Data/transcripts/1Wo6SqLNmLk_20241225194845.txt

ANDREW HUBERMAN: Welcome to
the Huberman Lab podcast,
where we discuss science
and science-based tools
for everyday life.
[MUSIC PLAYING]
I'm Andrew Huberman.
And I'm a professor of
neurobiology and ophthalmology
at Stanford School of Medicine.
My guests today are Mark
Zuckerberg and Dr. Priscilla
Chan.
Mark Zuckerberg,
as everybody knows,
founded the company Facebook.
He is now the CEO of Meta, which
includes Facebook, Instagram,
WhatsApp, and other
technology platforms.
Dr. Priscilla Chan
graduated from Harvard
and went on to do her medical
degree at the University
of California San Francisco.
Mark Zuckerberg and
Dr. Priscilla Chan
are married and the
co-founders of the CZI,
or Chan Zuckerberg Initiative,
a philanthropic organization
whose stated goal is to
cure all human diseases.
The Chan Zuckerberg Initiative
is accomplishing that
by providing critical funding
not available elsewhere,
as well as a novel
framework for discovery
of the basic
functioning of cells,
cataloging all the
different human cell
types, as well as providing
AI, or artificial intelligence,
platforms to mine
all of that data
to discover new pathways and
cures for all human diseases.
The first hour of
today's discussion
is held with both Dr. Priscilla
Chan and Mark Zuckerberg,
during which we discuss
the CZI and what it really
means to try and cure
all human diseases.
We talk about the motivational
backbone for the CZI
that extends well into each
of their personal histories.
Indeed, you'll learn quite a lot
about Dr. Priscilla Chan, who
has, I must say, an absolutely
incredible family story leading
up to her role as a
physician and her motivations
for the CZI and beyond.
And you'll learn from Mark, how
he is bringing an engineering
and AI perspective
to the discovery
of new cures for human disease.
The second half of
today's discussion
is just between Mark Zuckerberg
and me, during which we discuss
various Meta Platforms,
including, of course,
social media platforms, and
their effects on mental health
in children and adults.
We also discuss VR,
Virtual Reality, as well as
augmented and mixed reality.
And we discuss AI,
Artificial Intelligence,
and how it stands to transform
not just our online experiences
with social media and
other technologies,
but how it stands to
potentially transform
every aspect of everyday life.
Before we begin, I'd
like to emphasize
that this podcast is separate
from my teaching and research
roles at Stanford.
It is, however, part
of my desire and effort
to bring zero cost to
consumer information
about science and
science-related tools
to the general public.
In keeping with
that theme, I'd like
to thank the sponsors
of today's podcast.
Our first sponsor
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I've spoken many times before
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One of the key things to
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And that's because in order to
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Today's episode is also
brought to us by LMNT.
LMNT is an electrolyte drink
that has everything you need
and nothing you don't.
That means plenty of
electrolytes-- sodium,
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potassium-- and no sugar.
The electrolytes are absolutely
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I'm pleased to
announce that we will
be hosting four live events
in Australia, each of which
is entitled The Brain Body
Contract, during which I will
share science and
science-related tools
for mental health, physical
health, and performance.
There will also be a live
question and answer session.
We have limited
tickets still available
for the event in
Melbourne on February 10,
as well as the event in
Brisbane on February 24.
Our event in Sydney, at
the Sydney Opera House,
sold out very quickly.
So as a consequence,
we've now scheduled
a second event in Sydney
at the Aware Super Theatre
on February 18.
To access tickets to
any of these events,
you can go to
hubermanlab.com/events and use
the code Huberman at checkout.
I hope to see you there.
And as always, thank you for
your interest in science.
And now, for my discussion
with Mark Zuckerberg
and Dr. Priscilla Chan.
Priscilla, Mark, so
great to meet you.
And thank you for having
me here in your home.
MARK ZUCKERBERG: Oh, Thanks
for having us on the podcast.
PRISCILLA CHAN: Yeah.
ANDREW HUBERMAN: I'd like to
talk about the CZI, the Chan
Zuckerberg Initiative.
I learned about this
a few years ago,
when my lab was-- and
still is now-- at Stanford,
as a very exciting
philanthropic effort
that has a truly big mission.
I can't imagine
a bigger mission.
So maybe you could tell us
what that big mission is.
And then we can get into
some of the mechanics of how
that big mission can
become a reality.
PRISCILLA CHAN: So like
you're mentioning, in 2015,
we launched the Chan
Zuckerberg Initiative.
And what we were
hoping to do at CZI
was think about how do we build
a better future for everyone
and looking for ways
where we can contribute
the resources that we have
to bring philanthropically
and the experiences that
Mark and I have had,
for me as a physician
and educator,
for Mark as an
engineer, and then
our ability to bring teams
together to build the builders.
Mark has been a builder
throughout his career.
And what could we
do if we actually
put together a team to build
tools, do great science?
And so within our
science portfolio,
we've really been focused
on what some people think
is either an incredibly
audacious goal
or an inevitable goal.
But I think about
it as something
that will happen if we
continue focusing on it, which
is to be able to cure,
prevent, or manage
all disease by the
end of the century.
ANDREW HUBERMAN: All disease?
PRISCILLA CHAN: All disease.
So that's important, right?
And so a lot of times, people
ask like, which disease?
And the whole point is that
there is not one disease.
And it's really about taking
a step back to where I always
found the most hope
as a physician, which
is new discoveries
and new opportunities
and new ways of understanding
how to keep people well come
from basic science.
So our strategy at CZI is really
to build tools, fund science,
change the way basic
scientists can see the world
and how they can move
quickly in their discoveries.
And so that's what
we launched in 2015.
We do work in three ways.
We fund great scientists.
We build tools-- right
now, software tools
to help move science along and
make it easier for scientists
to do their work.
And we do science.
You mentioned Stanford
being an important pillar
for our science work.
We've built what we call
biohubs, institutes where teams
can take on grand
challenges to do work that
wouldn't be possible
in a single lab
or within a single discipline.
And our first
biohub was launched
in San Francisco, a
collaboration between Stanford,
UC Berkeley, and UCSF.
ANDREW HUBERMAN: Amazing.
Curing all diseases implies
that there will either
be a ton of knowledge gleaned
from this effort, which
I'm certain there will be--
and there already has been.
We can talk about some of those
early successes in a moment.
But it also sort of implies
that if we can understand
some basic operations
of diseases and cells
that transcend autism,
Huntington's, Parkinson's,
cancer and any other
disease that perhaps there
are some core principles that
would make the big mission
a real reality, so to speak.
What I'm basically saying is,
how are you attacking this?
My belief is that the cell sits
at the center of all discussion
about disease, given that
our body is made up of cells
and different types of cells.
So maybe you could
just illuminate for us
a little bit of what the
cell is, in your mind,
as it relates to disease and
how one goes about understanding
disease in the context of cells
because, ultimately, that's
what we're made up of.
MARK ZUCKERBERG: Yeah.
Well, let's get to the
cell thing in a moment.
But just even taking
a step back from that,
we don't think,
at CZI, that we're
going to cure, prevent
or manage all diseases.
The goal is to basically
give the scientific community
and scientists around
the world the tools
to accelerate the
pace of science.
And we spent a lot
of time, when we
were getting started
with this, looking
at the history of science and
trying to understand the trends
and how they've
played out over time.
And if you look over
this very long-term arc,
most large-scale
discoveries are preceded
by the invention of a new tool
or a new way to see something.
And it's not just
in biology, right?
It's like having
a telescope came
before a lot of discoveries
in astronomy and astrophysics.
But similarly, the microscope
and just different ways
to observe things or
different platforms,
like the ability to do
vaccines preceded the ability
to cure a lot of
different things.
So this is the engineering part
that you were talking about,
about building tools.
We view our goal is to
try to bring together
some scientific and engineering
knowledge to build tools
that empower the whole field.
And that's the big arc
and a lot of the things
that we're focused on, including
the work in single cell
and cell understanding,
which you can jump in and get
into that if you want.
But yeah, I think I
think we generally
agree with the
premise that if you
want to understand this
stuff from first principles--
people study organs a lot right.
You study how things
present across the body.
But there's not a very
widespread understanding
of how each cell operates.
And this is a big part of
some of the initial work
that we tried to do on the Human
Cell Atlas and understanding
what are the different cells.
And there's a bunch
more work that we want
to do to carry that forward.
But overall, I think, when we
think about the next 10 years
here of this long arc to
try to empower the community
to be able to cure, prevent
or manage all diseases,
we think that the next
10 years should really
be primarily about being
able to measure and observe
more things in human biology.
There are a lot
of limits to that.
It's like you want to look at
something through a microscope,
you can't usually
see living tissues
because it's hard to see through
skin or things like that.
So there are a lot of
different techniques
that will help us
observe different things.
And this is where the
engineering background
comes in a bit because--
I mean, when I think about this
is from the perspective of how
you'd write code or
something, the idea of trying
to debug or fix a code base,
but not be able to step
through the code
line by line, it's
not going to happen, right?
And at the beginning of any
big project that we do at Meta,
we like to spend a bunch of
the time up front just trying
to instrument things
and understand
what are we going to
look at and how are we
going to measure things so
we know we're making progress
and know what to optimize.
And this is such a
long-term journey
that we think that it actually
makes sense to take the next 10
years to build those
kinds of tools for biology
and understanding just how the
human body works in action.
And a big part of
that is, cells.
I don't know.
Do you want to jump and talk
about some of the efforts?
PRISCILLA CHAN: Sure.
ANDREW HUBERMAN: Could I just
interrupt briefly and just ask
about the different
interventions, so to speak,
that CZI is in a unique
position to bring to the quest
to cure all diseases?
So I can think of--
I mean, I know, as a scientist,
that money is necessary but not
sufficient, right?
When you have money, you
can hire more people.
You can try different things.
So that's critical.
But a lot of philanthropy
includes money.
The other component is you
want to be able to see things,
as you pointed out.
So you want to know that
normal disease process--
like, what is a healthy cell?
What's a diseased cell?
Are cells constantly being
bombarded with challenges
and then repairing those?
And then what we
call cancer is just
a runaway train of
those challenges
not being met by the cell
itself or something like that?
So better imaging tools.
And then it sounds like there's
not just a hardware component,
but a software component.
This is where AI comes in.
So maybe, at some point,
we can break this up
into two, three
different avenues.
One is understanding
disease processes
and healthy processes.
We'll lump those together.
Then there's hardware--
so microscopes,
lenses, digital
deconvolution, ways
of seeing things in bolder
relief and more precision.
And then there's how
to manage all the data.
And then I love the
idea that maybe AI
could do what human
brains can't do alone,
like manage
understanding of the data
because it's one thing
to organize data.
It's another to say, oh,
this as you point out
in the analogy with code,
that this particular gene
and that particular gene
are potentially interesting,
whereas a human
being would never
make that potential connection.
MARK ZUCKERBERG: Yeah.
PRISCILLA CHAN: So
the tools that CZI
can bring to the table--
we fund science, like
you're talking about.
There's lots of ways
to fund science.
And just to be
clear, what we fund
is a tiny fraction of what
the NIH funds, for instance.
ANDREW HUBERMAN: So you guys
have been generous enough
that it definitely holds
wait to NIH's contribution.
PRISCILLA CHAN: Yeah.
But I think every funder has
its own role in the ecosystem.
And for us, it's
really, how do we
incentivize new points of view?
How do we incentivize
collaboration?
How do we incentivize
open science?
And so a lot of our grants
include inviting people
to look at different fields.
Our first neuroscience RFA was
aimed towards incentivizing
people from different
backgrounds-- immunologists,
microbiologists--
to come and look
at how our nervous system works
and how to keep it healthy.
Or we ask that our
grantees participate
in the pre-print
movement to accelerate
the rate of sharing knowledge
and actually others being
able to build upon science.
So that's the
funding that we do.
In terms of building, we
build software and hardware,
like you mentioned.
We put together
teams that can build
tools that are more durable
and scalable than someone
in a single lab might
be incentivized to do.
There's a ton of great ideas.
And nowadays, most scientists
can tinker and build
something useful for their lab.
But it's really
hard for them to be
able to share that
tool sometimes
beyond their own laptop
or forget the next Lab
over or across the globe.
So we partner with scientists
to see what is useful,
what kinds of tools.
In imaging, Napari, it's
a useful image annotation
tool that is born from
an open source community.
And how can we
contribute to that?
Or a CELLxGENE, which works
on single cell data sets.
And how can we make it build a
useful tool so that scientists
can share data sets,
analyze their own
and contribute to a larger
corpus of information?
So we have software teams that
are building, collaborating
with scientists to make
sure that we're building
easy to use, durable,
translatable tools
across the scientific community
in the areas that we work in.
We also have institutes-- this
is where the imaging work comes
in-- where we are proud owners
of an electron microscope
right now.
It's going to be installed
at our imaging institute.
And that will really
contribute to the way
where we can see
work differently.
But more hardware does
need to be developed.
We're partnering with
the fantastic scientists
in the biohub network to build
a mini-phase plate to increase
to align the electrons through
the electron microscope
to be able to increase
the resolution,
so we can see in sharper detail.
So there's a lot of innovative
work within the network that's
happening.
And these institutes
have grand challenges
that they're working on.
Back to your
question about cells,
cells are just the smallest
unit that are alive.
