The Exchanges

Q Final one for you. What does no one see about the next three years that you see very clearly happening or not happening?

A I really see people will own their, every single company will own their own intelligence as a must-have. It's not optional. That's a trend I'm seeing because there's an analogy to software is there's a reason why every company build their own software stack. There's no standardized software you just use off the shelf to solve your problem because every single company is solving a unique problem. And they want to be a software because they want to have full control. And obviously they will pick and choose which part of the stack they want to build themselves, which part of the stack is common knowledge, there's no point of building. But every single company owns their own software stack. Obviously we're talking about this in the SAS time, right? So same, I think at a time every single company should own their own intelligence.

AI assessment note: “every single company will own their own intelligence as a must-have”

Q I mean, isn't the statement, you, you either die or you live long enough to build your own data centers? As, as Elon or Zuck now are spending, I think, ten billion on the latest Data center in Canada. Would you like to build data centers?

A So I have built data centers at Meta. Um, also lots of innovation possible there. There's no one-size-fits-all as well, and building a GPU native data center is also interesting, especially I think there is a potential direction of building, uh, so it's a trade-off, right? Um, from operation point of view, it's much better to build a heterogeneous deployment. Um, it's all the same chips, all the same SKU, as big as possible, And run multiple workloads, so it's fungible, right? It's very easy to manage. Um, bad notes, you can, you build one principle, one process to do maintenance operation. But, um, again, it goes to optimization, but once it's so big, then any optimization is going to drive a lot of economic return. Um, for example, we're talking about, um, NVIDIA recently acquired company also called Guac with Q. It's a large SRAM based, um, ASIC accelerator.

AI assessment note: “So I have built data centers at Meta. Um, also lots of innovation possible there.”

Q That's quite late. How do you reflect on being a 48 year old founder when we glorify starting a company when you're pretty much 15 these days?

A I didn't think deeply about that. I always want to have a tech business myself. I actually want to start a business in 2015, because I, I'm a first generation immigrant. I came to US in 2000. I did my PhD in distributed system, computer science, especially focused on databases. And database is a very concept, system to build, a lot of different objectives to optimize for, and pretty much touched, after I joined research lab, I pretty much touched every single aspect of processing data. And then I moved to LinkedIn to kind of further it down to build systems and products to be used to drive real impact. At that time, I feel I'm ready to start a company. I know all the tech. I know what product to build. I have a business proposal. I have a list of people I want to start a company with. And I spent time thinking about it and I paused because I don't think I have the skill set on people to build a company. It's not just about product. It's not just about tech. It's actually about people. And I decided I want to go to a place I can learn the most of people, and the best company at that time is Facebook. It's a rising star in Silicon Valley, and secretly I was planning to learn for one year and even go back to do my own business. I stayed there for seven years.

AI assessment note: “I didn't think deeply about that. I always want to have a tech business myself.”

Q Do you have to be full, we're going to Jensen's five-layered AI cake, do you have to then be full stack to win or to reduce dependencies? We've seen OpenAI come out with Jalapeno. Terrible name. Anthropic are talking to Samsung about building their own chips. Deep Seeker building their own chips. Zuck came out with Meta building their own chips. Do you have to be all, all of it?

A It really depends on the company philosophy. To us, agility is everything, and we need to earn the rise of building anything. So focus is everything for us, and we want to focus on where we add the biggest amount of value based on our strength. And, uh, we would like to leverage other people's strength to build on top of. So in particular, um, we want to run everywhere, uh, all possible air chips in the world. We don't want to limit it by how much chips we, uh, can bring into our data center, whether we're constructed or rented. Um, but over time, um, when the business grows very big, right, so I still remember when Matter was young, they, they don't build everything. And when they're big, Then it makes sense to build. You earn the rights to, to build for your own, um, you know, giant traffic, and this, if it save, like, five times more cost, then you should go do it, right? So, um, but I think at the early stage, that's why I give, tell you an interesting story, uh, in the coding space, we, I would say Cursor is the first company they have decided to work with us early on. Uh, I still remember when, They worked with us. They were single-digit million dollar. Wow. Very small. Uh, that's only two years ago. They grew by a hundred, a thousand X. Over two years. Something like that. Um, but they decided to work with us early on because they recognized they only want to focus on pr…

