Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2: Hello and welcome to another episode of the Out Thoughts podcast.
Speaker 2: I'm Tracy Alloway.
Speaker 3: And I'm Jill.
Speaker 2: Wasn't thal so Joe. We're still continuing our series. Recorded
Speaker 2: from the live show in New York. We had a
Speaker 2: bunch of great conversations. A couple of them were building
Speaker 2: off of discussions that we had had previously, and one
Speaker 2: of those discussions was in Chicago at another live show
Speaker 2: about six or seven months ago. Back in October, we
Speaker 2: spoke with Don Wilson of DRW about the trading environment,
Speaker 2: but also about his new venture.
Speaker 3: Right and so his new venture is one that actually
Speaker 3: there's quite a bit of competition in and quite of
Speaker 3: excitement in, and it's essentially like okay, GPUs. We know
Speaker 3: they're very important for the AI boom, et cetera. The
Speaker 3: question is can GPU capacity, which is scarce, can it
Speaker 3: become a tradable commodity such that I can buy futures
Speaker 3: to lock in my price of access to compute power.
Speaker 3: Could I resell those futures? Will there be speculators speculating
Speaker 3: on the upper down price of like an H one
Speaker 3: hundred running an H one hundred in video chip for
Speaker 3: an hour. This is a big question. We know there's
Speaker 3: a lot of interest in the actual compute, but whether
Speaker 3: there's interested in compute futures. It's tradable instruments is very TVD.
Speaker 2: Yeah, And the analogy that everyone always uses is compute
Speaker 2: is the new oil, right, so why can't it have
Speaker 2: you know, a market structure that looks somewhat like the
Speaker 2: oil market. And there are challenges. Fungibility is a big one,
Speaker 2: like one chip might not necessarily be equal to another
Speaker 2: chip or one ship.
Speaker 3: The same chip at one data center might equal to
Speaker 3: the same chip at a different data center exactly.
Speaker 2: And so even if you're not interested in AI, what
Speaker 2: I say here is like the market structure questions and
Speaker 2: the idea of building an entirely new market is really
Speaker 2: fascinating to me, and I think others will find it
Speaker 2: interesting too. And we really do have the perfect guest.
Speaker 2: We're speaking with Carmen Lee. She is the CEO of
Speaker 2: Compute Exchange and Silicon Data. These are the two companies
Speaker 2: that Wilson is invested in, and they've already announced that
Speaker 2: they're doing futures with the CME. So really the perfect
Speaker 2: person to speak to So take a listen. Last October
Speaker 2: we spoke with Don Wilson of DRW fame and he
Speaker 2: was talking to us about his new project, which was
Speaker 2: basically building out this compute exchange. Now we're here with
Speaker 2: you six months later. You're actually the one leading it.
Speaker 2: How far are you in this endeavor? And remind us
Speaker 2: what exactly are you trying to do here?
Speaker 4: Yeah, so thank you for the the quizing one great audience.
Speaker 4: Before I do that, I actually going to call back
Speaker 4: to six months ago in the DOM podcast you did.
Speaker 4: You asked them question, what if compute prices keep going
Speaker 4: to go up? At the time September October, compute prices
Speaker 4: were going down across our chips. Now see what happened.
Speaker 4: I think you called it. I think you called the
Speaker 4: market called it. So I'm the fund of CEO for
Speaker 4: Cilicond Data, so that's the index provider for GPU indcees.
Speaker 4: We recently announced partnership with CME, so we all been
Speaker 4: launching gp futurning options ME in a couple of months,
Speaker 4: pending the FDIC approval. Obviously that's quite exciting. We've been
Speaker 4: working on GPU indices for past two and a half years,
Speaker 4: starting twenty twenty four April, so it's been a while,
Speaker 4: and we launched for world's first GPU inducees at Bloomberg
Speaker 4: Terminal in twenty twenty five. A year later we launched
Speaker 4: the partnership with CMME, so it's quite exciting. Separately, I
Speaker 4: heard you mentioned Compute Change before, so thank you for
Speaker 4: doing AGIL. I'm the CEO for compute Change, which is
Speaker 4: sport marketplace for GPO procurement. So we do reserve contracts
Speaker 4: for contracts as well as ree for pitch contracts.
