October 14, 2025 • 22 mins
In this episode of Lead-Lag Live, I sit down with Derek Yan, CFA and Senior Investment Strategist at KraneShares, to cut through the noise surrounding the AI “bubble” narrative and unpack where the true opportunity still lies.
From infrastructure shortages and the rise of reasoning models to the challenge of accessing private AI leaders, Derek shares why the current cycle is still in its infancy and how investors can capture the next phase of growth.
In this episode:
– Why AI demand is outpacing computing capacity worldwide
– How reasoning models are reshaping the AI investment landscape
– Why the Magnificent 7 alone won’t capture the AI revolution
– The importance of balancing infrastructure vs application exposure
– How A-GIX connects investors to both public and private AI leaders
Lead-Lag Live brings you inside conversations with the financial thinkers who shape markets. Subscribe for interviews that go deeper than the noise.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
SPEAKER_00 (00:00):
So investors need to position the portfolio towards
that AI ecosystem.
And also, there's a dynamicallocation between
infrastructure and application.
That allocation is also veryimportant for investors to
really position their portfolioto really dynamically capture
the best growth from the AI ateach stage.
SPEAKER_01 (00:38):
Right now, you can't turn on CNBC or Bloomberg
without seeing headlines aboutartificial intelligence.
And just as often warnings aboutan AI bubble.
At the same time, trillions ofdollars are being poured into
infrastructure, data centers,and enterprise build-outs.
For investors, the big questionis whether this momentum is hype
(00:59):
or the start of something trulytransformative.
My guest today is Derek Yan, CFAand senior investment strategist
at Crane Shares.
Derek works closely on AGIX, astrategy designed to capture
opportunities across both publicand private AI companies.
Today, we're going to dig intohow investors should be thinking
(01:22):
about AI right now, where we arewithin the adoption cycle, and
what might be coming next.
Derek, welcome.
SPEAKER_00 (01:29):
Thank you for having me.
SPEAKER_01 (01:31):
So as I said, when you open CNBC or Bloomberg
today, the phrase AI bubble iseverywhere.
How should investors interpretthat?
Where are we within the hikecycle?
Or are we still sort of in theearly innings of a more
transformative trend?
SPEAKER_00 (01:46):
Yeah, definitely.
Like I think recently there's alot of news on a concern of AI
bubble, especially when you seelike Nvidia invested in the
OpenAI.
OpenAI going to use the money todeploy to like Oracle, Oracle to
buy more chips from Nvidia.
So that circle just reminds me alot of investors early-day
(02:10):
internet that like Ciscoproviding capital to their
clients.
So but we think like for Nvidia,it's more like strategic
investment because they foundOpenAI's business pretty
attractive.
So that's way, I don't thinklike that that's really a
bubble.
In contrast, actually, if youlisten to Jensen, if you listen
(02:34):
to a lot of like stakeholders,CEOs in the AI industry right
now, we have a shortage ofcomputing.
So just looking at a lot of ifyou use like OpenAI's ChatGPT,
or if you're a soft engineer orsoftware developers who use like
(02:54):
a lot code like for like um yourautomation in the coding space,
you realize there's come like aslowdown right now, like there's
a shortage of um AI outputbecause the demand is like
tremendous.
It's like you have, I don'tknow, it's like I think Jensen
(03:15):
have this idea, their previousforecast is like it will be 10x,
100x demand for AI tokensoutput.
But I think reality is likegonna be billions of billions X.
So that's crazy.
Just like imagine like how muchdemand is out there.
(03:35):
So if like OpenAI anthropic canramp up their capacity to
provide their users the capacityto really compute, their revenue
is gonna be double, triple.
So that shortage, that demand istrickling a lot of investment
into buying more computingpower, then the computing power
(03:57):
is going to buy more than videochips.
So that's why you see the lot offinancing demand from like model
companies like OpenAI.
So that makes me believe we'restill in a white early stage of
the AI build-out.
