September 29, 2025 • 34 mins
Don Wilson has built a career diving into some of trading’s thorniest problems, including figuring out ways to trade new and niche markets. Now, the founder and CEO of DRW has his sights set on the GPUs powering AI, which he thinks could end up being a bigger market than crude oil. In this episode, which was recorded live onstage at our show in Chicago, we talk about how such a market would work, including ways to ‘standardize’ the vast array of different types of semiconductors, and how this could change the capital stack of the industry. We also talk the evolution of trading over Don’s storied career and why he thinks most assets (and maybe even all of them) will be tokenized within the next five years.
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Episode Transcript
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Speaker 1 (00:02):
Bloomberg Audio Studios, podcasts, radio news.
Speaker 2 (00:18):
Hey, there are add lots listeners. You are about to
get a conversation with Don Wilson, founder and CEO of DRW,
sometimes called the smartest man in trading. This was recorded
live on stage at Chicago's Untitled Supper Club. We had
a blast and we hope you'll enjoy the show. All right, Don, Well,
thank you for being here. Really appreciate it.
Speaker 3 (00:40):
Great to be here.
Speaker 2 (00:41):
Truly the perfect guest to talk about what's next in trading.
But just to begin with why GPUs.
Speaker 4 (00:48):
Well, obviously AI is becoming more and more useful, and
as it becomes more useful, people use more of it,
which means they need to use more GPUs to write
inference or trade new models. And I actually have this
theory that within the next ten years, the world will
spend more per year on GPUs than it does.
Speaker 3 (01:12):
On crude oil, and that would of course make GPUs compute.
Speaker 4 (01:17):
The largest commodity in the world. So it seems like
you would kind of need a market for that.
Speaker 2 (01:23):
The very modest call just the largest market in the world.
Speaker 5 (01:27):
Yeah, it's funny because you know, I associately oil often
coming out of you know, sandy deserts, but now they're
literally turning the sand via chips into the commodity itself,
or like breathing life into the sand. Just to back up,
I have a million questions about this for those who
don't know, why don't you give us the sort of
you know, the thirty second or the forty five second
(01:49):
description of what you do or what DRW is.
Speaker 3 (01:52):
Yeah.
Speaker 4 (01:53):
So I started off standing in the trading pit in
Chicago and the ear dollar option pit, yelling and screaming.
And then I go home and write code on my
Macintosh computer and build models, and essentially, you know, I
don't stand in the pit and yell and scream anymore.
Most of the pits are gone. But we kind of
do the same thing now with computers.
Speaker 2 (02:13):
I heard a story that you were once on vacation
with your family and you were in Italy, I think
in Florence, and instead of I don't know, eating gelato
or something like that, you decided to invent a new
Greek letter for derivatives trading.
Speaker 5 (02:27):
So this is cool.
Speaker 4 (02:29):
Yeah, so here, I mean you're confusing two stories. So actually,
actually what happened was there was a new exchange that
had launched an interest rate swap futures contract. It was
called IDCG, and I looked at the contract and I
figured out that actually they had not designed the contracts properly,
(02:50):
and so although they were telling everybody that it was
economically equivalent to a regular interest rate swap, it wasn't
because it had the additional convexity bias in it, which
is we could talk about convexity bias. It goes even
more in the weeds than a lot of your podcasts
go into. But so when I was in Florence, I
(03:12):
had this idea of how you could create an interest
rate spap futures contract without this convexity bias problem, and
that is what I focused my time on there.
Speaker 3 (03:21):
Yeah, so back to the letter.
Speaker 4 (03:24):
The letter was about after a really unpleasant period in
the year dollar option pit where all the market makers
lost tons of money because the shape of the skew
shifted dramatically as the FED started hiking in a very
predictable manner, and nobody had really developed a measure for
(03:47):
linear skew. And so during the week I said, well,
this isn't that much fun. We're losing a lot of
money every day. But the good news is that that
means we have something to learn. And so I spent
the weekend working with the quants and we came up with,
you know, kind of a measure of the linear skew
between the calls and puts, and decided to use the
(04:09):
Greek letter ci to describe it. And you know, so
by Monday morning we had put it into the risk
and onto the sheets, and before the open I explained
to the traders how to talk about it, how to
use language around it, and before you know it, we
had made the money back because we were able to
trade manage this risk better than anybody else because we
(04:33):
had a whole language around it.
Speaker 2 (04:35):
So we've established your street cred. When it comes to
solving problems in contracts for financial instruments. If I think
about a GPU future or something like that, the first
problem that comes to my mind is standardization, because of course,
you know all different types of chips, different types of memory,
different latency. I guess, how do you go about addressing that?
Speaker 3 (04:58):
So that's a great question.
Speaker 4 (04:59):
And right now, so what we've done is if we
set up two companies. One is called comput Exchange, not
very creatively named. We have a tendency to do that.
Speaker 2 (05:08):
At DRW is your initials right?
Speaker 3 (05:10):
That was my trading badge and yes, also my initials.
Yeah you know.
Speaker 4 (05:15):
I mean we did better later on with Cumberland, our
cryptos trading arm. That was actually a reference to the
Grateful Dead song about the Cumberland minds.
Speaker 5 (05:24):
I didn't know that. I didn't know that.
Speaker 3 (05:25):
Yeah.
Speaker 4 (05:26):
One of my partners, who does the more creative naming,
came up with that one.
Speaker 3 (05:30):
He's a he's a dead fan anyway.
Speaker 4 (05:33):
The other company is called Silicon Data, and Silicon Data's
job is to create indices that will become tradable, you know,
will be viable to have futures contracts listed on them.
And right now they've created a number of different ones.
But one is the H one hundred index. Another one
(05:53):
is the A one hundred indecks and believe it or not,
those indices are both available on Bloomberg.
Speaker 5 (06:00):
Amazing. There's a love hearing about it. If we were
in the studio, I would already would already be looking
up the chart as you were talking about it. Who
are the natural participants? Because when I think about AI
or training, you know, imagine someone goes to one of
the big cloud vendors and they sign a long term
contract or whatever. Who are the participants who would be
(06:21):
better off in an environment where there was a liquid
market for compute.
Speaker 4 (06:26):
So what we found and DRW actually uses a compute
exchange source Compute, and we find that because there are
something like seventy different cloud providers that participate, you can
often get better pricing. And one of the things that
you can do is you can specify if let's say
that you're an AI company and you know roughly what
(06:50):
kind of cluster you want, you can specify that. You
can even say, you know what, I'm indifferent between locations,
or you know, I fits in the Middle East, I'm
still okay with it, but I want to pay twenty
cents per gpuur less whatever it is. You can kind
of express your preference curve comput Exchange can conduct an
auction and then you know, find the kind of best
(07:13):
price compute that matches your needs. So that's that's kind
of the idea of how it works. And you know,
it probably doesn't work if you want a ten thousand
cluster monster for doing a huge training run, but for
inference it works great or for smaller training runs, it
works really well.
Speaker 2 (07:31):
Is the broader impact The idea that once you establish
a liquid market where people can you know, presumably hedge
their exposure, that that would bring down the cost of capital.
Speaker 3 (07:41):
So that's right.
Speaker 4 (07:42):
So once you have a liquid market, then you have
much more confidence in the indices and you can then
list futures contracts. And so what does that do? It
enables the neo clouds that are going out capital buying
a bunch of GPUs, putting them in data centers and
(08:03):
kind of hoping that they can rent them out and
not really knowing what they're going to be able to
rent them out for six months from now, let alone
two years from now.
Speaker 3 (08:12):
So a neil.
