Latest / What's Your Problem? / The sold-out chips at the heart of AI

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The sold-out chips at the heart of AI · What's Your Problem?

0:15
Speaker 1: Pushkin. Brandon McBee was a commodities trader. His job was
0:23
Speaker 1: buying and selling things like natural gas and soybeans, and
0:27
Speaker 1: in twenty seventeen, he and a few of his friends,
0:30
Speaker 1: who were also commodities traders, got interested in crypto obviously,
0:34
Speaker 1: and as a hobby, Brand and his friends started buying
0:38
Speaker 1: specialized computer hardware to mine crypto, specifically to mind a
0:42
Speaker 1: token called ether. They were setting up computers in their
0:45
Speaker 1: offices and in garages, and then after a little while,
0:49
Speaker 1: other traders they knew wanted in on the action, and
0:52
Speaker 1: so Brand and his friends started buying more hardware and
0:56
Speaker 1: charging the other traders to use it.
0:58
Speaker 2: We were basically retrofitting warehouses in New Jersey.
1:02
Speaker 1: That sounds sketchy, it was.
1:05
Speaker 2: It was extremely sketchy, lots of wires everywhere, and could
1:08
Speaker 2: it be any more different than where we're at today?
1:11
Speaker 2: But we figured out how to run tens of thousands
1:15
Speaker 2: of GPUs in the harshest environments imaginable GPUs.
1:20
Speaker 1: Those were the key pieces of hardware that Brandon and
1:23
Speaker 1: his friends were using. GPU stands for graphics processing unit,
1:27
Speaker 1: and GPUs were originally developed to power things like video games,
1:31
Speaker 1: but they also turn out to be really good for
1:34
Speaker 1: some other things like mining cryptocurrency, running complex biological simulations,
1:39
Speaker 1: and also GPUs are really good for AI. And as
1:44
Speaker 1: Brandon and his buddies kept buying more and more GPUs,
1:47
Speaker 1: they thought to themselves, maybe we shouldn't just rent these
1:50
Speaker 1: out to people who want to mind crypto. Maybe we
1:52
Speaker 1: should see if people want to use them for other
1:54
Speaker 1: things like training and running AI. That turned out to
1:58
Speaker 1: be a very good call. Today, companies building and running
2:02
Speaker 1: AI models are clamoring to get access to GPUs, and
2:06
Speaker 1: Brannon and his friend's hobby has grown into a multi
2:09
Speaker 1: billion dollar company called core Weave that competes against companies
2:13
Speaker 1: like Google, Microsoft, and Amazon. I'm Jacob Goldstein. This is
2:22
Speaker 1: What's Your Problem, the show where I talk to people
2:24
Speaker 1: who are trying to make technological progress. My guest today
2:28
Speaker 1: is Brandon b He's the chief strategy officer at Coreweek
2:32
Speaker 1: and I wanted to talk to Brandon because you know,
2:34
Speaker 1: obviously we talk about AI all the time, and usually
2:37
Speaker 1: we talk about it as this very abstract thing, but
2:40
Speaker 1: in fact you need physical stuff for AI to exist.
2:44
Speaker 1: You need in particular GPUs, And so if you want
2:48
Speaker 1: to understand what's going on with AI. You need to
2:50
Speaker 1: understand what's going on with GPUs, and Brannon is at
2:54
Speaker 1: the center of that.
2:57
Speaker 2: We grew up building computers, right like I grew up
2:59
Speaker 2: building computers with my brother.
3:01
Speaker 1: Like were you a gamer? Is that one? Yeah?
3:03
Speaker 2: Absolutely?
3:03
Speaker 1: Yes, yeah, like in then you were very early to GPUs.
3:07
Speaker 2: Yes, yeah, in the nineties, like well as your Christmas
3:10
Speaker 2: present it was to go pick your new hard drive,
3:13
Speaker 2: or to get like a new sticker ram and bring
3:15
Speaker 2: it into your computer, or to you know, raid your
3:16
Speaker 2: uncle's office for their old compute equipment and take parts
3:20
Speaker 2: out of it.
3:21
Speaker 1: Uh huh.
3:22
Speaker 2: It's just a really fun space to be.
