Eiso Kant

And so what happens in our industry, and this is our path as well, is you train a very large model. where you can clearly see that there's more capabilities in the model. And then what we call, we distill it down to a smaller model.

And so what happens in our industry, and this is our path as well, is you train a very large model. where you can clearly see that there's more capabilities in the model. And then what we call, we distill it down to a smaller model.

And so what happens in our industry, and this is our path as well, is you train a very large model. where you can clearly see that there's more capabilities in the model. And then what we call, we distill it down to a smaller model.

And because this is the thing, learning from data models are really inefficient, but learning from data in combination with learning from a smarter, larger model is actually quite efficient. We make really big things that become really smart.

And because this is the thing, learning from data models are really inefficient, but learning from data in combination with learning from a smarter, larger model is actually quite efficient. We make really big things that become really smart.

And because this is the thing, learning from data models are really inefficient, but learning from data in combination with learning from a smarter, larger model is actually quite efficient. We make really big things that become really smart.

We then teach the smaller models to try to match as much of that intelligence as possible, which we can then economically viable, put in the market and make revenue from.

We then teach the smaller models to try to match as much of that intelligence as possible, which we can then economically viable, put in the market and make revenue from.

We then teach the smaller models to try to match as much of that intelligence as possible, which we can then economically viable, put in the market and make revenue from.

We should separate the price and the cost of models. If you look at what's happening in the world of general purpose LLMs, LLMs for everything, it's an incredibly competitive price war that's happening. And it's happening between the large hyperscalers, and it's happening between kind of referred to as the escape velocity AI companies, anthropic and open AI.

We should separate the price and the cost of models. If you look at what's happening in the world of general purpose LLMs, LLMs for everything, it's an incredibly competitive price war that's happening. And it's happening between the large hyperscalers, and it's happening between kind of referred to as the escape velocity AI companies, anthropic and open AI.

We should separate the price and the cost of models. If you look at what's happening in the world of general purpose LLMs, LLMs for everything, it's an incredibly competitive price war that's happening. And it's happening between the large hyperscalers, and it's happening between kind of referred to as the escape velocity AI companies, anthropic and open AI.

And then you throw in the mix, the vendors that are putting up the open source models from Meta and such. And I often think about what sits in that stack of costs. Well, what sits in the stack of the cost is a server, a box, the networking around it, a data center, the chips, right, the GPUs, and then the energy that goes into that.

And then you throw in the mix, the vendors that are putting up the open source models from Meta and such. And I often think about what sits in that stack of costs. Well, what sits in the stack of the cost is a server, a box, the networking around it, a data center, the chips, right, the GPUs, and then the energy that goes into that.

And then you throw in the mix, the vendors that are putting up the open source models from Meta and such. And I often think about what sits in that stack of costs. Well, what sits in the stack of the cost is a server, a box, the networking around it, a data center, the chips, right, the GPUs, and then the energy that goes into that.

And everything after that is marginal cost or variable cost of the running of the models. So we have to think about who has the lowest cost profile in the space, right? Who has the cheapest first principles capex that they're doing to run these models?

And everything after that is marginal cost or variable cost of the running of the models. So we have to think about who has the lowest cost profile in the space, right? Who has the cheapest first principles capex that they're doing to run these models?

And everything after that is marginal cost or variable cost of the running of the models. So we have to think about who has the lowest cost profile in the space, right? Who has the cheapest first principles capex that they're doing to run these models?

Well, that's the people who have as much of that vertically integrated and who have as much of that infrastructure already online and brought into the world. And this really is the hyperscalers. This is Amazon in number one, Microsoft in number two, Google in number three. But there's something interesting about all of those.

Well, that's the people who have as much of that vertically integrated and who have as much of that infrastructure already online and brought into the world. And this really is the hyperscalers. This is Amazon in number one, Microsoft in number two, Google in number three. But there's something interesting about all of those.