Speaker 4 (08:13):
Cloud could buy the GPUs, sell a strip of futures contracts,
and I envision that these will be traded kind of
like electricity futures, where there's one for every month, and
if you want to hedge the next three years, you
sell thirty six of them and now you've locked in
your pricing. Obviously, their cost of capital is going to
go down, which in turn should make GPUs.
Speaker 3 (08:36):
More readily available.
Speaker 4 (08:37):
And then on the flip side, if you're running an
AI company and you raise a finite amount of dollars
and you kind of know how much training you're going
to do, but you don't know exactly what configuration. You
can go ahead buy the compute in the derivatives market,
and then once you have a clear view on exactly
(08:58):
what configuration you want, then you can swap those derivatives
for actual computing.
Speaker 5 (09:05):
Talk to us a little bit more about the cell side,
So like we have these like big clouds, right, the
ones that everybody knows, and then you mentioned the neo clouds.
Do you see that changing? Like what do you see
as the future mix of cloud vendors in the future.
Speaker 3 (09:22):
So that is a great question.
Speaker 4 (09:25):
I think that the whole space is going to grow,
but that the aws gcps of the world will make
up a smaller percentage of the whole.
Speaker 5 (09:35):
Okay, that's my guess.
Speaker 3 (09:37):
But how come because there's such proliferation of other companies
buying GPUs and deploying them.
Speaker 5 (09:44):
Okay, that's a good answer.
Speaker 2 (09:46):
You know, Joe asked you who would be the natural
market participants for this? I'm going to ask you the
opposite question, who wouldn't want this? Because I think of
some of the hyperscalers they seem to like controlling the
GPU supply and maybe squeezing some of their competitors. Would
you expect resistance from.
Speaker 4 (10:04):
Them, Yeah, I mean I think the hyperscalers benefit from
opaque pricing and kind of bundled pricing, and of course
they would prefer to have all the GPUs could but
in video.
Speaker 2 (10:15):
I would also prefer to have all the GPUs.
Speaker 3 (10:17):
Yeah, yeah, that's always a good thing.
Speaker 4 (10:18):
But I think in Vidia wants the GPUs to be
widely distributed, and they're really the ones that make the call.
Speaker 5 (10:25):
This isn't the first time that there's been an attempt
to create futures markets out of technology. I think there's
been multiple efforts decades ago to Like d RAM futures
doesn't seem that fundamentally different, although maybe it is. Why
did those fail? Like when you think about, like what's
going to be different at this time, what was the
(10:46):
failure that caused? Like why didn't RAM futures take off?
Speaker 4 (10:49):
So the thing about d RAM was that the price
just kept on going down so in a very predictable way,
And so why would you want to buy a futures
contract if you know the price and future is going
to be lower?
Speaker 3 (11:01):
Whereas GPUs.
Speaker 4 (11:03):
You know, we've certainly gone through periods where GPU demand
was super high, and then we've gone through period where
you know, there was kind of some excess to.
Speaker 5 (11:12):
Not a consistent trajectory of pricing.
Speaker 4 (11:15):
I think that there will be a consistent trajectory lower
in terms of I don't know, however you want to
measure it dollars per flop put for dollars per token.
I think that that's going to continue to decline. But
you know, in each one hundred is going to be
a useful GPU for a very long time, and over
its life, I think there will be periods where there's
(11:37):
more demand, less demand, and you know, a little bit
more cyclicality and less predictability.
Speaker 2 (11:43):
So I know that the Trump administration has said that
they want this market to happen, right, So you seem
to have some regulatory I guess tailwind behind you.
Speaker 3 (11:53):
Yeah. I mean, I don't think that this is a
controversial thing.
Speaker 4 (11:57):
I think that it's pretty clear that once we figure
out the right index construction and have kind of sufficient
data that I don't think the CFTC would complain about
the product.
Speaker 5 (12:24):
This is a little bit of a sideways question from
your attempt to build this market. But speaking of the
cloud in your main business at DRW, I assume you're
sort of major customers or users of the CME. Are
you excited about the CME's migration of its back end
to Google Cloud because they tout it, they talk about
their partnership with Google, et cetera. As a client or customer,
(12:49):
are you enthusiastic about this move?
Speaker 2 (12:51):
We interviewed Terry earlier today and he was excited for sure.
Speaker 3 (12:55):
Yeah.
Speaker 4 (12:55):
So it depends on what you put into the cloud.
And it's totally fine to put a lot of things
into the cloud. But the thing that you don't want
to put into the cloud is a matching engine. And
the reason for that is you want the matching engine
to be as deterministic as possible. So that means that
if you send two orders into the matching engine, one
(13:21):
let's say a couple of microseconds behind the other one,
you want the one that gets there first to be
filled every time. Yeah, and if you put stuff into
the cloud, it's very hard to make that happen. You
wind up getting a wide distribution around which order will
be filled first. And even as you kind of stretch
(13:42):
those times out, you could have an order that comes
in maybe a couple milliseconds later be filled first. That
is super disruptive for liquidity providers and it means that
the liquidity in the market's going to suffer.
Speaker 5 (14:00):
Humhm. But this is you say, it's not ideal for
them to have a matching engine in the cloud, but
this is the direction it's going in.
Speaker 4 (14:06):
Yeah, and it's unclear exactly which part of the matching
engine moving the cloud. Is it some kind of a
dual structure. I don't know, But that's what matters, is
a deterministic matching engine. I mean, if Google can figure
out how to make matching engine and the cloud deterministic,
go for it. I'm very skeptical that that's even possible.
Speaker 5 (14:27):
Can you just describe the sort of theoretical problem. What
is it about cloud computing that makes this particular problem,
the deterministic aspect difficult as opposed to traditional infrastructure.
Speaker 4 (14:40):
Well, when you have on prem computers, you can it's
all right there, you can control where the wires go,
and so when it's in the cloud, it's a little
bit more well nebulous. I guess it's just harder to do.
Speaker 2 (14:54):
That's a good pun I admire it. So you mentioned
that you have this law and storied career in the
trading industry, starting from old school trading, and now we're
here talking about GPU trading and what's in the cloud
and what works and what doesn't tell us what your company,
what DRW is actually doing when it comes to practical
(15:14):
application of AI, this is a question we're asking everyone.
We asked all companies to spill all their proprietary secrets
about AI.
Speaker 5 (15:23):
Excluding the engineers. We know that, we know, we know
people are yes, we know that they're using clog code
or whatever.
Speaker 4 (15:30):
So besides the yeah, yeah, yeah, you're right, that's that's
kind of the boring answer.
Speaker 5 (15:34):
Yeah, And then the other thing is, then when we
ask this question, people cite a bunch of machine learning things,
which has been here for a while. So let's talk.
Speaker 3 (15:42):
About actual Ah.
Speaker 4 (15:44):
Yeah, So I think that the way that we make
trading decisions is going to change dramatically, and it already is.
You can use AI to interact with your proprietary data,
your propriety terry models, and suggest trades.
Speaker 3 (16:04):
That's pretty cool.
Speaker 5 (16:05):
Are you doing that right now?
Speaker 4 (16:06):
Yeah, so we're starting to do that, but we have
some tools that kind of do that now. And the
other thing that's really interesting is to fiddle around with
agents and have different agents interact, and so you could
kind of think about maybe you have a couple different
analysts AI analysts that both work on some stock, and
(16:27):
then you have kind of a risk taking agent or
maybe a couple different risk taking agents that interact with
those analysts and then come up with trades based on that. So,
I mean, these are you know, that's a little bit
of a theoretical concept, but I don't think we're that
far away from things like that.
