Introduction: A Race Against Time
Last November, the tech world witnessed the launch of the Sora Android app, OpenAI's tool capable of turning text prompts into vivid videos. The success was instant: hitting #1 in the Play Store with over a million videos generated in the first 24 hours. But the real technological miracle lies not just in the final product, but in the process that created it.
Behind this launch lies a story of extreme efficiency: the production version was built in just 28 days. Not by an army of developers, but by a lean team supported by Codex, using an early version of the GPT-5.1-Codex model. This article analyzes how AI is redefining Sora Android development, challenging software engineering conventions.
Context: Defying Brooks' Law
While Sora was exploding on iOS, the Android version was still stuck at an embryonic internal prototype, despite the pressure of a mounting number of pre-registrations. The traditional response to a looming deadline is to add human resources. However, Fred Brooks, a pioneer in computer architecture, famously warned that "adding more people to a late software project makes it later."
OpenAI chose to embrace this insight rather than fight it. Instead of bloating the team, they assembled a strong squad of four engineers, equipping them with Codex to multiply individual impact. The result? An internal build in 18 days and a global launch 10 days later, maintaining a 99.9% crash-free rate.
"Adding more people to a late software project makes it later."
Fred Brooks, Computer Architect
The Strategy: Codex as a Senior Engineer
To maximize the effectiveness of building Sora with Codex, the team treated the AI not as a simple autocomplete tool, but as a new senior engineer requiring onboarding. This approach revealed areas of excellence and critical limitations.
Where Codex Excels
- Understanding Code: Rapidly reads vast codebases, facilitating logic portability between platforms (e.g., Swift to Kotlin).
- Testing: Enthusiastically writes unit tests, ensuring broad coverage to prevent regressions.
- Feedback Loop: Reacts well to CI (Continuous Integration) error logs, proposing targeted fixes.
- Parallel Execution: Allows testing multiple approaches simultaneously, treating code as "disposable" to find the best solution.
Where Human Guidance is Needed
Left on its own, Codex lacks long-term architectural judgment. It tends to make things work "now" rather than cleanly. To mitigate this, the team used AGENT.md and AGENTS.md files to instruct the AI on formatting standards, best practices, and preferred architecture.
Practical Approach: Plan Before You Code
One of the key learnings was changing the workflow. Instead of asking "Build this feature," the team first asked Codex to analyze the existing system and propose an implementation plan.
- Analysis: Codex reads relevant files and summarizes how the feature works.
- Correction: Engineers refine the AI's understanding (e.g., correcting wrong abstractions).
- Planning: A miniature "design doc" is created.
- Execution: Codex implements the code following the approved plan.
This planning loop allowed Codex to run "unsupervised" for long periods, reducing time humans spent writing boilerplate code and shifting focus to high-level decisions.
Cross-Platform Development via Translation
Instead of using hybrid frameworks like React Native, the team used Codex to "translate" logic. Since data models and business logic are portable, Codex converted the iOS implementation (Swift) into Android (Kotlin) while preserving semantics. Providing concrete examples from the iOS repo allowed the AI to work with context, avoiding hallucinations or non-functional code.
Conclusion
OpenAI's experience demonstrates that AI does not reduce the need for rigor in software engineering; it increases it. Future engineers will need to excel in system understanding and collaboration with AI agents. Codex allowed the team to focus on creating an excellent product, delegating the code "grunt work."
For official technical details, you can check the original post on the OpenAI blog.
FAQ: Common Questions about Sora and Codex
How long did it take to develop Sora for Android?
The OpenAI team took just 28 days to take the app from prototype to global launch, using a small team of 4 engineers supported by Codex.
Which AI model was used to write the code?
An early version of the GPT-5.1-Codex model was used, the same agent now accessible to developers via various tools.
How was the transition from iOS to Android handled?
Instead of hybrid frameworks, the team used Sora Android development techniques to translate logic and patterns from the native iOS (Swift) app to the Android (Kotlin) app.
What is Brooks' Law mentioned in the project?
Brooks' Law states that adding manpower to a late software project makes it later. OpenAI avoided this risk by keeping the team small and empowering it with AI.
What was the role of the AGENTS.md files?
These files served to provide Codex with necessary context, style rules, and architectural best practices, acting as an onboarding guide for the AI.