The Big AI Terms Everyone Is Getting Wrong: Open Source, Open Weight And Proprietary

Open source AI is everywhere right now. The problem is that a lot of what gets called “open source” isn’t actually open source at all.

Terms like proprietary, open source and open weight are increasingly used to describe AI models and platforms, and they’re frequently confused or used interchangeably.

The distinction matters because these labels determine who controls the technology, what you can see inside it, whether you can modify it, and what you’re legally allowed to do with it.

For businesses choosing AI models, those differences can affect everything from cost and customization to data control, transparency and long-term dependence on a technology provider.

So what do proprietary, open source and open weight AI actually mean? Here’s a simple guide to the differences.

What Is Proprietary AI?

Proprietary AI refers to algorithms and tools that are owned as private IP by the corporations that develop or sell them. In many cases, the developers keep the models, the training data, and details of the mechanics of the training locked away on their own servers, and let their customers access it through APIs. ChatGPT is the most obvious example of this. It’s sometimes referred to as closed-source AI, and it effectively means the IP is private property. If it’s for sale, you can use it if you comply with the owner’s terms governing how it can be used. Proprietary software may even be available in a free version (again, ChatGPT is the best example), but use, distribution and modification are still controlled by the owner.

What Is Open Source AI?

Open source is the most transparent option, but in the world of AI, true examples are rare.

The precise meaning of open source is that the source code is freely available and can be used by anyone, in any way they like.

In reality, a lot of software described as open source, particularly in AI, is made available under a variety of licenses that do control what can be done with it.