At OpenAI, engineers use AI to build ideas instantly. This inverts the traditional product model, shifting the PM's role from upfront planning to evaluating already-built prototypes and deciding which ones to ship, dramatically accelerating development.
Instead of coding prototypes, OpenAI PMs use AI image generation to rapidly create multiple design mockups from a single screenshot and a text prompt. This offers a much faster iteration loop for exploring UI ideas before any code is written.
Codex can create temporary, event-triggered automations. For example, it can monitor for a specific Slack reply, execute a follow-up task like drafting an email, and then automatically delete the automation once its job is complete.
Instead of static documents that become instantly outdated, OpenAI PMs use AI to create a 'living' project overview site. The AI agent continuously scans project Slack channels and integrates updates, ensuring a perpetual source of truth for the team.
The low cost of AI-driven development makes 'disposable software' practical. Teams can build single-use tools on the fly—like a temporary web app to prioritize Slack messages—to solve immediate, specific workflow challenges without long-term overhead.
At OpenAI, teams of just one or two engineers leverage AI agents to own entire product lines. This model reduces human collaboration overhead and empowers engineers to make most micro-decisions autonomously, increasing speed and ownership.
By connecting to tools like Slack, Notion, and Linear, Codex allows OpenAI PMs to synthesize all historical context on a project in minutes. This dramatically reduces ramp-up time when joining new initiatives and enables them to operate with more leverage.
Users often underutilize AI with conservative requests. The key is to aim for outcomes that feel 10 times more ambitious than what you think is feasible. The AI will likely accomplish 90% of it, radically expanding your understanding of its capabilities.
After a productive, iterative conversation with Codex that achieves a desired outcome, you can ask it to analyze that same thread and create a reusable 'skill'. This templatizes the successful workflow, making it easy to replicate consistently.