Speaker 3: Let's talk about the variety of options that we have
Speaker 3: to financialize compute and so forth. So this, I mean,
Speaker 3: this came up in our conversation. The first conversation we
Speaker 3: had with we had with Ian Dunning. Who is the
Speaker 3: type of buyer who would want to buy compute on
Speaker 3: a spot market because right you talk about typically we
Speaker 3: think it's like these multi year contracts where some entity
Speaker 3: enters into a contract with a data center or a
Speaker 3: new cloud whatever, and this they have this for a while.
Speaker 3: So who is the market, who is the buyer or
Speaker 3: the user of these instruments that might want to buy
Speaker 3: spot compute or very short term short dated compute futures.
Speaker 4: It's a great question. So the compute market right now,
Speaker 4: for comput change, we have all our provider mostly are
Speaker 4: neo clouds around the world. It's one side. Another site
Speaker 4: is a big variety AI start up. So even though
Speaker 4: they are start up, millions of dollars on GPUs already.
Speaker 4: There are enterprises who are traditional businesses, but they are
Speaker 4: needing a note to notes, a few service here and
Speaker 4: there for their inferencing or I don't know, other deployment needs.
Speaker 4: They are providers. They are influencing providers right they don't
Speaker 4: own GPUs, but they provide open source, open weights model
Speaker 4: support for other use cases. So what see big variety.
Speaker 4: Most North American firms they do a variety of combination contracts.
Speaker 4: Obviously on demand give you the most flexibility. You don't
Speaker 4: pay and you don't use it. However, you're also at
Speaker 4: a mercy of defense. Apply curve at a given time,
Speaker 4: so translate to your price can go from three dollars
Speaker 4: to six to nine depends on demand supply curve shifting,
Speaker 4: so that doesn't help when you can have a predictable margin.
Speaker 4: And also in terms of scarce, you're not guaranteed to
Speaker 4: feel GPU resources for next hour or next month. So
Speaker 4: you see a lot of people shifting from on demand
Speaker 4: to reserve even four contracts right, so full contracts you
Speaker 4: back the lock in a deliverables for next whatever month, right,
Speaker 4: starting in September maybe or starting no member of forbare
Speaker 4: weapons right. So this all comes because of market condition.
Speaker 4: So computing cover that the physical GPO procurement also token,
Speaker 4: so we love to talk about token as well. On
Speaker 4: flip side, who's going to use the future options can
Speaker 4: be a similar set of people.
Speaker 1: Right.
Speaker 4: You look at oil market, which we all love double
Speaker 4: TM brand. Right, the people use double TM brand. A
Speaker 4: lot of them are naturally long oil. So the shells
Speaker 4: ran the producers. They need to hatch your revenue volatility
Speaker 4: by shorting futures or port options if you're naturally short
Speaker 4: oil americandline right, they want to control their cost volatility.
Speaker 4: They want to obviously use future options as well.
Speaker 3: Simple to the.
Speaker 4: Compute your new cloud or you're at anyone have the servers.
Speaker 4: Ideally you want to have predictable revenue streams.
Speaker 3: So the neo cloud would be the shell on this example.
Speaker 4: Exactly, you have GPS, right, all the banks were GPUs
Speaker 4: on your balance sheet right your long GPUs, then naturally
Speaker 4: you want to make sure revenue right is stable to
Speaker 4: certain degree, and then you want to use future to
Speaker 4: do so. If you are naturally short GPU, which is
Speaker 4: everybody in this room, amas you tell me you have
Speaker 4: GPUs right, then you depends how much you use. If
Speaker 4: you want to control your cost volatility, you want to
Speaker 4: use future to hatch as well.
Speaker 2: Just on the compute exchange side of things, if someone
Speaker 2: is buying like off the spot market, how do you
Speaker 2: guarantee I'm not sure quality is the right word for this,
Speaker 2: but how do you guarantee they're getting what they expect?
Speaker 4: This is a great question. So I canna flip to
Speaker 4: slide if you don't mind. So I usually don't let
Speaker 4: use slides, but this time because you mentioned really good questions.
Speaker 4: So we actually call it GPU lottery. So we published
Speaker 4: a paper earlier this year at GPGPO conference with Jefferson
Speaker 4: Lab on GPO performances. Well actually, so well we can
Speaker 4: have your quit link, you know, to the audience later on.
