Accelerating open source development with AI

In many open source communities, there’s a fair amount of skepticism around the use of generative AI (gen AI) tools for contribution and development. There are valid reasons for concern. Our goal in this article, and in Red Hat's own practice, is to address those concerns directly and not dismiss them. Our answers aren't just advice for others—they enable our own engineers, most of whom are open source contributors as well.  

We'll share with you the guidelines we've established for Red Hat engineers, based on our use of open source principles in practice. But first, we'd like to put the current wave of new tools into context.

A little historical context

For the last 4 decades, we've been regularly implementing new and improved tools and processes for software development. You name it: Compilers, version control systems, IDEs, virtual machines (both kinds), cloud instances, agile development, containers, configuration management, and automated testing. Every set of tools was once new, and many of them triggered heated arguments about authorship, quality, and legitimacy. There was a time when both compiler flags and auto-complete in IDEs were hot-button issues.  

AI-based development tooling is no different. Nor should it be. Over time, we’ll find that AI tools improve our development lives tremendously in some areas and not at all in others, and adoption will proceed accordingly. We use tools to solve problems in open source, and the new tools will help us solve old problems while discovering new ones.

If there’s a core problem in the world of open source, it can be expressed as, "too many projects, not enough maintainers." Today's project leader needs to do more than ever: faster releases, quicker security updates, secure software supply chains, CI/CD, regulatory compliance, and large-scale contributor management. These expectations are not sustainable without better tooling that helps maintainers to do more with less effort. Through principled use of AI, Red Hat believes that we can build the next generation of developer tools to meet this challenge.

Principles of AI adoption in open source

In order for the new tools to benefit open source, we need to adhere to the open source ethos that has built Red Hat and our industry. Accordingly, Red Hat has developed guidelines for AI-based open source contribution for our staff that are based on 3 principles:

1. Innovate responsibly
2. Be transparent
3. Respect the community

Innovating responsibly

Regardless of whether they’re using an AI tool, an IDE, a pair programming session's output, or any other method of producing code and docs, each contributor is fully responsible for what they contribute. The individual contributor is the human-in-the-loop who vouches for the quality, security, and compliance of the contribution. Contributors should understand the AI-assisted code just as if they wrote it entirely themselves. They should also be able to explain what it does, how it interacts with other code in the project, and why the change is necessary. We don’t see AI as a replacement for developers. The goal is to automate tedious tasks to free them up for complex, creative problem solving. We believe in a future where developers are amplified, not automated.

Our principle of human accountability reframes AI as a powerful assistant and tutor, not a replacement. A newcomer can use it to understand complex boilerplate and learn best practices, allowing them to focus on the core logic of their contribution while making fewer mistakes. Senior contributors can use new tools to perform more efficient and thorough review and testing. The responsibility remains with the people—senior members must mentor the contributor, not just the code, and junior members must be accountable for what they submit and demonstrate a willingness to learn.

Being transparent

Openness fosters trust. Marking substantial AI-assisted contributions, such as with an "assisted-by" line in the commit, helps communities develop best practices together and allows for auditing if issues arise. This also permits projects to learn, over time, which AI tools are helpful for their project development and which aren't working for them.  

Marking contributions also helps reviewers evaluate new contributions appropriately. Low-quality AI-generated contributions are a serious problem for projects. Red Hat will continue to work on practices and tools that we'll share with the whole ecosystem as we learn how to better address these challenges.

Respecting the community

Effective collaboration in open source relies on respecting each project's established contribution policies and social norms. Our first responsibility is to understand and engage with a community's chosen process for adopting new technologies like AI—or to help start discussion about creating such a process where one doesn’t exist. In other words, contributing to the conversation rather than attempting to dictate it. 

We know that some projects will welcome new tooling, some will prohibit it, and some will adopt specific policies around marking and acceptable uses. Where we can, Red Hat will help communities develop and adopt policies that help them maintain their community values, health, and quality standards. The key consideration is for projects to be able to use AI tools in a way that works for them.

Innovation in action at Red Hat

Our use of AI-powered automation for maintaining Red Hat Enterprise Linux (RHEL) packages is a real-world example of innovating responsibly. As detailed in this blog post by Laura Barcziová, building a reliable production system required a deep focus on accountability. The engineering team built in critical safeguards, such as dry-run modes and detailed tracing, so that a human can always understand and audit the AI's decisions. This focus on building for reliability and enabling human oversight is key to responsible innovation.

The Fedora Project's AI-Assisted Contributions Policy process is a powerful example of transparency and respect for community governance. Developed through extensive public debate, it requires accountability and disclosure, serving as a model for how open source projects can create their own clear, pragmatic guidelines for AI.

Open source is about principled innovation

Red Hat believes that AI offers tremendous opportunities for open source projects and contributors. We are committed to evolving our ecosystem in a way that preserves key open source principles. This commitment is rooted in a simple truth—our entire product portfolio is built on the innovation happening in upstream open source projects. The health, vibrancy, and productivity of these contributor communities are not just a priority, but are the very foundation of our success.

Our product strategy reflects this commitment—from delivering an enterprise-grade AI platform with Red Hat AI, to embedding AI capabilities across our entire portfolio, to sharing our own process innovations and discoveries that we use to improve quality and security.

This is a collaborative process, and we are approaching it with transparency. We’re tackling longstanding problems in open source that are bigger than Red Hat. We invite you to join us on this journey as we work with upstream communities to build the tools, define the standards, and shape the future of software development together.