The idea of artificial intelligence (AI) is not new, but recent advancements in related technologies have turned it from something more hypothetical-than-real to a tool that many of us use every day. The growing importance and proliferation of AI is at once exciting and potentially alarming as the foundations of many AI tools are essentially black boxes owned and controlled by a small number of powerful corporations.
At Red Hat we believe that everyone should have the ability to contribute to AI. AI innovation shouldn't be restricted to companies that can afford massive amounts of processing power and the specialist data scientists needed to train these increasingly large large language models (LLMs).
Instead we are applying our decades of open source experience to the development of AI tools and frameworks that will allow everyone to contribute to and benefit from AI while simultaneously helping shape its future and evolution. We believe the open source approach is the only way to achieve the full potential of AI, making it safer, accessible and democratized.
What is open source?
While the term "open source" originally referred to a software development methodology, it has since expanded to encompass a more general way of working that is open, decentralized and deeply collaborative. The open source movement now reaches far beyond the world of software, and the open source way has been embraced by collaborative efforts around the world, including in science, education, government, industry, healthcare and more.
Open source culture has some foundational principles and values that make it as effective and impactful as it is, including:
- Collaborative participation
- Shared responsibility
- Open exchange
- Meritocracy and inclusivity
- Community-oriented development
- Open collaboration
- Self-organization
- Respect and reciprocity
When open source principles form the foundation of collaborative efforts, history has shown that incredible things are possible. Some keystone examples range from the development and proliferation of Linux as the most powerful and ubiquitous operating system in the world, to the emergence and growth of Kubernetes and containers, to the development and expansion of the internet itself.
Open source and AI
So, does the open source way have any relevance in this new era of AI?
In our opinion, the short answer is, "Absolutely, yes." But let's expand on that and dig into why we believe this to be true.
6 advantages of open source in the era of AI
There are more than six advantages we could talk about here, but we'll start with the most important.
1. Increases speed of innovation
Unlike closed organizations and proprietary solutions, when technology is developed collaboratively and in the open, innovation and discovery can happen much more quickly.
When work is shared openly and others have the ability to build upon it, an enormous amount of time and effort is saved by teams not having to start from first principles with every new project. New ideas can build on the projects that came before. Not only does this save time and money, it also strengthens the results as more people work together to solve problems, share insights and review each other's work.
A larger, more collaborative community is simply able to achieve more—more people working together to solve complex problems are able to innovate more quickly and effectively than small, siloed groups working alone.
2. Democratizes access
Open source also democratizes access to these new and emerging AI technologies. When research, code and tools are shared openly, it helps eliminate some of the barriers that typically limit access to leading-edge innovations.
The InstructLab project is a perfect example of this. InstructLab is a model-agnostic open source AI project that simplifies the process of contributing skills and knowledge to LLMs. The project's goal is to enable anyone to help shape generative AI (gen AI), including people who don't have the data science skills and training normally required. This allows more individuals and organizations to contribute to training and refining LLMs in a trustworthy way, which leads to…
3. Improved safety, security and privacy
Since open source projects reduce the barriers to entry, a larger and more diverse group of contributors is able to help identify and resolve potential safety and bias issues in the AI models as they are being developed.
The data and methods used to train and fine-tune closed AI models are proprietary and kept closely guarded. Rarely are outsiders able to get any insight into how these models work, and whether they harbour any potentially dangerous data or inherent biases.
If a model and the data used to train it are open, however, anyone who cares to participate is able to examine them, so potential hazards are reduced and biases can be minimized. Furthermore, open source contributors can create tools and processes to track and audit future model and app development, helping improve and maintain their safety over time.
This openness and transparency also builds trust as users are able to directly examine how their data is being used and processed, so they can verify that their privacy and data sovereignty are being respected.
Finally, companies are able to protect their private, sensitive or otherwise proprietary data by using open source projects like InstructLab to create their own fine-tuned models over which they maintain strict control.
4. Provides flexibility and freedom of choice
While the monolithic, proprietary, black-box LLMs are what most people see and think about when it comes to gen AI, we're starting to see a growing drive towards smaller, independent, purpose-built AI models.
These small language models (SLMs) are generally trained on much smaller data sets to give them their basic functionality, and then those are further tailored for specific use cases using domain-specific data and knowledge.
These SLMs are significantly more efficient than their larger cousins, and have been shown to perform as well (if not better) when used for their intended purpose. They're faster and more efficient to train and deploy, and can be customized and adapted as much as needed.
And this is largely what the InstructLab project is designed to enable. With it, you can take a smaller open AI model—such as one of IBM's open source Granite models—and augment it with whatever additional data and training you like.
For example, you could use InstructLab to create a highly-tuned, purpose-built customer support chat bot that is trained on your internal knowledge and best practices, allowing you to provide the best of your customer service experience to everyone, everywhere, all the time.
And, more importantly, this allows you to avoid vendor lock-in, and provides flexibility in terms of where and how you deploy your AI model and any applications built upon it.
5. Enables a vibrant ecosystem
At Red Hat we believe that "no one innovates alone," and we've held true to this belief since we first launched Red Hat Enterprise Linux (RHEL). A huge part of that belief is based on the incredible value our partners bring not only for Red Hat, but for our customers.
This will continue to be true in the era of AI—where we're providing an array of open source tools and frameworks in the form of Red Hat AI, our partners will be building upon these to bring additional value to our customers. And this is all possible because we operate in the open and in cooperation and collaboration with our upstream projects and other researchers, companies and partners around the world.
No single vendor can provide everything an organization needs, or even hope to keep up with today's speed of technological evolution. Open source principles and practices accelerate innovation and enable a vibrant ecosystem by fostering partnerships and opportunities for collaboration across projects and industries.
6. Reduces costs
In early 2025, it is estimated that the average base salary for a data scientist in the United States is over $125,000, with increasingly senior data scientists able to command significantly more.
Obviously, there is a massive and growing demand for data scientists as AI has exploded in power and popularity, but few companies have much hope of attracting and retaining the specialized talent they need.
And the truly large LLMs are exorbitantly expensive to build, train, maintain and deploy, requiring entire warehouses full of highly optimized (and very expensive) computer equipment, and a massive amount of storage.
Open, smaller, purpose-built models and AI applications are significantly more efficient to build, train and deploy. Not only do they require a fraction of the computing power as the LLMs, projects like InstructLab make it possible for people without specialized skills and experience to actively and effectively contribute to training and fine-tuning AI models.
Clearly, the cost savings and flexibility that open source brings to AI development is beneficial for small and medium-sized enterprises who are hoping to embrace the competitive advantage that AI applications can bring.
Wrap up
We believe that it's essential that we build AI using open principles and with the same community that brought about cloud computing, the internet, Linux and so many other powerful and deeply innovative open technologies.
This is where Red Hat's AI product strategy is heading. We have always embraced the power of open source in our products and projects, and we are doing the same for AI.
Everyone should be able to benefit from AI, and so everyone should be able to help determine and shape its trajectory, and contribute to its development. Open source and collaborative innovation is essential to the future of AI in order that it remains accessible and beneficial for all.
resource
Open the future: An executive’s guide to navigating the era of constant innovation
About the author
Deb Richardson joined Red Hat in 2021 and is a Senior Content Strategist, primarily working on the Red Hat Blog.
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