The idea of artificial intelligence (IA) is not new, but recent advances in related technologies have turned it into a tool used by all of us daily. The growing importance and proliferation of AI is both exciting and potentially alarming, as the foundations of many AI platforms and capabilities are essentially black boxes controlled by a small number of powerful corporations.
Large organizations, such as Red Hat, believe that everyone should have the ability to contribute to AIAI innovation should not be restricted to companies that can afford the huge amounts of processing capacity and the data scientists needed to train those great language models (LLMs)
Instead, decades of open source experience for software development and community collaboration enable everyone to contribute to and benefit from AI while helping shape a future that meets our needs.
What is open source?
Although the term“open source” originally refers to a software development methodology, it has expanded to encompass a more general form of work that is open, decentralized, and deeply collaborative.The open source movement now goes far beyond the software world, and the way to be open source it has been embraced by collaborative efforts around the world, including sectors such as science, education, government, manufacturing, healthcare and more.
Open source culture has a few core principles and values that make it effective and meaningful, for example:
- Collaborative participation
- Shared responsibility
- Open exchanges
- Meritocracy and inclusion
- Community-oriented development
- Open collaboration
- Self-organization
- Respect and reciprocity
When open source principles form the basis of collaborative efforts, history shows that incredible things are possible.Some important examples range from the development and proliferation of the Linux as the most powerful and ubiquitous operating system in the world until the emergence and growth of Kubernetes and containers, in addition to the development and expansion of the Internet itself.
Six advantages of open source in the age of AI
There are numerous benefits to developing open source technologies, but six advantages stand out among the others.
1. Increasing the speed of innovation
When technology is developed collaboratively and openly, innovation and discovery can happen much faster, unlike closed organizations and proprietary solutions.
When work is shared openly and others have the ability to create based on it, teams save a huge amount of time and effort because they do not have to start from scratch.New ideas can broaden the projects that came before. This not only saves time and money, but also strengthens the results as more people work together to solve problems, share actionable insights, and review each other's work.
A broader, collaborative community is simply able to achieve more: by fostering people and connecting expertise to solve complex problems and innovate faster and more effectively than small, isolated groups.
2. Democratize access
Open source also democratizes access to new AI technologies.When research, code, and tools are shared openly, it helps eliminate some of the barriers that typically limit access to cutting-edge innovations.
The InstructLab the initiative is a model-independent open source AI project that simplifies the process of contributing skills and knowledge to LLMs.The goal of the effort is to enable anyone to help shape the Generative AI (gen AI), including those that do not have the data science skills and training typically needed.This allows more individuals and organizations to contribute to the training and refinement of LLMs reliably.
3. Enhanced security and privacy
Because open source projects reduce barriers to entry, a larger and more diverse group of contributors is able to help identify and solve potential security challenges present in AI models as they are being developed.
Most of the data and methods used to train and fine-tune AI models are closed and maintained by proprietary logics.Rarely people outside of these organizations are able to gain any insight into how these algorithms work and whether they harbor any potentially dangerous data or inherent biases.
If a model and the data used to train it are open, however, anyone interested can examine them, reducing security risks and minimizing platform biases.In addition, contributors to the open philosophy can create tools and processes to track and audit future model and application development, allowing them to monitor the development of different solutions.
This openness and transparency also engender trust, since users have the possibility to directly examine how their data is being used and processed, so that they can verify that their privacy and data sovereignty are being respected. Moreover, companies can also protect their private, confidential or proprietary information by using open source projects such as InstructLab to create their own adjusted models, over which they maintain strict control.
4. Provides flexibility and freedom of choice
While monolithic, proprietary, and black-box LLMs are what most people see and think about generative AI, we are beginning to see a growing push toward smaller, independent, purpose-built AI models.
These small language models (SLMs) are usually trained on much smaller datasets to give them their basic functionality, and then are further adapted for specific use cases with domain-specific data and knowledge.
These SLMs are significantly more efficient than their larger cousins, and have been shown to perform just as well (if not better) when used for their intended purpose.
And that's largely what the InstructLab project was created for. With it, you can take a smaller open source AI model and expand it with the additional data and training you want.
For example, you can use InstructLab to create a highly tuned, purpose-built customer service chatbot that leverages best practices in the organization. This practice allows you to deliver the best of your customer service experience to everyone, everywhere, in real time.
And, most importantly, it allows you to avoid getting stuck with a vendor and provides flexibility in terms of where and how you implement your AI model and any applications built on it.
5. Enables a vibrant ecosystem
In the open community, “nobody innovates alone“, and this belief has been held since the first months of the founding of the community.
This idea will continue to hold true in the AI era within Red Hat, an open solutions leader, which will provide various open source tools and frameworks in the form of Red Hat AI's, a solution with which partners will generate more value to end customers.
A single vendor cannot deliver everything an organization needs, or even keep up with the current speed of technological evolution.Open-source principles and practices accelerate innovation and enable a vibrant ecosystem by fostering partnerships and collaboration opportunities across projects and industries.
6. Reduce costs
In early 2025, esteemed that the average base salary of a data scientist in the United States is higher than US$ 125,000, with more experienced data scientists being able to earn significantly more.
Obviously, there is a huge and growing demand for data scientists with AI, but few companies have much hope of attracting and retaining the specialized talent they need.
And really 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 huge amount of storage.
Open, smaller, purpose-built models and AI applications are significantly more efficient to build, train, and implement. They not only require a fraction of the computing power of LLMs, projects like InstructLab enable people without specialized skills and experience to actively and effectively contribute to the training and fine-tuning of AI models.
Clearly, the cost savings and flexibility that open source brings to AI development are beneficial for small and medium-sized businesses hoping to achieve a competitive advantage with AI applications can bring.
In summary
To build democratic and open AI, it is crucial to use the open source principles that have enabled cloud computing, the internet, Linux and so many other open, powerful and deeply innovative technologies.
This is the path Red Hat is following to enable AI and other related tools. Everyone should benefit from the development of artificial intelligence, so everyone should be able to help determine and shape its trajectory, and contribute to its development. Collaborative innovation and open source are not essential as unavoidable for the future of the discipline.