Speaker 4: We actually this is a one hundred by the way,
Speaker 4: I know we didn't put tang on the day. Is
Speaker 4: a one hundred forty gigabytes memory bandwidth. We prove there's
Speaker 4: thirty eight percent performance variants for the same trip, and
Speaker 4: we decompose it into the chip self, into provider and
Speaker 4: inter provider. And there's many reasons for that, right and
Speaker 4: to your point, you can you don't know until you
Speaker 4: get your GPS. We have a PLAT for GPU. CARFAX
Speaker 4: for GPU depends how to look at it, so we
Speaker 4: in compute change. You actually verify the GPU before delivered
Speaker 4: to you. So basically you are you are. You CANFQ
Speaker 4: for say, hey, I one A two hundred BI two
Speaker 4: hundred notes. Obviously it will give you specs back and
Speaker 4: the commercial back same time, independently verify the performances on flops,
Speaker 4: memory bandwidth totals another information as ACE and other things.
Speaker 4: And as a user you can decide is price your
Speaker 4: most important criteria? Maybe it is, or maybe you're willing
Speaker 4: to pay a premium for dual location or the performances
Speaker 4: that you care more about on latency. Right, we believe
Speaker 4: gave people the option and transparency it is the most
Speaker 4: important thing.
Speaker 3: Let's stick with the oil analogy for a second. You know,
Speaker 3: there's a few benchmarks that we all know about. There's brand.
Speaker 3: There's WTI, there's others, but those are the two that
Speaker 3: we talk about. If we transpose this to chips for
Speaker 3: a second, okay, we say you have an H one
Speaker 3: hundred index. We did an episode of the podcast last week,
Speaker 3: I think with the CEO of Servius, which is another
Speaker 3: amazing company. Yep, yeah, but there are different Another type
Speaker 3: of chip for inference is your assumption that these indices
Speaker 3: are going to be close enough to the cost such
Speaker 3: that if you're okay, I'm running inference maybe on some
Speaker 3: service or TPUs or training whatever, some of these others,
Speaker 3: that an H one hundred index will be good enough
Speaker 3: as a hedging instrument.
Speaker 4: This is the whole goal for me sitting here. Actually, right,
Speaker 4: there's a meaningful every financial products in the functional reason
Speaker 4: for commodity it is for hadging. Right, This speculation is great,
Speaker 4: but really for people to hatch their relativity, to do
Speaker 4: risk allocation, to do risk transfer, and then asset capitala location.
Speaker 4: If we can't do what you said, then we fail
Speaker 4: at our job. Right, So that's why we when all
Speaker 4: the way back the way we cut we develop our
Speaker 4: index model is not a simple math. It's not Hey,
Speaker 4: you have two h one hundred to simple average, right,
Speaker 4: because then then you compare Apple to oranges, the two
Speaker 4: h one hundred can have different CPU different RAM defend
Speaker 4: this differential location depend memory, bandwidth. You cannot do simple math.
Speaker 4: What we do is we usually collect six months of
Speaker 4: historical training data from over one hundred data sources, and
Speaker 4: we see which factor to have the price differentiation. So
Speaker 4: every day over one hundred and fifty thousand traded prices
Speaker 4: in just our platform, and we normalize the traded prices
Speaker 4: based on the different characteristics of the model itself and
Speaker 4: normalized to a base case. And then we do the
Speaker 4: math of settlement price calculation. Right, So then this price
Speaker 4: will be highly correlated, ideally as much as it can
Speaker 4: to the price you pay at a new cloud for example. However,
Speaker 4: it won't be the same just like basis trading, right,
Speaker 4: like every other commodity is a basis risk. We're helping
Speaker 4: client colpeling the basis risk. So you know, hey, you
Speaker 4: us east, you may be BIPs higher or two than
Speaker 4: there's expectation a manageable correlation understanding of the indicies.
Speaker 2: You mentioned volatility just then, I mean the reason people
Speaker 2: need to hedg just because of volatility. Are you seeing
Speaker 2: enough of that in GPU prices that like, this model
Speaker 2: makes sense because if it's just a steady line up
Speaker 2: or steady line down, like it's gonna be a kind
Speaker 2: of boring market.
Speaker 4: So it's interesting. So last year, when GP prices are
Speaker 4: going down, the big conversation is why do you need
Speaker 4: indicies for something price will always go down? And this
Speaker 4: year is why do you want to inducease when price
Speaker 4: always go up? Literally is all the questions.
