I’m
a
practicing
attorney,
and
I
want
my
clients
to
use
AI.
That
might
sound
counterintuitive
coming
from
someone
who
earns
her
keep
selling
legal
advice.
Plus,
many
attorneys
express
frustration
when
clients
show
up
with
contracts
full
of
unlawful
provisions
drafted
by
ChatGPT
or
implausible
case
strategies
concocted
by
Gemini
and
then
have
to
spend
time
explaining
why
those
won’t
work.
But
after
more
than
three
decades
of
small
law
practice
where
I
frequently
harness
my
clients’
sweat
equity
to
fight
Big
Energy,
I
see
things
differently.
My
clients
who
use
AI
to
research
their
legal
situation
are
better
clients.
They
arrive
well-organized.
They
understand
the
documents
I’ve
sent
so
we
don’t
waste
precious
billable
hours
on
the
basics.
They’re
fully
engaged
in
the
case
without
monopolizing
my
time.
For
clients
on
a
budget
especially,
AI
can
be
transformational.
A
recent
federal
court
opinion
threatens
to
change
all
of
that.
In
a
memorandum
opinion
dated
February
17,
2026,
Judge
Jed
Rakoff
of
the
Southern
District
of
New
York
ruled
in
United
States
v.
Heppner
that
documents
a
defendant
generated
using
Claude
were
not
protected
by
attorney-client
privilege
or
the
work-product
doctrine.
The
defendant,
Bradley
Heppner,
charged
with
securities
fraud,
had
used
Claude
to
research
his
case
and
generate
detailed
legal
analyses.
When
the
FBI
seized
his
devices,
prosecutors
claimed
those
documents
were
fair
game.
The
court
agreed,
finding
that
(1)
Claude
is
not
an
attorney,
(2)
that
the
communications
were
not
confidential
given
Anthropic’s
privacy
policy
allowing
disclosure
to
third
parties,
and
(3)
that
even
assuming
the
documents
were
prepared
in
anticipation
of
litigation,
they
were
not
protected
by
work-product
privilege
because
they
were
not
prepared
by
or
at
the
behest
of
counsel.
The
ruling
may
be
defensible
under
existing
doctrine.
But
it
is
a
disaster
for
the
21st-century
justice
system.
To
understand
why,
consider
the
crisis
the
American
legal
system
already
faces.
According
to
the
Legal
Services
Corporation,
roughly
92
percent
of
low-income
Americans
receive
inadequate
or
no
legal
help
for
civil
legal
problems.
In
most
civil
cases
like
evictions,
debt
collections,
and
custody
disputes,
at
least
one
party
is
unrepresented.
For
these
people,
AI
is
a
lifeline
that
Heppner
turns
into
a
liability.
The
court
treated
Heppner’s
AI
conversations
as
no
different
than
a
Google
search,
which
we
all
know
from
Court
TV
is
not
protected
(think
of
all
the
convictions
that
have
flowed
from
queries
like
“how
do
I
clean
blood
stains
from
my
carpet?”).
But
that
equivalence
isn’t
quite
right.
When
you
Google
“elements
of
securities
fraud,”
you
generate
links
to
public
web
pages
and
factual
materials.
No
new
information
is
created.
AI
is
an
entirely
different
animal.
To
get
anything
useful
from
an
AI
model,
users
must
feed
it
specifics
like
a
timeline
and
perceived
vulnerabilities.
What
comes
back
is
a
custom
analysis
reflecting
a
user’s
mental
impressions
and
developing
legal
strategy.
Produced
in
discovery,
it
hands
your
adversary
your
strategic
calculations
and
your
assessment
of
where
a
case
is
weakest.
By
declaring
AI
research
discoverable,
Judge
Rakoff
allowed
the
government
to
rely
on
“wits
borrowed
from
its
adversary.”
This
is
exactly
what
the
Supreme
Court
sought
to
prevent
in
the
foundational
1947
case
Hickman
v.
Taylor,
which
established
the
work
product
doctrine.
The
Hickman
court
recognized
that
for
the
adversary
system
to
work,
a
party
must
have
a
“zone
of
privacy”
to
prepare
their
case.
Without
that
privacy,
the
court
warned,
“much
of
what
is
now
put
down
in
writing
would
remain
unwritten,”
and
“the
cause
of
justice
would
be
poorly
served.”
The
Heppner
decision
also
rests
on
a
legal
fiction
about
user
expectations.
The
court
found
Heppner
had
no
reasonable
expectation
of
privacy
because
the
terms
of
service
for
Claude
stated
that
data
may
be
disclosed.
