Canva's data makes smaller design models more economical

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 5:13

   Do you think it's interesting or do you think it's obvious that you did it? No, I think it's very interesting because I think it also applies to other companies maybe of similar size. when basically there's two factors. So first of all, you have unique data that nobody else has. And then the second part is deep vertical integration of AI models in your stack is going to provide incredible performance and cost reduction.

2. 5:39

   Those two factors are basically the driving input. And I'm sure there's a lot of companies that are like in the, you know, thousands of employees that are even smaller that will do that and where it makes sense. And so for us, it was kind of the discovery of the fact that you can have, you can fine tune or train a model that is two orders of magnitude fewer parameters if you have the data and you can beat the bigger model.

3. 6:06

   And then because it's smaller, you're going to be like another magnitude faster and cheaper, right? So that is the belief that we had. And right now we are in the 40X cheaper than the most expensive frontier models. They can do the same design task, but they are using others or many more parameters to do it. Instead, we have smaller models that we can fine-tune, we can do reverse learning with unique data that we have, and we can basically squeeze a lot more.

4. 6:38

   Same level of quality, sometimes better, but most importantly incredible speed and cost reduction. So that's one part. The second part is that the product itself with AI is evolving dramatically, right? And the way AI is helping humans generate design is really changing. And so there has been a lot of like, I would say siloed innovation where like, okay, I have an image model, the image model creates an image. Then I have a next step, an LLM looks at it and thinks, oh, we need to change something, goes back to the image model and says, hey, can you make something different?

5. 7:16

   And like in that back and forth, you lose control and you're also throwing away information. And so we have, with Craft, we have created like an image model that most of the work actually is creating a scene graph of what the design should look like. And then the conversion to pixel is just the last step that we train from scratch. But at the layout, let's call it the design layout stage, the LLM can already intervene and make changes before the pixels are generated.

6. 7:46

   And so we are seeing that there's incredible product differentiation by integrating the LLM world and the diffusion model world, if you want, in a more cohesive way. like the iterations can happen earlier and you have all non-destructive modifications with humans in the loop instead of having to bake the result, show it, iterate, and then to basically throw away in that process a lot of information every single time.

7. 8:16

   So those were, I think, the two main factors that drove our decisions. And also, if you look, the top models are now open source or open weights on the image generation space.