Speaker 1: I get it.
Speaker 4: It's pretty fascinating. So when we will look at volatility,
Speaker 4: we look at daily bomb volatility movement, not the price
Speaker 4: up and down. Right, the daily volatility for eight one
Speaker 4: hundred h one hundred is around twenty to thirty. So
Speaker 4: it's a very healthy commodity volatility range. So I don't
Speaker 4: manage volatility. It just happened to be the volatility that
Speaker 4: can change. It's all because we normalize it. If you
Speaker 4: look at each individual chip configuration at differential location, the
Speaker 4: voltity are different. There's some chips with eight percent volatility,
Speaker 4: some chips with over one hundred. Because normalization of indices,
Speaker 4: you actually get very healthy twenty to thirty daily bomb.
Speaker 3: I'm always fascinated by like, you know, we look at
Speaker 3: the Bloomberg terminal, for example, and there's a price on
Speaker 3: the screen and it's just there, and we started taking
Speaker 3: for granted that like it had to come from somewhere,
Speaker 3: and maybe some commodities have like a you know, there's
Speaker 3: an existing exchange and a public price, and then there's
Speaker 3: also a lot of commodities just bilateral traits. What is
Speaker 3: the actual process by which you collect the most recent data?
Speaker 3: So if you say, okay, an hour of h one
Speaker 3: hundred usage costs x right, whatever it is right now,
Speaker 3: how did you assemble that number? How did you gather
Speaker 3: that information from, say the inference providers?
Speaker 4: So it is a very can be lengthy, depends on
Speaker 4: what data sources, the nature of GPU spawn markets conpictures
Speaker 4: just one of them, and then many of my in
Speaker 4: neoclaushyperscular marketplaces all have very different contras, size, durations, backs,
Speaker 4: and their way to manage their data right, So it's
Speaker 4: a lot of licensing, conversation, negotiation well and also context
Speaker 4: love myself, I don't know, I was used for boomboard data,
Speaker 4: so I was in data basiness for period of time.
Speaker 4: So everything is pretty intuitive to me. It's very important
Speaker 4: to get a variety of data sources, especially for computing.
Speaker 3: Like do you call them up? Like so it's like, okay,
Speaker 3: the price is different on the sum them up.
Speaker 4: Well, you first of conversations say hey, I love what
Speaker 4: you do. You're bring your cloud? Can I license your data?
Speaker 4: And usually your feedback is what is in for me?
Speaker 1: Right?
Speaker 4: And I will tell our commercials And then your concern
Speaker 4: could be, hey, you know, if I give you all
Speaker 4: my data, I give my way all my secrets and
Speaker 4: I will go through traditional licensing agreement. What can I disclose?
Speaker 4: What I want from you? What I do not want
Speaker 4: from you? What's the pipeline look like? Are you right?
Speaker 4: You use a street market job? You are running my
Speaker 4: API to yours? Are you running into mind? It's a
Speaker 4: lot of conversations. It's actually pretty standard conversation. And right
Speaker 4: now with eight million pricing points globally around two hundred
Speaker 4: data sources, it's pretty much bau A lot. People will say, hey,
Speaker 4: always bring up you can I have your data? It's
Speaker 4: always my ending.
Speaker 2: You know, we were talking about GPU indices and you're
Speaker 2: not the only one doing GPU price indices for.
Speaker 4: Sure, not anymore.
Speaker 2: Yeah, not anymore. But when you look at some of
Speaker 2: the other ones, like sometimes they show different numbers or
Speaker 2: even different longer term trends, what accounts for the discrepancy there?
Speaker 2: What are you doing differently or what are they doing differently?
Speaker 2: I guess.
Speaker 4: So I can't come on other people's mythology because I
Speaker 4: actually don't know different raw data. Different mythology will eventually
Speaker 4: draft different prices. So the way I would look at
Speaker 4: this is, you know, it's always smart for anyone to
Speaker 4: look at multiple data sources and then figure out what
Speaker 4: is the actual decision you have to make, which datasps
Speaker 4: do you trust? The market always volte whilst us things
Speaker 4: start trading. The market will always gravitates what things actually
Speaker 4: help them hatch? Right, if you easily manupulatable, if you
Speaker 4: are not data source people acting actions, do you hatch?
