Compute, Carbon, and Cashflow Silicon Data’s Big Bet on GPU Markets

Welcome back to Data Driven, the podcast where we talk about how data and AI are changing the world. And sometimes we even understand it. Today's guest is the brilliant Carmen Lee, CEO of Silicon Data and former Bloomberg brainiac who's now on a mission to bring financial grade transparency to the wild west of GPU compute markets. If you've ever wondered how to hedge your AI infrastructure costs the way airlines hedge fuel, or what a futures market for GPUs even looks like, you're in for a

treat. Carmen's turning raw compute into a tradable commodity, normalizing chaos, and possibly building the Bloomberg terminal for AI infrastructure. Minus the beige keyboard, we cover everything from tokenomics and TSMC to why your AI startup's margins are flatter than the earth in a conspiracy forum. Oh, and there's a used GPU car lot somewhere in Virginia. Stick around. This one's a data geek's fever dream in the best way.

Hello and welcome back to Data Driven, the podcast where we explore the emerging field of data science, artificial intelligence, and this crazy AI world we live in. But it's all underpinned by data engineering. And with me, as always, is my favoritest data engineer in the world. Even my dog is barking, giving you a shout out. Andy Leonard. How's it going, Andy? It's going well, Frank. How are you? I'm doing well, I'm doing well. I'm keeping busy.

We were talking about other podcasts that we have and the other one is Impact Quantum. So go to impactquantum.com definitely check it out. And had a very fascinating conversation with our guest in the virtual green room. So without further ado, let's welcome Carmen Lee to the show. She's the CEO of Silicon Data and she is driven by a passion for developing and delivering cutting edge derivative products and data solutions that provide essential data, intelligence and efficiency to compute

markets worldwide. Her company's vision is to revolutionize these markets through unparalleled data transparency and financial innovation. Welcome to the show, Carmen. Thank you. You deliver up my tagline so well I might want to hire you to do the whatever. Thank you. This is like. Thank you. This is like I was looking the other day. This is almost our 400th show, so I do have a face for radio and apparent thankfully. But a voice for radio. So good for me.

This is great. And speaking of radio, we were geeking out because I started my career in New York in finance and Bloomberg. Having a Bloomberg terminal on your desk was a status symbol. There were the ones who had it and the ones who didn't and the ones who wanted it. And you know radio, right? Bloomberg radio, which we also get here in dc. And you used to work

for Bloomberg, so that's really cool. That's right. I had a great time working for Bloomberg and my team was part of the data team I thought is one of the most cutting edge data company especially in the financial services industry. Back then I cover all content, all product data integrations with any third

party ecosystems. So think about any training cycles from Fedmin back offices, think about any cloud providers and database Systems and even AI, LLMs, whatever you call them, different use cases, real time reference data, aesthetic data, anything. It's really fascinating. I learned a lot my background before that I was in all financial services and I don't know if I bore your audience at this point. I started my career in trading, high frequency trading

in Chicago. So to me transparency, efficiency and free market is sort of in my blood. 100% brainwashed at this point in life. So one of the things I noticed when I was a Bloomberg is there's a lot of interesting ecosystem platform came up last year, right? So they all leveraging gen AI. You're the first few adopters which is good for them and their client basis sometimes can be financial institutions. So boom, client

basis. So one of the things I noticed is it was a really fascinating conversation. So those startups, they're gaining a lot of tractions. Good for them. So obviously I was like oh you're doing so well. And they will complain to me saying that they were sassed, right? They were 100% SaaS revenue so static and then it's pivoting to AI driven SaaS. So their cost, think about last year The GPU per GPU per hour was like $9 or 6, 7, 9. Back to like 3 if

you own interruptible instances, right? So the swing is like 300% within the same day but then their revenue is static, right? So their margin like positive 40% to negative, 60 to positive and there's no way for them to manage it. And also same time it's not like they bring on more clients. They can enjoy the scalability. It's like again same thing, the margin is uncontrollable and they have this problem say how do they actually coming out a cash flow plan for next year and then

they obviously complain. Totally strikes me to be hey, this industry needs financial infrastructure layer, right? It's almost like talking to American Airlines. Say hey airline, you cannot hatch your oil prices fluctuation. How are they Going to price their tickets. They can't, right? And it's not like American Airline cost OPEX in like give me five year long contract. They don't do that. Every single of those commodities pricing discovery and hedging happens in divers market. So

futures options because there's a few reasons, right? Number one is it's just efficient. Number two is cheap. Both is flexible and then you and me, we can do the same thing. We have oil exposures, we don't have to be American airline. But today if you are crowing for hyperscalers, you can go to those, you know, whoever, right? Produce chips, right? And get a long term contract. But you, if you and me start Neo Cloud, guess what? We don't have access

to kind of pricing. It's not good. We you have a few players who have the pricing, who have that way to hedge it. But then the smaller Prius just couldn't get in the game, right? It's really not good for the ecosystem's health performance and the risk management. So that's really struck my core. Last year I was like man, someone needs to do the index, the pricing, the benchmarking layer of the GPU compute as human resource I feel like will be the biggest human resource in the next few years.

