October 1, 2025 • 20 mins
In this episode of Lead-Lag Live, I sit down with Kai Wu, Founder and CIO of Sparkline Capital, to break down the AI-driven capital cycle reshaping markets.
From Nvidia’s record-shattering deals to hyperscalers pouring trillions into infrastructure, Kai explains why today’s AI boom echoes past capital cycles—and why investors may be missing the real risk.
In this episode:
– Why mega-cap tech is shifting from asset-light to asset-heavy
– The historic link between rapid asset growth and underperformance
– Why concentration in the “Magnificent Seven” is an overlooked AI risk
– Lessons from the dot-com and railroad booms that apply to AI today
– How to position across the full AI adoption cycle
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
SPEAKER_01 (00:00):
These firms are starting to take on more debt,
you know, often in the forms ofthese kind of off-balance sheet
SPVs, which are a littleconcerning.
And then you mentioned in thevery beginning, kind of the
circularity around thecross-holdings of firms
investing in other firms toinvest in other firms to buy the
chips, which is, of course,something we saw in the dot-com
boom, which was, you know, um,you know, indicative of
shenanigans.
SPEAKER_00 (00:32):
I'm your host, Melanie Schaefer.
Welcome to Lead Lag Live.
Now, AI stocks are writing aseismic shift in capital flows.
Nvidia alone cinched a$100billion deal with OpenAI,
sending markets into overdrive.
Multiply that by the cascadingripple effects through
infrastructure, software, andchipmakers, and what felt like
(00:54):
hype is now shapingmulti-trillion dollar
valuations.
My guest today is Kai Wu,founder and CIO of Sparkline
Capital.
Sparkline Capital is aninvestment management firm
applying a cutting-edge machinelearning and computing to
uncover alpha in large,unstructured data sets.
Kai, thank you so much forjoining me today.
(01:17):
It's great to be here.
So let's start with the bigpicture.
Um hyperscalers are plowingthrough hundreds of billions or
are plowing hundreds of billionsinto AI infrastructure.
What do you what do you thinkthese capital flows tell you
about where the next leg of theopportunity could lie and what
risks are you watching?
SPEAKER_01 (01:34):
Yeah, I mean, this is something I'm watching very
closely.
Um, you know, as we all know,over the past 10, 15, maybe 20
years, the entire stock markethas been driven by the
performance of the so-calledmagnificent seven stocks.
Um, you know, Google, Apple,Nvidia, Microsoft, these guys.
Um, and, you know, really theirsuccess has been, at least to
(01:54):
me, um, on the back of theirbusiness models being in kind of
asset-light sectors, right?
So think of Google Search asthis perfect business that just
throws off cash and doesn'trequire too much capital to keep
afloat.
Um, you know, my my work is, myresearch is primarily in the
field of intangible assets.
Um, trying to understand howitems like brand equity, network
(02:16):
effects, human capital, andintellectual property allow
firms like these MagnificentSeven to earn supernormal
profits.
And, you know, for a long time,these companies have really been
the poster child of asset-likebusiness models, um, being able
to just really dominate in termsof um, you know, earnings and
profitability, and hence, youknow, the share prices have
(02:37):
followed.
Um, and to a large extent,they've become a significant
component of the stock market,right?
I think about a third or morenow of the SP 500 is now um, you
know, held up just by theseseven stocks.
And so what I've been watchingclosely is this transition away
from the model that's made themso successful, right?
What we've what we've seen isthese companies now set to
(02:57):
invest, you know, trillions ofdollars into AI data centers.
And that's you know, thesephysical infrastructure required
to power AI models, primarilyGPUs and chips, but also the you
know, physical data centers andum, you know, power and all this
other infrastructure.
Um, what we find is, you know,think of like the best example
might be Meta, right?
So Meta is, of course, um, youknow, the former Facebook,
(03:19):
social network, very asset-likebusiness.
They are now putting um about35% of revenues into physical
capital expenditures, right?
And that basically puts them umin line with the average
utility.
Uh so we we now we're now seeingthese these um formerly
asset-like companies transitioninto a more kind of asset-heavy
(03:42):
utility-like model.
And I think to, you know, thatthat's concerning for a few
reasons.
I think the first is is simplythe fact that if you look over
the history um of stock returns,you'll find that the asset heavy
companies have generallyunderperformed asset-like
companies.
Um, and these these stocks areagain holding up the stock
market and you know, are tradingat valuations that are still
reflecting their former glory.
(04:04):
And I think the second concernis around the the delta, right?
The growth in capital spending.
And we can probably get intothis later, but I mean, if you
study the history of capitalbooms, going back to the dot-com
boom, the railroads, um any biginfrastructure build-out um
around technology, it doesn'talways end well for the folks
building out the uh the pipes.
