OpenAI has revealed that it built the Android application for Sora, its AI video generation platform, in just 28 days using Codex, its AI powered coding system. The development timeline offers a glimpse into how AI assisted software engineering is beginning to compress traditional product cycles and change how applications are built and shipped.
According to details shared around the launch, Codex played a central role in accelerating the Android app’s development by assisting with code generation, debugging and iteration. Rather than replacing human developers, the system functioned as a collaborative tool that helped engineers move faster across different stages of the build process. The result was a fully functional mobile application delivered in under a month.
The announcement comes at a time when technology companies are increasingly exploring AI as a way to improve developer productivity. Writing and maintaining mobile applications is typically a resource intensive task, involving multiple layers of design, backend integration, testing and optimisation. By using Codex to handle repetitive or time consuming coding tasks, OpenAI was able to shorten development cycles significantly.
Sora, OpenAI’s text to video generation model, requires substantial engineering effort to translate its capabilities into user facing applications. Building a mobile app involves not only integrating AI services but also ensuring performance, usability and reliability on consumer devices. The rapid development of the Android app suggests that AI assisted coding tools can meaningfully reduce the friction involved in such projects.
Codex is designed to understand natural language instructions and convert them into working code across multiple programming languages. In the case of the Sora Android app, developers reportedly used Codex to generate boilerplate code, refine user interface components and iterate on features quickly. This allowed human engineers to focus more on architectural decisions and product experience.
The 28 day timeline stands out in an industry where mobile app development often takes several months, particularly for products built around advanced AI systems. While OpenAI has not provided a detailed breakdown of how tasks were divided between humans and AI, the example highlights the potential of AI coding assistants to act as force multipliers for small teams.
Industry observers say the development of Sora’s Android app illustrates a broader shift in software engineering workflows. AI tools are increasingly being used not just for experimentation but for production grade development. As these systems improve, they are expected to take on a larger share of routine coding tasks, allowing teams to move faster without proportionally increasing headcount.
The use of Codex also reflects OpenAI’s strategy of dogfooding its own products. By deploying its AI tools internally, the company can test their capabilities in real world scenarios and refine them based on practical feedback. Building a consumer facing app under tight timelines provides a demanding environment for evaluating the effectiveness of AI assisted development.
For developers outside OpenAI, the story has implications for how future software teams may operate. AI coding systems like Codex are increasingly being positioned as everyday tools rather than specialised assistants. As organisations adopt these systems, expectations around development speed and iteration cycles may shift accordingly.
However, experts caution that rapid development does not eliminate the need for oversight and quality control. AI generated code still requires review, testing and validation, particularly in applications that handle user data or rely on complex backend services. The success of AI assisted development depends on how well teams integrate these tools into established engineering practices.
The Sora Android app also highlights how AI can help bridge platform gaps more quickly. Mobile platforms often require separate development efforts, and releasing applications across operating systems can slow down product rollouts. AI assisted coding may help reduce these bottlenecks by accelerating adaptation across platforms.
The broader context is a growing emphasis on developer productivity across the technology sector. As demand for software continues to outpace available talent, companies are turning to AI to close the gap. Tools like Codex are part of a larger ecosystem that includes AI powered testing, documentation and deployment systems.
OpenAI’s experience building the Sora Android app may influence how enterprises view AI assisted development tools. Demonstrating a concrete outcome, such as a production app delivered in 28 days, provides a tangible example of what these systems can achieve. This may encourage wider adoption among organisations looking to optimise engineering workflows.
At the same time, the example raises questions about how development roles may evolve. As AI takes on more coding tasks, the value of human developers may increasingly lie in system design, problem framing and user experience rather than writing code line by line. The shift could reshape skill requirements and team structures over time.
The rapid development of the Sora Android app underscores the pace at which AI tools are being integrated into core software processes. What was once considered experimental is now being applied to real products with compressed timelines. As AI coding systems mature, similar stories may become more common across the industry.
For OpenAI, the project serves both as a product milestone and a demonstration of Codex’s capabilities. By showcasing how AI assisted development can deliver speed without sacrificing functionality, the company is reinforcing its narrative around practical, production ready AI tools.
The 28 day build of Sora’s Android app offers a snapshot of how software development may look in the coming years. AI is not eliminating the need for developers but changing how they work, enabling faster iteration and shorter paths from idea to execution. As organisations adapt to these tools, the boundaries of what can be built quickly are likely to continue expanding.