Nvidia Upgraded But Shares Now Overvalued

Our
$300
fair
value
estimate
assumes
a
fiscal
2024
(year
ending
January
2024)
adjusted
price/earnings
ratio
of
37
times.
We
project
revenue
will
increase
at
a
23%
compound
annual
growth
rate
through
fiscal
2028
as
the
firm
continues
to
diversify
its
revenue
sources
to
areas
of
strong
potential.

We
think
the
data
center
segment
will
rise
at
a
30%
CAGR
through
fiscal
2028
as
we
expect
the
firm
to
dominate
the
training
portion
of
deep-learning
AI
workloads.
Key
workloads
that
supported
this
growth
included
large
language
models
(such
as
ChatGPT),
other
forms
of
generative
AI,
deep
recommendation
engines,
and
autonomous
vehicle
fleet
data
processing
and
training.

While
we
expect
gaming
to
remain
a
major
source
of
revenue,
the
cryptocurrency
crash
has
materially
curbed
demand
for
Nvidia’s
GPUs
for
mining
purposes.
We
estimate
gaming
GPU
sales
will
be
up
7%
in
fiscal
2024
following
a
sharp
decline
of
27%
in
fiscal
2023.
Over
the
next
five
years,
we
project
that
gaming
revenue
will
rise
at
an
8%
CAGR.

In
automotive,
we
think
Nvidia
will
capture
a
healthy
portion
of
the
self-driving
opportunity,
culminating
in
a
39%
CAGR
in
automotive
revenue
through
fiscal
2028,
following
a
soft
fiscal
2021
and
2022
due
to
the
coronavirus
and
the
chip
shortage
that
hampered
auto
production.

Nvidia
price/fair
value
ratios
from
2020-23,
with
ratios
over
1
indicating
when
the
stock
is
overvalued,
while
ratios
below
1
indicate
when
the
stock
is
undervalued.

Bulls
Say:

1)
The
proliferation
of
the
AI
and
deep-learning
phenomena
that
rely
on
Nvidia’s
graphics
chips
presents
the
firm
with
a
massive
growth
opportunity.

2)
The
firm
has
a
first-mover
advantage
in
the
autonomous
driving
market
that
could
lead
to
widespread
adoption
of
its
Drive
PX
self-driving
platform.

3) 
The
increasing
complexity
of
graphics
processors
provides
a
barrier
to
entry
for
most
potential
rivals,
as
it
would
be
difficult
to
match
Nvidia’s
large
research
and
development
budget.

Bears
Say:

1)
The
artificial
intelligence
opportunity
remains
nascent,
and
it
is
not
a
foregone
conclusion
that
Nvidia’s
GPUs
will
dominate.

2)
Nvidia’s
automotive
endeavors
face
plenty
of
competition,
as
numerous
chipmakers
are
targeting
the
market.

3)
A
large
portion
of
sales
comes
from
the
maturing
PC
industry
via
PC
gaming.

Economic
Moat
Rating

We
believe
Nvidia
possesses
a
wide
economic
moat
stemming
from
its
intangible
assets
related
to
the
design
of
graphics
processing
units.
The
firm
is
the
originator
of
and
leader
in
discrete
graphics,
having
captured
the
lion’s
share
of
the
market
from
longtime
rival
AMD.
We
think
the
market
has
significant
barriers
to
entry
in
the
form
of
advanced
intellectual
property,
as
even
chip
leader
Intel INTC was
unable
to
develop
its
own
discrete
GPUs
(despite
its
vast
resources)
and
ultimately
needed
to
license
IP
from
Nvidia
to
integrate
GPUs
into
its
PC
chipsets
(though,
more
recently,
Intel
is
vying
to
develop
its
own
discrete
GPU).
To
stay
at
the
cutting
edge
of
GPU
technology,
Nvidia
has
a
large
research
and
development
budget
relative
to
AMD
and
smaller
GPU
suppliers,
which
allows
it
to
continuously
innovate
and
fuel
a
virtuous
cycle
for
its
high-margin
chips.

Nvidia
has
taken
steps
to
leverage
its
GPU
prowess
into
other
markets
such
as
data
center
and
automotive
that
represent
meaningful
growth
opportunities.
GPUs
are
being
used
to
accelerate
computation
workloads
with
the
goal
of
training
AI
systems
to
perform
complex
tasks
such
as
driving
cars.
We
note
these
tasks
are
computationally
intensive
endeavors
that
are
more
achievable
with
CPUs
and
GPUs
working
in
tandem
versus
CPUs
in
isolation.

Consumer
internet
and
cloud
behemoths
such
as
Alphabet GOOGL,
Meta META,
Amazon.com AMZN,
and
Microsoft MSFT have
found
GPUs
to
be
adept
at
accelerating
cloud
workloads
that
use
deep-learning
techniques
to
achieve
speech
recognition
(Siri,
Google
Now,
Alexa,
Cortana),
photo
recognition
(identifying
faces
in
pictures
on
Facebook,
videos
of
cats
on
YouTube),
and
recommendation
engines
(Netflix
and
Amazon).
More
recently,
large
language
models
that
use
generative
AI
such
as
ChatGPT
have
risen
in
prominence
and
utilize
thousands
of
GPUs
to
be
trained.

Nvidia
has
a
first-mover
advantage
in
the
accelerator
market
as
it
looks
to
foster
continued
AI
adoption
in
both
the
cloud
and
on
the
road.

DPUs
are
a
new
class
of
product
that
Nvidia
is
promoting
for
data
centers
that
offload
and
accelerate
workloads
that
may
run
on
a
CPU,
thus
allowing
CPUs
to
focus
on
core
applications.
Specifically,
DPUs
can
be
used
as
a
Smart
NIC,
or
network
interface
card,
for
software-defined
networking,
storage,
and
security.
Nvidia
CEO
Jensen
Huang
estimates
roughly
50%
of
the
computer
cores
in
modern
data
centers
are
handling
functions
that
could
run
on
a
DPU.
This
dynamic
puts
server
CPU
vendors
at
risk,
though
we
note
Intel,
Xilinx,
and
other
chip
firms
also
have
comparable
programmable
Smart
NIC
solutions.

Risk
and
Uncertainty

Our
Morningstar
Uncertainty
Rating
for
Nvidia
is
High.
The
firm
has
benefited
from
strong
PC
gaming
momentum
in
recent
years.
However,
many
of
the
most
popular
games
are
competitive
multiplayer
online
games
(esports)
that
require
low-end
discrete
GPUs
for
latency
reasons
versus
high-end
GPUs
for
cutting-edge
graphics.

Nvidia
has
a
first-mover
advantage
in
chip
solutions
for
AI
and
autonomous-vehicles,
though
its
lead
may
not
last
if
superior
alternatives
arise
(other
forms
of
acceleration
for
AI
or
other
self-driving
platforms).
Also,
the
rate
of
disruption
tends
to
be
quicker
in
these
markets
that
are
very
performance-sensitive.
We
note
GPUs
were
designed
to
do
one
thing
very
well:
render
graphics
for
realistic
images,
games,
videos,
and
so
on.
Leveraging
GPUs
in
deep-learning
applications
among
other
areas
mostly
occurred
due
to
a
lack
of
better
alternatives.
As
alternatives
arise,
Nvidia’s
recent
explosive
growth
could
be
difficult
to
maintain.

We
do
not
foresee
any
material
environmental,
social,
or
governance
issues
on
the
horizon.
Perhaps
the
greatest
risk
is
the
potential
scarcity
of
experienced
chip
design
talent
within
the
industry.

This
article
was
compiled
by
Muskaan
Hemrajani

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