AI engineers report burnout and rushed rollouts as ‘rat race’ to stay competitive hits tech industry

Late
last
year,
an

artificial
intelligence
engineer
at


Amazon
was
wrapping
up
the
work
week
and
getting
ready
to
spend
time
with
some
friends
visiting
from
out
of
town.
Then,
a
Slack
message
popped
up.
He
suddenly
had
a
deadline
to
deliver
a
project
by
6
a.m.
on
Monday.

There
went
the
weekend.
The
AI
engineer
bailed
on
his
friends,
who
had
traveled
from
the
East
Coast
to
the
Seattle
area.
Instead,
he
worked
day
and
night
to
finish
the
job.

But
it
was
all
for
nothing.
The
project
was
ultimately “deprioritized,”
the
engineer
told
CNBC.
He
said
it
was
a
familiar
result.
AI
specialists,
he
said,
commonly
sprint
to
build
new
features
that
are
often
suddenly
shelved
in
favor
of
a
hectic
pivot
to
another
AI
project.

The
engineer,
who
requested
anonymity
out
of
fear
of
retaliation,
said
he
had
to
write
thousands
of
lines
of
code
for
new
AI
features
in
an
environment
with
zero
testing
for
mistakes.
Since
code
can
break
if
the
required
tests
are
postponed,
the
Amazon
engineer
recalled
periods
when
team
members
would
have
to
call
one
another
in
the
middle
of
the
night
to
fix
aspects
of
the
AI
feature’s
software.

AI
workers
at
other
Big
Tech
companies,
including


Google
and


Microsoft,
told
CNBC
about
the
pressure
they
are
similarly
under
to
roll
out
tools
at
breakneck
speeds
due
to
the
internal
fear
of
falling
behind
the
competition
in
a
technology
that,
according
to


Nvidia
CEO
Jensen
Huang,
is
having
its
“iPhone
moment.”

The
tech
workers
spoke
to
CNBC
mostly
on
the
condition
that
they
remain
unnamed
because
they
weren’t
authorized
to
speak
to
the
media.
The
experiences
they
shared
illustrate
a
broader
trend
across
the
industry,
rather
than
a
single
company’s
approach
to
AI.

They
spoke
of
accelerated
timelines,
chasing
rivals’
AI
announcements
and
an
overall
lack
of
concern
from
their
superiors
about

real-world
effects,
themes
that
appear
common
across
a
broad
spectrum
of
the
biggest
tech
companies
—
from


Apple
to
Amazon
to
Google.

Engineers
and
those
with
other
roles
in
the
field
said
an
increasingly
large
part
of
their
job
was
focused
on
satisfying
investors
and

not
falling
behind
the
competition
rather
than
solving
actual
problems
for
users.
Some
said
they
were
switched
over
to
AI
teams
to
help
support
fast-paced
rollouts
without
having
adequate
time
to
train
or
learn
about
AI,
even
if
they
are
new
to
the
technology.

A
common
feeling
they
described
is
burnout
from
immense
pressure,
long
hours
and
mandates
that
are
constantly
changing.
Many
said
their
employers
are
looking
past

surveillance
concerns,
AI’s
effect
on
the
climate
and
other
potential
harms,
all
in
the
name
of
speed.
Some
said
they
or
their
colleagues
were
looking
for
other
jobs
or
switching
out
of
AI
departments,
due
to
an
untenable
pace.

This
is
the
dark
underbelly
of
the

generative
AI
gold
rush.
Tech
companies
are
racing
to
build
chatbots,
agents
and
image
generators,
and
they’re
spending

billions
of
dollars
training
their
own
large
language
models
to
ensure
their
relevance
in
a
market
that’s
predicted
to

top
$1
trillion
in
revenue
within
a
decade.

Tech’s
megacap
companies
aren’t
being
shy
about
acknowledging
to
investors
and
employees
how
much
AI
is
shaping
their
decision-making.

Microsoft
Chief
Financial
Officer
Amy
Hood,
on
an
earnings
call
earlier
this
year,
said
the
software
company
is “repivoting
our
workforce
toward
the
AI-first
work
we’re
doing
without
adding
material
number
of
people
to
the
workforce,”
and
said
Microsoft
will
continue
to
prioritize
investing
in
AI
as “the
thing
that’s
going
to
shape
the
next
decade.”