SPEAKER_00 (04:24):
Yeah, so that's what I wanted to ask you about next.
The the AI cycle is sort ofechoing past uh capital cycles.
Where do you see parallels todaybetween this AI CapEx wave and
earlier booms, like as youmentioned, the dot com and and
where do you think thedifferences online?
SPEAKER_01 (04:41):
Yeah, so I guess on the similarity side, um, you
know, we we we're seeing a smallgroup of concentrated players,
in this case the hyperscalers,um, you know, really drive the
the build out, right?
We saw the telecom firms um dothe same with the internet.
And, you know, of course,there's a cadre of uh companies
that were um laying out thetracks for the railroad boom um,
(05:02):
you know, over 100 years ago.
Um and so I think that there'sthat there's that similarity.
Um I think the differences, youknow, are primarily on the
balance sheet side, right?
So think of like global crossingas being perhaps a good example
in the dot-com era of a companythat um, you know, was over
levered and um you know wentbankrupt.
And I think a lot of therailroads as well went bankrupt
(05:23):
because of the way they arefinanced and they didn't really
have existing cash flows.
The difference, of course, isthat the Magnifist and 7 are, as
I mentioned, you know, supremelyprofitable businesses with up
until today sterling balancesheets.
Um now, this is starting tochange, right?
Where there are indications thatum, you know, these firms are
starting to take on more debt,um, you know, um often in the
(05:44):
forms of these kind ofoff-balance sheet SPVs, which
are a little concerning.
And then you mentioned in thevery beginning, kind of the
circularity around thecross-holdings of firms
investing in other firms toinvest in other firms to buy the
ships, um, which is of coursesomething we saw in the dot-com
boom, which was, you know, um,you know, indicative of
shenanigans.
Um so you know, I think we'recoming from a better starting
point for sure with thesecompanies.
(06:04):
And I don't think anyone isforecasting that, you know,
Google is going to go bankruptum because of the because of
overinvesting in in um AIinfrastructure.
But I think it that the concernis more around, you know, just
the dilution of their businessmodel, right?
If you're averaging somethingthat's very profitable and
something that's like not thatprofitable, you're not, you
know, ending up in as good aplace as you started off.
Um, and and just around therisks um, you know, given market
(06:27):
concentration, um, you know,around you know, how much is
relying on this one, you know,this handful of companies and
and more importantly, the onetheme of AI.
Um, you know, when AI does well,the economy does well, and when
AI does poorly, the economy willdo poorly.
And so I think that isdefinitely an area of concern
today.
SPEAKER_00 (06:44):
Yeah, and academic research often shows as well,
like firms with very high assetgrowth underperform.
What does that tell us aboutpulling back the hood on uh
growth today?
And how do classic signals failin this environment?
SPEAKER_01 (06:57):
Yeah, no, I'm glad you brought this up.
Um, yeah, there, you know, my mybackground is as more a quant
researcher, kind of, and and anda lot of the work I've done, you
know, especially earlier in mycareer, was studying a lot of
the factory literature.
So think of things like value,momentum, quality, and the
characteristics, characteristicsof companies that lead them to
succeed on average and and youknow, not.
(07:18):
So there's a factor, you know,in kind of the um, you know,
main models that people usecalled the investment factor,
which I find is kind of aconfusing name, but so I think
it's better um termed the assetgrowth factor.
And what that um what that showsis that companies that on a
trilling 12-month basis, youknow, in percentage terms grow
their assets, so grow theirbalance sheet um, you know, more
(07:40):
than average, tend tosubsequently underperform.
Right.
And this is a pretty robustfinding across markets and
across time.
And more importantly, it's notjust about um technology, right?
Of course, investment cycles canbe um, you know, tech-led, where
an innovation like AI or theinternet comes around and
everyone wants to hop in, but itcan be as mundane as you know,
(08:01):
cod fishing, or um, you canthink of examples in banking
with a financial crisis andhousing, right?
That's just the idea of um, youknow, the capital cycle, people
kind of invest too much and thenrealize subpour returns.
Um, what's interesting with thisfactor is I've actually been
doing a bit more work on it,trying to disentangle the
different components.
Because um firms, when they rateincrease their balance sheet,
(08:24):
they can be doing so um toaccomplish a couple of different
goals.
Um, you know, the most importantbeing are they spending the
money on building a physicalinfrastructure or simply on RD
marketing and perhaps creatingother uh you know um more
intangible assets?
And one thing you find is thatyou know, it's really the
physical capex that is mostlinked to the underperformance.
(08:45):
And I think that has to do withjust the the long-lived nature
of a lot of this um thisinfrastructure, right?
