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What
does
that
tell
me?
That's
very
interesting,
this
sort
of
strategic
backing
you've
got
beyond
just
the
size
and
scope
of
the
round.
First
of
all,
thanks
for
having
me.
I
think
one
of
the
key
things
of
our
role
in
the
AI
industry
is
that
we
really
are
an
infrastructure
provider.
The
three
pillars
of
AI
ultimately
are
data,
compute,
and
algorithms.
Folks
like
OpenAI
solve
the
algorithmic
piece,
folks
like
NVIDIA
solve
the
compute
piece,
and
our
role
at
scale
is
to
solve
the
data
pillar
for
AI.
Our
data
foundry
today
powers
nearly
every
leading
large
language
model,
including
those
from
OpenAI,
Meta,
Microsoft,
and
NVIDIA.
And
so
ultimately,
one
of
our
goals
with
this
financing
round
was
really
to
ensure
that
we
can
continue
serving
the
entire
AI
ecosystem.
So
when
you
look
at
a
lot
of
the
strategic
and
corporate
investors
that
we
brought
on
board,
it
really
was
to
sort
of
bring
together
the
entire
ecosystem
and
multiple
layers
of
the
stack.
So
this
includes
other
folks
in
the
AI
industry,
but
also
other
folks
in
the
kind
of
infrastructure
layer,
folks
like
NVIDIA,
AMD,
Intel,
as
well
as
folks
in
the
model
layer,
folks
like
Amazon
or
Meta,
and
then
lastly,
folks
in
the
application
layer,
folks
like
Cisco
or
ServiceNow.
And
so
our
goal
ultimately
was
to
ensure
that
we
can
continue
serving
the
entirety
of
the
AI
ecosystem
as
a
low
-level
infrastructure
provider
by
sort
of
like
bringing
together
that
entire
cohort.
What
about
Google
or
Microsoft?
Are
they
interested?
Is
that
for
the
later
dates,
or
can
we
only...
Can
we
only
go
with
a
certain
number
of
each
player
on
each
system?
You
know,
it's
always
like
herding
cats
with
these
corporations.
But
our
goal
is
to
continue
serving
the
entire
ecosystem.
We
want
to
ensure
that
artificial
intelligence
on
the
whole
is
able
to
accomplish
the
incredible
potential.
Our
goal
with
our
data
engine
is
to
generate
all
the
frontier
data
needed
to
fuel
us
to
AGI
and
potentially
even
beyond.
Our
view,
one
of
the
ways
we
think
about
it
is,
what
are
all
the
problems
in
data
that
need
to
be
solved
to
get
us
from
GPT
-4
to
GPT
-10,
and
how
do
we
ensure
that
we
have
the
means
of
production
to
do
all
of
that?
Let's
talk
about
the
means
of
production,
about
the
action
of
labeling
such
data,
because
I
know
that
you've
really
been
thinking
a
lot
about
AI
safety,
the
development,
the
biases,
ensuring
that
that's
something
that's
thought
about
within
your
business
and
the
modes
that
you
serve.
But
there
was
a
lot
of
concern
back
in
2023
about
who
you
employ
to
label
data,
and
a
lot
of
that
work
being
done
in
the
so
-called
global
system.
And
I
think
that's
a
big
part
of
the
problem.
And
I
think
that's
a
big
part
of
the
problem.
And
I
think
that's
a
big
part
of
the
problem.
So
I
think
that's
a
big
part
of
the
problem.
So
how
do
you
pay
them
yourself,
how
you're
paying
them,
at
what
rate
you're
paying
them?
How
is
that
being
solved
now
by
technology,
Alexander?
Yeah,
ultimately,
we
believe
that
the
future
of
AI
data
rests
on
three
principles,
data
abundance,
frontier
data,
and
measurement
and
evaluation.
In
terms
of
abundance,
I
think
this
is
one
of
the
clearer
areas.
We
need
to
ensure
that
we're
able
to
build
a
data
foundry
that
ushers
in
an
era
of
data
abundance.
These
models
are
becoming
increasingly
data
hungry
due
to
the
scaling
of
data.
And
so
I
think
that's
a
big
part
of
the
problem.
And
I
think
that's
a
big
part
of
the
problem.
And
I
think
that's
a
big
part
of
the
problem.
And
laws.
Every
successive
generation
of
models
requires
exponentially
more
data.
And
we
need
to
ensure
that
we
are
able
to
build
the
systems
and
the
means
of
production
that
allow
us
to
not
resign
ourselves
to
data
scarcity.
A
lot
of
the
key
for
us
comes
into
the
second
bullet
point,
though,
frontier
data.
As
we
develop
progressively
more
and
more
powerful
AI
systems,
we
need
to
be
building
frontier
data,
which
is
always
pushing
the
boundaries
of
AI
capabilities
towards
more
advanced
areas
such
as
complex
reasoning,
agents,
multimodality,
multilinguality,
and
more.
This
production
of
frontier
data
requires
human
experts
all
around
the
world.
And
so
our
view
is
that
humans
and
expertise
are
a
critical
component
of
this
production
process.
Alex,
I
remember
August
2019,
you
were
on
a
previous
iteration
of
this
show.
You
were
22
years
old,
and
you'd
just
done
a
$100
million
Series
C.
Fast
forward
to
today,
you've
just
announced
a
European
HQ
in
London.
You're
valued
at
$14
billion.
What's
that
like?
How
do
you
feel?
You
know,
I
think
ultimately
the
most
gratifying
piece
for
me
and
I
think
for
the
entire
company
has
really
been
how
far
AI
has
come
in
that
time
frame.
You
know,
in
2019,
it
was
the
very
primordial
early
days
of
what's
now
called
generative
AI.
You
know,
even
on
that
program,
we
didn't
talk
at
all
about
the
exciting
things
that
OpenAI
or
other
companies,
we
were
doing,
we
were
talking
about
self
-driving
cars.
If
you
fast
forward
to
today,
we've
gone
from
GPT
-2
to
GPT
-4
and
beyond,
or
4
and
4
.0
and
beyond.
We
have
AI
systems
that
are
significantly
more
capable.
And
I
think
we
see
a
path
to
AI
really
improving
everyone's
lives
in
a
way
that
was
more
of
a
pipe
dream
back
in
2019.
And
so
my
hope
is
that,
you
know,
five
years
later,
if
I'm
back
on
the
show,
that,
you
know,
we
can,
we're
looking
back
on
the
technology
and
seeing
even
further
development
in
our
industry.
I
think
we're
going
to
see
even
further
development
in
artificial
intelligence,
even
further
application
of
the
technology,
even
further
impact
from
ultimately
what
we
view
as
the
most
exciting
Frequently Asked Questions