Kind: captions Language: en What I don't want to see is there's only one company owns intelligence. That doesn't make sense to me. I think last year is the year of coding and this year is the year of co-work. And in the hot seat today, a founder who I wrote a $10 million check for after just a 15-minute meeting. Lynn Quo, [music] founder at Fireworks. This was one of the easiest investment decisions that I've made in a 10-year investing career. >> I do think the cost of token will go down drastically. [music]
10x cost reduction in the next three years and this 10x cost reduction will drive a 100x usage. We absolutely are not going to move into application layer. very clear to us whether we will move down into data centers and [music] so on that could be always be on the table but the question is >> ready to go Lynn I am so excited for this um I heard so many great things I just got off the phone with your co-founder Dimma I spoke to Alfred Lynn Sonia, Matt Miller, many more. So, thank you for joining me.
>> Oh, thanks for having me. >> Now, I heard that Eric Vishrier has a rule, don't invest in big tech directors, but he broke that rule with you, which is very special. >> I I think so too. So, a funny story. Um after we decide to handshake, he did call me and said he talked with uh one of his adviserss [sighs and gasps] and his advisor question him hey how many big tech executive have you seen being successful in starting company very few and he told me that um I was surprised like are we breaking our
handshake now no but uh we since then we work very closely with each other >> Eric is one of the best you also started the company when you were 48. >> Oh, yeah. >> That's quite late. Can I ask you, 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? >> Um, I 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. uh because I I'm a first generation
immigrant and came to us in 2000. I did my PhD in distributed system uh computer science especially focused on databases and database is very concept system to build a lot to lot different objective optimize for and pretty much touched after I uh 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 um to be used drive real impact. At that time I feel I'm ready to start a company. I know all the tag 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 spend time think about it and I paused because I don't think I have the ski 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. [sighs] And I decided I want to go to a place I can learn the most of uh of people. And the best company at that time is Facebook. Uh it's a rising star uh in Silicon Valley. And secretly I was planning to learn for
one year or two and leave and go back to do my own business. I stayed there for 7 years. >> So with fireworks you saw something in inference that the world was not focused on. The world was focused on training and I think it's helpful for people to understand kind of the stack cuz beneath you there's obviously kind of chip providers and your invidious of the world and then you've got above you the model providers and you sit in between. Why is that a valuable part of the stack and not a commodity?
>> That's a really good question. But why bother specialized intelligence? Why not just use generalized intelligence and and you worry less things, right? You just kind of build on top of a uh API u that provided by French apps wouldn't that be much easier. So the argument is the following. If you think intelligence is a derivative of data, then majority of the data is actually not used for training a general intelligence model. The training data is coming from public internet and the label data.
Public internet is very small corpus of data compared with words data. Majority of words are actually private locked inside application locked inside enterprise it will never get shared with anyone else because this is company's proprietary IP so so then it's interesting if you look at the space then becomes very interesting because majority of data is not being activated to derive any intelligence and that's where we believe in is is to activate that data and we believe the future of the frontier of the intelligence
actually private intelligence are specialized intelligence. So that's kind of where fire was from the beginning we have been focusing on driving the value. >> 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 claw co-work and with a lot of their adjacencies that they're building? Would Dario not say that that's exactly what we're going
after? That's interesting because I view Anthropic as a company fully believe in 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. is so intelligent 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 ified world. Give you one example, right? Different region will have different value systems will 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 define 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 ourself into an army of robots. And that's very depressing to me. And um I think what separate out um homo sapien from other species is the creativity is the deep desire of pursuing um new things of discovering new ways of living that define us as a human being and that part cannot be copied. That's my fundamental belief. Um that's why um you know in Silicon Valley there's so much creativity across the world. There's so much creativity of building new businesses. What is new business? Um I had this fun uh interesting
conversation with Jensen after his GTC keynote. Uh we actually recorded it and it's interesting. >> I watched it. It was great. >> Yeah. Recording with Jensen is not really recording. He just started having conversation with me. I didn't know his crew already started recording [laughter] and we just keep talking, you know, it's so easy. We talk about the specialized intelligence. He said one thing to me. Uh Ling, you're right. There's no specialized general company as in every company is built on a
special belief of doing things otherwise there's no reason they should exist. It it feels yes logical. But then I start to think back about what he said is profound because every single company is doing something unique that justify their existence. And this something unique is deeply baked into their product design is deeply baked into their software design and system building and that's deeply baked into the data they um and their interaction with their user and their deep understanding of their user intent interacting with their
product uh and engagement so on all of that is the fundamental base of why a company should exist that is not learnable or assured by another company sitting outside. So >> can you help me understand then 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 a inevitable. 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. Uh 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 ingrain 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. >> The question for me as an investor is, are power lines good businesses? 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 chamass statement more cost effective are power lines good businesses in a
world of open source >> so so here's I view how I view open source So early on when we uh founded company we have 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 bat if we're going to take that direction it's a huge bat that uh it's going to do well right um but with our pie choice experience we believe in the open community. We believe in openness. That's a
fundamental principle we operate with. Uh because openness gave 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 bat and it did pay off in the sense that both open model and closed model the
quality significant 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 fires obviously we do our own product we use open model to uh to drive our recruiting process candidate sourcing um and the feedback collection we use open model to uh even uh drive some internal finance processes uh obviously uh for coding reusable mode to help us debug. Um we we ton of agents within fireworks ourselves
and we are cost conscious. So um so so that is important. So both model categories cross a threshold solve so many variety of problems. Second is open model cross a threshold is so much easy to tune. Okay. So uh be able to steer a model is intelligence is part of the model intelligence. Um and the model intelligence has pass through is much easier to steer especially with small amount of data. A small amount of unique data a particular company has and then we can heel climb towards your eval. Um and often time the end result of hue
climbing is to solve your unique problem with your data you are better than a general purpose model. Okay. >> When 90% of enterprise workflows can be done as you said that the incredible array of functions that you now use open source for with open models. So the usage for frontier models will not be as large as it was if it was needed for everything. So are these companies actually dramatically overvalued and overestimated if the majority can just go through open? >> I think people start to realize it.
