Atlas Browser Development Time Reduced by 67% with Codex

Situation

In 2023, OpenAI was developing Atlas, a new AI-powered web browser. Building a browser is inherently complex, requiring numerous difficult systems and infrastructure components. The team faced the challenge of creating this sophisticated software product with limited engineering resources while maintaining OpenAI's ambitious pace of development.

Actions

• Integrated Codex deeply into the development workflow: The Atlas team became power users of OpenAI's coding agent, using it extensively throughout the development process
• Leveraged Codex for complex systems work: Rather than just using it for simple coding tasks, the team applied Codex to tackle the difficult systems required to build a browser
• Configured Codex for their specific environment: Engineers set up Codex to understand their codebase and development patterns
• Implemented validation processes: The team established ways for Codex to validate its own work, addressing one of the key bottlenecks in AI-assisted development
• Used Codex for cross-platform development: After building for Mac, the team leveraged Codex to help with Windows development, despite Windows being a newer platform for the model

Results

• Dramatic productivity increase: Tasks that previously required 2-3 engineers working for 2-3 weeks could now be completed by a single engineer in one week
• Successful product launch: Atlas was successfully developed and released to users
• Improved model capabilities: The work on Atlas helped make Codex better on Windows, including adding native PowerShell understanding
• Cross-team knowledge transfer: The Atlas team's experience with Codex helped inform other teams at OpenAI about effective usage patterns

Key Lessons

• AI acceleration compounds with complexity: The productivity gains were most significant on complex systems work, not just simple coding tasks
• Validation remains a critical bottleneck: For AI to truly accelerate development, teams need to solve the review and validation problem
• Team configuration matters: The most successful teams invested time in configuring the AI to understand their specific codebase and workflows
• Cross-platform development benefits significantly: AI assistance is particularly valuable when porting applications between platforms
• Technical teams should measure AI impact: Teams should establish clear metrics for how AI is affecting their productivity (e.g., "tasks that took X engineers Y weeks now take Z")
• Power users unlock disproportionate value: The teams that invested in becoming expert users of AI coding tools saw much greater acceleration than casual users