Hello and welcome to What Goes Up, a weekly markets podcast.
My name is Mike Regan, I'm a senior editor at Bloomberg.
And I'm Adana Heierk, a cross-acid reporter with Bloomberg.
And this week on the show, well, if you've been anywhere near the internet in the last
few months, you've probably read a poem or bits of a movie script or maybe even some
dad jokes that were written by a, well, a computer actually.
The experimental chat bot, ChatGPT, is taking the world by storm since its launch in November,
triggering a million questions about how this type of technology can disrupt various industries
and fueling a fresh wave of interest in how artificial intelligence can be used by investors.
We're going to get into it with the head of research at a company that's been using AI
for a few years now to pick stocks for an almost $2 billion ETF.
But first, Vildana, I try to go to ChatGPT.
Like everyone else, I've fallen into the hype of this ChatGPT and I went on trying to get
it to write us an intro to the podcast, but it's too busy.
There are too many people using it that they just turn me down.
Yeah, same. I tried for you also so that we can get a nice, fun intro from the robot
and it didn't work.
And I even tried to trick it.
I said, I have a very simple request and I'm on deadline.
Please, just can you help me out?
No, no love.
That didn't work?
No luck.
No, the robot doesn't like me.
I wonder the robot must have a PR rep that maybe we could finagle.
Hit up to complain.
Yeah.
It's probably a robot PR rep.
I don't know.
But a PR rep that is also a robot.
It's also a robot.
Yeah.
They'll just tell me to go to hell.
Is that what most of them say to you?
Some of them.
All right.
Well, you tell me which ones.
I'll get back to them.
But I do think our guest is the perfect guest to unpack this topic.
So why don't you bring him in?
Yeah, he is.
It's Matt Bartolini.
He's the head of Spider America's research at Stage Street Global Advisors.
And Matt, thanks so much for joining us.
Yeah, thank you for having me.
So we'll get into one of your AI ETFs in a bit, but maybe just to start, you can give
us sort of your journey into working with ETFs and what you do.
Yeah, sure.
So my journey in ETFs is working at Stage Street Bank, essentially since the middle
of 2000s and worked my way up throughout the organization on the county teams, portfolio
management teams, and then landed within the ETF teams, helping to conduct some of the
research on our products, different portfolio construction topics, investment thesis, market
outlooks, and market commentaries.
And that's really where my job is now at the head of Spider America's research.
Our job is to help make sense of a complex world by using data-driven insights.
And we write market commentaries, market outlooks, provides some portfolio of instruction discussions
to end advisors.
And hopefully help them select the right investment choice for them.
And if that's a spider ETF, then I think that's all great.
But in some cases, it doesn't.
And that's sort of how we operate, trying to be fair and balanced.
Yep.
And AI is obviously one of your areas of research, Matt.
So I'm curious, you know, this chat-GPT thing to me, and I think to a lot of people just
can't, it seemed to have come out of nowhere.
You know, it launched in November.
And you know, granted, I don't follow the space that closely.
But I think for a lot of people, it was just sort of dumb-founding how good this thing
is right at launch.
You know, to me, I would have expected sort of to see a cruder version of this that wasn't
quite as impressive.
But how did you see it?
Like, were you sort of as surprised as everyone else, or does your sort of research into AI,
had it led you to kind of know this type of thing was possible and in the pipeline?
Yeah, so a lot of the AI work that we've done is within sort of portfolio construction
and index selection on some of our funds.
So we were aware of the ability to use things like natural language processing, predictive
text, but also even just in our daily lives, I think some of the functions of chat-GPT,
we've probably just been benefiting from just in very small morsels.
You know, whether that is, you know, auto-correcting your text or the predictive text nature within
your iPhone of what you might say next, like that's sort of the same idea.
Or even, you know, when we use the Bloomberg terminal, we asked the help desk, we sometimes
get a very automated response back.
That's all sort of pieces of it.
The first time I saw it, you know, we were sort of playing around with it, you know,
write us a blog post about the benefits of ETFs.
And it got it probably 80% correct, you know, how we would want to structure the argument.
And I think that's sort of where chat-GPT is, is that it kind of gives you about an 80%.
And I was sort of joking with, you know, some of my colleagues who have older kids, that,
you know, chat-GPT would probably be a B minus student if it only ever turned in its homework.