Surpass all energy combined, right? So that's why I left Bloomberg right

away. Super passionate. I think we can bring so much transparency to ecosystem will benefit everybody, right? Not only benefiting other people, needs compute benefiting like you know, the end consumers. Because think about the whole funnel, right? You had finance and gpu the actual clusters cost, right? So if the banks don't have enough information or hedging for the

banks then they have to charge you high interest, they have no other way. Or you have to look for alternative capitals which traditionally they're more expensive, right? Because they're not banks. Banks are cheap as a cost of capital, right? So then the cost from you know, stage zero is high. Then think about the second stage, third stage and then people like you and me using Sora with OpenAI

everything will be more expensive because of that, right? So fix the problem with transparency from this from Gecko is really really critical and then their benchmarkings and encourage the secondary markets and all those flexibility and then availability will be really incredible to benefit the whole ecosystem. Interesting. So is it fair to say you've built basically a futures market for GPU. Compute I building a benchmark index layer. We are working with future exchange,

right? So I'm not a futures exchange so that would be something we will think about S and P. Right. So they license the index with a. Right, right, right, right, right. That's what we do. Right. Well, we will index to an exchange and they will have futures options on top of that and other financial products. That's a fascinating concept because like you're right, we need that because the scarcity of GPU compute is a real issue. It comes up. And if, if, if Amazon, the rate. Of volatility, how do

you. With, with. With like 40, 60% fluctuation every daily volatility and then it's just not a, a very transparent market which is. Breeds inefficiency. Right. Absolutely. So for those of. Oh, sorry. Go ahead, Andy. Okay. I was just going to ask. So are you tracking features and functionality and all of that? That, that would be the. How you value the GPU itself and compare that to the price and you're coming up with some ratio. Exactly. So

compute is not like. Unfortunately it's not as easy as electricity or even oil have different grid. Right. So even 100 has different configurations. Right. They all, it's not the same. Right. Different CPUs, different RAMs and geolocation matters. Right. So a lot of things. So normalization become very critical component to financially settle index. Right now we have H100A100 indexes published at Bloomberg and Refinitiv. So the way we do it is we have a base case and all

the factors normalize to the base case. And the way we normalize historical data, what factor is actually important to the users, the CPU matter? How much does it matter? What's the wave, whatever it contributes. How often do we calibrate? Maybe it matters today, maybe tomorrow. This, this particular. Whatever inputs value more. Right. So we do calibration, period of calibration as well.

Interesting. Yeah, it's fascinating to kind of see because I mean it always seemed like there's something missing around the GPU market. Right. Because it's just. And I also think too it's been a while since we had any kind of compute limitations on what we wanted to do. Right. Like that CPU is like. Yeah, it's cheap and you can get what you want and it's not supply demand kind of shifting.

Yeah, I agree. Right. So I didn't really think of like, you know, kind of this, this market kind of response to it, which I think is, is an interesting approach and I think, I think, I think it's fascinating. Yeah. Even if you think about AI SaaS company. Right. I don't know if you heard the saying that

SAS is 80% margin AI SaaS is 0% Mar. So I mean it depends on how you run your workflow. If you are not being thoughtful, right. You just dump everything, everything you need to do into the most expensive closed source model. And you're not optimizing your thinking tokens, your input tokens, output tokens. It can get very pricey very quickly, right? Not batching it, you're not doing all the right things. And even you do all the right things, it's gonna be such a meaningful

percentage of your cost. And then all those companies not ready for it. Right. Because in before what's the raw material cost? Electricity. Like really nothing. Right. But now every company becomes, you know, a which is great company but then their cost structure is shifting from zero cost to. To 40%, 60%, any percent to token or to GPU at the end. Right? Right. So how do you think about hedging that kind of cost component? Can you control that? Can you optimize for it? Can

you monitor it, can you benchmark it? You know, can you hedging it? So no, that's a good point. So do you think there's multiple, I guess, inputs and levers to this? Right. Because it doesn't seem like this would be a straight thing. So what's, you know, Andy mentioned that you were tracking certain benchmarks. Like what benchmarks are you tracking? Because I'm very curious about this. Right? So there's a few things, depends on your position at least this can change