Like, you know, it's the thesethings, you know,
semiconductors, for example,which is of course what we're
talking about to some extent, isa notoriously um cyclical
industry where if you look overthe past few decades, you'll
find these big booms and bustsbecause you know, companies um
(09:06):
see it see good times, theyoverforecast demand, they then
overbuild, it takes a few yearsfor the supply to come online,
it comes online, they'veoverbuilt, there's a collapse,
and so on and so forth.
Right.
And so this is just a historythat we've seen in these kind of
capital-intensive industries.
Um, and you know, it's I thinklinked largely to what what this
the academics find as well, withregards to the underperformance
(09:27):
of these stocks on average.
Um, the other thing you can dois you can disentangle the in
this uh asset growth anomalyinto two dimensions.
So one is like the effect ofjust a macro, which is you know,
you have these waves of um ofinvestment um, you know, both
both across the market, like wedid with the dot com, but even
within the individual sectors,like through airlines, let's
(09:48):
say, and then there'sconsolidation and expansion.
Um, and then also within eachindustry at the company level.
And what you find is that theeffect persists on both levels.
So both a macro effect, wherewhen an industry experiences a
lot of capital inflows, theytend to underperform, as well as
when individual companiesexperience um, you know,
aggressive ramp up in theircapital expenditures relative to
(10:10):
their peers, those companiesalso tend to underperform.
And again, like these findingsby the academics are not meant
to um be indicative of any onecompany.
Like we're not specificallypicking on meta in this case,
but um, you know, just is a kindof average finding across sets
of companies through time, justsaying, hey, this tends to not
work out that well if ifapplied, kind of if you were a
(10:30):
betting man, right?
And wanted to apply it um kindof consistently across names.
SPEAKER_00 (10:35):
What does this then potentially mean for the stock
market as a whole?
I mean, like we see extremeconcentration with uh a handful
of megacap tech names carryingthe market.
Do you view that as a new AIrisk factor?
And how do you manageconcentration risk uh in your
own framework?
SPEAKER_01 (10:51):
Yeah, and I think look, anyone investing in stocks
is generally using as a startingpoint a cap-weighted index,
right?
So they're going to be buyingstocks in proportion to their
market caps.
And of course, the Magnesin 7stocks are have become so big
that they comprise, as Imentioned, over a third of the
index.
Right.
So right off the bat, youalready um, you know, have your
fate in the hands of this umidea.
(11:14):
And then, of course, there are,you know, there is exposure just
uh at the economic level to AI,right?
That I think about the somepeople have estimated that about
half of the GDP growth of the USlast year was due to um in
investment in AI, right?
Because that's of course acomponent, private sector
investment is a big component ofAI.
And then there's, of course,like the labor market impacts
(11:36):
and and so forth of thetechnology.
And so to the extent where youknow investors are already
concentrated in the MagnificentSeven by dint of their exposure
to indices, um, you know, mythought as an investor would be,
you know, try to find ways todiversify.
Like you never, like maybe maybeAI will end up becoming the like
the the real deal, and we willend up see seeing us, you know,
(11:57):
a revolution in the way um theworld works.
And I and I do think so.
Um, but as we've seen in past umcycles, oftentimes that can take
a long time, right?
Like with the internet, theadoption curve was very long.
It took decades, right, for um,you know, the S curve to fully
materialize.
And in the interim, there wereplenty plenty of periods, um,
such as you know, the dot-combust in 2000, you know, 2002,
(12:20):
where stocks um, you know,investors were disillusioned and
stock prices um, you know,troughed.
So I think, you know, asinvestors, you want to be, you
know, pretty cautious withregards to exposure to any one
theme.
And, you know, while, you know,even four or five years ago,
when I was, you know, firstdoing research on this topic, I
was very bullish on AI sayingthat, look, this is the real
deal.
We should all just kind of bedoing AI.
(12:41):
Most folks I know areunderexposed to AI.
I think it's now flipped, where,you know, fast forward four or
five years, ChatGBT has nowreleased, everyone's talking
about AI.
I think the risks are actuallynow the opposite, where
everyone's now overexposed toAI.
Um, and in particular due tojust the cap-weighted indices um
favoritism towards the largerstocks.
Um so as an as an investor, Imean, my my thought would be to
(13:04):
look for ways to um investoutside of the Magnificent 7,
outside of these companies thatare not only exposed most to AI,
but are in fact, you know,exposed most of the kind of
ticks and shovels uh capitalexpenditure component of AI.
If you go back to the dot-comand railroad booms, you know,
the the folks who actually builtout the railroads, the folks who
(13:24):
actually built out the telecoms,those guys did did really
poorly.
Um now there are some firms umthat did well off those booms,
but um, the guys who actuallybuilt out the physical
infrastructure in in both casesdid not do well, right?
