DeepSeek made two critical changes.

Firstly. the architecture. OpenAI uses an AI architecture known as “fully dense”. This basically means that the architecture is comprised of a single, vast network that processes every request with all its parameters and data points. This is incredibly computationally dense, but the idea is that it can make it more capable in a broader application. DeepSeek is instead much more picky and uses a “mixture of experts” architecture. In this approach, the AI is split into many models designed to be better at answering certain queries, and there is a front-facing AI that can understand what kind of query it is being asked and triage it to the AI model best suited to answer it. This is a far more efficient model, as it only engages the parts of the AI needed to solve a problem and not the whole AI. It also means it requires less training, which is very costly, as these expert AIs are more focused and restricted in scope. However, as a trade-ff, this approach, in theory, should make a model have a less broad application than those with fully dense architecture.

Secondly, DeepSeek ensures that the finished product is open-source and not closed-source like most Western AI models. Closed-source AIs are developed in secret, and then use cases are found for the model once it is released, hence why OpenAI needs to use the costly “fully dense model” and why it seems like AI is being shoehorned into every possible corner of our lives, even if it doesn’t add any value. These closed-source models are expensive to build, and tech companies need to find applications to justify their astronomical expenditure on them. But open-source works the other way around. Users have specific use cases in mid and work with the developers to develop the AI to be usable in that specific application. This enables far more focused, efficient, and cheaper development that results in an AI that is more useful in areas that actually matter than in closed-source models.

So, Why are most Western AI companies using a closed-source approach?

This creates a proprietary AI that is wholly owned by the AI company. This enables them to charge more for its use and also helps them raise more investment, as it creates a more secure asset. After all, a tech giant or investment bank isn’t going to bankroll an open-source model that they can’t have total power and control over or sell on for billions of dollars.

OpenAI was originally meant to be open-source but it changed to closed-source. Why?

Western venture capitalists see AI as a way for them to replace the human workforce with their own AI in basically every sector of the economy, enabling them to wield significant control and reap huge profits. This is the hype that has been pushed for years now, despite scientists and AI developers themselves saying that AI can’t, and might not ever be able, to do this. But these venture capitalists didn’t care; they know that investing in AI and pushing this false narrative would increase the stocks and make them money, whether AI can achieve this feat or not. As such, OpenAI and almost every Western AI project switched to closed-source to enable such investment, as it enriches the investtors and the executives of these AI companies.

In fact, this is the real reason why American restricted GPU sales to China – not to get ahead in the AI race but to protect American investment and hegemonic domination of the sector.

Read: DeepSeek Just Exposed The Rot At The Core Of The AI Industry