Launch in 28 days: How OpenAI built the Sora app with Codex

OpenAI used its AI model Codex to port the Sora app from iOS to Android in a 28-day sprint. The system acted as a semantic translator, transforming existing Swift logic directly into native Kotlin code and thus massively shortening the usual development cycles.

Table of Contents

Key takeaways

Learn how the strategic use of AI coding tools can reduce months-long porting projects to just a few weeks. These insights will show you how to massively scale quality and speed through semantic translation instead of simple code generation.

Semantic translation beats pure generation. Use your existing code base as ground truth to transfer business logic directly into the target language using AI instead of reinventing features.
Speed as a feature can be measurably increased. The AI workflow increased the output to 600 plus lines per day and reduced the development time from the usual six months to just 28 days.
Test-driven development with AI ensures quality. Port your unit tests to the new platform first and let the AI generate the implementation until all tests are green.
Precise translation prompts replace generic commands. Formulate your inputs such as technical specifications that explicitly define architecture patterns and target libraries to minimize refactoring.
The developer role is radically changing to reviewer. The focus of daily work shifts from 70 percent pure typing to 80 percent validation of the generated architecture and logic.

Dive deep into the details now to transform your own development pipeline step by step.

The 28-day sprint: Anatomy of an AI-driven Android rollout

The pressure was enormous: after the successful iOS launch of Sora, OpenAI faced the classic challenge of not losing the Android community. The challenge was not just to port an app, but to mirror the user experience and performance of the iOS original 1:1 – and to do so in record time in order to secure market dominance and avoid fragmentation.

Normally, teams in this situation turn to cross-platform frameworks such as React Native or Flutter to use a common code base. However, OpenAI strategically decided against this. The chosen tech stack remained consistently native: Swift for iOS and Kotlin for Android. The goal was maximum native performance without the bloat of hybrid solutions. Instead of adding an abstraction layer, they used AI to keep two separate native codebases in sync.

This is where Codex – the model that also powers GitHub Copilot – fundamentally changed the game. It was not used as a mere autocomplete assistant for snippets, but acted as a semantic translator. Codex took complete logic modules from the iOS app and transformed them into functional Kotlin. This was not about linear “token replacement”, but about understanding the business logic, which was then re-articulated in the target language.

The result is speed as a feature. While a traditional native port of this complexity often takes 3 to 6 months and ties up large teams, the Sora rollout for Android was completed in just 28 days. This cycle shatters established developer metrics and proves that the bottleneck in software development is no longer necessarily the writing of code, but the efficiency of translation between platforms.

From coder to reviewer: Codex as architecture translator

A fundamental paradigm shift is taking place here: We are moving away from pure code generation (“write me a login function”) towards semantic translation. In this 28-day sprint, the existing iOS code base acted as absolute ground truth. Instead of reinventing business logic or UI states, the team used Codex to transfer the finished Swift architecture directly into Kotlin. This massively reduces the cognitive load: you no longer have to decide how a feature works, you just have to make sure that the translation fits into the target environment.

Technically, this is impressively complex. Codex not only has to swap syntax, but also map concepts:

Swift Optionals: the AI needs to understand when a Swift optional (?) is a simple Kotlin nullable type and when it requires more complex null-safety checks.
UI declarations: Probably the most difficult part is the translation from SwiftUI to Jetpack Compose. A @State property wrapper in Swift cannot be blindly copied; Codex must construct a remember { mutableStateOf(...) } from it for the state flow to work in Android’s recomposition cycle.

This approach enables parallel workflows that were previously unthinkable. While Codex delivers designs for front-end components and the data layer, you only validate the logic. You go from writer to reviewer. But this is exactly where caution is required: The “human-in-the-loop” necessity is non-negotiable. The tests showed that Codex tends to enforce iOS-specific patterns (such as delegates) too rigidly on Android or to hallucinate libraries that do not exist in the Android ecosystem. So your new core competence is not to type the code, but to mercilessly check the generated output for “Android idioms” and performance traps.

Benchmark: Traditional porting vs. AI coding workflow

If we hold the figures for the 28-day sprint against a traditional porting process, it quickly becomes clear that we are not dealing with an incremental improvement here, but with a quantum leap in development efficiency. The AI workflow changes fundamental KPIs of software development.

Here is a direct comparison of the metrics that OpenAI has observed in this project, as opposed to industry standards for comparable enterprise apps:

KPI (Key Performance Indicator)	Classic development	Codex-supported workflow
Output (LoC / Tag / Dev)	approx. 80 – 150 lines (incl. refactoring)	600 lines (validated code)
Time-to-first-compile	Often days (setup, boilerplate writing)	Hours (generate basic framework)
Developer focus	70% syntax/typing, 30% logic	20% prompting/review, 80% validation
Bug fixing vs. features	40% of time for bugs/regressions	25% fixes (as logic is copied from iOS)

Resource efficiency: The “one pizza team” scaling

The most radical result of this benchmark is the decoupling of output and headcount. Normally, a Sora-level Android app requires a dedicated team of 10-15 Android specialists to deliver in 6 months. With the Codex approach, small “elite squads” (2-3 developers) could generate the output of an entire department.

This eliminates the typical communication overhead (Brooks’s Law: “Adding manpower to a late software project makes it later”). Since the AI takes over the “undifferentiated heavy lifting” – i.e. the tedious writing of standard components and data classes – you scale the skills of your senior developers without increasing the complexity of the team.

Quality assurance: faster, but also more stable?

