{"id":6177,"date":"2026-06-02T13:32:32","date_gmt":"2026-06-02T17:32:32","guid":{"rendered":"https:\/\/redmonk.com\/sogrady\/?p=6177"},"modified":"2026-06-02T13:32:32","modified_gmt":"2026-06-02T17:32:32","slug":"g7-open-weights","status":"publish","type":"post","link":"https:\/\/redmonk.com\/sogrady\/2026\/06\/02\/g7-open-weights\/","title":{"rendered":"The G7 on Open Source vs Open Weights"},"content":{"rendered":"<p><a title=\"Daniel Jolivet, CC BY 2.0 &lt;https:\/\/creativecommons.org\/licenses\/by\/2.0&gt;, via Wikimedia Commons\" href=\"https:\/\/commons.wikimedia.org\/wiki\/File:Evian-les-Bains_(Haute-Savoie)_(10004827914).jpg\"><img decoding=\"async\" width=\"330\" alt=\"Evian-les-Bains (Haute-Savoie) (10004827914)\" src=\"https:\/\/upload.wikimedia.org\/wikipedia\/commons\/thumb\/3\/37\/Evian-les-Bains_%28Haute-Savoie%29_%2810004827914%29.jpg\/330px-Evian-les-Bains_%28Haute-Savoie%29_%2810004827914%29.jpg\"><\/a><\/p>\n<p>The term \u201copen source\u201d was <a href=\"https:\/\/opensource.com\/article\/18\/2\/coining-term-open-source-software\">coined in 1998<\/a>, at least in part, because the term that preceded it was unclear and required explanation. Free software was descriptive and understood within technical communities familiar with it, but misleading to newcomers who understood free in commercial rather than philosophical terms. It was clear, in other words, that a new descriptor was required, and open source was the result.<\/p>\n<p>Two years after the introduction of a proposed definition for open source AI, adoption has been minimal and there is an increasing awareness that unlike with pure source code, the assets that make up an AI model cannot be reduced to a single, binary open and closed definition.<\/p>\n<p>It\u2019s not that the licenses and promises of open source as they pertain to source code are simple. They can be complex, nuanced and difficult to explain. But they are, at least, that binary. Code is either open source or it\u2019s not. By contrast, it\u2019s now clear that open source AI &#8211; of which code is only a small part &#8211; is going to have to be defined along a spectrum from closed to open.<\/p>\n<p>The <a href=\"https:\/\/en.wikipedia.org\/wiki\/G7\">G7 nations<\/a> &#8211; with input from parties like <a href=\"https:\/\/opensource.org\/blog\/open-source-initiative-helps-g7-deliver-vision-on-ai-openness\">the OSI<\/a> &#8211; apparently concur. Their <a href=\"https:\/\/www.entreprises.gouv.fr\/files\/files\/Actualites\/2026\/g7\/vision-AI-openness-opportunities-and-shared-language.pdf\">paper<\/a> published this week, \u201cG7 Vision on AI openness opportunities and shared language,\u201d has several important takeaways. Among them:<\/p>\n<ul>\n<li>First, it clearly and unambiguously states that both open source and AI openness have immense societal benefits. It calls the latter, in fact, \u201c<em>an essential contributor to our economies<\/em>.\u201d<\/p>\n<\/li>\n<li>\n<p>Second, it acknowledges the risks and potential future harm that can result from the lack of clear, consistent definitions. \u201c<em>This lack of clarity in the field of AI tends to cast doubt on the degree of openness of such technologies, thereby undermining their benefits<\/em>.\u201d<\/p>\n<\/li>\n<li>\n<p>Third, it <em>explicitly<\/em> rejects a strict open or closed definition &#8211; \u201c<em>the openness of an AI is not binary<\/em>.\u201d<\/p>\n<\/li>\n<li>\n<p>Fourth, it <em>implicitly<\/em> rejects existing definitions &#8211; \u201c<em>the meaning of Open-Weight or Open Source AI remains contested<\/em>.\u201d<\/p>\n<\/li>\n<li>\n<p>Lastly, it proposes a four tier system for categorizing AI projects that sit along a spectrum of open.<\/p>\n<\/li>\n<\/ul>\n<p>The proposed classifications are similar in some respects to existing attempts like the Linux Foundation\u2019s <a href=\"https:\/\/docs.google.com\/document\/d\/1RUNrs4flAsYsikXTPu1jWBH1BAumCyeG\/edit?pli=1#heading=h.gjdgxs\">Model Openness Framework<\/a>. Both are built for a landscape in which projects will differ in what specifically is made available, the terms its made available under and what restrictions, if any, are placed on use. But where the MOF is quite granular, grading projects around 17 components across an entire development lifecycle, the G7 vision is simpler. It defines four tiers based on five components (weights, deployment code, training code, training data and use restrictions).<\/p>\n<p>In rough terms, those tiers can be described as follows ranging from most open to least:<\/p>\n<ul>\n<li><strong>Open Source AI with Open Data<\/strong>: everything is open and under an OSI license &#8211; code, data, weights, every asset. <\/li>\n<li><strong>Open Source AI<\/strong>: what\u2019s available is open, but it may or may not include training data, though it must include full training code. <\/li>\n<li><strong>Open Weights AI<\/strong>: weights and code are available and under an OSI license, but nothing else. <\/li>\n<li><strong>Weights Available AI<\/strong>: weights and code are available and open for inspection, but are released under a license which cannot be called open source due to use restrictions or other prohibited limitations. <\/li>\n<\/ul>\n<p>It remains to be seen whether or not the industry can adapt to a definition of open that depends on a sliding scale rather a fixed yes\/no. But it also doesn\u2019t have a choice. Two years of development and discussion and two years of living with a <a href=\"https:\/\/opensource.org\/ai\/open-source-ai-definition\">proposed definition<\/a> have gotten us no closer to an industry consensus. Subtly, however, what the G7 nations have done with this document intentionally or unintentionally is to both acknowledge that fact, make it irrelevant and implicitly propose their alternative.