3:24
Speaker 1: Then let's start macro and go micro. Okay, so like macro, Like,
3:28
Speaker 1: let me think of planet Earth, Like where on planet
3:32
Speaker 1: Earth are the data centers that you are in?
3:35
Speaker 2: So we operate in North America currently. I think you'll
3:38
Speaker 2: see us expand globally within the next twelve eighteen months
3:41
Speaker 2: or so.
3:42
Speaker 1: Where in North America? Like how many data centers in?
3:45
Speaker 2: Yeah? So in North America will be in about fourteen
3:48
Speaker 2: data centers by Q one. Okay, So that's co located,
3:52
Speaker 2: which means someone else built and operates the actual shell
3:56
Speaker 2: and all the power and cooling and all these like
3:58
Speaker 2: really engineering tins of components within that data center. Then
4:01
Speaker 2: we come in and build out our infrastructure and the
4:06
Speaker 2: infrastructure and the data center. It's one of the coolest
4:09
Speaker 2: things you can ever walk into, right, It's like walking
4:11
Speaker 2: into a spaceship.
4:12
Speaker 1: I love it. So so tell me what it's like
4:15
Speaker 1: when you go to one of these data senters. What
4:16
Speaker 1: does it look like? Just like a giant warehouse out
4:18
Speaker 1: in the middle of nowhere, somewhere where power is cheap.
4:22
Speaker 2: Uh, you know, power's in there, but it's more about
4:24
Speaker 2: redundancy of power. It's more about connectivity to like the
4:28
Speaker 2: broader Internet backbone across North America. And it's about security
4:32
Speaker 2: as well. So when you pull up to one of
4:33
Speaker 2: these sites, you know it's it's twenty foot walls, it's
4:36
Speaker 2: security guards, it's you know, multiple badges worth of security
4:40
Speaker 2: to get back to where you need to. It's no cameras,
4:43
Speaker 2: it's completely clean rooms like, no food, no drinks, Like
4:48
Speaker 2: you can't have anything being pulled into the service, right
4:50
Speaker 2: because there's so much airflow moving through you know, the
4:54
Speaker 2: entire room.
4:55
Speaker 1: Okay, so you badge, you badge the security guy, you park,
4:58
Speaker 1: you get in, walk into the room. What's it look like?
5:01
Speaker 1: Is it hot? Is allowed?
5:03
Speaker 2: It is? It feels like it's screaming at you. It
5:07
Speaker 2: is extremely loud. You can barely have a conversation. And
5:10
Speaker 2: that's a reflection of the amount of power intensity ultimately, right,
5:15
Speaker 2: because that's just fans pushing air through the servers and
5:20
Speaker 2: very aggreat to cool them, to cool them, yes, because
5:24
Speaker 2: they're consuming power on an extremely dense basis. Right, So
5:29
Speaker 2: the more power you consume in a smaller area, the
5:31
Speaker 2: more you know, socialated cooling you have to there. So
5:33
Speaker 2: it's loud, and i'd say one of the more interesting
5:37
Speaker 2: things that you're in there, it's the miles of fiber
5:42
Speaker 2: optic that's required to transport information and connect everything. So
5:47
Speaker 2: a typical let's say sixteen thousand GPU cluster that we build,
5:51
Speaker 2: it has forty eight thousand discrete connections amongst it that
5:56
Speaker 2: all have to be done perfectly. Then it has about
5:59
Speaker 2: five hundred miles of fiber optic cabling run between all
6:04
Speaker 2: those connections. So this is one sort of cluster that
6:07
Speaker 2: you have built. Yeah, and it's like one part of
6:11
Speaker 2: a bigger data center. Yeah, that's right.
6:14
Speaker 1: Okay, So let's talk. Let's talk about the cluster. You
6:17
Speaker 1: said it has forty eight thousand connections, meaning like does
6:21
Speaker 1: a human being when you're building it, actually have to
6:23
Speaker 1: go and like plug forty eight thousand things into forty
6:27
Speaker 1: eight thousand things one at a time, no or yes.
6:29
Speaker 2: Yes, yeah, And it all has to be done correctly.