Speaker 5 (16:44):
Just on the cloud trading a little bit more. I
am really interested in this topic. What is the current
stay today, just so that we understand where you're at,
Like what is today's snapshot of usage of the platforms?
Speaker 3 (16:57):
I mean as far as where the matching engines are or.
Speaker 5 (17:00):
Oh sorry, on the on the GPU trading is it
right now?
Speaker 3 (17:03):
Oh?
Speaker 5 (17:04):
Like where is the state of the business?
Speaker 4 (17:05):
Oh, you know, I think last month we conducted five
or six auctions, so it's early, but it's happening.
Speaker 2 (17:15):
So when I think about how like futures contracts are born,
it's usually bespoke options and then you get the index.
I guess, and then you get a forward and then
a future. That's kind of how I think about it
in my head, is that the process that you imagine.
Speaker 3 (17:30):
For this h not necessarily. I think that the simplest.
Speaker 4 (17:35):
I mean, yeah, I suppose you could do some privately
negotiated compute swap or something, and maybe that will happen first,
but no, I think the first thing is a futures
contract that settles to an index. If the spot market
becomes really liquid and you have very standardized auctions, and
you know, one of the things that you asked about was, well,
(17:56):
how do you deal with the lack of standardized you know?
And so one thing is you go to a certain
type of GPU, you know, H one hundred for instance.
But even within that, you can configure them in different ways.
You could use in finiband, you can use some other
way of connecting them. And so what's important is you
(18:16):
need to decide on some benchmark. And one of the
things that Silicon Data has done is they've actually built
some measurement tools that measure how fast a GPU cluster is,
and so you can then say, okay, well, in order
for this GPU to be kind of eligible to be
(18:36):
in the index it needs to meet a certain standard,
and you can there are a couple different vectors you
can measure by, so I think that that's kind of
how you would do it. And then if you got
very liquid auctions, you could actually have a futures contract
that cash settles to the auction price, and then people
(18:57):
can have the option of either essentially just cash settling
their derivative and walking away, or cash selling their derivative
and participating in the auction, and they would know that
price would transfer from one thing to another. That might
be a future state of the world, and the initial
state is probably just a generic index, and the future
(19:17):
is cash settled.
Speaker 3 (19:18):
To the index.
Speaker 2 (19:19):
What would a market failure look like in GPU trading,
because your analogy is the oil market, and you know,
weird stuff happens in the oil market. Could we get
negative GPU prices? Or if everyone wakes up one day
and decides they want to use chat GPT as their
psychotherapist or whatever some people are doing, could you have
a GPU shortage where maybe people can't deliver into the contract.
Speaker 4 (19:43):
There are lots of ways that markets can break and
go wrong. And I remember to this day that when
oil futures went negative.
Speaker 3 (19:50):
It was during COVID. I was sitting at home.
Speaker 4 (19:52):
I was trading oil futures and I bought oil futures
for negative prices.
Speaker 5 (19:57):
You were one of the one team made money.
Speaker 3 (19:59):
Yeah.
Speaker 4 (20:00):
My, Uh then what was that twenty twenty one? So yeah,
my then fourteen year old said to me, please, please please,
I want to buy negative priced futures contracts. And I said, well,
you have no way.
Speaker 3 (20:14):
Of taking delivery the oil.
Speaker 4 (20:17):
And he said, I will go to Cushing, Oklahoma and
figure out how to do it.
Speaker 5 (20:21):
You've really raised a son, daughter, son, son. You've really uh,
he's been learning.
Speaker 2 (20:29):
We have we have an episode about taking physical possession
of oil. I do not recommend. It. Turns out if
you keep it on your desk for long enough, it
evaporates into the atmosphere and poisons your colleagues.
Speaker 3 (20:39):
Yeah, anyway, a little bit of a tangent.
Speaker 4 (20:43):
So I think on the upwards rejectory, if there's tons
of demand, you know, that's something that commodity markets are
really good at dealing with. The price will go up
and more supply will come in, and I think that's
all good.
Speaker 3 (20:55):
On the downward side, you know.
Speaker 4 (20:57):
You can always just turn the GPUs off, so I
don't think they trade negative.
Speaker 5 (21:01):
How much of the volatility that do you when you
anticipate market volatility in the price of GPUs? How much
is that like embedded electricity costs, So when you buy compute, right,
you're buying the chip, but also the power, Like, how
much of that volatility will be the power?
Speaker 4 (21:17):
So the industry lingo that's used is total cost of ownership,
And you know what percentage of the total cost of
ownership is the power price? And for an H one hundred,
it's less than fifteen.
Speaker 2 (21:29):
Percent less than fifteen Okay, So GPU trading obviously one
of the things you're working on, but you're a busy
guy and you've got other stuff up your sleeve. What
are you doing in the realms of tokenized trading?
Speaker 4 (21:42):
So that is an area that we're super excited about,
and we've been thinking about this for a very long time.
So in twenty twelve, when we started talking about bitcoin
at Dow and there were a number of traders at
DORW that were very excited about.
Speaker 2 (21:58):
Bitcoin, you were very early into it. Twenty twelve was
still pretty.
Speaker 4 (22:01):
Early, very early, Yeah, So we were having these discussions
of why is this interesting. Is it interesting? What about
it is interesting? And we came away with the following thesis,
there's some small chance that bitcoin could be digital gold.
I don't know, you know, call it one percent. It's
kind of an interesting product, so we should probably make
markets in it. So we set up Cumberland as the
(22:23):
and you know, we didn't call it DRW because at
the time, everybody knew that anybody trading crypto was obviously
a crook, so so you know, we wanted to kind
of separate the brand a little bit. But you know,
the other thing was this idea that you could move
value instantaneously in a trustless ecosystem was super interesting to me,
(22:44):
and I said, wow, if you could do that in
traditional financial markets, that would make the market so much better,
so much more resilient, and so we should really figure
out how to do that. So we started a company called,
again not very creatively named Digital Asset Holdings, which created
the Canton blockchain. Initially, the Canton blockchain was a private,
(23:04):
permissioned chain, but last summer it actually became a public chain,
and that chain was designed specifically with tokenization of traditional
financial instruments in mind. So it has a couple of characteristics.
One is it has configurable privacy, and believe it or not,
for people who are in the finance business, they don't
(23:28):
want to broadcast to the entire world when they are
buying or selling something. I mean, obviously, if it's above
the reporting thresholds, you do. So that was kind of
a fundamental characteristic of this chain. It's different than Ethereum
or Solona or any of these other things where if
you tokenize something and put it on top and you
move it around, everybody sees it move around. So that's
(23:48):
kind of something we've been working on for quite a while.
Speaker 5 (23:52):
How big could this get? Like? Could it swallow everything?
Could you imagine a world in which, given any financial instrument,
do bond et cetera, that it all sort of ends
up on chin.
Speaker 3 (24:04):
Yeah, I think that everything will be on chain.
Speaker 5 (24:07):
Wow by when give us a year?
Speaker 3 (24:10):
No, I'm always way too early on this stuff.
Speaker 4 (24:12):
But I think in the next five years all of
these instruments will be on chain.
Speaker 5 (24:17):
Okay, that's good. Primarily we will have a live episode
in twenty we'll come back to Chicago, US that question.
Speaker 2 (24:23):
Yeah, is the ideal with tokenized assets also that you
could use that for collateral management and use it as
(24:45):
a way to move collateral.
Speaker 4 (24:48):
And so everybody's talking about moving to twenty four five
or twenty four to seven markets, and if you want
to do that, it's really important to be able to
move collateral twenty four five or twenty four seven and
move variation margin twenty four or five or twenty four seven.