every single day. Like our ecosystem is so nuts, right? So it depends your positioning, the whole workflow, right. So think about if you are new clouds, you are selling token, right? The cost for you is a gpu, right. So then your margin becomes the diff between the margin and the GPU cost. And that's the way we calculate it, right. Which is different units. And then your worry is okay, so for token survey for the tokens, how much money can I get rate

from one particular gpu? The flops, right? How can I optimize for that? And what if I'm doing even hosting open source models? And how do I make sure people using that open source model, should I shifting it? What's the pricing for that? Think about that strategy and GPU said okay, am I renting GPUs? I'm like outright purchase those GPUs and put on my books and depreciate it. How

long can I depreciate it for? How do I let's say if everyone's the latest and greatest, I'm selling the GPU after second, third year, what's the terminal value for the GPUs? Who should validate that? Which bank should depreciate the asset classes. So it's a lot of things coming to the new

cloud space. If you think about your inferencing infrastructure, right? So let's say you're AI tech company, right? Then your revenue is token, right? Ideally they're paying you based on token use cases as well. And then your cost is token which is easier but same time for you is thinking through okay so right now open source tokens, the price

they do move up and down. For example if you look at Deep Seq, even Deep Seq, they host their own servicing but then the price changes, they have the off peak hours and that change all the time. Or you can do closed source which the price is pretty static. The way I think about it is again it's extremely free market approach, right? Is how can we make sure especially open source ones, the token prices is driven by the market demand supply curve,

right? Let's say if everyone, if I have like 100 GPUs right now and obviously let's say I choose to host only one llama open source model and then I know I can produce X amount of tokens, both input output tokens, right? And I can just auction off and you guys and you can buy a million token and one day he's like I'm not going to use it, why do I sell it to Frank? Can this be some market where right now you are stuck with it, right? In

my mind, unfortunately I'm very brainwashed to free market. I feel like you have to give people option. The more option you give people and any have flexibility, franchise flexibility and people more willing to participate because they know they can get out. Because right now you're stuck

with hyperscaler GPUs or any tokens, you're stuck with it and then you're less likely to commit because you know you can get out or you get fined even worse, right? You know those cases, you get fined millions of dollars when you back out on cloud deals. That's one of the things I really think I should encourage people thinking about tokens

and GPUs as a main cost structure. How can we drive efficiency so people can commit and then get out if they need to and then swap out and everyone gets more value and efficiency from those transactions. So is it more like an exchange or an auction? What's the mechanism? Right? So from token GPU side obviously there's Spot exchange already like compute exchange, where you can actually tell them, hey, I need this configuration how many nodes? And then they will

say okay, let's do an auction. And then the best price, best quality, whatever combination wins. Right? Yeah. You can potentially do other asset class as well. Right. So we're. Siliconita is a data company. So think about us as the Bloomberg and there's the Nices, NASDAQqs and everybody, right. This spot, right, you can actually get GPUs. You, you can actually get stocks from those exchanges. And the FAST is we collect data from those exchanges like

Bloomberg. Right. And then we'll produce financial products on top of that. Right. So that's right, there's spot, which is the nasdaq. Right. You can buy and sell, get actual physical deliveries, all the compute or token you need. And there's data side which is making data the Bloomberg. Right. And then FAST is structurally the financial products layer. Right, data layer. And then we're agnostic, meaning we look agnostic of chips, agnostic of spot markets, agnostic of everything. Right.

And it's a future exchange which they license our indexes to create futures product. Ideally we're settling to spa. Maybe some of them will sell at spa. Right. So it's pretty standard practices. So would the currency or the coin of the realm be tokens or compute time or compute seconds? Things change. It's, it's making my life really fun and you know, also different. Yeah, all the time. And then you, you mentioned you have this quantum thing, right? Right.

It's a lot. We track all compute. So it doesn't for us what chips and what, what architecture framework and you know, we don't really care. We benchmark the performances and the data inside. And everything we don't know for us is getting ready for everything. So we want to create product that's actually going to be helpful to the marketplaces, not just creating things like gambling table. People bet on binary things. Right. For us, how can we make it useful for the people who actually

naturally long compute? So the Neo clouds everybody else, they need product to hedge their revenue fluctuation. Right. So they issue short futures and whoever naturally short compute. So you need computer and for you is a cost management. So I want to make sure my product is usable by them. It depends on how they pay. Right. If they pay tokens,

nothing to create token products. You're very right now paying people paying per GP power and you create product for that. If they pay things all right, then it's different contracts for that. So it really depends on how people using it today and tomorrow. And then, you know, we. We hyped to create products that may not be the S&P 500, which live forever. We probably create financial products live for next five to 10 years. Because guess what? Chips