6:33
Speaker 2: And then all the components have to provision correctly. When
6:36
Speaker 2: I say provisioning, that means they have to get their
6:38
Speaker 2: you know, operational instructions, like you turn something on, it
6:41
Speaker 2: doesn't know who it is, right, like what networks of
6:44
Speaker 2: what it's supposed to do. So you have to develop
6:46
Speaker 2: a bunch of bunch of software that we've done that
6:49
Speaker 2: informs the machine. Hey, hello world, here's what we'd like
6:53
Speaker 2: for you to do. So that that's called provisioning.
6:57
Speaker 1: Okay, so forty eight thousand connections and how many GPUs
7:01
Speaker 1: did you say are in this cluster?
7:03
Speaker 2: So that that's for a sixteen thousand GPU clubs.
7:06
Speaker 1: And so so we've started at the macro, We started
7:08
Speaker 1: a planning that we're getting, then we got the data center,
7:12
Speaker 1: and then we got your cluster, and then now let's
7:14
Speaker 1: get to that GPU because like that is a really interesting.
7:20
Speaker 1: It's the center of this whole thing, right in this
7:22
Speaker 1: cluster you're talking about, Is it, in fact the.
7:25
Speaker 2: Nvidia H one hundred, Yes, oh go, that's right.
7:28
Speaker 1: So I want to talk about the Nvidia H one
7:31
Speaker 1: hundred for a minute, because like it's a big deal
7:33
Speaker 1: in the world right now, right like everybody wants them.
7:37
Speaker 1: Apparently you can't get them, or you can't get as
7:41
Speaker 1: many as you want. You can search for Nvidia H
7:44
Speaker 1: one hundred on Amazon and it comes up, and the
7:47
Speaker 1: first one that came up for me costs thirty seven thousand,
7:51
Speaker 1: seven hundred and ninety dollars, doesn't say weather shipping is included.
7:57
Speaker 1: And that's just one of them. That's just one of them.
8:00
Speaker 1: And you're talking about how many in this cluster.
8:04
Speaker 2: So that would be sixteen thousand and a sixteen thousand
8:07
Speaker 2: GPU cluster, and we're you know, we're building multiple of those.
8:10
Speaker 1: And so presumably you're not paying retail one hopes you're
8:14
Speaker 1: not paying thirty seven thousand dollars per But but what
8:18
Speaker 1: is the order of magnitude of what the what the
8:22
Speaker 1: GPUs cost you in one of these clusters.
8:24
Speaker 2: We're not going to talk about that.
8:26
Speaker 1: Okay, but Let's be clear, I'm just the same as
8:30
Speaker 1: everyone else's thirty seven thousand.
8:32
Speaker 2: Yeah, it's it's less. It's it's going to be comparable
8:34
Speaker 2: with the what the hyperscalers payers, or so we think
8:37
Speaker 2: right end of the day, we don't.
8:38
Speaker 1: Really know how much does it cost to build one
8:41
Speaker 1: of these clusters, like to an order of magnitude? Sure,
8:45
Speaker 1: one hundred million dollars, is that the right order of magnitude.
8:48
Speaker 2: It's in the hundreds of millions of dollars, that's.
8:50
Speaker 1: Correct to build one of these clusters.
8:52
Speaker 2: It's a built one sixteen thousand GPU cluster. But to
8:55
Speaker 2: be clear, like we build clusters of different sizes, we
8:57
Speaker 2: build multiples of these clusters. It's it's all kind of
9:00
Speaker 2: based on what that end client is looking for. And
9:04
Speaker 2: you know we do on demand compute. May all kind
9:07
Speaker 2: of qualified as our our capital expins at the company
9:10
Speaker 2: levels measured in the billions of dollars a year.
9:12
Speaker 1: Sure, it's a very capital intensive business because you have
9:15
Speaker 1: to buy and for the fundamental reason that you have
9:18
Speaker 1: to buy these GPUs that everybody wants that are profoundly expensive.
9:22
Speaker 2: That's correct.
9:23
Speaker 1: I mean an interesting twist in this part of the
9:26
Speaker 1: story is you know, in Vidia is this really interesting
9:28
Speaker 1: company right now. Their stock has gone crazy this year
9:31
Speaker 1: because they make the GPUs that are in wild demand
9:34
Speaker 1: right now, and they are also an investor in your company,
9:37
Speaker 1: right which is very interesting. Like how does that piece
9:42
Speaker 1: of it, that in Vidia investment in core weave, Like,
9:44
Speaker 1: how does that work in terms of the broader dynamics.