Because that's kind of the surface level it gets.
And I have a friend who's a professor at a college, and they've actually started to
work on how to figure out, you know, essays and reports that are written through AI.
And the big thing is you've got to look at the nuance.
And chat-GPT doesn't really understand the complexity of nuances, particularly for topics
like ETFs, where there actually is a lot of operational nuance.
You know, as a B minus student myself, that explains why I was so impressed, Phil Donah,
I think.
If you are a student right now, you could use it to help boost your grades a little bit.
Yeah.
Well, that's, I think, the fear of everyone has.
I have a nine-year-old son who had to do a penguin project, and he, instead of looking
in a book, he yelled out to Alexa, you know, how fast you to penguin swim.
And I had to tell him that he can't do that anymore.
So this is a new reality that we're all living in.
I know.
How fast can penguins swim?
I don't know.
I don't know what to ask Alexa.
But, oh, don't ask Alexa.
Well, no, my kids, it's the same thing.
They're sitting there doing their homework, and I hear them, y'all, hey, Google, what's,
you know, nine times 37.
And, I mean, in some ways, it's just a calculator, and I think educators are going to have to
get used to it and allow it to some degree.
I don't know.
It's such a strange new world.
But Matt, talk to us about the Spider-Kensho new economy ZTF, which actually has been using
AI to pick stocks.
You know, we're sort of the laymen among us out there.
How exactly does AI help in this stock-picking effort?
Yeah, so the artificial intelligence behind it is natural language processing.
And this is run by the index provider S&P.
It actually started with a firm Kencho.
There was a small startup that was incubated out of Goldman Sachs.
S&P bought that firm and all of the IPL on with it, and that's our index provider for
the fund.
And the NLP or natural language processing, what it does is it scans through prospectus
and other regulatory filings from companies because you want to start with a strong source.
Regulatory filings have to be quite prescriptive.
And if you make falsehoods about that, there's penalties, right?
So it scans through regulatory documents, searching for key terms to identify how these
firms' material operations correlate back to areas of innovation, whether it's like enterprise
collaboration, clean energy, advanced transport systems, drones.
So it scans through all of these regulatory documents, looking for the frequency of a
term used, but also the words around it.
So a company saying that drone technology is incredibly important for the future growth
of our business.
That really shows some emphasis towards that type of innovation.
So that will be scanned and recorded and classified appropriately into 25 different areas of innovation.
And then from there, stocks are weighted in more of a modified, equal-weighted structure
where core firms to a specific innovation are overweighted to non-core firms.
So basically, the way we sort of describe it is that the AI process selects the stocks,
and then there's a quantitative weighty methodology to weight the stocks.
But the reason why we went down this path of using AI is that we wanted something forward-looking,
something dynamic, because back in 2018, we understood that in the ETF world, there weren't
a lot of strategies that were this forward-looking, innovative type paradigm.
A lot of it was based on revenue, and revenue is what has already been realized.
That is a backward-looking approach, and we wanted something that was more dynamic and
a forward-looking approach, and the AI process was able to deliver that for us.
Okay, so before you tell us more about that, I am interested in sort of the mechanics.
So once the AI runs through and chooses with these companies that it thinks is the right
word, but that it chooses as fitting the right criteria, do you then have a human go through
the results and say, okay, this actually sounds pretty good, or maybe we don't want to have
XYZ company as part of this portfolio.
So within the index methodology, there is sort of a human control element to it, most
like a quality control.
So for instance, if a company is classified as innovating within clean energy, they use
the term, wind and solar quite significantly, it says part of the material operations, but
when it comes down to it, there is a check and balance from the index committee that says,
okay, does firm XYZ offer a product and service in this category, or they just some sort
of, this is probably a bad term, but some sort of shell company that doesn't actually provide
a product or service, they just, yeah, this is what they do, they just say something that
doesn't correlate back to their actual products and services.
So that's where there's a little bit of a manual quality check to ensure that these
firms are actually engaged in these areas of innovation and are not just talking about
it sort of, you know, extemporaneously.
And the other thing is too, that helps in terms of, you know, get, say, perfect, you
know, we have a champagne problem that this fund becomes, you know, 190 billion dollars
and someone wants to get into it and they just use the word drone a thousand times to
game it, that helps, right, that sort of open manual override sort of quality check.
