West

This year's Fortune 500 marks the 63rd running of the list.

Fortune 500

This year's Fortune 500 marks the 62nd running of the list.

This year's Fortune 500 marks the 61st running of the list. Wal-Mart claims the top spot again. See who made the list.

Wal-Mart stayed on top as its sales crept closer to half-a-trillion dollars. Apple moved into the top five. And UnitedHealth Group continued its steady climb. 

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West (WSTC): Company Profile, Stock Price, News, Rankings

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West (WSTC): Company Profile, Stock Price, News, Rankings | Fortune

This week, Allie speaks with Rebecca Lynn, Managing Director at Canvas Prime and trailblazing venture capitalist. Together they discuss Rebecca’s roots in the Midwest, her path to Silicon Valley and what she’s learned as an entrepreneur turned venture capitalist. Also in this episode: the unusual deal between Nvidia and the US Government, and the new class of AI billionaires.

Nvidia + AMD’s Deal & Unfiltered Chat with OG VC, Canvas Prime’s Rebecca Lynn

Tech

When no one was looking, the global semiconductor business became a smash-mouth, no-tariffs-barred affair. But in an age of AI, business is still (very) good. The founder and CEO of one of SoftBank's newest multibillion-dollar acquisitions weighs in on the future of AI compute, the changing cast of industry power players, and the shifting geopolitical landscape ahead.

