Open Weights: Why Would Anyone Give Away the Most Expensive Part of an AI Model?

Companies release open weights to shift value from the model itself to the ecosystem around it. By giving away the trained parameters, they encourage adoption, customisation and integration into third party systems. This builds a standard, attracts developers, and reduces the risk of customers being locked into a single provider's API, while still monetising through services and support.

1. A strange thing is happening in AI

There is something slightly strange happening in artificial intelligence. Companies are spending enormous amounts of money buying GPUs, building data centres, collecting data, employing researchers and training increasingly capable models. Training a serious frontier model is not like writing an ordinary piece of software. Much of the value is created through an expensive process that turns an initially useless neural network into something that can write software, analyse documents, reason about problems and increasingly operate tools on someone’s behalf.

Then, having spent all that money, some of these companies turn around and put the most valuable result of that entire process on the internet for anyone to download. They publish the weights. At first glance this sounds commercially insane. Why would you spend a fortune building something and then allow other companies, including competitors, to download it, run it themselves, modify it and in some cases build competing products with it? To understand why, you first need to understand what weights actually are.

2. Think of an AI model as a racing engine

Imagine several companies trying to build the world’s best racing engine. The architecture of the engine tells you roughly how the machine is constructed. It tells you that there are cylinders, valves, a turbocharger and a fuel system. AI has an equivalent. Researchers publish architectures such as the Transformer and techniques such as mixture of experts and attention mechanisms, and anyone can read the papers.

But possessing the design of an engine does not mean you possess the engine. The really valuable object is the finished engine after thousands of engineering decisions, adjustments, tests and refinements have been made. The weights of an AI model are a little like all of those adjustments frozen into the finished machine. Technically, weights are enormous collections of numerical parameters learned during training. The Open Source Initiative describes them as the learned parameters layered over the architecture that determine how a model responds to an input, and a modern frontier model may contain hundreds of billions or even trillions of these parameters.

Before training, those numbers are largely meaningless. During training they are adjusted again and again as the model processes enormous quantities of information, and what eventually emerges is the behaviour we recognise as the model. This distinction matters because the architecture itself is often not particularly secret. Two companies can understand broadly how one another’s engines work while still being unable to reproduce the finished engine without doing the enormously expensive engineering themselves. The weights contain the result of that work, and that is what makes them valuable.

3. Closed models: you can drive the car, but the bonnet is welded shut

With a traditional proprietary model, you normally interact with it through somebody else’s service. You send a request to an API and receive an answer. You may be able to adjust prompts, tools and system instructions, but the model itself remains on the provider’s infrastructure. You cannot download the underlying weights, inspect them, or take the model home and run it on your own hardware. You cannot simply decide that from tomorrow your bank, hospital or government department will run the model inside its own data centre.

The easiest analogy is renting a racing car from its manufacturer. You can drive it incredibly quickly, but the bonnet is welded shut. This model has real advantages. The provider can continuously improve the system, operate enormous computing infrastructure and hide a great deal of complexity from the customer, which is exactly what many users want. The disadvantage is dependency. If the provider changes the price, changes the model, changes its policies, withdraws a feature or decides that a particular use case is no longer allowed, there may be very little the customer can do about it. Your intelligence layer belongs to somebody else.

4. Open weights: here are the keys and here is the engine

An open weight model changes that relationship entirely. Instead of merely allowing you to send requests to a model, the creator makes the trained weights available. Depending on the licence and the model, you can download those weights, operate the model on infrastructure you control, adapt it for a particular purpose, fine tune it, quantise it, distil it or build another system around it.