Speaker 4: There was the point a sideh one speculation, right, So
Speaker 4: you know I'd love to say, I mean, I also
Speaker 4: strongly believe what the best but again I will let
Speaker 4: the market decide, which will happen very soon.
Speaker 3: So of course, like yes, there's the economic rationale for
Speaker 3: the existence of a hedging instrument, and we can understand
Speaker 3: that someone who is an entity that from time that
Speaker 3: needs compute their short implicitly short GPUs, they want to hedge,
Speaker 3: et cetera. But the liquid markets also really do need
Speaker 3: speculators and they need people betting on price. What are
Speaker 3: you seeing right now in terms of traders or institutions,
Speaker 3: et cetera, who economically can take both sides of the
Speaker 3: trade and how active is this getting where it's just
Speaker 3: a compute trading desk that is separate from their economic needs.
Speaker 4: The conversation has been going on for a very long
Speaker 4: time with various banks, various multi participants, speculators. They are
Speaker 4: very excited. So some banks obviously have those both sides
Speaker 4: of the trade right, so they can cross off some
Speaker 4: positions internally. That's great. Always some they have to use
Speaker 4: leverage external products. So that's where we come in. The
Speaker 4: way I encourage them to do is I selfishly, I
Speaker 4: want them to start trading desk and compute. The more
Speaker 4: people trade, the better for me, right selfishly. The same time,
Speaker 4: it is important for people understand GPU trading. It's not
Speaker 4: like you can just move someone from oil electricity with
Speaker 4: no background context jobing to GPU compute futures. There's a
Speaker 4: lot of context where number one GPU it is not
Speaker 4: homogenous product number two. You have to understand the use
Speaker 4: cases of eight one hundred h one hundred right now,
Speaker 4: they are not that correlated. Is that right? Maybe that's
Speaker 4: not right. I don't know. There are use cases which
Speaker 4: they're pretty separated, but maybe their use cases they can
Speaker 4: be transferred and also their software layer to this. Right,
Speaker 4: so right now you can art give sooner use cases
Speaker 4: some large amount of models cannot be deployed and the
Speaker 4: legacy chips, but doesn't mean six months later you cannot
Speaker 4: do so. As the software layer compression model compression gets better,
Speaker 4: optimization gets better, things can change. So really understand not
Speaker 4: just the hardware configuration, this local supply demand curve for
Speaker 4: the service self also software layer that's kind of critical, right,
Speaker 4: that's really changed the supply demand curve and all the
Speaker 4: way to the user behavior. So it's all it's going
Speaker 4: to take some times as we have engaged with a
Speaker 4: lot of participants make sure they have the right set up.
Speaker 2: I have what is possibly a dumb question, but the
Speaker 2: compute futures, how are those actually settled? Because I have
Speaker 2: like images in my mind of taking physical delivery of
Speaker 2: like maybe one of those big.
Speaker 4: Okay, that'll be fun. So for the CME, futures will
Speaker 4: be financially settled just like the traditional oil settlement price.
Speaker 4: That priceis goes four contracts. Well obviously we do four
Speaker 4: right now, I can fut change, but we always open
Speaker 4: to do you know, physically deliver futures, especially given we
Speaker 4: do have silica mark which is GPO benchmarking. So imagine
Speaker 4: the future. You can do, Hey, I want to twenty
Speaker 4: grade A B two hundred, this configuration, this shape of
Speaker 4: servers in US East and then at the end we'll
Speaker 4: get that. Well, usually API costs so you don't get
Speaker 4: physical and it's not as coo as physically give you
Speaker 4: a way way for but you get APIA costs.
Speaker 2: One can dream.
Speaker 3: How do you literally trade it is in like let's
Speaker 3: say there's probably some very bright people in the room
Speaker 3: now with an institution. When it's all listed in everything,
Speaker 3: does it need to go through like a future's broker?
Speaker 3: Is it like a could it be like a prediction
Speaker 3: market if you just go to a website? Like what
Speaker 3: is the actual How does someone actually get in this setting?
Speaker 3: Aside whether they're sophisticated enough of whether they know what
Speaker 3: they're doing a lot of people trade to have no
Speaker 3: idea what they're doing. Yeah, setting all this side. Yes,
Speaker 3: you know, only trade what you know. But like, what
Speaker 3: is it through a prime broker? Like how will people
Speaker 3: actually be able to participate.
Speaker 2: In this market?
