Clayton
Collins:
What
does
AI
mean
for
the
world?

Andrew
Maas:
AI
refers
to
a
broad
category
of
technologies
that
can
be
used
for
a
lot
of
different
things.
And
sometimes
when
people
hear
AI,
they
think
of
a
single
sentient
intelligence,
or
what
we
would
call
sort
of
general
AI,
or
hard
AI.
For
the
most
part,
people
like
me
are
not
thinking
about
that
day
to
day
—
we
don’t
think
that
that’s
very
close.
Systems
like

ChatGPT
—
it’s
become
clear
over
the
last
two
years
that
we’re
not
seeing
the
sort
of
rapid
progress
of
something
that’s
going
to
become
as
smart
as
a
five
-ear
old
anytime
soon.
In
terms
of
general
reasoning
and
common
sense,
those
are
the
things
that
are
very
hard.

What
is
true
is
these
language
models
are
very
good
at
fooling
us
humans…
we
want
to
believe
that
they’re
smart,
and
they’re
good
as
little
chatbots.
And
increasingly,
we’re
now
engineering
these
different
components
around
them
and
onto
them
that
are
making
them
more
useful
and
more
reliable
in
terms
of
factual
accuracy,
interfacing
with
the
outside
world.

But,
in
terms
of
broad
AI,
think
of
it
as
sort
of
adopting
the
internet
almost.
There
wasn’t
one
way
that
a
business
said,
here’s
how
we’re
going
to
use
the
internet
to
improve
our
business.
And
AI,
especially
the
kind
of
new
AI
systems
that
we
can
now
start
to
build
and
adopt,
you
should
think
of
it
in
the
same
way,
where
it’s
not
that
there’s
going
to
be
a
single
AI
agent
that
I
hire
instead
of
an
employee
and
it’s
the
perfect
employee
and
it
sells
every
mortgage
to
every
person
ever.
No,
it’s
that
there’s
100
different
little
use
cases.

Rick
Roque:
Our
industry
…tends
to
be
a
bit
antiquated.
We
tend
to
be
overly
focused
on
human
resources,and
on
repeated
tasks
that
might
prevent
scale,
or
that
might
introduce
inaccuracies
or
aspects
of
errors
that
in
our
business
mean
added
margin.
Like
we
don’t
even
know
how
many
inefficiencies
there
are
in
the
mortgage
chain
so
we
just
price
it
out,
right?
Which
is
why
so
many
innovations
like

AI
or
blockchain
help
alleviate
the
aspects
of
doubt
around
the
quality
of
what
it
is
that
you’re
producing.
And
as
a
result,
your
need
to
price
into
the
mortgage
pricing
throughout
the
capital
markets
chain
gets
diminished,
because
you
know
you
have
a
greater
degree
of
confidence
in
what
you’re
receiving.

When
I
was
on
the
leadership
team
at

Cross
Country,
I
said,
this
is
really
a
necessary
conversation
that
not
just
Cross
Country,
but
really
all
lenders
in
the
industry
need
to
have.
Because
it’s
clear
that
the
great
divide
that’s
coming
in
the
business
is
that
lenders
that
leverage
AI,
and
this
radical
adjustment
of
efficiency
into
their
pipeline,
this
is
going
to
be
the
difference
between
businesses
that
leveraged
fax
and
couriers
versus
businesses
that
adopted
email.
I
genuinely
believe
the
efficiencies
will
be
that
dramatic,
both
in
pricing
and
the
consumer
experience,
and
in
profitability
for
the
lender,
will
be
that
stark
of
a
contrast
with
between
those
who
adopt
and
those
who
don’t.

CC:

What
do
you
see
companies
building
right
now?

AM:
That’s
where
there’s
a
huge
amount
of
upheaval
right
now.
Because
basically,
where
we
are
today,
there
is
a
tremendous
amount
of
new
use
cases
which
are
enabled
by
the
technology
advances
and
the
large
public
models
that
are
available
with
friendly
commercial
licensees
now.
And
we
have
probably
five
years
of
work
to
do,
honestly,
in
terms
of
enterprise
adoption
and
integration
—
even
assuming
that
everybody’s
onboard,
everybody
wants
to
do
it
and
depending
on

regulatory
stuff.