9:48
Speaker 2: Yeah, I think you see strategic investors on cap tables
9:51
Speaker 2: pretty often. We have a differentiated product, We have a
9:56
Speaker 2: fantastic way to get their infrastructure into the market, but
10:01
Speaker 2: we don't have any preferential treatment. We don't have any prioritization.
10:04
Speaker 1: We're just not easier for you to get the GPUs.
10:07
Speaker 2: No, it is not easier for us to get the
10:09
Speaker 2: GPUs whatsoever.
10:12
Speaker 1: So these GPUs, these Nvidia GPUs, why is this the
10:16
Speaker 1: one that everybody wants? What's so special about the H
10:19
Speaker 1: one hundred?
10:20
Speaker 2: So Jensen and the Vidia team did this.
10:22
Speaker 1: Jensen is the founder c Yeah.
10:25
Speaker 2: Yes, did this amazing job of enabling the artificial intelligence
10:30
Speaker 2: space from an early early onset, like back in the
10:33
Speaker 2: early twenty tens, by releasing the drivers, the software that
10:38
Speaker 2: actually control the GPUs into the market and making it
10:42
Speaker 2: accessible to those developers. Right, so when you go into
10:46
Speaker 2: like GitHub, for example, you search AI project, they're all
10:49
Speaker 2: going to come back referencing Kuda drivers, which is the
10:53
Speaker 2: software in Cressent right there, using x eighty six.
10:55
Speaker 1: So Nvidia very cleverly sort of created the language that
10:59
Speaker 1: most of the people who write software, who write AI
11:01
Speaker 1: software speak essentially you, that's totally right. And so if
11:05
Speaker 1: there might be another chip where the sort of specs
11:08
Speaker 1: are comparable, but the language is different, and everybody speaks
11:12
Speaker 1: this one language and there's sort of a network effect
11:15
Speaker 1: at play where it's like everybody develops on this chip
11:17
Speaker 1: because everybody develops on this chip, and so that is
11:20
Speaker 1: like a classic killer network effect.
11:24
Speaker 2: Yep, they just did an amazing job.
11:27
Speaker 1: Interesting, you're competing against Amazon, Microsoft, Google, and Oracle, so
11:34
Speaker 1: more or less the biggest companies in the world. Yes,
11:36
Speaker 1: you're like four guys who were just in a warehouse
11:41
Speaker 1: in New Jersey, Like what what happened there?
11:46
Speaker 2: Like?
11:46
Speaker 1: How is that happening?
11:48
Speaker 2: It's ultimately it was through that specialization dot that really
11:52
Speaker 2: goddess thing. Like you have an at scale existing market
11:55
Speaker 2: and you figure out how to do something better. You've
11:58
Speaker 2: decommoditized a market through performance. So the analogy I like
12:03
Speaker 2: is like, we figured out how to refine a barrel
12:05
Speaker 2: of oil better.
12:06
Speaker 1: Yeah, so everyone has to come use us because you
12:09
Speaker 1: can do it more efficiently, more cheaply.
12:12
Speaker 2: Yeah, which means it's more efficient for our clients. At
12:14
Speaker 2: the end of the day.
12:15
Speaker 1: Presumably these companies are spending billions, tens of billions, maybe
12:20
Speaker 1: hundreds of billions of dollars to catch up with you.
12:23
Speaker 1: I mean, I wouldn't be inclined to bet against them, respectfully.
12:28
Speaker 2: I couldn't agree with you more. They absolutely Isn't that
12:31
Speaker 2: bad for you?
12:31
Speaker 1: Isn't that scary for you?
12:33
Speaker 2: Like?
12:33
Speaker 1: Why are you talking about to be like sprinting to
12:35
Speaker 1: try and not get caught by Google and Amazon?
12:38
Speaker 2: Look, these are amazing companies with amazing depth of engineering
12:42
Speaker 2: talent and a massive amount of resources. To your point,
12:45
Speaker 2: they're the largest companies on the Planet's super smart. It's
12:48
Speaker 2: very good.