Speaker 4: The beauty of CME is you can do the same
Speaker 4: thing you're doing now treating semi products. Okay, the same
Speaker 4: process and processing margin. That's why you get great margin optimization. Right,
Speaker 4: everything is BAU it's no different. We don't have anything
Speaker 4: right now.
Speaker 3: So any commodities broker that someone has, they will be
Speaker 3: able to on that platform. They will have access to these.
Speaker 4: Instruments exactly right. Yeah, we make it easy for people.
Speaker 2: Would you be upset if a prediction market set up
Speaker 2: a GPU price contract of some sort with that into
Speaker 2: your business?
Speaker 4: Not at all. So we actually work with poly market.
Speaker 4: Last year someone actually listed my product at polymarket with
Speaker 4: my consent. It's pretty it's always start like that. And
Speaker 4: then someone told me that, and we try to Polymarket
Speaker 4: and say, hey, do you want to do something you
Speaker 4: know more real? So we did. Fabruary settled and April
Speaker 4: settled a few contracts on polymarket. I just with test
Speaker 4: the water right obviously we're exclusively with CME right now.
Speaker 4: But yeah, so I think obviously you have to do
Speaker 4: it right, licensing, nonminal, pillared, it all the right things. Yeah,
Speaker 4: you know, I don't. Mark can do whatever they want,
Speaker 4: and then people will choose the best product for them
Speaker 4: to use.
Speaker 3: Sitting aside the financial instruments for the moment, would people
Speaker 3: think about AI and they think about the use of GPUs,
Speaker 3: they mostly still probably in their mind think of like
Speaker 3: open AI and Thropic and Google basically, and that's kind
Speaker 3: of it. But obviously, as you've stated, like the world
Speaker 3: of entities that serve inference in some form or another
Speaker 3: is much greater than these three companies that we talk about.
Speaker 3: Talk to us a little bit more about what the
Speaker 3: actual world of inference provision looks like outside of the
Speaker 3: big household AI name.
Speaker 4: So the ones you mentioned, they mostly are closed source
Speaker 4: models as we call it, right, but they do have
Speaker 4: some open source versions, but they're famous for their closed
Speaker 4: source models. So we actually track three hundred open source
Speaker 4: open weights closed source models globally. Upon pricing and consumption
Speaker 4: point of view, it is really interesting if we have actually,
Speaker 4: you know, we haven't really formally launched token in disease.
Speaker 4: You can currently look at Bloomberg and it's on Bloomberg.
Speaker 4: What's interesting is people are depends. It's all based on
Speaker 4: your choices. Right now. The price actually doubled file indusees
Speaker 4: from now from December first last year. It's like two
Speaker 4: dollars twenty one dollar per million token. It's a mixture
Speaker 4: of input up and token prices reach weighted by consumption
Speaker 4: by buscut models. It's not here. This is a GPU unfortunately, which.
Speaker 3: Are just well, since we have this specific chart up
Speaker 3: right now, what is the y access in this church?
Speaker 4: So you look yet that the per GPO power rental
Speaker 4: rate on demand for three chips. The top one the
Speaker 4: yellow line is B two hundred new cloud on demand
Speaker 4: per flower. So it's a mouthful. The line the air
Speaker 4: line is interesting, right. So every new chip we came
Speaker 4: out based on historical data one hundred usually came out
Speaker 4: to be high and then comes down as more supply
Speaker 4: you know, came life, and then price came down and
Speaker 4: then stabilizes. So that's the trend we have absorbed for
Speaker 4: A one hundred and for H one hundred. So when
Speaker 4: B two country came out, we pollished the data last
Speaker 4: year at Bloomberg this early this year, the price was
Speaker 4: high and it came down, which is kind of what
Speaker 4: I expected. But the slope was less steep than I expected.
Speaker 4: I was like, hm, that's interesting, the slope wasn't as steep,
Speaker 4: and I'm quickly observed the price just came up and
Speaker 4: now it's higher than the initial opened whatever you call that,
Speaker 4: right launch prices. That shows your demands apply craft in
Speaker 4: a different stage than whatever stage we had before. So
Speaker 4: the A one the red line is H one hundred
Speaker 4: neil cloud on demand per jubile power rate. So you
Speaker 4: can see the price came down last year a little
Speaker 4: bit sort about the skill so you don't see much,
Speaker 4: but came down and came back up quite a bit.