what our style, right? The A100 people still using it, but like L4s, people are using it, but like other chips like the V's, the, you know, probably not as much. Right. Then similarly, my financial products associated with that underlying asset probably will, you know, retire, be retired. Right? Which is fine. That's cool. I'm sorry, go ahead, Andy. I was just thinking about it and a couple of ideas popped into my head as you were describing that, Carmen. One is

capacity. It sounds like you're literally selling compute capacity, GPU capacity, time, just whatever. But it kind of falls into that bucket under one hand. But then on the other hand, it seems like that it almost creates this utility market. Is that fair or am I missing something, right? No, you're right. But two pieces. So one is a compute exchange part, right? This is where you can actually get either depends on what people, the mode of people preferences. You can get GPUs or get

tokens, whatever, right. Physically delivered, you do you. You don't have to touch any financial products, right. It is literally like you going to a store buying stuff. And then the more option based, right. You can actually get instances. And the silicon data is. You cannot actually getting any compute. Right? Like you cannot get any stocks from Bloomberg. Well, you can get this data.

What asset is trading, what prices? So that informal decision, ideally in your spot market be like, hey, I think everyone, you know, the H100 price is a little too high, in my opinion. I'm not going to. Right. Right now, like, forget about this. And I can totally use a 100. Right. It's fine. So this data is data layer, which is liquid data, right? So those are those the sort of two pieces to I guess resolve the workflow equation. So it's kind of like when you go to the supermarket. I'm

sorry, Andy. When you go to. That's okay, go ahead. When you go to the supermarket, you buy the beef, you buy the pork, but you don't think about the pork belly futures and stuff like that. It's kind of abstracted away from you. Exactly. The farmers will think about this, right? Yeah, farmers think about it. Yeah. They need to hatch the corn futures, right? But if you are farmer, you still say you were someone to eat the corn. You go supermarket, you don't think about, hey,

Right. So you may have covered this already, but how does or does fungibility come into play? It's a great question. So I went through so many different iterations about this. Initially I was like, okay, why don't I just normalize across flops? And I was like, nope, can't do that because there's just, there's so many things wrong with this approach. But obviously we can dig into details, but we're not going to do that. And then secondly is okay, why don't we do like inferencing

chips? Like just make a pot and then we realize, okay, how can. So again back to the initial question. I want to make product actually going to help people hedging. Right. If you do a combination of different chips, then if you are and you know we're using of a lot of people, are you going to really use that to hatch? How would some correlation look like. Right.

Maybe you just rather have different chip types and then just hatch accordingly because the correlation will be much higher than the combination of indexes. Maybe the composition of indexes is good for just tracking general, but not for actually financial products. So we have, we have, we can have all. Some of them will be tradable. Some of them. Well, right. For us is if people start, if, if we move to the world where it's not going to be Nvidia only kind of play

in the like amd. We can eventually, it'll probably end eventually. Well, we'll see when, right? We'll see quantum happens first or everyone catching up first. I have no idea. Right. So if it's like a more vibrant ecosystems. Right. And then maybe we're thinking about, hey, maybe we can do like doing some of the chips. Even different firms would normalize it and then we do something like a inferencing chips, chaining chips. I don't

know. So that's another thing. Or like token, token indexes. Right. So can we do open source ones? Multimodality? Is multimodality going to be a thing in a few years? Everything going to go back to one model only? Because right now with different models. But maybe it's the interim stage. Right. We. I don't know. So it's one of the things we have to keep like looking and thinking and just moving things forward. Yeah, I was thinking too about, you know, the, the amortization that people

do in their heads at least when they buy a new car. Yeah. So that's the math is you drive it off the lot, it's worth what, a 75, 80% of what you pay for. So we need a Carfax for GPUs, right? So that's what we do too for silicon Mark. So what we do is okay, everything. Well at least right now or before Last year or T minus 1, everything is brand new. So okay, we'll take whatever the number they published and tdbs, the flops, we all know there's like haircut to that number.

That's funny, right? And then a year later, right, A year later I say, Andy, you're growing great in great data centers. Your thermal cooling was doing great. I'm old data center, I don't have the latest cooling. Obviously my chip is after year. You can argue they own different curves, decay curve. And are we treating the same prices even though same configuration? Probably we shouldn't. Should it be reflection of the actual quality? So that's something Mark does.

And then we do things even more basic than that. So number one is when you tell me you have H100 like 100 nodes, each node has say 8 GPUs, right? Yeah. Is that true? Can I

number one verify the UID of that? And you see, it's all the CPUs and this operation systems on all the nodes, they all live connected. Number one, can we just verify are they connected? What's the latency? So that's very basic things, right? So we do that piece at least, you know, are they truly UIDs and CPUs? The machine, is the machine ever changed? Because we do mesh IDs based on CPU changes. We know something changes, right? And then the UID of every

chip. So we do the decay curve for the individual chips and also the machine level and then thermal staggeration, everything. So we do that and then we do validation. Almost like Bloomberg Validate fixing compound. Because you have to understand the issuers and it's a bridge and it's a school and with cash flows and all those stuff. So we do that for GPUs. The geolocation. If you build data centering somewhere in North Korea, it's great, but no one going to use it, right?