Iron in the Fire

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Peter and Daniel. So there's a lot of excitement about what AI can do, right? That's why we're here. But none of it's possible without the right infrastructure and the right chips. The competition to build the next generation of AI hardware isn't just a business story, it's a strategic one touching on everything from investment and innovation to geopolitics. Today we're joined by one of the most respected leaders in technology and semiconductors to help us understand what's at stake and where the future is heading. So please welcome Renee James, chairman and CEO of Ampere. Hi Renee. Good to see you. All right. Thank you for joining us. So You worked at Intel for a very long time, um, and you worked with the great Andy Grove. You were for a long time, you were first as chief of staff, is that right? And then you, you know, right before you left Intel, you were the president of Intel. So, When you look at that, I promise I will ask you questions about your company Ape, which was acquired by SoftBank for $6.5 billion earlier this year, but I promise we'll get there, but I have to know as an, as an Intel vet. What would Andy Groves say about the state of Intel today? Thanks. Yes, um. He would be very disappointed. Why? Because it's an industry that Intel um invented literally invented the means by which you fabricate modern semiconductors, um, the what we think of as fabs that everyone likes to talk about and weirdly wants to build everywhere. Uh, and the process that goes into them was pioneered by Intel, developed by Intel. The modern transistor technology was invented by Intel, um, the first modern CPU that powered, you know, computing and the entire PC mobile, everything. Was Intel. So when you think about that, and these were people who left Fairchild because they had a different view of how the future could be and started with venture capital, if you can believe it, in the late 60s, 6869, this is, you know, their life's work and then to see Intel fall behind. Um, a company that was a great partner, has been a great partner, still is, I think of Intel forever, my supplier TSMC was started in collaboration with Intel, um, because we needed an additional source. So you look at it and you think, wow. That's kind of scary and sad. And Intel is not that sort of enzymatic uh uh uh actor that it was in in its heyday is, is kind of what you're inferring. And a lot of the technology that we use today, I mean, the Mellanox interconnect technology of Nvidia was invented at Intel and spun out there, you know, my company, uh, not just me but several of the key leaders, we're X Intel, we built every server product that built every data center. it started, you know, all of this cloud stuff. So, you know, a lot of good came out of it and a lot of spinoff companies, you know, the modern way that we think about building silicon and semiconductors that we all take for granted like a miracle occurs. You turn something on and it just works and like, you know, 70 billion of something you don't even know what they are is going on and on a little piece of silicon and we don't know, you know, it just works it it truly is miraculous, but then you think about the state of the company today and And I, I wish them recovery. I really do. I think they're super important to global semiconductors. They're super important to the future of where we're going because at its base Intel is the company that invents the future of transistor. still today, even in their current situation, super important. We will come back to the sort of global implications of that in a second, but I do want to, I do want to talk about Amper for a second. So Renee, you're one of two women who run semiconductor companies. It's extraordinary. And then you I'm happy that there's someone else now. That's right. That's right. And you went even further because you said, I'm going to start one. Like that isn't exactly how that went, but yeah, I think Andy said, what are you doing with your life? Why don't you start the next one? So what what what leads one to start a semiconductor company because I've always gotten the sense that um sort of like an automaker, you can't just turn around and it's not exactly a software company, you know, you can't just spin it up overnight. You need a lot of capital. Yes, um, well, so first of all you have to have a passion and a vision for where you think things are going because you, you know, you're not, you wouldn't start a company at the kind of the scale of a CPU company or GPU company, um, from the ground up at this point in the history of semis given the the hurdle rate and the investment rate and costs, um, unless you had a passion for doing something that hadn't been done before because you know you're not gonna start a company to like compete with Nvidia. That's ridiculous. Um, so we said the problem that we hadn't solved, the thing I was passionate about after 30 years of building microprocessors, was we have always, whenever we got into trouble we would just say like, oh, you know what we can do, we'll just put more power onto it. We can just release the power and I'll get higher performance or we'll put down more transistors. I mean, we did a lot of things that at the time it didn't matter. It didn't cost as much. It was, you know, no one was going to freak out that we went from 350 watts to 400 watts to, you know, whatever. I mean these were conscious decisions to change because you have to change the spec on what the server is and what goes into the data center and all that stuff, but more was more, more was more and you could get paid for it. Um, so the thing we didn't do was something that we knew about. I mean, good and physicists know that at some point, you know, you need to constrain the power and figure out how to improve the performance through other techniques, and that's a lot of R&D and a lot of thinking. About thinking differently, thinking differently about how we designed the architecture of X86 for the last 20 years that we had been working on it, um, before I was president until I ran R&D for a long time, so we spent a lot of time thinking about this, so we, um. I said, you know what, that's what I want to work on. I want to work on how we build out what comes next, knowing that not