SPEAKER_01 (04:11):
Derek, from an investment perspective, where do
you think we are within the AIadoption and monetization
cycles?
Are we still in theinfrastructure build-out phase
or are we already moving intoapplication phase?
SPEAKER_00 (04:23):
Yeah, so that's a good question because I think
like if you look at the past,but like when we have like the
GPT moment, then the GPT modelis basically pre-trained.
So a lot of resources, likedata, data laboring, um, then
you need like a lot of chips anddata center built out uh to
(04:45):
train a model.
Then you use that model.
Um, so story is like that, andyou have a lot, you need a lot
of infrastructure investment uhto develop new models because
new models can be better.
However, that narrative reallychanged.
Uh, I think earlier this year,in back in December last year,
(05:06):
January this year, we have likeOpenAIS 01 model, and you have
the DeepSeq R1 model.
So those new types of modelscalled reasoning model, the cost
like per uh disclosure by likeDeepSeq, the cost of building a
new model is much lower.
(05:27):
So that's just like scared, Ithink, global investors, because
like if that's the case, themodels become like commodity.
You don't need a lot ofinfrastructure to invest in the
chips to build new models.
The story totally changed.
You have some like a panicthere, then all of a sudden,
(05:47):
like all the value migrate tothe application layers.
Oh, the software company, theSaaS company benefit.
Um, because if the model isgoing to be so cheap going
forward, the application isgonna benefit.
But however, that narrative nowshifts back again.
Um, part of the reason is justlike you do have that wave of
(06:12):
efficiency gain for a softwarecompany because they deployed AI
models.
So their margin improved, thereare a lot of workflow automated
already.
So those financial results arealready being published, so
people know that.
So people don't think that'sgonna go much higher because
there's so much efficiency togain.
(06:32):
Um, there's I don't think that'sa lot of ad right now.
So people are looking for newavenue gain for those software,
the application layer.
But now with the new reasoningmodel, actually the inferencing
demand, as I said, like peopleneed new AI coding, people need
(06:53):
the O1 to do more in theconsumer-facing applications,
the demand is like not enough.
Just like the sorry, thecapacity just not enough that we
need more infrastructure andchips to actually just support
that.
Uh and like just look at a unieconomy.
Like, you think about likeusually OpenAI or Anthropic,
(07:17):
they quote um the clients, likein API, right?
Like the million tokens peroutput.
Like usually the price can beranged like$12 or like$20.
Um, then you have the like thecost is like currently it's
lower.
So you do have like uniqueeconomy um for each of the AI
(07:39):
models.
Um, so when you have that, justlike if you can have chips, you
have computing power, you canmake more money.
So I think like currently theinfrastructure is back.
And like that's where theinvestors are now focusing on
the investment focus on AI.
SPEAKER_01 (07:58):
Garrett, what would you say to investors who say, I
already have exposure to the AItrade through the Magnificent 7
or the Nasdaq 100?
What's the incremental case foradding a dedicated AI strategy
like AGIX?
SPEAKER_00 (08:12):
Yeah, I think like just similar to the internet
era, right?
Like you have um a long-termgrowth structure uh happening in
AI, but the dynamic is just likemore than I think seven names.
Um when you look at even likeMac7 this year, like there's
(08:33):
some dispersion.
Um so and we are still the king.
You have Meta um who's like umusing AI to run ads, and those
uh monetizations fast, and thethey'll they have been
performing well.
However, then then there's somelike lags.
Um you have the Apple, then theApple's strategy on AI, the
(08:55):
talents uh is not ready.
They don't have a good strategy.
So you do have like some laggingperformance, uh, even among Mac
7.
Then it's even more obvious likeamong NASDAQ 100 because a lot
of business within the NASDAQ100 are going to be disrupted.
Their business model is probablygonna lose the AI raise.
(09:19):
Um, and their whole business canbe just replaced by many AI
native startups or otherincumbents in the technology
space.