OpenAI released its gpt oss models in August 2025 under the Apache 2.0 licence specifically so they could be customised and operated on infrastructure the user controls, and the model card describes them as open weight reasoning models available under that licence together with a usage policy.[^1] DeepSeek has released the weights and inference code for its core models, including V3 and R1, under the MIT licence, while Alibaba’s Qwen family includes numerous models released under Apache 2.0, with the licence occasionally reverting to a more restrictive Qwen licence for some flagship releases.[^2] Mistral has followed a similar strategy with a mixture of commercial and open weight models, and Moonshot AI has pushed the idea remarkably far. Its Kimi K3 model, announced in July 2026, is a 2.8 trillion parameter mixture of experts model with roughly 104 billion active parameters, and Moonshot has described it as the first open model at what it calls “3T class” scale. The full weights were published on Hugging Face under a custom Kimi K3 licence rather than a conventional permissive licence, together with the technical report and configuration files needed to run it.[^3]

You are no longer merely renting the car. Someone has given you the engine.

5. Open weights does not necessarily mean open source

This is where the terminology becomes messy. People regularly describe models as open source simply because the weights can be downloaded, but those two things are not equivalent. You might have the finished engine without receiving every drawing, every test result, every manufacturing process or a record of every material that went into building it. AI can work the same way. A company may release the model weights while keeping some or all of the training data, training process and internal tooling private.

The Open Source Initiative published its Open Source AI Definition in October 2024, and by that definition a model must also provide access to information about the training data, the architecture and the training code before it can properly be called open source.[^4] By this stricter test, most of the models people casually call open source, including Llama, Gemma and Qwen, are actually open weight rather than fully open. They give you the finished engine, not the factory that built it.

Licensing terms vary considerably across the field. DeepSeek uses the MIT licence for its own models. Qwen mostly uses Apache 2.0, though Alibaba retains a separate Qwen licence for some of its largest models where it wants more control over how they are commercialised. OpenAI uses Apache 2.0 for gpt oss. Meta describes Llama as open source, but Llama actually ships under Meta’s own community licence, which includes a cap on the number of monthly active users a company can have before it needs a separate commercial agreement with Meta, along with restrictions on how derivative models must be named.[^5] So it is worth being precise. Open weights tells you that you can obtain the trained parameters. It does not automatically tell you everything about how the model was created, or everything you are legally permitted to do with it.

6. So why on earth would anyone give the weights away?

This is the genuinely interesting question. There is a tendency to assume companies release open models out of charity, and there is undoubtedly a real research culture behind some of these decisions, but there are also extremely powerful commercial reasons for doing it.

The first reason is brutally simple: sometimes the model is not the business. Meta is the clearest example. Meta makes its money primarily through the enormous advertising businesses surrounding Facebook, Instagram and its other products, so it does not need to charge every developer who runs Llama for that model to be strategically valuable to Meta. Mark Zuckerberg has been unusually explicit about this. In his 2024 essay accompanying the release of Llama 3.1, he compared the trajectory of open AI to the way Linux eventually overtook closed Unix systems, and argued that an open ecosystem prevents Meta from becoming dependent on a closed technology stack controlled by a competitor.[^6] More recently, in August 2026, Meta has doubled down on this framing again, with Zuckerberg publishing a further essay and opinion piece arguing that American open models need to compete more aggressively against Chinese open models such as DeepSeek and Kimi, and positioning Meta’s continued commitment to openness as both a commercial strategy and a matter of national technology policy.[^7] Giving something away can therefore be a genuine form of competitive defence, aimed as much at other American labs as at China.

7. Sometimes you give away the engine because you want everyone to build roads for it

Technology becomes enormously powerful when an ecosystem forms around it. Developers optimise software for it, hardware manufacturers support it, cloud companies host it, researchers improve it, consultants understand it, universities teach it and startups build products around it. Once that begins to happen, the model stops being merely a model. It becomes a platform.

Meta has deliberately encouraged exactly this around Llama, working with cloud providers, hardware companies and enterprise technology firms to make deployment, fine tuning and optimisation easier, with the explicit goal of making Llama the industry standard.[^8] This is one of the oldest strategies in technology. You do not always need to own the tollbooth if you can influence where the road gets built.