AI in the way that we think of it today, but we were working on machine learning and everything already. We knew that was the next big thing. We knew that cloud computing, the way that the cloud used software was different than the way that the enterprise consumed software, like in this linear scaling, always on, always on demand. Um, and, and the microprocessors that we had built were meant to turn on and off in a way to be efficient for different workloads but not for the cloud, so that was our thesis and um. I gathered around people that were retired or that had worked for me before and I said we should do this and they're all like, you've lost your mind because we had built like 80 microprocessors, 80, and they were like, really, are you sure? I'm like, we can do this. If not us, then who? And then Andy says. He was still alive and not really that helpful in the sense that he was like, we taught you everything, you know, this is what we what Bob and Gordon and I did. This is what, you know, this is, this is the history of Silicon Valley. Someone has to do it, so that's what we did and I was really fortunate because the Carlisle Group, who doesn't. Usually do venture um back to us in our first round um and then we had a couple of strategic, our largest strategic being Oracle over time um and we ended up raising billions of dollars over the course of the last 8 years and um last month announced that, um, SoftBank Group is buying the company for $6.5 billion. In cash. So let's talk, let's talk a little bit about that. So what. Some folks were scratching their heads when that deal was, was, was announced. What does SoftBank give Ape? What does Ampere give SoftBank? Um, well, of course the deal isn't closed, right, so we're in the regulatory period, but in general, um, we've pioneered, we have a lot of patents and a lot of technology we've built, um, 5 generations of product already that have been deployed into major hyper scale data centers, um, how to build very, very high performance in an air cooled, meaning 500 watts or less envelope, um, and we could release the power and use liquid cooling. And go even, but the performance is that of like an AMD leading edge processor today we have integrated AI acceleration inside of that envelope. We can add additional functionality on and only release the power, you know, 50, 100 watts. So I think that that whole thing allows you to build out a vision of AI, you know, scale data centers that are more efficient over time. Um, we were, we are an arm architecture licensee, so a sister company in the SoftBank Group, um, so we had taken an architecture that's inherently low power but had not been known for the highest performance, and we had, um, invented ways to bring it up to. The level of, you know, that kind of performance, and I think that it's very synergistic with their overall vision and the mission that Masasan has for Stargate. For sure, for sure, and, and we will get to that in a second. I, I wanna ask you, I want to come back to the, the geopolitics question so. When you and I were talking about Intel previously, um, you were expressing to me kind of the importance of that company in the overall ecosystem, um, honestly, in the US, but, but in as as outdated the terms as it is with the West. Um, we are in a very US versus China moment in time right now and you're very concerned. Um, what is the future of SEI right now and What's it look like if we don't get it right here in the UK? In the UK, um, what do you mean by don't get it right in the UK? What, what is the landscape, right? What is the playing field given these tensions and you know what, how do we remain competitive given that sort of binary choice, you know, semiconductors historically have been a global product that have been available globally, um. That you know the predominance of semi semiconductor business is distributed around the world both in supply chain and design. Um, some of the great design teams in different segments of semiconductors are in Europe, and many of the industrial companies in Europe do their own semiconductors, so super important. Um, the predominant and and currently an EUV only lithography equipment is from Europe, so I think we have a long history of a very open design design and manufacturing ecosystem. I, I do think that there is, you know. An importance in the global nature that this business has always been, it was never just a US business that went global. It was global from day one. So I think we have to think about where the people are who do this, how, how the, you know, how it moves, how it affects every segment of the economy in all these different countries, because computing is not just about people doing office work anymore, it's how we live our lives. It's everywhere, right? So, um, I think as long as we're conscientious about open market and and how we move forward on that I think that's important. The China thing is a separate situation because China has capabilities that are self-contained and to some extent I don't know what direction that will go it, you know, I have always participated in a global market, so it's hard for me to speculate on what that would look like, but it's the US and. Um, Taiwan, Southeast Asia, European, you know, design capability for global semiconductors is profound. I mean, it is hard to imagine doing, you know, some level of networking and mobility and everything without teams in France, for example, right? Like there's just specialties around the world, Poland. Right, right, I will come to the audience for a question in just a moment. I wanna just quickly ask you to tell me, you know, does AP have a role in the Stargate project? And if so, what? Um, well, I can't comment too much on that, um, but as we said in the press release, we, we do have a role in that, so yeah. All right. All right, let's go to the audience for questions. Questions for Renee James. They're all wishing they were in semiconductors, like, really? All right, well, I have, oh yes, right here up in the front, just wait, wait for the mic to reach you. Way up here. It's on the couch. And as always, your name and who you're with when it comes to you, OK? Thank you. I'm Doctor Shiva, I bank, and like I said yesterday, we're building a data center and also I'm from Sri Lanka. I don't know whether you