So if you think about that, likeyou do need to be very selective
right now in the AI play goingforward because it's not a Mac7,
(09:40):
it's not NASDAQ, it's gonna bethe AI winners.
Like, who's gonna be where AIwinners are gonna deliver those
long-term structural growth?
Um, so investors need toposition the portfolio to really
um towards that um AI ecosystem.
And also, uh, I think like, as Isaid, you have to, there's a
(10:03):
dynamic allocation betweeninfrastructure and application.
So that allocation is also veryimportant for investors to
really position um theirportfolio uh to really
dynamically capture the bestgrowth from the AI at each
stage.
SPEAKER_01 (10:20):
Derek, just to get a little uh more deep into that,
what are some of the biggestchallenges in trying uh to get
exposure to AI and how does AGIXhelp solve some of those issues?
SPEAKER_00 (10:31):
Yeah, I think like there's three challenges.
Um if you think about that, likeone is just an allocation, I
said.
How do you really, even eventhough, even though like
everybody knows, oh, AI is theplay in the long term, you need
adequate AI, but how can you doit?
Then you have the AI hardware,you have AI infrastructure, you
(10:55):
have you have AI applications.
So, how do you really allocatethe capital across each layer?
That's uh very strategic, orsometimes can be tactical,
dynamic.
So you need a framework toreally deploy the capital uh
according to the current demand,right?
(11:18):
So because you think about it,that's the the down upstream to
downstream, then everybody'sfighting for margin.
So where's the bottleneck?
So you really need to emphasizeyour where you need to really uh
tilt your portfolio to thatbottleneck.
The second challenge is goingforward, I think every company
is gonna declare they're gonnabe AI companies.
(11:40):
Because everyone's gonna use AI.
Um so a lot of business is gonnaclaim, oh, we're AI native, uh,
we're gonna use AI.
But uh in the end, they're justmore than a height where there's
their business model more likelyto be disrupted by either the
model companies, open AI isgonna destroy a lot of business,
(12:02):
actually, then um many SaaSplayare actually gonna lead in many
verticals.
So we think you need to be, youneed to think like AI native
researchers, and you needinsights from AI native
researchers to talk to the AIresearchers within each firm to
gain that insight of each one'sbusiness model.
(12:24):
Um that's the second challenge.
Um, the third one is that mostpeople's portfolio is only the
public, right?
So that'll give you a lot oflike exposure to hyperscaler uh
applications um like chips.
But um one critical part of theecosystem uh is missing, which
(12:47):
is the foundational modelcompanies, because most
foundational model companies areprivate.
Um however, that's where uh Ithink at this stage and going
forward, uh most of the value isgoing to be created.
So um many investors missingthat value creation within their
portfolio.
(13:07):
So we as at creatures, we weidentified those three
challenges like two years ago.
Um that's why we started thisproject to work with a bunch of
AI native researchers um tocreate a portfolio to solve
those three uh issues, I wouldthink.
So we we work with AIresearchers.
(13:28):
So those AI researchers theyprovide a score uh for each
company within the universe thathelps us navigate uh the dynamic
of each company's businessmodel, how the business model is
ready for the AI disruption.
And also the AI score is gonnabe dynamic each quarter, so the
(13:51):
weight towards each um category,uh hardware infrastructure
application is gonna be uhdynamically reflected to
position uh the best as thecurrent stage of AI.
And also because those AI nativeresearchers, they are also
venture investors, they have aclose relationship with a lot of
(14:14):
AI uh model companies.
So we've been able actually toget access to a lot of private
AI investment.
Um, AGX, uh, on behalf of CleanShires Trust, um uh we invested
into the Anthropic um in Marchthis year, uh in their CSE
(14:35):
round.
So this is a direct investment.
We sit on the cap table.
And also we invest in anthropic,sorry, the XI in July uh through
their$10 for round.
So we also sit on their captable.