And your body,
all of our bodies,
have many, many, many cells.
Some estimate of like
37 trillion cells,
different cells in your body.
And what are they all doing?
And what do they look
like when you're healthy?
What do they look
like when you're sick?
And where we're at right now
with our understanding of cells
and what happens
when you get sick
is basically we've gotten pretty
good at, from the Human Genome
Project, looking at
how different mutations
in your genetic
code lead for you
to be more susceptible
to get sick or directly
cause you to get sick.
So we go from a mutation
in your DNA to, wow,
you now have Huntington's
disease, for instance.
And there's a lot that
happens in the middle.
And that's one of the questions
that we're going after at CZI,
is what actually happens.
So an analogy that I like to
use to share with my friends
is, right now, say we
have a recipe for a cake.
We know there's a
typo in the recipe.
And then the cake is awful.
That's all we know.
We don't know how the
chef interprets the typo.
We don't know what
happens in the oven.
And we don't actually
know how it's exactly
connected to how the
cake didn't turn out
or how you had expected it.
A lot of that is unknown.
But we can actually
systematically try
to break this down.
And one segment of that
journey that we're looking at
is how that mutation
gets translated and acted
upon in your cells.
And all of your cells
have what's called mRNA.
mRNA are the actual instructions
that are taken from the DNA.
And our work in
Single-Cell is looking
at how every cell in your
body is actually interpreting
your DNA slightly
differently and what
happens when healthy cells
are interpreting the DNA
instructions and
when sick cells are
interpreting those directions.
And that is a ton of data.
I just told you, there's
37 trillion cells.
There's different large
sets of mRNA in each cell.
But the work that we've been
funding is looking at how--
first of all, gathering
that information.
We've been incredibly
lucky to be
part of a very fast-moving
field where we've gone from,
in 2017, funding some
methods work to now
having really not complete,
but nearly complete
atlases of how the human body
works, how flies work, how mice
work at the single-cell
level and being
able to then try
to piece together
how does that all come
together when you're healthy
and when you're sick.
And the neat thing about
the inflection point
where we're at in AI is that
I can't look at this data
and make sense of it.
There's just too much of it.
And biology is complex.
Human bodies are complex.
We need this much information.
But the use of large
language models
can help us actually
look at that data
and gain insights,
look at what trends
are consistent with health and
what trends are unsuspected.
And eventually, our
hope, through the use
of these data sets that
we've helped curate
and the application of
large language models,
is to be able to formulate a
virtual cell, a cell that's
completely built off of
the data sets of what
we know about the human body,
but allows us to manipulate,
and learn faster and
try new things to help
move science and
then medicine along.
ANDREW HUBERMAN:
Do you think we've
cataloged the total number
of different cell types?
Every week, I look
at great journals
like Cell Nature and Science.
And for instance, I saw
recently that, using single cell
sequencing, they've categorized
18 plus different types
of fat cells.
We always think of like a fat
cell versus a muscle cell.
So now, you've got 18 types.
Each one is going to express
many, many different genes
and mRNAs.
And perhaps one of
them is responsible
for what we see in
advanced type 2 diabetes,
or in other forms of obesity,
or where people can't lay down
fat cells, which turns out
to be just as detrimental
in those extreme cases.
So now, you've got all
these lists of genes.
But I always thought of single
cell sequencing as necessary,
but not sufficient, right?
You need the information, but
it doesn't resolve the problem.
And I think of it more as
a hypothesis-generating
experiment.
OK, so you have all these genes.
And you can say, well,
this gene is particularly
elevated in the diabetic
cell type of, let's say,
one of these fat cells or
muscle cells for that matter,
whereas it's not
in non-diabetics.
So then of the millions
of different cells,
maybe only five of them
differ dramatically.
So then you generate
a hypothesis.
Oh, it's the ones that
differ dramatically
that are important.
But maybe one of those genes,
when it's only 50% changed,
has a huge effect because of
some network biology effect.
And so I guess what I'm
trying to get to here
is how does one
meet that challenge.
And can AI help
resolve that challenge
by essentially placing
those lists of genes
into 10,000 hypotheses?
Because I'll tell you
that the graduate students
and postdocs in my lab
get a chance to test one
hypothesis at a time.
PRISCILLA CHAN: I know.
ANDREW HUBERMAN: And that's
really the challenge,
let alone one lab.
And so for those
that are listening
to this-- and
hopefully, it's not
getting outside the scope
of standard understanding
or the understanding
we've generated here.
But what I'm
basically saying is,
you have to pick at some point.
More data always sounds great.
But then how do you
decide what to test?
PRISCILLA CHAN: So no, we
don't know all the cell types.
I think one thing that was
really exciting when we first
launched this work
was cystic fibrosis.
Cystic fibrosis is caused
by mutation in CFTR.
That's pretty well known.
It affects a certain channel
that makes it hard for mucus
to be cleared.
That's the basics
of cystic fibrosis.
When I went to medical
school, it was taught as fact.
ANDREW HUBERMAN: So their
lungs fill up with fluid.
These are people who
are carrying around
sacks of fluid filling up.
PRISCILLA CHAN: Yep.
ANDREW HUBERMAN: I've worked
with people like that.
And they have to literally
dump the fluid out.
PRISCILLA CHAN: Exactly.
ANDREW HUBERMAN: They can't
run or do intense exercise.
Life is shorter.
PRISCILLA CHAN: Life is shorter.
And when we applied single-cell
methodologies to the lungs,
they discovered an
entirely new cell type
that actually is affected by
a mutation in the CF mutation,
in cystic fibrosis
mutation, that
actually changes
the paradigm of how
we think about cystic fibrosis.
ANDREW HUBERMAN: Amazing.
PRISCILLA CHAN: [? Just ?]
[? unknown. ?] So I don't think
we know all the cell types.
I think we'll continue
to discover them.
And we'll continue to discover
new relationships between cell
and disease, which leads me
to the second example I want
to bring up, is
this large data set
that the entire
scientific community has
built around single cell.
It's starting to allow us to
say this mutation, where is it
expressed?
What types of cell
types it's expressed in?
And we actually
have built a tool
at CZI called CELLxGENE, where
you can put in the mutation
that you're interested in.
And it gives you a heat
map of cross cell types
of which cell types are
expressing the gene that you're
interested in.
And so then you can
start looking at, OK,
if I look at gene X and I know
it's related to heart disease--
but if you look at
the heat map, it's
also spiking in the pancreas.
That allows you to
generate a hypothesis.
Why?
And what happens when
this gene is mutated
and the function
of your pancreas?
Really exciting way to look
and ask questions differently.
And you can also
imagine a world where
if you're trying to develop a
therapy, a drug, and the goal
is to treat the
function in the heart,
but you know that
it's also really
active in the pancreas again.
So is there going to be
an unexpected side effect
that you should think
about as you're bringing
this drug to clinical trials?
So it's an incredibly
exciting tool
and one that's only
going to get better
as we get more and
more sophisticated
ways to analyze the data.
ANDREW HUBERMAN:
I must say, I love
that because if I look at
the advances in neuroscience
over the last 15
years, most of them
didn't necessarily come from
looking at the nervous system.
They came from the understanding
that the immune system
impacts the brain.
Everyone prior to that
talked about the brain
as an immune-privileged organ.
What you just said
also bridges the divide
between single cells,
organs and systems, right?
Because ultimately,
cells make up organs.
Organs make up systems.
And they're all
talking to one another.
And everyone nowadays is
familiar with gut-brain axis
or the microbiome
being so important.
But rarely is the discussion
between organs discussed,
so to speak.
So I think it's wonderful.
So that tool was
generated by CZI.
Or CCI funded that tool?
MARK ZUCKERBERG: We built that.
PRISCILLA CHAN: We built it.
ANDREW HUBERMAN: You built it.
So is it built by Meta?
Is this Meta?
MARK ZUCKERBERG: No, no,
it has its own engineers.
ANDREW HUBERMAN: Got it.
MARK ZUCKERBERG: Yeah.
They're completely
different organizations.
ANDREW HUBERMAN: Incredible.
And so a graduate
student or postdoc
who's interested in
a particular mutation
could put this mutation
into this database.
That graduate student
or postdoc might
be in a laboratory known
for working on heart,
but suddenly find that
they're collaborating
with other scientists that work
on the pancreas, which also
is wonderful because
it bridges the divide
between these fields.
Fields are so
siloed in science--
not just different
buildings, but people
rarely talk, unless things
like this are happening.
PRISCILLA CHAN: I mean, the
graduate student is someone
that we want to empower
because, one, they're
the future of
science, as you know.
And within CELLxGENE,
if you put in the gene
you're interested in and
it shows you the heat map,
we also will pull up the most
relevant papers to that gene.
And so read these things.
ANDREW HUBERMAN:
That's fantastic.
As we all know,
quality nutrition
influences, of course, our
physical health, but also
our mental health and our
cognitive functioning--
our memory, our ability to
learn new things and to focus.
And we know that one of
the most important features
of high quality
nutrition is making sure
that we get enough vitamins
and minerals from high quality,
unprocessed, or
minimally processed
sources, as well as enough
probiotics, and prebiotics
and fiber to support
basically all
the cellular
functions in our body,
including the gut microbiome.
Now, I, like most everybody
try to get optimal nutrition
from whole foods, ideally mostly
from minimally processed or non
processed foods.
However, one of the challenges
that I and so many other people
face is getting enough
servings of high quality fruits
and vegetables per
day, as well as
fiber and probiotics that
often accompany those fruits
and vegetables.
That's why, way back in
2012, long before I ever
had a podcast, I
started drinking AG1.
And so I'm delighted that AG1
is sponsoring the Huberman Lab
podcast.
The reason I started taking
AG1 and the reason I still
drink AG1 once or
twice a day is that it
provides all of my
foundational nutritional needs.
That is, it provides
insurance that I
get the proper amounts of those
vitamins, minerals, probiotics
and fiber to ensure optimal
mental health, physical
health and performance.
If you'd like to try AG1, you
can go to drinkag1.com/huberman
to claim a special offer.
They're giving away
five free travel
packs plus a year's
supply of vitamin D3 K2.
Again, that's
drinkag1.com/huberman to claim
that special offer.
MARK ZUCKERBERG: I just think
going back to your question
from before are there going
to be more cell types that
get discovered?
I mean, I assume so, right?
I mean, no catalog of
this stuff is ever--
it doesn't seem like
we're ever done.
we keep on finding more.
But I think that that
gets to one of the things
that I think are the
strengths of modern LLMs,
is the ability to imagine
different states that things
can be in.
So from all the work that
we've done and funded
on the Human Cell Atlas, there
is a large corpus of data
that you can now train a
kind of large-scale model on.
And one of the things
that we're doing at CZI,
which I think is
pretty exciting,
is building what we think is one
of the largest non-profit life
sciences AI clusters.
It's on the order of 1,000 GPUs.
And it's larger than what
most people have access
to in academia that you can do
serious engineering work on.
And by basically
training a model
with all of the
Human Cell Atlas Data
and a bunch of other
inputs as well,
we think you'll be able
to basically imagine
all of the different
types of cells and all
the different states that they
can be in, and when they're
healthy and diseased,
and how they'll
interact with different--
interact with each
other, interact
with different potential drugs.
But I think the state
of LLMs, I think
this is where it's
helpful to understand--
have a good understanding
and be grounded
in the modern state of AI.
I mean, these things
are not foolproof.
I mean, one of the
flaws of modern LLMs
is they hallucinate.
So the question is,
how do you make it
so that that can be an advantage
rather than a disadvantage?
And I think the way that it
ends up being an advantage
is when they help you
imagine a bunch of states
that someone could be in, but
then you, as the scientist
or engineer, go and validate
that those are true,
whether they're solutions
to how a protein can
be folded or possible
states that a cell could
be in when it's interacting
with other things.
But we're not yet
at the state with AI
that you can just take the
outputs of these things
as gospel and run from there.
But they are very good,
I think as you said,
hypothesis generators or
possible solution generators
that then you can go validate.
So I think that that's
a very powerful thing
that we can basically--
building on the first
five years of science work
around the Human Cell Atlas
and all the data that's
been built out-- carry
that forward into something
that I think is going to be a
very novel tool going forward.
And that's the type
of thing that I
think we're set up to do well.
I mean, you had this exchange a
little while back about funding
levels and how CZI is just a
drop in the bucket compared
to NIH.
The thing that I think we
can do that's different
is funding some of these
longer term, bigger projects.
It is hard to galvanize
the and pull together
the energy to do that.
And it's a lot of what most
science funding is, relatively
small projects
that are exploring
things over relatively
short time horizons.
And one of the things
that we try to do
is build these tools over
5, 10, 15-year periods.
They're often
projects that require
hundreds of millions
of dollars of funding
and world-class engineering
teams and infrastructure to do.
And that, I think, is a pretty
cool contribution to the field
that I think is--
there aren't as
many other folks who
are doing that kind of thing.
But that's one of
the reasons why
I'm personally excited
about the virtual cell stuff
because it just this perfect
intersection of all the
stuff that we've
done in single cell,
the previous collaborations
that we've done with the field
and bringing together
the industry and AI
expertise around this.