Speaker 4: I think the last three months came up to like
Speaker 4: eight percent for AGE one hundred. The one hundred is older,
Speaker 4: oldest chips among the three, right, they're pretty you know,
Speaker 4: put don't you calm audit? At this point the price
Speaker 4: came down, they stabilized, but the price came up about
Speaker 4: ten to fifteen percent for the past three months. Remember
Speaker 4: the A one hundred right there, they're not the latest
Speaker 4: and greatest at all. So this also tells you to
Speaker 4: supply deman croft shifting.
Speaker 2: Oh yeah, actually that reminds me, could you talk to
Speaker 2: us because you're doing refurbishment of chips as well, right, like,
Speaker 2: which seems like challenging in many ways and kind of
Speaker 2: reminds me a lot about like the sort of carbona
Speaker 2: model of compute or something like that. How are you
Speaker 2: actually doing this? Like how does that business work?
Speaker 4: So this is cool in two different things. One is
Speaker 4: for people come to conpect change saying that, hey, I
Speaker 4: want to you know, it's Union Cloud provider. Right, if
Speaker 4: you get a piece of land, you again a g
Speaker 4: CO location, Great, congratulations? Then your option is number one.
Speaker 4: Should I get the latest and greatest, the B three hundred,
Speaker 4: the gbs the roubmen with a few months or do
Speaker 4: you want to get refrabit trips and turn out maybe sooner? Right? Then,
Speaker 4: to you, it's become our I calculation for the most part. Right,
Speaker 4: what's you expected, you know, future revenue generation? What's your
Speaker 4: residual VALUD calculation? How much you can purchase by right,
Speaker 4: it's actually pretty simple cash flow based in our I calculation.
Speaker 4: So the way we approach residual value and refurb trans
Speaker 4: transaction is you know, based on our I you know
Speaker 4: this is your potential break even look at h one hundred, right,
Speaker 4: obviously you're not going to charge its high speed two hundred,
Speaker 4: but your cost base is also lower, so you can
Speaker 4: do the future. You're assume a few years of four
Speaker 4: country's signing three years this kind of cash flowback. That's
Speaker 4: your misgivell now, right, So we do that calculation with
Speaker 4: people so they understand, Hey, what was the value supposed
Speaker 4: to generate? And it was the treating the market prices
Speaker 4: for refurbished or used GPUA and you have to test
Speaker 4: you to make sure things works, and there's out of
Speaker 4: nuances to that. But we helped go to the understanding
Speaker 4: of the whole research of value and that's why the
Speaker 4: whole bubble thinking about but go ahead, what.
Speaker 3: Month was it last year when like everyone got really
Speaker 3: obsessed with like the life span of chips. Remember, like
Speaker 3: tweeted something about He's like, oh the lifespan, They're like right,
Speaker 3: and everyone was free, spent like three weeks free and
Speaker 3: then moved on from that conversation, right, like that was
Speaker 3: what do we know about chip life spans? Are there
Speaker 3: misconceptions out there about the how long yes, these can
Speaker 3: get be productive.
Speaker 4: I'm going in to view a few times, but I don't.
Speaker 4: I mean, I'm not important. Still, I'm more important, but
Speaker 4: back then that even less relevant. I was telling reporters.
Speaker 4: I was like, look, I don't know what did that
Speaker 4: you're looking at based on my I actually have blocks.
Speaker 4: So my website, which is completely can to search for
Speaker 4: it last year because of that conversation, I want you
Speaker 4: currect later on the second year h one hundred residual
Speaker 4: value of reseal value for refribit chips about eighty five
Speaker 4: cents one dollar, So a year later you can sell
Speaker 4: eighty five to one dollar. That's pretty good. I would
Speaker 4: say the thirty is eighty four cents one dollar, and
Speaker 4: I think my cards appreciate win more than that, right,
Speaker 4: And I enjoined my car forboard every ten ten years,
Speaker 4: So it's I had a data. But again I'm not
Speaker 4: going to argue it against narrative, which is so.
Speaker 3: But there was there's a fairly sived a decent drop
Speaker 3: from year one to year two. That's what after that.
Speaker 3: You see your general level.
Speaker 4: That's November December analysis. Right now, it's a little different.
Speaker 4: I haven't refreshed to study but our code is there.
