Why OpenAI is merging Codex and ChatGPT and the future of knowledge work | Andrew Ambrosino
OpenAI's Codex lead reveals why 90% of the company uses a developer tool despite it being "actively hostile" to non-engineers—and what this says about the collapse of traditional product roles.
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TLDR
• AI has inverted product development: implementation is now cheap, taste/curation is expensive—leading to 90 uncoordinated prototypes instead of PRDs
• The Codex app would have failed if launched in November vs February (same product, smarter models = different outcome)—so teams now build features that don't work yet and wait for AI to catch up
• Product managers now operate in "zone defense"—spreading out for coverage rather than owning swim lanes, since everyone can build anything
• OpenAI tried building separate tools for different personas but everyone stayed in Codex, revealing the future is a unified AI home base that coordinates all work
• Andrew automates his entire job inside Codex (Slack briefs, release management, status tracking)—the app even builds its own Premiere Pro extensions when needed
In Detail
Andrew Ambrosino describes a fundamental inversion in how products get built at OpenAI. The traditional process assumed implementation was expensive, so you derisk everything upfront through documents and research. Now implementation is free—anyone can build anything—which creates a new problem: 90 people building 90 different prototypes of the same idea, all looking production-ready but actually in early exploration. The bottleneck has shifted from "can we build this" to "should we build this and how should it work"—pure taste and curation.
This shift is forcing role collapse, but not in the simplistic "everyone's a builder now" way. OpenAI uses a "zone defense" model where product managers spread out to provide coverage rather than owning features, since engineers are product-minded and designers write code. Roles are defined by the average of what you do, not rigid boundaries. The dangerous version is companies eliminating product/design entirely and losing decades of discipline-specific knowledge. The healthy version is more overlap with specialists who can work across the stack.
The most striking insight is about timing and model intelligence. The exact same Codex app would have failed in November but succeeded in February—the only difference was model capability. This means teams now build features that don't work yet, let them sit, and retry with each model improvement. OpenAI initially tried building separate tools for different personas (Codex for engineers, ChatGPT for others), but discovered everyone stayed in Codex despite it being "actively hostile" to non-technical users. This revealed the right shape: a unified home base that coordinates work across tools, whether through connectors, computer use, or building its own extensions (like when a videographer used Codex to build a Premiere Pro plugin so Codex could edit videos for him).