AI assessment note: “It really depends on the company philosophy. To us, agility is everything”

Q How do you think about that question for yourself when, when, when you're sitting there in an armchair on a Sunday afternoon thinking, hmm, we're optimizing for growth now. When is that time to optimize for gross margin?

A Well, I would say we optimize, we want to optimize for both. So, so here's how I think about it. Um, optimize for growth is a, requires a lot of business planning. Assuming there's product market fit. Optimize for growth margin is optimized for differentiation. Um, I, I think I want to avoid over-optimizing for growth margin, but we should optimize for growth margin continuously, as in we should optimize for product differentiation continuously. There's no question about it. And, uh, um, I think we want to continue to optimize towards a healthy growth margin, which allow us to grow really fast, and it's a trade-off And we don't want to take compromises. Um, the compromise as in we over optimize growth margin to result in a very slow growth, right? And one possible way to optimize growth margin, we do not grow at all. We just optimize the heck out of it. I know we can heal climb to a high number, but that's absolutely a disaster outcome.

AI assessment note: “we should optimize for gross margin continuously”

Q You said cursor being the front runners in terms of how innovative they've been. I completely agree with you, but I heard, and you know, I, I really stalk you before shows, but I heard that, you know, CTO Dima was embedded at cursor for months building the RL infrastructure. Is that how it has to be done? And is that scalable?

A So what's happening is Usually in the early adoption curve of new technology, the early adopters are all hackers. A hacker is not in a bad way. It's not, it doesn't have a negative connotation. They, they have deep expertise in certain area, and they want to control a lot of things. Um, versus in the late stage of a new tech adoption curve, it starts to get more accessible, um, to a much bigger cohort user. Doesn't have deep Expertise, and they, they need less control. So it always go into deep control first, usually, and, ah, little control later. So we definitely are aiming towards the later stage as the ultimate time we want to target, but it's also extremely valuable to understand, ah, what is required to get there. So, so that's why we partnered deeply with Cursor. They are the pioneer trying those ideas. They do have researchers from frontier labs, and they want to control every single thing, and at the same time, we're also pushing to the boundary. We're doing, we're doing things that never existed before. We're doing things that never existed before, because we push the boundary that is unique, ah, to, to this particular setting. Ok, what is unique is here. Typically, if you think about training, training happens, training is very capital intense. Um, and, uh, and it usually happens in big companies. They have a lot of money. They put those money to buy very expensive t…

AI assessment note: “we definitely are aiming towards the later stage as the ultimate time we want to target”

Q With that in mind, will you not build your own open router of the world to cater to that? Yes.

A You, you can argue they're the best builder because they deeply understand their use case, and they have the evals. So again, my thinking of what is the frontier is not just this one model. The frontier could be your special routing mechanism, For your business. And, uh, you decompose that based, um, hey, in order to, uh, fulfill this task and you, uh, usually you need a highly intelligent layer, maybe the most expensive open, uh, closed models to, to be, uh, to judge, uh, you know, the highest complexity and just Usually people will also be a sub-agent to solve smaller problems, then those can go to smaller open models, and those can also further being customized to fit into your special design. Um, so I've seen a lot of people already doing that today, and we also think there's a space to build a automatic routing system that can learn by itself. Um, and that compound with automatic tuning system eventually, We think it should all be automated, and then you can see a self-evolving system based on, uh, what flow through, uh, your product, and your product keeps evolving. Your product is, is live, right? So you, you keep, uh, deploying and launching new features, and to interact with your users, and, uh, that just kind of, it, it will be, um, totally self-evolving automated system.