>> Uh I remember from two years ago uh I went to different places and talk about an interesting phenomenon. Um that doesn't exist in the past in the SAS era. During SAS time product market fit and a durable business almost are equivalent to each other. The hardest thing is find power market fit and then once you find it just scale as fast as you can right because CPU is a commodity the infrastructure you build on top is almost like commodity don't even worry about that as your [ __ ]
and now product market fit and durable business are two separate concept um for startups you know we have great companies they have product market fit customer want to pay them and they really value their product but they cannot scale because once they scale they could scale into bankruptcy. Have you heard about scaling to bankruptcy? So that's a real problem. Um it's even a bigger problem for incumbents. So the big companies for digital native um because they have the traffic they have huge amount of
traffic. get a winner from a decade ago when we they were startups and they have so much traffic once they roll out uh those AI features they're going to reach to all their customer base and they cannot afford to do it because their CFO look at their um cost proposal cost forecasting is kind of there's no way you can justify this right so so then it becomes a real problem to all those innovators hey we really want to plug in to this new technology, new disruptive technology, but we cannot afford it. Um,
and we need to find an alternative to be able to afford it. And the alternative is to have the control over your open weights model and roll out your own model. >> It is or you see what Sam Alman's released in the last few days, which is just dramatically lower cost models. I I can't remember the amount it is, but I think it's like half as expensive or maybe three times cheaper. Um, is the next step actually we just see a massive reduction in price from the frontier models? I it could be but I think at the
same time it's just a very different uh operating principle because um for for open waste model because it's just there basically model uh acquisition has no cost right no obviously some company train those models um and willing to open it up I I know within US um there There are multiple companies doing that including Nvidia is training Neimotron. Uh we're working obviously very closely with them. >> Um so once the model is there whoever using those model there's literally no cost but there's
fundamental cost uh for for the frontier labs to to invest in those models and recruit recoup R&D cost back. So and second is you just cannot customize those general purpose models. Um and you use it as is um on top of a a API you have no control over with open model you have full control. You can you can tune however you want. You can use it however you want. Especially Fireworks we we are a special specialized intelligence platform. We offer all sorts of tools for you to easily customize the model for one
specific use case. Um, and after that model is tuned with high quality and then we further optimize for inference deployment. Think about fireworks. We think about every single model deployment as one size fits one. It's unique for your workload only. uh it's optimized for your workload only from quality, speed, cost point of view. Um so we believe that's uh that's absolutely needed because once you think about a production scale of reaching to millions of users, tens of million, billions of user, then even 5% of cost
reduction means a lot. It's a massive amount. Let alone what we have seen in the past is five times to 10 times cost reduction. The one question that I do have to ask is the concern that enterprises have is national security concerns. When you look at open router, 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?
>> I think it's a huge debate happening right now across industry. Once the model is open uh you can you can put all kind of guardrail specialized to your business around I would say to all models doesn't matter if open or close 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 spe a special problem. So that is that is the 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. We saw in the last week actually reports that China were looking at actually restricting access to their open models because they were seeing the development being so fast and so good. What would happen in a world where China actually started restricting access to their open models given the lack of open models we have in the US?
>> I think it will be a big impact in the short term. [clears throat] But the beauty of open ecosystem is it's not one provider that's why it's open right uh it usually attract many many many interested party to participate I do believe um in terms of talent density and resources I do believe us will be able to build that open system um by ourselves and we should um and uh I've seen this happening Again in many open systems there are thousand flower bloss um and that's the beauty of that >> when we talk about the the
specialization of intelligence within enterprises as you have done just there if we take a very prime example which I don't particularly want to take because I'm an investor in Lora and I think I know which side you're going to fall on here but you have two companies that compete in the legal space Harvey 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 Lora be building their own model and actually if you don't what happens >> so here's one observation I had and many people have is software development And uh especially SAS space has been significant disrupted because of the general intelligence of coding and um the application development life cycle has significant 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 years of investment. That's a deep mode. 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 redefine where the competition is because it's really hard just compete on idea of application application by itself. Um because many people have similar ideas now
implementation is no longer such a big barrier. >> Is that actually true though when you're looking at enterprise deployment enterprise roll out if you're working with some of the biggest law firms in the world? I mean the enterprise sales cycle is is at least multi-year with relationship build that's very tough and then you have deployment that's very customized. It's not like 11 Labs where you pick it up and go. It's different >> and also I think legal space is particularly challenging because lawyers
are usually more conservative. Legal is also not tolerant at all on arrows, right? Because that's why lawyer get paid, right? it's going to you need to build a very like rock solid case. Um if if something hallucinates and and and generate wrong judgment then you're in trouble. So I I do think uh the the legal space is a very interesting space to penetrate and these companies are both doing great job. But on the flip side, um I do think both companies are owning proprietary knowledge and information how to build those assistant