Um, and so I think that's inparticular kind of the epicenter
of risk for the AI boom.
That look, the AI AI maycontinue to um be transformative
(13:47):
and may ultimately be adoptedum, you know, in well by
enterprise and by by consumers,in which case, um, you know, the
beneficiary, there will be manybeneficiaries and also might
not.
But even if AI, you know,disappoints um, you know, in the
in the investments that thesecompanies are making in data
centers underwhelm, that'sactually almost better for the
for the adopters, right?
(14:09):
Like to, you know, when when thedot com bust happened and fiber
collapsed um and was unused fora long time, companies could
then, um, you know, likeNetflix, for example, could then
utilize that fiber at a belowcost in a subsidized way to
build other businesses.
Um, and so I'd say, you know,focus more on the beneficiaries
of AI as opposed to the picksand shovels.
(14:29):
Um, I think that's probably aslightly safer way um to deal
with the um you know theconsolidation of risk around
this single theme um in thecurrent market.
SPEAKER_00 (14:38):
So to just get in a little more deeply into this,
could you walk uh investorsthrough how they could position
themselves um through the fulltech adoption cycle if if that
comes uh topics?
SPEAKER_01 (14:50):
Yeah, I think the best way to think about this is,
you know, by analogy to thedot-com boom, because we
actually have a full cycle ofhistory, we can kind of say,
hey, at least with hindsight,how would I have done that?
Um so basically with the dot-comboom, you kind of saw the
adoption of the internet unfoldin waves, right?
Whereas in the very beginning,the say mid-90s, early 90s,
mid-90s, you know, there was alot of the innovation was just
(15:13):
by, you know, these kind of puretech companies, these pure play
internet names, um, you know,AOL, Cisco, those would have
been the best investments.
But what happened was everyonefigured that out.
And, you know, these stocks werenow trading at, you know, price
to sales ratios above 30.
Um, and I use price to salesbecause in some cases these
companies were not evenprofitable.
Um, so fantastic valuations.
Everyone was betting on the youknow new um economy, and these
(15:36):
companies got bit up.
So, what would have then beenthe right play, again, with
hindsight, would have been tothen rotate into phase two of
the game plan, which is to notbuy the innovators, but to buy
the early adopters.
Right.
So, early adopters in this case,I mean companies that were
positioned to benefit from theusage of the internet um in
their own business lines, butweren't necessarily kind of pure
(15:58):
play internet names.
They're not, you know, telecomsor or dot-com companies.
Those companies were stilltrading at relatively um
affordable multiples, not toodifferent from the market in
general, um, yet still had a lotof upside um from the um further
adoption of the internet.
Um, right.
And then and then from there,once those names got bit up, you
(16:19):
kind of went on to kind of moremainstream companies that again,
you know, we all got a GDP bumpfrom AI um from the internet,
um, and and those guys wouldhave been the kind of the third
stage.
Right.
So you can kind of see how thisplays out.
Um, you know, as adoption umgoes, you want to kind of uh
always be moving, right?
If you only bought AOL, youwould have done really well in
(16:39):
the beginning and then donereally poorly and end up you
know losing money.
Right.
So I think the key here is to bedynamic and to be value
conscious because value,valuations in a way, kind of
help you organically navigatethe cycle.
It helps you kind of determine,hey, when am I starting to get
to the point where you know thewe go from phase one to phase
two, right?
So they're gonna help younaturally do that.
(17:00):
And that that's how one wouldhave first uh kind of um done
the first cycle.
Um, I think in AI, we are atleast in my mind, starting to
reach the tipping point betweenum phase one and phase two,
where Nvidia is, you know, atthis point, everyone knows it's
an AI stock, right?
It's no secret anymore.
You know, if you bought it fiveyears ago, you were probably
ahead of the curve, you're nolonger ahead of the curve by
(17:21):
doing that.
And you know, to a large extent,you know, the both the upside
and downside of AI continuing tooutperform or underperform is
now is currently kind ofperfectly priced into NVIDIA.
And so if you really want toprofit from um the continuance
of AI, you want to look for thenext wave of guys, um, the kind
of early adopter category.
(17:41):
Um so that's kind of where I seeus currently positioned.
Um, but again, like who knows,it could take longer or shorter
to play out um than in pastcycles.
Um, history only, you know,rhymes doesn't repeat.
SPEAKER_00 (17:52):
Yeah, and I think that's some really great advice
uh and reminders for investors.
Just lastly, Clyde, for viewerswho uh want to learn more about
your work and even connect withyou, what's the best way to do
that and where can they go tolearn more?
SPEAKER_01 (18:05):
Sure, yeah.
I I I have a website, umsparklinecapital.com, and I post
a bunch of different researchpapers, including some of the
research that I've alluded to onthis um you know talk.