We took all those in considerations when we created those data models. So then we figured out, okay, so based on the setup and we run a benchmark on specific GPUs, this is our grade and then this is our validation. Obviously you can do whatever you want. And then you can say hey, screw that, I believe this is much higher price. You can do that as well, Right? But this is our valuation. So almost like a scoring system.

That's interesting. So My mind immediately went to, when, when we started talking about cars, my mind immediately went to, you know, the used GPR lot some guy in bib overhauls out here in Farmville, Virginia kicking the tires. What's it going to take to get you into this gpu? Yep. See, there we go. And network them together. Right. Like I think there's also, you know, maybe, you know, I don't know if you've been tracking the, the DGX Spark device that Nvidia has, but apparently they have ports

in them so you can network I think up to four together. I'm not sure but yeah, I'm sorry I cut you off but like. No, no, no. Nvidia we definitely leveraging a lot of. So we do the container within container and we do integrate with Nvidia DGX benchmarking. So they have open sourced some of their LM benchmarking based on GPUs and we do streamline their products so you can test lms. So Nvidia Digitex testing

through system data. The benefit is if you do it all right yourself, number one, you can obviously people want but people can just change up the, the benchmark results themselves. Right? It's open source but through us it's data Oracle. You can't really change results. Number two things is more streamlined. It takes a few hours to run versus take weeks because you've download a bunch of things you may or may not need. You may or may not need.

Well, I also think too like, you know, how does this, you know, you mentioned you, you kind of skirted around the location thing with sovereign AI, right? So like if I'm okay with using Google Services, right. And I can, I have access to TPUs, right. I have a lot more access to whatever Amazon's chip. Microsoft I think is working on something. Custom that's on prices too, right. The Geolocation they have different prices and different carbon footprint. We haven't even touched that.

Right, right, right. We do track that as well based on local grid power grid information. We do track the carbon cost associated with different AI workflows. I think it's important, I think so for me is let me at least surfacing the number to you and you decide what to do with it. Right. So I think that's a good idea

or you know, maybe it turns out that you know, this type of model of GPU is you know, depending on what your core. I think it's, I think it's great because I think one of the things that I've heard And I didn't Peter Drucker. What gets measured gets managed, right? So you're, what you're doing is you're providing ways to measure GPUs and GPU performance. Right.

So if I don't care. One of the things I heard about and I'm sure you have some thoughts on this is like cloud providers that are starting up and they're just doing GPUs, right. They're just doing kind of training loads. Right. And they don't need to be located anywhere special. Right. Like they don't need to be in the northeast corridor. They could be in the middle of nowhere as long as they have power. Right. And because you're going to run a load, right, you're going to run a load on

the thing, it's going to take 72 hours say to run. You don't really care if the latency is, you know, 150 milliseconds versus 3. Right. It doesn't really matter. Yes. That's why you see a lot of us get built up in like Iceland, Finland, the users can be in Americas, can be in Asia. Right, right. For them is can they get the capacity looking for and you hard deal if you're thermal powered data centers, cheap electricity. Yeah.

And then it's cleaner supposedly. Right. As long as you're not on the volcano belt. Right. As long as it's not going to blow up. Yeah. But yeah, so we definitely see that trend and a lot of energies, you know, what do we call it oversupply sometimes can be in Spain because overbuilt and the grid couldn't handle it. And then they need to get data center up and running like now to take over the power. But then it takes a lot to make the racks start running. Right.

More than just the GPU itself, you need the connectivities and network and that could be in shortage. So you need to solve a lot of different pieces to actually deliver the actual computer. But that's why it's fascinating industry for us because we see things from dsml, tsmc, side. So anything supply demand shifting will have an impact on the whole ecosystem. And then this industry is winner takes off from LTSMC to a solution level. You have to be the solution. Your alternative solution just not

going to work. So. So every single piece is so critical to the whole chain packaging. Right. You have to work, right. If you don't know how to do it, then you just can't do it. It's not like you can buy a cheaper pair of socks or whatever so we do. We're from end to end, right. From the SM of production, tsmc. So we're official TSMC partners are going to be actually TSMC conferences to this November. Very cool. It is really cool. I kicked out by those stuff very quickly. And all the way to

the model A, the token layer. Right. Agentic layer. So we sort of see things all the way. Which I think my brain get overclocked every single. I know what you mean because I get till the time of like 2:33pm and I'm like, I can't take any more input. Like and the muscle, my brain muscle just dead. I know. How do you do that? How do you get a roller in my brain, just like relax my brain muscles? I. I found going for a walk is a. Is a good way to do it. Right.

