A surge of openness in AI has defined 2026. Amid calls for both caution and support, prevailing trends point to a future in which models are built and released openly. Recent research offers new insight into the adoption of open AI systems, who benefits, and the role of Open Source across the AI technology stack. The success of open-weight and Open Source AI reflects growing adoption, lower costs, narrowing performance gaps, and a desire for greater control over how AI is designed and implemented.

Recent reports document a considerable increase in the number of open AI projects on popular platforms such as GitHub, Hugging Face, and OpenRouter. The scale of AI development on public platforms is striking, even if public availability does not necessarily imply meaningful openness. Data from the 2026 Stanford AI Index Report shows that over 5 million AI-related projects were available on GitHub as of late 2025, up from a million in 2020. Similarly, the number of models available on HuggingFace has grown to over two million. This upward trend is likely to continue, seeing vocal calls in support of Open Source by a growing number of governments and organizations.

Several interrelated factors are driving the shift toward openness. Open weights and Open Source are attractive to those seeking a greater control surface. Auditability, modifiability, data privacy, security, and the ability to own and control implementation decisions are driving adoption. Additionally, a significant share of the growth in adoption can be explained by a cost-performance calculus that favors open-weight and Open Source AI over closed, proprietary models.

According to data from Mozilla’s inaugural State of Open Source AI report, open models are approaching performance parity on certain tasks, including coding, with the gap narrowing to 3% by some measures. At the same time, inference costs for open models have decreased between six and fifty fold over the last three years.

Mozilla’s report also highlights positive usage trends. The most popular open-weight models saw usage increase by more than 90% month-over-month in June 2026. Yet, while open models account for 20% of usage by token volume, they capture only 4% of revenue. These figures highlight both sides of open-model economics: adopters benefit from lower costs, while maintainers of widely used models may have fewer resources to sustain them. For now, the status quo is subject to imbalances between the benefits of developing AI in the open and the costs of sustaining it.

Open-weights is Opportune; Open Source is Quintessential

Among the ten models with the highest usage in the Mozilla report, half are released under an OSI-approved license. While this does not mean they meet the Open Source AI Definition (OSAID), it suggests that developers are choosing to signal openness in ways that align with community expectations.

Importantly, while open-weights and Open Source AI are often used interchangeably, Open Source AI models provide distinct benefits over open-weights. For adopters and developers, open-weights provide immediate value, enabling adoption outside the sphere of a specific vendor or technology stack.

But weights alone do not provide the access needed to use, study, share, and modify AI systems. If neither data nor code are shared as prescribed by the OSAID, upstream development decisions become the defaults downstream. The centralization of design decisions––and the consequences therein––are heightened by the concentration of downloads and reuse of AI models. According to data from Hugging Face, a small minority of models, about 200 or 0.01% of models, account for nearly half of all downloads on the platform.

For many, limited disclosures within open-weights provide grounds for mistrust, as both what is withheld and the rationale for withholding are unknown. Without the materials needed to make meaningful modifications, downstream users may be unable to fully audit, adapt, improve, or secure AI systems.

In the present, past, and future, Open Source offers a strategic path to digital innovation, competition, and security. Open Source enables collaboration and control over how technologies are developed, governed, and deployed. Looking ahead, there are ample opportunities for Open Source to impact not only AI models but the many software layers between models, developers, and users.

A growing body of evidence suggests that openness is a pillar of the AI ecosystem, presenting a compelling and timely path for AI development. While the tendency toward openness is clear, robust Open Source standards remain essential to ensuring that AI evolves in ways that ensure anyone can use, study, modify, and share these technologies. Through the Open Source AI fellowship, OSI will expand on this work by conducting original research, measuring trends, and building community consensus around what it means for an AI system to carry the Open Source label. We invite you to engage with us and help inform our research and advocacy.