Right
now,
in
general,
my
advice
is
to
look
very
hard
at
vendors
you’re
working
with,
because
they
might
be
building
something
for
the
very
first
time
or
promising
you
something
that
they
know
is
possible,
but
they
haven’t
actually
built.
It’s
just
that
kind
of
time.

CC:

You
mentioned
models
are
trained
off
of
public
data.
Are
there
two
different
AI
marketplaces
that
are
emerging
—
like
models
that
are
trained
around
proprietary
or
in-house
datasets
versus
models
that
are
trained
around
publicly
available
information
on
the
internet?

AM:
That’s
what
people
thought
might
emerge
two
years
ago,
and
it
didn’t
actually
go
that
way.
Originally,
as
chatGPT
was
starting
to
come
out,
there
was
a
bunch
of
buzz
that
every
large
enterprise
would
use
all
of
their
internal
data
and
train
a
custom
version
of
those
types
of
models
and
it
would
just
solve
a
bunch
of
problems.
And
what
we
found
is,
if
you
just
take
all
your
internal
data
and
train
one
of
these
big
models
on
your
private
data,
you
end
up
with
something
that
kind
of
talks
a
little
more
like
your
private
data
talks,
but
it
doesn’t
actually
solve
some
of
the
most
valuable
use
cases
for
you.
You
still
have
to
engineer
components
on.

And
so
more
recently,
I
think
the
consensus
that’s
emerging
is
you
think
of
the
language
model
as
kind
of
like
a
paintbrush
—
I
might
have
a
paintbrush
from
public
data
that’s
pretty
good
at
talking
about
mortgage
and
financial
terms
and
those
sorts
of
things.
If
we
use
some
of
the
publicly
available
GPT
models,
they
can
talk
about
mortgage
terms
pretty
well.
They
don’t
actually
integrate
in
any
useful
way
with
your
back-end
systems
or
your
structured

data
to
say:
here’s
the
mortgages
that
I
offer
and
here
are
my
terms.
And
so
that’s
the
extra
engineering
work
you’re
going
to
have
to
do,
whether
you
have
a
private
model
or
you’re
using
a
public
model.

As
a
result,
what
we
see
now
is
people
doing
the
engineering
work
to
integrate
their
proprietary
workflows,
their
proprietary
data,
but
not
necessarily
training
one
of
these
LLMs
themselves.

CC:

How
are
executives
starting
to
lay
the
groundwork
for
how
this
impacts
their
businesses?

AM:
Something
I’ve
heard
a
lot
in
the
enterprise
AI
discussion
circuit
recently
is:
when
and
where
is
the
enterprise
value
for
all
of
this
AI
buzz
going
to
emerge?
Because
it’s
been
two
years
since
we
had
a
bunch
of
hype
around
ChatGPT.
Some
executives
spent
a
bunch
of
money
on
projects
that
were
poorly
scoped.
And,
for
example,
built
one
of
these
very
large
private
models
that
we
were
just
talking
about,
and
then
they
deployed
it
internally
and
they
found,
okay,
we
still
have
a
bunch
of
use
cases
that
are
not
solved
by
this
model.
Now,
how
do
we
actually
go
solve
those
use
cases?

And
some
of
those
might
be
out
of
reach,
because
they’re
a
hard
problem
that
the
language
modeling
piece
doesn’t
solve.
And
so
we
saw
a
ton
of
buzz,
a
ton
of
investment,
a
ton
of
startups
get
founded
and
product
ideas
kick
off,
and
we’re
now
trying
to
pick
through:
who’s
actually
doing
a
process
with
AI
that’s
saving
money
or
creating
new
revenue?
And
now,
it’s
getting
into
more
of
the
details.
So
executives
that
I’ve
seen
more
recently,
they’re
not
thinking
of
how
do
I
invest
in
AI?
Instead,
they’re
thinking:
what
are
the
use
cases
where
AI
is
going
to
be
really
transformative?
And
how
is
that
impacting
my
bottom
line?
Or
what
is
the
ROI?
And
I
think
that’s
the
right
starting
point.

