{"id":374869,"date":"2026-04-11T18:20:10","date_gmt":"2026-04-11T18:20:10","guid":{"rendered":"https:\/\/www.newsbeep.com\/nz\/374869\/"},"modified":"2026-04-11T18:20:10","modified_gmt":"2026-04-11T18:20:10","slug":"the-inevitable-need-for-an-open-model-consortium","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/nz\/374869\/","title":{"rendered":"The inevitable need for an open model consortium"},"content":{"rendered":"

Recently, I was talking with Percy Liang<\/a>, Stanford professor and lead of the Marin<\/a> project (another fully-open model lab), and it set in on me that there will eventually be a consortium of companies funding a foundational set of open models used across industry. It\u2019s not clear when this\u2019ll emerge, and Nemotron (Coalition<\/a>) is Nvidia\u2019s attempt to bankroll and bootstrap this approach within a single wealthy company, but a consortium is the only long-term stable path to well-funded, near-frontier open models.<\/p>\n

In recent months, we\u2019ve seen a lot of turnover in open<\/a> model<\/a> labs, with high-profile departures at Qwen and Ai2 (my comment<\/a>). This shouldn\u2019t be super surprising to followers of the ecosystem \u2014 it\u2019s happened before with Meta shifting its focus away from Llama<\/a>, and it\u2019ll only happen more as the cost of trying to keep pace at the frontier of AI only increases. The other leading labs with models available today include Chinese startups such as Moonshot AI, MiniMax, and Z.ai \u2014 all of which look precarious on their ability to fund continued growth in the cost of training or R&D. Releasing one\u2019s strongest models openly today is in active tension with the option of spending focus and resources on AI products that can currently generate meaningful revenue (and profits).<\/p>\n

Share<\/a><\/p>\n

We\u2019re going to see business models emerge around releasing some, or even many, models openly, but these will largely be smaller models that enable a long-tail of functionality, rather than models at the absolute frontier. This class of companies that\u2019ll release many, strong fine-tunable models will include the likes of Arcee AI<\/a>, Thinking Machines, OpenAI, Google with Gemma, and more in that class. The cost and relative advantage of keeping the best models closed in a business environment with many opportunities for revenue are too high. To summarize \u2014 there will be an ever increasing number of companies releasing models that are good for creating a lively niche of smaller, custom models, but an ever decreasing number of companies willing to release fully open, near-frontier models. <\/p>\n

This is the core thesis of why I\u2019m pushing hard for more people to do more research on how these smaller models can complement the best closed agents, the science of finetunability, etc. See my post below \u2014 it\u2019s about creating a sustainable open model ecosystem, whether or not the frontier of open keeps paced with closed:<\/p>\n

\"WhatWhat comes next with open models<\/a><\/p>\n

It\u2019ll take years for this equilibrium to become more obvious, seen through the lens of more open model families coming and going. This year, it seems likely we\u2019ll see Nvidia\u2019s Nemotron reach new heights, Reflection AI challenge some of the Chinese models with a strong, large MoE, maybe Meta releases a new open-weight model, and so on. True pressure to change strategy will only come when the capital environment punishes the less efficient spend on resources (e.g. giving away your competitive advantage, in having an in-house model). This pressure will likely hit Chinese startups training these models first. <\/p>\n

All of Moonshot AI, MiniMax, and Zhipu AI will show signs of financial challenge in the coming years if they retain their strategy, on top of their models falling further behind the best open models in terms of generality. This is inevitable pressure to evolve open models to areas that are profitable and complementary of the frontier of AI.<\/p>\n

Nvidia, which is best positioned to support the open ecosystem in the near term to support its core GPU business, could face many pressures to pull back its open model efforts. It could:<\/p>\n

Realize it\u2019s too competitive to their biggest customers as they succeed too much with Nemotron, <\/p>\n

Fall to competition on their core business and lose the free cash flow buffer needed to fund this (e.g. it\u2019s 2031 and OpenAI, Anthropic, Google, and the other frontier labs are worth so much they build their own chips). <\/p>\n

Start succeeding beyond their initial goals and keep the chips for them to build ASI themselves, as a closed-weight model. <\/p>\n

The pressures for new funding mechanisms for open models are based on the assumptions of continued, substantive progress on the capabilities of frontier models. Mechanisms such as self-improvement<\/a> and scaling all stages of the training pipeline are underway. This progress of capabilities will only increase the potential profit in selling models as and in products, not giving them away. The scale of investment required has already begun to push away non-profits from the game of making truly frontier-scale models. Capitalism is designed to make companies ruthless and chase down leads on profitability, not donate technology as charity.<\/p>\n

As the economic environment shifts companies away from releasing the strongest models openly, more companies that rely on these models will look for an outlet of securing model access into the future. This is going to be compounded by a growing group of companies who come to rely on open-weight models for their workflows. <\/p>\n

These points loop back into how model training is getting more expensive, so where desire to have the models will go up, ability to procure them will go down for many players. There are x-factors that could multiply the demand for institutions to ensure the existence of open models, such as the best frontier models not even being available via API (such as if Claude Mythos<\/a> never goes general access).<\/p>\n

As training relevant models is shifting to cost billions of dollars, rather than millions, few companies well be able to afford it. many companies will bite at the cost of paying 1\/10th of the cost to train a frontier model, or if the consortium works, 1\/50th. The upside for companies will be some mechanism to steer development (e.g. model sizes) or getting early access to develop internal and open-source tooling for the model. <\/p>\n

It is in my nature to, by default, say this idea will fail, as training models is inherently a complex and high-focus endeavor, one that requires integration of every part of the stack and focusing specifically on your own vision and needs, rather than trying to serve every possible user. Eventually the need for open intelligence \u2014 and economic pressure to build it \u2014 will make a model consortium inevitable.<\/p>\n","protected":false},"excerpt":{"rendered":"Recently, I was talking with Percy Liang, Stanford professor and lead of the Marin project (another fully-open model…\n","protected":false},"author":2,"featured_media":374870,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[365,363,364,111,139,69,145],"class_list":{"0":"post-374869","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-artificial-intelligence","8":"tag-ai","9":"tag-artificial-intelligence","10":"tag-artificialintelligence","11":"tag-new-zealand","12":"tag-newzealand","13":"tag-nz","14":"tag-technology"},"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/posts\/374869","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/comments?post=374869"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/posts\/374869\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/media\/374870"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/media?parent=374869"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/categories?post=374869"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/nz\/wp-json\/wp\/v2\/tags?post=374869"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}