What I find fascinating about it, something like 560 holdings, you know, so it's not a
very concentrated fund.
And, you know, when you're looking for innovative sort of startup type of companies, a lot of
times that means really small, even maybe micro cap companies that you have to dig through,
which are not typically very heavily followed by, you know, the Wall Street analyst class,
just by definition, you know, if there's thousands of them.
And this really surprised me, and you know what you say, about 48% of the holdings have
less than 10 analysts covering the stock, is that almost a benefit for this type of strategy
that it helps you sort of find these hidden gems that are maybe being completely overlooked
by the messes out there?
Yeah, I mean, AI at its heart is to help increase efficiency and productivity.
And what this does is it allows us to cover the uncovered.
So if you're using an analyst recommendations, analysts can only cover so many stocks within
a given day.
And there can be some firms that are quite innovative, that are, you know, performing
and producing some really interesting things within our economy, you know, whether it's
things within advanced healthcare like wearables, that aren't really covered by Wall Street
analysts because they might be smaller capitalization securities.
And we sort of just know this even from like traditional finance that the majority of analysts
recommendations are in that large cap space.
And then small caps and make caps sort of do not get as much notoriety or coverage.
And AI basically is one way to solve that problem to give you a deeper breadth of opportunities
and really broaden your scope of companies that may be considered innovative.
So I want to give a shout out to Katie Greifield and Sam Potter on the Crosshaster team at Bloomberg
because they had this really fascinating story that said something like we asked chat GPT
to create an ETF for us.
And here's the results.
And actually it had done a really good job putting something together.
And you were part of this story and Katie and I were chatting about it afterwards.
And she said, Matt had all these insights into the composition aspect of, because you guys
have your own AI ETF.
And I do wonder about that.
Like, is the power of the AI being able to create an ETF?
Is the power, does it lie in the sheer amount of work that it can do?
Whereas you might not be able to have like a team of humans combing through so many different
things to the point where they get to an ETF that has 560 components.
Yeah, it's all about creating efficiencies and being able to capture undiscovered or
unrepresented areas within the equity markets.
Even just looking in core portfolios, disruption happens further down the cap spectrum.
And that's why using something that is able to explore data sets that are really unstructured.
Because revenue, profiles, balance sheets, those are more structured data sets.
But using textual language processing to identify firms based on what their material operations
are saying is one way to help classify them into these areas of innovation.
And I think one of the things about this fund in particular is that we do understand that
it is not innovation does not just benefit the pure place is that the ecosystem around
it can benefit the whole idea during the gold rush of the 1800s of it would rather mind
for gold or sell the pickaxes and the tents to go along with it.
You probably had a pretty good business model of your selling a lot of pickaxes in the 1800s.
And that's sort of the idea here is the ecosystem is also beneficial.
And how do you identify that ecosystem?
You affirm like Nividia, for example, they make all of the sensory technology within autonomous
vehicles.
That's a supplier to that ecosystem.
And as autonomous vehicles take off, they're going to benefit as well.
So using AI to detect that can really help create a really targeted but diversified portfolio
of innovative stocks.
And basically this ETF has many more components than it would if a team of humans was putting
it together, right?
Yeah, so the statistic there of over 10 analysts, 48%.
So let's just say we used that as an example.
It needs to at least be covered by 10 analysts.
Well, right then and there we lose half the portfolio.
And 10 is not a big number.
So if we were to take more human based approach to it, it would be far more concentrated
of a portfolio.
And that's what we see with the other broad innovation ETFs out there is that they're
far more concentrated and they're also far more geared towards large cap security.
So you do not get the differentiation that you would want in something that is supposed
to be innovative and not largely represented within core portfolios.
Right.
When this fund was launched, I guess it was three or four years ago, growth stocks, innovative,
disruptive stocks were the hottest things going in the market and the fund did great.
2019 up 37%, 2020 up 61% up about 4%, 21.
Then obviously last year was kind of the rug pull out from under growth and innovation
down 32%.
So I'm wondering, is there a way to layer AI on top of a fund like this to allow to sort
of shift to a value strategy or to kind of sniff out the market cycle into what's kind
of the new hot factor to get into?
