Nvidia Stock Post-Earnings: Buy, Sell or Hold?

Nvidia’s
forecast
was
also
ahead
of
expectations,
although
the
firm
did
concede
that
it
will
see
a
significant
decline
in
revenue
from
China
due
to
US
export
restrictions.
Nvidia
will
seemingly
have
no
trouble
making
up
for
this
lost
revenue
by
shipping
even
more
GPUs
to
customers
in
the
rest
of
the
world,
but
it’s
possible
that
such
demand
will
be
satisfied
a
bit
quicker
than
previously
expected.

Nonetheless,
Nvidia
is
firing
on
all
cylinders,
unlike
any
other
technology
company
we
can
recall.
It
is
in
a
nearly
perfect
position
in
AI
semiconductors.
Even
as
others
catch
up
and
customers
undoubtedly
diversify
with
other
vendors
over
time,
we
think
the
firm
will
remain
the
market
leader
for
years
to
come.

Bullish
investors
saw
some
great
data
points.
Most
obviously,
there
was
Nvidia’s
financial
outperformance
on
the
top
and
bottom
lines
in
the
third
quarter,
as
well
as
a
fourth-quarter
forecast
that
was
ahead
of
expectations.
The
company
touted
its
strong
performance
in
AI
inference
(when
the
model
processes
queries)
semis –
it’s
the
unquestioned
leader
in
AI
training
GPUs,
but
it
has
been
questioned
whether
it
would
also
dominate
here.
Nvidia’s
prospects
seem
promising
here.
We’re
also
encouraged
that
its
networking
business,
led
by
the
InfiniBand
technology
needed
in
AI,
is
running
at
a
$10
billion
(£7.9
billion)
annual
run
rate,
up
significantly
versus
a
few
quarters
ago.

A
bearish
investor
may
have
also
seen
some
things
they
were
looking
for.
Nvidia
conceded
that
it
will
lose
a
good
chunk
of
revenue
in
China.
Also,
AI
revenue
has
certainly
been
lumpy,
as
the
firm
has
earned
more
than
10%
of
revenue
with
a
new
customer
in
the
third
quarter
and
a
different
customer
in
the
second
quarter
(which,
notably,
was
not
a
10%
customer
in
the
third
quarter).
If
a
bearish
investor
believes
all
the
AI
spending
is
an
up-front
purge
and
won’t
be
followed
by
a
longer-lived
revenue
stream,
these
results
might
back
them
up.
We
don’t
foresee
an
AI
chip
bubble
any
time
soon,
but
we
can’t
rule
out
the
potential
that
some
of
these
large
cloud
vendors
will
take
a
breather
on
GPU
purchases
at
some
point.

After
such
a
terrific
quarter,
we’re
modestly
more
confident
that
Nvidia
will
reach
our
long-term
target
of
$100
billion
of
data
center,
or
DC,
revenue
in
fiscal
2028.
However,
there
are
many
moving
pieces,
such
as
the
lumpy
buying
patterns
of
large
customers
and
risks
around
U.S.
sanctions
on
China.
Thus,
we
reiterate
our
Very
High
Uncertainty
Rating,
even
as
we
maintain
our
$480
fair
value
estimate

Fair
Value
Estimate
for
Nvidia
Stock

With
its
3-star
rating,
we
believe
Nvidia’s
stock
is
fairly
valued
compared
with
our
long-term
fair
value
estimate.

Our
fair
value
estimate
is
$480
per
share,
which
implies
an
equity
value
of
over
$1.1
trillion.
Our
fair
value
estimate
implies
a
fiscal
2024
price/adjusted
earnings
multiple
of
45
times
and
a
fiscal
2025
forward
price/adjusted
earnings
multiple
of
31
times.

We
anticipate
a
massive
expansion
in
the
AI
processor
market
in
the
decade
ahead,
and
we
see
room
for
tremendous
revenue
growth
both
at
Nvidia
and
at
competing
solutions,
whether
they
be
external
chipmakers
(like
Advanced
Micro
Devices
[AMD] or
Intel
[INTC])
or
in-house
solutions
developed
by
hyperscalers
(such
as
chips
from
Alphabet
[GOOGL] or
Amazon
[AMZN]).

Nvidia’s
DC
business
has
already
achieved
exponential
growth,
rising
from
$3
billion
in
fiscal
2020
to
$15
billion
in
fiscal
2023.
The
firm
should
see
an
even
higher
inflection
point
in
fiscal
2024,
as
we
expect
DC
revenue
to
more
than
double
to
$41
billion.

We
don’t
view
this
spike
as
coming
from
frontloaded
orders
or
a
buildup
of
excess
capacity,
as
we
model
46%
growth
in
fiscal
2025
DC
revenue
to
over
$60
billion.
We
model
growth
of
23%,
20%,
and
13%
in
the
following
three
years,
driving
DC
revenue
to
$100
billion
in
fiscal
2028,
compared
with
AMD’s
estimate
for
the
AI
accelerator
total
addressable
market
to
be
$150
billion
by
calendar
2027.
We
think
this
prediction
is
reasonable,
given
the
massive
investments
and
interest
in
AI.
We
doubt
that
any
enterprise
wants
to
be
left
behind;
nor
does
any
cloud
computing
provider
want
to
be
shorthanded
in
providing
AI
GPUs
to
its
customers.

