The Exchanges

Q How did you think about distribution? You know, you've seen, you know, HuggingFace has partnered with, um, Amazon. You've seen, obviously, OpenAI with Microsoft. When you think about distribution and your competitive edge there, where did you land?

A So my business model is actually very simple. I haven't really talked about it much. Um, stimulate open. We're one of the biggest providers of, uh, grants to open source software, tens of millions already, and then take the best of open, which hopefully we build ourselves, and then an open base with an open data, and then commercial variants with licensed data, and then national variants. So you have Hindi insurance adjusted stable chat. Or Indonesian pharmaceutical worker stable chat that's available in every cloud on-prem on device with licensing fees, royalties, and revenue share. And the system integrators work with us as well. Lots of announcement to come. And so by standardizing and stabilizing all the complexity to these very sophisticated building blocks, these very intentionally built models, that really helps the world integrate this stuff by building playbooks and other things. That's the core business, because it doesn't require actual innovation. We are still innovative in the leaders and media in particular. Instead, it requires data and distribution. Data to the models, the models are open and interpretable, and models to the data via our partners. And that's valuable, because the private data in the world is far more valuable than the data that you will send to proprietary models. And it's not a race to the bottom either. So, that's what we are. We're a modeling…

AI assessment note: “available in every cloud on-prem on device with licensing fees, royalties, and revenue share”

Q How do we think about open source versus closed source human healthcare data? Cause like obviously for us all to benefit as one, you know, MS sufferers around the world need to submit that data around, you know, responses to certain treatments.

A Yeah. So I think the wonderful thing about these models is they're few shot learners, so they don't need to have much information. And so isn't the classical big data problem. HDR UK has been one of the pioneers here with the UK Biobank, Federated Learning and others. And there are kind of, um, with FL seven, HLR and other standards being built around this to allow for full federated learning. If you have open source, Language models that are fully auditable, understand, I call them organic free-range models, the ones we're building with no web scrape data. Those can sit on device, like Google yesterday announced, um, POM-II. The smallest POM-II model is four hundred million parameters. It works on your Google Pixel phone. You don't need giant models anymore, and then that model can just share the specific information that preserves your privacy with the bigger thing, and then it can take from that global knowledge base as well. So you'll have big global models, on-device models, And I think open works for that, because you don't need to have all the data open. You just need to know that Harry is old enough to have a drink, not that all the details about Harry and his birthplace and everything like that.

AI assessment note: “I think open works for that, because you don't need to have all the data open.”

Q What does that look like? Sorry, AI plus humans.

A AI plus humans means that you have information coming in and then the stories are, drafts are automatically written, reviewed by humans who then give their input to train it better. This is kind of the feedback flow, and then what happens is it comes out, and there's a factual anchors, and then it gets customized to Alabama, and then Alabama context, and all sorts of other things, because you can tell it, TLDR, too lazy didn't read, explain it like I'm five, make it more complex, and so you're going to see something very interesting here, which is the right news at the right time. The localization will return, but again, through AI first. I think this is the thing, we're seeing AI integrated, But the next wave is going to be, once we understand design patterns, AI first, everything and information flows, once these technologies are a bit more mature.

AI assessment note: “drafts are automatically written, reviewed by humans who then give their input”

Q What does that proper partnership mean, and who's that with? That's with IBM, that's with SAP, that's with Apple.

A So, we've announced Amazon, let's say we have lots of other announcements, the biggest companies in the world, where they have amazing teams, but they can only move so fast. And I'm building dedicated teams to help them move and understand the whole sector, Without trying to, like, sell them on services. I'm trying to say, I will build you a customized model if you want, but I'm only doing that with a dozen companies, you know, so I can kind of focus down. And I will tell you that GPT-IV is great, or Cohere is great, or all this stuff. All the latest research through the communities we support, I will make sure you're on top of, rather than into your sector, and you've got dedicated people helping you in this transition period.

AI assessment note: “we've announced Amazon... I'm building dedicated teams to help them move”

Q And one thing I heard you talk about before, which I thought was fascinating, was your access to super compute, and you compared it to existing large incumbents. Why do you have more super compute than other people?

