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OpenAI Codex lead on the new shape of product work | Andrew Ambrosino

Lenny's PodcastJune 28, 20261h 9m
Topics50
Codex Usage at OpenAI0:00Why AI Struggles with Design0:30The New Shape of Product Work0:45Defining Taste6:30Prototypes vs Documents7:00The Primal Mark Concept8:30Mediums No Longer Signal Process Stage9:00Taste in Practice10:30Why AI Is Not Good at Design12:00Culture and Novelty in Design14:00The Abstraction Layer Between Design and Code14:30Codex as a New Paradigm16:00The Design Process Is Not Dead17:00Role Collapse at Codex21:30Dogfooding Loop23:00Eliminating Traditional Roles24:00Boundaries and Skill Specialization25:30Easier Role Switching and Reduced Gatekeeping26:30Codex Team Structure27:30Large Teams with ICs28:30Zone Defense for Product Work29:00High Agency, High Taste People30:30ICs and Managers Both Managing31:00Planning at High Velocity32:00Prototyping and Letting Features Bake33:00Codex App Timing Example33:45Building Things That Are Not Yet Working35:30Historical Thread of the Same Feature36:00Being Too AGI-Pilled36:45Balancing Optimization and Exploration38:30Supervised vs Unsupervised AI-Written Code40:00Using Codex for Personal Work42:00Aligning Tool Usage with Job Growth43:30Daily Brief from Slack Channels44:30Setting Up Automations45:00The Chatbot Shape Problem46:00Building an Email Filter App46:30Computer Use and Browser Integration47:25Personal Workflows and Memory Features48:00Running SaaS Apps Inside Codex49:30Browser Evolution at OpenAI49:31Technical Challenges of the App Shape51:00Vision for Codex52:01Unexpected Cross-Disciplinary Usage53:00The Developer Tool vs General Knowledge Work Tool Question54:00Home Base Philosophy55:31The Premiere Pro Extension Story57:02Fail Corner59:31Lightning Round1:02:01Additional Commentary1:07:01
In a Nutshell

The core shift in product work is that implementation has become cheap, so taste, curation, and steering now determine outcomes—teams generate dozens of uncoordinated builds, and the bottleneck is identifying which attempts are worth folding in. AI accelerates coding far more than design because design feedback loops require human taste and novelty that current models lack, leaving an abstraction layer between visual output and coherent system design out of reach. The highest-value people combine high agency with strong taste to shepherd ideas from concept to production, and the winning organizations design processes that support both bottom-up exploration and ongoing optimization rather than trying to do both at once.

AI-Generated Notes

These notes were generated by AI and may contain inaccuracies.

90% of people at OpenAI use Codex. Not 90% of engineers. That's 90% of the entire company. The goal for Codex is to create the best desktop app that has ever existed. The quality bar had to be so high that there was never hesitation opening the app to do the next thing, making it the natural choice, similar to how people open a browser tab.

Since January, Codex usage has grown 6x with over 5 million weekly active users. Internally at OpenAI, nearly 100% of employees use Codex weekly, and that includes non-engineers.

Design is harder to grade because the human aspect of taste is part of the feedback mechanism needed, which remains out of reach with current technology.

Everybody at OpenAI is very agentic, has great ideas, and everybody's building everything. The implementation is actually not the expensive part anymore. It's taste.

The product process has inverted. Previously, research, ideation, and prototyping came before implementation because implementation was expensive, so teams tried to derisk everything upfront through documents, research, and prototypes. Now that has changed completely.

There are 90 different uncoordinated teams implementing and trying different approaches to the same feature. The curation process has become critical: of those 90 attempts, what is good about them, what should be folded into other aspects, how should they be framed, should they be part of another feature, how many segments should be in the toggle.

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