AI assessment note: “we also think there's a space to build a automatic routing system”

Q When we think about the general intelligence part, just before we move kind of further into the stack of like multi-model, Sam proffered the five percent kind of gifting Of open AI and others to the administration. Do you think we've reached a stage where model development is so advanced and so important to society that they will in part be government or administration owned?

A That's very interesting question. I think I think there were precedence of that. If we think about the foundation tier of those, uh, general intelligence model as fundamentally a base infrastructure for, uh, for the big, big economy to operate around, there has been precedence of, like, PG&E, almost electricity and gas, um, and, and, uh, and so on, right? So, um, I, I actually don't know, but I, I don't want, I, what I don't want to see is there's only one company owns intelligence. I think that doesn't make sense to me because there are different, as I said, there are different flavors of intelligence. There's this general common intelligence that benefits everyone, um, and then there's a specialized intelligence that actually Help us advance in, in history to, uh, to think differently, to, uh, create new paradigm of, of living or new paradigm of doing business and shaping the industry. I don't want that to die because there's only one company can do that. I don't think that makes sense.

AI assessment note: “I think there were precedence of that... I actually don't know”

Q and Lagora, and Harvey have committed to building their own model, and then Lagora have not. Um, A year ago, it looked like companies that didn't commit to their own model were right, because, you know, frontier models were increasing so fast in terms of capability. Now it looks like they're wrong. Should companies like Harvey and Lagora be building their own model? And actually, if you don't, what happens?

A So here's one observation I had, and many people have, is software development, uh, especially SaaS space has been significantly disrupted because of the general intelligence of coding. And, um, the application development life cycle has significantly collapsed in terms of the timeline and the resource needed. In the past, it requires Tens of very strong product engineers and PMs to convert from idea to implementation to production scale. Multiple quarters of new years of investment. That's a deep mold. And today, one person, a few weeks, can possibly launch their ideas into a product and scale quickly. Um, this is unprecedented, and that's also create interesting dynamics in redefining where the competition is, because It's really hard just to compete on the idea of application, application by itself, um, because many people have similar ideas. Now implementation is no longer such a big barrier.

AI assessment note: “software development, uh, especially SaaS space has been significantly disrupted”

Q You said about PyTorch and Open, and the Open ecosystem. Open source in the last, I would say three months, we've all realized is actually accelerating so fast, and the capabilities have increased to such an extent that it's not comparable, quite, but it's getting 90% as efficient with, you know, 15 times to Chamath's statement, more cost effective. Are power lines good businesses in a world of open source?

A So, so here's how I view, how I view open source. So early on, when we, um, founded company, we had pretty deep debate among the co-founders, What do we do? Do we build our own models or we build on top of open models? At that time, open model was not almost like at its infancy. It's a big bet. If we're going to take that direction, it's a huge bet that, ah, it's going to do well, right? Um, but with our prioritized experience, we believe in the open community. We believe in openness. That's a fundamental principle we operate with. Um, because openness gives control. Openness gave control to the user. Uh, think about open models, right? Um, once the model is released, you have the full control of the weights. You can change it however you want. It's yours. Um, and then you can build on top of it, right? So, so that is a fundamental different operating principle that we believe in because of our roots, um, in open source before. So we took that bet, and it did pay off in the sense that both open model and closed model, the quality significantly increased, improved over the past two years. Um, to the point, both of it, both of these two streams cross the threshold, cross a quality threshold, it can solve so many problems, right? So within, uh, within Fireworks, obviously, with Dogfoot, our own product, we use OpenModel to, um, to drive our recruiting process, candidate sourcing, …

AI assessment note: “So, so here's how I view, how I view open source.”

Q The one question that I do have to ask is the concern that enterprises have is national security concerns. When you look at Open Rooster, I think the top six models today are Chinese models, and they're incredible quality. The speed of development is incredible. But they are Chinese models. Do we have serious national security concerns when analyzing the power of Chinese open source?

