple prototype experiments simultaneously and scaling the winners based on real usage patterns from their own team.
• Counter-Positioning Against Giants: How Linear successfully competed with industry incumbents like Jira and Asana by focusing obsessively on the individual contributor experience over middle management’s reporting needs.

Here's how to connect with us:

• Find Nan on LinkedIn⁠ and X
• Follow Matt on LinedIn⁠ and ⁠X⁠
• Learn more about Linear
• And make sure to check out Sauce AI⁠ and follow us on LinkedIn⁠!

Timestamps:

00:00 - Introduction and Guest Welcome

00:28 - What Will Feel Archaic in Five Years

01:00 - Building Better Products vs Shipping More Features

02:05 - Decision Making and Product Clarity

03:07 - High-Performing vs Low-Performing Product Teams

04:23 - The Problem with Traditional Backlogs

07:12 - Understanding Taxonomies and Organization Systems

09:03 - The Journey from Old to New Ways of Thinking

10:27 - Customer Behavioral Shifts and On-Ramps

12:42 - Linear's Engagement Strategy: Distinct Issue Creators

15:16 - AI Strategy Part 1: Sharpening Existing Data

17:02 - AI Strategy Part 2: Synthetic Actors and Agents

18:42 - Which Workflows Will AI Take Over First

20:26 - Barriers to AI Adoption in Development

21:57 - Building AI Products: Augmentation vs Replacement

23:58 - Measuring Success with AI Products

25:10 - Business Model Evolution with AI

26:26 - Low-Cost AI Experimentation Process

28:01 - AI Project Examples: Winners and Failures

30:15 - Integrating AI Without Compromising Simplicity

32:32 - ROI and Impact of AI Products

33:17 - Future Impact on Product Team Structure

37:00 - Starting with Customer Problems in AI Development

39:46 - Customer Development and Beta Testing Process

41:27 - Focusing on Individual Contributors vs Buyers

44:15 - Effective Customer Interview Techniques