What does it mean when an AI is open source vs. closed?
When you start looking into the world of artificial intelligence, you’ll quickly run into two camps: the “open” crowd and the “closed” crowd. Companies like OpenAI, Anthropic, and Google generally keep their most powerful models behind a digital curtain, while others like Meta and DeepSeek release models that anyone can download and run.
At its simplest, this debate is about who gets to see how the engine works and who is allowed to drive the car. But the lines have blurred, and a new term—“open weight”—has become just as important for understanding how your favorite AI tools actually function.
The black box: Closed AI models
If you’ve used ChatGPT, Claude, or Gemini, you’ve used a closed AI model. These are often called “proprietary” or “black box” models. You can interact with them through a website or an app, but you don’t have access to the underlying code, the specific data used to train them, or the “weights”—the billions of mathematical values that determine how the AI responds to your questions.
The companies behind these models (the “frontier” players) argue that keeping things closed is safer. By controlling access, they can prevent people from using the AI for malicious purposes and ensure it follows specific safety guidelines. From a business perspective, it also protects their multi-billion dollar investment; if they shared everything, a competitor could simply copy their homework.
For you, the user, closed models are usually the easiest to use. You don’t need a powerful computer to run them because they live on the company’s servers. However, you are also at the mercy of that company’s policies. If they decide to change how the model behaves or how it handles your data, you don’t have much say in the matter.
The transparent engine: Open-source AI
In the world of traditional software, “open source” means the recipe (the source code) is available for anyone to inspect, modify, and share. For a long time, the AI industry used this term loosely, but in late 2024, the Open Source Initiative (OSI) released an official definition to bring some order to the chaos.
True open-source AI is rare. According to the OSI, a model only earns the “open source” label if the developers share:
- The training data: Detailed information about what the AI “read” to learn.
- The code: The full set of instructions used to build and run the model.
- The weights: The final mathematical “brain” of the model.
The goal is transparency. If a model is truly open source, researchers can audit it for bias, developers can fix bugs, and anyone can run it on their own hardware without asking for permission.
The middle ground: Open-weight models
Most of what we call “open” today is actually “open weight.” This is a crucial distinction. When Meta releases a Llama model or DeepSeek shares R1, they aren’t necessarily giving you the full recipe or the training data. Instead, they are giving you the finished “brain.”
Think of it like a car. A closed AI is like a taxi service; you tell the driver where to go, but you don’t own the car or know what’s under the hood. An open-weight model is like being given the keys to the car. You can drive it wherever you want, you can take it apart in your garage, and you can even change the tires. You just don’t have the original blueprints used to design it at the factory.
Open-weight models are a huge deal for privacy and control. Because you can download the weights and run them on your own computer, your data never has to leave your house. This is why many enthusiasts and privacy-conscious businesses prefer open-weight models over the big frontier services.
Why the distinction matters for you
The choice between open and closed isn’t just for developers—it affects how you use technology every day.
- Privacy: If you are asking an AI for medical advice or help with sensitive work documents, a closed model means your data is going to a corporate server. An open-weight model run locally keeps that data on your device.
- Cost and Accessibility: Closed models usually require a subscription for the best performance. Open-weight models are often free to download, though you’ll need a decent computer (or a “vibe coding” setup) to run them smoothly.
- Innovation: Open models allow for a “community effect.” When a company releases a model like Kimi-K2 or Qwen, thousands of independent developers immediately start finding ways to make it faster and better. This competition keeps the big frontier companies on their toes.
Looking ahead
The gap between the “big three” (OpenAI, Anthropic, and Google) and the open community is shrinking. While the frontier models are still the most capable for complex reasoning tasks, open-weight models have become “good enough” for the vast majority of daily tasks.
Ultimately, the best choice depends on what you value. If you want the cutting edge with zero setup, the closed “frontier” models are your best bet. But if you want to own your tools and keep your data private, the world of open weights has never looked better.