A I think it's a huge debate happening right now across the industry. Once the model is open, um, you can, you can put all kind of guardrails specialized to your business around. I would say to all models. Doesn't matter if it's open or closed. You should put your own guardrail around it. The fundamental reason is the following. A model provider will infuse their own judgment. Their own taste into the model training process. You cannot guarantee it matches yours. Remember, it goes back to Jensen's comment, there's no specialized general company. Every company is special. Every company will have a special design principle. Every company will have a special taste. Every company will have a special target audience to serve. Because of that specialty, it's guaranteed that The judgment, the taste, the design principle from one company would mismatch, would misalign with your company, which is special, is solving a special problem. So that is, that is a reason you need to tune those models to match yours. Um, and I really believe the future will be, will not be a few small number of AGI models. Dominant world. I really believe the future will be, it may be scary, but I think that's true. It will be millions of specialized model, one per application per use case.

AI assessment note: “I think it's a huge debate happening right now across the industry.”

Q Is that not where you would bet long on China with the greatest of respects? Especially in the US, one of the biggest, ah, barriers to data center deployment is policy and is kind of local legal infrastructure that prevents it. In China you don't have any of that and data center deployment is much, much faster.

A I think in general infrastructure, the base, uh, the physical infrastructure construction in China is going really fast. I literally see, um, some kind of, um, crossover bridge is being built within a week. Uh, the velocity is very, very high there. Um, and, uh, there's a highway close to my home. After one year, it's not done yet, so it's also a crossover. Um, so I, I do think there's a unique strength probably because of, uh, the population density, and, uh, um, and they are specializing in those kind of construction, um, really work. So, um, but I do think, I do think here, um, we, we also have those specialty people. It's just even, I heard even electrician is under severe shortage. We are under global supply chain constraint here.

AI assessment note: “physical infrastructure construction in China is going really fast”

Q You said about PyTorch and Open, and the Open ecosystem. Open source in the last, I would say three months, we've all realized is actually accelerating so fast, and the capabilities have increased to such an extent that it's not comparable, quite, but it's getting 90% as efficient with, you know, 15 times to Chamath's statement, more cost effective. Are power lines good businesses in a world of open source?

A So, so here's how I view, how I view open source. So early on, when we, um, founded company, we had pretty deep debate among the co-founders, What do we do? Do we build our own models or we build on top of open models? At that time, open model was not almost like at its infancy. It's a big bet. If we're going to take that direction, it's a huge bet that, ah, it's going to do well, right? Um, but with our prioritized experience, we believe in the open community. We believe in openness. That's a fundamental principle we operate with. Um, because openness gives control. Openness gave control to the user. Uh, think about open models, right? Um, once the model is released, you have the full control of the weights. You can change it however you want. It's yours. Um, and then you can build on top of it, right? So, so that is a fundamental different operating principle that we believe in because of our roots, um, in open source before. So we took that bet, and it did pay off in the sense that both open model and closed model, the quality significantly increased, improved over the past two years. Um, to the point, both of it, both of these two streams cross the threshold, cross a quality threshold, it can solve so many problems, right? So within, uh, within Fireworks, obviously, with Dogfoot, our own product, we use OpenModel to, um, to drive our recruiting process, candidate sourcing, …

AI assessment note: “So, so here's how I view, how I view open source.”

Q Can you help me understand, you know, as a podcaster, I specialize in asking basic questions, so forgive me, but why then do people like Dario, like Sam, like Larry and Sergey talk about AGI in the way that they do as an inevitable?

A I think what they build is fantastic because they are basically building power line to distribute a really great source of intelligence that everyone else can build on top of. That's how I view their contribution. And if, if we don't have this fundamental infrastructure Then we will not have all kind of appliances living in our home. I love my coffee machine, um, and it's special branded, right? So, but without that power, then we don't get to do the things that are fun, that's a unique, that's a special, uh, that ingrained my, our, encode our taste. Um, so, so I do think that's very, very important. But the question is, is this power line going to replace everything we do? I don't think so.