8. Open weights also recruit thousands of researchers you do not employ

There is another consequence of releasing weights that is easy to underestimate. The rest of the world starts experimenting with your model. Researchers discover better ways to quantise it. Developers make it run on cheaper hardware. Other researchers fine tune it for medicine, coding, mathematics or languages the original developers barely considered, and infrastructure companies optimise inference engines specifically around it. Suddenly an enormous distributed research and development organisation exists around your technology, and most of the people in it do not work for you.

This is one reason open technology can move so quickly. Mistral has argued publicly that open models allow systems to be adapted to specialised business problems in ways closed models cannot easily match, and Meta has repeatedly pointed to its broader developer ecosystem as the mechanism through which Llama and the surrounding tooling keep improving.[^9] The original creator does not capture all of that value, but it may capture enough of it to make the trade worthwhile.

9. And then there is distribution

There is a second battle happening in AI alongside the battle for the smartest model, and that is the battle for adoption. A technically brilliant model nobody builds around can end up being strategically less valuable than a slightly weaker model that becomes embedded everywhere. Open weights remove one of the biggest barriers to distribution.

A university can download a model. A startup can experiment with it. A government can deploy it internally. A cloud provider can host it. A hardware manufacturer can optimise for it. A bank can put it inside an environment where sensitive customer information never needs to leave its own infrastructure. OpenAI itself now highlights exactly this set of reasons, control, local deployment, data residency and customisation, as the justification for gpt oss.[^10] Once a model is running in thousands of different places, replacing it becomes much harder. Giving the model away can therefore create something selling API calls cannot create quite as easily, and that is ubiquity.

10. This is part of why the Chinese models matter so much

This is where DeepSeek, Qwen and Kimi become strategically important. The AI discussion in the West was initially dominated by a handful of American companies with closed models, with the implicit assumption that access to the most capable intelligence would increasingly be purchased through APIs operated by those companies. Open weight Chinese models challenge that assumption directly.

DeepSeek releases weights and technical reports for its major models under the permissive MIT licence. Qwen has released a broad family of models across different sizes, making it possible to choose something appropriate for anything from modest consumer hardware to a large data centre deployment. Moonshot AI has now put Kimi K3, at 2.8 trillion parameters, on Hugging Face, pushing genuinely frontier scale intelligence further into the open model world than any Western lab has yet matched.[^11] Independent tracking of open model benchmarks in mid 2026 places Kimi K3 as the strongest open weight model on aggregate intelligence measures, ahead of GLM 5.2, though Moonshot’s own comparisons acknowledge it still trails the very best closed models from Anthropic and OpenAI on most tasks.[^12]

This matters because it changes the competitive question. It is no longer simply which company has the best chatbot. It increasingly becomes which model becomes the foundation everybody else builds on, and those are very different battles to win.

11. Open models change the economics of AI

There is another consequence that may ultimately matter more than any of the above, and that is downward pressure on the price of intelligence. If only a handful of companies possess highly capable models, those companies have enormous pricing power. If a capable model can instead be downloaded and operated by Amazon, Microsoft, Google, a specialist inference provider or the customer itself, inference starts behaving more like a competitive computing workload rather than a scarce resource controlled by one seller.

The model creator may have spent an enormous amount developing the underlying intelligence, but once the weights are available, many companies can compete to operate it efficiently. That changes the economics substantially. Optimisation becomes valuable. GPUs compete with alternative accelerators. Quantisation improves. Smaller distilled versions appear. Inference software improves. Hardware utilisation improves. The cost of consuming intelligence begins to move away from whatever the original creator decides to charge and closer to the underlying cost of computing it, which is an important shift for anyone budgeting for AI at scale.

12. There is still a reason to keep models closed

None of this means every model will become open weight. There are powerful reasons to keep the very best models proprietary. A company that has spent billions developing a genuine capability advantage may decide that selling access to that advantage is far more valuable than distributing it. Keeping the weights private also gives the developer more control over safety mechanisms, usage, intellectual property and the way the model is operated in practice.