12:48
Speaker 1: At building big, efficient things.
12:52
Speaker 2: Yes, but they are generalized. These are huge teams, and
12:56
Speaker 2: we both know how long it takes to move a
12:58
Speaker 2: big ship in a new direction. And I mean, to
13:02
Speaker 2: put a sense of scale to it, They're working with
13:04
Speaker 2: tens of thousands of engineers. We're in the hundreds of engineers,
13:07
Speaker 2: and it just allows for us to focus on a
13:10
Speaker 2: task much more discreetly. This is all we do. We
13:14
Speaker 2: build supercomputers. I'm not trying to serve every single corner
13:19
Speaker 2: of the cloud. I'm trying to serve the part of
13:22
Speaker 2: the cloud that needs access to supercomputers, and we've just
13:24
Speaker 2: become really good at doing it.
13:30
Speaker 1: We'll be back in a minute after a break for
13:33
Speaker 1: some ants. I know some of your clients I've read about, right,
13:54
Speaker 1: I imagine some might be private, but some are public, right, Like, so,
13:57
Speaker 1: who are some of the people who are using the
13:59
Speaker 1: things that you have built?
14:01
Speaker 2: Some of the public ones. Inflection AI is one that
14:05
Speaker 2: we really enjoy working with that team, especially on Jeanie most.
14:08
Speaker 1: Of us solely my right, who was one of the
14:09
Speaker 1: founders of DeepMind and who just wrote a book and
14:11
Speaker 1: who are trying to book for the show. That's his company, right,
14:14
Speaker 1: that's the company that he founded. That's correct Mind, And
14:16
Speaker 1: so they are training like a big sort of a
14:19
Speaker 1: GBT size model, right, and they're using your computers to
14:22
Speaker 1: do it.
14:24
Speaker 2: That's right, yeap?
14:25
Speaker 1: Who else you know?
14:26
Speaker 2: A smaller team that we just love. The engineers at
14:29
Speaker 2: is a company called novel Ai. Novel Ai has kind
14:34
Speaker 2: of a storytelling based inference product and market. They do
14:36
Speaker 2: text to image, text to story. But there's we have
14:40
Speaker 2: over six hundred AI clients on our platform today.
14:43
Speaker 1: And are you still doing you know, drug discovery stuff.
14:47
Speaker 1: Are you still doing entertainment stuff? Or has AI? Is
14:49
Speaker 1: the demand for AI and willingness to pay just so
14:52
Speaker 1: high that it's mostly.
14:54
Speaker 2: That now, that's just where all the attention is is
14:56
Speaker 2: on AI. But we're absolutely cruiting clients on the media
14:59
Speaker 2: and entertainment side and the drug discovery side.
15:02
Speaker 1: And is it getting way more expensive for those clients
15:05
Speaker 1: because of the AI demand? Are they in the sort
15:07
Speaker 1: of unfortunate kind of backwash of like this thing they
15:11
Speaker 1: need to use just got wildly more expensive because there's
15:13
Speaker 1: so much demand and so much money flowing in for
15:16
Speaker 1: these other use cases.
15:17
Speaker 2: We have not increased pricing due to that. I think
15:21
Speaker 2: what you've seen instead is just it's just more difficult
15:24
Speaker 2: for people to get.
15:24
Speaker 1: To Supply is constrained. Price hasn't gone up, but supply
15:27
Speaker 1: is constrained. Well, let's let's talk about that, because supply
15:29
Speaker 1: constraints are like an interesting part of the story right there.
15:32
Speaker 1: Was a quote from a guy at OpenAI who's like,
15:35
Speaker 1: what people, what my friends talk about when we get
15:38
Speaker 1: together is like, how are we going to get more GPUs?
15:40
Speaker 1: Like have you heard where are their GPUs? It's like
15:43
Speaker 1: it's like mad Max or something, but instead of fuel,
15:46
Speaker 1: what everybody needs is GPUs. Like that is really interesting, right,
15:50
Speaker 1: Like one doesn't think of technology these you know, tech
15:55
Speaker 1: firms with all the money they could want constrained because
15:59
Speaker 1: they just can't get these chips, these GPUs at any price.
16:04
Speaker 1: Like that's a really interesting state of affairs.