Speaker 3 (25:06):
And so yes, that is a very important use case.
Speaker 5 (25:10):
So speaking of very exciting sexy topics in trading, right
after you, we're going to be speaking with Terrek Mansur
of Kelshi And so prediction markets are super hot. Where
are you at with them? Is dorw making markets in
any of these in any of the spaces right now?
Speaker 4 (25:27):
So a million years ago we actually made markets and
prediction markets. I think it was, I don't know, in
trade or something, yeah, and it never went anywhere.
Speaker 3 (25:37):
Nobody cared.
Speaker 4 (25:37):
And I always thought, you know, prediction markets should be
a thing everybody should care.
Speaker 3 (25:42):
But nobody did. And then Auger came out.
Speaker 4 (25:45):
I was like, oh, this is really cool, this is
going to take off, and nobody cared, and so it's it's.
Speaker 3 (25:49):
Taken a long time.
Speaker 4 (25:50):
So at this point we use it as a reference price,
you know, obviously during the election, it was super helpful
to use that as a gauge.
Speaker 2 (25:58):
Of So you were actually using that because you know,
we hear stories about institutional investors maybe finding prediction markets
useful perhaps, but you were looking at it.
Speaker 4 (26:07):
We were definitely looking at it. We were not using
it as a hedge. And it was funny. Shane messaged
me and said, hey, you know it's it's it's up
on Bloomberg now and I was like, oh, that's awesome, Shane.
Speaker 5 (26:19):
Shane Coplin from Yeah, that's right. Yeah, But currently, like
do you foresee, like are you going to enter not
either making markets on some of these exchanges, and would
you get into the sports contracts?
Speaker 3 (26:32):
I mean, so we're not here.
Speaker 4 (26:34):
I think it's highly likely that we'll start trading some
of the prediction markets. Some of our competitors already trade
in the sports markets pretty actively. We don't, so it's
not necessarily a natural fit. But I don't have like
a religious opposition to it.
Speaker 2 (26:50):
Would there be different considerations for trading in a prediction
market versus a traditional financial asset? Are there different things
you have to think about, either in terms of like
price seeing the trade or maybe risk management.
Speaker 3 (27:02):
Well, I think it depends on what the prediction market is.
Speaker 4 (27:04):
I mean, if you're trading a prediction market on I
don't know whether somebody will throw a rubber object onto
a WNBA court, then I mean that's something that people
in the audience can control. And so it seems like
providing liquidity in that you would be at a disadvantage.
Speaker 2 (27:26):
That was a very particular example. By the way, I
was going to go with Taylor Swift getting married.
Speaker 3 (27:32):
But you went with that one.
Speaker 5 (27:34):
Well, these are markets that people can directly intervene.
Speaker 3 (27:38):
Directly as opposed to, for instance, their.
Speaker 5 (27:42):
Own yeah, antisocial behavior, that's.
Speaker 3 (27:45):
Right, and.
Speaker 4 (27:47):
As opposed to will the Fed cut twenty five or
fifty or stay on hold? I mean you can trade
that in sofur, you can trade that in in the
FED funds futures. There some binaries you can trade. And
so the prediction market version of that is totally fits
in with the risk that we already trade.
Speaker 5 (28:09):
So we mentioned in the intro there's gonna be this
big meeting in DC next week, and we just happened
to sort of catch a bunch of the participants. When
you look at the landscape for these new futures, platforms
because that's what they are, right. The CME has regulation
been part of their dominance. Has regulation made it harder
(28:30):
for other entrants to cut into CME margins or volumes.
So I'm trying to ask questions that are going to
create some tension around the table.
Speaker 3 (28:40):
Next week, Yeah still here, I mean, oh yeah, you.
Speaker 2 (28:43):
Should hear what Terry said about Howard Lutnick.
Speaker 5 (28:47):
It'll be on the podcast.
Speaker 3 (28:48):
I'm sure I can.
Speaker 4 (28:49):
I could probably repeat it without having heard it. So
once you have a liquid market in something, it becomes
a natural monopoly. It's very hard to move that to
a different venue. It's happened before. I was living in
London in the mid nineties and the bond futures were
(29:11):
on the floor of the Life. It was this huge
trading pit with a bunch of guys pushing and shoving,
and over the course of twelve months, the DTV now
called the UX was able to move the entire bond
futures complex onto the computer on a different exchange. Now,
I mean they gave hefty incentives to people. I think
(29:34):
they went to all the German banks and they said,
don't you dare trade on Life anymore. So it's possible,
but I think that these things are generally I don't
think that it's really a regulatory issue that causes them
to be sticky. I think it's more just kind of
a natural state of affairs.
Speaker 2 (29:52):
Network effect, I guess. So our theme for this evening
is obviously the future of trading, and one of the
things that seems to be happening is the sort of
intermingling of professional and retail trading. And we again talked
about that with Terry. I'm sure we're about to talk
about it with the Calhi CEO. But from your perspective,
and again, you started this career back when I don't
(30:14):
think there were any retail traders doing day trading. Really,
how has that changed the way you think about trading?
And can you envision a future where I don't know,
AI fires all of us and we're all going to
be just day trading from home as an insurance policy,
Robinhood is really a full employment program.
Speaker 5 (30:31):
Maybe for us workers.
Speaker 4 (30:34):
Yeah, So, I mean that is a thesis that I
have heard, is that what's happening is a bunch of
relatively successful people are losing their jobs and they're retiring,
but in their retirement they decide to just manage their
portfolios on robinhood And so there's this surge in trading
(30:56):
activity that wouldn't have happened ten years ago, and it's
only going to grow from here.
Speaker 3 (31:03):
And I don't know, maybe that's right.
Speaker 5 (31:05):
It feels to me like culturally, because you're talking about, right,
why have prediction markets taken off when they've been around
for over twenty years. I think I first heard about
them in like two thousand and two or two thousand
and three. They've suddenly taken off. There was never a
bright line between what's gambling and what's sort of hedging
or what's trading, but there's clearly whatever line that is
just feels like it's completely collapsing. Is this good? Do
(31:28):
you have an opinion? Like should? Is there is? And
I don't know if any of our opinions matter on
the question, because it feels like culturally we're entering that
world where everything will be tradeable on any app and
there's you know, you're gonna see a price for gold
futures right next to one day, the line on a
football match, et cetera.
Speaker 1 (31:46):
Is it?
Speaker 5 (31:47):
Do we want this world?
Speaker 3 (31:49):
So?
Speaker 4 (31:50):
I don't think there's anything particularly wrong with it, But
I am a little bit confused about whether prediction markets
and sports are actually consistent with what the Commodity Exchange
Act says is permissible.
Speaker 3 (32:08):
And so I know that your next your.
Speaker 4 (32:11):
Next guest is perfect is benefits from his ability to
list these contracts. And and I don't know if you know,
the CFTC is just kind of asleep, and I know
they're kind of understaffed now or uh or or maybe
they've decided that actually these are economically important transactions that
(32:36):
are consistent with the CEA.
Speaker 3 (32:37):
It's unclear to me.
Speaker 2 (32:39):
All Right, well, we're going to have to leave it there.
But Don Wilson, founder and CEO of dr W, thank
you so much for being here.
Speaker 3 (32:47):
Really appreciated, Thank you for having me.
Speaker 2 (33:02):
That was our conversation with DRW founder and CEO Don Wilson,
recorded live on stage in Chicago. I'm Tracy Alloway. You
can follow me at Tracy Alloway.
Speaker 5 (33:13):
And I'm Jill Wisenthal. You can follow me at the Stalwart.
