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

There are roughly 100x more people who use code than who can write code.1 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 make the power of software accessible to people who do not write code. In this episode, Akshay joins swyx and Vibhu to unpack the launch of ChatGPT Work, why Codex unexpectedly took off among non-developers inside OpenAI, and the company’s broader plan to bring useful agents from software engineers to knowledge workers and eventually everyone.

We go deep on the shared agent harness behind Codex and ChatGPT Work, why OpenAI brought the experiences together without making them identical, and how persistent computers, artifacts, Sites, plugins, memory, and sub-agents are changing what people can delegate to AI. Akshay explains why some teams are replacing decks and spreadsheets with interactive websites, how agents can gather context across code, Slack, documents, and local files, and what OpenAI learned from personal-agent products like OpenClaw.

Side note: also don’t miss Abhihek’s sandbox track keynote at AIE, which now powers a lot of the sandboxing for ChatGPT Work… and yes was also broken by an unreleased OpenAI model in the recent HuggingFace incident.

Akshay also reflects on how AI is transforming product development itself: why more people will become generalists with a specialty, why ideas and taste become the bottlenecks when almost anyone can build, why LLMs still struggle to generate genuinely grounded new ideas, and why teams must distinguish increased motion from actual progress.

We discuss:

Why Codex unexpectedly took off among non-developers inside OpenAI

Why employees felt like using Codex gave them a new superpower

The product insight that led OpenAI to build ChatGPT Work

Why Codex and ChatGPT Work share the same underlying agent harness

How their UX, Git visibility, artifacts, and sandboxing defaults differ

Why OpenAI merged its agent experiences instead of building separate products

How AI is blurring the boundaries between engineering, design, strategy, and operations

Why OpenAI wants the default model configuration to work for most users

When power users should use deeper reasoning, Ultra, or multi-agent modes

Artifacts, agentic spreadsheets, and creating high-fidelity work products

Why interactive Sites may replace decks and spreadsheets

The challenge of designing a simple interface for an agent that can build almost anythingWhy users should retry tasks that models could not handle three or six months ago

How AI can gather context for performance reviews without replacing human judgment

The OpenAI automation that turns internal Slack and document activity into memes

What reaching ten million ChatGPT Work and Codex users means for the product

How OpenClaw inspired persistent environments, scheduled tasks, and personal agents

Using ChatGPT for financial planning, budgeting, workouts, meals, and household management

The design tradeoffs behind sub-agents and how much of their work users should see

ChatGPT memory, Chronicle, and long-term context

Why AI may make more people generalists with deep specialties

Why ideas and taste become more important when almost anyone can build

Why LLMs still struggle with the instruction “bring me new ideas”

Measuring productivity through quality at-bats instead of commits, tokens, or pull requests

The critical difference between AI-generated motion and meaningful progress

Akshay Nathan

LinkedIn: https://www.linkedin.com/in/akshaynathan/

X: https://x.com/akshaynathan_

Timestamps

00:00:00 Introduction and Bringing the Power of Code to Everyone

00:01:33 Joining OpenAI and Preserving a Startup Culture

00:02:40 What OpenAI Learned from Enterprise AI Adoption

00:05:28 Why OpenAI Built ChatGPT Work

00:07:17 Codex vs. ChatGPT Work and the Shared Agent Harness

00:12:07 Why OpenAI Merged Its Agent Experiences

00:16:24 Models, Reasoning Levels, and Choosing the Right Default

00:20:26 Artifacts, Agentic Spreadsheets, and Model–Product Collaboration

00:24:22 Why Sites Could Replace Decks and Spreadsheets

00:30:08 Designing an Agent That Can Build Almost Anything

00:34:28 From Developer Agents to Knowledge Work—and Everyone

00:36:07 Power-User Advice and AI-Assisted Performance Reviews

00:40:41 OpenAI’s Internal AI Memes and the Ten-Million-User Launch

00:44:39 OpenClaw, Personal Agents, and ChatGPT as an Operating System

00:50:24 Sub-Agents, Ultra Mode, and How Much Control Users Need

00:54:39 ChatGPT Memory, Personalization, and Chronicle

01:00:19 How AI Is Reshaping Product Development and Tech Roles

01:03:15 Ideas, Taste, and Why LLMs Struggle to Generate New Ideas

01:04:42 Measuring Productivity, Quality At-Bats, and Motion vs. Progress

Transcript

Introduction: Akshay Nathan, ChatGPT Work, and the No-Code Arc

Swyx [00:00:00]: We’re here in the studio with Akshay from OpenAI. Welcome.