16:07
Speaker 2: Yes, and I think the driver ultimately look like AI
16:11
Speaker 2: software adoption is the steepest adoption curve we've ever seen,
16:15
Speaker 2: and we're trying to build supercomputers at that pace, and
16:19
Speaker 2: it's a massive logistical and engineering challenge build to keep
16:23
Speaker 2: pace with that.
16:24
Speaker 1: Duct right, stuff doesn't scale like tof.
16:27
Speaker 2: Is a massive engineering challenge as well, Like it.
16:31
Speaker 1: Are the GPUs the rate limiting step? I mean, even
16:33
Speaker 1: if you had them, it would be hard, But is
16:34
Speaker 1: the fundamental it's a good question bottleneck the GPUs.
16:40
Speaker 2: GPUs themselves are an extremely high demand, but increasingly it's
16:44
Speaker 2: data center space, which which we've found has been extremely
16:48
Speaker 2: interesting because data center space is an area that's been
16:51
Speaker 2: undercapitalized or underinvested in for probably the last decade or so,
16:55
Speaker 2: and there's only a certain number of data centers that
16:59
Speaker 2: can actually run this infrastructure due to that increase in
17:03
Speaker 2: power density relative to kind of legacy infratis sructual.
17:09
Speaker 1: Right, So, like in this given amount of space, the
17:12
Speaker 1: kinds of clusters you're building, kinds of machines you're building,
17:16
Speaker 1: are going to use way more power. And that's a
17:18
Speaker 1: problem because these in a given amount of these BEATA
17:20
Speaker 1: centers aren't set up to provide that much power per
17:24
Speaker 1: unit space.
17:24
Speaker 2: And cooling most more importantly as well, like you have
17:28
Speaker 2: to be able to cool that area, and.
17:30
Speaker 1: They generate more heat because they consume more.
17:32
Speaker 2: Power exactly right. So there's only a certain number of
17:36
Speaker 2: data centers in the US I'm really speaking to the
17:38
Speaker 2: US here that can run that. And now that's the bottleneck,
17:42
Speaker 2: and that's going to be the bottleneck for the next
17:43
Speaker 2: two years arguably, because like all that space has now
17:47
Speaker 2: been leased.
17:48
Speaker 1: What are you trying to figure out that you have
17:49
Speaker 1: not yet figured out?
17:51
Speaker 2: It's that scaling, it's that onslaught of demand. I think
17:56
Speaker 2: we're we have it figured out, We know the engineering,
18:00
Speaker 2: we have the right product, we have the capital, the
18:03
Speaker 2: funding to go do it. We have the right people
18:05
Speaker 2: on the ground, we have the right management team. Now
18:08
Speaker 2: it's just growth like we have the next twelve to
18:12
Speaker 2: eighteen months in front of us outlined, and it's just
18:15
Speaker 2: go build, like, be focused on building. It's arguably some
18:19
Speaker 2: of the largest technology infrastructure projects and the world are
18:22
Speaker 2: happening at our company.
18:24
Speaker 1: When you think about like the next year, you have
18:26
Speaker 1: to spend billions of dollars, billions of dollars, it's like
18:30
Speaker 1: three guys who are playing around in the garage four
18:34
Speaker 1: years ago spending billions of dollars, like you worried you
18:39
Speaker 1: might do it wrong.
18:42
Speaker 2: If I worried, so, I think it's an excellent question.
18:47
Speaker 2: But we've also had some of the most well informed,
18:53
Speaker 2: intelligent participants in the artificial intelligence space look at our
18:57
Speaker 2: engineering teams, look at our product and say that's who
19:00
Speaker 2: I'm going with. We're doing it every day, We're building
19:03
Speaker 2: it every day, and now it's just keep doing it,
19:06
Speaker 2: remain focused, keep building. Don't get this dude.
19:11
Speaker 1: Now you're just grinding me like you seem super successful,
19:14
Speaker 1: but you're telling me is now you just have to
19:16
Speaker 1: grind it out for a year and like every day.
19:18
Speaker 2: Grind it out and keep growing. Yes, we'll be back in.
19:26
Speaker 1: A minute with the lightning Okay, now let's do the
19:41
Speaker 1: lightning round. How many screens do you have? How many
19:45
Speaker 1: monitors do you have on your computer? Six? It's like
19:48
Speaker 1: former commodities trader once, commodities trader always.