ANDREW HUBERMAN:
Yeah, I completely
agree that the model of science
that you're putting together
with CZI isn't just
unique from NIH,
but it's extremely
important that
the independent
investigator model is what's
driven the progression of
Science in this country
and, to some extent, in Northern
Europe for the last 100 years.
And it's wonderful,
on the one hand,
because it allows for that
image we have of a scientist
tinkering away or the people
in their lab, and then
the eurekas.
And that hopefully translates
to better human health.
But I think, in my opinion,
we've moved past that model
as the most effective model
or the only model that
should be explored.
MARK ZUCKERBERG: Yeah, I
just think it's a balance.
You want that.
But you want to
empower those people.
I think that that's these
tools empower those folks.
ANDREW HUBERMAN: Sure.
And there are mechanisms
to do that, like NIH.
But it's hard to do
collaborative science.
It's interesting that we're
sitting here not far--
because I grew up right
near here as well.
I'm not far from the garage
model of tech, right?
The Hewlett-Packard model,
not far from here at all.
And the idea was the tinkerer
in the garage, the inventor.
And then people often
forget that to implement
all the technologies
they discovered
took enormous factories
and warehouses.
So there's a similarity there
to Facebook, Meta, et cetera.
But I think, in
science, we imagine
that the scientists
alone in their laboratory
and those eureka moments.
But I think, nowadays, the
big questions really require
extensive collaboration and
certainly tool development.
And one of the tools that
you keep coming back to
is these LLMs, these
large language models.
And maybe you could
just elaborate,
for those that aren't familiar.
What is a large language model?
For the uninformed, what is it?
And what does it allow us to
do that different, other types
of AI don't allow?
Or more importantly,
perhaps what
does it allow us to do that a
bunch of really smart people,
highly informed in a
given area of science,
staring at the data--
what can it do
that they can't do?
MARK ZUCKERBERG: Sure.
So I think a lot of the
progression of machine learning
has been about building systems,
neural networks or otherwise,
that can basically make sense
and find patterns in larger
and larger amounts of data.
And there was a breakthrough
a number of years
back that some folks
at Google actually made
called this transformer
model architecture.
And it was this
huge breakthrough
because before then there
was somewhat of a cap
where if you fed more
data into a Neural Network
past some point,
it didn't really
glean more insights from
it, whereas transformers
just-- we haven't seen
the end of how big that
can scale to yet.
I mean, I think that
there's a chance
that we run into some ceiling.
ANDREW HUBERMAN: So
it never asymptotes?
MARK ZUCKERBERG: We
haven't observed it yet.
But we just haven't built
big enough systems yet.
So I would guess that--
I don't know.
I think that this
is actually one
of the big questions
in the AI field today,
is basically, are transformers
and are the current model
architectures sufficient?
If you just build larger
and larger clusters,
do you eventually
get something that's
like human intelligence
or super intelligence?
Or is there some kind
of fundamental limit
to this architecture that
we just haven't reached yet?
And once we get a little bit
further in building them out,
then we'll reach that.
And then we'll need
a few more leaps
before we get to the
level of AI that I
think will unlock
a ton of really
futuristic and amazing things.
But there's no doubt
that even just being
able to process
the amount of data
that we can now with
this model architecture
has unlocked a lot
of new use cases.
And the reason why they're
called large language models is
because one of the first uses
of them is people basically
feed in all of the language
from, basically, the world
wide web.
And you can think about them as
basically prediction machines.
You put in a prompt.
And it can basically
predict a version
of what should come next.
So you type in a headline
for a news story.
And it can predict what it
thinks the story should be.
Or you could train
it so that it could
be a chat, bot
where, OK, if you're
prompted with this question,
you, can get this response.
But one of the
interesting things
is it turns out that there's
actually nothing specific
to using human language in it.
So if instead of feeding
it human language, if you
use that model architecture
for a network and instead
you feed it all of the
Human Cell Atlas Data,
then if you prompt it
with a state of a cell,
it can spit out
different versions
of how that cell can
interact or different states
that the cell could be
in next when it interacts
with different things.
ANDREW HUBERMAN: Does it have
to take a genetics class?
So for instance, if you give
it a bunch of genetics data,
do you have to say, hey,
by the way, and then
you give it a genetics class so
it understands that you've got
DNA, RNA, mRNA, and proteins?
MARK ZUCKERBERG: No, I think
that the basic nature of all
these machine learning
techniques is they're
basically pattern
recognition systems.
So there are these very
deep statistical machines
that are very efficient
at finding patterns.
So it's not actually--
you don't need to teach
a language model that's
trying to speak a language
a lot of specific things
about that language either.
You just feed it in
a bunch of examples.
And then let's say you teach
it about something in English,
but then you also give
it a bunch of examples
of people speaking Italian.
It'll actually be able to
explain the thing that it
learned in English in Italian.
So the crossover and just
the pattern recognition
is the thing that is pretty
profound and powerful
about this.
But it really does apply to
a lot of different things.
Another example in the
scientific community
has been the work
that AlphaFold,
basically the folks at DeepMind,
have done on protein folding.
It's just basically a lot of
the same model architecture.
But instead of
language, there they
fold they fed in all
of these protein data.
And you can give it a state.
And it can spit out solutions to
how those proteins get folded.
So it's very powerful.
I don't think we know
yet, as an industry, what
the natural limits of it are.
I think that that's one
of the things that's
pretty exciting about
the current state.
But it's certainly allows
you to solve problems
that just weren't solved with
the generation of machine
learning that came before it.
ANDREW HUBERMAN:
It sounds like CZI
is moving a lot of work that was
just done in vitro, in dishes,
and in vivo, in
living organisms,
model organisms are humans,
to in silico, as we say.
So do you foresee a future where
a lot of biomedical research,
certainly the work of CZI
included, is done by machines?
I mean, obviously,
it's much lower cost.
And you can run millions
of experiments, which,
of course, is not to
say that humans are not
going to be involved.
But I love the idea that we
can run experiments in silico
en masse.
PRISCILLA CHAN: I think
in silico experiments are
going to be incredibly helpful
to test things quickly,
cheaply and just unleash
a lot of creativity.
I do think you need to be
very careful about making
sure it still translates
and matches the humans.
One thing that's
funny in basic science
is we've basically cured
every single disease in mice.
We know what's going on when
they have a number of diseases
because they're used
as a model organism.
But they are not humans.
And a lot of times,
that research
is relevant, but not
directly one-to-one
translatable to humans.
So you just have to be really
careful about making sure
that it actually
works for humans.
ANDREW HUBERMAN: Sounds
like what CZI is doing
is actually creating
a new field.
As I'm hearing all of
this, I'm thinking, OK,
this transcends immunology
department, cardiothoracic
surgery, I mean neuroscience.
I mean, the idea of a new field,
where you certainly embrace
the realities of
universities and laboratories
because that's where most of
the work that you're funding
is done.
Is that right?
MARK ZUCKERBERG: Mm-hmm.
ANDREW HUBERMAN:
So maybe we need
to think about what it means
to do science differently.
And I think that's one of the
things that's most exciting.
Along those lines, it seems
that bringing together
a lot of different
types of people
at different major
institutions is going
to be especially important.
So I know that the initial
CZI Biohub, gratefully,
included Stanford.
We'll put that
first in the list,
but also UCSF, forgive me.
I have many friends at
UCSF and also Berkeley.
But there are now some
additional institutions
involved.
So maybe you could
talk about that,
and what motivated the decision
to branch outside the Bay Area
and why you selected those
particular additional
institutions to be included.
MARK ZUCKERBERG: Well,
I'll just say it.
A big part of why we wanted
to create additional biohubs
is we were just so
impressed by the work
that the folks who were
running the first biohub did.
PRISCILLA CHAN: Yeah.
And you should walk
through the work
of the Chicago Biohub
and the New York Biohub
that we just announced.
But I think it's actually an
interesting set of examples
that balance the
limits of what you want
to do with physical
material engineering
and where things are
purely biological
because the Chicago team
is really building more
sensors to be able to understand
what's going on in your body.
But that's more of a physical
kind of engineering challenge,
whereas the New York
team-- we basically
talk about this as like a
cellular endoscope of being
able to have an immune
cell or something that
can go and understand,
what's the thing that's
going on in your body?
But it's not a physical
piece of hardware.
It's a cell that you can
basically have just go report
out on different things that
are happening inside the body.
ANDREW HUBERMAN: Oh, so making
the cell the the microscope.
PRISCILLA CHAN: Totally.
MARK ZUCKERBERG: And
then eventually actually
being able to act on it.
But I mean, you should go
into more detail on all this.
PRISCILLA CHAN: So
a core principle
of how we think about biohubs
is that it has to be--
when we invited
proposals, it has
to be at least
three institutions,
so really breaking down the
barrier of a single university,
oftentimes asking for the
people designing the research
aim to come from all different
backgrounds and to explain why
that the problem that
they want to solve
requires interdisciplinary,
inter-university, institution
collaboration to
actually make happen.
We just put that
request for proposal
out there with our
San Francisco Biohub
as an example,
where they've done
incredible work in single cell
biology and infectious disease.
And we got--
I want to say--
like 57 proposals
from over 150 institutions.
A lot of ideas came together.
And we were so, so
excited that we've
been able to launch
Chicago and New York.
Chicago is a collaboration
between UIUC,
University of Illinois
Urbana-Champaign,
and University of
Chicago and Northwestern.
Obviously, these universities
are multifaceted.
But if I were to describe
them by their stereotypical
strength, Northwestern has
an incredible medical system
and hospital system.
University of Chicago
brings to the table
incredible basic
science strengths.
University of Illinois is
a computing powerhouse.
And so they came
together and proposed
that they were going
to start thinking
about cells in tissue,
so one of the layers
that you just alluded to.
So how do the cells that we know
behave and act differently when
they come together as a tissue?
And one of the first tissues
that they're starting with
is skin.
So they've already been
able to, as a collaboration
under the leadership, of
Shana Kelly design engineered
skin tissue.
The architecture looks the
same as what's in you and I.
And what they've done is
built these super, super thin
sensors.
And they embed these sensors
throughout the layers
of this engineered tissue.
And they read out the data.
They want to see what
these cells are secreting,
how these cells
talk to each other
and what happens when
these cells get inflamed.
Inflammation is an
incredibly important process
that drives 50% of all deaths.
And so this is another
disease-agnostic approach.
We want to understand
inflammation.
And they're going to
get a ton of information
out from these sensors that tell
you what happens when something
goes awry because
right now we can say,
when you have an
allergic reaction,
your skin gets red and puffy.
But what is the
earliest signal of that?
And these sensors can
look at the behaviors
of these cells over time.
And then you can apply
a large language model
to look at the earliest
statistically significant
changes that can allow you to
intervene as early as possible.
So that's what Chicago's doing.
They're starting
in the skin cells.
They're also looking at the
neuromuscular junction, which
is the connection between where
a neuron attaches to a muscle
and tells the muscle
how to behave--
super important in things
like ALS, but also in aging.
The slowed transmission
of information
across that
neuromuscular junction
is what causes old
people to fall.
Their brain cannot trigger their
muscles to react fast enough.
And so we want to
be able to embed
these sensors to understand how
these different, interconnected
systems within our
bodies work together.
In New York, they're doing a
related, but equally exciting
project where they're
engineering individual cells
to be able to go in and identify
changes in a human body.
So what they'll do is--
they're calling it--
ANDREW HUBERMAN: It's wild.
I mean, I love that.
I mean, this is--
I don't want to go on a tangent.
But for those that want to
look it up adaptive optics,
there's a lot of
distortion and interference
when you try and look
at something really
small or really far away.
And really smart
physicists figured out,
well, use the interference
as part of the microscope.
Make those actually
lenses of the microscope.
MARK ZUCKERBERG: We
should talk about imaging
separately after you talk
about the New York Biohub.
ANDREW HUBERMAN: It's extremely
clever, along those lines.
It's not intuitive.
But then when you hear it, it's
like it makes so much sense.
It's not immediately intuitive.
Make the cells that already
can navigate to tissues
or embed themselves in
tissues be the microscope
within that tissue.
I love it.
PRISCILLA CHAN: Totally.
The way that I explain
this to my friends
and my family is this
is Fantastic Voyage,
but real life.
We are going into
the human body.
And we're using the immune
cells, which are privileged
and already working to
keep your body healthy,
and being able to target them
to examine certain things.
So you can engineer an immune
cell to go in your body
and look inside your
coronary arteries and say,
are these arteries healthy?
Or are there plaques?
Because plaques
lead to blockage,
which lead to heart attacks.
And the cell can then
record that information
and report it back out.
That's the first half
of what the New York
Biohub is going to do.
ANDREW HUBERMAN: Fantastic.
PRISCILLA CHAN: The
second half is can you
then engineer the cells to
go do something about it.
Can I then tell
a different cell,
immune cell that is able
to transport in your body
to go in and clean that
up in a targeted way?
And so it's incredibly exciting.
They're going to
study things that
are immune privilege, that
your immune system normally
doesn't have access to--
things like ovarian
and pancreatic cancer.
They'll also look at a number
of neurodegenerative diseases,
since the immune system doesn't
presently have a ton of access
into the nervous system.
But it's both mind blowing
and it feels like sci-fi.
But science is
actually in a place
where if you really push
a group of incredibly
qualified scientists
say, could you do this
if given the chance, the
answer is like probably.
