CoinBearer Trading Center: The Future of Decentralized AI

Openness
fosters
innovation,
and
recent
advances
in
artificial
intelligence
(AI)
have
showcased
its
global
utility
and
influence.
As
computing
power
increases
through
resource
integration,
centralization
issues
are
likely
to
arise,
with
entities
possessing
superior
computing
capabilities
gaining
dominance.
This
centralization
could
hinder
the
pace
of
innovation.
Decentralization
and
Web3
technologies
offer
promising
alternatives
to
maintain
the
openness
of
AI.

Decentralized
Computing
for
Pre-Training
and
Fine-Tuning

Crowdsourced
Computing
(CPUs
+
GPUs)

Supporting
Argument:
The
crowdsourcing
model,
similar
to
those
used
by
platforms
like
Airbnb
and
Uber,
could
be
adapted
for
computing.
This
model
would
aggregate
idle
computing
resources
into
a
marketplace,
potentially
offering
lower-cost
computing
solutions
for
specific
use
cases
and
providing
censorship-resistant
resources
for
training
models
that
may
face
future
regulations
or
bans.

Opposing
Argument:
Crowdsourced
computing
may
not
achieve
the
economies
of
scale
necessary
for
high-performance
tasks,
as
most
high-performance
GPUs
are
not
consumer-owned.
The
concept
of
decentralized
computing
seems
contradictory
to
high-performance
computing
principles.

Decentralized
Inference

Running
Open-Source
Model
Inference
Decentralized

Supporting
Argument:
Open-source
models
are
approaching
the
capabilities
of
closed-source
models
and
gaining
traction.
Centralized
services
such
as
HuggingFace
or
Replicate
for
model
inference
introduce
privacy
and
censorship
concerns.
Decentralized
or
distributed
vendors
could
address
these
issues.

Opposing
Argument:
Local
inference,
facilitated
by
dedicated
chips
capable
of
handling
large
parameter
models,
may
ultimately
prevail.
Edge
computing
offers
solutions
for
privacy
and
resistance
to
censorship.

On-Chain
AI
Agents

On-Chain
Applications
Using
Machine
Learning

Supporting
Argument:
AI
agents,
which
require
a
transaction
coordination
layer,
can
benefit
from
cryptocurrency
payments,
as
they
are
inherently
digital
and
cannot
utilize
traditional
banking
systems.
On-chain
AI
agents
mitigate
platform
risks,
such
as
sudden
changes
in
plugin
architectures
by
entities
like
OpenAI,
which
can
disrupt
services
without
warning.

Opposing
Argument:
Current
AI
agents,
such
as
BabyAGI
and
AutoGPT,
are
not
yet
ready
for
production.
Additionally,
entities
creating
AI
agents
can
use
payment
services
like
Stripe
without
relying
on
cryptocurrency.
The
argument
regarding
platform
risk
has
been
previously
used
to
justify
crypto,
but
it
has
yet
to
materialize.

Data
and
Model
Sources

Autonomous
Management
and
Value
Collection
for
Data
and
Machine
Learning
Models

Supporting
Argument:
Data
ownership
should
reside
with
users
who
generate
the
data,
rather
than
the
companies
collecting
it.
As
data
is
a
crucial
resource
in
the
digital
era,
its
monopolization
by
major
tech
companies
and
inadequate
monetization
are
significant
concerns.
A
more
personalized
internet
requires
portable
data
and
models,
allowing
users
to
transfer
data
across
applications
similar
to
moving
cryptocurrency
wallets
between
dapps.
Blockchain
technology
may
provide
a
viable
solution
to
data
sourcing
challenges,
particularly
in
light
of
increasing
fraud.

Opposing
Argument:
Data
ownership
and
privacy
concerns
may
not
be
a
priority
for
users,
as
evidenced
by
high
registration
numbers
for
platforms
like
Facebook
and
Instagram.
Trust
in
established
entities
like
OpenAI
may
overshadow
concerns
about
data
ownership.

Token-Incentivized
Apps
(e.g.,
Companion
Apps)

Envisioning
Crypto
Token
Rewards

Supporting
Argument:
Crypto
token
incentives
are
effective
for
encouraging
network
growth
and
behavioral
engagement.
Many
AI-centric
applications
are
expected
to
adopt
this
model.
The
AI
companion
market
presents
significant
opportunities,
with
the
potential
to
become
a
multi-trillion
dollar
sector.
Historical
data,
such
as
the
$130
billion
spent
on
pets
in
the
U.S.
in
2022,
suggests
a
strong
market
for
AI
companions.
AI
companion
apps
have
already
shown
significant
engagement,
with
average
session
lengths
exceeding
one
hour.
Crypto-incentivized
platforms
could
capture
substantial
market
share
in
this
and
other
AI
application
areas.