<\/p>\n<p>The challenge for any single definition of open source AI is that it is not possible to please both definition purists and definition pragmatists. The former point out that any definition that allows for any omission of training data is effectively granting the term open source to a project that cannot ever be independently replicated. Which is legitimate. The latter, on the other hand, point to issues with datasets ranging from the byzantine nature of data licensing to the sheer impracticality of the size of these datasets. Which are also legitimate. You can please one of these groups about an open source definition, but not both.<\/p>\n<p>What the G7 is proposing serves as a recognition that that debate is a lost cause. Instead, for all intents and purposes, the G7 is proposing to deprecate the term open source AI in favor of open weights.<\/p>\n<p>It is true, on the one hand, that there are not one but two different tiers in the G7\u2019s framework that explicitly cite the term open source. So the deprecation is not literal. There is a definition of open source AI in existence.<\/p>\n<p>But if no major competitive models can satisfy that definition, does the definition matter?<\/p>\n<p>Two weeks ago, we <a href=\"https:\/\/redmonk.com\/sogrady\/2026\/05\/15\/open-ai-models\/\">surveyed<\/a> a large, representative sample of relevant models. Since then we\u2019ve added a few new models to track. A quick survey of the licensing terms for this sample are suggestive. 28 of the surveyed models are closed, and thus irrelevant in a discussion of openness. Of the 40 remaining in our sample, half are Weights Available AI (non-OSI license) and half are Open Weights AI (OSI license). Which in turn means that none are Open Source AI and none are are Open Source AI with Open Data.<\/p>\n<p>To be clear, there are borderline cases: IBM publishes detailed training documentation and methodology, but not the code. Meta has released fine-tuning recipes and scripts (llama-recipes) alongside Llama, but again not the code. Deepseek, meanwhile, arguably went furthest, providing reinforcement learning training code and distillation scripts &#8211; but not the full pipeline. None of these, therefore, are considered open source AI by the G7&#8217;s definition.<\/p>\n<p>Outside of our sample, meanwhile, there are models that provide data, code and weights: AI2&#8217;s OLMo and EleutherAI&#8217;s Pythia most notably. But they aren&#8217;t particularly competitive with the selected open and closed models tracked here and thus are not considered.<\/p>\n<p>Put simply, the G7\u2019s proposal at once codifies the open source AI definition while simultaneously making it irrelevant. Open weights becomes the de facto term of art, then, by default &#8211; at least until such time in future that truly open source models become more competitive. Instead of blurring the definition of open source AI to meaninglessness, a new, more descriptive term in open weights seeks to mitigate the shortcomings of its predecessor &#8211; much as open source itself once did for free software.<\/p>\n<p>It is not clear whether even an august body like the G7 can compel adoption of their proposed framework, and there are questions about who should be defining industry terminology: governments, or industry bodies? But if this effort is successful, and the term open source is effectively relegated to just describing source code, that could be the <a href=\"https:\/\/redmonk.com\/sogrady\/2024\/10\/22\/from-open-source-to-ai\/\">best possible outcome<\/a>. Vendors who wish to benefit from the halo of open can do so with a term separate and distinct from open source, and thus one insulated from contamination from use restrictions, lack of training data and other issues that would violate the original spirit and intent of the open source definition.<\/p>\n<p>In past debates between open source purists and pragmatists, the latter would frequently argue that a definition of open source that was too strict would never be used.<\/p>\n<p>The mistake all along may have been assuming that that was a bad thing.<\/p>\n<p><strong>Disclosure<\/strong>: neither the G7 nor the OSI are RedMonk clients.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The term \u201copen source\u201d was coined in 1998, at least in part, because the term that preceded it was unclear and required explanation. Free software was descriptive and understood within technical communities familiar with it, but misleading to newcomers who understood free in commercial rather than philosophical terms. It was clear, in other words, that<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"spay_email":"","footnotes":"","jetpack_publicize_message":"","jetpack_is_tweetstorm":false},"categories":[599,61],"tags":[],"class_list":["post-6177","post","type-post","status-publish","format-standard","hentry","category-ai","category-open-source"],"jetpack_featured_media_url":"","jetpack_publicize_connections":[],"jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/redmonk.com\/sogrady\/wp-json\/wp\/v2\/posts\/6177","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/redmonk.com\/sogrady\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/redmonk.com\/sogrady\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/redmonk.com\/sogrady\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/redmonk.com\/sogrady\/wp-json\/wp\/v2\/comments?post=6177"}],"version-history":[{"count":0,"href":"https:\/\/redmonk.com\/sogrady\/wp-json\/wp\/v2\/posts\/6177\/revisions"}],"wp:attachment":[{"href":"https:\/\/redmonk.com\/sogrady\/wp-json\/wp\/v2\/media?parent=6177"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/redmonk.com\/sogrady\/wp-json\/wp\/v2\/categories?post=6177"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/redmonk.com\/sogrady\/wp-json\/wp\/v2\/tags?post=6177"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}