Give us enough time, the
bright team and resources.
It's doable.
MARK ZUCKERBERG: Yeah.
I mean, it's a 10
to 15-year project.
But it's awesome,
engineered cells, yeah.
ANDREW HUBERMAN: I
love the optimism.
And the moment you said make
the cell the microscope,
so to speak, I was
like yes, yes and yes.
It just makes so much sense.
What motivated the decision
to do the work of CZI
in the context of existing
universities as opposed to--
there's still some real
estate up in Redwood City
where there's a bunch of
space to put biotech companies
and just hiring people
from all backgrounds
and saying, hey, have at it and
doing this stuff from scratch?
I mean, it's a very
interesting decision
to do this in the
context of an existing
framework of graduate students
that need to do their thesis
and get a first author
paper because there's
a whole set of structures
within academia
that I think both
facilitate, but also limit
the progression of science.
That independent
investigator model
that we talked about
a little bit earlier,
it's so core to the way
science has been done.
This is very different
and frankly sounds
far more efficient, if I'm
to be completely honest.
And we'll see if I renew my
NIH funding after saying that.
But I think we all
want the same thing.
As scientists and
as humans, we want
to understand the way we work.
And we want healthy people
to persist to be healthy.
And we want sick
people to get healthy.
I mean, that's really
ultimately the goal.
It's not super complicated.
It's just hard to do.
PRISCILLA CHAN: So the
teams at the biohub
are actually independent
of the universities.
ANDREW HUBERMAN: Got it.
PRISCILLA CHAN: So each
biohub will probably
have in total maybe 50 people
working on deep efforts.
However, it's an acknowledgment
that not all of the best
scientists who can
contribute to this area
are actually going to, one,
want to leave a university
or want to take on the
full-time scope of this project.
So it's the ability to
partner with universities
and to have the faculty
at all the universities
be able to contribute
to the overall project,
is how the biohub is structured.
ANDREW HUBERMAN: Got it.
MARK ZUCKERBERG: But a lot of
the way that we're approaching
CZI is this long-term,
iterative project
to figure out-- try a
bunch of different things,
figure out which things produce
the most interesting results,
and then double down on those
in the next five-year push.
So we just went
through this period
where we wrapped
up the first five
years of the science program.
And we tried a lot
of different models,
all kinds of different things.
And it's not that
the biohub model--
we don't think it's
the best or only model.
But we found that it was
a really interesting way
to unlock a bunch
of collaboration
and bring some
technical resources that
allow for this longer
term development.
And it's not something that
is widely being pursued
across the rest of the field.
So we figured, OK, this
is an interesting thing
that we can help push on.
But I mean, yeah, we do
believe in the collaboration.
But I also think that
we come at this with--
we don't think that the way
that we're pursuing this
is the only way to
do this or the way
that everyone should do it.
We're pretty aware of what
is the rest of the ecosystem
and how we can play
a unique role in it.
ANDREW HUBERMAN: It
feels very synergistic
with the way science
is already done
and also fills an incredibly
important niche that,
frankly, wasn't filled before.
Along the lines of
implementation--
so let's say your large language
models combined with imaging
tools reveal that a
particular set of genes acting
in a cluster--
I don't know-- set
up an organ crash.
Let's say the pancreas
crashes at a particular stage
of pancreatic cancer.
I mean, it's still one of the
most deadliest of the cancers.
And there are others that you
certainly wouldn't want to get.
But that's among the ones you
wouldn't want to get the most.
So you discover that.
And then and the
idea is that, OK,
then AI reveals
some potential drug
targets that then bear
out in vitro, in a dish
and in a mouse model.
How is the actual implementation
to drug discovery?
Or maybe this target is
druggable, maybe it's not.
Maybe it requires
some other approach--
laser ablation
approach or something.
We don't know.
But ultimately,
is CZI going to be
involved in the implementation
of new therapeutics?
Is that the idea?
MARK ZUCKERBERG: Less so.
PRISCILLA CHAN: Less so.
This is where it's important
to work in an ecosystem
and to know your
own limitations.
There are groups, and
startups and companies
that take that and bring it to
translation very effectively.
I would say the
place where we have
a small window into
that world is actually
our work with rare
disease groups.
We have, through our
Rare As One portfolio,
funded patient advocates
to create rare disease
organizations where patients
come together and actually pool
their collective experience.
They build
bioregistries, registries
of their natural history.
And they both partner
with researchers
to do the research
about their disease
and with drug developers to
incentivize drug developers
to focus on what they may
need for their disease.
And one thing that's
important to point out
is that rare
diseases aren't rare.
There are over
7,000 rare diseases
and collectively impact
many, many individuals.
And I think the thing
that's, from a basic science
perspective, the incredibly
fascinating thing
about rare diseases is that
they're actually windows to how
the body normally should work.
And so there are often
mutations that when
genes that when they're mutated
cause very specific diseases,
but that tell you how the
normal biology works as well.
ANDREW HUBERMAN: Got it.
So you discussed basically the
major goals and initiatives
of the CZI for the next,
say, 5 to 10 years.
And then beyond
that, the targets
will be explored by
biotech companies.
They'll grab those targets, and
test them and implement them.
MARK ZUCKERBERG:
There's also, I think,
been a couple of teams from
the initial biohub that
were interested in spinning
out ideas into startups.
So even though it's
not a thing that we're
going to pursue because
we're a philanthropy,
we want to enable
the work that gets
done to be able to get turned
into companies and things
that other people
go take and run
towards building
ultimately therapeutics.
So that's another zone.
But that's not a thing
that we're going to do.
ANDREW HUBERMAN: Got it.
I gather you're both optimists.
Yeah?
Is that part of what
brought you together?
Forgive me for switching
to a personal question.
But I love the
optimism that seems
to sit at the root of the CZI.
PRISCILLA CHAN: I
will say that we
are incredibly hopeful people.
But it manifests in different
ways between the two of us.
MARK ZUCKERBERG: Yeah.
PRISCILLA CHAN: How
would you describe
your optimism versus mine?
It's not a loaded question.
MARK ZUCKERBERG: I don't know.
Huh.
I mean, I think I'm more
probably technologically
optimistic about
what can be built.
And I think you, because of
your focus as an actual doctor,
have more of a
sense of how that's
going to affect actual
people in their lives,
whereas, for me, it's like--
I mean, a lot of my
work is we touch a lot
of people around the world.
And the scale is immense.
And I think, for
you, it's like being
able to improve the
lives of individuals,
whether it's students at any of
the schools that you've started
or any of the stuff that we've
supported through the education
work, which isn't the
goal here, or just
being able to improve people's
lives in that way I think
is the thing that I've seen
be super passionate about.
I don't know.
Do you agree with
that characterization?
I'm trying I'm trying to--
PRISCILLA CHAN: Yeah,
I agree with that.
I think that's very fair.
And I'm sort of
giggling to myself
because in day-to-day
life, as life partners,
our relative optimism
comes through
as Mark just is overly
optimistic about his time
management and will get
engrossed in interesting ideas.
MARK ZUCKERBERG: I'm late.
PRISCILLA CHAN: And he's late.
ANDREW HUBERMAN: Physicians
are very punctual, yeah.
PRISCILLA CHAN: And
because he's late,
I have to channel Mark
is an optimist whenever
I'm waiting for him.
MARK ZUCKERBERG: That's
such a nice way of--
OK, I'll start using that.
PRISCILLA CHAN:
That's what I think
when I'm in the driveway with
the kids waiting for you.
I'm like, Mark is an optimist.
And so his optimism
translates to some tardiness,
whereas I'm a how is this
going to happen like.
I'm going to open a spreadsheet.
I'm going to start
putting together a plan
and pulling together
all the pieces,
calling people to bring
something to life.
MARK ZUCKERBERG: But it is one
of my favorite quotes, that
is optimists tend
to be successful
and pessimists tend to be right.
And yeah, I mean, I
think it's true in a lot
of different aspects of life.
ANDREW HUBERMAN: Who said that?
Did you say that,
Mark Zuckerberg?
MARK ZUCKERBERG: No, I did not.
PRISCILLA CHAN: Absolutely not.
MARK ZUCKERBERG: No, no, no.
I like it.
I did not invent it.
ANDREW HUBERMAN:
We'll give it to you.
We'll put it out there.
MARK ZUCKERBERG: No, no, no.
ANDREW HUBERMAN: Just
kidding, just kidding.
MARK ZUCKERBERG: But I do
think that there's really
something to it, right?
I mean, if you're
discussing any idea,
there's all these reasons
why it might not work.
And those reasons
are probably true.
The people who are stating them
probably have some validity
to it.
But the question is, is that
the most productive way to view
the world?
Across the board,
I think the people
who tend to be the
most productive
and get the most done--
you kind of need
to be optimistic
because if you don't believe
that something can get done,
then why would
you go work on it?
ANDREW HUBERMAN:
The reason I ask
the question is that these days
we hear a lot about the future
is looking so dark in
these various ways.
And you have children.
So you have families.
And you are a family, excuse me.
And you also have
families independently
that are now merged.
But I love the
optimism behind the CZI
because, behind
all this, there's
a set of big
statements on the wall.
One, the future can be
better than the present,
in terms of treating disease,
maybe even, you said,
eliminating diseases,
all diseases.
I love that optimism.
And there's a tractable
path to do it.
We're going to put literally
money, and time, and energy,
and people, and technology
and AI behind that.
And so I have to ask,
was having children
a significant modifier in terms
of your view of the future?
Like wow, you hear all
this doom and gloom.
What's the future going
to be like for them?
Did you sit back and
think, what would it
look like if there was a
future with no diseases?
Is that the future, we
want our children in?
I mean, I'm voting a big yes.
So we're not we're not
going to debate that at all.
But was having
children an inspiration
for the CZI in some way?
MARK ZUCKERBERG: Yeah.
So
PRISCILLA CHAN: I think
my answer to that--
I would dial backwards for me.
And I'll just tell a very
brief story about my family.
I'm the daughter of
Chinese-Vietnamese refugees.
My parents and grandparents
were boat people,
if you remember
people left Vietnam
during the war in these small
boats into the South China Sea.
And there were stories about
how these boats would sink
with whole families on them.
And so my
grandparents, both sets
of grandparents who
knew each other,
decided that there was a
better future out there.
And they were willing
to take risks for it.
But they were afraid of
losing all of their kids.
My dad is one of six.
My mom is one of 10.
And so they decided
that there was something
out there in this bleak time.
And they paired up their
kids, one from each family,
and sent them out on
these little boats
before the internet, before
cell phones, and just said,
we'll see you on the other side.
ANDREW HUBERMAN: Wow.
PRISCILLA CHAN:
And the kids were
between the ages of like
10 to 25, so young kids.
My mom was a teenager, early
teen when this happened.
And everyone made it.
And I get to sit
here and talk to you.
So how could I not believe
that better is possible?
And like I hope that that's
in my epigenetics somewhere
and that I carry on.
ANDREW HUBERMAN: That
is a spectacular story.
PRISCILLA CHAN: Isn't that wild?
ANDREW HUBERMAN:
It is spectacular.
PRISCILLA CHAN: How can I
be a pessimist with that?
ANDREW HUBERMAN: I love it.
And I so appreciate that
you became a physician
because you're now
bringing that optimism,
and that epigenetic
understanding,
and cognitive understanding
and emotional understanding
to the field of medicine.
So I'm grateful to the people
that made that decision.
PRISCILLA CHAN: Yeah.
I've always known that story.
But you don't understand
how wild that feels
until you have your own child.
And you're like,
well, I can't even--
I refuse to let her use glass
bottles only or something
like that.
And you're like, oh my God,
the risk and the willingness
of my grandparents to believe
in something bigger and better
is just astounding.
And our own children give
it a sense of urgency.
ANDREW HUBERMAN: Again,
a spectacular story.
And you're sending knowledge
out into the fields of science
and bringing knowledge
into the fields of science.
And I love this.
We'll see you on the other side.
I'm confident that it
will all come back.
Well, thank you
so much for that.
Mark, you have the
opportunity to talk about--
did having kids
change your worldview?
MARK ZUCKERBERG: It's really
tough to beat that story.
ANDREW HUBERMAN: It is
tough to beat that story.
And they are also your children.
So in this case, you get two for
the price of one, so to speak.
MARK ZUCKERBERG: Having
children definitely changes
your time horizon.
So I think that
that's one thing.
There are all these things that
I think we had talked about,
for as long as we've known
each other, that you eventually
want to go do.
But then it's like,
oh, we're having kids.
We need to get on this, right?
So I think that there's--
PRISCILLA CHAN:
That was actually
one of the checklists, the baby
checklist before the first.
MARK ZUCKERBERG: It was
like, the baby's coming.
We have to start CZI.
PRISCILLA CHAN: Truly.
MARK ZUCKERBERG: I'm like
sitting in the hospital
delivery room finishing
editing the letter that we
were going to publish
to announce the work.
PRISCILLA CHAN: Some people
think that is an exaggeration.
It was not.
We really were editing
the final draft.
ANDREW HUBERMAN:
Birthed CZI before you
birthed the human child.
Well, it's an
incredible Initiative.
I've been following it
since its inception.
And it's already been
tremendously successful.
And everyone in the
field of science--
and I have a lot of
communication with those
folks--
feels the same way.
And the future is even
brighter for it, it's clear.