Speaker 4: If you're my data client, you can around my cold
Speaker 4: you get a number right away. Another thing I want
Speaker 4: to point out is l forties. They're like you know
Speaker 4: the ogs right at that time, the people still use them.
Speaker 4: They charge you hypercure charge your forties as p JB
Speaker 4: per hour. So you know, I don't know about two
Speaker 4: years where's the net number coming from? But I will
Speaker 4: I will do that trade every single day. You sell
Speaker 4: you're two years old at ten cents a dollars, I
Speaker 4: will buy it.
Speaker 2: There's a sort of big question looming in the background
Speaker 2: of a lot of these discussions, which is the B question.
Speaker 2: I guess whether or not we're in an AI bubble right,
Speaker 2: And you sort of touched on it earlier. You have
Speaker 2: all this granular data on how people are actually using compute,
Speaker 2: GPU prices, all of that. What what's your take on
Speaker 2: the big question?
Speaker 4: So as an index provider, I cannot give any full
Speaker 4: of guidance, no blamer, nor do I know, right being fairness,
Speaker 4: I know what do I know? So the way I
Speaker 4: look at is we had defined a bubble right, so
Speaker 4: you look at style bubble right and the nest that
Speaker 4: showed up to two hundred percent and came back down
Speaker 4: eighty four percent whatever back. Then that's it's a bubble. Right.
Speaker 4: The way I look at bubble is can is your valiation?
Speaker 4: Can your future cash flows support today's valuation of yours? Right?
Speaker 4: So then I'm not talking about like opening and everyone
Speaker 4: else valuation. I'm not ob busy. I don't. I don't.
Speaker 4: I don't understand that process. The way I look at
Speaker 4: GPUs the machines, it's actually pretty simple. Look at a
Speaker 4: future cash flow of your forward contracts and then you
Speaker 4: discounted back. Can you get money back for the price
Speaker 4: you pay? Right, It's actually pretty straightforward for the machine level. Right,
Speaker 4: But to your point, right, you can say, hey, what
Speaker 4: happened if demand jobbed? No one gonna use your whatever
Speaker 4: things you have. But remember the four contract is a
Speaker 4: signed contract. If you have that, you can't know obviously
Speaker 4: if you have things, the biggest concern is people have
Speaker 4: over built. You've overbuilt, then by theory, then all your
Speaker 4: prices will calmed down because it over supply of the market. Right,
Speaker 4: So then you talk about supply demain equilibrium. How do
Speaker 4: we know about future demand of GPUs? Right? I don't
Speaker 4: know that everyone's guess is better than mine. Probably the
Speaker 4: way I look at it is not. It's not that
Speaker 4: easy to bring any GP online, right you can use
Speaker 4: you hear all those side big deals in the bill,
Speaker 4: twenty five million dollars invested, but they don't translate to
Speaker 4: immediate GPU availability right in the servers which you have
Speaker 4: to be waylisted if you buy brand new stuff co location,
Speaker 4: you need optic fiber, so there's a lot of unfortunately
Speaker 4: STAR has to be aligned.
Speaker 2: Wait, but people can default on contracts, right, so even
Speaker 2: if you have a long term contract signed like that
Speaker 2: could not work out. Could you envision like credit default
Speaker 2: swaps or something in the compute market like so.
Speaker 4: That happens in every other markets, right, aman market if
Speaker 4: you do OTC trade, you have the rice some monual
Speaker 4: defon you. Doesn't matter who they are, right, So there's
Speaker 4: vary There's a lot of mechanism to hatch that. The
Speaker 4: things you cannot hatch is GPU cost write the price
Speaker 4: you entered. So that's something exactly will seemmy futures for
Speaker 4: you can have a transparency the liquidity and then the
Speaker 4: easiness of treating it out and had your position.
Speaker 3: Carmen Lee, thank you so much for joining us.
Speaker 2: That was our conversation with Carmen Lee of Compute Exchange
Speaker 2: and Silicon Data, recorded live at our New York show.
Speaker 2: I'm Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 3: And I'm Joe Wisenthal. You can follow me at the Stalwart.
Speaker 3: Follow our guest Carmen Lee at Carmen Lee. Follow our
Speaker 3: producers Carmen Rodriguez at Carmen armand dash Ol Bennett at Dashbot,
Speaker 3: kil Brooks at Kile Brooks and Kevin Lozano at Kevin
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