OpenAI’s Vision for the AI Super App — Akshay Nathan, OpenAI
About this episode
From the show’s notesFrom building no-code products at Airtable to leading Core Product 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 nondevelopers 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.
Read the show’s notes in full
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 nondevelopers 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, spreadsheets, and traditional reports • Building a playable board game and research environment with 1.7 billion tokens • The challenge of designing a simple interface for an agent that can do almost anything • OpenAI’s path from developer agents to knowledge work and personal productivity • 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 building context from years of interactions • Why AI will make more people generalists while preserving 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