And thank you for expanding
to the Midwest and New York.
And we're all very excited to
see where all of this goes.
I share in your optimism.
And thank you for
your time today.
PRISCILLA CHAN: Yeah, thank you.
MARK ZUCKERBERG: Thank you.
A lot more to do.
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And now for my discussion
with Mark Zuckerberg.
Slight shift of topic here--
you're extremely
well-known for your role
in technology development.
But by virtue of your
personal interests
and also where Meta
technology interfaces
with mental health
and physical health,
you're starting to become
synonymous with health,
whether you realize it or not.
Part of that is because
there's posts, footage
of you rolling jiu jitsu.
You won a jiu jitsu
competition recently.
You're doing other forms of
martial arts, water sports,
including surfing,
and on and on.
So you're doing it yourself.
But maybe we could just
start off with technology
and get this issue out
of the way first, which
is that I think many people
assume that technology,
especially technology that
involves a screen, excuse
me, of any kind is going to
be detrimental to our health.
But that doesn't necessarily
have to be the case.
So could you explain
how you see technology
meshing with, inhibiting,
or maybe even promoting
physical and mental health?
MARK ZUCKERBERG: Sure.
I mean, I think this is
a really important topic.
The research that we've
done suggests that it's not
all good or all bad.
I think how you're
using the technology has
a big impact on
whether it is basically
a positive experience for you.
And even within technology,
even within social media,
there's not one type of
thing that people do.
I think, at its best, you're
forming meaningful connections
with other people.
And there's a lot of research
that basically suggests
that it's the
relationships that we have
and the friendships that bring
the most happiness in our lives
and, at some level,
end up even correlating
with living a longer
and healthier life
because that grounding
that you have in community
ends up being
important for that.
So I think that aspect
of social media,
which is the ability to connect
with people, to understand
what's going on
in people's lives,
have empathy for them,
communicate what's
going on with your life,
express that, that's
generally positive.
There are ways that
it can be negative,
in terms of bad interactions,
things like bullying,
which we can talk about because
there's a lot that we've
done to basically make sure that
people can be safe from that
and give people tools and
give kids the ability to have
the right parental controls.
Their parents can oversee that.
But that's the interacting
with people side.
There's another
side of all of this,
which I think of as just
passive consumption, which,
at its best, is entertainment.
And entertainment is an
important human thing, too.
But I don't think that that
has quite the same association
with the long-term well-being
and health benefits
as being able to help people
connect with other people does.
And I think, at its worst, some
of the stuff we see online--
I think, these days,
a lot of the news
is just so relentlessly
negative that it's just
hard to come away
from an experience
where looking at the
news for half an hour
and feel better about the world.
So I think that
there's a mix on this.
I think the more
that social media
is about connecting
with people and the more
that when you're consuming
and using the media
part of social media to
learn about things that
enrich you and can provide
inspiration or education as
opposed to things that
just leave you with a more
toxic feeling, that's the
balance that we try to get
right across our products.
And I think we're pretty
aligned with the community
because, at the end of
the day, I mean, people
don't want to use a product
and come away feeling bad.
There's a lot that
people talk about--
evaluate a lot of
these products in terms
of information and utility.
But I think it's
as important, when
you're designing a
product, to think
about what kind of
feeling you're creating
with the people who
use it, whether that's
an aesthetic sense when
you're designing hardware,
or just what do you
make people feel.
And generally, people don't
want to feel bad, right?
That doesn't mean that
we want to shelter people
from bad things that are
happening in the world.
But I don't really think that--
it's not what people
want for us to just
be just showing all this super
negative stuff all day long.
So we work hard on all these
different problems-- making
sure that we're helping connect
people as best as possible,
helping make sure that
we give people good tools
to block people who
might be bullying them,
or harass them, or
especially for younger folks,
anyone under the age of 16
defaults into an experience
where their
experience is private.
We have all these
parental tools.
So that way, parents can
understand what their children
are up to in a good balance.
And then on the
other side, we try
to give people tools
to understand how
they're spending their time.
We try to give people tools
so that if you're a teen
and you're stuck in some
loop of just looking
at one type of content,
we'll nudge you and say, hey,
you've been looking at content
of this type for a while.
How about something else?
And here's a bunch
of other examples.
So I think that there
are things that you
can do to push this in
a positive direction.
But I think it just
starts with having
a more nuanced view of this
isn't all good or all bad.
And the more that you
can make it a positive
thing, the better this
will be for all the people
who use our products.
ANDREW HUBERMAN: That
makes really good sense.
In terms of the negative
experience, I agree.
I don't think anyone wants
a negative experience
in the moment.
I think where some people
get concerned perhaps--
and I think about my own
interactions with, say,
Instagram, which I use all the
time for getting information
out, but also
consuming information.
And I happen to love it.
It's where I
essentially launched
the non-podcast segment of
my podcast and continue to.
I can think of experiences
that are a little bit
like highly
processed food, where
it tastes good at the time.
It's highly engrossing.
But it it's not
necessarily nutritious.
And you don't feel
very good afterwards.
So for me, that would
be the little collage
of default options to
click on in Instagram.
Occasionally, I
notice-- and this just
reflects my failure, not
Instagram's, that there
are a lot of street
fight things,
like people beating
people up on the street.
And I have to say, these have a
very strong gravitational pull.
I'm not somebody that enjoys
seeing violence, per se.
But you know I find myself--
I'll click on one of
these, like what happened?
And I'll see someone get hit.
And there's a little melee
on the street or something.
And those seem to be
offered to me a lot lately.
And again, this is
my fault. It reflects
my prior searching experience.
But I noticed that it has a bit
of a gravitational pull, where
I didn't learn anything.
It's not teaching me any
useful street self-defense
skills of any kind.
And at the same time,
I also really enjoy
some of the cute animal stuff.
And so I get a
lot of those also.
So there's this
polarized collage
that's offered to me that
reflects my prior search
behavior.
You could argue that the
cute animal stuff is just
entertainment.
But actually, it fills
me with a feeling,
in some cases, that
truly delights me.
I delight in animals.
And we're not just
talking about kittens.
I mean, animals I've
never seen before,
interactions between
animals I've never seen
before that truly delight me.
They energize me
in a positive way
that when I leave Instagram,
I do think I'm better off.
So I'm grateful for the
algorithm in that sense.
But I guess, the direct question
is, is the algorithm just
reflective of what one
has been looking at a lot
prior to that moment
where they log on?
Or is it also trying to do
exactly what you described,
which is trying to give people
a good-feeling experience that
leads to more good feelings?
MARK ZUCKERBERG: Yeah.
I mean, I think we try to
do this in a long-term way.
I think one simple
example of this
is we had this issue
a number of years back
about clickbait
news, so articles
that would have basically
a headline that grabbed
your attention,
that made you feel
like, oh, I need
to click on this.
And then you click on it.
And then the article is
actually about something that's
somewhat tangential to it.
But people clicked on it.
So the naive version of this
stuff, the 10-year-old version
was like, oh, people seem
to be clicking on this.
Maybe that's good.
But it's actually a pretty
straightforward exercise
to instrument the system to
realize that, hey, people
click on this, and
then they don't really
spend a lot of time reading
the news after clicking on it.
And after they do
this a few times,
it doesn't really correlate
with them saying that they're
having a good experience.
Some of how we
measure this is just
by looking at how
people use the services.
But I think it's also
important to balance
that by having real people
come in and tell us,
OK-- we show them, here are
the stories that we could have
showed you, which of these
are most meaningful to you,
or would make it so that you
have the best experience,
and just mapping the
algorithm and what
we do to that ground truth of
what people say that they want.
So I think that, through
a set of things like that,
we really have made large
steps to minimize things
like clickbait over time.
It's not like gone
from the internet.
But I think we've done a
good job of minimizing it
on our services.
Within that though,
I do think that we
need to be pretty
careful about not
being paternalistic about what
makes different people feel
good.
So I mean, I don't
know that everyone
feels good about cute animals.
I mean, I can't
imagine that people
would feel really bad about it.
But maybe they don't have as
profound of a positive reaction
to it as you just expressed.
And I don't know.
Maybe people who are
more into fighting
would look at the
street fighting videos--
assuming that they're within
our community standards.
I think that there's
a level of violence
that we just don't want
to be showing at all.
But that's a separate question.
But if they are, I
mean, then it's like--
I mean, I'm pretty into MMA.
I don't get a lot of
street fighting videos.
But if I did, maybe I'd feel
like I was learning something
from that.
I think at various times
in the company's history,
we've been a little bit too
paternalistic about saying,
this is good content, this
is bad, you should like this,
this is unhealthy for you.
And I think that we want to
look at the long-term effects.
You don't want to get
stuck in a short term
loop of like, OK,
just because you
did this today doesn't
mean it's what you
aspire for yourself over time.
But I think, as long as you
look at the long-term of what
people both say they want and
what they do, giving people
a fair amount of latitude to
like the things that they like,
I just think feels like
the right set of values
to bring to this.
Now, of course, that
doesn't go for everything.
There are things that are truly
off limits and things that--
like bullying, for example, or
things that are really inciting
violence, things like that.
I mean, we have the
whole community standards
around this.
But I think, except
for those things
which I would hope that
most people can agree, OK,
bullying is bad--
I hope that 100% of
people agree with that.
And not 100%, maybe 99%.
Except for the things that
kind of get that very--
that feel pretty extreme
and bad like that,
I think you want to
give people space
to like what they want to like.
ANDREW HUBERMAN: Yesterday, I
had the very good experience
of learning from the Meta team
about safety protections that
are in place for kids who
are using Meta Platforms.
And frankly, I was really
positively surprised
at the huge number of
filter-based tools and just
ability to customize the
experience so that it can stand
the best chance of enriching--
not just remaining neutral,
but enriching their
mental health status.
One thing that came about
in that conversation,
however, was I realized
there are all these tools.
But do people really know
that these tools exist?
And I think about my own
experience with Instagram.
I love watching Adam Mosseri's
Friday Q&As because he explains
a lot of the tools that
I didn't know existed.
And if people haven't
seen that, I highly
recommend they watch that.
I think he takes
questions on Thursdays
and answers them
most every Fridays.
So if I'm not aware of the tools
without watching that, that
exists for adults,
how does Meta look
at the challenge of making sure
that people know that there
are all these tools--
I mean, dozens and dozens
of very useful tools?
But I think most of us just
know the hashtag, the tag,
the click, stories versus feed.
We now know that--
I also post to Threads.
I mean, so we know the
major channels and tools.
But this is like
owning a vehicle that
has incredible features
that one doesn't
realize can take you off road,
can allow your vehicle to fly.
I mean, there's a lot there.
So what do you
think could be done
to get that information out?
Maybe this conversation could
cue people to [INAUDIBLE]..
MARK ZUCKERBERG: I mean, that's
part of the reason why I wanted
to talk to you about this.
I mean, I think most of the
narrative around social media
is not, OK, all of
the different tools
that people have to
control their experience.
It's the narrative of
is this just negative
for teens or something.
And I think, again,
a lot of this
comes down to how is the
experience being tuned.
Are people using it to
connect in positive ways?
And if so, I think
it's really positive.
So yeah, I mean, I
think part of this
is we probably just need to
get out and talk to people more
about it.
And then there's an
in-product aspect,
which is if you're a
teen and you sign up,
we take you through a pretty
extensive experience that
tries to outline some of this.
But that has limits, too,
because when you sign up
for a new thing, if you're
bombarded with here's
a list of features, you're like,
OK, I just signed up for this.
I don't really understand much
about what the service is.
Let me go find some
people to follow
who are my friends on
here before I learn
about controls to prevent people
from harassing me or something.
That's why I think it's
really important to also show
a bunch of these
tools in context.
So if you're
looking at comments,
and if you go to
delete a comment,
or you go to edit something, try
to give people prompts in line.
It's like, hey, did that
you can manage things
in these ways around that?
Or when you're in the inbox
and you're filtering something,
remind people in line.
So just because of
the number of people
who use the products
and the level of nuance
around each of the controls,
I think the vast majority
of that education, I think,
needs to happen in the product.
But I do think that through
conversations like this
and others that we
need to be doing,
I think we can create a broader
awareness that those things
exist so that way at
least people are primed
so that way when those things
pop up in the product people,
they're like, oh yeah, I knew
that there was this control.
And here's how I would use that.
ANDREW HUBERMAN: I find
the restrict function
to be very useful, more than the
block function in most cases.
I do sometimes have
to block people.
But the restrict
function is really useful
that you could filter
specific comments.
You might recognize that
someone has a tendency
to be a little aggressive.
And I should point out that
I actually don't really
mind what people say to me.
But I try and maintain
what I call classroom rules
in my comment section, where
I don't like people attacking
other people because I
would never tolerate that
in the university classroom.
I'm not going to tolerate
that in the comments section,
for instance.
MARK ZUCKERBERG: Yeah.
And I think that the example
that you just used about
restrict versus block gets to
something about product design
that's important, too, which
is that block is this very
powerful tool that if someone
is giving you a hard time
and you just want them to
disappear from the experience,
you can do it.
But the design trade-off with
that is that in order to make
it so that the person is
just gone from the experience
and that you don't
show up to them,
they don't show up to you--
inherent to that is
that they will have
a sense that you blocked them.