So those um those relationshipswere critical, um, as well as
those private investments, Ithink is critical to provide uh
(14:58):
ecosystem that includes both uhthe hardware, the
infrastructure, applications,and models.
So that gives you the investorthe whole picture of AI.
SPEAKER_01 (15:09):
I want to pivot uh just a little bit.
Looking out, say three to fiveyears, what's one thing about AI
or the investment landscape thatyou think most investors are
underestimating uh today?
SPEAKER_00 (15:20):
That's a good question.
I mean, like, first, I thinklike people keep underestimating
the demand for AI.
Um, just like all the thinkabout even like Nvidia, um then
their capex is probably likelike shorting, providing the the
to really accommodate thedemand.
(15:41):
All the hyperscalers, even theyhave like huge numbers in the
capex.
Like in the cloud era, like itturned out to be shortage.
Now with all the infrastructureinferencing and reasoning
demand, uh, it's shortage again.
Um so we we're likely to seemore like um underestimate of
(16:04):
the demand for computing powerand also the demand for AI.
Um you have seen that likeacross Wall Street, I think
starting in the internet era, umlike most of the Wall Street
like analysts, they theyunderestimate the demand for the
Capax need.
Um so that's number one.
(16:24):
Um and number two, I thinkpeople now focus a lot of the
kind of like digalization, um,because AI is really happening
in the digital world.
If you look at the industry, um,which industry is like now
adopting AI penetration rate forthe AI is highest among the
(16:47):
technology sector, um, becausetechnology sector, their
workflow uh is all around code,around data.
Um, so like if you think aboutlike typical workload of
software developers, productmanagers, those a lot of
workflow can be automated usingAI coding, AI agent.
(17:08):
Um so that's that's a goodopportunity right now.
Um, but if you look down, um, alot of industries, their
penetration is quite low.
Uh, think about likemanufacturing, think about like
healthcare, a lot of servicesector.
Um, I think those are potentialopportunities for AI going
(17:29):
forward in more longer term.
Because think about like the AIcan be two things.
One is the uh digital AI, butthen now we increasingly see
more chance from the physicalAI.
Um now you have like Elon Musktrying to develop like R2D2 for
(17:49):
everybody.
So like the humanoid robotics,like we have like a humanoid
robotic phone as well, uh,called uh KOID coid.
That is really investing in theecosystem to build out those
like R2D2 components.
Um that like we bring one of thehumanoids actually to green the
(18:10):
belt NASDAQ.
So once you see the performance,the accuration system for the
humanoid, it can run, it canmove, it can wave, it can shake
hands.
Like human now uh is much bettercompared to like years ago,
where you probably see this likefootage from like Boston
dynamic.
Uh, but if you look at like inthe real real life, the
(18:33):
performance of the humanoidrobotics right now, today, it
just will be shocking.
So I think that just like giveeverybody like optimistic um uh
outlook uh on the physical AI,because now when you have AI
models can incorporate likeaction data, our environment
(18:57):
data, um, image data with thevision technologies, the sensor
technologies, you can actuallycreate like a whole physical
world AI.
Um the factories, like if youlook at the latest factories,
they have like digital twins,right?
So they create a digitalfactory.
(19:17):
So like we can do that like forevery everything going forward.
Like even when our human bodycan have a digital twin, so all
the healthcare robot can work,right?
So that's that's something Ithink like is really
underestimated or missedposition in everybody's
portfolio.
Um, so um I would think like uhas uh as cranchers, um, that's
(19:39):
why we launched uh KOI decoy uhas a player to really tap into
that early opportunity.
SPEAKER_01 (19:45):
It was epic uh to see the footage of that humanoid
at the New York Stock Exchange.
Derek, just to before we wrapup, for anyone who wants to
learn more about AGIX or toconnect with you or the team,
where's the best place for themto go?
SPEAKER_00 (19:58):
Yeah, they can um uh craneships website, we actually
publish a lot of research on AIand we have a white paper on the
AGIX of FAQ.
