{"id":1401,"date":"2026-01-15T11:09:39","date_gmt":"2026-01-15T11:09:39","guid":{"rendered":"https:\/\/simoninstitute.ch\/?p=1401"},"modified":"2026-01-15T13:11:44","modified_gmt":"2026-01-15T13:11:44","slug":"open-ai-models-an-introduction","status":"publish","type":"post","link":"https:\/\/simoninstitute.ch\/blog\/post\/open-ai-models-an-introduction","title":{"rendered":"Open AI Models: An Introduction"},"content":{"rendered":"\n

Open-source and open-weight AI are receiving significant attention from policymakers. The development of open AI models (along with open-source software and open data) will be one of seven priority topics<\/a> for the first UN Global Dialogue on AI Governance in July 2026.1<\/sup> Open AI models are also promoted in national and regional policies, including China\u2019s AI Plus initiative, the EU\u2019s Apply AI Strategy, and more recently the US AI Action Plan. Growing policy attention has come alongside a narrowing of the performance gap between open-weight and closed-weight models, according to multiple benchmarks.2<\/sup><\/p>\n\n\n\n

This article is the first in a series on open AI development, why it matters, and where it is headed. After clarifying core concepts, this article examines the policy and commercial drivers for open model promotion as well as safety and governance considerations.<\/p>\n\n\n\n

What does openness in AI development mean?<\/h3>\n\n\n\n

AI systems exist on a spectrum of openness depending on how many of their components (e.g., training and inference code, model architecture, training data, model weights) are publicly available and to what degree of accessibility.3<\/sup><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Spectrums of accessibility for models, training code and training data. Adapted from Appendix A, How behind are open models? | Epoch AI<\/a>, with examples of training code and datasets added by the author.<\/em><\/p>\n\n\n\n

Definitions and classifications are contested. \u201cOpen-weight\u201d can refer to models whose weights are freely available for unrestricted download and use, or those whose weights are downloadable only for certain purposes or for a maximum number of end-users (see right-hand side of top row in infographic above). The definition of open-source AI, according to the Open Source Initiative\u2019s v1.0 proposal<\/a> endorsed by organizations including Mozilla and EleutherAI, is more demanding. It requires the accessibility of parameters (or weights), code and data information (\u201csufficiently detailed information about the data used to train the system so that a skilled person can build a substantially equivalent system\u201d). As very few competitive AI models today meet this definition, \u201copen (AI) models\u201d in this series denotes open-weight models.  

The further along a system is on the openness spectrum, the greater the ease with which others can use, study, modify, and share it. This may be good or bad depending on the purpose the deploying party uses it for. However, even highly open models face practical constraints on downloads and usage, particularly compute requirements and technical know-how. The most widely downloaded models on Hugging Face\u2014the largest public repository of AI models and datasets\u2014tend to be smaller models that can be run on laptops or a single consumer GPU rather than multi-GPU clusters. For users with limited technical capacity, accessing models via intuitive web interfaces\u2014or, for higher-volume or more customized use, through application programming interfaces (APIs)\u2014remains more practical than deploying open-weight models locally. Cloud service providers play an important enabling role for developers and IT teams by offering hosted access to open-weight models, along with tools to streamline fine-tuning and deployment.<\/p>\n\n\n\n

Policy Drivers<\/h3>\n\n\n\n

Several policy considerations are increasing demand for and interest in open models.<\/p>\n\n\n\n

First, open models are perceived as conducive to AI development and diffusion due to their cost and accessibility advantages over proprietary alternatives. Companies and governments (including the providers of digital public infrastructure such as identity or payment platforms) can deploy models without recurring licensing or API fees, making experimentation and scaling more feasible. In addition, developers can rapidly build models with new or improved capabilities by fine-tuning open models on task-specific data or through distillation, in which a smaller model is trained on outputs generated by a larger model to inherit the latter\u2019s capabilities.4<\/sup> For these reasons, a partner at American venture-capital firm Andreessen Horowitz estimates<\/a> that 80% of entrepreneurs pitching the firm are using a Chinese open model.<\/p>\n\n\n\n

Second, sovereignty concerns make open options appealing. The ability to do further pre-training or fine-tuning of open models facilitates adaptation to local languages, norms, and needs. For instance, the SEA-LION model series<\/a> builds upon open models from Google, Alibaba and others to optimize for Southeast Asian language performance. Furthermore, adopting open models rather than licensing them from overseas commercial providers allows data to be stored and processed in-country and reduces foreign dependencies. Achieving these benefits depends on local talent, data and compute infrastructure; open models alone are not a silver bullet. <\/p>\n\n\n\n

Third, there are benefits for transparency and trust. Model weights are essential for several strands of research across AI security and safety, including white-box robustness testing<\/a> and mechanistic interpretability<\/a>, both of which rely on direct access to internal model structure.<\/p>\n\n\n\n

In addition, norms of participatory, multistakeholder, and transparent governance prevalent in earlier discussions about Internet and data governance continue to influence expectations around AI, increasing demand for open approaches.<\/p>\n\n\n\n

Commercial Drivers<\/h3>\n\n\n\n

Beyond the factors outlined above, several other perceived benefits are driving commercial demand for open AI models from both public and private customers: <\/p>\n\n\n\n