Codex from 0 to 10M Users: Building ChatGPT Work — Akshay Nathan, OpenAI
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- latent.space
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- Latent Space
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It lays out how OpenAI is unifying one — persistent compute, memory, sub-agents, plugins — across coding and general knowledge work, which is the clearest public signal yet of how artifact-generating agents will be architected. Useful if you're designing your own runtime or deciding where the ceiling of 'describe the outcome, get the artifact' actually sits.
- agent harness — The scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.
- AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
“There are roughly 100x more people who use code than who can write code.”
“In June, OpenAI said knowledge workers already accounting for roughly 20% of Codex’s user base and growing more than 3x as quickly as developers.”
“Codex, what we learned was that, like, if you give the agent access to this infinitely flexible environment as a computer, it can do really powerful things.”
Akshay Nathan
“I think maybe the trap is like conflating motion and progress. I think motion is much easier now than ever before because of the tooling that we have.”
Akshay Nathan
“But once LLMs came onto the scene, it became clear that, this was the missing piece, like, the missing technology required to, like, bring the magic of code to everyone without them having to know what’s going on underneath the hood.”
Akshay Nathan
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