Follow our producers Carmen Rodriguez at Carmen armand dash Ol
Bennett at Dashbot and kil Brooks at Kilbrooks. Form more
odd lots content go to Bloomberg dot com slash odd
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You can chat about all of these topics twenty four
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Speaker 2 (33:33):
And if you enjoy odd Lots, if you like it
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Bloomberg Audio Studios, podcasts, radio news.
Speaker 2 (00:18):
Hey, there are add lots listeners. You are about to
get a conversation with Don Wilson, founder and CEO of DRW,
sometimes called the smartest man in trading. This was recorded
live on stage at Chicago's Untitled Supper Club. We had
a blast and we hope you'll enjoy the show. All right, Don, Well,
thank you for being here. Really appreciate it.
Speaker 3 (00:40):
Great to be here.
Speaker 2 (00:41):
Truly the perfect guest to talk about what's next in trading.
But just to begin with why GPUs.
Speaker 4 (00:48):
Well, obviously AI is becoming more and more useful, and
as it becomes more useful, people use more of it,
which means they need to use more GPUs to write
inference or trade new models. And I actually have this
theory that within the next ten years, the world will
spend more per year on GPUs than it does.
Speaker 3 (01:12):
On crude oil, and that would of course make GPUs compute.
Speaker 4 (01:17):
The largest commodity in the world. So it seems like
you would kind of need a market for that.
Speaker 2 (01:23):
The very modest call just the largest market in the world.
Speaker 5 (01:27):
Yeah, it's funny because you know, I associately oil often
coming out of you know, sandy deserts, but now they're
literally turning the sand via chips into the commodity itself,
or like breathing life into the sand. Just to back up,
I have a million questions about this for those who
don't know, why don't you give us the sort of
you know, the thirty second or the forty five second
(01:49):
description of what you do or what DRW is.
Speaker 3 (01:52):
Yeah.
Speaker 4 (01:53):
So I started off standing in the trading pit in
Chicago and the ear dollar option pit, yelling and screaming.
And then I go home and write code on my
Macintosh computer and build models, and essentially, you know, I
don't stand in the pit and yell and scream anymore.
Most of the pits are gone. But we kind of
do the same thing now with computers.
Speaker 2 (02:13):
I heard a story that you were once on vacation
with your family and you were in Italy, I think
in Florence, and instead of I don't know, eating gelato
or something like that, you decided to invent a new
Greek letter for derivatives trading.
Speaker 5 (02:27):
So this is cool.
Speaker 4 (02:29):
Yeah, so here, I mean you're confusing two stories. So actually,
actually what happened was there was a new exchange that
had launched an interest rate swap futures contract. It was
called IDCG, and I looked at the contract and I
figured out that actually they had not designed the contracts properly,
(02:50):
and so although they were telling everybody that it was
economically equivalent to a regular interest rate swap, it wasn't
because it had the additional convexity bias in it, which
is we could talk about convexity bias. It goes even
more in the weeds than a lot of your podcasts
go into. But so when I was in Florence, I
(03:12):
had this idea of how you could create an interest
rate spap futures contract without this convexity bias problem, and
that is what I focused my time on there.
Speaker 3 (03:21):
Yeah, so back to the letter.
Speaker 4 (03:24):
The letter was about after a really unpleasant period in
the year dollar option pit where all the market makers
lost tons of money because the shape of the skew
shifted dramatically as the FED started hiking in a very
predictable manner, and nobody had really developed a measure for
(03:47):
linear skew. And so during the week I said, well,
this isn't that much fun. We're losing a lot of
money every day. But the good news is that that
means we have something to learn. And so I spent
the weekend working with the quants and we came up with,
you know, kind of a measure of the linear skew
between the calls and puts, and decided to use the
(04:09):
Greek letter ci to describe it. And you know, so
by Monday morning we had put it into the risk
and onto the sheets, and before the open I explained
to the traders how to talk about it, how to
use language around it, and before you know it, we
had made the money back because we were able to
trade manage this risk better than anybody else because we
(04:33):
had a whole language around it.
Speaker 2 (04:35):
So we've established your street cred. When it comes to
solving problems in contracts for financial instruments. If I think
about a GPU future or something like that, the first
problem that comes to my mind is standardization, because of course,
you know all different types of chips, different types of memory,
different latency. I guess, how do you go about addressing that?
Speaker 3 (04:58):
So that's a great question.
Speaker 4 (04:59):
And right now, so what we've done is if we
set up two companies. One is called comput Exchange, not
very creatively named. We have a tendency to do that.
Speaker 2 (05:08):
At DRW is your initials right?
Speaker 3 (05:10):
That was my trading badge and yes, also my initials.
Yeah you know.
Speaker 4 (05:15):
I mean we did better later on with Cumberland, our
cryptos trading arm. That was actually a reference to the
Grateful Dead song about the Cumberland minds.
Speaker 5 (05:24):
I didn't know that. I didn't know that.
Speaker 3 (05:25):
Yeah.
Speaker 4 (05:26):
One of my partners, who does the more creative naming,
came up with that one.
Speaker 3 (05:30):
He's a he's a dead fan anyway.
Speaker 4 (05:33):
The other company is called Silicon Data, and Silicon Data's
job is to create indices that will become tradable, you know,
will be viable to have futures contracts listed on them.
And right now they've created a number of different ones.
But one is the H one hundred index. Another one
(05:53):
is the A one hundred indecks and believe it or not,
those indices are both available on Bloomberg.
Speaker 5 (06:00):
Amazing. There's a love hearing about it. If we were
in the studio, I would already would already be looking
up the chart as you were talking about it. Who
are the natural participants? Because when I think about AI
or training, you know, imagine someone goes to one of
the big cloud vendors and they sign a long term
contract or whatever. Who are the participants who would be
(06:21):
better off in an environment where there was a liquid
market for compute.
Speaker 4 (06:26):
So what we found and DRW actually uses a compute
exchange source Compute, and we find that because there are
something like seventy different cloud providers that participate, you can
often get better pricing. And one of the things that
you can do is you can specify if let's say
that you're an AI company and you know roughly what
(06:50):
kind of cluster you want, you can specify that. You
can even say, you know what, I'm indifferent between locations,
or you know, I fits in the Middle East, I'm
still okay with it, but I want to pay twenty
cents per gpuur less whatever it is. You can kind
of express your preference curve comput Exchange can conduct an
auction and then you know, find the kind of best
(07:13):
price compute that matches your needs. So that's that's kind
of the idea of how it works. And you know,
it probably doesn't work if you want a ten thousand
cluster monster for doing a huge training run, but for
inference it works great or for smaller training runs, it
works really well.
Speaker 2 (07:31):
Is the broader impact The idea that once you establish
a liquid market where people can you know, presumably hedge
their exposure, that that would bring down the cost of capital.
Speaker 3 (07:41):
So that's right.
Speaker 4 (07:42):
So once you have a liquid market, then you have
much more confidence in the indices and you can then
list futures contracts. And so what does that do? It
enables the neo clouds that are going out capital buying
a bunch of GPUs, putting them in data centers and
(08:03):
kind of hoping that they can rent them out and
not really knowing what they're going to be able to
rent them out for six months from now, let alone
two years from now.
Speaker 3 (08:12):
So a neil.
Speaker 4 (08:13):
Cloud could buy the GPUs, sell a strip of futures contracts,
and I envision that these will be traded kind of
like electricity futures, where there's one for every month, and
if you want to hedge the next three years, you
sell thirty six of them and now you've locked in
your pricing. Obviously, their cost of capital is going to
go down, which in turn should make GPUs.
Speaker 3 (08:36):
More readily available.