A Because I went and I did it. So we had articles coming out saying about our burn. I'm like, I have oil wells when everyone wants to build petrochemicals. Every day we have companies coming to us asking us for our super compute because it's not available on the market. You need these chips lined up with interconnect, and we've got 7000 A-one hundreds now. You know, we have TPUs, we have all these things, and we know how to use them, and we can share them with people because we're open. You know, whereas Anthropic and others cannot. So this is like, at the worst case, I'll build a foundation model as a service company, and I'll make a hundred million dollars in profit this year without having to charge even market rates, and I can retire, but I wouldn't do that. I wouldn't bring this to the world. So I think compute is misunderstood. It's not like Bird and, you know, all these scooter companies and others, they spent money on marketing. This is actually an asset right now that's scarce. And so there's no harm in scaling compute, and then with the top chip manufacturers, they're building us dedicated teams, and again, they're coming in and supporting us because our models drive demand for their chips. The more open models there are, the more open demand is, so it's a virtuous circle there as well, and so we get compute before everyone else.

AI assessment note: “top chip manufacturers, they're building us dedicated teams”

Q Why? Like, what would I, just tell me, AGI to build a sustainable business, because at the end of the day.

A They're building an AGI to turn the world into utopia, it's written in their path to AGI thing, that they think this can basically bring about utopia. Apple's a black box, right? So we'll see a WWDC, and so they could surprise us all, But let's face it, Siri's crap. But they have all the ingredients in place. The identity architecture, the secure enclave, other things. Neural engine. A stable diffusion was the first model ever optimized on the neural engine, etc. But let's see that one. Amazon have moved faster than I think they've moved before. Amazon's interesting because they're an engineering organization. So they have self-driving cars. They have satellite internet. Because once they've got it and they can take it from research to engineering, it's there. One of the struggles they've had is that it's not moved from the research side yet. You're still evolving on research. They're like, what do we do now? But they are inclusive. Jeff Bezos said for his first hundred billion in revenue, he envisioned half of it being proprietary and half of it being marketplace, and they're having the same approach with Bedrock and things. Meta, I think, is the dark horse. I think Mark's probably pissed off that OpenAI bought AI.com, so you couldn't change it from meta to AI. But again, having him at the head, he can shift these things, right? Because the metaverse, obviously, is a complete …

AI assessment note: “They're building an AGI to turn the world into utopia... Apple's a black box”

Q Why? Like, what would I, just tell me, AGI to build a sustainable business, because at the end of the day.

A They're building an AGI to turn the world into utopia, it's written in their path to AGI thing, that they think this can basically bring about utopia. Apple's a black box, right? So we'll see a WWDC, and so they could surprise us all, But let's face it, Siri's crap. But they have all the ingredients in place. The identity architecture, the secure enclave, other things. Neural engine. A stable diffusion was the first model ever optimized on the neural engine, etc. But let's see that one. Amazon have moved faster than I think they've moved before. Amazon's interesting because they're an engineering organization. So they have self-driving cars. They have satellite internet. Because once they've got it and they can take it from research to engineering, it's there. One of the struggles they've had is that it's not moved from the research side yet. You're still evolving on research. They're like, what do we do now? But they are inclusive. Jeff Bezos said for his first hundred billion in revenue, he envisioned half of it being proprietary and half of it being marketplace, and they're having the same approach with Bedrock and things. Meta, I think, is the dark horse. I think Mark's probably pissed off that OpenAI bought AI.com, so you couldn't change it from meta to AI. But again, having him at the head, he can shift these things, right? Because the metaverse, obviously, is a complete …

AI assessment note: “They're building an AGI to turn the world into utopia”

Q Shared narrative, psychological safety, two of the biggest contributors. So now running stability, how do you think about integrating those two?

A So we've got the shared narrative. We're going to build the foundation to activate humanity's potential, and then the motto is make people happier. But it's been a learning process. A year ago, we were basically a mom and pop shop in some ways. My wife and I were working at it. Like, had lots of meetings out of our, like, sitting room and things because the office didn't have Wi-Fi and all sorts. Now it's like, Growing up, we're a 170 people, we're going global, we'll have stabilities in every country, and the next year we're going multinational, and that's difficult. So we really try to put in processes in place, but it's not easy. Part of this is, like, we went closed source on a bunch of stuff like Dream Studio. I'm open sourcing everything now. From next week, we're going to build our language models in the open and share what works and what doesn't work.

AI assessment note: “So we've got the shared narrative. We're going to build the foundation”

Q What do you think the business model of the future is for those models moving into enterprise?