And that's why I think some
stuff like restrict or just
filtering, like
I just don't want
to see as much stuff
about this topic--
people like using different
tools for very subtle reasons.
I mean, maybe you want the
content to not show up,
but you don't want
the person who's
posting the content to know that
you don't want it to show up.
Maybe you don't want to get the
messages in your main inbox,
but you don't want to tell the
person actually that you're not
friends or something like that.
You actually need to give
people different tools that
have different levels
of power and nuance
around how the social
dynamics around using them
play out in order
to really allow
people to tailor the experience
in the ways that they want.
ANDREW HUBERMAN:
In terms of trying
to limit total amount
of time on social media,
I couldn't find really
good data on this.
How much time is too much?
I mean, I think
it's going to depend
on what one is looking at, the
age of the user, et cetera.
MARK ZUCKERBERG: I agree.
ANDREW HUBERMAN: I
know that you have
tools that cue the
user to how long
they've been on
a given platform.
Are there tools
to self-regulate--
I'm thinking about the Greek
myth of the sirens and people
tying themselves to the
mast and covering their eyes
so that they're not
drawn in by the sirens.
Is there a function aside from
deleting the app temporarily
and then reinstalling it every
time you want to use it again?
Is there a true lockout,
self-lockout function
where one can lock themselves
out of access to the app?
MARK ZUCKERBERG: Well, I
think we give people tools
that let them manage this.
And there's the tools
that you get to use.
And then there's the
tools that the parents
get to use to basically
see how usage works.
But yeah, I think that
there's different--
I think, for now,
we've mostly focused
on helping people
understand this,
and then give people reminders
and things like that.
It's tough, though, to
answer the question that you
were talking about before.
Is there an amount of
time which is too much?
Because it does really
get to what you're doing.
If you fast forward
beyond just the
apps that we have today
to an experience that
is like a social
experience in the future
of the augmented reality
glasses or something
that we're building,
a lot of this
is going to be you're
interacting with people
in the way that you
would physically
as if you were like
hanging out with friends
or working with people.
But now, they can
show up as holograms.
And you can feel like you're
present right there with them,
no matter where
they actually are.
And the question is,
is there too much
time to spend interacting
with people like that?
Well, at the limit,
if we can get
that experience to be
as rich and giving you
as good of a sense of presence
as you would have if you were
physically there
with someone, then I
don't see why you would want to
restrict the amount that people
use that technology
to any less than what
would be the amount of time
that you'd be comfortable
interacting with
people physically,
which obviously is not
going to be 24 hours a day.
You have to do other stuff.
You have work.
You need to sleep.
But I think it really gets to
how you're using these things,
whereas if what you're
primarily using the services for
is you're getting stuck in loops
reading news or something that
is really getting you into
a negative mental state,
then I don't know.
I mean, I think that
there's probably
a relatively short
period of time
that maybe that's a good thing
that you want to be doing.
But again, even
then it's not zero
because just because news
might make you unhappy
doesn't mean that
the answer is to be
unaware of negative things that
are happening in the world.
I just think that
different people
have different tolerances for
what they can take on that.
And I think it's
generally having
some awareness is probably
good, as long as it's not more
than you're constitutionally
able to take.
So I don't know.
I try not be too paternalistic
about this as our approach.
But we want to empower
people by giving them
the tools, both people and,
if you're a teen, your parents
to have tools to understand
what you're experiencing
and how you're using these
things, and then go from there.
ANDREW HUBERMAN: Yeah.
I think it requires of all of us
some degree of self-regulation.
I like this idea of not
being too paternalistic.
I mean, it seems like
the right way to go.
I find myself
occasionally having
to make sure that I'm not
just passively scrolling,
that I'm learning.
I like foraging for, organizing
and dispersing information.
That's been my life's career.
So I've learned so
much from social media.
I find great
papers, great ideas.
I think comments are a
great source of feedback.
And I'm not just saying that
because you're sitting here.
I mean, Instagram in particular,
but other Meta platforms
have been tremendously
helpful for me to get science
and health information out.
One of the things that
I'm really excited about,
which I only had the chance to
try for the first time today,
is your new VR platform,
the newest Oculus.
And then we can talk about
the glasses, the Ray-Bans.
MARK ZUCKERBERG: Sure.
ANDREW HUBERMAN:
Those two experiences
are still kind of blowing
my mind, especially
the Ray-Ban glasses.
And I have so many
questions about this.
So I'll resist.
But--
MARK ZUCKERBERG: We
can get into that.
ANDREW HUBERMAN: OK.
Well, yeah, I have some
experience with VR.
My Lab has used VR.
Jeremy Bailenson's
Lab at Stanford
is one of the pioneering
labs of VR and mixed reality.
I guess they used to call it
augmented reality, but now
mixed reality.
I think what's so
striking about the VR
that you guys had me try today
is how well it interfaces
with the real room, let's
call it, the physical room.
MARK ZUCKERBERG: Physical.
ANDREW HUBERMAN: I
could still see people.
I could see where
the furniture was.
So I wasn't going to
bump into anything.
I could see people's smiles.
I could see my
water on the table
while I was doing this what
felt like a real martial arts
experience, except I
wasn't getting hit.
Well, I was getting
hit virtually.
But it's extremely engaging.
And yet, on the
good side of things,
it really bypasses a lot
of the early concerns
that Bailenson Lab--
again, Jeremy's Lab-- was
early to say that, oh, there's
a limit to how much VR one
can or should use each day,
even for the adult brain
because it can really
disrupt your vestibular
system, your sense of balance.
All of that seems
to have been dealt
with in this new
iteration of VR.
I didn't come out of it
feeling dizzy at all.
I didn't feel like I was
reentering the room in a way
that was really jarring.
Going into it is
obviously, Whoa,
this is a different world.
But you can look to your left
and say, oh, someone just
came in the door.
Hey, how's it going?
Hold on, I'm playing
this game, just
as it was when I was a
kid playing in Nintendo
and someone would walk in.
It's fully engrossing.
But you'd be like, hold on.
And you see they're there.
So first of all,
bravo, incredible.
And then the next question
is, what do we even
call this experience?
Because it is
truly really mixed.
It's a truly mixed
reality experience.
MARK ZUCKERBERG: Yeah.
I mean, mixed reality
is the umbrella term
that refers to the
combined experience
of virtual and
augmented reality.
So augmented reality is
what you're eventually
going to get with some future
version of the smart glasses,
where you're primarily
seeing the world,
but you can put holograms in it.
So we'll have a
future where you're
going to walk into a room.
And there are going to
be as many holograms
as physical objects.
If you just think about all the
paper, the art, physical games,
media, your workstation--
ANDREW HUBERMAN: If
we refer to, let's
say, an MMA fight, we could just
draw it up on the table right
here and just see it repeat
as opposed to us turning
and looking at a screen.
MARK ZUCKERBERG: Yeah.
I mean, pretty much
any screen that exists
could be a hologram in the
future with smart glasses.
There's nothing that
actually physically needs
to be there for that
when you have glasses
that can put a hologram there.
And it's an interesting
thought experiment
to just go around and think
about, OK, what of the things
that are physical in the world
need to actually be physical.
Your chair does, right?
Because you're sitting on it.
A hologram isn't
going to support you.
But like that art
on the wall, I mean,
that doesn't need to
physically be there.
So I think that that's the
augmented reality experience
that we're moving towards.
And then we've had these
headsets that historically we
think about as VR.
And that has been something
that is like a fully
immersive experience.
But now, we're getting
something that's
a hybrid in between
the two and capable
of both, which is a headset
that can do both virtual reality
and some of these augmented
reality experiences.
And I think that
that's really powerful,
both because you're going to
get new applications that allow
people to collaborate together.
And maybe the two of
us are here physically,
but someone joins us and
it's their avatar there.
Or maybe it's some
version in the future.
You're having a team meeting.
And you have some
people there physically.
And you have some
people dialing in.
And they're basically like
a hologram, there virtually.
But then you also
have some AI personas
that are on your
team that are helping
you do different things.
And they can be embodied as
avatars and around the table
meeting with you.
ANDREW HUBERMAN:
Are people are going
to be doing first dates that
are physically separated?
I could imagine that
some people would--
is it even worth leaving
the house type date?
And then they find out.
And then they meet
for the first time.
MARK ZUCKERBERG: I mean, maybe.
I think dating has physical
aspects to it, too.
ANDREW HUBERMAN: Right.
Some people might
not be-- they want
to know whether
or not it's worth
the effort to head out or not.
They want to bridge
the divide, right?
MARK ZUCKERBERG: It is possible.
I mean, I know
some of my friends
who are dating basically
say that in order
to make sure that they have
a safe experience, if they're
going on a first
date, they'll schedule
something that's shorter and
maybe in the middle of the day.
So maybe it's coffee.
So that way, if they
don't like the person,
they can just get out
before going and scheduling
a dinner or a real, full date.
So I don't know.
Maybe in the future,
people will have
that experience where
you can feel like you're
kind of sitting there.
And it's and it's even easier,
and lighter weight and safer.
And if you're not having
a good experience,
you can just teleport
out of there and be gone.
But yeah, I think that this
will be an interesting question
in the future.
There are clearly a lot of
things that are only possible
physically that--
or are so much
better physically.
And then there are
all these things
that we're building up that
can be digital experiences.
But it's this weird
artifact of how
this stuff has been developed
that the digital world
and the physical world
exist in these completely
different planes.
When you want to interact
with the digital world--
we do it all the time.
But we pull out a small screen.
Or we have a big screen.
And just basically,
we're interacting
with the digital world
through these screens.
But I think if we
fast forward a decade
or more, I think one of the
really interesting questions
about what is the
world that we're
going to live in, I think
it's going to increasingly
be this mesh of the
physical and digital worlds
that will allow us to feel, A,
that the world that we're in
is just a lot richer
because there can be all
these things that people create
that are just so much easier
to do digitally than physically.
But B, you're going to have a
real physical sense of presence
with these things and
not feel like interacting
in the digital world
is taking you away
from the physical world,
which today is just
so much viscerally
richer and more powerful.
I think the digital world
will be embedded in that
and will feel just as
vivid in a lot of ways.
So that's why I
always think-- when
you were saying before, you
felt like you could look
around and see the real room.
I actually think there's
an interesting kind
of philosophical distinction
between the real room
and the physical room,
which historically I
think people would have said
those are the same thing.
But I actually
think, in the future,
the real room is going
to be the combination
of the physical world with
all the digital artifacts
and objects that are in there
that you can interact with them
and feel present, whereas the
physical world is just the part
that's physically there.
And I think it's possible
to build a real world that's
the sum of these two
that will actually
be more profound experience
than what we have today.
ANDREW HUBERMAN:
Well, I was struck
by the smoothness of the
interface between the VR
and the physical room.
Your team had me try a--
I guess it was an exercise
class in the [INAUDIBLE]..
But it was essentially
like hitting mitts boxing,
so hitting targets boxing.
MARK ZUCKERBERG:
Yeah, super natural.
ANDREW HUBERMAN: Yeah, and it
comes at a fairly fast pace
that then picks up.
It's got some tutorial.
It's very easy to use.
And it certainly got
my heart rate up.
And I'm in at
least decent shape.
And I have to be
honest, I've never
once desired to do any of
these on-screen fitness things.
I mean, I can't think of
anything more aversive than a--
I don't want to insult
any particular products,
but riding a stationary
bike while looking
at a screen pretending
I'm on a road outside.
I can't think of
anything worse for me.
MARK ZUCKERBERG: I do
like the leaderboard.
Maybe I'm just a very
competitive person.
If you're going to be
running on a treadmill,
at least give me a
leaderboard so I can beat
the people who are ahead of me.
ANDREW HUBERMAN: I like
moving outside and certainly
an exercise class
or aerobics class,
as they used to call them.
But the experience I tried
today was extremely engaging.
And I've done enough
boxing to at least know
how to do a little bit of it.
And I really enjoyed it.
It gets your heart rate up.
And I completely
forgot that I was
doing an on-screen experience
in part because, I believe,
I was still in
that physical room.
And I think there's
something about the mesh
of the physical room and
the virtual experience that
makes it neither of
one world or the other.
I mean, I really felt at
the interface of those.
And I certainly got
presence, this feeling
of forgetting that I was
in a virtual experience
and got my heart rate
up pretty quickly.
We had to stop because we
were going to start recording.
But I would do that for a good
45 minutes in the morning.
And there's no amount of
money you could pay me truly
to look at a screen
while pedaling on a bike
or running on a treadmill.
So again, bravo, I think
it's going to be very useful.
It's going to get people
moving their bodies more,
which certainly--
social media, up until now,
and a lot of technologies
have been accused of limiting
the amount of physical activity
that both children and
adults are engaged in.
And we know we need
physical activity.
You're a big proponent
of and practitioner
of physical activity.
So is this a major goal
of Meta, to get people
moving their bodies more
and getting their heart
rates up and so on?
MARK ZUCKERBERG: I think
we want to enable it.
And I think it's good.
But I think it comes more from a
philosophical view of the world
than it is necessarily--
I mean, I don't go
into building products
to try to shape
people's behavior.
I believe in empowering
people to do what they want
and be the best version of
themselves that they can be.
ANDREW HUBERMAN: So no agenda?
MARK ZUCKERBERG: That said,
I do believe that there's
the previous
generation of computers
were devices for your mind.
