Eiso Kant

i think we need to separate spend from getting the world's most possible capable ai by closing this gap getting to agi closing the gap between human intelligence and are they not the same thing they're not the same thing because if you look at these models as investments that we're making to get intelligence out on the other end that needs to be economically valuable to end users right with lots of layers and applications and things in between the model the creation of models is capex

The operating of them, the inference to running of them, is OPEX. But the OPEX to run them requires extremely large-scale physical footprint in the world. Very simple. If we would spend, you know, $100 making a model and it will ever return $2 or $3 in terms of value to the world, it makes no sense, right? The world will punish it. It won't exist.

The operating of them, the inference to running of them, is OPEX. But the OPEX to run them requires extremely large-scale physical footprint in the world. Very simple. If we would spend, you know, $100 making a model and it will ever return $2 or $3 in terms of value to the world, it makes no sense, right? The world will punish it. It won't exist.

The operating of them, the inference to running of them, is OPEX. But the OPEX to run them requires extremely large-scale physical footprint in the world. Very simple. If we would spend, you know, $100 making a model and it will ever return $2 or $3 in terms of value to the world, it makes no sense, right? The world will punish it. It won't exist.

The huge scale-out that has to happen in the world for AI to become something that can tackle all of our world's problems and can feed in all of, from our software to our daily lives, requires huge footprint to run these models. It requires massive inference. And that means it requires data centers all over the world, close to end users, latency matters. And that requires a massive build-out.

The huge scale-out that has to happen in the world for AI to become something that can tackle all of our world's problems and can feed in all of, from our software to our daily lives, requires huge footprint to run these models. It requires massive inference. And that means it requires data centers all over the world, close to end users, latency matters. And that requires a massive build-out.

The huge scale-out that has to happen in the world for AI to become something that can tackle all of our world's problems and can feed in all of, from our software to our daily lives, requires huge footprint to run these models. It requires massive inference. And that means it requires data centers all over the world, close to end users, latency matters. And that requires a massive build-out.

And I think this is one of the largest build-outs that we've seen in physical infrastructure since, you know, the last couple of decades in the cloud.

And I think this is one of the largest build-outs that we've seen in physical infrastructure since, you know, the last couple of decades in the cloud.

And I think this is one of the largest build-outs that we've seen in physical infrastructure since, you know, the last couple of decades in the cloud.

We're in a world today where the amount of data centers that can hold and power and have enough energy to power increasingly magnitude order larges of clusters is a very small number. I think he's absolutely right in this sense.

We're in a world today where the amount of data centers that can hold and power and have enough energy to power increasingly magnitude order larges of clusters is a very small number. I think he's absolutely right in this sense.

We're in a world today where the amount of data centers that can hold and power and have enough energy to power increasingly magnitude order larges of clusters is a very small number. I think he's absolutely right in this sense.

Now, the data centers from two years ago versus the data centers in terms of size and power requirement that we're going to see in the next two years look radically different, not just because the scale of number of servers and nodes that we're interconnecting,

Now, the data centers from two years ago versus the data centers in terms of size and power requirement that we're going to see in the next two years look radically different, not just because the scale of number of servers and nodes that we're interconnecting,

Now, the data centers from two years ago versus the data centers in terms of size and power requirement that we're going to see in the next two years look radically different, not just because the scale of number of servers and nodes that we're interconnecting,

this is the difference between inference right for inference we don't need all of the machines to be connected to each other in the same place for training we need them all to be connected to each other in the same room in the same place and so that massively changes what a data center looks like i think the show has done so well because i ask questions that people think why do you need that for training and not for inference i think it's a good question

this is the difference between inference right for inference we don't need all of the machines to be connected to each other in the same place for training we need them all to be connected to each other in the same room in the same place and so that massively changes what a data center looks like i think the show has done so well because i ask questions that people think why do you need that for training and not for inference i think it's a good question

this is the difference between inference right for inference we don't need all of the machines to be connected to each other in the same place for training we need them all to be connected to each other in the same room in the same place and so that massively changes what a data center looks like i think the show has done so well because i ask questions that people think why do you need that for training and not for inference i think it's a good question

When we're scaling up the size of these models and we're training them on more and more data and we're using more and more compute for it, at every single step that we're taking in the learning, every set of samples of data that we show the model, we need them to communicate with each other and share what they've learned. across the optimization landscape.