Codex from 0 to 10M Users: Building ChatGPT Work — Akshay Nathan, OpenAI

July 28, 2026

AI Summary

5 min read

When Akshay Nathan joined OpenAI in 2023, the company had about 500 people and felt more “startupy” than any early-stage company he had ever worked at. That culture of bottoms-up ambition, he says, has not changed. What has changed is the product surface. Nathan now leads the productivity team behind ChatGPT Work, the product that merged Codex’s agentic coding harness into the main ChatGPT experience and hit 10 million users within a month of launch. For Nathan, the launch is a culmination of a career-long thesis: that the magic of code—automation, iteration, flexible tooling—can be brought to everyone, not just developers. But the path from that thesis to a shipped product required hard tradeoffs about simplicity, defaults, and what to show the user.

From Codex to ChatGPT Work: The Merge Decision

Continue reading the full summary in the app — free to try.

Read Full Summary →

Free • No credit card required

Listen to Audio Summary Open in App

Never miss an episode of Latent Space: The AI Engineer Podcast

Get every new episode summarized in your inbox — free, ~5 minutes to read.

No spam. Unsubscribe anytime.

What you'll learn

1 Codex from 0 to 10M Users: Building ChatGPT Work — Akshay Nathan, OpenAI
2 (00:03) **Full-Circle Journey: From No-Code to Super App** - Akshay traces his path from consumer fintech through Walrus and Airtable to OpenAI
3 (02:39) **Enterprise Lessons: No One-Size-Fits-All** - What Akshay learned from ChatGPT Enterprise that shaped ChatGPT Work
4 (05:31) **The Decision to Merge: Codex → Super App** - Why ChatGPT Work exists and the internal insight that triggered the merge
5 (07:16) **Positioning: Productivity, Not Just Enterprise** - Who ChatGPT Work is for and how it differs from Codex
6 (09:39) **Harness Architecture: Same Engine, Different UI** - How the underlying agent harness compares between Codex and Work
7 (12:08) **The Merge Decision: Why Not Separate Apps?** - What they considered and rejected when designing the unified experience

+ Full timestamped outline available in the app

Show Notes

There are roughly 100x more people who use code than who can write code. As code that “just works” becomes easier to generate, this group may be the biggest prize of all — if you can get the agentic interface right.

A key trend we have been tracking over at AINews is the absolute explosion in Codex usage this year, with MAU now up >10x from Jan 2026. Less than two weeks after their July 9th launch, OpenAI said ChatGPT Work and Codex had reached 10M million users combined (as we cover in the pod, Codex now powers ChatGPT Work, so all ChatGPT Work users are now users of the Codex harness, even if they aren’t traditional engineers) — showing the early innings of what happens when you graduate from coding agents to knowledge work agents:

We’ve been calling out how coding agents are “breaking containment” to do everything else this year to power every other part of knowledge work - and it started with the org chart, with a major reorg last month that amounted to two of Codex’s most prominent leaders, Greg and Tibo, taking responsibility over product and ChatGPT specifically, completing a “Superapp” consolidation cycle first discussed in March.

With these updates Codex is no longer just a coding tool. 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. A product dedicated for knowledge workers was being pulled out of the Codex team.

However, knowledge work has a different set of problems and environments than coding. For decades, knowledge work has been scattered across different primitives like documents for writing, spreadsheets for analysis, slide decks for communication, and specialized applications for everything else. ChatGPT Work now enables users to work across every primitive with agents. Instead of opening an application and manually operating its features, the user can describe an outcome and collaborates with an agent that can assemble the tools, context, and artifact needed to reach it.

From building no-code products at Airtable to leading Productivity Engineering at OpenAI, Akshay Nathan has spent much of his career trying to