With
the
explosion
of
generative
artificial
intelligence
projects,
computational
power
has
become
a
hotly
contested
resource.
As
AI
becomes
more
ubiquitous
and
the
race
for
graphic
processing
unit
(GPU)
supplies
intensifies,
the
need
for
wider
and
more
democratized
access
to
computing
power
has
become
an
urgent
priority
for
non-MAANG
companies.
Combine
this
red-hot
demand
with
scarcity
that
is
quickly
transforming
into
resource
exclusivity,
and
the
ugly
likely
result
is
an
AI
ecosystem
is
largely
being
molded
by
a
small
handful
of
massive
tech
corporations.
Mark
Rydon
is
the
Co-Founder
and
Head
of
Strategy
at
Aethir,
a
decentralized
enterprise-grade
cloud
computing
network.
This
op-ed
is
part
of
CoinDesk’s
new
DePIN
Vertical,
covering
the
emerging
industry
of
decentralized
physical
infrastructure.
If
we
are
to
avoid
this,
the
future
of
AI,
and
its
ethical
implications,
hinges
on
the
ability
to
distribute
these
resources
widely
rather
than
relying
on
a
handful
of
corporations
to
monopolize
this
power.
Addressing
the
Supply
Side
of
Compute
Demands
As
the
demand
for
computing
surges,
the
current
infrastructure
struggles
to
keep
pace.
As
reported
in
the
Washington
Post,
several
states
are
running
short
of
power.
Northern
Virginia,
for
example,
needs
several
large
nuclear
power
plants
to
serve
all
the
new
data
centers
planned
and
under
construction.
Additionally,
the
escalating
costs
of
model
training
raise
critical
questions
about
the
future
of
AI
development:
Where
will
this
necessary
computing
power
come
from?
China
recently
announced
that
it
aims
to
boost
its
computing
capacity
by
50%
in
the
next
decade
and
a
half,
but
this
avenue
won’t
be
available
to
all.
One
way
to
address
this
is
through
a
decentralized
model.
Decentralized
Physical
Infrastructure
Networks
(DePINs)
can
be
used
to
aggregate
underutilized
enterprise
GPUs
and
put
them
to
use,
redistributing
previously
inaccessible
supply
back
into
the
market.
They
can
also
help
leverage
the
latent
compute
capacity
in
consumer
devices,
creating
a
vast,
accessible
network
of
GPUs
that
can
be
utilized
for
AI
training
and
other
compute-intensive
tasks.
These
approaches
democratize
the
supply
and
access
to
computational
resources,
challenging
traditional
GPU
monopolies
and
fostering
innovation.
Moreover,
distributed
infrastructure
optimizes
resource
use,
ensuring
that
unused
computational
power
can
contribute
to
significant
AI
projects.
This
approach
maximizes
efficiency
and
aligns
with
ESG
principles
of
reducing
energy
waste
and
environmental
impacts
associated
with
large-scale
data
centers.
Unlocking
New
Data
Oceans
Not
only
can
DePINs
address
the
supply
and
resource
challenge
that
drives
compute
accessibility.
They
can
also
help
unlock
new
data
oceans
that
can
provide
the
diverse
datasets
needed
to
train
more
specialized,
robust
and
inclusive
AI
models.
This
approach
enhances
the
quality
of
AI
systems
and
promotes
data
sovereignty
and
privacy.
DePINs
use
blockchain
technology
and
advanced
encryption
methods
to
ensure
data
remains
secure
and
ownership
is
clearly
defined.
This
decentralized
approach
broadens
the
spectrum
of
information,
including
that
of
underrepresented
regions
and
communities,
leading
to
more
accurate
and
inclusive
AI
models.
Furthermore,
DePINs
give
data
owners
more
control
over
their
information,
enhancing
privacy
while
encouraging
widespread
data
sharing.
For
instance,
consider
a
healthcare
scenario
where
a
patient’s
data
from
various
hospitals
and
clinics
can
be
securely
shared
without
compromising
on
privacy.
By
leveraging
DePINs,
researchers
can
access
a
rich,
diverse
dataset
that
enhances
their
ability
to
develop
better
diagnostic
tools
and
treatment
plans.
Similarly,
in
the
environmental
science
field,
DePINs
can
facilitate
sharing
climate
data
from
various
sensors,
often
located
on
private
homes
and
properties
worldwide,
leading
to
more
accurate
models
and
predictions.
The
Ethical
Imperative
It’s
also
worth
noting
how
the
concentration
of
AI
development
within
a
few
Big
Tech
companies
raises
significant
ethical
concerns.
When
advanced
AI
model
training
and
deployment
are
monopolized
by
a
few
entities,
it
restricts
AI’s
potential
to
benefit
all.
This
centralized
control
can
reinforce
existing
inequalities
and
curtail
the
scope
of
AI’s
positive
impact
on
society.
The
concentration
of
power
can
lead
to
biased
AI
systems
that
reflect
the
perspectives
and
priorities
of
a
narrow
segment
of
the
population,
exacerbating
social
and
economic
disparities.
Such
a
scenario
contradicts
AI’s
democratizing
potential,
where
innovations
should
ideally
serve
diverse
communities
and
address
a
wide
range
of
societal
challenges.
Democratizing
access
to
GPU
resources
is
not
just
an
imperative
for
the
industry
–
it
is
an
ethical
necessity.
By
ensuring
that
researchers,
startups,
and
innovators
worldwide
can
access
the
computational
power
required
to
develop
AI
technologies,
we
can
promote
a
more
inclusive
and
equitable
AI
landscape.
NVIDIA
CEO
Jensen
Huang
who
coined
the
term
“Sovereign
AI,”
also
emphasizes
that
nations
must
create
AI
to
ensure
cultural
preservation.
This
broader
access
encourages
diverse
perspectives
in
AI
development,
leading
to
fairer,
more
balanced,
and
more
effective
AI
solutions
that
can
benefit
society.
Impact
on
Innovation
The
potential
impact
of
decentralized
GPU
infrastructure
on
innovation
and
research,
particularly
in
emerging
markets,
cannot
be
overstated.
For
instance,
our
recent
collaboration
with
TensorOpera
AI
to
advance
large-scale
language
model
(LLM)
training
on
a
decentralized
cloud
infrastructure
showcased
the
tangible
benefits
of
this
approach.
By
harnessing
the
power
of
decentralized
GPUs,
TensorOpera
can
now
conduct
significant
LLM
training
runs
without
relying
on
traditional,
centralized
resources.
This
democratization
of
computing
power
now
paves
the
way
for
innovative
projects
and
research
endeavors
previously
unattainable
due
to
resource
constraints.
Bridging
the
compute
divide
Decentralized
GPU
infrastructure
represents
a
pivotal
step
towards
bridging
the
compute
divide
and
democratizing
access
to
AI
resources.
By
distributing
computational
power
more
equitably,
we
can
ensure
that
the
benefits
of
AI
are
realized
by
a
broader
spectrum
of
society,
thereby
increasing
innovation
across
the
board.
This
approach
addresses
the
ethical
challenges
posed
by
AI
monopolies
and
fosters
global
innovation
and
research,
particularly
in
emerging
markets.
As
we
move
forward,
embracing
decentralized
models
and
leveraging
latent
computational
capacities
will
be
crucial
in
meeting
the
growing
demands
of
AI
development.
The
future
of
AI
depends
on
our
ability
to
build
a
more
inclusive,
equitable
and
decentralized
computational
landscape.
Note:
The
views
expressed
in
this
column
are
those
of
the
author
and
do
not
necessarily
reflect
those
of
CoinDesk,
Inc.
or
its
owners
and
affiliates.