all know Bain graphite, the pure graphite is in Sri Lanka and only 1% is exported. And now there are people looking at graphene, which is the GPU. Uh, you know, for graphene chips, you need graphite. Is there any, you know, experiments that you are doing or what's the plan? Yeah, so we don't do base level. Um, Ampere is a fabulous semiconductor company unlike Intel. We don't do um base level metallurgic, um, material science at MPR. We do design, advanced design and IP um development, um. So I am sure there are people looking at that, but most of that work is done in process fabrication, um, in conjunction with that. So at TSMC and Intel and places like that. Yeah. We have a question. Thank you for your question. We have one right back here, yes, your name and who you're with. Uh, I like, yes a business school. So I had a question about the overall ecosystem around CPUs and GPUs. So where do you see this going? Nvidia is now very dominant in the GPUs. Um, you know, X86 is still powerful in the CPUs. Where, where do you see this in 5 years, you know, Nvidia, ARM, everything. Yeah, thanks for the question. Um, in the, in the, you know. GPUs, as you know, grew out of gaming. So a large portion of the GPU, you know, the shaders and the things you need for rendering and gaming aren't used to um train large language models. So, Um, while it's a lot of horsepower, it isn't the only way that you can do the computation required for AI. And in fact, um, as we've discussed in multiple roundtables here, um, as we go to smaller models, as we use, let me say the appropriate AI technology or machine learning technology for the job at hand, you might just run it on an enhanced CPU. In fact, that's probably the most efficient way. So all the CPUs are being enhanced with AI acceleration, and we have a product that we've talked about Ampeer1 Aurora, that actually has um the AI functionality because we have 512 cores you can put, you know, AI cores and compute cores and into that's one of the great innovations that we've been working on. Um, so I think that 5 years from now what we're going to see is a convergence of AI and compute and architecturally there's an underlying thing that is super insular, uh, Silicon Valley Wars kind of thing which is the micro architecture of computing. Will it be X86 or will it be something else? Um, Nvidia is an ARM licensee. Nvidia builds their things on ARM as well. I do think given the progress we've made and now under our new ownership that um our proposed ownership that that arm is looking like a long term it's the architecture that Apple uses they're in a licensee that Samsung uses that Qualcomm uses that Broadcom uses. And um because of the developer community being on phones, laptops, you see, you get this bulk of developers that know that environment so I, I, I think the future is relatively bright there. And of course, SoftBank owns arm. Yes, right here. Hi Seth Dober in Quantum AI and Infinity Adventures. Hi Renee. Um, so one of the biggest challenges in, in AI, especially training is not necessarily compute, it's the distance between compute and memory. Are you guys doing anything in that space? That's my favorite topic. Oh my God. See, I, yesterday at lunch I was like, I love that guy. I, uh. Yes, Seth, of course we're working on that problem that we've had for the last 30 years. Um, if it exists, if it, if it was a problem in mainframes, it's still a problem. Um, of course there's high speed memories, but one of the things, of course I, I'm not gonna reveal how we're doing it, but all of us have been working on moving memory closer, staging memories, uh, flexible different kinds of memories that you might want to put in and out depending on costs, so there's a bunch of things that we're doing to, um. Deal with moving the workload in and out more efficiently. It's not just memory, it's a lot of the die to die interconnect as well, as you know, right? So we've done a lot of innovation. We have patents in it and die to die and how you move things across, but you know, the other thing that's quite fascinating only to me but probably not the rest of this room, but maybe to you, the way you lay out the chip where you put computation versus IO actually matters because it determines some of the. Length of travel and so we've been very attentive to that as well. Thank you for your question. I feel like I saw one more. Yes, was just my memory. I wanna talk about semiconductors. Come on now. Oh yes, right up here. Oh yes, your co-host. Yes, that's right. Just wait for it right here, Maddie. Thank you. Hi Renee, Jason Del Ray from Fortune. Um, I spent a lot of time, probably too much time thinking and reporting about Amazon, and I'm curious what you see as their potential future in the chip space. Um, I know they've made a lot of investments and are you bullish, bearish on them in the future, and either way, where, where do you see them fitting in? Yeah, so, um. Thank you for that question. It's, it's Amazon uniquely has an outstanding team that they've had together for years, and one of the hallmarks of a successful semiconductors is how many generations has a team done together, and the answer to Amazon is a lot, and they also use ARM architecture and have been quite successful in building chips, as has Google, by the way, um, that are specific to the pathology. They have a really unique way of accelerating data inside their own network and they're and they have an outstanding silicon team that's been able to build, you know, really great products that for their own use. I think given you know their their might and their team and everything that they'll continue to be very successful with that strategy. I, you know, the hyper scalers have the wherewithal to. Do that kind of thing, but the other half of the market is the enterprises of the world that need to use AI that don't have it, and that's where I think, you know, commercial chips have to fill that gap and they're not all gonna build out massive Nvidia infrastructure either, right? So there's still an enormous market opportunity and let us not forget, despite the fact that we never talk about it. Pure computational data, non-AI is growing as well at a double-digit kegger. So there's kind of a, there's other stuff going on as well. Thank you. Everyone, the fantastic Renee James.