So people can go to Cranchis.comslash AGIX.
Um where anyone wants to knowCOID, they can go to
Craneships.com slash COID.
(20:19):
Um, yeah, just like that's thego-to place.
SPEAKER_01 (20:22):
Fantastic.
Well, Derek, thank you so muchuh for joining me again, and
thanks to everyone for watching.
Be sure to like, share, andsubscribe for more episodes of
Lead Live Lives.
See you next time.
So investors need to position the portfolio towards
that AI ecosystem.
And also, there's a dynamicallocation between
infrastructure and application.
That allocation is also veryimportant for investors to
really position their portfolioto really dynamically capture
the best growth from the AI ateach stage.
SPEAKER_01 (00:38):
Right now, you can't turn on CNBC or Bloomberg
without seeing headlines aboutartificial intelligence.
And just as often warnings aboutan AI bubble.
At the same time, trillions ofdollars are being poured into
infrastructure, data centers,and enterprise build-outs.
For investors, the big questionis whether this momentum is hype
(00:59):
or the start of something trulytransformative.
My guest today is Derek Yan, CFAand senior investment strategist
at Crane Shares.
Derek works closely on AGIX, astrategy designed to capture
opportunities across both publicand private AI companies.
Today, we're going to dig intohow investors should be thinking
(01:22):
about AI right now, where we arewithin the adoption cycle, and
what might be coming next.
Derek, welcome.
SPEAKER_00 (01:29):
Thank you for having me.
SPEAKER_01 (01:31):
So as I said, when you open CNBC or Bloomberg
today, the phrase AI bubble iseverywhere.
How should investors interpretthat?
Where are we within the hikecycle?
Or are we still sort of in theearly innings of a more
transformative trend?
SPEAKER_00 (01:46):
Yeah, definitely.
Like I think recently there's alot of news on a concern of AI
bubble, especially when you seelike Nvidia invested in the
OpenAI.
OpenAI going to use the money todeploy to like Oracle, Oracle to
buy more chips from Nvidia.
So that circle just reminds me alot of investors early-day
(02:10):
internet that like Ciscoproviding capital to their
clients.
So but we think like for Nvidia,it's more like strategic
investment because they foundOpenAI's business pretty
attractive.
So that's way, I don't thinklike that that's really a
bubble.
In contrast, actually, if youlisten to Jensen, if you listen
(02:34):
to a lot of like stakeholders,CEOs in the AI industry right
now, we have a shortage ofcomputing.
So just looking at a lot of ifyou use like OpenAI's ChatGPT,
or if you're a soft engineer orsoftware developers who use like
(02:54):
a lot code like for like um yourautomation in the coding space,
you realize there's come like aslowdown right now, like there's
a shortage of um AI outputbecause the demand is like
tremendous.
It's like you have, I don'tknow, it's like I think Jensen
(03:15):
have this idea, their previousforecast is like it will be 10x,
100x demand for AI tokensoutput.
But I think reality is likegonna be billions of billions X.
So that's crazy.
Just like imagine like how muchdemand is out there.
(03:35):
So if like OpenAI anthropic canramp up their capacity to
provide their users the capacityto really compute, their revenue
is gonna be double, triple.
So that shortage, that demand istrickling a lot of investment
into buying more computingpower, then the computing power
(03:57):
is going to buy more than videochips.
So that's why you see the lot offinancing demand from like model
companies like OpenAI.
So that makes me believe we'restill in a white early stage of
the AI build-out.
SPEAKER_01 (04:11):
Derek, from an investment perspective, where do
you think we are within the AIadoption and monetization
cycles?
Are we still in theinfrastructure build-out phase
or are we already moving intoapplication phase?
SPEAKER_00 (04:23):
Yeah, so that's a good question because I think
like if you look at the past,but like when we have like the
GPT moment, then the GPT modelis basically pre-trained.
