How can we make sure that everyone has access to AI? Can small models outperform large models? With Stability AI’s Emad Mostaque

**Emad Mostaque** (0:07)
What if the route to AGI is not one big model to rule them all, trained on the whole internet, and then narrowed down to human preferences, but instead millions of models that reflect the diversity of humanity that are then brought together? I think that is an interesting way to kind of look at it, because that will also more likely to be a human-aligned AGI, rather than trying to make this massive elder god of weirdness bound to your will, which is what it feels like at the moment.

**Sarah Guo** (0:41)
This is the No Priors podcast. I'm Sarah Guo.

**Elad Gil** (0:44)
I'm Elad Gil.

**Sarah Guo** (0:45)
We invest in, advise and help start technology companies.

**Elad Gil** (0:48)
In this podcast, we're talking with the leading founders and researchers in AI about the biggest questions.

**Sarah Guo** (1:01)
AI-generated images have been everywhere over the past year, but one company has fueled an explosive developer ecosystem around large image models, and that's Stability AI AI. This week on the podcast, we'll talk to Emad Mostaque, the founder and CEO. Stability AI builds open AI tools. They're most known for stable diffusion, the unreasonably effective AI model where a user puts in a natural language prompt and the AI generates images. But they're also engaged in progressing models in natural language, voice, video and biology. We'll talk about how this barely one-year-old London-based company has changed the AI landscape, scaling laws, progress in different modalities, safety and why he thinks the future of AI is open.
Emad, welcome to No Priors.

**Emad Mostaque** (1:42)
Thank you for having me on, Sarah, Elad.

**Sarah Guo** (1:43)
Let's start with the personal story. You have a background in computer science and you were working in the hedge fund world.
That's a hard left turn or it looks like it from that world to being a driving force in the AI of state of the art. How did you end up working in this field?

**Emad Mostaque** (1:57)
Yeah, I've always been interested in AI and technology. On the hedge fund, I was one of the largest investors in video games and artificial intelligence. But then my real interest came when my son was diagnosed with autism and I was told there was no cure or treatment. I was like, well, let's try and see what we can do. I built up a team and did AI-based literature review. This was about 12 years ago of the existing treatments and papers to try and figure out commonalities.
Then did some biomolecular pathway analysis of neurotransmitters for drug repurposing, and came down to a few different things that could be causing it.
Worked with doctors to treat him and he went to mainstream school, and that was fantastic. Went back to running a hedge fund, won some awards, and then I was like, let's try and make the world better. The first one was non-AI enhanced education tablets for refugees and others. That's Imagine Worldwide, my co-founder's charity. Then in 2020, COVID came and I saw something like autism, a multi-systemic condition that existing mechanisms that extrapolated the future from the past, wouldn't be able to keep up with and thought, could we use AI to make this understandable?
I set up an AI initiative with the World Bank, UNESCO and others to try and understand what caused COVID, and try and make that available to everyone. Then I hit the institutional wall in a variety of places and realized that the models and technologies that had evolved were far beyond anything that happened before. There were some interesting arbitrage opportunities from a business perspective, and more on that, a bit of a moral imperative to make this technology available to everyone because we're now going to very narrow superhuman performance and everyone should have access to that.

**Sarah Guo** (3:42)
It's an amazing journey and congratulations on all the impact you've already had. As you say, or as you imply, the AI field in recent years has been increasingly driven by labs and private companies. One of the most obvious paths to performance progress is to just make models bigger, scaling data parameters, GPUs, which is very expensive. Then in reaction, just to set the stage a little bit, there's been some efforts over the previous years to be more community-driven and open and build alternatives like Luther.
How did you start engaging in that and how did Stability AI change the game here?

**Emad Mostaque** (4:20)
Yeah, so when I was doing the COVID work, we tried to get access to various models. In some cases, the companies blew up. Other cases, we weren't given access, despite it being a high-profile project.
And so I started supporting Luther AI as part of the second wave. So, you know, Stella and Connor and others kind of led it on the language model side. But really, one of my main interests was the image model side. I have aphantasia, so I can't visualize anything in my brain, which is more common than people would think. In fact, a lot of the developers in this space have that. Like, we've got nothing in our brain.