Funding Open-Source Generative AI With Crypto

The
intersection
between
generative
artificial
intelligence
and
Web3
is
one
of
the
most
active
areas
of
research
and
development
in
crypto
circles
over
the
last
few
months.
Decentralized
compute,
zero-knowledge
AI,
smaller
foundation
models,
decentralized
data
networks,
and
AI-first
chains
are
some
of
the
recent
trends
that
aim
to
enable
Web3-native
rails
for
AI
workloads.

These
trends
are
technological
innovations
that
seek
to
bridge
the
worlds
of
Web3
and
AI,
representing
a
natural
friction
against
the
centralized
nature
of
generative
AI.
While
creating
technological
bridges
with
AI
is
foundational
for
the
evolution
of
Web3,
they
don’t
represent
the
only
integration
path
for
these
technology
trends.

What
if
the
path
for
integrating
Web3
and
AI
was
financial
instead
of
purely
technical?
It
turns
out
that
the
programmable
finance
and
capital
formation
capabilities
of
crypto
could
be
useful
for
one
of
the
biggest
challenges
facing
the
current
generative
AI
market.

What
challenge
are
we
referring
to?
Nothing
other
than
the
funding
challenges
of
open-source
generative
AI.

Despite
the
recent
level
of
innovation
in
decentralized
generative
AI,
the
gap
with
centralized
AI
tech
is
increasing
rather
than
decreasing.
Many
people
agree
that
blockchains
represent
the
best
technology
alternative
to
the
increasing
centralized
AI
control
of
large
tech
platforms.
However,
the
adoption
challenges
for
decentralized
AI
platforms
are
monumental.

Decentralized
compute
is
a
clear
pillar
for
decentralized
AI
but
proves
impractical
for
pretraining
and
fine-tuning
workloads
that
require
GPUs
in
close
proximity
with
access
to
datasets
that
often
sit
behind
corporate
firewalls.
Zero-knowledge
ML
is
too
expensive
to
be
practical
in
large
foundation
models
and
hasn’t
seen
any
real
demand
in
the
market.
Decentralized
data
marketplaces
need
to
overcome
the
same
issues
that
have
prevented
data
marketplaces
from
becoming
large
tech
businesses.

While
decentralized
AI
strives
to
overcome
these
frictions,
centralized
alternatives
are
accelerating
at
a
frantic
pace,
creating
a
scary
gap
between
the
two.
The
one
trend
that
is
keeping
the
hopes
for
a
world
in
which
decentralized
AI
can
succeed
is
the
rapid
evolution
of
open-source
generative
AI.

All
decentralized
AI
trends
rely
on
a
healthy
open-source
generative
AI
ecosystem,
yet
that
ecosystem
might
not
be
as
healthy
as
it
seems.

In
the
last
couple
of
years,
we
have
witnessed
an
explosion
of
innovation
in
open-source
large
generative
AI
as
an
alternative
to
platforms
such
as
OpenAI/Microsoft,
Google
or
Anthropic.
Meta
has
become
a
surprising
undisputed
champion
of
open-source
generative
AI
with
the
release
of
the
Llama
models.
Companies
like
Mistral
have
raised
billions
in
venture
funding,
enterprise
platforms
like
Databricks
or
Snowflake
are
pushing
open-source
models,
and
there
is
a
growing
number
of
open-source
generative
AI
releases
on
a
weekly
basis.

While
the
momentum
in
open-source
generative
AI
is
strong,
a
more
detailed
analysis
shows
a
different
reality.
Open-source
generative
AI
is
facing
a
massive
funding
issue.
When
it
comes
to
large
foundation
models,
only
large
companies
such
as
Databricks,
Snowflake,
Meta
or
well-funded
startups
like
Mistral
are
keeping
up
with
the
performance
of
large
closed
models.
Most
of
the
releases
from
other
labs,
like
Databricks
and
Snowflake,
are
focused
on
optimized
enterprise
workloads,
while
most
of
the
recent
open-source
research
is
focusing
on
complementary
techniques
rather
than
on
new
models.

The
reason
behind
this
phenomenon
can
be
attributed
to
the
astronomical
costs
of
building
large
frontier
models.
Any
pre-training
cycle
for
a
20
billion-plus
parameter
model
could
cost
between
ten
to
a
hundred
million
dollars
and
involves
a
multi-month
process
with
many
failed
attempts.
These
costs
fall
outside
the
budget
of
most
university
labs.
To
make
matters
more
interesting,
many
of
the
grants
for
AI
university
labs
come
from
large
tech
incumbents,
which
then
are
the
immediate
beneficiaries
of
the
outputs.

Making
money
with
open
source
has
historically
been
hard,
and
making
money
with
open-source
generative
AI
is
hard
at
AI
scale.
As
a
result,
open-source
generative
AI
is
experiencing
a
massive
funding
crunch
that
can
create
a
serious
gap
with
the
AI
incumbents.

The
capital
formation
primitives
of
crypto
seem
like
one
of
the
few
viable
alternatives
to
address
the
funding
crunch
in
generative
AI.
Throughout
its
history,
crypto
tokens
have
been
a
primary
vehicle
for
capital
formation
for
Web3
projects
through
bull
and
bear
market
cycles.
Could
some
of
these
principles
be
applied
to
open-source
generative
AI?
There
is
certainly
more
than
one
interesting
option.

Gitcoin
represents
one
of
the
most
successful
examples
of
funding
open-source
innovation
in
Web3.
The
quadratic
funding
mechanism
pioneered
by
Gitcoin
could
apply
directly
to
generative
AI.
Bringing
native
generative
AI
capabilities
to
Web3
is
paramount
for
the
evolution
of
the
space,
so
it
is
natural
to
expect
that
generative
AI
projects
will
drive
community
attention.

Let’s
say
that
a
university
AI
lab
needs
to
raise
$10
million
for
pre-training
an
LLM
based
on
novel
architecture.
Multiple
DAOs
and
foundations
can
contribute
to
a
Gitcoin
grant
that
can
also
be
matched
by
the
grantors,
creating
a
more
efficient
funding
mechanism.
This
mechanism
is
far
more
efficient
than
the
current
alternatives
in
the
market.

Funding
open-source
projects
enables
mechanisms
in
which
the
value
created
by
those
projects
can
benefit
the
original
funding
community.
When
it
comes
to
Web3
and
open
generative
AI,
an
interesting
idea
is
to
establish
a
license
in
which
any
commercial
application
using
a
model
funded
using
Web3
tokens
should
contribute
part
of
that
revenue
back
in
the
form
of
that
specific
token.
This
mechanism
can
even
be
enforced
via
smart
contracts.

Financing
vehicles
for
open-source
AI
are
one
of
the
most
important
challenges
to
address
in
the
current
generative
AI
landscape.
Open
source
is
traditionally
hard
to
finance,
and
open-source
generative
AI
is
even
more
so,
considering
the
expensive
computational
requirements.

Not
enabling
proper
funding
channels
to
foster
open-source
innovation
in
generative
AI
can
create
a
systemic
risk
to
the
entire
space
as
the
balance
will
shift
entirely
to
closed
commercial
platforms.
Crypto
has
established
some
of
the
most
sophisticated
and
battle-tested
channels
for
funding
open-source
innovation.
Maybe,
the
first
bridge
between
Web3
and
generative
AI
will
be
financial
and
not
necessarily
technical.

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.