The biggest concern with AI speed is usually technical debt. Interestingly, the benchmark showed the opposite: stability increased. Why? Because Codex never gets “tired” of writing unit tests.

In traditional benchmarks, tests are often omitted under time pressure. In the AI workflow, Codex was instructed to immediately generate the appropriate JUnit tests in Kotlin for each translated logic block (e.g. video rendering pipelines). This led to a level of test coverage (code coverage) that is often only achieved months after the release in manual sprints. So speed was not at the expense of quality here, but the “translation-first” approach forced greater consistency between iOS and Android behavior.

Guidance for dev teams: the blueprint for AI-native development

If you want to replicate the speed of the Sora team, it’s not enough to simply have a chatbot open. You need a structured process that integrates LLMs deep into your pipeline. Here’s the roadmap for the transformation to AI-native development:

Step 1: Context preparation (Context is King)

Never dump your entire monolith into a prompt window. LLMs work best when they stay focused. You need to modularize your source code (the “ground truth”, e.g. your iOS app). Break down business logic into isolated components.
For larger projects, a local RAG (Retrieval Augmented Generation) approach is recommended: Index your code base so that the AI automatically pulls only the relevant dependencies and interface definitions when a query is made. Only cleanly isolated input delivers clean output.

Step 2: The “translation prompt”

Forget generic commands like “Translate this to Kotlin”. Your prompts must be formulated like technical specifications. Define target libraries and architecture patterns explicitly.
Example prompt:

“Analyze this Swift struct VideoModel. Convert it to a Kotlin data class. Use @SerializedName annotations for Gson, implement Parcelable for state transfer and transform all Swift optionals to null-safe Kotlin types. Keep the immutability.”
The more precise your constraints, the less refactoring work you will have later.

Step 3: Test-driven development (TDD) with AI

Turn the workflow around: Don’t let the AI port the code, but the tests first. Take the unit tests from the original platform and have them translated for the target system. Only when you have a valid test suite in the new language do you let the AI generate the actual implementation until all tests are green. This guarantees functional equivalence and prevents logic errors from creeping in during translation.

Tooling integration

Copy-paste from the browser breaks the flow. You have to use the AI where the code lives.

Cursor: A fork of VS Code that offers AI-native features like “Composer” to apply changes across multiple files at once.
GitHub Copilot Workspace: Allows you to plan and execute complex refactorings or ports based on issues directly in a cloud-based IDE environment.
JetBrains AI Assistant: For those who are deeply rooted in the IntelliJ/Android Studio ecosystem to perform context-sensitive refactorings without switching plugins.

Strategic Implications: Risks, costs and the skill shift

This massive increase in speed doesn’t come without a price tag – and we’re not just talking about the OpenAI bill. Using Codex at this level fundamentally changes the DNA of your development team and requires a rethink in management.

The metamorphosis of the senior dev
The classic “junior-level” coding – writing boilerplate, translating trivial logic or building standard models – is almost completely taken over by AI. This drastically shifts the requirements profile. Coding becomes primarily reviewing and orchestration. Your senior developers will have to type less code, but will need an even deeper understanding of system architecture, performance and edge cases. They go from being craftsmen to site managers who have to ensure that the AI solution not only compiles, but also scales in the long term.

The trap of “technical phantom debt”
There is a real risk in AI-supported sprinting: if teams generate code that they do not fully understand in detail, you are building a ticking time bomb. “Blind copy-paste” leads to code bases that work initially but lose a lot of their maintainability. You have to make sure that every generated block is understood and owned by the team. Code that nobody can explain becomes a nightmare in the first bugfixing cycle.

Security: Your IP in the prompt
Data protection is a critical aspect. If you feed proprietary algorithms or sensitive business logic into an LLM, you need to know exactly where the data is going. For Sora-level projects, the use of enterprise instances with strict zero data retention policies is mandatory. The risk of the company’s own intellectual property (IP) inadvertently contributing to the training of public models is too high without appropriate contracts.

ROI: tokens vs. developer time
Financially, the calculation is usually clear. Even intensive API usage and high token prices for powerful models pale in comparison to the salaries of a specialized mobile team over, say, four months of development. If you use Codex to shorten the Android market launch from six months to 28 days, the ROI is immense – provided you haven’t ceded control of the code quality to the algorithm.

Conclusion: Native quality in fast motion

The Sora case proves it: The classic trade-off between native performance and fast time-to-market no longer exists. We are experiencing the end of linear scaling – you no longer need huge teams to port large apps, but smart processes. AI does not act as a simple code generator here, but as a semantic interpreter that bridges the gap between iOS and Android without the compromises of hybrid frameworks.

However, this does not mean that developers become superfluous. On the contrary: the role is shifting radically from “line-by-line writer” to system architect and quality manager. Those who blindly trust generated code build up technical debt; those who manage AI as a junior partner with a strict review process, however, gain unassailable speed.

Your next steps for the AI transition:

Start the pilot: Choose an isolated module of your iOS app (e.g. a data model or a utility class) and translate it to Kotlin via LLM and “Translation Prompt”.
Tooling check: Integrate AI where the code is created. Tools such as Cursor or GitHub Copilot Workspace are mandatory to avoid context loss through copy-paste.
Safety first: Establish clear guidelines for your prompts. Sensitive business logic only belongs in enterprise environments with a data protection guarantee, not in public chatbots.
Skill upgrade: Train your team in code reviewing and TDD (Test Driven Development). The ability to find errors in generated code is becoming more important than writing boilerplate.

Don’t wait for the next cross-platform marvel – the most efficient connection between your platforms is now your own AI workflow.