And I think that we are
not brains and tanks.
I think that there's a
philosophical view of people
of like, OK, you are
primarily what you think about
or your values or something.
It's like, no, you
are that and you
are a physical manifestation.
And people were very physical.
And I think building a computer
for your whole body and not
just for your mind is very
fitting with this worldview
that the actual essence
of you, if you want
to be present with
another person,
if you want to be fully engaged
in experience is not just--
it's not just a video conference
call that looks at your face
and where you can share ideas.
It's something that you
can engage your whole body.
So, yeah I mean, I
think being physical
is very important to me.
I mean, that's a lot of the
most fun stuff that I get to do.
It's a really important
part of how I personally
balance my energy
levels and just get
a diversity of experiences
because I could spend all
my time running the company.
But I think it's good for people
to do some different things
and compete in different areas
or learn different things.
And all of that is good.
If people want to do really
intense workouts with the work
that we're doing with Quest
or with eventual AR glasses,
great.
But even if you don't want to
do a really intense workout,
I think just having a computing
environment and platform which
is inherently physical captures
more of the essence of what
we are as people than any of
the previous computing platforms
that we've had to date.
ANDREW HUBERMAN: I
was even thinking just
of the simple task of getting
better range of motion a.k.a.
flexibility.
I could imagine, inside
of the VR experience,
leaning into a stretch, standard
type of lunge-type stretch,
but actually seeing a
meter of are you are you
approaching new
levels of flexibility
in that moment
where it's actually
measuring some
kinesthetic elements
on the body in the joints,
whereas normally, you
might have to do that in front
of a camera, which then would
give you the data on a screen
that you'd look at afterwards
or hire an expensive coach or
looking at form and resistance
training.
So you're actually
lifting physical weights.
But it's telling you whether
or not you're breaking form.
I mean, there's just
so much that could
be done inside of there.
And then my mind
just starts to spiral
into, wow, this is very
likely to transform
what we think of as,
quote unquote, "exercise."
MARK ZUCKERBERG:
Yeah, I think so.
I think there's still
a bunch of questions
that need to get answered.
I don't think most people
are going to necessarily want
to install a lot of
sensors or cameras
to track their whole body.
So we're just over
time getting better
from the sensors that are on
the headsets of being able to do
very good hand tracking.
So we have this
research demo where
you now, just with the hand
tracking from the headset,
you can type.
It just projects a little
keyboard onto your table.
And you can type.
And people type like 100
words a minute with that.
ANDREW HUBERMAN: With
a virtual keyboard?
MARK ZUCKERBERG: Yeah.
We're starting to be able to--
using some modern AI
techniques, be able to simulate
and understand where
your torso's position is.
Even though you
can't always see it,
you can see it a
bunch of the time.
And if you fuse
together what you
do see with the accelerometer
and understanding
how the thing is
moving, you can kind of
understand what the body
position is going to be.
But some things are
still going to be hard.
So you mentioned boxing.
That one works pretty well
because we understand your head
position.
We understand your hands.
And now, we're increasingly
understanding your body
position.
But let's say you
want to expand that
to Muay Thai or kickboxing.
OK.
So legs, that's a
different part of tracking.
That's harder because that's
out of the field of view
more of the time.
But there's also the
element of resistance.
So you can throw a
punch, and retract it,
and shadow box and do
that without upsetting
your physical balance that much.
But if you want to
throw a roundhouse kick
and there's no
one there, then, I
mean, the standard way that you
do it when you're shadowboxing
is you basically
do a little 360.
But I don't know.
Is that going to feel great?
I mean, I think there's
a question about what
that experience should be.
And then if you want
to go even further,
if you want to get
grappling to work,
I'm not even sure
how you would do
that without having resistance
of understanding what the force
is applied to you would be.
And then you get
into, OK, maybe you're
going to have some
kind of bodysuit that
can apply haptics.
But I'm not even sure that even
a pretty advanced haptic system
is going to be able to be
quite good enough to simulate
the actual forces that would be
applied to you in a grappling
scenario.
So this is part of what's
fun about technology,
though, is you keep on
getting new capabilities.
And then you need to
figure out what things you
can do with them.
So I think it's really
neat that we can do boxing.
And we can do the
supernatural thing.
And there's a bunch
of awesome cardio,
and dancing and
things like that.
And then there's also
still so much more
to do that I'm excited
to get to over time.
But it's a long journey.
ANDREW HUBERMAN: And what
about things like painting,
and art and music?
I imagine-- of course,
different mediums--
I like to draw with
pen and pencil.
But I could imagine trying to
learn how to paint virtually.
And of course, you could
print out a physical version
of that at the end.
This doesn't have to depart
from the physical world.
It could end in
the physical world.
MARK ZUCKERBERG: Did
you see the demo,
the piano demo where you--
either you're there
with a physical keyboard
or it could be a
virtual keyboard.
But the app basically
highlights what keys
you need to press in
order to play the song.
So it's basically like
you're looking at your piano.
And it's teaching you how to
play a song that you choose.
ANDREW HUBERMAN:
An actual piano?
MARK ZUCKERBERG: Yeah.
ANDREW HUBERMAN: But it's
illuminating certain keys
in the virtual space.
MARK ZUCKERBERG: Yeah.
And it could either be a
virtual piano or a keyboard
if you don't have a
piano or keyboard.
Or it could use your
actual keyboard.
So yeah, I think
stuff like that is
going to be really fascinating
for education and expression.
ANDREW HUBERMAN: And excuse
me, but for broadening access
to expensive equipment.
I mean, a piano is
no small expense.
MARK ZUCKERBERG: Exactly.
ANDREW HUBERMAN: And it
takes up a lot of space
and needs to be tuned.
You can think of all
these things, the kid that
has very little
income or their family
has very little
income could learn
to play a virtual piano
at a much lower cost.
MARK ZUCKERBERG: Yeah.
And it gets back
to the question I
was asking before about this
thought experiment of how
many of the things
that we physically have
today actually need
to be physical.
The piano doesn't.
Maybe there's some
premium where--
maybe it's a somewhat better,
more tactile experience
to have a physical one.
But for people who don't
have the space for it,
or who can't afford
to buy a piano,
or just aren't sure
that they would want
to make that investment at
the beginning of learning how
to play piano, I
think, in the future,
you'll have the option
of just buying an app
or a hologram piano which
will be a lot more affordable.
And I think that's going to
unlock a ton of creativity too
because instead of the
market for piano makers
being constrained to like a
relatively small set of experts
who have perfected
that craft, you're
going to have kids or developers
all around the world designing
crazy designs for potential
keyboards and pianos
that look nothing like
what we've seen before,
but maybe bring even
more joy or even more
fun into the world
where you have fewer
of these physical constraints.
So I think there's going to be
a lot of wild stuff to explore.
ANDREW HUBERMAN:
There's definitely
going to be a lot of
wild stuff to explore.
I just had this
idea/image in my mind
of what you were talking
about merged with our earlier
conversation when
Priscilla was here.
I could imagine a time
not too long from now
where you're using mixed reality
to run experiments in the lab,
literally mixing
virtual solutions,
getting potential outcomes,
and then picking the best
one to then go actually do
in the real world, which
is very both financially
costly and time-wise costly.
MARK ZUCKERBERG: Yeah.
I mean, people are already using
VR for surgery and education
on it.
And there's some study that
was done that basically tried
to do a controlled experiment
of people who learned how
to do a specific surgery
through just the normal textbook
and lecture method
versus you show the knee
and you have it be a
large, blown-up model.
And people can manipulate
it and practice
where they would make the cuts.
And like the people in
that class did better.
Yeah, I think that it's
going to be profound
for a lot of different areas.
ANDREW HUBERMAN: And the last
example that leaps to mind--
I think social media
and online culture
has been accused of creating
a lot of real world--
let's call it physical world
social anxiety for people.
But I could imagine practicing
a social interaction.
Or a kid that has a
lot of social anxiety
or that needs to advocate
for themselves better
learning how to do
that progressively
through a virtual
interaction, and then taking
that to the real world because,
in my very recent experience
today, it's so blended
now with real experience
that the kid that
feels terrified
of advocating for
themselves, or just talking
to another human
being, or an adult,
or being in a new circumstance
of a room full of kids, you
could really experience
that in silico
first and get comfortable,
let the nervous system
attenuate a bit, and then take
it into the, quote unquote,
"physical world."
MARK ZUCKERBERG:
Yeah, I think we'll
see experiences like that.
I mean, I also think that
some of the social dynamics
around how people
interact in this kind
of blended digital world will
be more nuanced in other ways.
So I'm sure that there will
be new anxieties that people
develop too, just like teens
today need to navigate dynamics
around texting
constantly that we just
didn't have when we were kids.
So I think it will
help with some things.
I think that there will be new
issues that hopefully we can
help people work through too.
But overall, yeah, I
think it's going to be
really powerful and positive.
ANDREW HUBERMAN: Let's
talk about the glasses.
MARK ZUCKERBERG: Sure.
ANDREW HUBERMAN: This was wild.
Put on a Ray-Bans--
I like the way they look.
They're clear.
They look like any
other Ray-Ban glasses,
except that I could
call out to the glasses.
I could just say,
hey Meta, I want
to listen to the
Bach variations--
the Goldberg Variations of Bach.
And Meta responded.
And no one around me could hear.
But I could hear with
exquisite clarity.
And by the way, I'm not getting
paid to say any of this.
I'm just still
blown away by this.
Folks, I want a
these very badly.
I could hear, OK, I'm
selecting those now--
or that music now.
And then I could hear
it in the background.
But then I could still
have a conversation.
So this was neither headphones
in nor headphones out.
And I could say,
wait, pause the music.
And it would pause.
And the best part was I
didn't have to, quote unquote,
"leave the room" mentally.
I didn't even have
to take out a phone.
It was all interfaced through
this very local environment
in and around the head.
And as a neuroscientist,
I'm fascinated by this
because, of course, all of
our perceptions-- auditory,
visual et cetera--
are occurring inside the casing
of this thing we call a skull.
But maybe you could
comment on the origin
of that design for you,
the ideas behind that,
and where you think it
could go because I'm sure
I'm just scratching the surface.
MARK ZUCKERBERG:
The real product
that we want to
eventually get to is
this full augmented
reality product
in a stylish and comfortable
normal glasses form factor.
ANDREW HUBERMAN: Not a dorky
VR headset, so to speak?
MARK ZUCKERBERG: No, I mean--
ANDREW HUBERMAN: Because
the VR headset does
feel kind of big on the face.
MARK ZUCKERBERG: There's
going to be a place for that,
too, just like you
have your laptop
and you have your workstation.
Or maybe the better analogy
is you have your phone
and you have your workstation.
These AR glasses are going to
be like your phone in that you
have something on your face.
And you will, I think,
be able to, if you want,
wear it for a lot of
the day and interact
with it very frequently.
I don't think that
people are going
to be walking around the
world wearing VR headsets.
ANDREW HUBERMAN: Let's hope.
MARK ZUCKERBERG: But yeah,
that's certainly not the future
that I'm hoping we get to.
But I do think that there
is a place for having--
because it's a
bigger form factor,
it has more compute power.
So just like your workstation
or your bigger computer
can do more than
your phone can do,
there's a place for
that when you want
to settle into an intense task.
If you have a doctor
who's doing a surgery,
I would want them doing
it through the headset
not through the phone equivalent
or the lower powered glasses.
But just like phones
are powerful enough
to do a lot of things, I think
the glasses will eventually
get there, too.
Now, that said, there's a
bunch of really hard technology
problems to address in order
to be able to get to this point
where you can put full
holograms in the world.
You're basically
miniaturizing a supercomputer
and putting it into a glasses
so that the glasses still
look stylish and normal.
And that's a really
hard technology problem.
Making things small
is really hard.
A holographic display
is different from what
our industry has optimized
for for 30 or 40 years now,
building screens.
There's a whole
industrial process
around that goes into phones,
and TVs, and computers,
and increasingly so many things
that have different screens.
There's a whole pipeline
that's gotten very good
at making that kind of screen.
And the holographic
displays are just
a completely different thing
because it's not a screen.
It's a thing that
you can shoot light
into through a laser or some
other kind of projector.
And it can place that as
an object in the world.
So that's going to need to be
this whole other industrial
process that gets built up to
doing that in an efficient way.
So all that said,
we're basically
taking two different approaches
towards building this at once.
One is we are trying to keep
in mind what is the long-term
thing that--
it's not super far off.
Within a few years,
I think we'll
have something that's a first
version of this full vision
that I'm talking about.
I mean, we have something
that's working internally
that we use as a dev kit.
But that one, that's
a big challenge.
It's going to be more expensive.
And it's harder to get
all the pieces working.
The other approach has
been, all right, let's
start with what
we know we can put
into a pair of
stylish sunglasses
today and just make
them as smart as we can.
So for the first
version, we worked with--
we did this collaboration
with Ray-Ban
because that's well-accepted.
These are well-designed glasses.
They're classic.
People have used
them for decades.
For the first version, we
got a sensor on the front,
so you could capture moments
without having to take
your phone out of your pocket.
So you got photos and videos.
You had the speaker
and the microphone,
so you can listen to music.
You could communicate with it.
But that was the
first version of it.
We had a lot of
the basics there.
