Valerio
Baselli:
Hello
and
welcome
to
Morningstar.
Everyone
wants
to
invest
in
artificial
intelligence,
but
there
are
several
ways
to
do
so.
Which
one
is
the
most
suitable
at
the
moment?
Today,
I’m
joined
by
Rahul
Bhushan,
Global
Head
of
Index
at
ARK
Invest
Europe.
Rahul,
Nvidia
(NVDA)
recently
became
the
most
valuable
company
in
the
world.
Its
impressive
rally
has
pushed
many
investors
towards
the
theme
of
AI.
But
now,
you
at
ARK
Invest,
think
it’s
time
to
start
showing
some
caution.
Can
you
explain
why?
Rahul
Bhushan:
Yeah.
First
of
all,
thanks
Valerio
for
having
us
on.
The
first
thing
is,
we’ve
gone
through,
and
we
might
still
be
going
through
a
massive,
unprecedented
AI
CapEx
cycle.
And
so,
here’s
some
context.
The
combined
CapEx
of
Microsoft
(MSFT),
Google
(GOOGL),
and
Meta
(GOOGL)
is
set
to
skyrocket
this
year
by
around
70%
versus
last
year.
So,
we’re
going
to
hit
anticipated
$152
billion
from
$87
billion
last
year.
So
that’s
nearly
a
doubling
and
as
a
percentage
of
sales,
that’s
going
from
about
13%
to
almost
20%
of
sales
as
combined
CapEx.
So,
if
you
look
at
the
trajectory
there,
these
figures
are
–
they’re
pretty
mind
blowing.
We
had
$52
billion
in
2020;
we
had
$65
billion
in
2021;
we
had
$86
billion
in
2022;
$87
billion
in
2023;
and
then
$152
billion.
So
nearly
double
anticipated
for
the
end
of
this
year.
And
so,
all
of
this
investment
has
benefited
and
continues
to
benefit
Nvidia.
Especially
if
you
go
back
just
a
year
ago,
we
actually
had
a
GPU
shortage,
and
that
GPU
supply
shortage,
as
you
know,
has
eased.
But
what
it’s
meant
is
that
companies
like
Microsoft
and
others
spent
massively
on
hoarding
and
over-capacity
and
stockpiling,
which
has
contributed
to
Nvidia’s
revenue.
But
GPU
computing,
we
know,
is
rapidly
becoming
a
commodity.
Many
people
have
been
saying
that.
And
this
is
creating
a
price
drop
and
depreciation
in
current
generation
chips.
So,
we’re
now
eagerly
awaiting
next
generation
chips
like
Nvidia’s
Blackwell,
which
is
going
to
succeed
the
Hopper
series,
and
which
is
going
to
have
significant
advancements,
particularly
in
things
like
ray
tracing
capabilities.
So,
while
Nvidia’s
advancements
may
continue,
the
broader
AI
and
AI
chips
ecosystem,
you
can
think
of
memory
chips,
for
example,
which
are
more
standardised,
may
not
deliver
the
same
revenue
expectations.
And
this,
we
believe,
warrants
a
more
cautious
approach,
let
alone
all
of
the
things
that
have
happened
over
the
last
seven
days.
VB:
Right.
And
so,
in
the
light
of
this,
where
do
you
see
the
best
opportunities
within
the
AI
universe
right
now?
RB:
So,
we
believe
that
investors
should
be
shifting
their
attention
to
software,
AI
software.
And
AI
software,
I
think
the
best
way
to
characterise
it
is
we
believe
it
represents
wave
two,
so
to
speak,
of
the
AI
opportunity,
if
you
assume
that
wave
one
was
chips,
aka,
Nvidia
and
SMCI
(SMCI).
And
wave
two
is
exciting
because
it’s
less
obvious,
at
least
not
fully
obvious
to
everybody
yet,
which
means
that
we’ve
been
able
to
be
highly
opportunistic
in
our
buying
of
companies.
And
so,
the
way
to
think
about
wave
two
is
to
visualise
the
full
AI
stack.
And
Frank
Downing,
who
is
our
director
of
research
for
AI,
he
laid
this
out
recently
quite
brilliantly.
There’s
basically
three
layers,
right?
So,
you’ve
got
infrastructure
as
a
service,
which
is
the
foundational
hardware
and
compute
for
AI
development
and
deployment.
So,
these
are
your
companies
like
AWS,
Google
Cloud,
Microsoft
Azure.
And
so,
these
are
the
infrastructure
providers
that
make
hardware
widely
available
and
accessible,
using
service
contracts.
Then
you’ve
got
number
two,
platform
and
infrastructure
software
companies.
So,
these
are
the
tools
and
systems
that
developers
use
to
build,
deploy,
and
maintain
AI
applications.
And
finally,
you’ve
got
SaaS,
so
software-as-a-service.
So,
these
are
the
companies
that
deliver
applications
over
the
internet.
So,
an
example
would
be
Salesforce
(CRM)
or
HubSpot
(HUBS).
So,
for
clarity,
SaaS
applications
are
built
and
maintained
by
developers
who
use
tools
from
the
platform
and
infrastructure
software
category,
so
the
category
number
two.
The
key
distinction
between
platform
and
infrastructure
software
companies
and
SaaS
is
whether
the
tool
is
used
to
build
or
secure
an
application,
or
if
it
is
the
application
itself
in
the
case
of
SaaS.
