Arvind Narayanan

Yeah, thank you for asking that. That's not obvious at all. My view is that in a lot of cases, the adoption of these models is not bottlenecked by capability. If these models were actually deployed today to do all the tasks that they're capable of, it would truly be a striking economic transformation. The bottlenecks are things other than capability. And one of the big ones is cost.

Yeah, thank you for asking that. That's not obvious at all. My view is that in a lot of cases, the adoption of these models is not bottlenecked by capability. If these models were actually deployed today to do all the tasks that they're capable of, it would truly be a striking economic transformation. The bottlenecks are things other than capability. And one of the big ones is cost.

Yeah, thank you for asking that. That's not obvious at all. My view is that in a lot of cases, the adoption of these models is not bottlenecked by capability. If these models were actually deployed today to do all the tasks that they're capable of, it would truly be a striking economic transformation. The bottlenecks are things other than capability. And one of the big ones is cost.

And cost, of course, is roughly proportional to the size of the model. And that's putting a lot of downward pressure on model size.

And cost, of course, is roughly proportional to the size of the model. And that's putting a lot of downward pressure on model size.

And cost, of course, is roughly proportional to the size of the model. And that's putting a lot of downward pressure on model size.

And once you get a model small enough that you can run it on device, that of course opens up a lot of new possibilities, both in terms of privacy, you know, people are much more comfortable with on device models, especially if it's something that's going to be listening to their phone conversations or looking at their desktop screenshots, which are exactly the kinds of AI assistance that companies are building and pushing.

And once you get a model small enough that you can run it on device, that of course opens up a lot of new possibilities, both in terms of privacy, you know, people are much more comfortable with on device models, especially if it's something that's going to be listening to their phone conversations or looking at their desktop screenshots, which are exactly the kinds of AI assistance that companies are building and pushing.

And once you get a model small enough that you can run it on device, that of course opens up a lot of new possibilities, both in terms of privacy, you know, people are much more comfortable with on device models, especially if it's something that's going to be listening to their phone conversations or looking at their desktop screenshots, which are exactly the kinds of AI assistance that companies are building and pushing.

And just from the perspective of cost, you don't have to dedicate servers to run that model. So I think those are a lot of the reasons why companies are furiously working on making models smaller without a big hit in capability.

And just from the perspective of cost, you don't have to dedicate servers to run that model. So I think those are a lot of the reasons why companies are furiously working on making models smaller without a big hit in capability.

And just from the perspective of cost, you don't have to dedicate servers to run that model. So I think those are a lot of the reasons why companies are furiously working on making models smaller without a big hit in capability.

You're right. Cost is going down dramatically. In certain applications, cost is going to become much less of a barrier, but not across the board.

You're right. Cost is going down dramatically. In certain applications, cost is going to become much less of a barrier, but not across the board.

You're right. Cost is going down dramatically. In certain applications, cost is going to become much less of a barrier, but not across the board.

So there's this interesting concept called Jevons Paradox. And this was first in the context of coal in England in the 18th century. I think when coal mining got cheaper, there was more demand for coal. And so the amount invested into coal mining actually increased. And I predict that we're going to see the same thing with models. When models get cheaper, they're put into a lot more things.

So there's this interesting concept called Jevons Paradox. And this was first in the context of coal in England in the 18th century. I think when coal mining got cheaper, there was more demand for coal. And so the amount invested into coal mining actually increased. And I predict that we're going to see the same thing with models. When models get cheaper, they're put into a lot more things.

So there's this interesting concept called Jevons Paradox. And this was first in the context of coal in England in the 18th century. I think when coal mining got cheaper, there was more demand for coal. And so the amount invested into coal mining actually increased. And I predict that we're going to see the same thing with models. When models get cheaper, they're put into a lot more things.

And so the total amount that companies are spending on inference is actually going to increase. In an application like a chatbot, let's say, you know, it's text in, text out, no big deal. I think costs are going to come down. Even if someone is chatting with a chatbot all day, it's probably not going to get too expensive.

And so the total amount that companies are spending on inference is actually going to increase. In an application like a chatbot, let's say, you know, it's text in, text out, no big deal. I think costs are going to come down. Even if someone is chatting with a chatbot all day, it's probably not going to get too expensive.