Housekeeping: No voiceover for this post as I’m traveling.
The oldest comparison people try to make is how what’s happening with open models compares to foundational open-source software projects like the Linux operating system. There are fairly clean analogies, but they paint a narrow path forwards for the self-sustaining nature of the open-source model ecosystem, where once Linux got big enough it was going to be self-fulfilling as the best possible tool for many jobs. The open-source language model – i.e. only models that come with a full training recipe, data, code, etc. – is a closer analogue to the open-source operating system. The open weight models you use – those with just model weights and inference code to run them – are closer to specific versions of software that you install in a project built upon them.
Model weights are very transient on average, but they still have a long shelf life, as with a lot of heavily used software. It’s why many companies are still using workflows built on Llama 3, despite agentic behaviors taking off years later. The open-source recipe, typified in modern times by the Olmo models I helped build at Ai2, with its predecessors like Pythia from EleutherAI, is a resource intensive process that any company can pick up, modify, and press “run” on to produce a new set of model weights. In the best cases, the community can contribute improvements in data or training code back into the next model too! This is why Nvidia is investing so much in nearly open-source models – for their Nemotron models they release all the data they legally can and the training code, etc. Nvidia wants a world where countless people can build token machines, so intelligence is not monopolized. This is a world with massive demand for inference across many companies, all of which want to buy Nvidia’s offerings.
Open-source AI has a tricky future, as building the best models is extremely capital intensive. The ability to build competitive models has stayed more accessible in industry longer than many would’ve expected. The default expectation for many is that training models is too expensive and the open-source recipe is too far behind, so building a new lab centered on some part of training LLMs will not be tractable.
There are two futures from here. First is if “it works” – if the open-source recipe works for Nvidia, they’ll be creating far more demand for their chips (and profits) than it costs to build the models. Right now it’s reported that Nvidia is spending $26 billion on this endeavor. It’s not clear if this will work, or if AI’s capital intensiveness will drive more and more companies out of the training game. We haven’t seen many signs of this starting. In fact, the companies bowing out – like Databricks and 01.ai – seem like anomalies.
The open-source ecosystem will become increasingly dependent on Nvidia’s financing in the coming years. This is an existential window, where within a few years the profits of this approach need to return to them, or another open model company needs to cultivate platform-like financial feedback loops on their openness. This economic reward needs to be proportional to the profits generated by Anthropic and OpenAI’s APIs to keep pace over decades of language model development. This can be driven by competitiveness on performance or by the AI boom just being so big that the open model training, inference, and fine-tuning companies all have vast quantities of demand.
The second future is if one of these two financially positive paths doesn’t play out, open models will fork to a different development path than the leading closed models – one more focused on efficiency, modifiability, specialization, etc. I put this mentally as my most likely outcome – open models are still incredibly useful, but fill a long-tail ecosystem relative to the closed counterparts that have monopoly ownership stakes in the most valuable areas like knowledge work collaboration, drug discovery, SWE, etc. The long-tail is something like enterprise-specific agents that run on-prem with private data on repetitive business tasks.
Part of why I think this open-source training will have a hard time catching on is because training is getting more complex and more abstracted. The current open model ecosystem is buoyed by an explosion in interest in post-training open models. These people take models like DeepSeek V4 Flash, Inkling Small, or GLM 5.X and finetune them for their specific agentic tasks (e.g. in Tinker, the most popular finetuning API today).
Over the last few years, post-training largely referred to the whole process of modifying the base model to make it intelligent and usable. There is a shift happening where the ability to train a base model to be a general agentic reasoner is becoming opaque like at-scale pretraining practices from a few years ago. This could go so far as to change the established pretraining, midtraining, post-training lexicon that has been standard for a few years. It could come to be something closer to pretraining, reasoning training, and post-training.
As there’s less interest in training the entire model, there’s less interest in investing in open-source AI. These are the only sort of hints we will get, but we cannot do much to fight the economic gravity of these situations. This trend is the next step in the number of open model builders who release base models (the model versions before core reasoning training) continuing to decrease. It goes hand in hand with open model builders experimenting with revenue share licenses for downstream use in products or inference. These are experiments in keeping the financing viable for building near frontier open-weight models – a lot hinges in the near future on how successful they are. These are the people that need to succeed for Nvidia’s demand-growth strategy around open-source to succeed, and last.