I know that's not the goal of this fund, but I wonder if you think about that.
Is there a way to not only pick the individual stocks under a certain theme or strategy like
this, but to also have that strategy sort of evolve over time and try to isolate the
upcoming market cycle and what's the leadership's going to be in case growth does have a down
draft like this?
So that's when that becomes just market timing, so to speak.
Right.
You're now doing some form of factor rotation.
I think you could perhaps create more of a style of neutral innovative portfolio, but
that becomes much harder because then you're going to have in an optimization framework,
the optimizer is going to be working really hard to mitigate any of that small cap bias.
So then you're just going to basically look like a large cap growth tech exposure.
So you then it's always this trade off of like, do I want to mitigate some of these implicit
style factors and get sort of close up that tracking risk to traditional benchmarks or
maintain the purity of what we're trying to do of innovative exposures.
So you always try to find that balance and if you try to create more style neutral or
something that is less impacted by market cyclical factors, then you're going to lose
some of that purity of your intended focus.
And I think when we are having discussions around performance, we always just go back
to attribution and we will use fundamental risk models.
And if we look at it since inception, industry and stock selection effects relative to the
S&P 1500 for example, industry and stock selection effects have been positive to the
fund's return.
It has been additive to performance.
The industry part is interesting because there are some industries like semiconductor software,
you sort of wearable technologies within healthcare.
Those industries are going to be more innovative than say some firms within like staples, you're
sort of consumer goods products.
So industry effects is byproduct of the focus of innovation.
Stock selection effects is byproduct of the AI selection methodology and then the weighting
process.
The detractors of returns have been style factors, namely higher volatility, lower quality and
high growth, you know, since inception.
But those factors are implicit because it's not what we're seeking to obtain, but they're
also cyclical.
So high volatility, low quality, high growth were being famously rewarded starting in 2020
through sort of mid 2021.
So that was a tailwind to returns back then.
So that's how we always like to frame the performance conversation is breaking those three
components out, noting that the style components are going to be cyclical and move in and out
based on market directions.
I'm always curious how ETF issuers decide on a theme or a topic or, you know, putting
an ETF together.
So a couple of years ago was AI something that you guys when you got together were thinking
was going to be a big deal in the coming years?
Or is it sort of, which I think this happens a lot in the ETF space?
Let's just put it out there, give it a try and see what happens.
So it's definitely not the latter within our firm.
We're definitely not going to say that.
Yeah, we're never going to be like, hey, this is a hot dot.
Let's throw it out there and see if it works.
That's just not what we do.
With respect to these funds, we have a pretty strong heritage within sector and industry
investing.
And we know that there are thematic investors out there.
We see it all the time within our traditional industry suite.
Someone that wants to play a rally in oil stocks will go by XOP, our oil and gas ETF.
And that's a thematic investor.
We knew that thematic investing was going to be on the rise because there's some thematics
like autonomous vehicles or cyber security or clean energy that are hard to gain exposure
to under a traditional GICS framework because some of these firms operate across GICS sectors.
Clean energy is a perfect example.
You have firms within the legacy energy sector, the utility sector, industrial sector, technology
sector.
So you want to go across the sector.
So we were like, well, how do you go about doing this?
Again, we wanted something that was forward looking.
We knew that revenue was backward looking.
So this is how we landed on firm like Kencho and then later obviously S&P Kencho as the
combined entity of having a really unique value proposition of using natural language
processing to detect firms that are listing out these innovative services or innovative
corporate designs as part of their material operations.
So that was really the impetus for it.
And I think I remember one instance is I think it was obviously before we launched it was
probably 2015, 2016 timeframe when we were really starting to kick the tires on this.
The Pokemon augmented reality iPhone app was just really, really popular.
I remember playing softball and seeing a bunch of people hanging out by the left field tree.
I mean, no idea why.
And someone had put a Pokemon stand.
I don't play this, so I have no idea.
And I remember talking to folks internally like, you know, it would be really interesting
if you could have something that focused on these type of firms, you know, innovating
within virtual reality and augmented reality coincided at the same time as we're kicking
the tires on this process.
And that's kind of the idea now owning 20 stocks and augmented reality is that a, you
know, pure play investment thesis for the long term, probably not, but having it part
of a more diversified innovative exposure probably is and that's sort of where we ended
up.