But
the
design
of
AI
tools
suggests
otherwise.
The
conversational
interface,
the
personalized
one-on-one
format,
and
the
way
the
AI
chatbots
invite
users
to
share
their
situation
cultivates
the
sense
of
a
confidential
consultation.
The
Heppner
ruling
expects
ordinary
people,
often
at
their
darkest
hour,
to
parse
complex
terms
of
service
that
most
lawyers
skip,
while
the
product
itself
beckons
with
a
siren’s
song
to
“tell
me
everything.”
And
the
more
these
tools
absorb
your
facts
to
sharpen
their
analysis,
the
more
damaging
the
trail
they
leave
behind.
To
follow
the
Heppner
logic
to
its
conclusion
rewards
ignorance
and
disempowerment.
Do
no
research,
and
you
have
no
trail.
Try
to
be
a
diligent,
informed
participant
in
your
own
legal
matter,
and
you
hand
your
opponent
a
gift.
What
is
most
aggravating
about
the
Rakoff
ruling
is
how
tone-deaf
it
is
to
the
high
cost
of
legal
services
and
to
AI’s
potential
to
reduce
those
costs.
Heppner’s
Quinn
Emanuel
lawyers
bill
upwards
of
$3000/hr,
so
why
wouldn’t
Heppner
try
using
AI
to
save
a
few
bucks?
The
opinion
also
acknowledges
AI’s
novelty
–
but
instead
of
crafting
an
approach
to
encourage
use
of
AI
to
level
the
playing
field,
it
defaults
to
relying
on
an
article
penned
by
Ira
Robbins,
an
ivory
tower
academic
who
arrives
at
the
mean
and
utterly
unimaginative
conclusion
that
privilege
for
AI
communications
is
“neither
doctrinally
justified
nor
normatively
sound.”
Privilege
isn’t
some
type
of
inherent
protection.
Some
privileges
like
attorney-client
privilege
are
created
by
legislatures
while
others
like
work
product
doctrine
have
been
crafted
by
courts
or
even
ethics
regulators.
For
example,
ABA
Opinion
477
says
that
unencrypted
email
carries
with
it
a
sufficient
expectation
of
privacy
to
confer
confidentiality
–
even
though
we
all
know
that’s
a
fiction.
So
why
can’t
we
just
say
that
an
expectation
of
privacy
applies
to
generative
AI
and
speak
a
privilege
into
existence?
Under
Rakoff’s
opinion,
work-product
protection
arguably
survives
if
a
client
undertakes
AI
research
at
the
direction
of
an
attorney.
But
that
only
makes
life
more
complicated,
requiring
lawyers
like
me
to
micromanage
our
clients’
work
and
remind
them
like
a
nagging
parent
to
include
the
magic
words
“prepared
under
lawyer’s
direction”
every
time
they
enter
an
AI
chatbox.
And
that
narrow
exception
doesn’t
protect
pro
se
litigants
or
clients
who
want
to
do
their
homework
before
ever
stepping
foot
into
an
attorney’s
office.
Judge
Rakoff’s
ruling
mechanically
applied
old
rules
to
a
new
world.
Today,
millions
of
Americans
are
turning
to
interactive
AI
to
survive
a
legal
system
that
has
become
too
expensive
and
too
complex
for
ordinary
people
to
navigate.
As
a
lawyer,
I
want
my
clients
and
my
potential
clients
to
keep
using
AI
tools.
The
law
should
encourage
them
to
do
so,
not
punish
them
for
it.
Carolyn
Elefant
is
one
of
the
country’s
most
recognized
advocates
for
solo
and
small
firm
lawyers.
She
founded
MyShingle.com
in
2002,
the
longest-running
blog
for
solo
practitioners,
where
she
has
published
thousands
of
articles,
resources,
and
guides
on
starting,
running,
and
growing
independent
law
practices.
She
is
the
author
of
Solo
by
Choice,
widely
regarded
as
the
definitive
handbook
for
launching
and
sustaining
a
law
practice,
and
has
spoken
at
countless
bar
events
and
legal
conferences
on
technology,
innovation,
and
regulatory
reform
that
impacts
solos
and
smalls.
Elefant
also
develops
practical
tools
like
the AI
Teach-In to
help
small
firms
adopt
AI
and
she
consistently
champions
reforms
to
level
the
playing
field
for
independent
lawyers.
Alongside
this
work,
she
runs
the
Law
Offices
of
Carolyn
Elefant,
a
national
energy
and
regulatory
practice
that
handles
selective
complex,
high-stakes
matters.