19:50
Speaker 2: I can't help it. I literally can't help it.
19:55
Speaker 1: It's so like one screen to you, you feel like
19:57
Speaker 1: you're looking at an Apple Watch or something. One monitor.
20:01
Speaker 1: Do you use all six monitors?
20:04
Speaker 2: I do? I use all six. Now, whether it's all
20:07
Speaker 2: relevant at the same time, I don't know, but but
20:09
Speaker 2: I do use.
20:10
Speaker 1: Also looking at six different things.
20:13
Speaker 2: No, it's like dedicated chat Windows. There's dedicated you know, browser, Windows,
20:18
Speaker 2: information Windows. I can get stats on all of our infrastructure.
20:23
Speaker 2: I just can't help it. I can't get that commodity
20:26
Speaker 2: trader out of me.
20:27
Speaker 1: Will you go to eight?
20:29
Speaker 2: I've been at eight. I stepped down to six. Six.
20:34
Speaker 1: It hurt me a bit it As a former commodities trader,
20:40
Speaker 1: what do you understand about the world that most people don't?
20:46
Speaker 2: Risk management say more so acknowledging that there's always going
20:52
Speaker 2: to be trade offs and what you do, but reducing
20:56
Speaker 2: the potential downside of that trade off as much as
21:00
Speaker 2: possible through through hedging, through negotiations, through contractual agreements, and
21:06
Speaker 2: being able to filter out the noise.
21:09
Speaker 1: Overrated or underrated? Bitcoin underrated, Ethereum overrated. I would not
21:17
Speaker 1: have guessed Bitcoin underrated, Ethereum overrated, given in particular your
21:23
Speaker 1: experience that you started your company mining ethereum. Why why
21:27
Speaker 1: is eum overrated and why is Bitcoin underrated?
21:29
Speaker 2: I think ethereum had so many promises.
21:32
Speaker 1: Of what it would do global computer.
21:35
Speaker 2: I just what are they delivered on. I just haven't
21:38
Speaker 2: seen like any real use cases. They've never got enterprise adoption, nothing,
21:43
Speaker 2: whereas Bitcoin is getting government It was.
21:45
Speaker 1: Supposed to be new global money. It was. So why
21:50
Speaker 1: do you think it's underrated?
21:53
Speaker 2: Because I think it's still moving that direction. It has
21:56
Speaker 2: the potential for it. It remains really interesting. It's slow moving,
22:03
Speaker 2: but it's it's making progress, whereas I feel like Ethereum
22:06
Speaker 2: has kind of been more sideways, if anything, interesting to down.
22:10
Speaker 1: Even what's one time when you gave up on something?
22:14
Speaker 2: Oh my gosh, I mean in the trading world, every
22:16
Speaker 2: single day, like.
22:19
Speaker 1: You abandon a thesis, basically abandon a trade.
22:22
Speaker 2: Is that? Yeah, I mean look like you're you're wrong
22:25
Speaker 2: every single day. And that was like one of the
22:27
Speaker 2: really interesting kind of outcomes of spending a decade in
22:30
Speaker 2: that career is is learning how to be wrong. You're
22:33
Speaker 2: wrong all the time, but it's it's like, how do
22:36
Speaker 2: you admit you're wrong, accept it, learn from it, and
22:39
Speaker 2: move on to the next. And I thought that the
22:41
Speaker 2: commodity trading world was just such an fascinating platform for that.
22:46
Speaker 2: Just get it reinforced every single day. It's not like
22:48
Speaker 2: a quarterly performance, It's an intra day, twenty times a day.
22:53
Speaker 1: Sing it on eight screens in front of your face
22:55
Speaker 1: in real time.
22:56
Speaker 2: I said, red numbers telling you how badly you're doing.
23:00
Speaker 2: Like it's immediately quantified. So I've been wrong a lot.
23:05
Speaker 1: Great, I appreciate your time.
23:07
Speaker 2: Yeah, that was fantastic. Really enjoyed the conversation.
23:15
Speaker 1: Brandon McNee is the chief strategy Officer at Corweath
23:21
Speaker 2: M