Meta
CEO

Mark
Zuckerberg
spent
much
of
his
opening
remarks
on
his
company’s
earnings
call
last
week

focused
on
AI
products
and
services
and
the
advancements
in
its
large
language
model
called

Llama
3.

“This
leads
me
to
believe
that
we
should
invest
significantly
more
over
the
coming
years
to
build
even
more
advanced
models
and
the
largest
scale
AI
services
in
the
world,”
Zuckerberg
said.

At
Amazon,
CEO
Andy
Jassy
told
investors

last
week
that
the “generative
AI
opportunity”
is
almost
unprecedented,
and
that
increased
capital
spending
is
necessary
to
take
advantage
of
it.

“I
don’t
know
if
any
of
us
has
seen
a
possibility
like
this
in
technology
in
a
really
long
time,
for
sure
since
the
cloud,
perhaps
since
the
Internet,”
Jassy
said.

On
the
ground
floor,
where
those
investments
are
taking
place,
things
can
get
messy.

The
Amazon
engineer,
who
lost
his
weekend
to
a
project
that
was
ultimately
scuttled,
said
higher-ups
seemed
to
be
doing
things
just
to “tick
a
checkbox,”
and
that
speed,
rather
than
quality,
was
the
priority
while
trying
to
recreate
products
coming
out
of
Microsoft
or
OpenAI.

In
an
emailed
statement
to
CNBC,
an
Amazon
spokesperson
said,
the
company
is “focused
on
building
and
deploying
useful,
reliable,
and
secure
generative
AI
innovations
that
reinvent
and
enhance
customers’
experiences,”
and
that
Amazon
is
supporting
its
employees
to “deliver
those
innovations.”

“It’s
inaccurate
and
misleading
to
use
a
single
employee’s
anecdote
to
characterize
the
experience
of
all
Amazon
employees
working
in
AI,”
the
spokesperson
said.

Last
year
marked
the
beginning
of
the
generative
AI
boom,
following
the
debut
of
OpenAI’s
ChatGPT
near
the
end
of
2022.
Since
then,
Microsoft,
Alphabet,
Meta,
Amazon
and
others
have
been
snapping
up
Nvidia’s
processors,
which
are
at
the
core
of
most
big
AI
models.

While
companies
such
as
Alphabet
and
Amazon
continue
to

downsize
their
total
headcount,
they’re
aggressively
hiring
AI
experts
and

pouring
resources
into
building
their
models
and
developing
features
for

consumers
and
businesses.

Eric
Gu,
a
former
Apple
employee
who
spent
about
four
years
working
on
AI
initiatives,
including
for
the
Vision
Pro
headset,
said
that
toward
the
end
of
his
time
at
the
company,
he
felt
“boxed
in.” 

“Apple
is
a
very
product-focused
company,
so
there’s
this
intense
pressure
to
immediately
be
productive,
start
shipping
and
contributing
features,”
Gu
said. He
said
that
even
though
he
was
surrounded
by “these
brilliant
people,”
there
was
no
time
to
really
learn
from
them.

“It
boils
down
to
the
pace
at
which
it
felt
like
you
had
to
ship
and
perform,”
said
Gu,
who
left
Apple
a
year
ago
to
join
AI
startup
Imbue,
where
he
said
he
can
work
on
equally
ambitious
projects
but
at
a
more
measured
pace.
 

Apple
declined
to
comment.

An
AI
engineer
at
Microsoft
said
the
company
is
engaged
in
an “AI
rat
race.”

When
it
comes
to
ethics
and
safeguards,
he
said,
Microsoft
has
cut
corners
in
favor
of
speed,
leading
to
rushed
rollouts
without
sufficient
concerns
about

what
could
follow.
The
engineer
said
there’s
a
recognition
that
because
all
of
the
large
tech
companies
have
access
to
most
of
the
same
data,
there’s
no
real
moat
in
AI.

Microsoft
didn’t
provide
a
comment.

