The Vertical vs. Horizontal AI Strategy
by Logan Kilpatrick • Head of Developer Relations at OpenAI
Logan leads developer relations at OpenAI, supporting millions of developers building on ChatGPT and the API. Previously, he was a Machine Learning Engineer at Apple and advised NASA on open-source policy.
🎙️ Episode Context
Logan Kilpatrick discusses the internal culture at OpenAI that drives their rapid innovation, specifically focusing on 'high agency' and 'urgency.' He shares practical frameworks for prompt engineering, strategies for building defensible AI products in a landscape dominated by foundation models, and the future of AI agents.
Problem It Solves
Helps startups and PMs avoid being 'Sherlocked' or disrupted by future general model updates (like GPT-5).
Framework Overview
Startups should not build general purpose assistants or wrappers that compete with core model capabilities. Instead, focus on deep vertical integrations or novel interfaces that leverage model intelligence for specific workflows.
🧠 Framework Structure
Avoid General Reasoning Competition: ...
Deep Vertical Integration: Build for ...
Interface Innovation: Move beyond the...
When to Use
During product discovery and strategic planning for any AI-native application.
Common Mistakes
Building a thin UI layer over GPT-4 for a general task (e.g., 'Writing Assistant') that will likely be absorbed into the base model.
Real World Example
Harvey (legal AI) is cited as a success because they build custom models and tools specifically for lawyers, which OpenAI's general models won't inherently solve deeply.
If you're going to try to build the next general assistant... it has to be so radically different.
— Logan Kilpatrick