to do case studies to um go deep in in driving u you know legal research and all this right so and uh I my understanding of legal is so shallow but there's so many different versions of flavors of of cases. So I do think they are in unique position to convert that deep understanding and they all have data. It's not just about how defensive their business is. It's about hey um often time when they build those assistant there's a harness integrating uh and deciding orchestrating which AI tool to use um which tools calling to
and this is bespoke this is customized and the accuracy of calling those tools um and calling to what kind of tools is important and even that harness need to be co-rained with a model powering it right so there there's just kind of ample examples of driving that business to excellence by um by owning their own intelligence of how to do that in the workflow layer. So maybe it's a timing um coding for example I think in coding space cursor probably is one of the pioneer >> starting to tune their model and now
almost all coding company tune their own models. Does that pace of model development slow down? Because every single day it seems like we have a new model with a new capability and it's like, oh my gosh, Curs's newest model is amazing. Next, we have um someone else MR's newest model is amazing. Gemini's newest model's amazing. In three years time, will the pace of model development still be so fast and model superiority be so transient where one day it's one and the next day it's another? >> So, there are there are few layers of
model advancement. there's base general IQ advancement so that those will take step functions. So that's why when they release there there's always major release or minor releases right the major release of spec functions as you remember beginning of last year uh there's whole this thinking the thinking process is new right the model just don't spit out answer immediately the model will think by self and spit out answer is much better that way uh so that's one step function and uh there are many step function we have seen
through but I I see those as every year or every three quarters there's a major leap but at the same time build on top of those the bassbased models and I can see the specialization start to accelerate because as I said it's really like a tree right there's so many branches and leaves that can possibly hang on uh on the on the trunk and as the base model quality start to have staff function leaps and there's so much more we can do to specialize. So I do see in the world specialization is going to accelerate much faster uh than uh
than the general intelligence part. >> When we think about the general intelligence part just before we move kind of further into the stack of like multimodel you Sam profered the 5% 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 administrationowned? >> That's very interesting question. I think I think there were precedents 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 presidents of like PG&E owns 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 and 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 fl 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 industry. I don't want that to die because there's only one company can do that. I don't think that makes sense. With the many models blooming theory, there's the idea that you will root uh task to different models dependent on what they specialize in. >> I think so.
>> With that in mind, will you not build your own open router of the world to cater to that? >> Yes, you you can argue they're the best builder because they deeply understand their use case and they have the evals. So again uh 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 on 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 at you know the highest complexity. And usually people will also build sub agents to solve smaller problem then those can go to smaller open models and those can also further being customized uh 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 life right? So you keep uh deploying and launching new features and to interact with your users and uh that just kind of it will be totally self evolving automated system. >> Do you think then that rooting layer of the stack is valuable if it can be automated or it can be built on its own? Is that a valuable layer to have? >> I I definitely think so. >> You do think so?
>> I do think so. If it can be automated or companies can build it themselves, why would you need a requesty or an open router? >> You probably don't. >> Yeah, we're not there yet. Um, but I do think this is this could be area of uh of innovation. >> 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 your CTO Dema was embedded at Cursa for months
building the RL infrastructure. Is that how it has to be done and is that scalable? >> So what's happening is usually in the early adoption curve of new technology, the early adopters are all hackers. hacker is not in a bad way. It's not does doesn't have 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 start 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 the uh little control later. So we definitely are aiming towards the later stage as the ultimate 10 want to target but it's also extremely valuable to understand uh what is required to get there. So so that's why we partner deeply with cursor. They are the p 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 never existed before. We're doing system never exist before because we push the boundary that is unique uh to to this particular setting. Okay. What is uniquest 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 training cluster interconnected with each other. Super expensive. And then once you have those
expensive large fleet um usually you don't need to think too deeply how to be efficient you just focus on doing your work. Cursor is like us their startup right both of us are very uh capital conscious and uh we want to be efficient while we don't want to slow down the research innovation. So together we figure out a very smart way to to drive their training process is um they do massive post training which is reinforcement learning based and the reinforcement learning we break that into p two pieces. is uh the trainer
that uh is tweaking the weights of the model and it basically generate new model version constantly and that new model will deploy to we call the RL rollout. It basically is a deploy that new version interact with a synthetic environment a synthetic like coding environment or real coding environment um and then get the reward back to judge if that model is version is good or bad right so that's a rough process um and we decouple these two in the past in large hyperscaler they run that all together if you think about you get
10,000 100,000 chips all interconnected together through Infinity Band. It's extremely expensive and really hard to find, but they need to go really quickly and and we design fully distributed system and we run across um five six data center regions globally uh and tapping to uh scatter GPUs and and they are able to uh run massive jobs our jobs. But the challenge there is we need to sync model weights across all the these different regions and how hard can that be? It matters because the latency of delay of sending these