Morry
Kolman,
an
independent
software
engineer
and
digital
artist
who
has
worked
on
viral
projects
that
have
garnered
more
than
200,000
users,
said
that
in
the
age
of
rapid
advancement
in
AI, “it’s
hard
to
figure
out
where
is
worth
investing
your
time.”

“And
that
is
very
conducive
to
burnout
just
in
the
sense
that
it
makes
it
hard
to
believe
in
something,”
Kolman
said,
adding,
“I
think
that
the
biggest
thing
for
me
is
that
it’s
not
cool
or
fun
anymore.”

At
Google,
an
AI
team
member
said
the
burnout
is
the
result
of
competitive
pressure,
shorter
timelines
and
a
lack
of
resources,
particularly
budget
and
headcount.
Although
many
top
tech
companies
have
said
they
are
redirecting
resources
to
AI,
the
required
headcount,
especially
on
a
rushed
timeline,
doesn’t
always
materialize.
That
is
certainly
the
case
at
Google,
the
AI
staffer
said. 

The
company’s
hurried
output
has
led
to
some
public
embarrassment.
Google
Gemini’s
image-generation
tool
was
released
and

promptly
taken
offline
in
February
after
users
discovered
historical
inaccuracies
and
questionable
responses.
In
early
2023,
Google
employees
criticized
leadership,
most
notably
CEO

Sundar
Pichai,
for
what
they
called
a “rushed”
and “botched”
announcement
of
its
initial
ChatGPT
competitor
called
Bard.

The
Google
AI
engineer,
who
has
over
a
decade
of
experience
in
tech,
said
she
understands
the
pressure
to
move
fast,
given
the
intense
competition
in
generative
AI,
but
it’s
all
happening
as
the
industry
is
in
cost-cutting
mode,
with
companies
slashing
their
workforce
to
meet
investor
demands
and “increase
their
bottom
line,”
she
said. 

There’s
also
the
conference
schedule.
AI
teams
had
to
prepare
for
the
Google
I/O
developer
event
in
May
2023,
followed
by
Cloud
Next
in
August
and
then
another
Cloud
Next
conference
in
April
2024.
That’s
a
significantly
shorter
gap
between
events
than
normal,
and
created
a
crunch
for
a
team
that
was “beholden
to
conference
timelines”
for
shipping
features,
the
Google
engineer
said.

Google
didn’t
provide
a
comment
for
this
story.

The
sentiment
in
AI
is
not
limited
to
the
biggest
companies.

An
AI
researcher
at
a
government
agency
reported
feeling
rushed
to
keep
up.
Even
though
the
government
is
notorious
for
moving
slower
than
companies,
the
pressure “trickles
down
everywhere,”
since
everyone
wants
to
get
in
on
generative
AI,
the
person
said.

And
it’s
happening
at
startups.

There
are
companies
getting
funded
by “really
big
VC
firms
who
are
expecting
this
10X-like
return,”
said
Ayodele
Odubela,
a
data
scientist
and
AI
policy
advisor.

“They’re
trying
to
strike
while
the
iron
is
hot,” she
said.

Regardless
of
the
employer,
AI
workers
said
much
of
their
jobs
involve
working
on
AI
for
the
sake
of
AI,
rather
than
to
solve
a
business
problem
or
to
serve
customers
directly. 

“A
lot
of
times,
it’s
being
asked
to
provide
a
solution
to
a
problem
that
doesn’t
exist
with
a
tool
that
you
don’t
want
to
use,”
independent
software
engineer
Kolman
told
CNBC. 

The
Microsoft
AI
engineer
said
a
lot
of
tasks
are
about “trying
to
create
AI
hype”
with
no
practical
use.
He
recalled
instances
when
a
software
engineer
on
his
team
would
come
up
with
an
algorithm
to
solve
a
particular
problem
that
didn’t
involve
generative
AI.
That
solution
would
be
pushed
aside
in
favor
of
one
that
used
a
large
language
model,
even
if
it
were
less
efficient,
more
expensive
and
slower,
the
person
said.
He
described
the
irony
of
using
an “inferior
solution”
just
because
it
involved
an
AI
model.