They were looking for, for, for Pokeballs, I think, right?
Yeah.
Some rare Pokemon character, something, I don't know.
I never played it either, but it.
Me neither, but I remember being a big kid.
I do remember people wandering around staring at their phones, bumping into each other.
It was, I think it kind of came and went though, which is weird, you know, it's, it's,
I almost thought that type of gaming would have caught on more, you know, that using
that location element of your phone more, but who knows, maybe, maybe something is coming.
I'm curious just if you can give us kind of a 30,000 foot view of how you're thinking
about AI now, like I said at the beginning, you know, this chat GPT does seem to, to sort
of us lay them in like a big innovation, like suddenly the innovation in AI has accelerated
faster than I think people realized.
Tell me if you agree with that or disagree, but also in general, where do you see AI?
What industries do you see being most susceptible to disruption from AI going forward?
Yeah.
I mean, I think for the most part, you know, AI investment, I think is projected to increase
is something you're, you know, respected like 115% over the next three years.
That like the statistics around AI investment is astounding.
You see it every day, big numbers, big percentages.
I think from an industry perspective, something that like paralegal services could be something
like that.
Research documentation, we're able to scan something very quickly.
And I think you can even see that in some of like the McKinsey studies that you talk about
how, you know, upwards of 25% of the workforce will need to change jobs as a result of advances
in artificial intelligence.
Legal requests are likely to be one of those because, you know, going and pulling all of
the specific, you know, court cases over the last 50 years related to one topic.
Yeah, that could be done quite easily through natural language processing, you know, using
predictive text, searching for text.
I think that's just one, one of those.
Just even within my team, we're trying to use, you know, some form of AI to help, you know,
write weekly notes for us.
That's something that, you know, since I put out in our plans for this year, it's just,
you know, again, creating more efficiencies.
And some of the weekly notes are more about, you know, fund flows and market performance
and, you know, having something easily done quicker that also can be helped from a compliance
perspective too, because everything's rules-based.
But yeah, the legal one is always one that comes to mind, any sort of document search,
document retrieval, those, that's where AI think is some of the more low-leaning fruits.
It doesn't sound as flashy, but, you know, that's, that's one of them.
Yeah.
Well, if you're a law firm, you're certainly going to save a boatload of money.
If you can, you know, hire fewer paralegals to do all that, that sort of legwork.
Yeah.
But I think podcast hosts are totally fine.
Oh boy.
Right.
Don't shank us.
Yeah.
Don't shank us.
I don't know.
The chat JBT wrote some pretty good dad jokes.
So I'm feeling threatened.
You might be out of that job.
Yeah.
Well, you can talk about this job for now.
Who would have thought that, you know, they would create a young Luke Skywalker in the
most recent or last seasons of the Mandalorian, you know, all of a sudden they can use, you
know, AI and some other stuff to create, you know, different voice structures.
Who knows?
And podcast hosts have a good chance of outlasting it for at least the next 20 years.
Fun podcast hosts.
Maybe just to bring it back to the market.
I'm wondering like which sectors maybe can stand to benefit the most from AI.
It's sort of tough because I think, you know, obviously within technology, a law firm for
our already starting to use AI and their processes, I would probably say within the industrial
sector, sort of supply chain logistics.
They're sort of, you know, consumer oriented areas in terms of consumer service.
So you can obviously already see it with Amazon and some of the way they interact with consumers
and using AI.
I would say probably those three would probably be the biggest around industrials.
So you can supply chain and then consumer and then tech is just going to benefit because
they're the ones who are creating the innovation.
Yeah.
You know, Matt, I know, so we've been talking a lot about AI, which is one of your focuses,
but not the only one.
So I'm curious if you can just give us kind of the state of play in the ETF market as
a whole.
What kind of flows are you seeing this year?
You know, market obviously off to this super strong start growth and innovation doing well
again.
Where are you seeing the flows?
Are people chasing that sort of rebound in innovation and growth or they still going
in a value?
What's what are the flows look like?
Yeah.
So thematic ETFs last year in 2022 actually had outflows and they had outflows for the
first time since 2013.