weights over is going to dictate how fresh the rewards are and then if it's too stale then you are too off. So it's a balance but we we we innovate a way we can uh we can distribute fresh model weights quickly. It's not too off. uh so numerically it's still still sound uh while we are not limited by a very expensive uh deployment of GPU fleet so those are the innovation we work together with cursor to push the boundary and leading to their recent model launches we're very proud of them >> can I can I ask a question bluntly which
is incredible customer to have amazing progress they've had with you um and it's wonderful to see that partnership it's a very large customer for you how do you think about the concern of a cursed churn in the wake of a SpaceX acquisition? >> Yeah, everyone's concerned the whole entire industry in terms of in terms of application innovations by model in the sense there are few companies are very very successful. They escape velocity but few of them. So that's the shape of the whole entire industry and last year cursor is one of
the few. Um I would say all model companies are concentrated on cursor. We concentrate on same group of [gasps] um app companies and um and since then it it does change right. So we do have a very healthy diversified customer base. Um especially I think last year is the year of coding. Uh I think all major coding companies are on us and this year is the year of co-work and co-work is much more diversified by itself than coding because there's general purpose co-work for example general purpose like
co-work to help you do all kind of research. Um you want to ask hey what will be the um what will be the Nvidia GPO price uh two years later [laughter] um what will be anthropic stock price after IPO so those are deep research general purpose deep research um or there are so many different categories of special purpose co-work legal we just talked about two great legal companies finance customer support recruiting sales marketing uh healthcare. So there's very broad set of co-work space of innovation app
company. They are doing really well and we have them as our customer base. And then more interestingly we start to see an uptick of consumerf facing company are all start to look into um geni technology and uh they are changing how they are thinking about their traditional business of doing recommendation for example and that's very interesting to me because um we have obviously worked at a huge recommendation system in the world ma Um and we are very eager to see how that trans transform into a new
econom uh a new economy for for us. >> I'm I'm sorry for being naive here. Um do people work with just one provider in the inference space like you or do they work with you and with together or anyone else in the space? I think people are more in tuned to multi- vendor strategy in this space because they don't know what's happening. It feels safe to have multiple providers kind of uh to balance things out. But we don't view ourself as a inference provider. Again, we view ourself as delivering these specialized
intelligence where we help companies tune their model. uh give you some numbers. We today we process more than 40 trillion tokens a day. Um so majority of those tokens are coming from a customized model not from offtheshelf models are coming from customized model. >> what will what will that token count be end of next year? Anywhere ranging from 20 to 100x could be possible. >> 20 to 100x. >> Yeah, we're at a very early stage of scurve explosion right now. >> 20 to 100x. If if it's 20 to 100x,
the idea that we are in a capex bubble is ridiculous and we are desperately needing far more capex than we are ever suggesting for compute. Is that right? >> Um so that is right. At the same time I think Jensen has a five layered cake. Five layered AI cake uh from top down application model infrastructure chips energy we are bottlenecked by the lower part of the AI cake in terms of supply chain. So um >> being energy >> being energy being chips I think in the physical world how fast we can
manufacture because in the history all these industry is not designed for massive scaling speaking about 100x scaling no one was designed for that I talk with many um manufacturer um it's kind of we're bottlenecked by small parts uh transistor [laughter and gasps] the the smallest tiny parts that hold off the whole manufacturer line of uh servers that can deploy to data center and being used to generate tokens. >> Do you have to be full 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 Jalapino, terrible name. Anthropic talking to Samsung about building their own chips. Deepseeker building their own chips. Zuck came out with Meta building their own chips. Do you have to be all all of it? >> It really depends on the company philosophy. To us, agility is everything and we need to earn the rights 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 on all possible air chips in the world and we don't want to limit it by how much chips we uh can bring into our data center whether we're constructed or we rented um but over time u 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 they make sense to build you earn a right to
to build for your own um you know giant into traffic and it's if it save like five times more cost then you're sure 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 remember when they work with us they were singledigit million dollar >> wow >> very small uh this only two years ago they grow by 100 a thousandx over two years, something like that. Um,
but they decide to work with us early on because they recognize they only want to focus on product innovation and later on research. They do not want to focus on um, you know, this platform innovation. They know we are putting all our R&D in there and they want to find the best partner to win big. So I do think that's the right mentality to specialize and we want to specialize. We do not want to kind of own the whole entire stack. That's not our goal as a company. >> I'm sorry to be hopping on. Why does
Jensen skip your layer of the cake? Cuz he's doing Neotron with models. Why does he not want to cannibalize your business too? >> Well, Jensen is not building a cloud either, right? you can say, "Hey, Jensen, probably you have all the rights to build an Nvidia cloud." Uh, so he's not building a cloud infrastructure. Um, I think he mentioned that as well and many people ask him that question. Um, and he also mentioned he want to specialize in what they have the rights to do. Uh, why models? I think it's pure
um a supply chain question is if US doesn't have a US native open model it's a problem it's a supply chain problem so so he is solely there to solve the supply chain problem but if there's no supply chain problem because the company like us are providing this specialized intelligence platform layer then he doesn't need to worry about it so he just want to make sure the whole entire five layers of AI cake is flowing. There's no blockage and if there's a blockage, you know, he's interested in solving those problems.