AI assessment note: “That's how I view their contribution.”

Q and Lagora, and Harvey have committed to building their own model, and then Lagora have not. Um, A year ago, it looked like companies that didn't commit to their own model were right, because, you know, frontier models were increasing so fast in terms of capability. Now it looks like they're wrong. Should companies like Harvey and Lagora be building their own model? And actually, if you don't, what happens?

A So here's one observation I had, and many people have, is software development, uh, especially SaaS space has been significantly disrupted because of the general intelligence of coding. And, um, the application development life cycle has significantly collapsed in terms of the timeline and the resource needed. In the past, it requires Tens of very strong product engineers and PMs to convert from idea to implementation to production scale. Multiple quarters of new years of investment. That's a deep mold. And today, one person, a few weeks, can possibly launch their ideas into a product and scale quickly. Um, this is unprecedented, and that's also create interesting dynamics in redefining where the competition is, because It's really hard just to compete on the idea of application, application by itself, um, because many people have similar ideas. Now implementation is no longer such a big barrier.

AI assessment note: “software development, uh, especially SaaS space has been significantly disrupted”

Q I have so many questions to ask you. I totally understand you in terms of the values in private data within some of these largest companies. Is that not the premise of what Anthropics Enterprise business is though? With Claude Cowork and with a lot of their adjacencies that they're building, would Dario not say, That's exactly what we're going after.

A That's interesting because I view Anthropic as a company fully believing AGI. The definition of AGI is there's this one model that can solve all the problem in the best way. That, to me, that's the definition of AGI. To me, that means you do not need to specialize. And that one model should be able to solve all the problems. It's so intelligent. I have so much knowledge of every parts of, um, of the businesses. Every Parts of the jobs it can, it can, it can fulfill. Then why do you need to bother specialize? So, so that itself is a validation that we're living in a world that's not ruled by one principle. We are living a fully diversified world. Give you one example, right? Different region. We'll have different value systems. We'll have different policies. Uh, we'll have different way of conducting business. We'll have different lifestyle. It's all taste, choices, judgment combined. Um, I think that's what defines us as human. We are not robots. If, if our future world is going to be ruled by one standard, a taste dictated by one company, we turn ourselves into an army of robots, and that's very depressing to me. And, um, I think what separates out, um, Homo sapiens from other species is the creativity, is the deep desire of pursuing Um, new things. Of discovering new ways of living. That defines us as a human being. And that part cannot be copied. That's my fundamental belief…

AI assessment note: “That's interesting because I view Anthropic as a company fully believing AGI.”

Q How do you see the more mature state of your market? Is it like a cloud market where you have obviously Azure, AWS, GCP, or is it an Uber and a Lyft where one takes 90% and the others kind of fight for scraps?

A We're, we're in the adoption curve where a lot more companies, they are in the airspace, start to seriously think about moving to specialized intelligence. To start to seriously think about owning Their intelligence is better than renting. Um, because going back to this optimization, when is the good timing, right? So it's the same question we're answering for ourselves when build versus buy, and our customers also think about build versus buy, or build versus rent, or own versus rent, right? I think AI journey or AI adoption journey has gone further along into a lot of company has meaningful traffic. A lot of company is deploying AI into production. A lot of company is at the phase of scaling, um, and that's where optimization kicks in. When optimization kicks in, you need to have control to optimize. If you don't have control, you have, you just don't have the range to optimize. And for you to have the control, then you have to build on top of some, like, open model. You have to kind of turn your data into your intelligence. That's pretty much the, the path that we have seen. So many companies, uh, across the industry, they reach the same conclusion that are moving towards this direction.

AI assessment note: “We're, we're in the adoption curve where a lot more companies”