The Mexican immigrant and son of a Holocaust survivor is on a mission to do the KIND thing. 


How KIND’s founder went from mowing lawns to selling his company for $5 billion—and becoming the newest ‘Shark Tank’ judge 

Features

Daniel Lubetzky is on a mission to do the KIND thing. 


KIND Snacks' founder pulled off a $5 billion sale—and now he’s judging entrepreneurs on 'Shark Tank'

KIND’s founder went from mowing lawns to selling his company for $5 billion

So I literally would receive boxes from each factory and I would try a product from every single batch. So like I would take all of these 2030 40 bars a day, cut them into pieces. And I would try from every product every day. That was like for many years, if you look at my face, I looked like a kind bar because that's like how many kind bars I was eating at the time. Hello, this is Daniel Lski. I'm the founder of Kind and this is how I made my first million. I grew up in Mexico City. We came to United States when I turned 16 to San Antonio Texas. My mom was a homemaker. She took very good care of us. And my dad was an entrepreneur. My dad was an extraordinary figure. He arrived to Mexico with a third grade education. He had been a Holocaust survivor that robbed from an education at the age of nine and was liberated by American soldiers when he was 15.5 and was in a refugee camp and arrived in Mexico when he was 17 and he didn't speak Spanish or English only had a third grade education. So he educated himself by reading used books and using encyclopedias covered to cover volume, a volume B volume C, he spoke nine languages. By the time he passed away, he had read thousands of books. He was one of the most educated self made people and he started working in factories two or three shifts in a row. So eventually he and his father had a little bit of money and they started a small jewelry shop which grew. So they owned ultimately a chain of duty free stores on the border between Mexico and the United States. I first went to a Jewish Mexican immigrant school in Mexico City where they taught us Yiddish and Hebrew and Spanish as kids. Then I went to a public high school, Robert Lee High School in San Antonio, Texas. Well, when I moved to the United States, I didn't have had a permit to live here. We moved here legally, but I didn't have a permit to work. So I had to be an entrepreneur because I was allowed to do that. So I would start my own business. I had a lawn mowing business, but I didn't have a lawn mower. So I had to borrow the one from the people whose lawns I tried to mow and I had a car washing business with my cousin. I also had little businesses here and there, culminating before law school with uh business selling watches, I sold them in flea markets. Eventually we had different booths uh in different uh shopping malls, selling watches. And I had a network of students selling watches to all the administrators and the teachers and the professors and to each other. And it was a lot of fun. I went to Trinity University in San Antonio and Stanford Law School. I worked at a couple of law firms for little stint during the summers. But these were all stints right after law school or in between law school. But what happened is I was very passionate about the Arab Israeli conflict when I'm trying to resolve or contribute towards resolving the Arab Israeli conflict. And I got a fellowship to go to the Middle East to research, to turn my college thesis into practice. And I actually ended up taking a leave of absence from the law firm that I was going to join and studying peace works. The partner thought that I was going to give up after six months. I thought that it was going to become an incredible success, but it ended up being my vocation and my passion for 10 years. But it's a peace works where I learned so much of the food space. And it's all those mistakes at peace works that with the benefit of self reflection and improvement benefited me when we launch time when I was doing piece works just so you get an idea. I was literally knocking on doors and walking the streets of Manhattan going from 100 and 20 seconds and Broadway down all the way to Financial District where we were filming then going crossing and going all the way up Broadway. I would start east and the west or west and go east and along the way when I was like spending the long day skipping lunch or dinner and I wanted a healthy snack. I couldn't find something that I could feel good about eating. And I'm like, wow, if I don't find it, I'm sure others also have that need. So I was on the lookout for a healthy snack or something that was wholesome, but also convenient. That was healthy, but also tasty. And that's how we came up with the idea for what became kind. When we started making kind bars, it was all done by hand. All of the industry relies on what's called extruded products. Every one of our leading competitors takes a bunch of ingredients, macerates them beyond recognition and then plops them what's called slab bars because it's a slab of a slab of this homogeneous stuff, but it's unrecognizable. You don't know what you're eating with kind. We wanted to celebrate