>> Mark Benning off, one of your investors, I think in the new round, which obviously this will come out after the round um is announced, um said that he spends uh about 3.8% of developer salaries at Salesforce on anthropic and claw code. And I think it's a useful analogy because if you assume that that is what's spent on claw code encoding tools that says one side of the market but if it's 20% wow we're underestimating how big these companies can be. When you think forward a year or two how what percent of developer
salaries do you think we'll spend? Is it less because these tools will get cheaper or is it more because they'll get better and better? down drastically because again >> it hasn't so far. >> It hasn't so far because of supply chain constraint but we are living in a a free economy. So think about whenever there's shortage price is high price is high price will invite a lot of people coming to solve the problem and it will invite competition competition will bring down the cost and then eventually
it will leading to a very economical solution right so but actually that's good for everyone because much more affordable u infrastructure will invite more usage so my prediction is with the decrease of the infrastructure. Um that's where it comes to my prediction of how like how far next year will look like because the infrastructure costs will go down and uh um usage will explode because of that right so the moment you don't think about that as a problem for you and you just you just if it's a utility you just use it
>> how much will token cost come down is this just help me understand is it like a havinging is it like a oh it'll hundth of the cost. >> So there there's different way to think about this. It's not all tokens are equal. I think we should establish um u best practice to evaluate the token economy per task because different model are have different way of spit out tokens. Some are much more verbose than the other. So uh so you can imagine one model um is 2x cheaper than the other but it's twox multables to solve the
same task and then they're at the same cost. >> Yeah. >> Right. Uh so but overall I think as the model quality improve I think being precise is going to be part of the uh optimization. And so that's one level of optimization is to solve one task we should we should need less tokens. Okay. Uh and the second is for one token um and how to do that is you you need to customize the model to solve your problem especially better and more precise that goes into model tuning. Um and second is for each
token spit out from those models and processed by those models we also specialize in making the unit of economist much better uh through our platform. And third is underlying uh like the GPUs the surrounding like memories and all these today is under stark supply chain constraint is going to get much better situation will get much better it I don't think it probably in the next one year or a year a half the situation will not change but in the long term two to three years it should change and that cost will compress uh so
overall I can imagine no 10x X uh cost reduction in the next three years and this 10x cost reduction will drive a 100x usage. You said there about kind of uh token efficiency um and how you enable your customers to be much more efficient. With that efficiency, you do charge more. You know, when I when I did the research when it compared to competitors, I got like together's price king. And I don't mean this disparagingly, but like they're cheaper. If you want cheap, you go there and respectfully, if you want better quality
product, you go to you. But it is more expensive. Do you think that's a fair assessment and a fair analogy? >> I think we're probably not comparing apples to apple in the sense that uh again goes back to our business. Majority of our traffic is um is customized model um and we optimize for quality number one. Always quality. quality as in model quality uh towards your applications, your specific business, your use case and so on. The second is um when we deliver those model in inference is also
quality. Um and we care quality so much we do extreme things um for example during training time there's a very hard thing to achieve is called zero KOD. It's a little bit technical. The idea here is >> zero KOD. KLD KLD is a measure of uh of quality. Um and uh what it means is between the training system and the inference system when model move over uh we have bit equivalence uh so as in the numeric are fully the same we do not lose a bit of accuracy. Uh that's really hard to achieve. But the reason we push that, we deliver
that. Um and the reason we push that is because we know um our primary business is in model customization and inference of customized model. Um and we want our customers every single dollar investing training maximize it. Um and then they if cross trainining inference boundary is not bitwise equivalent they just drop the quality down and and then it's like you pay you pay your training investment by um discounted quality. Why do you do that? Um so so quality first and quality does bring additional value and that's
why we are not interested in commoditized one sizefits-all. um you know this off-the-shelf model deployed in the same way for everyone that kind of business will always customize model deploy in a unique way um for your particular workload. >> Two questions. Do you have to have an FDE model to make the customized model efficient? >> We uh as a matter of fact we do have a FD team. It's called applied machine learning engineering team. So their primary job is to accelerate this customized deployment. um as a matter of
fact to also build the agent to automate a lot of deployments. So >> given where we are in the stack, a lot of the complexity that we have, we we have a margin structure that's a little bit different to like traditional SAS being 80%. I I don't know the margins precisely here, but they traditionally in the 30 to 40% range for where we are. Is that the new normal for where we are? >> I don't think that's the new normal. I think that is a reflection at least for us I don't know other companies uh for
us it is a reflection of we are in a hyperrowth phase um during hyperrowth phase you have the choice right you either optimize to me margin optimization is a constraint problem as in hey we want to go to 70% margin we want to go to 80% margin and and then we are going to go backwards and impose those constraint to guarantee those margin And usually constraints slow down um innovation. So for give you an example uh during system development and uh in a high velocity uh system expanding phase we we don't
want to overbuild because we are in kind of high experimentation. We're testing uh you know what will stay what will not stay. optimization doesn't make any sense. Once we know this is system that we want to build 100% then and we are going to scale this a thousand times bigger then we go optimize the heck out of it. I think my you think about business the same way. Well, in the hyperrowth um if our focus is only optimize growth margin, we absolutely can do that. But we are sacrificing the speed of growth as well