Akshay Nathan [00:00:07]: Thank you.

Swyx [00:00:08]: And with our trusty co-host, Vibhu. So you recently launched ChatGPT Work. You lead Core Product Engineering. It’s been a long journey, into all this. I find it very interesting that you started with no code or low code, with Walrus and Airtable. And to some extent, ChatGPT Work is like the super app of super apps of, well, here is the ultimate no code. You just write a prompt.

Akshay Nathan [00:00:32]: Yeah. It’s funny how things come, full circle. I think for a long time in my career, I started my career working consumer fintech, but then after that, like, there’s this hypothesis that, the things that we were able to do with code, like, as engineers, like, if we could bring that to many more people in a more, accessible way, then that would be truly magical. We were working on a startup. It’s funny, like, before LLMs, before vision LLMs, on how to do automated testing with AI. It was just kinda jank, back then, but doing what we can, and then worked at Airtable for a while on the same thesis that, like, if we can bring a database or the primitives behind a database to people, that’d be really useful to them. 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. And so, like, I think this launch and a lot of the stuff that we’ve been up to is, like, the manifestation of that.

From Walrus and Airtable to OpenAI

Vibhu [00:01:33]: How was stuff when you joined? So you joined OpenAI 2023. Now we’ve got, so much more stuff, so ChatGPT, Codex app, ChatGPT Work. Have things changed?

Joining OpenAI and What Hasn’t Changed

Akshay Nathan [00:01:44]: I think the more interesting thing is how things haven’t changed. Like, one, I joined I remember when I joined, it was, like, five hundred people. One thing I was worried about was, like, I was looking for something, more early stage and, like, was it gonna feel startup enough? And I joined, and I was like, “This feels even more startup-y than I could ever imagine.” And, like, that really hasn’t changed even till now. I think the, like, level of, like, bottoms-up ambition and, like, the ability of anyone to, like, do anything or have an idea and ship it is really cool. But on the, like, mission side, I think what was really compelling to me is this mission of, bringing frontier intelligence to everyone. Like, building AGI and then bringing it to everyone. And, I think acknowledging back then that, like, that vision is gonna, not be a linear progression. Like, we’re probably gonna, like, try different products and have different things that succeed and don’t. But the vision has stayed the same, and the mission has stayed the same, and we’re starting to see the pieces, fall together, and that’s really cool.

Enterprise Lessons: No One-Size-Fits-All AI

Swyx [00:02:40]: You worked on Enterprise. What A lot of people never touch ChatGPT Enterprise. What is something that you learned from there that you’re bringing into your work now?

Akshay Nathan [00:02:52]: I think how there’s no one-size-fits-all solution in Enterprise. I remember in the early days of ChatGPT Enterprise, like, when we talked to customers and, like, everyone. That was, like, when I think it was a year after ChatGPT was released, and everyone was so excited to bring, AI into their enterprise. And, there were all these teams being stood up. It was, like, the AI deployment team with, like, these enormous budgets. And if you asked anyone, like, what were they excited about? Like, what were they excited about solving? Like, at first, you’d get, like, kinda like the baseline answers of, like, “Yeah, we have all this context and data and all this stuff.” But then if you ask them, like, “What was, like, a discrete use case that, like, they want AI to enable in their workplace?” You get such a different, like, variance, like, explosion of, different types of answers. And it’s interesting, like, you using, like, these models and these products, you have this box, and you can say anything to it, which is the magic. But it’on the flip side, it also means that, like, you don’t know what to do with it. And in Enterprise, I think a big part of that is, like, meeting the users where they are, like, what use case were they trying to solve, and then teaching them how they can use AI to, like, gain leverage there.

Swyx [00:03:56]: Do you meaningfully differentiate that from forward-deployed engineering?

Akshay Nathan [00:04:01]: I think there is the go-to-market side of it and then there is the product side of it. I think you need someone on the product side. And I think, like, however good we get at FDE motion, like, I think at the end of the day, if we have a user who’s, like, looking at their computer or looking at their phone, like, it’s our job in the product to, like, be enabling them and showing them where

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