We need to look at how an AI factory works and how things are exponential as compared to traditional factory.
Here is where we see the ability to generate enormous valie. It is exactly what Big Tech is following. In fact, Jensen Huang, the CEO of Nvidia, often talks about the AI factory.
So what is this?
It is actually something that mirrors a traditional factory, with one major difference. It is that which changes everything.
Fortunately, we are in the process of developing an AI factory on Leo.
Before getting into the AI side of things, let us look at the traditional factory.
What does a company do to in this situation? It all starts with the plant, the physical structure. That is built. Then we have equipment going in which is the assembly line. After that, workers feed in raw materials which results in a product coming off the assembly line.
This is a fairly simply concept to follow.
It is the same for an AI factory. We willl list the traditional compoonents and parallel that to an AI factory.
Factory:
Plant - Neurel Network
Equipment - Algorithms
Workers - Data producers
Raw Materials - Data
Product - Data
As you can see, with an AI factory, the raw materials and the output are the same. Naturally, the data going is raw whereas the output is data in a specific form. It could be text information, an image, or video. Whatever the model is designed for, that is the output.
Do you see how this concept follows the same process as traditional factories?
With an AI factory, there is a key diffence that changes everything.
In a traditional factory, the output (product) is manufactured and sent to a customer. Thus, the process of making another item starts with the process beinging anew. A new set of raw materials is acquired and fed into the equipment to produce more units.
This is something that hold true for AI factories also. More data is continually fed into the neural network adding to the capabilities of the model output.
Here is the difference maker and why we are dealing with an exponential.
With an AI factory, the output also serves as raw materials. This would be akin to feeding the finished product back into the assembly line. Hence, we not only want to keep adding new data but the output by the model also serves to train future iterations of the model, increasing the amount of raw material available.
Naturally, this changes once robotics are introduced and we are dealing with phyisical products. But when it comes to purely AI models, the output is also part of the input.
It is why data is so important. It is also why a company like OpenAI could be in trouble. While they could keep creating synthetic data (output), they lack a continual flow of new data (raw materials) in.
Leo has this. We simply need to keep adding to the volume. If data is the new oil, we have the ability to generate as much as we desire.