No, like. And a co worker of mine calls it everything turns to hieroglyphic hieroglyphics when he's like looking at like stuff. And I was like, yeah, that's a good way to put it. Because it's just kind of like, yeah, I can't. I don't want to have time by a daughter. So I usually spend time with my daughters. I feel like they've been silly. And I would tell them, I'm so stressed out. When my daughter was like, me too. I was like, what are you stressed about one last donut

than the other guy. I was like, that's very important thing. I agree with that. That's very stressful. I will be really upset if I get one less donut. So. Yeah, so definitely put things in perspective. Yeah, that's cool. I think one of the best things. Any other questions? No, plenty, plenty. Like, I'm just fascinated by this. I know we're kind of short on time, but one of the things that you mentioned was

tcmc. Tsmc. So for those who don't know who they are and how important they are to the global economy, could you explain for those folks and why I was so excited that you're going to one of their conferences? I didn't know they had conferences, so. I don't think I would do the justice of explaining how important TSMC is. All right, how about I explain it and then you tell me where I'm wrong. I'm sure you'll do a better job than I can. So. Tsmc. Taiwan

Semiconductor Manufacturing Company. That's right. They are based in Taiwan. And the reason why. Nvidia. There's a fascinating story in the book called the Nvidia Way. I Don't know if you've listened to that or read that book. Really awesome book. But basically one of the advantages Nvidia had early on and arguably now was that they off they outsourced their chip manufacturing to this company tsmc. I'll get it right that time. They are basically what they call a fab. And you could, I mean not

now they're so busy like you know, you kind of the you in general. Right. Like I couldn't call them up and be like hey, I have some prints for you. I have some chip designs I want you to make for me. Can you send me. They're not at that scale but so they're a fab. And so what happens is people like Nvidia, companies like Nvidia, a few other companies too will go and they will, they will design their chips and then they'll, they'll basically not drop ship but effectively kind of print to order

chips. Which frees up a company like Nvidia from having to build their own fabs. Kind of like intel does. Is that a good description? 100 so I usually call on Nvidia and AMD like design houses and then sometimes confused with people who's like oh, are they like Louis Vuitton was like no, Right, right. Or like graphic designers? Yeah, yeah. So they're design houses and then they are Fabless. Right. And intel, which is interesting because they do both. Right? Yeah, yeah.

Intel like as I was saying that intel doesn't. Yeah, they do both. Yeah. Right. And then it could be a great strategy. Could work or. Well, depends on many things. Right then anyways, so TSMC is like the, as I said before, this industry, I don't know if it's good or bad but it's a winner takes all market. Right. So TNC is definitely

the winner for a lot of different reasons. I think for the leadership, self and technical team for the whole supply chain ecosystem. The gravity, all the years, the hard work they've put in. So it's a position where I don't think anyone can seriously challenge them in a meaningful way in the next whatever years. So they're very critical. And then the good thing interesting about them, they're the agnostic of design houses,

right. So they have great relationship with Nvidia for sure and I'm sure with them, with everybody, right. It's their job to produce those chips and then it's interesting enough it's aligned with mine. Silicon Data. Because

I'm agnostic of chips, right. So obviously I want to create products that's most important to the ecosystem. So right now people care a few chips and those chips happen to be from one design houses. But let's say if another design house start picking up a lot of momentum. For me, it's like, how can I help everybody in ecosystem compare, contrast hashing, right? Use them benchmarking, normalize it in a meaningful way. So it's my job to work with all the design

houses. It's their job to produce chips that can be usable for defunding the houses too. So we're very aligned in that sense. And anything they do, right? So think about, they are future looking because they're not thinking about next year or next quarter. They think about 20 years, 10 years. It takes them five, six years to build a fab, right? And then they need a fab to

be utilized. And they have a threshold, right? If you're building a fabric and that's not utilized by year eight, they plan right now by year a year 10, they are losing a lot of money. A lot like billions of dollars, right? Like can you make sure the fab will be utilized, the demand will be there by year 10. Forecasting from today. It's very, very, very hard job to do. And it's not like it's not like a new reim, you know, like what are minings and all things that you can hedge it, right?