Now, KOMP actually had inflows, a little bit of a divergence there, maybe speaking to our
efforts, but a lot of it was for performance related.
Roughly only 13% of all thematic ETFs on the ETF industry beat the S&P 500 last year.
That's actually been different this year.
We're around 86% of thematic ETFs are beating the S&P 500, yet at the end of January, flows
were still negative for the broader category.
So ETF investors are still a little skeptical, which I think is not too surprising given the
Dow performance results from last year.
But, you know, again, within our suite, we've actually seen inflows, which, you know, perhaps
speaks to the efficacy of the product type, the structure, the rationale, and the investor
motivation.
Matt, we can't let you go without asking you about SPY, which is probably the best known
ETF out there.
And it just turned 30 years old.
So happy birthday to SPY.
I know you guys threw it a couple birthday parties.
But can you maybe tell us about this?
Like, it's been around for 30 years now.
I think we had a story on Bloomberg saying, you know, it's held the crown for so long,
but can it continue to hold on to this sort of the crown of being the most prominent
and well-known ETF?
So maybe just tell us about SPY a little bit, just because we have you here and you're the
sort of preeminent figure on talking about this.
Yeah.
So I mean, SPY, like I said, you know, without SPY, there's a lot, there's no KMP.
There's not a lot of other ETFs out there.
It started the industry.
The infrastructure that it has is the reason why we do have ETFs, the ability to do in
kind of creation redemption, and it's been time tested throughout those past 30 years.
And what I think this year taught us, SPY had a record amount of users come to that product
in terms of it had $9.5 trillion of trading volume.
It had a record amount of overall shares traded.
And you know, roughly, is 25% of all trading volume in ETFs was on, was on SPY.
So I think that's just really, you know, a good indicator of how much usage it still
gets even though it's 30 years after its inception.
And it was interesting when we were talking about CHET-GPT earlier, that when you do ask
CHET-GPT, what is the best ETF?
It does come back SPY.
And I think it's with the reason, you know, it's the biggest, it's the most liquid, it's
the longest trajectory.
And for that reason, CHET-GPT recognizes being one of the better ETFs out in the marketplace.
Bringing it full circle there.
I like it.
Oh, Matt Bartolini is the head of Spiders America's research at State Street Global Advisors.
Great stuff.
We really appreciate your time.
We cannot let you go though.
So we hear the craziest thing you've seen in markets this week.
Viltata, as always, why don't you get us started?
Okay, so mine is in crypto this week.
And it's this report by Chainalysis, which I don't know if you don't know about Chainalysis,
they sort of do like forensics, basically, of the blockchain and within the crypto space.
So it's interesting that such a report will come from a crypto company specifically.
But basically they found that thieves stole a record $3.8 billion worth of cryptocurrency
last year.
And at North Korea itself, it's estimated still $1.7 billion in 2022, up from 400 million
in the year prior, which is just crazy amounts of money.
It's mine boggling.
People in crypto don't like to talk about this aspect of crypto, but then you have this
crypto company actually coming out with this report talking about.
I wonder if that's at current market prices or it's probably at the price of the assets
when they were stolen, I would imagine.
I would think so.
Yeah, I would think so.
But I mean, yeah, but even if you think about where Bitcoin was, yeah, a couple months
ago versus now.
I guess.
And they probably don't answer this in that report, but I wonder how much of that is sort
of trapped.
You have to steal some crypto and it's stuck in a while and everyone knows it's there.
And it's sometimes hard to to launder that.
I'd be curious to see how much of that actually, you know, these thieves are enjoying the benefits
of that.
Yeah, there are some companies, some crypto, like researchers that look into when sizable
sums of coins are moved or like 19,000 coins that hadn't moved in 10 years or so.
Right.
That's like that, which are really interesting.
And that's when the, but you never know where they're going or what's happening.
That's when the 30s always catch them too is the minute you try to move it into something
else.
Yeah, exactly.
That's pretty good ones.
How about you about you see anything crazy recently?
I mean, actually, one of the craziest things was the market reaction to the most recent
Federal Reserve rate hike.
I didn't think it would be that overwhelmingly positive.
I had Powell was still pretty persistent on the need to hike rates.
And right now you have a two year yield that is roughly 50 basis points below with the
Fed funds is that doesn't really happen.