CC:

Rick,
what
are
you
hearing
in
mortgage
boardrooms?
What
conversations
are
being
had
about
AI
and
the
executive
circles
that
you
run
in?

RR:
I’d
say
complete
confusion.
The
focus
in
all
boardrooms
is
on
production…
There’s
a
tremendous
amount
of
focus
on
the
impending
winner
come
October,
November,
December.
Now
the
higher
you
get
in
the
enterprise
class
and
mortgage
lenders,
there
is
a
focus
that
in
2025
and
2026
demand
is,
without
a
doubt,
becoming
extreme
—
the
demand
for
housing
is
percolating
to
alarming
proportions.
We
all
see
the
opportunity
in
production
volume
in
2025
and
2026
and
2027,
which
isn’t
too
far
away.

And
so,
the
larger
you
get,
in
some
of
those
conversations
I’m
a
part
of,
whether
you’re
a

New
American
Funding
or
an

NFM
or
a

Sierra
Pacific,
the
conversation
is:
how
do
we
triple
our
production
without
adding
any
overhead?
What
do
we
need
to
do?
Either
outsourcing
or
leveraging
AI
—
I
think
AI
is
really
the
next
innovation.
The
last
10
years,
the
focus
for
a
lot
of
us
on
the
enterprise
side
has
been:
can
we
outsource
to
India
or
Philippines
to
reduce
our
cost
of
production?

CC:
It’s
been
a
theme
on
this
show
for
years.
How
do
we
build
an
accordion-type
cost
structure
that
can
fluctuate
with
changes
in
volume,
whether
it
be
purchase
or
refi
volume,
that
doesn’t
mean
that
you
have
to
go
out
and
scale
up
with
thousands
of
new
processors
and
underwriters
and
ops
talent
every
time
we
see
an
inflow
of
volume?

RR:
And
the
problem
is,
outsourcing
doesn’t
change
the
paradigm,
because
you’re
still
throwing
hundreds
of
people
at
it.
At
AFN,
we
had,
I
think
400
or
500
employees
in
the
Philippines.
I
mean,
we’ll
easily
scale
that.
I
know
Cross
Country
will
easily
scale
that,
but
again,
you’re
not
really
changing
the
paradigm,
you’re
just
adding
cheaper
labor.
You’re
adding
the
same
bodies,
it’s
just
that
those
bodies
aren’t
living
in
the
United
States.
You’re
applying
incredibly
talented
resources
at
a
third
of
the
cost
or
25%
of
the
cost
in
the
Philippines.
I
think
AI
completely
shatters
the
paradigm.

CC:
What
makes
AI
different?
And
how
does
the
housing
executive
mentality
have
to
change
to
actually
build
a
better
cost
structure?

RR:
Let
me
just
chime
in
and
say
that
I
disagree
with
your
point
that
it
hasn’t
introduced
efficiencies,
because
it
has
—
the
problem
is
the
regulatory
environment,
which
wasn’t
really
built
for
automation.
It
wasn’t
built
around
how
to
streamline
efficiencies
in
compliance,
other
than
maybe
audits.
OCR
solutions
were
kind
of
the
big
thing
in
2008,
2009,
2010
,2011.
But
if
it
didn’t
correct
it
100%
of
the
time,
then
it
really
was
worthless,
because
you
still
had
to
go
back
and
preview
with
everything.
It’s
almost
like
the
way
Siri
—
if
I
voice
text
today
and
I’m
driving,
it
really
doesn’t
help
all
that
much.
Because
now
I’m
proofing
everything
that
incorrectly
translated
from
voice
to
text.
And
now
I’m
probably
at
greater
risk
as
I’m
driving.

So
I
would
say
what
technology
did
is
provide
a
tremendous
amount
of
efficiencies
where
it
was
designed…It
had
nothing
to
do
with
our
ability
to
close
loans
faster.
AI
can
change
that.