And then in addition, you canjust reach out to me directly on
social media.
Um I'm on both Twitter andLinkedIn under the handle C
Kaiwoo, C K A I W U.
SPEAKER_00 (18:27):
Okay, thanks again for joining me, and thanks to
everyone for watching.
Be sure to like, share, andsubscribe for more episodes of
Deep Light Live.
These firms are starting to take on more debt,
you know, often in the forms ofthese kind of off-balance sheet
SPVs, which are a littleconcerning.
And then you mentioned in thevery beginning, kind of the
circularity around thecross-holdings of firms
investing in other firms toinvest in other firms to buy the
chips, which is, of course,something we saw in the dot-com
boom, which was, you know, um,you know, indicative of
shenanigans.
SPEAKER_00 (00:32):
I'm your host, Melanie Schaefer.
Welcome to Lead Lag Live.
Now, AI stocks are writing aseismic shift in capital flows.
Nvidia alone cinched a$100billion deal with OpenAI,
sending markets into overdrive.
Multiply that by the cascadingripple effects through
infrastructure, software, andchipmakers, and what felt like
(00:54):
hype is now shapingmulti-trillion dollar
valuations.
My guest today is Kai Wu,founder and CIO of Sparkline
Capital.
Sparkline Capital is aninvestment management firm
applying a cutting-edge machinelearning and computing to
uncover alpha in large,unstructured data sets.
Kai, thank you so much forjoining me today.
(01:17):
It's great to be here.
So let's start with the bigpicture.
Um hyperscalers are plowingthrough hundreds of billions or
are plowing hundreds of billionsinto AI infrastructure.
What do you what do you thinkthese capital flows tell you
about where the next leg of theopportunity could lie and what
risks are you watching?
SPEAKER_01 (01:34):
Yeah, I mean, this is something I'm watching very
closely.
Um, you know, as we all know,over the past 10, 15, maybe 20
years, the entire stock markethas been driven by the
performance of the so-calledmagnificent seven stocks.
Um, you know, Google, Apple,Nvidia, Microsoft, these guys.
Um, and, you know, really theirsuccess has been, at least to
(01:54):
me, um, on the back of theirbusiness models being in kind of
asset-light sectors, right?
So think of Google Search asthis perfect business that just
throws off cash and doesn'trequire too much capital to keep
afloat.
Um, you know, my my work is, myresearch is primarily in the
field of intangible assets.
Um, trying to understand howitems like brand equity, network
(02:16):
effects, human capital, andintellectual property allow
firms like these MagnificentSeven to earn supernormal
profits.
And, you know, for a long time,these companies have really been
the poster child of asset-likebusiness models, um, being able
to just really dominate in termsof um, you know, earnings and
profitability, and hence, youknow, the share prices have
(02:37):
followed.
Um, and to a large extent,they've become a significant
component of the stock market,right?
I think about a third or morenow of the SP 500 is now um, you
know, held up just by theseseven stocks.
And so what I've been watchingclosely is this transition away
from the model that's made themso successful, right?
What we've what we've seen isthese companies now set to
(02:57):
invest, you know, trillions ofdollars into AI data centers.
And that's you know, thesephysical infrastructure required
to power AI models, primarilyGPUs and chips, but also the you
know, physical data centers andum, you know, power and all this
other infrastructure.
Um, what we find is, you know,think of like the best example
might be Meta, right?
So Meta is, of course, um, youknow, the former Facebook,
(03:19):
social network, very asset-likebusiness.
They are now putting um about35% of revenues into physical
capital expenditures, right?
And that basically puts them umin line with the average
utility.
Uh so we we now we're now seeingthese these um formerly
asset-like companies transitioninto a more kind of asset-heavy
(03:42):
utility-like model.
And I think to, you know, thatthat's concerning for a few
reasons.
I think the first is is simplythe fact that if you look over
the history um of stock returns,you'll find that the asset heavy
companies have generallyunderperformed asset-like
companies.
Um, and these these stocks areagain holding up the stock
market and you know, are tradingat valuations that are still
reflecting their former glory.
(04:04):
And I think the second concernis around the the delta, right?
The growth in capital spending.
And we can probably get intothis later, but I mean, if you
study the history of capitalbooms, going back to the dot-com
boom, the railroads, um any biginfrastructure build-out um
around technology, it doesn'talways end well for the folks
building out the uh the pipes.
SPEAKER_00 (04:24):
Yeah, so that's what I wanted to ask you about next.
The the AI cycle is sort ofechoing past uh capital cycles.
Where do you see parallels todaybetween this AI CapEx wave and
earlier booms, like as youmentioned, the dot com and and
where do you think thedifferences online?