So a lot of resources, likedata, data laboring, um, then
you need like a lot of chips anddata center built out uh to
(04:45):
train a model.
Then you use that model.
Um, so story is like that, andyou have a lot, you need a lot
of infrastructure investment uhto develop new models because
new models can be better.
However, that narrative reallychanged.
Uh, I think earlier this year,in back in December last year,
(05:06):
January this year, we have likeOpenAIS 01 model, and you have
the DeepSeq R1 model.
So those new types of modelscalled reasoning model, the cost
like per uh disclosure by likeDeepSeq, the cost of building a
new model is much lower.
(05:27):
So that's just like scared, Ithink, global investors, because
like if that's the case, themodels become like commodity.
You don't need a lot ofinfrastructure to invest in the
chips to build new models.
The story totally changed.
You have some like a panicthere, then all of a sudden,
(05:47):
like all the value migrate tothe application layers.
Oh, the software company, theSaaS company benefit.
Um, because if the model isgoing to be so cheap going
forward, the application isgonna benefit.
But however, that narrative nowshifts back again.
Um, part of the reason is justlike you do have that wave of
(06:12):
efficiency gain for a softwarecompany because they deployed AI
models.
So their margin improved, thereare a lot of workflow automated
already.
So those financial results arealready being published, so
people know that.
So people don't think that'sgonna go much higher because
there's so much efficiency togain.
(06:32):
Um, there's I don't think that'sa lot of ad right now.
So people are looking for newavenue gain for those software,
the application layer.
But now with the new reasoningmodel, actually the inferencing
demand, as I said, like peopleneed new AI coding, people need
(06:53):
the O1 to do more in theconsumer-facing applications,
the demand is like not enough.
Just like the sorry, thecapacity just not enough that we
need more infrastructure andchips to actually just support
that.
Uh and like just look at a unieconomy.
Like, you think about likeusually OpenAI or Anthropic,
(07:17):
they quote um the clients, likein API, right?
Like the million tokens peroutput.
Like usually the price can beranged like$12 or like$20.
Um, then you have the like thecost is like currently it's
lower.
So you do have like uniqueeconomy um for each of the AI
(07:39):
models.
Um, so when you have that, justlike if you can have chips, you
have computing power, you canmake more money.
So I think like currently theinfrastructure is back.
And like that's where theinvestors are now focusing on
the investment focus on AI.
SPEAKER_01 (07:58):
Garrett, what would you say to investors who say, I
already have exposure to the AItrade through the Magnificent 7
or the Nasdaq 100?
What's the incremental case foradding a dedicated AI strategy
like AGIX?
SPEAKER_00 (08:12):
Yeah, I think like just similar to the internet
era, right?
Like you have um a long-termgrowth structure uh happening in
AI, but the dynamic is just likemore than I think seven names.
Um when you look at even likeMac7 this year, like there's
(08:33):
some dispersion.
Um so and we are still the king.
You have Meta um who's like umusing AI to run ads, and those
uh monetizations fast, and thethey'll they have been
performing well.
However, then then there's somelike lags.
Um you have the Apple, then theApple's strategy on AI, the
(08:55):
talents uh is not ready.
They don't have a good strategy.
So you do have like some laggingperformance, uh, even among Mac
7.
Then it's even more obvious likeamong NASDAQ 100 because a lot
of business within the NASDAQ100 are going to be disrupted.
Their business model is probablygonna lose the AI raise.
(09:19):
Um, and their whole business canbe just replaced by many AI
native startups or otherincumbents in the technology
space.
So if you think about that, likeyou do need to be very selective
right now in the AI play goingforward because it's not a Mac7,
(09:40):
it's not NASDAQ, it's gonna bethe AI winners.
Like, who's gonna be where AIwinners are gonna deliver those
long-term structural growth?
Um, so investors need toposition the portfolio to really
um towards that um AI ecosystem.
And also, uh, I think like, as Isaid, you have to, there's a
(10:03):
dynamic allocation betweeninfrastructure and application.