I think that's pretty crazy is that borrowing money two years out is cheaper than overnight
rates at the Federal Reserve.
So I think that's I'd be interested to see what happens in the ensuing days.
If that course corrects.
Yeah, that is a it is a bizarre upside down world.
And I don't think the market reaction was anything what he had intended.
I've joked that Palps should probably have a Bloomberg terminal in front of them when
he's giving the press conference to to amend his answers to have the desired effect because
I don't think I don't think that's what he was after that day.
We should send him one.
Yeah, I bet he well he I'm assuming he has one.
I know I think he has one yet.
But he's just got to start bringing it with him.
If he does it.
All right, I'll give you mine as a Vellana as I've you know, I'm not really a car guy.
I'm more of a pedestrian, but I really what happened to those four pourshes that you know,
they're they're still imaginary.
They're still imaginary.
I am into when people pay ridiculous prices for collectable items, as you know, though.
So the story is courtesy of CNN.
So if you ever heard of the car company Bugatti, they make these like hot rod supercars, they
call them.
Yeah, I heard you have two of them.
Yes, yes.
Matchbox size.
But so Bugatti apparently is transitioning to electric.
They're going to go hybrid first, but they're done making strictly gas powered cars.
So they recently produced the last pure gasoline powered car they're ever going to make.
It's a I'm probably going to butcher this pronunciation.
The Bugatti, she run profalee.
I believe I didn't take French, but something like that.
So what up for auction?
Instead of just selling it, they put it up for auctions with a Sotheby's, I believe.
I'm just going to tell you what it went for on auction.
It's boring to name the price.
10.7 million this car went up million brand new car center record for the highest priced
new car sold at auction.
But what I'm going to make you guys square off against each other in our game show is
what do you think the max speed is that this vehicle is capable of reaching?
The fastest it can go in miles per hour for a 10.7 million dollar car.
How fast do you think you get to go in that car when you floor it?
It's only fair if we can name it in kilometers.
All right.
Well, feel free to do that, but you need to translate it to miles for me.
Oh my gosh.
I'm guessing it's not as high, but I know nothing about cars.
Am I going first?
There's Matt going first.
I think you go first.
Fine.
I'm going to go with 260.
260 miles an hour.
I don't know.
Is that a lot?
That's a lot.
That's way too much.
That's like a flame.
Yeah.
I would say I'd probably like 215.
215.
Oh my gosh.
I think we have our first tie in the price is precise.
236.
Wow.
So you guys are pretty close.
Hello, traditional rules.
You went over, Vildontas.
I think we got to give it to Matt.
I went over.
It's fine.
The guests can win.
That's fine.
236 miles an hour.
Price is right.
That's right.
Here.
Price is precise rules.
Don't get our lawyers involved.
This is called a price is precise.
Yeah.
Here is the crazier thing though, is that's not the fastest car Bugatti's ever sold.
The fastest could go 300 miles an hour.
They say, quote, in theory, Adam, not sure everyone, anyone's ever managed to get it
up to 300.
I don't know if you could.
Tom Cruise would if you gave him a chance for one of his movies.
I'm sure he could.
He probably had several of these, but I'm not sure if you could theoretically, if you
could drive a car 300 miles an hour, I feel like it would take off like a rocket ship
at that point.
I would, my heart would burst from it.
Like I'd be so scared.
You definitely have to live.
If you're driving that fast, you got to listen to your podcasts at double speed, I think.
So if we have any Bugatti drivers out there, allow them to double speed us.
Two X.
Two X.
Yeah.
Pretty good though.
You guys are both in the ballpark.
I'm not sure what I would have guessed.
I'm not sure if I would have gone over 200.
It just seems insane to drive over 200 miles an hour.
Anyway.
You don't go 200 miles an hour in the New Jersey turnpike?
Oh, New Jersey transit I do.
Yeah.
That's the other train.
Oh, transit.
Different.
When we're late.
Anyway, Matt Bartolini from State Street Global Advisors is real honor to be able to
pick your brain on all these topics.
Wish you all the best and hopefully you'll come back and talk to us again someday.
Yeah.
Thanks, Cass.
Thanks, Matt.
What goes up will be back next week until then you can find us on the Bloomberg terminal,
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See you next time.
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Thank you.
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