There is therefore likely to be a continuing tension between the two approaches. Some companies will monetise scarcity. Others will monetise the ecosystem surrounding abundance. Some, like Mistral, will try to do both at once, running a portfolio that mixes open weight models with commercial ones reserved for production use under separate licensing terms.[^13] That structure may prove to be fairly common as the field matures.

13. Weights make intelligence portable

This is probably the simplest way to explain why open weights matter. With a closed model, intelligence is a service. With an open weight model, intelligence starts becoming an asset you can actually possess. You can put it in your data centre. You can put it in your cloud account. You can optimise it for your hardware. You can modify it for your organisation. With sufficiently small models, you can even put it directly onto personal devices.

That difference is profound. We spent the first years of the generative AI wave becoming accustomed to the idea that intelligence lived behind somebody else’s API. Open weights challenge that assumption, and the 2026 push by Meta to reassert itself against Chinese open models, alongside DeepSeek, Qwen and Moonshot continuing to publish frontier scale weights, suggests the trend is accelerating rather than fading. Once you understand that, the decision to release a model’s weights no longer seems quite so strange. The company doing it may be giving away an extraordinarily valuable engine, but what it really wants is for the entire world to start building around that engine. In technology, owning the standard can sometimes be worth considerably more than owning the product.

References

[^1]: OpenAI, “gpt oss 120b & gpt oss 20b Model Card,” August 2025, https://openai.com/index/gpt-oss-model-card/
[^2]: Ollama, DeepSeek R1 model card notes on MIT licensing and Qwen/Llama distilled variants, https://ollama.com/library/deepseek-r1:8b; Hugging Face, Qwen3 235B A22B model card, Apache 2.0 licence
[^3]: Simon Willison, “Kimi K3, and what we can still learn from the pelican benchmark,” July 2026, https://simonwillison.net/2026/Jul/16/kimi-k3/; Tom’s Hardware, “China’s 2.8 trillion parameter Kimi K3,” July 2026, https://www.tomshardware.com/tech-industry/artificial-intelligence/moonshot-releases-2-8-trillion-parameter-kimi-k3
[^4]: QubitTool, “Open Source AI Licenses 2026: Apache 2.0 to RAIL Guide,” summarising the OSI Open Source AI Definition (OSAID) v1.0, October 2024, https://qubittool.com/blog/open-source-ai-license-compliance-guide
[^5]: Code To Cloud, “Open Source LLMs for Developers,” May 2026, on the Llama 4 Community Licence monthly active user cap and naming requirements, https://codetocloud.io/blog/open-source-llms-developers/
[^6]: Mark Zuckerberg / Meta, “Open Source AI Is the Path Forward,” July 2024, https://about.fb.com/news/2024/07/open-source-ai-is-the-path-forward/
[^7]: Fortune, “Mark Zuckerberg makes his case for American open source AI over Chinese rivals,” August 2026, https://fortune.com/2026/08/10/meta-brandishes-open-source-ai-models-again-as-zuckerberg-media-blitz-emphasizes-battle-against-chinese-rivals/
[^8]: About Meta, “Open Source AI Is the Path Forward,” July 2024 (on ecosystem partnerships with Amazon, Databricks and NVIDIA)
[^9]: Mistral AI public documentation on open weight licensing strategy, https://docs.mistral.ai/
[^10]: OpenAI Help Center, “OpenAI open weight models (gpt oss),” https://help.openai.com/en/articles/11870455-openai-open-weight-models-gpt-oss
[^11]: Amplifi Labs, “Kimi K3: The Complete Guide to Moonshot AI’s 2.8T Model,” July 2026, https://amplifilabs.com/post/kimi-k3-the-complete-guide-to-moonshot-ais-2-8t-model
[^12]: ComputingForGeeks, “Open Source LLM Comparison Table (2026),” https://computingforgeeks.com/open-source-llm-comparison/
[^13]: Mistral AI documentation on its mixed portfolio of open weight and commercial licensed models, https://docs.mistral.ai/