In this episode, a16z GP Martin Casado sits down with Sherwin Wu, Head of Engineering for the OpenAI Platform, to break down how OpenAI organizes its platform across models, pricing, and infrastructure, and how it is shifting from a single general-purpose model to a portfolio of specialized systems, custom fine-tuning options, and node-based agent workflows.

They get into why developers tend to stick with a trusted model family, what builds that trust, and why the industry moved past the idea of one model that can do everything. Sherwin also explains the evolution from prompt engineering to context design and how companies use OpenAI’s fine-tuning and RFT APIs to shape model behavior with their own data.
Highlights from the conversation include:

• How OpenAI balances a horizontal API platform with vertical products like ChatGPT
• The evolution from Codex to the Composer model
• Why usage-based pricing works and where outcome-based pricing breaks
• What the Harmonic Labs and Rockset acquisitions added to OpenAI’s agent work
• Why the new agent builder is deterministic, node based, and not free roaming

Timestamps:
00:00 Introduction
8:36 Horizontal vs vertical OpenAI
12:18 Why you can’t “disintermediate” the model
15:11 People build relationships with models
17:30 Not one AGI model, but many
20:10 Fine-tuning, RFT, and customer data choices
24:44 Prompt engineering isn’t the point anymore
28:06 What an “agent” really is
31:55 How OpenAI thinks about pricing
36:46 Why open-weights don’t kill the API
42:57 Different stacks for text, images, video
45:47 How the agent builder actually works

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