Along the way we’re still in for a ton of action in open-weight models, as releasing access to intelligence is one of the strongest business strategies available. This additional type of player, who monetizes the AI indirectly, is typified by Meta and other hyperscalers with massive balance sheets. Meta releasing its very-strong Muse Spark 1.2 model as open-weights would severely hamper the revenue growth rate of their competitors in Anthropic and OpenAI who rely on selling tokens. These companies are both commoditizing their complements, but they’re doing it in different ways. Nvidia wants to teach everyone to fish for tokens, so the ecosystem is self-sustaining, but Meta is strategically flooding the zone with tokens.
Facts Only
* The comparison made is between open models and foundational open-source software like the Linux operating system.
* Open-source language models are analogous to open-source operating systems that require a full training recipe, data, and code.
* Open-weight models consist of model weights and inference code to run them.
* Model weights have a long shelf life similar to heavily used software.
* The open-source recipe involves a resource-intensive process for creating new model weights that can be modified by the community.
* Nvidia releases data and training code for Nemotron models, seeking to allow countless people to build token machines.
* Building competitive models remains capital intensive, leading to the expectation that training is too expensive for many.
* One potential future is if the open-source recipe works for Nvidia, driving demand for chips.
* The second future involves open models diverging into a path focused on efficiency, modifiability, and specialization.
* Post-training open models involve finetuning base models like DeepSeek V4 Flash or Inkling Small for specific agentic tasks using tools like Tinker.
* The trend shows a decrease in the release of base models before core reasoning training.
* Hyperscalers like Meta are releasing models as open weights, which could impact revenue growth for competitors relying on token sales.
Executive Summary
The open-source model ecosystem is being compared to foundational open-source software like Linux, suggesting a path toward self-sustaining growth. Open-source language models are analogous to open-source operating systems, while open-weight models with only weights and inference code are akin to specific installed software versions. Model weights have a long shelf life, enabling continued use of workflows built on older models like Llama 3. The process of creating open-source model recipes, exemplified by the Olmo models and predecessors, is resource-intensive but allows community contributions to improve future models. Nvidia invests in open-source models to foster a world where intelligence is not monopolized, creating massive demand for their chips across many companies.
The future faces two potential paths: either the open-source recipe proves economically viable, leading to greater demand for Nvidia's hardware, or open models will diverge from closed models toward specialization and efficiency. The latter suggests open models will occupy a long-tail ecosystem focused on enterprise-specific agentic tasks running on-premise, rather than dominating knowledge work collaboration or drug discovery where closed models currently hold monopolies.
A shift is occurring in the open-source trend away from training entire models toward post-training fine-tuning of existing base models. This change may result in reduced interest in large-scale model training and investment in the broader ecosystem. This trend coincides with a decrease in the release of base models and an increase in revenue share experiments for downstream use, suggesting a focus on financial viability for building frontier open-weight models.
Full Take
The narrative suggests an inherent tension between the ideal self-sustaining nature of the open-source ecosystem and the stark reality of capital intensity in building frontier AI models. The core conflict is whether economic incentives can align with open development, specifically concerning Nvidia's investment and the relative power held by closed model providers like Anthropic and OpenAI.
The shift from training entire models to post-training fine-tuning marks a crucial inflection point. If full pretraining becomes too intractable for most, the ecosystem may pivot away from broad foundational research toward niche, application-specific value creation. This bifurcation implies that open models may not displace closed models in domains requiring deep, proprietary knowledge (like drug discovery or core knowledge work) but will thrive in specialized, on-premise agentic applications where data privacy and specific task execution are paramount.
The tension between Nvidia's goal of democratized intelligence and the hyperscalers' strategy of token monetization represents a fundamental contest over ownership of the AI value chain. The potential outcome—either alignment via economic feedback loops or divergence into a specialized, long-tail space—hinges on whether investment strategies can overcome the current monopoly positions. Further inquiry is needed into the viability of platform-like financial structures for open models and the long-term impact of commoditizing complements versus democratizing access to intelligence.