SPEAKER_01 (04:41):
Yeah, so I guess on the similarity side, um, you
know, we we we're seeing a smallgroup of concentrated players,
in this case the hyperscalers,um, you know, really drive the
the build out, right?
We saw the telecom firms um dothe same with the internet.
And, you know, of course,there's a cadre of uh companies
that were um laying out thetracks for the railroad boom um,
(05:02):
you know, over 100 years ago.
Um and so I think that there'sthat there's that similarity.
Um I think the differences, youknow, are primarily on the
balance sheet side, right?
So think of like global crossingas being perhaps a good example
in the dot-com era of a companythat um, you know, was over
levered and um you know wentbankrupt.
And I think a lot of therailroads as well went bankrupt
(05:23):
because of the way they arefinanced and they didn't really
have existing cash flows.
The difference, of course, isthat the Magnifist and 7 are, as
I mentioned, you know, supremelyprofitable businesses with up
until today sterling balancesheets.
Um now, this is starting tochange, right?
Where there are indications thatum, you know, these firms are
starting to take on more debt,um, you know, um often in the
(05:44):
forms of these kind ofoff-balance sheet SPVs, which
are a little concerning.
And then you mentioned in thevery beginning, kind of the
circularity around thecross-holdings of firms
investing in other firms toinvest in other firms to buy the
ships, um, which is of coursesomething we saw in the dot-com
boom, which was, you know, um,you know, indicative of
shenanigans.
Um so you know, I think we'recoming from a better starting
point for sure with thesecompanies.
(06:04):
And I don't think anyone isforecasting that, you know,
Google is going to go bankruptum because of the because of
overinvesting in in um AIinfrastructure.
But I think it that the concernis more around, you know, just
the dilution of their businessmodel, right?
If you're averaging somethingthat's very profitable and
something that's like not thatprofitable, you're not, you
know, ending up in as good aplace as you started off.
Um, and and just around therisks um, you know, given market
(06:27):
concentration, um, you know,around you know, how much is
relying on this one, you know,this handful of companies and
and more importantly, the onetheme of AI.
Um, you know, when AI does well,the economy does well, and when
AI does poorly, the economy willdo poorly.
And so I think that isdefinitely an area of concern
today.
SPEAKER_00 (06:44):
Yeah, and academic research often shows as well,
like firms with very high assetgrowth underperform.
What does that tell us aboutpulling back the hood on uh
growth today?
And how do classic signals failin this environment?
SPEAKER_01 (06:57):
Yeah, no, I'm glad you brought this up.
Um, yeah, there, you know, my mybackground is as more a quant
researcher, kind of, and and anda lot of the work I've done, you
know, especially earlier in mycareer, was studying a lot of
the factory literature.
So think of things like value,momentum, quality, and the
characteristics, characteristicsof companies that lead them to
succeed on average and and youknow, not.
(07:18):
So there's a factor, you know,in kind of the um, you know,
main models that people usecalled the investment factor,
which I find is kind of aconfusing name, but so I think
it's better um termed the assetgrowth factor.
And what that um what that showsis that companies that on a
trilling 12-month basis, youknow, in percentage terms grow
their assets, so grow theirbalance sheet um, you know, more
(07:40):
than average, tend tosubsequently underperform.
Right.
And this is a pretty robustfinding across markets and
across time.
And more importantly, it's notjust about um technology, right?
Of course, investment cycles canbe um, you know, tech-led, where
an innovation like AI or theinternet comes around and
everyone wants to hop in, but itcan be as mundane as you know,
(08:01):
cod fishing, or um, you canthink of examples in banking
with a financial crisis andhousing, right?
That's just the idea of um, youknow, the capital cycle, people
kind of invest too much and thenrealize subpour returns.
Um, what's interesting with thisfactor is I've actually been
doing a bit more work on it,trying to disentangle the
different components.
Because um firms, when they rateincrease their balance sheet,
(08:24):
they can be doing so um toaccomplish a couple of different
goals.
Um, you know, the most importantbeing are they spending the
money on building a physicalinfrastructure or simply on RD
marketing and perhaps creatingother uh you know um more
intangible assets?
And one thing you find is thatyou know, it's really the
physical capex that is mostlinked to the underperformance.
(08:45):
And I think that has to do withjust the the long-lived nature
of a lot of this um thisinfrastructure, right?
Like, you know, it's the thesethings, you know,
semiconductors, for example,which is of course what we're
talking about to some extent, isa notoriously um cyclical
industry where if you look overthe past few decades, you'll
find these big booms and bustsbecause you know, companies um
(09:06):
see it see good times, theyoverforecast demand, they then
overbuild, it takes a few yearsfor the supply to come online,
it comes online, they'veoverbuilt, there's a collapse,
and so on and so forth.
Right.