Speaker 4 (08:37):
And then on the flip side, if you're running an
AI company and you raise a finite amount of dollars
and you kind of know how much training you're going
to do, but you don't know exactly what configuration. You
can go ahead buy the compute in the derivatives market,
and then once you have a clear view on exactly
(08:58):
what configuration you want, then you can swap those derivatives
for actual computing.
Speaker 5 (09:05):
Talk to us a little bit more about the cell side,
So like we have these like big clouds, right, the
ones that everybody knows, and then you mentioned the neo clouds.
Do you see that changing? Like what do you see
as the future mix of cloud vendors in the future.
Speaker 3 (09:22):
So that is a great question.
Speaker 4 (09:25):
I think that the whole space is going to grow,
but that the aws gcps of the world will make
up a smaller percentage of the whole.
Speaker 5 (09:35):
Okay, that's my guess.
Speaker 3 (09:37):
But how come because there's such proliferation of other companies
buying GPUs and deploying them.
Speaker 5 (09:44):
Okay, that's a good answer.
Speaker 2 (09:46):
You know, Joe asked you who would be the natural
market participants for this? I'm going to ask you the
opposite question, who wouldn't want this? Because I think of
some of the hyperscalers they seem to like controlling the
GPU supply and maybe squeezing some of their competitors. Would
you expect resistance from.
Speaker 4 (10:04):
Them, Yeah, I mean I think the hyperscalers benefit from
opaque pricing and kind of bundled pricing, and of course
they would prefer to have all the GPUs could but
in video.
Speaker 2 (10:15):
I would also prefer to have all the GPUs.
Speaker 3 (10:17):
Yeah, yeah, that's always a good thing.
Speaker 4 (10:18):
But I think in Vidia wants the GPUs to be
widely distributed, and they're really the ones that make the call.
Speaker 5 (10:25):
This isn't the first time that there's been an attempt
to create futures markets out of technology. I think there's
been multiple efforts decades ago to Like d RAM futures
doesn't seem that fundamentally different, although maybe it is. Why
did those fail? Like when you think about, like what's
going to be different at this time, what was the
(10:46):
failure that caused? Like why didn't RAM futures take off?
Speaker 4 (10:49):
So the thing about d RAM was that the price
just kept on going down so in a very predictable way,
And so why would you want to buy a futures
contract if you know the price and future is going
to be lower?
Speaker 3 (11:01):
Whereas GPUs.
Speaker 4 (11:03):
You know, we've certainly gone through periods where GPU demand
was super high, and then we've gone through period where
you know, there was kind of some excess to.
Speaker 5 (11:12):
Not a consistent trajectory of pricing.
Speaker 4 (11:15):
I think that there will be a consistent trajectory lower
in terms of I don't know, however you want to
measure it dollars per flop put for dollars per token.
I think that that's going to continue to decline. But
you know, in each one hundred is going to be
a useful GPU for a very long time, and over
its life, I think there will be periods where there's
(11:37):
more demand, less demand, and you know, a little bit
more cyclicality and less predictability.
Speaker 2 (11:43):
So I know that the Trump administration has said that
they want this market to happen, right, So you seem
to have some regulatory I guess tailwind behind you.
Speaker 3 (11:53):
Yeah. I mean, I don't think that this is a
controversial thing.
Speaker 4 (11:57):
I think that it's pretty clear that once we figure
out the right index construction and have kind of sufficient
data that I don't think the CFTC would complain about
the product.
Speaker 5 (12:24):
This is a little bit of a sideways question from
your attempt to build this market. But speaking of the
cloud in your main business at DRW, I assume you're
sort of major customers or users of the CME. Are
you excited about the CME's migration of its back end
to Google Cloud because they tout it, they talk about
their partnership with Google, et cetera. As a client or customer,
(12:49):
are you enthusiastic about this move?
Speaker 2 (12:51):
We interviewed Terry earlier today and he was excited for sure.
Speaker 3 (12:55):
Yeah.
Speaker 4 (12:55):
So it depends on what you put into the cloud.
And it's totally fine to put a lot of things
into the cloud. But the thing that you don't want
to put into the cloud is a matching engine. And
the reason for that is you want the matching engine
to be as deterministic as possible. So that means that
if you send two orders into the matching engine, one
(13:21):
let's say a couple of microseconds behind the other one,
you want the one that gets there first to be
filled every time. Yeah, and if you put stuff into
the cloud, it's very hard to make that happen. You
wind up getting a wide distribution around which order will
be filled first. And even as you kind of stretch
(13:42):
those times out, you could have an order that comes
in maybe a couple milliseconds later be filled first. That
is super disruptive for liquidity providers and it means that
the liquidity in the market's going to suffer.
Speaker 5 (14:00):
Humhm. But this is you say, it's not ideal for
them to have a matching engine in the cloud, but
this is the direction it's going in.
Speaker 4 (14:06):
Yeah, and it's unclear exactly which part of the matching
engine moving the cloud. Is it some kind of a
dual structure. I don't know, But that's what matters, is
a deterministic matching engine. I mean, if Google can figure
out how to make matching engine and the cloud deterministic,
go for it. I'm very skeptical that that's even possible.
Speaker 5 (14:27):
Can you just describe the sort of theoretical problem. What
is it about cloud computing that makes this particular problem,
the deterministic aspect difficult as opposed to traditional infrastructure.
Speaker 4 (14:40):
Well, when you have on prem computers, you can it's
all right there, you can control where the wires go,
and so when it's in the cloud, it's a little
bit more well nebulous. I guess it's just harder to do.
Speaker 2 (14:54):
That's a good pun I admire it. So you mentioned
that you have this law and storied career in the
trading industry, starting from old school trading, and now we're
here talking about GPU trading and what's in the cloud
and what works and what doesn't tell us what your company,
what DRW is actually doing when it comes to practical
(15:14):
application of AI, this is a question we're asking everyone.
We asked all companies to spill all their proprietary secrets
about AI.
Speaker 5 (15:23):
Excluding the engineers. We know that, we know, we know
people are yes, we know that they're using clog code
or whatever.
Speaker 4 (15:30):
So besides the yeah, yeah, yeah, you're right, that's that's
kind of the boring answer.
Speaker 5 (15:34):
Yeah, And then the other thing is, then when we
ask this question, people cite a bunch of machine learning things,
which has been here for a while. So let's talk.
Speaker 3 (15:42):
About actual Ah.
Speaker 4 (15:44):
Yeah, So I think that the way that we make
trading decisions is going to change dramatically, and it already is.
You can use AI to interact with your proprietary data,
your propriety terry models, and suggest trades.
Speaker 3 (16:04):
That's pretty cool.
Speaker 5 (16:05):
Are you doing that right now?
Speaker 4 (16:06):
Yeah, so we're starting to do that, but we have
some tools that kind of do that now. And the
other thing that's really interesting is to fiddle around with
agents and have different agents interact, and so you could
kind of think about maybe you have a couple different
analysts AI analysts that both work on some stock, and
(16:27):
then you have kind of a risk taking agent or
maybe a couple different risk taking agents that interact with
those analysts and then come up with trades based on that. So,
I mean, these are you know, that's a little bit
of a theoretical concept, but I don't think we're that
far away from things like that.
Speaker 5 (16:44):
Just on the cloud trading a little bit more. I
am really interested in this topic. What is the current
stay today, just so that we understand where you're at,
Like what is today's snapshot of usage of the platforms?
Speaker 3 (16:57):
I mean as far as where the matching engines are or.
Speaker 5 (17:00):
Oh sorry, on the on the GPU trading is it
right now?
Speaker 3 (17:03):
Oh?
