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 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 anything * Why 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 worke