And so this is just a historythat we've seen in these kind of
capital-intensive industries.
Um, and you know, it's I thinklinked largely to what what this
the academics find as well, withregards to the underperformance
(09:27):
of these stocks on average.
Um, the other thing you can dois you can disentangle the in
this uh asset growth anomalyinto two dimensions.
So one is like the effect ofjust a macro, which is you know,
you have these waves of um ofinvestment um, you know, both
both across the market, like wedid with the dot com, but even
within the individual sectors,like through airlines, let's
(09:48):
say, and then there'sconsolidation and expansion.
Um, and then also within eachindustry at the company level.
And what you find is that theeffect persists on both levels.
So both a macro effect, wherewhen an industry experiences a
lot of capital inflows, theytend to underperform, as well as
when individual companiesexperience um, you know,
aggressive ramp up in theircapital expenditures relative to
(10:10):
their peers, those companiesalso tend to underperform.
And again, like these findingsby the academics are not meant
to um be indicative of any onecompany.
Like we're not specificallypicking on meta in this case,
but um, you know, just is a kindof average finding across sets
of companies through time, justsaying, hey, this tends to not
work out that well if ifapplied, kind of if you were a
(10:30):
betting man, right?
And wanted to apply it um kindof consistently across names.
SPEAKER_00 (10:35):
What does this then potentially mean for the stock
market as a whole?
I mean, like we see extremeconcentration with uh a handful
of megacap tech names carryingthe market.
Do you view that as a new AIrisk factor?
And how do you manageconcentration risk uh in your
own framework?
SPEAKER_01 (10:51):
Yeah, and I think look, anyone investing in stocks
is generally using as a startingpoint a cap-weighted index,
right?
So they're going to be buyingstocks in proportion to their
market caps.
And of course, the Magnesin 7stocks are have become so big
that they comprise, as Imentioned, over a third of the
index.
Right.
So right off the bat, youalready um, you know, have your
fate in the hands of this umidea.
(11:14):
And then, of course, there are,you know, there is exposure just
uh at the economic level to AI,right?
That I think about the somepeople have estimated that about
half of the GDP growth of the USlast year was due to um in
investment in AI, right?
Because that's of course acomponent, private sector
investment is a big component ofAI.
And then there's, of course,like the labor market impacts
(11:36):
and and so forth of thetechnology.
And so to the extent where youknow investors are already
concentrated in the MagnificentSeven by dint of their exposure
to indices, um, you know, mythought as an investor would be,
you know, try to find ways todiversify.
Like you never, like maybe maybeAI will end up becoming the like
the the real deal, and we willend up see seeing us, you know,
(11:57):
a revolution in the way um theworld works.
And I and I do think so.
Um, but as we've seen in past umcycles, oftentimes that can take
a long time, right?
Like with the internet, theadoption curve was very long.
It took decades, right, for um,you know, the S curve to fully
materialize.
And in the interim, there wereplenty plenty of periods, um,
such as you know, the dot-combust in 2000, you know, 2002,
(12:20):
where stocks um, you know,investors were disillusioned and
stock prices um, you know,troughed.
So I think, you know, asinvestors, you want to be, you
know, pretty cautious withregards to exposure to any one
theme.
And, you know, while, you know,even four or five years ago,
when I was, you know, firstdoing research on this topic, I
was very bullish on AI sayingthat, look, this is the real
deal.
We should all just kind of bedoing AI.
(12:41):
Most folks I know areunderexposed to AI.
I think it's now flipped, where,you know, fast forward four or
five years, ChatGBT has nowreleased, everyone's talking
about AI.
I think the risks are actuallynow the opposite, where
everyone's now overexposed toAI.
Um, and in particular due tojust the cap-weighted indices um
favoritism towards the largerstocks.
Um so as an as an investor, Imean, my my thought would be to
(13:04):
look for ways to um investoutside of the Magnificent 7,
outside of these companies thatare not only exposed most to AI,
but are in fact, you know,exposed most of the kind of
ticks and shovels uh capitalexpenditure component of AI.
If you go back to the dot-comand railroad booms, you know,
the the folks who actually builtout the railroads, the folks who
(13:24):
actually built out the telecoms,those guys did did really
poorly.
Um now there are some firms umthat did well off those booms,
but um, the guys who actuallybuilt out the physical
infrastructure in in both casesdid not do well, right?
Um, and so I think that's inparticular kind of the epicenter
of risk for the AI boom.
That look, the AI AI maycontinue to um be transformative
(13:47):
and may ultimately be adoptedum, you know, in well by
enterprise and by by consumers,in which case, um, you know, the
beneficiary, there will be manybeneficiaries and also might
not.
