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.

OpenAI's design process is not one-size-fits-all. For core, lasting features, they obsess over details and try hundreds of options. For experimental features where technology is in flux, they embrace speed and "building in public." The key is to be adaptable and not be precious about work that may become obsolete tomorrow.

The Codex team resists optimizing their own workflows. Instead, they use the product to perform those tasks, even when it's not the best tool. This painful dogfooding loop forces them to make the product better at solving real-world process problems, turning internal pain into user value.

OpenAI operates with a "truly bottoms-up" structure because it's impossible to create rigid long-term plans when model capabilities are advancing unpredictably. They aim fuzzily at a 1-year+ horizon but rely on empirical, rapid experimentation for short-term product development, embracing the uncertainty.

Framing OpenAI as a new hyperscaler, rather than a typical product company, rationalizes its numerous experimental launches. Like Google, it's expected that many "bets" will fail, but the strategy is to explore many fronts to find the next major growth engine.

At OpenAI, the development cycle is accelerated by a practice called "vibe coding." Designers and PMs build functional prototypes directly with AI tools like Codex. This visual, interactive method is often faster and more effective for communicating ideas than writing traditional product specifications.

OpenAI runs numerous parallel research projects (expansion), knowing most will fail. When a few show promise, it consolidates talent and resources onto those winners (contraction) to scale them up, before spreading out again to explore the next frontier. This cycle is applied to product as well.

The Codex team's core mandate was to create a tool they loved and used daily for their own development. This intense dogfooding—including building the app on itself—served as the ultimate validation and quality bar before they considered shipping it externally.

The Codex team combines research, product, and engineering, allowing them to solve problems at either the product level or the core model level. This tight integration creates a flywheel where product needs drive research and research breakthroughs are immediately applied to the product.