The two types of Agentic Engineering, and their teams

About this episode

The episode discusses the impact of AI tools on app development and team dynamics in engineering.

We return from a break to discuss the effects of an avalanche of app making due to Claude and Codex, including “Camp,” an experiment by Nabeel in native multiplayer AI-assisted group work beyond shareable outputs. We cover: In order to be a founder leading AI transformation do you need to lead by example? Conductor’s viral “prompt feature requests” workflow, reality of one-shot apps versus iterative prompting, how teams may use less open-source, Gemini's comparative strengths, and what does it mean when the engineering pod optimal size has moved from six to two. We end by discussing Granola’s MCP and why data moats are fragile, favoring best interfaces and customer-centric access. 00:00 Divergent Paths: Two types of Engineering post Claude 4.5 00:00 Introduction: The New Reality of Coding 00:21 Building Camp: Multiplayer Knowledge Work 03:12 Open Source in the Age of Models 08:56 The Recommendation Problem: From Average to Expert 15:29 Why Gemini Works for Personalization 18:32 Submit a Prompt: Conductor's Product Innovation 21:50 Two types of Engineering: Automatic vs Iterative 32:08 Rethinking Team Structure: From Six to Two 35:01 Can you AI transform without living it…

People in this episode

Hosts: Fraser Kelton, Nabeel Hyatt

Topics covered

• AI transformation
• multiplayer AI-assisted work
• open-source software
• team structure
• data moats
• engineering types

Keywords

• AI
• engineering
• team structure
• open-source
• data moats
• multiplayer
• Codex
• Claude
• Gemini

Mentioned in this episode

Organizations: Conductor

Products: Claude, Codex, Camp, Gemini, Granola

Books & works: MCP

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