Mohammad Norouzi

I think, yeah, we need to find that taste maker.

Yeah.

We focused on the details of the model and we know we can win on scaling.

I used to work for Google.

I don't think even if we raise 10x the amount we've raised so far, we can beat Google in terms of the number of chips that we can dedicate to each model training.

So instead, we focused on innovation.

We think there's still so much more to do to innovate.

We are also focusing on differentiation.

I don't think a lot of labs are focusing on design, graphic design in particular, editable text that I'm talking about.

And then we also decided to go open weight to really partner with a lot of other platforms to be at least another option for people who care about design.

And so, yeah, so we focus on the small model primarily because

We think there's still so much to do.

We think now is actually a good time for us to scale.

Given the quality of the model at 9.3 billion parameters, you should imagine what if this model is 100x bigger and there are a mixture of experts, architectures that don't make the model necessarily slower, but they make the model a lot more powerful.

So I think that's one new frontier for us to kind of scale this model 10x, 100x.

Right, so one thing that you kind of alluded to is the fact that this can run on consumer GPU now.

And we think there is a new frontier that you do a lot of editing on your phone, a lot of image generation on your phone.

And it's not only about pushing quality at

you know 100 billion parameter 1 trillion parameter range we think it's really important to have small models that can run on device obviously a lot of companies care about privacy and we are really excited to partner with the industry to push the kind of small model size quality further um

Now, in terms of the research team, it's an interesting question whether you can focus on a very small, narrow field in image generation.