Speaker 5 (17:04):
Like where is the state of the business?
Speaker 4 (17:05):
Oh, you know, I think last month we conducted five
or six auctions, so it's early, but it's happening.
Speaker 2 (17:15):
So when I think about how like futures contracts are born,
it's usually bespoke options and then you get the index.
I guess, and then you get a forward and then
a future. That's kind of how I think about it
in my head, is that the process that you imagine.
Speaker 3 (17:30):
For this h not necessarily. I think that the simplest.
Speaker 4 (17:35):
I mean, yeah, I suppose you could do some privately
negotiated compute swap or something, and maybe that will happen first,
but no, I think the first thing is a futures
contract that settles to an index. If the spot market
becomes really liquid and you have very standardized auctions, and
you know, one of the things that you asked about was, well,
(17:56):
how do you deal with the lack of standardized you know?
And so one thing is you go to a certain
type of GPU, you know, H one hundred for instance.
But even within that, you can configure them in different ways.
You could use in finiband, you can use some other
way of connecting them. And so what's important is you
(18:16):
need to decide on some benchmark. And one of the
things that Silicon Data has done is they've actually built
some measurement tools that measure how fast a GPU cluster is,
and so you can then say, okay, well, in order
for this GPU to be kind of eligible to be
(18:36):
in the index it needs to meet a certain standard,
and you can there are a couple different vectors you
can measure by, so I think that that's kind of
how you would do it. And then if you got
very liquid auctions, you could actually have a futures contract
that cash settles to the auction price, and then people
(18:57):
can have the option of either essentially just cash settling
their derivative and walking away, or cash selling their derivative
and participating in the auction, and they would know that
price would transfer from one thing to another. That might
be a future state of the world, and the initial
state is probably just a generic index, and the future
(19:17):
is cash settled.
Speaker 3 (19:18):
To the index.
Speaker 2 (19:19):
What would a market failure look like in GPU trading,
because your analogy is the oil market, and you know,
weird stuff happens in the oil market. Could we get
negative GPU prices? Or if everyone wakes up one day
and decides they want to use chat GPT as their
psychotherapist or whatever some people are doing, could you have
a GPU shortage where maybe people can't deliver into the contract.
Speaker 4 (19:43):
There are lots of ways that markets can break and
go wrong. And I remember to this day that when
oil futures went negative.
Speaker 3 (19:50):
It was during COVID. I was sitting at home.
Speaker 4 (19:52):
I was trading oil futures and I bought oil futures
for negative prices.
Speaker 5 (19:57):
You were one of the one team made money.
Speaker 3 (19:59):
Yeah.
Speaker 4 (20:00):
My, Uh then what was that twenty twenty one? So yeah,
my then fourteen year old said to me, please, please please,
I want to buy negative priced futures contracts. And I said, well,
you have no way.
Speaker 3 (20:14):
Of taking delivery the oil.
Speaker 4 (20:17):
And he said, I will go to Cushing, Oklahoma and
figure out how to do it.
Speaker 5 (20:21):
You've really raised a son, daughter, son, son. You've really uh,
he's been learning.
Speaker 2 (20:29):
We have we have an episode about taking physical possession
of oil. I do not recommend. It. Turns out if
you keep it on your desk for long enough, it
evaporates into the atmosphere and poisons your colleagues.
Speaker 3 (20:39):
Yeah, anyway, a little bit of a tangent.
Speaker 4 (20:43):
So I think on the upwards rejectory, if there's tons
of demand, you know, that's something that commodity markets are
really good at dealing with. The price will go up
and more supply will come in, and I think that's
all good.
Speaker 3 (20:55):
On the downward side, you know.
Speaker 4 (20:57):
You can always just turn the GPUs off, so I
don't think they trade negative.
Speaker 5 (21:01):
How much of the volatility that do you when you
anticipate market volatility in the price of GPUs? How much
is that like embedded electricity costs, So when you buy compute, right,
you're buying the chip, but also the power, Like, how
much of that volatility will be the power?
Speaker 4 (21:17):
So the industry lingo that's used is total cost of ownership,
And you know what percentage of the total cost of
ownership is the power price? And for an H one hundred,
it's less than fifteen.
Speaker 2 (21:29):
Percent less than fifteen Okay, So GPU trading obviously one
of the things you're working on, but you're a busy
guy and you've got other stuff up your sleeve. What
are you doing in the realms of tokenized trading?
Speaker 4 (21:42):
So that is an area that we're super excited about,
and we've been thinking about this for a very long time.
So in twenty twelve, when we started talking about bitcoin
at Dow and there were a number of traders at
DORW that were very excited about.
Speaker 2 (21:58):
Bitcoin, you were very early into it. Twenty twelve was
still pretty.
Speaker 4 (22:01):
Early, very early, Yeah, So we were having these discussions
of why is this interesting. Is it interesting? What about
it is interesting? And we came away with the following thesis,
there's some small chance that bitcoin could be digital gold.
I don't know, you know, call it one percent. It's
kind of an interesting product, so we should probably make
markets in it. So we set up Cumberland as the
(22:23):
and you know, we didn't call it DRW because at
the time, everybody knew that anybody trading crypto was obviously
a crook, so so you know, we wanted to kind
of separate the brand a little bit. But you know,
the other thing was this idea that you could move
value instantaneously in a trustless ecosystem was super interesting to me,
(22:44):
and I said, wow, if you could do that in
traditional financial markets, that would make the market so much better,
so much more resilient, and so we should really figure
out how to do that. So we started a company called,
again not very creatively named Digital Asset Holdings, which created
the Canton blockchain. Initially, the Canton blockchain was a private,
(23:04):
permissioned chain, but last summer it actually became a public chain,
and that chain was designed specifically with tokenization of traditional
financial instruments in mind. So it has a couple of characteristics.
One is it has configurable privacy, and believe it or not,
for people who are in the finance business, they don't
(23:28):
want to broadcast to the entire world when they are
buying or selling something. I mean, obviously, if it's above
the reporting thresholds, you do. So that was kind of
a fundamental characteristic of this chain. It's different than Ethereum
or Solona or any of these other things where if
you tokenize something and put it on top and you
move it around, everybody sees it move around. So that's
(23:48):
kind of something we've been working on for quite a while.
Speaker 5 (23:52):
How big could this get? Like? Could it swallow everything?
Could you imagine a world in which, given any financial instrument,
do bond et cetera, that it all sort of ends
up on chin.
Speaker 3 (24:04):
Yeah, I think that everything will be on chain.
Speaker 5 (24:07):
Wow by when give us a year?
Speaker 3 (24:10):
No, I'm always way too early on this stuff.
Speaker 4 (24:12):
But I think in the next five years all of
these instruments will be on chain.
Speaker 5 (24:17):
Okay, that's good. Primarily we will have a live episode
in twenty we'll come back to Chicago, US that question.
Speaker 2 (24:23):
Yeah, is the ideal with tokenized assets also that you
could use that for collateral management and use it as
(24:45):
a way to move collateral.
Speaker 4 (24:48):
And so everybody's talking about moving to twenty four five
or twenty four to seven markets, and if you want
to do that, it's really important to be able to
move collateral twenty four five or twenty four seven and
move variation margin twenty four or five or twenty four seven.
Speaker 3 (25:06):
And so yes, that is a very important use case.
Speaker 5 (25:10):
So speaking of very exciting sexy topics in trading, right
after you, we're going to be speaking with Terrek Mansur
of Kelshi And so prediction markets are super hot. Where
are you at with them? Is dorw making markets in
any of these in any of the spaces right now?