nature and use whole nuts, whole fruits, whole grains and really, really preserve their identity. So they don't oxidize them so that they are as nature intended. And that's actually much harder to do because it doesn't flow as easily through the line. So initially everything was made by hand and we had to cut the product in the lines. And over the course of years, we developed ways to automate the process. And today, you know, we make tens of millions of bars a day. The center of the tasting for kind bars was meal for many, many, many years. Like that was my lunch and dinner because I was so obsessed as that I would not even once we were making the product and toil, we were scaling and then we have one factor and then two factors and three factors and four factors. I was very worried about quality control because in my early years of peace works had made the mistake of not being obsessive our quality and disappointed my consumers and they had punished me for it by stopping to buy our peace works products. So I was determined not to make that mistake with kind. So I literally would receive boxes from each factory and I would try a product from every single batch. So like I would take all of these 2030 40 bars a day, cut them into pieces. And I would try from every product every day. That was like for many years, if you look at my face, I looked like a kind bar because that's like how many kinds I was eating at the time when I started kind, it was an offshoot of peace works and I had not delivered an investment for my investors at peace works. So I rolled them over into kind so that they would have a chance of getting their money back. And what would have turned out to be probably the worst investment in history probably became one of the best investments in history. They got anywhere from 1000 X to 5000 X, which is crazy. But that's how kind uh exploded once we start doing things, right? But honestly, other than my initial investment of $100,000 from my closest friends, nobody wanted to invest in me. So I I held on to most of the company, not out of choice or wisdom, but just nobody wanted to invest. It was not until late 2008, early 2009 that I finally brought in a private equity investment of $5.1 million. And that was the only time that we brought in money for primary shares into the company to supposedly grow the company. We actually didn't need the money because the company was profitable. And uh that was 2009 and that was the last time we ever received primary shares, we never needed more. One of the mistakes I made that I encourage entrepreneurs not to make is that I left my fate in the hands of my investors, of my private equity investors and their interest ended up not being aligned with me. You really need to be careful when you get an investment together with someone that's aligned with you. And that really wants to have the vision and also recognize that the vision might change. Because when I did this thing, I said five years later, I want to sell kind that's fine. But then I realized I had reached that zenith and realized that it was a much higher one. And I was just getting started and I wanted to continue doing it. And the private equity investor pushed really hard for us to sell the company and said, no, I don't want to sell it. So it was a very tough negotiation where I ended up buying them back at a very crazy valuation they put in uh I think between the primary shares and the money that I brought in in secondary for myself and for all of our investors, I would think we had brought in $16 million. They got like a hit $227 million back plus distributions in the course of 4.5 years. But I got over 80% of the company at the time, the period when the private equity investors were pushing me to sell, uh the company was very painful and difficult. I didn't sleep well for, for many years, there's just so much intensity in the journey. And so very often times I couldn't put myself to bed. And that period was one of the many when I was really, really really worried and I just worked through it. But right now I can be a little bit nostalgic and proud. But back then I was frankly just scared this business advice I got was from Martin when there, when I was a freshman in college and I asked him to come speak to my class and he said, whatever you do, do it to the best of your ability. Give it the very, very best you got, give everything you got to whatever you choose. Because every morning something, someone is waking up and doing that. And if you really want to stand out and win, you need to, wherever you're focusing, really deliver you very, very best. We did a transaction where we saw a controlling stake of kind. I still own a minority, a significant portion of kind but not uh controlling stake. But when we did the closing dinner with my partners, I ended up doing a magic show and it was a mentally performance when my colleagues, my bankers actually thought that I had read their minds and they were actually like, really upset for a while that had like used that in the negotiations and like, like, you know, it's magic. It's not, I can't really read your mind, but maybe I can.