A
software
engineer
at
a
major
internet
company,
which
the
person
asked
to
keep
unnamed
due
to
his
group’s
small
size,
said
the
new
team
he
works
on
dedicated
to
AI
advancement
is
doing
large
language
model
research “because
that’s
what’s
hot
right
now.”

The
engineer
has
worked
in
machine
learning
for
years,
and
described
much
of
the
work
in
generative
AI
today
as
an
“extreme
amount
of
vaporware
and
hype.”
Every
two
weeks,
the
engineer
said,
there’s
some
sort
of
big
pivot,
but
ultimately
there’s
the
sense
that
everyone
is
building
the
same
thing.

He
said
he
often
has
to
put
together
demos
of
AI
products
for
the
company’s
board
of
directors
on
three-week
timelines,
even
though
the
products
are “a
big
pile
of
nonsense.”
There’s
a
constant
effort
to
appease
investors
and
fight
for
money,
he
said.
He
gave
one
example
of
building
a
web
app
to
show
investors
even
though
it
wasn’t
related
to
the
team’s
actual
work.
After
the
presentation, “We
never
touched
it
again,”
he
said.

A
product
manager
at
a
fintech
startup
said
one
of
his
projects
involved
a
rebranding
of
the
company’s
algorithms
to
AI.
He
also
worked
on
a
ChatGPT
plug-in
for
customers.
Executives
at
the
company
never
told
the
team
why
it
was
needed.

The
employee
said
it
felt “out
of
order.”
The
company
was
starting
with
a
solution
involving
AI
without
ever
defining
the
problem.

An
AI
engineer
who
works
at
a
retail
surveillance
startup
told
CNBC
that
he’s
the
only
AI
engineer
at
a
company
of
40
people
and
that
he
handles
any
responsibility
related
to
AI,
which
is
an
overwhelming
task.

He
said
the
company’s
investors
have
inaccurate
views
on
the
capabilities
of
AI,
often
asking
him
to
build
certain
things
that
are “impossible
for
me
to
deliver.” He
said
he
hopes
to
leave
for
graduate
school
and
to
publish
research
independently.

The
Google
staffer
said
that
about
six
months
into
her
role,
she
felt
she
could
finally
keep
her
head
above
water.
Even
then,
she
said,
the
pressure
continued
to
mount,
as
the
demands
on
the
team
were “not
sustainable.”

She
used
the
analogy
of “building
the
plane
while
flying
it”
to
describe
the
company’s
approach
to
product
development.

The
Amazon
AI
engineer
expressed
a
similar
sentiment,
saying
everyone
on
his
current
team
was
pulled
into
working
on
a
product
that
was
running
behind
schedule,
and
that
many
were “thrown
into
it”
without
relevant
experience
and
onboarding.

He
also
said
AI
accuracy,
and
testing
in
general,
has
taken
a
backseat
to
prioritize
speed
of
product
rollouts
despite “motivational
speeches”
from
managers
about
how
their
work
will “revolutionize
the
industry.”

Odubela
underscored
the
ethical
risks
of
inadequate
training
for
AI
workers
and
with
rushing
AI
projects
to
keep
up
with
competition.
She
pointed
to
the
problems
with
Google
Gemini’s
image
creator
when
the
product
hit
the
market
in
February.
In
one
instance,
a
user
asked
Gemini
to
show
a
German
soldier
in
1943,
and
the
tool
depicted
a

racially
diverse
set
of
soldiers
wearing
German
military
uniforms
of
the
era,
according
to
screenshots

viewed
by
CNBC.

“The
biggest
piece
that’s
missing
is
lacking
the
ability
to
work
with
domain
experts
on
projects,
and
the
ability
to
even
evaluate
them
as
stringently
as
they
should
be
evaluated
before
release,”
Odubela
said,
regarding
the
current
ethos
in
AI.

At
a
moment
in
technology
when
thoughtfulness
is
more
important
than
ever,
some
of
the
leading
companies
appear
to
be
doing
the
opposite.

“I
think
the
major
harm
that
comes
is
there’s
no
time
to
think
critically,”
Odubela
said.