So that allocation is also veryimportant for investors to
really position um theirportfolio uh to really
dynamically capture the bestgrowth from the AI at each
stage.
SPEAKER_01 (10:20):
Derek, just to get a little uh more deep into that,
what are some of the biggestchallenges in trying uh to get
exposure to AI and how does AGIXhelp solve some of those issues?
SPEAKER_00 (10:31):
Yeah, I think like there's three challenges.
Um if you think about that, likeone is just an allocation, I
said.
How do you really, even eventhough, even though like
everybody knows, oh, AI is theplay in the long term, you need
adequate AI, but how can you doit?
Then you have the AI hardware,you have AI infrastructure, you
(10:55):
have you have AI applications.
So, how do you really allocatethe capital across each layer?
That's uh very strategic, orsometimes can be tactical,
dynamic.
So you need a framework toreally deploy the capital uh
according to the current demand,right?
(11:18):
So because you think about it,that's the the down upstream to
downstream, then everybody'sfighting for margin.
So where's the bottleneck?
So you really need to emphasizeyour where you need to really uh
tilt your portfolio to thatbottleneck.
The second challenge is goingforward, I think every company
is gonna declare they're gonnabe AI companies.
(11:40):
Because everyone's gonna use AI.
Um so a lot of business is gonnaclaim, oh, we're AI native, uh,
we're gonna use AI.
But uh in the end, they're justmore than a height where there's
their business model more likelyto be disrupted by either the
model companies, open AI isgonna destroy a lot of business,
(12:02):
actually, then um many SaaSplayare actually gonna lead in many
verticals.
So we think you need to be, youneed to think like AI native
researchers, and you needinsights from AI native
researchers to talk to the AIresearchers within each firm to
gain that insight of each one'sbusiness model.
(12:24):
Um that's the second challenge.
Um, the third one is that mostpeople's portfolio is only the
public, right?
So that'll give you a lot oflike exposure to hyperscaler uh
applications um like chips.
But um one critical part of theecosystem uh is missing, which
(12:47):
is the foundational modelcompanies, because most
foundational model companies areprivate.
Um however, that's where uh Ithink at this stage and going
forward, uh most of the value isgoing to be created.
So um many investors missingthat value creation within their
portfolio.
(13:07):
So we as at creatures, we weidentified those three
challenges like two years ago.
Um that's why we started thisproject to work with a bunch of
AI native researchers um tocreate a portfolio to solve
those three uh issues, I wouldthink.
So we we work with AIresearchers.
(13:28):
So those AI researchers theyprovide a score uh for each
company within the universe thathelps us navigate uh the dynamic
of each company's businessmodel, how the business model is
ready for the AI disruption.
And also the AI score is gonnabe dynamic each quarter, so the
(13:51):
weight towards each um category,uh hardware infrastructure
application is gonna be uhdynamically reflected to
position uh the best as thecurrent stage of AI.
And also because those AI nativeresearchers, they are also
venture investors, they have aclose relationship with a lot of
(14:14):
AI uh model companies.
So we've been able actually toget access to a lot of private
AI investment.
Um, AGX, uh, on behalf of CleanShires Trust, um uh we invested
into the Anthropic um in Marchthis year, uh in their CSE
(14:35):
round.
So this is a direct investment.
We sit on the cap table.
And also we invest in anthropic,sorry, the XI in July uh through
their$10 for round.
So we also sit on their captable.
So those um those relationshipswere critical, um, as well as
those private investments, Ithink is critical to provide uh
(14:58):
ecosystem that includes both uhthe hardware, the
infrastructure, applications,and models.
So that gives you the investorthe whole picture of AI.
SPEAKER_01 (15:09):
I want to pivot uh just a little bit.
Looking out, say three to fiveyears, what's one thing about AI
or the investment landscape thatyou think most investors are
underestimating uh today?
SPEAKER_00 (15:20):
That's a good question.