Emad Mostaque, Chief Executive Officer, Stability.Ai

Brainstorm AI 2022: Silicon Valley’s Newest Gold Rush

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Brainstorm A.I. 2022: Generative A.I.: Silicon Valley’s Newest Gold Rush

Welcome back. I hope you all had a chance to stretch your legs and grab a coffee. And now let's continue with our main stage conversations. There is probably no hotter topic in A I these days than generative A I. It's an umbrella term for software that instead of just analyzing existing data can create original blog posts, Wikipedia entries, artistic images, video code, and more up next, a demo of the remarkable tech from Stability A I followed by a conversation with the firm's founder, Ahmad Mustaq. He'll lay out the economic and creative upside that will follow from making this open source free to use code available to anyone who wants it. Please welcome to the brainstorm A I stage, Ahmad Mustaq founder and CEO of Stability A I. We'll get to the demo in just a minute. But first of all, I want to ask you, why did you do this? And especially why did you do this as an open source project? I mean, potentially, you know, this could make a lot of money in the film entertainment industry. Why do it open source, fun and profit? I think I saw this technology and this technology classical A I was you built an air for one thing, this is there that can do anything. And I realized it would be like servers and databases which are almost all open source and it's all about scale and service customization. So I thought rather than wait five years to get there, let's just go there at once and allow it to spread everywhere which it has. So like for example, stable diffusion has just overtaken Bitcoin and Ethereum on github stars in terms of developer popularity, the fastest growing open source software ever with the biggest community building. The craziest things. Wow. Um Now, one of the things when stability of 1.0 came out, that was a little bit controversial is that unlike some of your competitors like dolly, you didn't apply any filters to either the training data or the output to make sure that people couldn't generate pornography or other sort of not safe for work images. Now, with the 2.0 version that was just debuted last week, I believe you, you've actually rolled that back a bit. You have applied some filters that make it much harder to generate pornography. Why the reversal on that? So the first part was actually a research initiative led by the LMU team at the University of Munich with collaboration from ourselves runway ML and some others that providing the main compute infrastructure. And Robin Rombach, our lead GS of code are doing it and they said, let's start it as a base and make it just an image of the internet and see what we can do and that's what came out. But we put a safety filter on it and an ethical use license as well to limit the amount of output for our own release of stability. We decided and listened to the feedback that we would filter out, not safe for work elements so more people could use it. We decided that we would also make it a more open dataset. So we could really understand how the model worked and we millions of dollars kind of doing that as well, which is now results in the model that anyone can use in a commercial and corporate setting. Great. Well, let's let's, let's see the model and I this is a special demonstration. This is the first time I think you've shown this particular version of the model. Can you, can you explain what you're going to be showing us? Sure. So basically, um we released 2.0 12 days ago, we released the original 1 August 23rd, released 2.1 tomorrow. Um This is the pace of kind of innovation here. 2.1 is very special because when we released 1.01 0.4 actually in August 23rd, it was 5.6 seconds for a generation. We release that release that to 0.9 seconds for an image. Um just a few days ago as of next week, it will be 30 images in a second. So we designed it to be fast and now we've got to real time. This unfortunately isn't quite real time. Um But we've got the one second generation and again, it can generate anything. So you take words, you put it in and for example, is on stage screen. Yes, you can do a lovely Labrador, right? Labradoodle. We like doodles, Labradoodle. And then in a second, it generates a picture of multiple lovely labradoodles with a hat and then boom, it generates it with a hat. I think it's just loading on the internet and then we can do by Van Gogh and it becomes by Van Gogh. Um So this is actually a very interesting thing because 100,000 gigabytes of images compressed into a 1.6 gigabyte file that works on your Macbook. And as of last week, it works natively. So you'll be able to generate these images in a second in a stained glass window. So you can generate just about anything because it understands 2 billion different concepts. And this is just unimaginable, right? And like I said, as of next week, it will be 30 frames. A 2nd 2.1 adds back a lot of the artistic elements. We got some lovely stained glass windows. Isn't that nice? But then it's also very flexible. So for example, what we can do, oops the wrong way we can do. Let's do nine images. Here. We took four images of Jeremy and we decided to create a Jeremy Khan model. This is just with four images. Who knows if this will work? Uh a picture of Jeremy Khan as an astronaut. You want to go to space and then we can do HD map painting. Why not live demo of a new technology who sees knows if it will work or not. We'll find out in a second. The suspense is killing me. OK. OK. How are you doing there? Maybe we should make it professional. Actually, it wasn't professional enough. So it's interesting because it understands all these kind of modifiers. If you do a professional, not professional, you also do negative prompts, you can say not ugly and it becomes not ugly. Um You gotta do not ugly for me. Definitely Jeremy like any of these like, how's this one here? Yeah, not bad. It's fine. Not bad. There we go. Just the four images. We've got Jeremy there and um now we obviously do by banker. There we go, right? So I think, you know, super powerful technology, they can create any image unimaginable just a year ago. That that's not me. That's not you. Another Jeremy's emerged. So mat professional, let's do photo with some 35 millimeter studio lighting to make it good. So, I mean, I think this is the thing, this technology is still nascent, right? This is unimaginable. Just a few months ago, let alone in the last year and it's just evolving at such a rapid rate. Still not, well, it's closer, but this technology is designed to be kind of zero shot where you just put something in, you get out, it's part of a whole design process. So we've seen people take this and create entire movies from it because they take frame by frame like into the Spider Verse and they transform it into an anime movie. We've seen people take this and create architectural designs at scale and you know, again, it's part of the process. It's this amazingly powerful tiny file, we put it out to the world and now people are developing around it and developing an ecosystem that they can then take and build really useful applications because that's what you need actual useful things. This is why the number one App Store app one app Store of the year is Wombo, which uses our technology. Number one on the App Store right now is Lenzo which creates these types of customized profiles based on this version of the technology as well. So I think it's going to be very interesting to see how this develops going forward. Absolutely. And there, I mean, there are skeptics out here sort of would say this is great. It's a lot of pretty pictures. But what's, you know, what's the real business impact of this? So like if you look at movies for example, this will cut down movie generation time by half. So we have an example. Our creative director is shooting a movie in a desert about serial killer with some very famous acts and actresses. Serial killer had a book of all the victims