Speaker 4 (25:27):
So a million years ago we actually made markets and
prediction markets. I think it was, I don't know, in
trade or something, yeah, and it never went anywhere.
Speaker 3 (25:37):
Nobody cared.
Speaker 4 (25:37):
And I always thought, you know, prediction markets should be
a thing everybody should care.
Speaker 3 (25:42):
But nobody did. And then Auger came out.
Speaker 4 (25:45):
I was like, oh, this is really cool, this is
going to take off, and nobody cared, and so it's it's.
Speaker 3 (25:49):
Taken a long time.
Speaker 4 (25:50):
So at this point we use it as a reference price,
you know, obviously during the election, it was super helpful
to use that as a gauge.
Speaker 2 (25:58):
Of So you were actually using that because you know,
we hear stories about institutional investors maybe finding prediction markets
useful perhaps, but you were looking at it.
Speaker 4 (26:07):
We were definitely looking at it. We were not using
it as a hedge. And it was funny. Shane messaged
me and said, hey, you know it's it's it's up
on Bloomberg now and I was like, oh, that's awesome, Shane.
Speaker 5 (26:19):
Shane Coplin from Yeah, that's right. Yeah, But currently, like
do you foresee, like are you going to enter not
either making markets on some of these exchanges, and would
you get into the sports contracts?
Speaker 3 (26:32):
I mean, so we're not here.
Speaker 4 (26:34):
I think it's highly likely that we'll start trading some
of the prediction markets. Some of our competitors already trade
in the sports markets pretty actively. We don't, so it's
not necessarily a natural fit. But I don't have like
a religious opposition to it.
Speaker 2 (26:50):
Would there be different considerations for trading in a prediction
market versus a traditional financial asset? Are there different things
you have to think about, either in terms of like
price seeing the trade or maybe risk management.
Speaker 3 (27:02):
Well, I think it depends on what the prediction market is.
Speaker 4 (27:04):
I mean, if you're trading a prediction market on I
don't know whether somebody will throw a rubber object onto
a WNBA court, then I mean that's something that people
in the audience can control. And so it seems like
providing liquidity in that you would be at a disadvantage.
Speaker 2 (27:26):
That was a very particular example. By the way, I
was going to go with Taylor Swift getting married.
Speaker 3 (27:32):
But you went with that one.
Speaker 5 (27:34):
Well, these are markets that people can directly intervene.
Speaker 3 (27:38):
Directly as opposed to, for instance, their.
Speaker 5 (27:42):
Own yeah, antisocial behavior, that's.
Speaker 3 (27:45):
Right, and.
Speaker 4 (27:47):
As opposed to will the Fed cut twenty five or
fifty or stay on hold? I mean you can trade
that in sofur, you can trade that in in the
FED funds futures. There some binaries you can trade. And
so the prediction market version of that is totally fits
in with the risk that we already trade.
Speaker 5 (28:09):
So we mentioned in the intro there's gonna be this
big meeting in DC next week, and we just happened
to sort of catch a bunch of the participants. When
you look at the landscape for these new futures, platforms
because that's what they are, right. The CME has regulation
been part of their dominance. Has regulation made it harder
(28:30):
for other entrants to cut into CME margins or volumes.
So I'm trying to ask questions that are going to
create some tension around the table.
Speaker 3 (28:40):
Next week, Yeah still here, I mean, oh yeah, you.
Speaker 2 (28:43):
Should hear what Terry said about Howard Lutnick.
Speaker 5 (28:47):
It'll be on the podcast.
Speaker 3 (28:48):
I'm sure I can.
Speaker 4 (28:49):
I could probably repeat it without having heard it. So
once you have a liquid market in something, it becomes
a natural monopoly. It's very hard to move that to
a different venue. It's happened before. I was living in
London in the mid nineties and the bond futures were
(29:11):
on the floor of the Life. It was this huge
trading pit with a bunch of guys pushing and shoving,
and over the course of twelve months, the DTV now
called the UX was able to move the entire bond
futures complex onto the computer on a different exchange. Now,
I mean they gave hefty incentives to people. I think
(29:34):
they went to all the German banks and they said,
don't you dare trade on Life anymore. So it's possible,
but I think that these things are generally I don't
think that it's really a regulatory issue that causes them
to be sticky. I think it's more just kind of
a natural state of affairs.
Speaker 2 (29:52):
Network effect, I guess. So our theme for this evening
is obviously the future of trading, and one of the
things that seems to be happening is the sort of
intermingling of professional and retail trading. And we again talked
about that with Terry. I'm sure we're about to talk
about it with the Calhi CEO. But from your perspective,
and again, you started this career back when I don't
(30:14):
think there were any retail traders doing day trading. Really,
how has that changed the way you think about trading?
And can you envision a future where I don't know,
AI fires all of us and we're all going to
be just day trading from home as an insurance policy,
Robinhood is really a full employment program.
Speaker 5 (30:31):
Maybe for us workers.
Speaker 4 (30:34):
Yeah, So, I mean that is a thesis that I
have heard, is that what's happening is a bunch of
relatively successful people are losing their jobs and they're retiring,
but in their retirement they decide to just manage their
portfolios on robinhood And so there's this surge in trading
(30:56):
activity that wouldn't have happened ten years ago, and it's
only going to grow from here.
Speaker 3 (31:03):
And I don't know, maybe that's right.
Speaker 5 (31:05):
It feels to me like culturally, because you're talking about, right,
why have prediction markets taken off when they've been around
for over twenty years. I think I first heard about
them in like two thousand and two or two thousand
and three. They've suddenly taken off. There was never a
bright line between what's gambling and what's sort of hedging
or what's trading, but there's clearly whatever line that is
just feels like it's completely collapsing. Is this good? Do
(31:28):
you have an opinion? Like should? Is there is? And
I don't know if any of our opinions matter on
the question, because it feels like culturally we're entering that
world where everything will be tradeable on any app and
there's you know, you're gonna see a price for gold
futures right next to one day, the line on a
football match, et cetera.
Speaker 1 (31:46):
Is it?
Speaker 5 (31:47):
Do we want this world?
Speaker 3 (31:49):
So?
Speaker 4 (31:50):
I don't think there's anything particularly wrong with it, But
I am a little bit confused about whether prediction markets
and sports are actually consistent with what the Commodity Exchange
Act says is permissible.
Speaker 3 (32:08):
And so I know that your next your.
Speaker 4 (32:11):
Next guest is perfect is benefits from his ability to
list these contracts. And and I don't know if you know,
the CFTC is just kind of asleep, and I know
they're kind of understaffed now or uh or or maybe
they've decided that actually these are economically important transactions that
(32:36):
are consistent with the CEA.
Speaker 3 (32:37):
It's unclear to me.
Speaker 2 (32:39):
All Right, well, we're going to have to leave it there.
But Don Wilson, founder and CEO of dr W, thank
you so much for being here.
Speaker 3 (32:47):
Really appreciated, Thank you for having me.
Speaker 2 (33:02):
That was our conversation with DRW founder and CEO Don Wilson,
recorded live on stage in Chicago. I'm Tracy Alloway. You
can follow me at Tracy Alloway.
Speaker 5 (33:13):
And I'm Jill Wisenthal. You can follow me at the Stalwart.
Follow our producers Carmen Rodriguez at Carmen armand dash Ol
Bennett at Dashbot and kil Brooks at Kilbrooks. Form more
odd lots content go to Bloomberg dot com slash odd
Lots with the daily newsletter and all of our episodes.
You can chat about all of these topics twenty four
to seven in our discord Discord dot gg slash online.
Speaker 2 (33:33):
And if you enjoy odd Lots, if you like it
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Joe Weisenthal
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