But even if AI, you know,disappoints um, you know, in the
in the investments that thesecompanies are making in data
centers underwhelm, that'sactually almost better for the
for the adopters, right?
(14:09):
Like to, you know, when when thedot com bust happened and fiber
collapsed um and was unused fora long time, companies could
then, um, you know, likeNetflix, for example, could then
utilize that fiber at a belowcost in a subsidized way to
build other businesses.
Um, and so I'd say, you know,focus more on the beneficiaries
of AI as opposed to the picksand shovels.
(14:29):
Um, I think that's probably aslightly safer way um to deal
with the um you know theconsolidation of risk around
this single theme um in thecurrent market.
SPEAKER_00 (14:38):
So to just get in a little more deeply into this,
could you walk uh investorsthrough how they could position
themselves um through the fulltech adoption cycle if if that
comes uh topics?
SPEAKER_01 (14:50):
Yeah, I think the best way to think about this is,
you know, by analogy to thedot-com boom, because we
actually have a full cycle ofhistory, we can kind of say,
hey, at least with hindsight,how would I have done that?
Um so basically with the dot-comboom, you kind of saw the
adoption of the internet unfoldin waves, right?
Whereas in the very beginning,the say mid-90s, early 90s,
mid-90s, you know, there was alot of the innovation was just
(15:13):
by, you know, these kind of puretech companies, these pure play
internet names, um, you know,AOL, Cisco, those would have
been the best investments.
But what happened was everyonefigured that out.
And, you know, these stocks werenow trading at, you know, price
to sales ratios above 30.
Um, and I use price to salesbecause in some cases these
companies were not evenprofitable.
Um, so fantastic valuations.
Everyone was betting on the youknow new um economy, and these
(15:36):
companies got bit up.
So, what would have then beenthe right play, again, with
hindsight, would have been tothen rotate into phase two of
the game plan, which is to notbuy the innovators, but to buy
the early adopters.
Right.
So, early adopters in this case,I mean companies that were
positioned to benefit from theusage of the internet um in
their own business lines, butweren't necessarily kind of pure
(15:58):
play internet names.
They're not, you know, telecomsor or dot-com companies.
Those companies were stilltrading at relatively um
affordable multiples, not toodifferent from the market in
general, um, yet still had a lotof upside um from the um further
adoption of the internet.
Um, right.
And then and then from there,once those names got bit up, you
(16:19):
kind of went on to kind of moremainstream companies that again,
you know, we all got a GDP bumpfrom AI um from the internet,
um, and and those guys wouldhave been the kind of the third
stage.
Right.
So you can kind of see how thisplays out.
Um, you know, as adoption umgoes, you want to kind of uh
always be moving, right?
If you only bought AOL, youwould have done really well in
(16:39):
the beginning and then donereally poorly and end up you
know losing money.
Right.
So I think the key here is to bedynamic and to be value
conscious because value,valuations in a way, kind of
help you organically navigatethe cycle.
It helps you kind of determine,hey, when am I starting to get
to the point where you know thewe go from phase one to phase
two, right?
So they're gonna help younaturally do that.
(17:00):
And that that's how one wouldhave first uh kind of um done
the first cycle.
Um, I think in AI, we are atleast in my mind, starting to
reach the tipping point betweenum phase one and phase two,
where Nvidia is, you know, atthis point, everyone knows it's
an AI stock, right?
It's no secret anymore.
You know, if you bought it fiveyears ago, you were probably
ahead of the curve, you're nolonger ahead of the curve by
(17:21):
doing that.
And you know, to a large extent,you know, the both the upside
and downside of AI continuing tooutperform or underperform is
now is currently kind ofperfectly priced into NVIDIA.
And so if you really want toprofit from um the continuance
of AI, you want to look for thenext wave of guys, um, the kind
of early adopter category.
(17:41):
Um so that's kind of where I seeus currently positioned.
Um, but again, like who knows,it could take longer or shorter
to play out um than in pastcycles.
Um, history only, you know,rhymes doesn't repeat.
SPEAKER_00 (17:52):
Yeah, and I think that's some really great advice
uh and reminders for investors.
Just lastly, Clyde, for viewerswho uh want to learn more about
your work and even connect withyou, what's the best way to do
that and where can they go tolearn more?
SPEAKER_01 (18:05):
Sure, yeah.
I I I have a website, umsparklinecapital.com, and I post
a bunch of different researchpapers, including some of the
research that I've alluded to onthis um you know talk.
And then in addition, you canjust reach out to me directly on
social media.
Um I'm on both Twitter andLinkedIn under the handle C
Kaiwoo, C K A I W U.
SPEAKER_00 (18:27):
Okay, thanks again for joining me, and thanks to
everyone for watching.
Be sure to like, share, andsubscribe for more episodes of
Deep Light Live.
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