Read
more
about
Nvidia’s
Fair
Value
Estimate

Economic
Moat
Rating

We
assign
Nvidia
a
wide
economic
moat,
thanks
to
intangible
assets
around
its
graphics
processing
units
and,
increasingly,
switching
costs
around
its
proprietary
software,
such
as
its
Cuda
platform
for
AI
tools,
which
enables
developers
to
use
its
GPUs
to
build
AI
models.

Nvidia
was
an
early
leader
and
designer
of
GPUs,
which
were
originally
developed
to
offload
graphics
processing
tasks
on
PCs
and
gaming
consoles.
The
company
has
emerged
as
the
clear
market
share
leader
in
discrete
GPUs
(over
80%
share,
per
Mercury
Research).
We
attribute
this
leadership
to
intangible
assets
associated
with
GPU
design,
as
well
as
the
associated
software,
frameworks,
and
tools
developers
need
to
work
with
these
GPUs.
We
don’t
foresee
any
emerging
companies
becoming
a
third
relevant
player
in
the
market
alongside
Nvidia
and
AMD.
Even
Intel,
the
chip
industry
behemoth,
has
struggled
for
many
years
with
trying
to
build
a
high-end
GPU
that
would
be
adopted
by
gaming
enthusiasts,
and
its
next
effort
at
a
discrete
GPU
is
slated
to
launch
in
2025.

In
our
view,
the
nature
of
parallel
processing
in
GPUs
is
at
the
heart
of
Nvidia’s
dominance
in
its
various
end
markets.
PC
graphics
were
the
initial
key
application,
facilitating
more
robust
and
immersive
gaming
over
the
past
couple
of
decades.
Cryptocurrency
mining
also
involves
many
mathematical
calculations
that
can
run
in
parallel.
And
over
the
past
decade,
parallel
processing
has
been
found
to
more
efficiently
run
the
matrix
multiplication
algorithms
needed
to
power
AI
models.

GPUs
are
best
suited
to
make
the
many
billions
of
calculations
needed
for
large
language
models
to
predict
the
next
word
in
a
query
(GPT-3
was
trained
on
175
billion
parameters,
for
example).
More
importantly,
Nvidia
made
shrewd
moves
to
expand
Cuda,
creating
and
hosting
a
variety
of
libraries,
compilers,
frameworks,
and
development
tools
allowing
AI
professionals
to
build
their
models.
Cuda
is
proprietary
to
Nvidia
and
only
runs
on
its
GPUs,
and
we
believe
this
hardware-plus-software
integration
has
created
high
customer
switching
costs
in
AI,
contributing
to
the
firm’s
wide
moat.

Read
more
about
Nvidia’s
moat
rating

Risk
and
Uncertainty

We
assign
Nvidia
a
Very
High
Uncertainty
Rating.
For
better
or
worse,
its
stock
price
will
be
driven
by
its
prospects
for
DC
and
AI
GPUs.
We
see
a
host
of
tech
leaders
vying
for
its
leading
AI
position.
We
think
it
is
inevitable
that
leading
hyperscale
vendors,
such
as
Amazon’s
AWS,
Microsoft
[MSFT],
Alphabet,
and
Meta
Platforms
[META],
will
seek
to
reduce
their
reliance
on
Nvidia
and
diversify
their
semiconductor
and
software
supplier
base,
including
through
developing
in-house
solutions.

Our
uncertainty
rating
is
based
on
the
uncertainty
around
this
market.
Nvidia
dominates
AI
today,
and
the
sky
is
the
limit
for
the
company’s
profitability
if
it
can
maintain
this
lead
over
the
next
decade.
However,
any
semblance
of
the
successful
development
of
alternatives
could
meaningfully
limit
the
company’s
upside.

Read
more
about
Nvidia’s
risk
and
uncertainty

NVDA
Bulls
Say

• Nvidia’s
  GPUs
  offer
  industry-leading
  parallel
  processing,
  which
  was
  historically
  needed
  in
  PC
  gaming
  but
  has
  since
  expanded
  into
  crypto
  mining,
  AI,
  and
  perhaps
  future
  applications;
• Nvidia’s
  DC
  GPUs
  and
  Cuda
  software
  platform
  have
  established
  it
  as
  the
  dominant
  vendor
  for
  AI
  model
  training,
  a
  use
  case
  that
  should
  rise
  exponentially
  in
  the
  years
  ahead;
• The
  firm
  has
  a
  first-mover
  advantage
  in
  the
  autonomous
  driving
  market
  that
  could
  lead
  to
  the
  widespread
  adoption
  of
  its
  Drive
  PX
  platform.

NVDA
Bears
Say

• Nvidia
  is
  currently
  a
  leading
  AI
  chip
  vendor,
  but
  other
  powerful
  chipmakers
  and
  tech
  titans
  are
  focused
  on
  in-house
  chip
  development;
• Although
  Cuda
  is
  a
  leader
  in
  AI
  training
  software
  and
  tools,
  other
  cloud
  vendors
  would
  likely
  prefer
  to
  see
  greater
  competition
  in
  this
  space,
  and
  they
  may
  shift
  to
  alternative
  open-source
  tools
  if
  they
  arise;
• Nvidia’s
  gaming
  GPU
  business
  has
  often
  seen
  boom-or-bust
  cycles
  based
  on
  PC
  demand
  and,
  more
  recently,
  cryptocurrency
  mining.

This
article
was
compiled
by
Tom
Lauricella

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