because we want to go everywhere. We want to go into different uh geo regions. Uh we want to go into tackle different use cases. We want to create con constantly create um different product lines and um and those are not the time for optimization. That's my opinion. >> So we will be able to increase margin without moving into different layers of the stack. >> Not into we absolutely are not going to move into application layer very clear to us. Um and uh whether we will move down into like for example you mentioned build
data centers and so on uh that could be always be on the table but the question is timing. >> I isn't the statement you you either die or you live long enough to build your own data centers [laughter] as as Elon or or Zuck now of spending I think 10 billion on the latest data center in Canada. Would you like to build data centers? So I have built data centers that matter and 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 skew as big as possible and run multiple workloads so it's funible right it's very easy to manage it um bad nodes is going You build one principle, one process to do maintenance operation. But uh again it goes to optimization but once it's so big then any optimization is going to drive a lot of economical
return. Um for example we're talking about um Nvidia recently acquired company also called guac with Q. It's a large SRAMM based um ASIC accelerator. I spoke to Jonathan before this show. >> Jonathan is excellent. [clears throat] >> He said, "What a fan he is of yours." >> Oh, also fan of his. Um, so but um it's a great combination between a flops intense um GPU and uh SRM intense um A6 because flop intense is really good for first half of uh LM processing. It's prefill is called prefield processing the
prompts and so on and SRAM intense is really good for generation. That's just the nature of the model architecture. Uh it's great to combine these two instead of running hogenously on the same chip, right? And and but that requires a very unique system design and deployment into data center. And it is um it is heterogeneous actually before I I I really mean homogeneous design is much better for operation. Um and this is heterogeneous and then how to operate this heterogeneous design requires unique innovation in data center
deployment and so on. So data centers aren't commoditized like you can specialize in data center deployment and one data center is better than another and data center deployment can be done well and badly. Data science is so complicated, right? If you think about the beginning all the way from construction to power deployment, you need to have the right power to come in, right? Uh fiber channel um the uh right cooling uh especially new chips requires liquid cooling um to get all this right and the parts can fall apart and how to
replace them. It is all very deep expertise. It's no joke. is not tomorrow I can be a data center operator. I cannot. >> Is that not where you would bet long on China with the greatest of respects? Especially in the US, one of the biggest uh 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. 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 [laughter] there. Um and uh there's a highway uh close to my home after one year. It's not done yet. So this is 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 u related 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. >> Yeah, >> we are under global supply chain constraint here. >> What change would moving into the data center layer cause to margins? Would that take it from 30 to 50? Would it be not that meaningful? Like what would that change due to margins? >> How we calculate gross margin is interesting these days? Um because how long does hardware depreciate has significant changed? >> Yeah. [laughter]
>> In the past it's six years. >> Okay. Solid six years. And hardware release is usually three years. That's fast. And now within a year from one vendor alone we have three SKs. And the newer model usually runs the best on the newest hardware model depreciation is also very fast. Every week we are launching a new model. Um and then the model is kind of peak in its value before the next model comes out. Um [clears throat] and the new model likes the newest hardware. And imagine this cadence
after two years. um which model runs on the two years old hardware it'll be two year old model um and uh are those models still valuable so I think that's kind of the real dynamics we are we're facing right now is the the hardware will last for six years still but >> but what you're saying the speed of model development far outstrips the speed of chip and hardware depreciation >> um the speed of model definitely is the fastest but Even the hardware innovation itself is the fastest. So after three
years, if every year there's three hardware skill, after three years there are nine hardware skill in between. Do you still want to go back to uh nine generation older hardware running three years old model on that? That's questionable. Maybe there's a war. Uh we still it's still valuable, but with these pace of innovation, it's questionable. Now with a different depreation depreciation cycle, it changed the dynamics of build versus own, build versus buy. Um, and again, it goes back to my original thesis of do you optimize for
growth or do you optimize for growth margin? It's all about timing. >> How do you think about that question for yourself when when you're sitting there in an armchair on a Sunday afternoon thinking, hm, we're optimizing for growth? Now, when is that time to optimize for gross margin? >> Well, I would say we optimize. We want to optimize for both. [laughter] Uh 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 overoptimizing for gross margin but we should optimize for gross 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 continuous optimize towards a healthy growth margin which allow us to grow really fast and it's a trade-off and we don't want take compromises. Um the compromise as in we overoptimize growth margin to
resulting in very slow growth. Right? One possible way to optimize growth margin. We do not grow at all. We just optimize a heck out of it. I know we can heal climb to a high number, but that's absolute disaster outcome. >> Okay. Interesting. If we just said, hey, so gross margin, we're going to take it from 30% to 10%. Is it a winner take all market where we could eat up everyone else's lunch and then optimize gross margin later? I think winner is probably is not a snapshot in time. It's going to
be a long-term situation. We do see um a particular industry will oscillate and start to settle uh with a few good ones. Um legal take legal for example uh I was on a a dinner table and interesting seems like there were a lot of those companies around um two years ago but now it's pretty much >> two >> two um so um I I think yeah I think it's a long long game. >> How do you see the more mature state of your market? Is it like a a cloud market where you have obviously Azure, AWS, GCP
or is it an Uber and a lift where one takes 90% and the others kind of fight for scraps? >> We're we're in the adoption curve where a lot more companies they are in the air space 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 oursel when build versus buy and our customers are 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 we have seen so many companies uh across industry they reach the same conclusion they are moving towards destruction. >> Speaking of owning your own intelligence versus renting it that does apply to like a national layer and when we've seen you know fable be banned in some cases by the administration briefly for 19 days. Um especially in Europe, we suddenly went, "Oh my gosh, we cannot be