that he had and they were gonna have to hire 25 actresses to fill that book, put them in seventies makeup and you know, fly them out sag rates, make up all this kind of thing. $600,000. He trained a custom model overnight on seventies pictures, it was done. Save $600,000. Wow, that's just one example when you look at video games, movies, other things that transforms in any type of design process, we working with automotive manufacturers to create brand new car designs because you can do these kind of dynamically, you know, you can just type in like an awesome super car in San Francisco and it will generate those but you can customize it with your own assets. This thing when you take the standard model and customize it, it becomes super duper powerful. That's an awesome supercar in San Francisco. Yeah. Wow. And are you, are you thinking of using this beyond just images? Are you thinking about using the same underlying technology for generating other sorts of things? Yeah. So we have the most popular language model in the world G TJ Neo and others from our Luther A I kind of community and we actually do models of every single type. So we have audio models that can generate any type of music. We've got 3D models coming video models. Ultimately, the whole thing is that there's no barrier to creation when you have this next generation of models. Because we move from a big data, we took big data extrapolated for very specific models to these very general models that understand principles like understands the word Supercar, San Francisco, we Twitter about Jeremy, maybe it needs more than four images, but we have bigger Jeremy, right? And this is the next stage in A I and the next stage in real usability of this technology, right? And for you guys, it stability, what's your kind of business model going to be going forward every single creative industry in the world is going to have to react to this because like I said, as of next week, this is 30 images. A second which is basically video brand new experience where you can create anything dynamically. We're going to go to them, take the extra bytes, petabytes of data and convert them into models because again, 100,000 gigabytes to 1.6 gigabytes. Something quite crazy. Yeah, absolutely. Um I said I wasn't going to take questions from the audience, but I think I will because we have a little bit of time who has questions for Ahmad? Anyone have question right down here in front, wait for the mic to get to you, please and please stand and say who you are. What would you say to you? Can you, can you tell us who you are? Sorry? My name is Ian Util Ceo of attention live. What would you say to creatives? Because this can either augment a creatives development of what they do or they can kind of feel like, oh no, this is gonna take my job. So on the optimistic perspective, how are you encouraging creative and creative teams to leverage this technology to do what they do better? So I think this is the thing, what is creativity, right? So this can mash together concepts that so we still have an iterative process to get there. There is just one part of the process. So the ideation phase, the fine tuning phase, the kind of like we have text to image, image, to text in painting so I can remove, I could say it make that car blue and it will make that car blue. In the next version, you have to forget how to integrate this into your processes just as you did with after effects Photoshop and all these other tools because it's just a tool, it doesn't do anything by itself. So what we found is the best artists got even better. And this is what we saw where there was an A I that uh beat Lisa Dollar Go, who was the best Go player? The average Go score. It looks like this over time. Is that because they all got better because they use the technology. This is how you view it as a tool to extend and embrace it because it's here and you should use it right now and then you'll outperform all of your competitors. All other questions over here. Hi there, Ahmad Rishi Gar, a partner at the Mayfield Fund. Um uh You've got a lot of other projects on the website that you're working on. You've obviously started with image generation. But one of the things that's captivated everyone's mind is text to text threading and text generation because of the broad applications. Could you talk a little bit about the future horizon for stability and all the different applications you want to build into and how and how kind of starting with image get you to a place where you can do multimodal applications over time. So that's as a business perspective. When I saw image, I saw it as we going from a 10% to a 90%. Like if I've shown you this a year ago, it's impossible, right? And the fact that it's real time now is also impossible. So you wake up and do some impossible things before breakfast. Text. By contrast, we got 85% to 95% there. And I struggle to see where the edge is going to be when you have a chat bot that can do anything like you use chat GPT somewhere over now. It's fantastic. All the A I models will be like that. Whereas an image I saw that there are giant asset stores of IP that need to be converted because you'll always want Mickey Mouse or Halo or any of these other things. So when you can create everything, something becomes valuable, but it's not enough just to have image because image will be standardized. Instead, what we're doing is we're building the ready player. One oasis hock where anyone can create anything they can imagine. And that's not only media, no more powerpoints, no more slide creation in a few years, just describe it and say make it more peppy, make it nicer and it'll do it automatically recreate Game of Thrones season eight because it was a bit rubbish. You know, these types of things is what will enable seamlessly. And can you talk a little bit about how you build stability itself? Because I think a lot of people were surprised you guys almost came out of nowhere with this fantastic technology. I think there are a lot of businesses out there where they, you know, they talk all the time about the struggle they have hiring A I talent. You've obviously been able to build, you know, really cutting edge stuff. How did you do it? So we built a community when some of the large open source models were open source, the big entities could do it. And I looked at where's the arbitrage ties and the gaps. The gap was super compute data and talent. The talent came from our communities and we hired primo from our communities. So the 1st 96 employees, only one phd so differentiated talent then data, we built large open source data sets like lion 5.6 billion images. The previous launch was 100 million to power this. And then finally super compute worked closely with Amazon to build the 10th fastest public supercomputer in the world. And so that was overwhelming amounts relative to what happened. So 4100 the fastest in the UK was 640. And that unlocked all of this because we allowed people to experiment and play and figure out and make mistakes that then optimized for this crazy model right here. But also other models that were released in our generation to the new language ones, to the others because you need all the components all in one. And that's why creative stability to be like this foundation layer for next generation generative A I and you now have a sort of a partnership with Aws. Is that right? Yeah, so Aws to be a long term partner, now it's accelerating up, we're going to power Generative A I through kind of Amazon and make this available to everyone. It becomes a differentiator for different people's clouds. Obviously, Azure has got their relationship with open A I AWS is you guys, it's kind of an interesting thing. Exactly. And you know, and there, there's different approaches like Azure is quite integrated. Open air is very close to Microsoft. Whereas we're just trying to get this out to the entire world. Great Ahmad, thank you so much for joining us. This has been fantastic, really exciting stuff.

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