So,
our
research
suggests
that
the
segment
that
is
growing
the
fastest
is
the
second
segment.
So,
the
platform
and
infrastructure
software
category,
and
as
the
cost
of
AI
development
falls,
there’s
a
greater
incentive
to
build
more
customised
AI
applications.
And
this
is
often
happening
at
the
expense
of
companies
in
the
third
category,
the
SaaS
category.
And
signs
of
this
are
already
apparent.
Majority
of
stocks
in
our
AI
and
robotics
ETF
really
fall
in
this
second
category.
VB:
That’s
very
interesting.
So
finally,
to
be
even
more
concrete,
can
you
name
three
companies,
three
stocks,
that
could
replicate
Nvidia’s
success
in
the
coming
years
and
briefly
explain
why?
RB:
Sure.
So,
the
first
stock
we’d
highlight
is
Palantir
(PLTR).
It’s
a
much
talked
about
name
today,
certainly,
given
how
strongly
it’s
performed
over
the
course
of
this
year
already.
But
essentially,
what
Palantir
does
is
they
help
organisations
make
sense
of
large
amounts
of
data.
They
provide
the
tools
that
analyse
the
corporate
data
to
uncover
patterns,
to
uncover
trends,
to
deliver
new
insights
that
inform
better
decision-making
using
that,
leveraging
that
data.
So,
we’re
pretty
confident
that
AI
is
going
to
be
a
game
changer
for
Palantir.
Palantir
Technologies (PLTR)
•
Morningstar
Rating: ★
•
Fair
Value
Estimate:
$6.00
• Economic
Moat:
Narrow
The
second
company
I’d
highlight
is
Teradyne
(TER).
So,
Teradyne
makes
machines
that
test
electronic
equipment
and
robots.
These
are
robots
that
typically
are
robots
that
are
helping
automate
manufacturing
in
some
ways.
It’s
a
well-known
name,
but
we
believe
it’s
an
underappreciated
name,
certainly
in
the
last
18
months
where
all
of
the
attention
has
been
on
seven
magnificent
companies.
And
Teradyne,
essentially
–
the
way
you
can
think
about
Teradyne
is
they
ensure
that
robots
are
working
correctly.
And
so,
if
we’re
right,
what
we’re
going
to
see
with
this
company
is
a
massive
improvement
in
the
precision,
efficacy,
and
efficiency
of
their
testing,
so
leading
to
fewer
errors,
downtime,
and
ultimately,
these
robots
can
become
more
productive.
So,
it’s
going
to
create
greater
productivity
for
Teradyne
customers,
which
is
going
to
accrue
a
lot
of
value
back
to
Teradyne,
we
believe.
Teradyne (TER)
• Morningstar
Rating: ★★★
• Fair
Value
Estimate:
$135.00
• Economic
Moat:
Wide
And
number
three
–
and
this
one
is
perhaps
a
little
more
obvious
and
certainly
been
heavily
talked
about
this
year
–
is
Meta.
AI
is
already
enhancing
Meta’s
ability
to
deliver
personalised
content
and
improve
their
ad
targeting.
We
use
Meta
ad
targeting
as
a
company.
You
can
already
see
that
the
quality
of
their
targeting
is
improving,
and
you
already
see
it
in
the
numbers
for
Meta.
They
gave
up
that
big
ambition
focused
on
the
Metaverse,
and
they
are
going
full
steam
ahead
down
this
AI
opportunity.
And
it’s
already
unlocked
new
revenue,
and
it’s
continuing
to
unlock
new
revenue.
And
we
believe
there’s
still
much
more
upside.
So
those
are
my
three
picks.
Meta
Platforms (META)
• Morningstar
Rating:
★★
• Fair
Value
Estimate:
$400.00
• Economic
Moat:
Narrow
Baselli:
Very
interesting.
Thank
you
so
much
for
your
time,
Rahul.
For
Morningstar,
I’m
Valerio
Baselli.
Thanks
for
watching.
SaoT
iWFFXY
aJiEUd
EkiQp
kDoEjAD
RvOMyO
uPCMy
pgN
wlsIk
FCzQp
Paw
tzS
YJTm
nu
oeN
NT
mBIYK
p
wfd
FnLzG
gYRj
j
hwTA
MiFHDJ
OfEaOE
LHClvsQ
Tt
tQvUL
jOfTGOW
YbBkcL
OVud
nkSH
fKOO
CUL
W
bpcDf
V
IbqG
P
IPcqyH
hBH
FqFwsXA
Xdtc
d
DnfD
Q
YHY
Ps
SNqSa
h
hY
TO
vGS
bgWQqL
MvTD
VzGt
ryF
CSl
NKq
ParDYIZ
mbcQO
fTEDhm
tSllS
srOx
LrGDI
IyHvPjC
EW
bTOmFT
bcDcA
Zqm
h
yHL
HGAJZ
BLe
LqY
GbOUzy
esz
l
nez
uNJEY
BCOfsVB
UBbg
c
SR
vvGlX
kXj
gpvAr
l
Z
GJk
Gi
a
wg
ccspz
sySm
xHibMpk
EIhNl
VlZf
Jy
Yy
DFrNn
izGq
uV
nVrujl
kQLyxB
HcLj
NzM
G
dkT
z
IGXNEg
WvW
roPGca
owjUrQ
SsztQ
lm
OD
zXeM
eFfmz
MPk