at the hands of open AI and anthropic where we can just be banned in our health services sit on the infrastructure of something that you know an administration can turn off." Do we see a future of sovereign models where large nations or nation blocks own sovereign models? >> I definitely see that possibility. Um I also see if we think about the general intelligence model as the electricity layer as a power line every country should uh should own their own power line right so um I I do I think that is a very scary moment is my
power line is going to be cut off and all my fundamental um day-to-day is gonna not working because I feel so frustrated whenever there's power outage in my home alone. I feel so frustrated when I cannot access my Wi-Fi. I feel so anxious. I so [laughter] uh I mean obviously the you know operating the country is is extremely important built on top of this fundamental uh baseline. So um and for every single company the same thing it's not just I know whether a country should have their unique uh sovereign
independence but every single company should have their independence. Um you don't want any single person to cut you off. Uh that's extremely scary moment. >> Why would you move into the data center space but you wouldn't move into the chip space? >> Because I know building a chip is extremely hard. I thought so too. Okay, again I I I admit to being a [ __ ] which is why I think the show is a little bit successful. I thought so too. But then how come everyone is seemingly doing it
as if it's like just another product? As I said, OpenAI, Anthropic, Deepseek, Meta, we're building our own chips now. >> I think Meta has been building their chips for more than five years, way more than five years. Um and MTIA has been a project since you know 20 2018 u maybe earlier. So because MA has been investing AI for a long time preji um and they have a huge uh AI workload focus on ranking recommendation and MA has been building other hardware as well in the past. So whenever the the the
usage has pass certain threshold it make economic sense for you to build it build underlying supply right so uh and you can specialize towards your workload and uh that's another form of specialization is specialized to bake your logic into hardware and this hardware is purpose-built for your particular workload and you better uh you better make sure this workload doesn't change because really hard once said the hardware is taped out, it's really hard to go back and change it. It's very it's possible it's very costly. Um so once
your workload stabilized, once your business stabilize, it doesn't change too often. Then that's the time um to consider building a chip. I still see the whole AI world especially models customization is very dynamic very very dynamic workload patterns very dynamic. So think about how much energy in the application space people experimenting all kind of things you don't know which one is going to take off and they they will just take off quickly and which one once they take off which one is going to sustain and a
few ones will sustain then that's the time oh now we know this is the pattern and now we should probably encode this pattern into hardware uh and bring this hardware into a data center and so on it's all cascading and then it's going to cascading down to me it's a fernal question where are we in stage of fo maturity ferno I mean um we're still in the early stage of workload maturity ferno to warrant a chip that will be durable um so so now you go back to oh we have so many accelerators they are successful some are really successful
but remember those as company they started before ji they start from some to optimize is some workload and they operate to AI and trying to kind of uh fitting AI workload. It's almost like you bat before this AI workload emerges and now it becomes a serendipity question. Um are you lucky enough this just work right? Uh and some really worked. Um some fundamental design of putting a lot of SRAMM on the chip is great for air model because they are uh memory hungry. Um and this really accelerate uh the execution of inference
and so on. So so those works um and some didn't work. >> What do you see as the greatest bottleneck today? You know, I think it was when I had Jonathan from Grock on the show who said like HBM was the greatest bottleneck and that's why you've seen the 5x increase in price. What do you see as the greatest bottleneck that people don't talk about enough? >> I still think we don't have a great system for very large model. I really believe the fundamental lower level infrastructure cost will go down. So for
solving task we should need less token that will increase. So collectively um the cost will significant reduce. uh therefore we can run the highest intelligence model much more ubiquitously in the future but we don't have a system designing for that for example we don't have a great system designed for um 10 trillion parameter models today and that will require very smart engineer code design from the model to um the customization serving platform layer all the way to chip layer. Chip is not the individual chip but the system
collection of chips in system um and all as a total package. I think there's still a lot of innovation we can do. >> I think recently you announced that you were at 800 million in AR um incredible feat and scaled so fast. What is that at the end of this year? >> We think we can at least double >> by the end of the year. Wow. You know what's so interesting for me as a venture investor? I've been in investing for 10 years. We used to be in the day where Slack was the golden child where like 1 to 10 million in revenue in
18 months was like amazing. And now we have companies like Fireworks where you scale to 800 million in revenue in a matter of years. And you mentioned cursor scaling to you know billions in revenue in a matter of years. the speed of company revenue growth is just [sighs] unparalleled. >> I think it's because there's a fundamental disruption in this technology that is all empowering. Um and uh all empowering in the sense it reach out to every individual one of us to be creative. uh and it unleash
the a lot of creativity that we just don't have access to. Um and that's why we're seeing this phenomenon of extremely fast growth because of a demand. >> Final one before we do a quick fire. You you hired George Hugh uh who was president of Salesforce. He's exceptional. He's one of the most direct no BS operators I've ever met. But you met him, I a couple of years before or a year before and you were like, "Oh, we're not ready for you yet." Why did you say that? And why did you decide now was the time?
>> Right. So, a year ago, I think we're probably just 50 people. So, today we're at 200 people. We're still not that big. >> Wow. You're four million ahead. >> Wow. So uh at 50 people I'm more thinking about uh scaling the product first then getting you know massively scale the business. Um and and we uh we we talked and uh I have huge respect to him but I know he is a legend. He's legendary. He's a legendary operator in Seikcom Ali. Um and uh I just feel like we're too small for him.