A I think it's the same as always. You've got good products, good distribution, you know, you lock it in, like, 1.5 million people still use AOL. You know, like, HCL bought Lotus Notes for 1.5 billion a few years ago. Like, 40% of the world still doesn't have internet. Again, we're super privileged where we are, right? And so you look at that, and I look at emerging markets, I'm like, all of finance is securitization and leverage, and securitization is telling a story. The only thing that matters for a stock is the marginal story and how it evolves. What if you have massive information about every child in Africa, and every business in India, and they embrace this technology properly? Massive financial growth.

AI assessment note: “I think it's the same as always. You've got good products, good distribution”

Q What do you think the business model of the future is for those models moving into enterprise?

A I think it's the same as always. You've got good products, good distribution, you know, you lock it in, like, 1.5 million people still use AOL. You know, like, HCL bought Lotus Notes for 1.5 billion a few years ago. Like, 40% of the world still doesn't have internet. Again, we're super privileged where we are, right? And so you look at that, and I look at emerging markets, I'm like, all of finance is securitization and leverage, and securitization is telling a story. The only thing that matters for a stock is the marginal story and how it evolves. What if you have massive information about every child in Africa, and every business in India, and they embrace this technology properly? Massive financial growth.

AI assessment note: “I think it's the same as always. You've got good products, good distribution”

Q Is there any extent to how low it can go?

A We have no idea. You already said this is impossible. Two years ago, you're like, no way. You have a single file that's maybe a few hundred gigabytes that can pass every exam apart from English Lit. There is no such thing as an unbiased model. DALI-II, when OpenAI had that, they introduced a bias filter. Any non-gendered word that had a random gender and a random ethnicity. So you typed in sumo wrestler and you get Indian female sumo wrestler. This is why you need national data sets. You need cultural data sets. You need personal data sets that can interact with these base models and customize to you and your stories. Cause you and I both have our stories that make up our psyche and understand that context is so important to have AIs that can work for us, not on us.

AI assessment note: “We have no idea. You already said this is impossible.”

Q Is there any extent to how low it can go?

A We have no idea. You already said this is impossible. Two years ago, you're like, no way. You have a single file that's maybe a few hundred gigabytes that can pass every exam apart from English Lit. There is no such thing as an unbiased model. DALI-II, when OpenAI had that, they introduced a bias filter. Any non-gendered word that had a random gender and a random ethnicity. So you typed in sumo wrestler and you get Indian female sumo wrestler. This is why you need national data sets. You need cultural data sets. You need personal data sets that can interact with these base models and customize to you and your stories. Cause you and I both have our stories that make up our psyche and understand that context is so important to have AIs that can work for us, not on us.

AI assessment note: “We have no idea. You already said this is impossible.”

Q Is there any extent to how low it can go?

A We have no idea. You already said this is impossible. Two years ago, you're like, no way. You have a single file that's maybe a few hundred gigabytes that can pass every exam apart from English Lit. We need to feed these models better data and other stuff, and that's why we're moving so hard at stability. There should be no more web script data in here. There should be national data sets that are good quality to feed these free-range organic models and national and proprietary models and others. There is no such thing as an unbiased model. DALI-II, when OpenAI had that, and they introduced the bias filter, any non-gendered word, they had a random gender, and a random ethnicity. So you typed in sumo wrestler, and you get Indian female sumo wrestler. That was a good picture I got to save somewhere. This is why you need national data sets, you need cultural data sets, you need personal data sets, that can interact with these base models, and customize to you and your stories, because you and I both have our stories that make up our psyche. And so that's why, and the reason I signed that letter, because I think there's a six month pause to get all of our shit together, Before, things go completely insane, and next year, this is everywhere, and everyone's investing in everything, and it's just absolute chaos. You will have national champions and others. I think it's incredibly diffi…

AI assessment note: “We have no idea. You already said this is impossible.”

Q Shared narrative, psychological safety, two of the biggest contributors. So now running stability, how do you think about integrating those two?

A So we've got the shared narrative. We're going to build the foundation to activate humanity's potential, and then the motto is make people happier. But it's been a learning process. A year ago, we were basically a mom and pop shop in some ways. My wife and I were working at it. Like, had lots of meetings out of our, like, sitting room and things because the office didn't have Wi-Fi and all sorts. Now it's like, Growing up, we're a 170 people, we're going global, we'll have stabilities in every country, and the next year we're going multinational, and that's difficult. So we really try to put in processes in place, but it's not easy. Part of this is, like, we went closed source on a bunch of stuff like Dream Studio. I'm open sourcing everything now. From next week, we're going to build our language models in the open and share what works and what doesn't work.

AI assessment note: “Part of this is, like, we went closed source on a bunch of stuff”