I mean, like, first, I thinklike people keep underestimating
the demand for AI.
Um, just like all the thinkabout even like Nvidia, um then
their capex is probably likelike shorting, providing the the
to really accommodate thedemand.
(15:41):
All the hyperscalers, even theyhave like huge numbers in the
capex.
Like in the cloud era, like itturned out to be shortage.
Now with all the infrastructureinferencing and reasoning
demand, uh, it's shortage again.
Um so we we're likely to seemore like um underestimate of
(16:04):
the demand for computing powerand also the demand for AI.
Um you have seen that likeacross Wall Street, I think
starting in the internet era, umlike most of the Wall Street
like analysts, they theyunderestimate the demand for the
Capax need.
Um so that's number one.
(16:24):
Um and number two, I thinkpeople now focus a lot of the
kind of like digalization, um,because AI is really happening
in the digital world.
If you look at the industry, um,which industry is like now
adopting AI penetration rate forthe AI is highest among the
(16:47):
technology sector, um, becausetechnology sector, their
workflow uh is all around code,around data.
Um, so like if you think aboutlike typical workload of
software developers, productmanagers, those a lot of
workflow can be automated usingAI coding, AI agent.
(17:08):
Um so that's that's a goodopportunity right now.
Um, but if you look down, um, alot of industries, their
penetration is quite low.
Uh, think about likemanufacturing, think about like
healthcare, a lot of servicesector.
Um, I think those are potentialopportunities for AI going
(17:29):
forward in more longer term.
Because think about like the AIcan be two things.
One is the uh digital AI, butthen now we increasingly see
more chance from the physicalAI.
Um now you have like Elon Musktrying to develop like R2D2 for
(17:49):
everybody.
So like the humanoid robotics,like we have like a humanoid
robotic phone as well, uh,called uh KOID coid.
That is really investing in theecosystem to build out those
like R2D2 components.
Um that like we bring one of thehumanoids actually to green the
(18:10):
belt NASDAQ.
So once you see the performance,the accuration system for the
humanoid, it can run, it canmove, it can wave, it can shake
hands.
Like human now uh is much bettercompared to like years ago,
where you probably see this likefootage from like Boston
dynamic.
Uh, but if you look at like inthe real real life, the
(18:33):
performance of the humanoidrobotics right now, today, it
just will be shocking.
So I think that just like giveeverybody like optimistic um uh
outlook uh on the physical AI,because now when you have AI
models can incorporate likeaction data, our environment
(18:57):
data, um, image data with thevision technologies, the sensor
technologies, you can actuallycreate like a whole physical
world AI.
Um the factories, like if youlook at the latest factories,
they have like digital twins,right?
So they create a digitalfactory.
(19:17):
So like we can do that like forevery everything going forward.
Like even when our human bodycan have a digital twin, so all
the healthcare robot can work,right?
So that's that's something Ithink like is really
underestimated or missedposition in everybody's
portfolio.
Um, so um I would think like uhas uh as cranchers, um, that's
(19:39):
why we launched uh KOI decoy uhas a player to really tap into
that early opportunity.
SPEAKER_01 (19:45):
It was epic uh to see the footage of that humanoid
at the New York Stock Exchange.
Derek, just to before we wrapup, for anyone who wants to
learn more about AGIX or toconnect with you or the team,
where's the best place for themto go?
SPEAKER_00 (19:58):
Yeah, they can um uh craneships website, we actually
publish a lot of research on AIand we have a white paper on the
AGIX of FAQ.
So people can go to Cranchis.comslash AGIX.
Um where anyone wants to knowCOID, they can go to
Craneships.com slash COID.
(20:19):
Um, yeah, just like that's thego-to place.
SPEAKER_01 (20:22):
Fantastic.
Well, Derek, thank you so muchuh for joining me again, and
thanks to everyone for watching.
Be sure to like, share, andsubscribe for more episodes of
Lead Live Lives.
See you next time.
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