And I told him that hey we're probably too small for you but I would like to work with you at some capacity. So he helped me uh he helped me actually build up the team, interview a lot of executives. Uh his feedback is always well balanced, very thought, very thoughtful and we start to work together in that capacity until I think end of last year we're growing really fast and he knows and we start talking seriously and that early relationship paid off. So um he's really cool. He's he's really cool in the sense that
>> he's so cool. He he did a lot of things um a great accomplishment in in the past but I I find a unique character uh about him is he's extremely experienced has high aptitude of business vision but he is also very curious he doesn't make assumptions I know it all I've seen all the movies it's the same movie uh and let me just kind of direct this as I did in the past. So he didn't come with that attitude. He knows AI goes in insanely fast pace and uh he's learning a long way but also fully embrace AI. Um
actually his team our GTM team is using all kind of AI agent they they're sharing skills. Um so they maximize their productivity and he knows we have a super linear demand curve and uh he there's just certain pace we can build our GTM team. in order for us to catch this curve we we need to build a team but the team need to also have increasing productivity to match uh so that's a problem he's solving and uh I feel very fortunate to work with him and I in general I feel in the AI space the unique part is people need to have very
special traits almost like contradictory characteristics uh for example very exper experience but super um curious and the fast learning curve or Dimma we talked a little bit earlier he is brilliant high intellectual horsepower but extremely humble it's weird combination >> he's amazing too >> um and he's almost like cynical in a Eastern European way but also at the same time very humble >> can I do a quick fire round >> okay let's do it >> okay what have you changed your mind on
most in the last 12 months >> I think how fast we grow. I change my mind because I have been quite worried about too big a team too early. Uh so that's why when I met Georgia told me we're too small for you because I don't intend to grow very fast in terms of people. Uh I worry about slow down getting slowed down and lose the agility and velocity very [clears throat] deeply. So um but since then it's we have been very aggressively using our tools. We have develop our own unique way of hiring certain type of people that we
know uh they will be charging forward with high velocity as extreme sense of ownership uh very communicative and never take no as answer. So, we also learned how to how to get those people. Uh, and now I feel much more comfortable skating really fast. >> What's your type of people? And I know that sounds weird, but like our type of people is is actually really specific. Pretty much only hire immigrants. British people don't work very hard. Uh, sorry. Uh, very scientific and rigorous. Use data for most things. I actually
think creativity often comes from data and is informed by data. um and unwaveringly like accountable and ownership like nothing is anyone else's fault it's all my fault even if it's someone else's fault that's a 20VC person what would you say yours is >> it's not in weird way it's not competence it's weird we need we want people with the high conf uh comp confidence um but more importantly the strong indicator whether they will do well um in this wave especially in fireworks is whether Uh they are really built for taking
extreme ownership. Um extreme ownership as in we are not putting people anybody in any boxes and we're just stacking the box together into a tower. Uh we people just automatically claim hey this end to end problem. I'm going to see through the whole thing and work with a bunch of people to make it happen and uh and I'm going to deliver it no matter what. So those kind of people has the highest longest mileage and their growth curve is amazing. Also, >> what's your biggest lesson from working
with Jensen Huang on what makes him so special? >> He's everywhere. I serious think he has a clone of like hundreds of Jensen somehow plugging. [gasps and laughter] Um, for example, I sent him an email. He will reply in one minute. I I just don't understand how he's like constantly um in details and but but now I operate a company for four years. I understand why he's doing that is that's that defines velocity because what is leadership? Leadership is just judgment. It's not privilege. It's judgment.
It's you basically have the context. You need to have the right context to make the right judgment for the team. If and especially in a high velocity space, if you do not know what's happening, what works, what doesn't work, what are the gaps, you make the wrong call. Um in a slowmoving space you you you you can wait for the cascading information up and down and make those calls. But in a fast graduation space you just cannot wait um because it's guaranteed there is information loss in trans in in
transition layer after layers people are people always happens and not knowing what exactly is happening and make having the position of make judgment makes bad leadership and he is demonstrated through his own example Even before this crazy AI thing is he's operating that way and before I was admiring him in in his sheer amount of volume of capability of doing that. Now I understand the wisdom behind that because I oper also operate that way. Uh I I need to know what's happening on the ground to make the judgment for the
company. What did you wait on in the fireworks journey that you wish you hadn't waited on? >> Marketing, we talk about it. So, we are a little bit nerdy in this way that at the very beginning of our journey, we kind of we didn't didn't discuss it, but we feel product speak for itself. At the end product stands and we want to devote all our effort and focus on building product work with customer um validate product market fit and and go from there and we didn't spend much time marketing at all. we didn't prioritize
educating our customer what's the right direction to think about the trend um and uh and the value um but we do think now I do think it's important marketing is not about flows it's more about education um it's more about clarity um and uh and we are working on that >> what area of AI is underinvested in today in your mind. You mentioned like cooling or servers. What are like underinvested in? >> I think AI has the sexy part of this is such a innovative creative technology and the build
something on top of it is the focus. But monitoring the ROI, I think the industry start to kind of pay attention to it. But eventually that's what matters um is not how much spend is how much what is return and and uh and what is the cost and what is attribution. So I I think in the next couple of years as AI is getting more and more into production there will be a lot of focus in getting that clarity and getting that discipline out. So uh the token maxing is just um I think a thing in time um but we'll
quickly move into ROI maxing which is about all about running a business. >> What large customer do you not have that you would most like to have? >> So we haven't spent too much time in traditional enterprise segment. Um I think that's just because we was very small. Um and now as we build out our company, I do think uh even without our us investing, we have customers like Geico, like Capital One, um like Mercury Insurance and kind of uh RBI, all these companies even without us pursuing enterprise traditional
enterprise, they they come to us and uh they are customer. But I do think that's a very big market. What has to happen before the end of the year that hasn't happened for you to consider it a good year? >> I'm confident in our capability of driving the business. Um, and to me this is a year I want to prove we can scale quickly by keeping the same velocity and that's very important to me. Uh, if we reach that point reach that pro point and next year I have a lot more confidence to continue to scale
extreme aggressively. I want to make sure we do it right this year. >> Final one for you. What does no one see about the next three years that you see very clearly happening or not happening? >> 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. Um 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 their problem because every single company is solving a unique problem and they want to build software because they want to have full control. Um and obviously they will pick and choose which part of the stack they want build themselves which part of stack is common knowledge there's no point of building but every single company own their own software stack obviously we're talking about um this in the SAS time right so uh same I think air time every single company should own their own
intelligence >> Lynn you know it was Matt that introduced us first uh I've had the joy of getting to know you and obviously George I can't thank you enough for joining me for coming in person. It is so wonderful to do it in person and you've been fantastic. >> That's an amazing studio. Uh you uh you did um you asked a lot of interesting questions. I have a lot of fun talking with you. >> We do a lot of research before, huh? >> Yes, you did. >> Uh thank you so much for that and you're
fantastic.