A survey of more than 700 technology leaders and senior developers across 41 countries found that business leaders are embracing open source AI tools as essential components of their technology stacks. Overall, more than three-quarters of respondents—76%—expect their organizations to increase use of open source AI technologies over the next several years according to the survey, which was conducted by McKinsey, the Mozilla Foundation, and the Patrick J. McGovern Foundation.
The uptake of open source AI by business can be explained by looking at technological and geopolitical trends.
Open source AI innovations are having impact on two key AI technology developments: privacy-centric Edge applications powered by small language models (SLMs) and the emergence of reasoning models with higher inference-time compute, according to the survey report.
Embracing open source is also increasingly part of the political zeitgeist as governments seeks alternatives to U.S. and Chinese closed models. During the global AI Action Summit in Paris in February political leaders expressed concern about concentration of power in the hands of a few AI companies. Some 58 of the countries attending the summit – which together represent one half of the global population – signed a statement committing to promoting AI accessibility to reduce digital divides; ensuring AI is open, inclusive, transparent, ethical, safe, security and trustworthy and avoiding market concentration.
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“The future of AI belongs to ecosystems, not empires,” says Vilas S. Dhar, president, Patrick J. McGovern Foundation, a contributor to the survey report and a participant at the AI Action Summit.
In a May 1 interview with The Innovator Dhar argued that open source enables open innovation. “By democratizing access to innovation ecosystems, open source puts the tools of creation into everyone’s hands, allowing regionally appropriate AI models to develop,” he says. He points to an initiative by Chile’s Ministry of Science, Technology, Knowledge and Innovation and the National AI Center to launch Latam GPT, a large language model designed to understand and represent the history and culture of Latin America. The official launch is scheduled for June, with capabilities comparable to OpenAI’s ChatGPT 3.5.
Latam GPT was developed with the support of experts, institutions, and research centers across Mexico, Argentina, Colombia, Ecuador, the United States, Spain, Peru, and Uruguay. The project’s vision is to democratize access to advanced AI technologies, ensuring every country in the region can develop and implement AI systems in their governments and industries.
“This takes the idea of open source and uses it to build a product that will drive an entire ecosystem,” says Dhar. “Open source can unlock thousands of new approaches by breaking the link between dependence on costly foundation models and innovation in the fast, affordable applications that are built on top of them.”
Sovereignty through collaboration is an idea that is taking hold in Europe as well. “In a world where exponential technologies have shifted the concentration of power and value capture to a handful of non-European companies, mostly through proprietary and lock-in techniques, openness is the only radical and non-conflicting public policy that can reverse the trend immediately,” Yann Lechelle, CEO of Probabl, a spin-off of French research center Inria that has been financing a global open source data science library called scikit-learn, a tool widely used for performing complex AI and machine learning tasks, said in a recent interview with The Innovator. “For the European Commission stimulating, supporting and adopting more open science, open data, open source and open weights, open standards and open hardware , may be the strongest path to transforming the economic landscape. It’s a weapon that can be used for a massive leveling of the playing field.”
Big Tech is, in fact, playing a big role in open source as well. The most common open source AI tools used by 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, according to the survey report.
“I see this as Big Tech recognizing the growing momentum around open source as a path to build more inclusive and participatory ecosystems,” says Dhar. “It also reflects a realization that market dominance will not be about holding on to the AI model but about sustaining and supporting developer communities that build on top,” he says. “More innovation is better for everyone.”
The Advantages of Open Source
Hyperscalers such as Amazon Web Services (AWS), Google Cloud, and Microsoft Azure are releasing industry-specific, cost-efficient SLM models tailored for specialized tasks and distilled into domain-specific tools to power applications for sectors such as manufacturing and finance. But open source developers are also playing an important role in creating these SLMs, enabling the distillation process of general-purpose LLMs with smaller models that can match or even exceed the performance of larger ones, according to the survey report.
Small models enable Edge applications and on-device intelligence for organizations that prioritize latency and/or privacy. Some examples of small-model hubs that distribute open source (and other) models cited in the survey report include the Qualcomm AI Hub, which addresses the needs of Edge AI product OEMs, and Ollama, which offers a framework and tools to deploy open models to the PCs of individual advanced users. The expectation is that hubs like these will add trusted third-party evaluation certification tools, enhancing customer trust and confidence.
The second key trend is the emergence of reasoning models, which employ higher compute during inference time (rather than in their pretraining time) to excel at specific tasks, says the survey report. While the initial wave of reasoning models were proprietary (such as OpenAI’s o1 reasoning model), open source alternatives—including China’s DeepSeek-R1 and a similarly capable model from Alibaba—have quickly followed. Other players are building on and adapting these. The survey report mentions how Perplexity has modified a version of DeepSeek3 to provide more unbiased and accurate information and Smolagents from Hugging Face has created an alternative Deep Research model, challenging offerings from OpenAI and Google DeepMind.
Other open technologies are emerging to help builders optimize and enhance their model-training pipelines and processes. DeepSeek, for example, has continued to offer open source repositories, including parallelism and integration capabilities, for its reasoning models, says the survey report.
While the capabilities of open source models once lagged proprietary ones, base models have improved significantly, says the survey report. And while enterprises may face challenges in tailoring some of the components of reasoning models and the time to value is often longer “the bottom line is that open source offerings now allow model service providers to bring together a full stack of technologies that deliver an effective developer experience, enable modularity, and capture the advantages of community-based development,” says the survey report. The report argues that open source provides organizations greater flexibility and choice to deploy AI either on the Edge or in the Cloud, depending on their privacy, latency, and performance needs. And it says the open source’s operating model and architectural flexibility “can help build more resilient AI systems.”
Navigating The Risks
Amid the benefits and value of open source AI, there are risks, primarily related to security, that could affect their adoption, says the survey report. The most relevant AI risks cited include cybersecurity (62% of respondents), regulatory compliance (54%), and intellectual property (50%).
The survey report recommends four ways businesses can control the risks when implementing an AI model-based system, whether open source or proprietary:
- Guardrails: The establishment of robust guardrails—such as automated content filtering, input/output validation, and human oversight—can help ensure responsible use and secure outputs.
- Third-party evaluations: Conduct regular assessments with standardized benchmarks that allow for certification. During such benchmarking, private evaluations assure that test data sets are kept private from the model.
- Documentation and monitoring: Operationally, a software bill of materials can help track version discrepancies and vulnerabilities by maintaining detailed inventories of open source components. Quantitative risk assessments can assess the severity of vulnerabilities in open source systems.
- Cybersecurity practices: To secure data privacy and system integrity running models in trusted execution environments may help to ensure sensitive data remains encrypted during processing. Incorporating differential privacy and federated learning techniques during training can prevent models from memorizing confidential information. Strong access controls within model repositories, network segmentation between training and inference servers, continuous monitoring of security incidents, and cryptographic hash verification to confirm that models are from trusted repositories can help address both content safety and cybersecurity challenges in production AI environments, the survey report says.
A Foundation For A More Innovative Future
The survey found that many companies are opting for hybrid open source and proprietary systems. Still, the momentum behind open source AI is undeniable, says Mozilla President Mark Surman, a contributor to the survey report. “In just the past year, we’ve seen countless examples proving that community-driven innovation can not only compete with but even outperform proprietary models,” he said in a statement in the report. “The next big bet is building open tools and a stack that make AI truly accessible—like an AI Lego box that anyone can use. If we get this right, open source AI won’t just be an alternative to closed systems. It will be the foundation for a more competitive, creative, and innovative future.”
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