Like there's a way to hatch the future curve. But like it's not like they can forecast, forecast and do a swap on that because the market is so concentrated and then very binary and a huge size. Who's taking the other side? I don't know. It's very hard over the concentrate to do so for them is to get clarity supply demand curve in 10 years. I mean they do also edge computing chips as well, not just data center chips. Right? But how do they think through that? I think that's

really challenging. I think will be really challenging for me for sure. I'm sure they have way smarter people there to think through those problems. But yeah, it's an interesting problem to have. That's why TSMC and I, for example, they sell to their clients who are in the vds of the world. So they have that kind of transparency. But what they don't have, which may be a different indicator for the supply demand curve in 10 years is end users

pricing volatility. Right? And then you know, okay, so if every single chip, every single chip I produced, right, Data center quality chips, one dying price, right. Is the indicator for supply demand shifting. Maybe it is Maybe it's not right. At least you have some, some data points which your immediate sales and revenues which is T0 won't give you because then a few degrees removed from end user experiences you give Nvidia and Nvidia packages it to

AWS and GCP and end users and you and me. Right. So that's something that for them to think through as well.

Interesting. One of the stories I heard and I wonder if it's true, was that part of the reason why there was part of the reason why Nvidia was able to really capitalize on this. There's a lot of reasons, but one of them was the fact that in the crypto craze, the run up to get chips for that Nvidia had purchased. Now what you said makes a lot more sense now. Nvidia had purchased the. They basically purchased a certain amount of capacity at TSMC

for like three to four years, something like that. And then that happened to coincide with the AI boom. Is that, is that true? And that. I guess that's a market too, right? Like you know, like hey, so I wasn't. I know so 7 so I'm not following all ASICS so they have a specific for. For the, for. For the mining chips. That could be true. So I think

not because I'm straight, I mean and a girl can dream. I'm strapped to be like, you know, to really help the industry and then be, you know, like the company the team hopefully can propel the industry move forward. Right. I'm strive to point zero over percent people and then competency is very important. Obviously execution, your hard work is important. Not a big piece is you have to be really, really lucky. That is also everyone's control.

And then Nvidia puts so much time effort into everything they do. You can argue they were really great company even before the AI boom and everything. But the lock piece and how do you control that? How do you. How do you know quota gonna be like the piece that's needed? Right. Well, some. Someone said that, you know, Jensen Wang is like the epitome of, you know, the better you, the harder you work, the more luck you have. True. Like there's a lot to that and I know it's

complicated but like I'm just, I just. It's interesting how the crypto kind of boom and bust really kind of also propel us into the AI. Not, not all by itself but it definitely I think gave. There was some momentum where no momentum was expected, if that makes sense. Right. Yeah, I agree, I agree Timing is so interesting, but we just have to two point like the heart of your world. You have to do everything you can with the environment. Right? That's

cool. That's cool. All data. So we'll see happens what I mean. That'S the importance of data. Right. Like, you know, people don't realize that. And I go calling back to Bloomberg. So I'm referring to Michael Bloomberg, former mayor of New York. But before he was mayor he basically started a company called Bloomberg. And he was not the only factor but like a big part of, you know, people getting into, you know, his

philosophy. As I understood it, if there's a good, if there's a good biography book on him, I totally would want to listen to it. But basically getting the traders access to data gave them an advantage. Right. And it was really, he was really early on in the idea of that data is not just something that's created as a byproduct of transactions, but can actually be, you know, monetized and arguably weaponized. Right. Like so.

And you know, Bloomberg terminals before, you know, it was interesting because he basically sold these custom terminals so you'd not to rely on like local ID who were still struggling with like, you know, just keeping the network up and running, you know, these separate

devices that became status symbols. And ultimately he, that's become like this media empire that, you know, I can watch Bloomberg on my tv, I can listen to it, you know, whether it's a satellite radio or the app or you know, FM or AM radio stations. You know, I think it's in San Francisco, New York and D.C. they have a big office in D.C. they always have an interesting show called Political Capital. I think that plays

at 5pm every day. I listen to it because it's kind of the policy side of finance and kind of what's going on in the world around. And AI has come up a lot digital sovereignty. So it's interesting how all of these worlds, I like your thoughts on this, right. The worlds of finance, the worlds of tech and the worlds of policy, politics and dare I say war. Right. They're all kind of like crashing together in this giant thing. And it's kind of cool, kind of scary.

I think it can be. I mean, sometimes I'm scared I was like, you know, because you see a few things, it's like, whoa. There's a lot I feel like for people born post Covid, not born, but grew up post Covid, I would call Jen the second Gen Z Gen Alpha. Yes. I think Gen Z's apparently now like I'm all confused. But for them it's like, of course they should. They should. My AI should be my boyfriend, girlfriend. Right. Like whatever. And then for me it's like, this is not comfortable at all. Weird.

