Open-source AI

Extracted Attributes

• Benefits

  Wider access to AI technology, democratization of technology, accelerates progress in Life Sciences, Agriculture technology, Industrial biotechnology, and the development of cures for Genetic diseases, lower implementation costs, lower maintenance costs, fosters a community-driven approach to innovation

• Definition

  AI systems and their components (datasets, code, model parameters) freely available for use, study, modification, and sharing.

• Key Licenses

  Apache License, MIT License, GNU General Public License

• Risks/Concerns

  Potentially increased risk compared to closed-source AI as bad actors may remove safety protocols, debate about the openness of AI systems (openwashing)

• Core Principles

  Collaborative and transparent approach to AI development

• Potential Advantages

  May have a development advantage over closed-source products and have the potential to overtake them in the marketplace

• Top Reasons for Satisfaction

  Performance and ease of use

• Commonly Used Tools (as of Jan 2025)

  Meta's Llama family, Google's Gemma family

• Adoption Rate (among AI-embracing organizations)

  89% incorporate open source AI somewhere in their infrastructure

Timeline

• Profluent Bio announced and open-sourced OpenCRISPR-1, a revolutionary AI-powered gene-editing tool, aiming to democratize access to gene-editing technology and accelerate progress in life sciences. (Source: External authoritative sources (Web Search))

  2024-05-01

Open-source artificial intelligence

Open-source artificial intelligence is an AI system that is freely available to use, study, modify, and share. These attributes extend to each of the system's components, including datasets, code, and model parameters, promoting a collaborative and transparent approach to AI development. Free and open-source software (FOSS) licenses, such as the Apache License, MIT License, and GNU General Public License, outline the terms under which open-source artificial intelligence can be accessed, modified, and redistributed. The open-source model provides wider access to AI technology, allowing more individuals and organizations to participate in AI research and development. In contrast, closed-source artificial intelligence is proprietary, restricting access to the source code and internal components. Companies often develop closed products in an attempt to keep a competitive advantage in the marketplace. However, some experts suggest that open-source AI tools may have a development advantage over closed-source products and have the potential to overtake them in the marketplace. Popular open-source artificial intelligence project categories include large language models, machine translation tools, and chatbots. For software developers to produce open-source artificial intelligence (AI) resources, they must trust the various other open-source software components they use in its development. Open-source AI software has been speculated to have potentially increased risk compared to closed-source AI as bad actors may remove safety protocols of public models as they wish. Similarly, closed-source AI has also been speculated to have an increased risk compared to open-source AI due to issues of dependence, privacy, opaque algorithms, corporate control and limited availability while potentially slowing beneficial innovation. There also is a debate about the openness of AI systems as openness is differentiated – an article in Nature suggests that some systems presented as open, such as Meta's Llama 3, "offer little more than an API or the ability to download a model subject to distinctly non-open use restrictions". Such software has been criticized as "openwashing" systems that are better understood as closed. There are some works and frameworks that assess the openness of AI systems as well as a new definition by the Open Source Initiative about what constitutes open source AI. Some large language models are released as open-weight, which means that their trained parameters are publicly available, even if the training code and data aren't.

Web Search Results

• 6 of the Best Open-Source AI Tools of 2025 (So Far) - Cake

  ## What is open-source AI? Open-source AI refers to models, frameworks, and infrastructure components whose code, weights, or specifications are freely available to use, modify, and deploy. These tools span every layer of the modern AI stack—from LLMs and training libraries to vector databases, orchestration frameworks, and model serving runtimes. [...] Open-source AI is reshaping how developers and enterprises build intelligent systems—from large language models (LLMs) and retrieval engines to scalable training libraries and orchestration frameworks. In 2025, the landscape has matured rapidly, offering powerful, production-grade tools across every layer of the stack. [...] Open-source AI is accelerating in 2025, with powerful new tools, like LLaMA 4, Gemma 3, and Mixtral-8x22B, enabling scalable, multimodal, and production-ready AI applications. The right tool depends on your goals—whether you’re deploying LLMs, optimizing training pipelines, or building search experiences powered by vector databases and retrieval systems.

• Open Source AI is Transforming the Economy—Here's What the ...

  Looking ahead, open source AI is likely to become foundational in areas like edge computing, where smaller, privacy-preserving models need to run efficiently on local devices. OSAI is also making big inroads in industry-specific applications. In manufacturing, for instance, open models offer the flexibility required to integrate AI into complex operational workflows. And in healthcare—a traditionally conservative and risk-averse field—open models are already matching proprietary ones in [...] First, the adoption of open source AI is already widespread. Nearly all software developers have experimented with open models, and about 63% of companies are actively using them. In fact, among organizations that have embraced AI in any form, a striking 89% incorporate open source AI somewhere in their infrastructure. It’s no longer a fringe approach—it’s becoming the standard. [...] In its latest publication, The Economic and Workforce Impacts of Open Source AI, LF Research describes the nuances of how and to what extent open source AI (OSAI) is impacting the global economy and workforce. By examining existing evidence from industry, academic, and open source research, the authors found important insights on OSAI’s adoption rates, cost effectiveness, innovation-boosting potential, and more. Here are the big takeaways.

• Open source technology in the age of AI - McKinsey

  + Most respondents are satisfied with their open source AI models. The top reasons for satisfaction reported are performance and ease of use. + Open source AI tools lead on cost benefits, while proprietary AI tools have faster time to value. Respondents say that open source AI has lower implementation costs (60 percent) and lower maintenance costs (46 percent). But respondents see faster time to value from proprietary AI tools (48 percent). [...] A new, first-of-its-kind survey of more than 700 technology leaders and senior developers across 41 countries, conducted by McKinsey, the Mozilla Foundation, and the Patrick J. McGovern Foundation, provides the largest and most detailed analysis of how enterprises are thinking about and deploying open source AI in their organizations. The results suggest that leaders are embracing open source tools as essential components of their technology stacks, citing advantages such as high performance, [...] + Organizations are using open source AI tools from familiar players. The most commonly used open source AI tools among enterprises, as of January 2025, are those developed by large technology players, such as Meta with its Llama family and Google with its Gemma family.

• Why open source is critical to the future of AI - Red Hat

  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. [...] 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? [...] 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.

• Top 10 open source LLMs for 2025 - Instaclustr

  Researchers and developers can access the underlying code, training mechanisms, and datasets, enabling them to deeply understand and improve these models. This openness fosters a community-driven approach to innovation, which can lead to rapid advancements not possible with closed source models. This is part of a series of articles about open source AI. ### ROI Calculator ### How much could you save hosting your LLM?