Yeah, yeah, yeah. For me it's not. I have no idea what's going on. Like, I just so creeped out by this. But for lot of people it's like, of course you do that. Of course you tell AI all your secrets. Of course they can. My phone can record my conversation. Of course you can train, you know, your AI model. My model use my all my Gmail content information. All edge computing. I have my own AI model. Of course you can wear, you know, glasses and then record everything you and me talk

about. And how secure is everything right now? Right. The hardware level encryption is only available on a very specific few chips. TPU can do that. You rely on software encryption. No, it's true. And software encryption that is vulnerable to a quantum attack which is not that far away. We are not the software and use cases moving so quickly. The hardware hasn't been able to cut up. And it's expensive to do hardware encryption. It takes

longer and it's more expensive. That's why sometimes the hyperscaler charging higher premium for that reason. Right. Are you willing to spend a token and time and effort to do so? Some use cases, you can argue. Yes, yes, absolutely. No edge computing chips can do that kind of hardware level encryption. And it's happening like now. Right, Right. I was talking to a startup called Quantum Knight. Nate claimed to have a solution that is a low, low compute kind of post

quantum ready thing. So I can send you their link and information. Yeah, we, we track quantum computing prices as well. Very different than GPU pricing and like, you know, like a thousand per second per minute pricing versus hourly. Right. This is like different cycles you run. And then GPU become like error correction component to the whole thing. But for us it's like, okay, so computers compute now, GPU and tpu, whatever, pu. And then it becomes like quantum. How we think through that? I don't

know. My brain just like, you know. Yeah, I know. At some point it just becomes like. I'm not smart enough right now to, to. To. To figure that out. I tell you, like I go

through like quantum stuff and like I always joke with Andy, like I'd be like 15 minutes, I get a migraine, which is basically like my brain's version of blue screening. And like, just like, okay, I can stop. I can get to about. I can get to about 45 minutes now, which is, you know, an improvement. But this is actually a good book. And he was actually a guest recently on the Quantum Computing podcast. It's a thick book. It's a thick book. But I'll tell you this.

The, the, the, the first three chapters, introducing the concepts are probably the single best introduction to the concept I have ever read. Yeah, I will send you the link. Yeah, yeah. Dancing with Cubits. Really interesting book. Super nice author too. He's a, he's a trip. But it, it. No, you're right. Like, these are. The thing that really worries me is I kind of think about this like we built our entire economy and we're, we're on a house of sand. Can we start on this? That's

another thing. We'll have to have you back on the show for a second one. But like other countries where they lay off hundreds and thousands of people, not. Not just by American companies. Right? Yeah. Don't even get me s on that. Well, like, and like, you know, we're all based on. And, and the other thing, the elephant in the room, right, is the fact that TC the, the T in TSMC stands for Taiwan. Right. Kind of. I know, I know it's very dangerous to talk about this, but, but like. It'S

kind of like, shoot. So I won't say much, but I'll just say it's contested real estate. How about that? Right. That's a pretty safe way to say it. Right? It's contested. Right. And you know, the entire world effectively revolves around the kind of modern civilization revolves around the manufacturing that happens there. And God forbid, like, you know, whether it's man made or a tsunami or a bad earthquake, like, I mean, our world, I mean, we, we get sent back

to the 1700s pretty quickly. You know, 1700 is not, you know, there are still people, they're still human beings in the hundreds. That could be worse than that. That's true. It could be way worse than that. That is a good point. I was trying to keep it. I was trying to end it on a positive. And I know you're traveling there like no humans. Well, no, I mean, like, I mean, there's a lot of ways that the, you know, this apocalypse could go,

so to speak. Right. It could be, you know, but like, it's a very. And like, just from an infrastructure point of view and supply chain point of view, like, you know, we, we. We've really championed globalism and kind of all of these extended supply chains for, you know, there were reasons there's always reasons, but like at the cost of resilience. Right, right. That's kind of scary. I assume you've read Taleb, right? The like anti fragile.

I'm so sorry. No, that's fine. That's fine. But I really appreciate you taking the time. Where can folks find out more about you? Silicon Data.com Silicon Data.com awesome. And we'd love to have you back on the show. And you can tell us what these conferences were like. The. The ts. Let's see how much I can understand first. Right, right, right, right, right. That wasn't a good question. That's why you got to be like the kids today and record all your conversations

so you can talk to the transcript later. All right, nice seeing you guys. All right, thank you. And we'll let our AI finish the

show. And that wraps up another episode of Data Driven, the podcast where we ponder the future of AI data and occasionally the fate of humanity if we don't get GPU pricing under control. Big thanks to Carmen Lee for joining us and blowing our minds with compute market mechanics, financial innovation, and just a touch of economic existentialism. Be sure to check out silicondata.com to learn more. Just don't try to day trade

H1 hundreds after midnight. If you liked what you heard, subscribe, leave a review, or send us compute credits. Until next time, stay curious, stay caffeinated, and remember, in a world of exponential AI, transparency might just be the killer app.