But we saw how people used it.
And we tuned it.
We made the camera twice as
good for this new version
that we made.
The audio is a lot
crisper for the use cases
that we saw that people actually
used, which is-- some of it
is listening to music.
But a lot of it is people want
to take calls on their glasses.
They want to listen to podcasts.
But the biggest thing that
I think is interesting
is the ability to get AI running
on it, which it doesn't just
run on the glasses.
It also kind of proxies
through your phone.
But I mean, with all
the advances in LLMs--
we talked about this a
bit in the first part
of the conversation.
Having the ability to have
your Meta AI assistant
that you can just
talk to and basically
ask any question
throughout the day is--
I think it'd be
really fascinating.
And like you were
saying about how
we process the world as
people, eventually, I
think you're going
to want your AI
assistant to be able to see what
you see and hear what you hear.
Maybe not all the time.
But you're going to
want to be able to tell
it to go into a mode where it
can see what you see and hear
what you hear.
And what's the
device design that
best positions an
AI assistant to be
able to see what
you see and hear
what you hear so it
can best help you?
Well, that's glasses,
where it basically
has a sensor to be able
to see what you see
and a microphone that is
close to your ears that
can hear what you hear.
The other design goal
is, like you said,
to keep you present
in the world.
So I think one of the
issues with phones
is they pull you away from
what's physically happening
around you.
And I don't think that the
next generation of computing
will do that.
ANDREW HUBERMAN: I'm
chuckling to myself
because I have a friend.
He's a very well
known photographer.
And he was laughing about
how people go to a concert.
And everyone's filming
the concert on their phone
so that they can be the
person that posts the thing.
But there are literally
millions of other people
who posted the exact same thing.
But somehow, it feels important
to post our unique experience.
With glasses, that
would essentially
smooth that gap completely.
You could just worry about
it later, download it then.
There are issues, I
realize, with glasses
because they are so seamless
with everyday experience,
even though you and I
aren't wearing them now.
It's very common for
people to wear glasses--
issues of recording and consent.
[INTERPOSING VOICES]
ANDREW HUBERMAN: Like if I go
to a locker room at my gym,
I'm assuming that the people
with glasses aren't filming.
Whereas right now, because
there's a sharp transition when
there's a phone in the room
and someone's pointing it,
people generally say, no phones
in locker rooms and recording.
So that's just one instance.
I mean, there are
other instances.
MARK ZUCKERBERG: We have
the whole privacy light.
Did you get--
ANDREW HUBERMAN: I didn't
get a chance to explore that.
MARK ZUCKERBERG: Yeah.
So anytime that it's
active, that the camera
sensor is active, it's basically
pulsing a white bright light.
ANDREW HUBERMAN: Got it.
MARK ZUCKERBERG: Which is, by
the way, more than cameras do.
ANDREW HUBERMAN: Right.
Someone could be
holding a phone.
MARK ZUCKERBERG: Yeah.
I mean, phones aren't showing
a light, bright sensor
when you're taking a photo.
ANDREW HUBERMAN:
People oftentimes
will pretend they're texting
and they're actually recording.
I actually saw an instance
of this in a barber shop
once, where someone
was recording
and they were pretending
that they were texting.
And it was interesting.
There was a pretty intense
interaction that ensued.
And it was like, wow, it's
pretty easy for people
to feign texting while
actually recording.
MARK ZUCKERBERG: Yeah.
So I think when
you're evaluating
a risk with a new technology,
the bar shouldn't be is it
possible to do anything bad.
It's does this new
technology make it easier
to do something bad than
what people already had.
And I think because you have
this privacy light that is just
broadcasting to everyone
around you, hey,
this thing is recording now--
I think that makes it
actually less discreet
to do it through the
glasses than what you could
do with a phone already, which
I think is basically the bar
that we wanted to get over
from a design perspective.
ANDREW HUBERMAN: Thank
you for pointing out
that it has the privacy light.
I didn't get long
enough in the experience
to explore all the features.
But again, I can think
of a lot of uses--
being able to look at a
restaurant from the outside
and see the menu, get a
status on how crowded it is.
As much as I love--
I don't want to call
out-- let's just
say app-based map functions
that allow you to navigate
and the audio is OK.
It's nice to have a conversation
with somebody on the phone
or in the vehicle.
And it'd be great if the road
was traced where I should turn.
MARK ZUCKERBERG:
Yeah, absolutely.
ANDREW HUBERMAN:
These kinds of things
seem like it's going to be
straightforward for Meta
engineers to create.
MARK ZUCKERBERG: Yeah, in
a version, we'll have it
so it'll also have the
holographic display, where
it can show you the directions.
But I think that there
will basically just
be different price points
that pack different amounts
of technology.
The holographic
display part, I think,
is going to be more
expensive than doing
one that just has the AI, but
is primarily communicating
with you through audio.
So I mean, the current
Ray-Ban Meta glasses are $299.
I think when we have one
that has a display in it,
it'll probably be some
amount more than that.
But it'll also be more powerful.
So I think that
people will choose
what they want to use based
on what the capabilities are
that they want and
what they can afford.
But a lot of our goal
in building things
is we try to make things that
can be accessible to everyone.
Our game as a company isn't to
build things and then charge
a premium price for it.
We try to build things that
then everyone can use, and then
become more useful because a
very large number of people
are using them.
So it's just a very
different approach.
We're not like Apple or some
of these companies that just
try to make something and
then sell it for as much
as they can, which, I mean,
they're a great company.
So I mean, I think that
model is fine, too.
But our approach
is going to be we
want stuff that
can be affordable
so that way everyone in
the world can use it.
ANDREW HUBERMAN:
Long lines of health,
I think the glasses will
also potentially solve
a major problem in
a real way, which
is the following for
both children and adults.
It's very clear that viewing
objects in particular screens
up close for too many hours
per day leads to myopia.
It literally changes the
length of length of the eyeball
and nearsightedness.
And on the positive
side, we know,
based on some really
large clinical trials,
that kids who spend--
and adults who spend two hours
a day or more out of doors
don't experience that and maybe
even reverse their myopia.
And it has something to do
with exposure to sunlight.
But it has a lot to do
with long view, viewing
things at a distance greater
than three or four feet away.
And with the glasses,
I realize, one
could actually do digital
work out of doors.
It could measure and
tell you how much time
you've spent looking at things
up close versus far away.
I mean, this is just another
example that leaps to mind.
But in accessing
the visual system,
you're effectively
accessing the whole brain
because it's the only
two bits of brain that
are outside the cranial
vault. So it just
seems like putting technology
right at the level of the eyes,
seeing what the
eyes see, has just
got to be the best way to go.
MARK ZUCKERBERG: Yeah.
Well, multimodal, I think, is--
you want the visual sensation.
But you also want
text or language.
ANDREW HUBERMAN: Sure.
That all can be brought to
the level of the eyes, right?
MARK ZUCKERBERG: What
do you mean by that?
ANDREW HUBERMAN:
Well, I mean, I think
what we're describing
here is essentially
taking the phone, the
computer, and bringing it
all to the level of the eyes.
And of course, one would like--
MARK ZUCKERBERG: Oh,
Physically at your eyes?
ANDREW HUBERMAN: Physically
at your eyes, right?
MARK ZUCKERBERG: Yeah.
ANDREW HUBERMAN: And one
would like more kinesthetic
information, as you mentioned
before-- where the legs are,
maybe even lung function.
Hey, have you taken
enough steps today?
But that all can be-- if it can
be figured out on the phone,
it can be-- by the phone, it can
be figured out by the glasses.
But there's additional
information there,
such as what are you
focusing on in your world.
How much of your time is spent
looking at things far away
versus up close?
How much social time
did you have today?
It's really tricky to
get that with a phone.
If my phone were
right in front of us
as if we were at
a standard lunch
nowadays, certainly
in Silicon Valley,
and then we're peering
at our phones, I mean,
how much real direct attention
and was in the conversation
at hand versus something else?
You can get issues
of where are you
placing your attention
by virtue of where
you're placing your eyes.
And I think that information
is not accessible
with a phone in your
pocket or in front of you.
Yeah, I mean, a little bit, but
not nearly as rich and complete
information as one
gets when you're really
pulling the data from
the level of vision
and what kids and
adults are actually
looking at and attending to.
MARK ZUCKERBERG: Yeah, yeah.
ANDREW HUBERMAN: It
seems extremely valuable.
You get autonomic information,
size of the pupils.
So you get information
about internal states.
MARK ZUCKERBERG: I mean, there's
internal sensor and outside.
So the sensor on the Ray-Ban
Meta glasses is external.
So it basically allows
you to see what you see--
sorry, the AI system to
see what you're seeing.
There's a separate
set of things which
are eye tracking, which are
also very powerful for enabling
a lot of interfaces.
So if you want to
just look at something
and select it by looking
at it with your eyes
rather than having to drag
a controller over or pick up
a hologram or
anything like that,
you can do that
with eye tracking.
So that's a pretty profound and
cool experience, too, as well
as just understanding
what you're
looking at so that way you're
not wasting compute power
drawing pixels and
high resolution
in a part of the world
that's going to be
in your peripheral vision.
So yeah, all of
these things, there
are interesting design
and technology trade-offs,
where if you want the external
sensor, that's one thing.
If you also want
the eye tracking,
now that's a different
set of sensors.
Each one of these
consumes compute,
which consumes battery.
They take up more space.
So it's like, where are the eye
tracking sensors going to be?
It's like, well, you
want to make sure
that the rim of the glasses is
actually quite thin because--
I mean, there's a variance
of how thick can glasses
be before they look more
like goggles than glasses.
So I think that there's
this whole space.
And I think people are going
to end up choosing what
product makes sense for them.
Maybe they want something
that's more powerful,
that has more of the
sensors, but it's
going to be a little
more expensive,
maybe like slightly thicker.
Or maybe you want
a more basic thing
that just looks very similar
to what Ray-Ban glasses are
that people have been wearing
for decades but has AI in it
and you can capture
moments without having
to take your phone out
and send them to people.
In the latest version, we got
the ability in to live stream.
I think that that's pretty
crazy, that now you can be--
going back to your concert case
or whatever else you're doing,
you can be doing
sports or watching
your kids play something.
And you can be watching.
And you can be live streaming
it to your family group,
so people can see it.
I think that stuff is--
I think that's pretty
cool, that you basically
have a normal looking glasses at
this point that can live stream
and has an AI assistant.
So the stuff is making
a lot faster progress
in a lot of ways than
I would have thought.
And I don't know.
I think people are going
to like this version.
But there's a lot
more still to do.
ANDREW HUBERMAN: I think
it's super exciting.
And I see a lot of technologies.
This one's particularly
exciting to me
because of how smooth
the interface is
and for all the reasons
that you just mentioned.
What's happening with and
what can we expect around
AI interfaces and
maybe even avatars
of people within social media?
Are we not far off
from a day where
there are multiple
versions of me
and you on the
internet or people?
For instance, I get
asked a lot of questions.
I don't have the opportunity to
respond to all those questions.
But with things
like ChatGPT, people
are trying to generate
answers to those questions
on other platforms.
Will I have the
opportunity to soon
have an AI version of
myself where people
can ask me questions about
what I recommend for sleep
and circadian rhythm, fitness,
mental health, et cetera based
on content I've
already generated
that will be accurate so they
could just ask my avatar?
MARK ZUCKERBERG: Yeah,
this is something
that I think a lot
of creators are going
to want that we're
trying to build
and I think we'll probably
have a version of next year.
But there's a bunch
of constraints
that I think we need to
make sure that we get right.
So for one, I think it's
really important that--
it's not that there's a
bunch of versions of you.
It's that if anyone is creating
an AI assistant version of you,
it should be something
that you control.
I think there are some platforms
that are out there today
that just let people like make--
I don't know-- an AI bought
of me or other figures.
And it's like, I don't know.
I mean, we have
platform policies for--
and for decades,
since the beginning
of the company at this point,
which is almost 20 years,
that basically don't
allow impersonation.
Real identity is like
one of the core aspects
that our company was started on.
You want to authentically
be yourself.
So yeah, I think if
you're almost any creator,
being able to engage
your community--
and there's just going
to be more demand
to interact with you than
you have hours in the day.
So there are both
people who out there
who would benefit from
being able to talk
to an AI version of you.
And I think you,
and other creators,
would benefit from being able
to keep your community engaged
and service that demand that
people have to engage with you.
But you're going to want to
know that that AI version of you
or assistant is going
to represent you
the way that you would want.
And there are a
lot of things that
are awesome about
these modern LLMs.
But having perfect
predictability
about how it's going
to represent something
is not one of the
current strengths.
So I think that
there's some work that
needs to get done there.
I don't think it needs to be
100% perfect all the time.
But you need to have very
good confidence, I would say,
that it's going to represent
you the way that you'd
want for you to
want to turn it on,
which, again, you
should have control over
whether you turn it on.
So we wanted to start in
a different place, which
I think is a somewhat easier
problem, which is creating
new characters for AI personas.
So that way, it's not--
we built one of the
AIs is like a chef.
And they can help you
come up with things
that you could cook and
can help you cook them.
There's a couple
of people that are
interested in different
types of fitness that
can help you plan
out your workouts
or help with recovery or
different things like that.
There's an AI that's
focused on DIY crafts.
There's somebody who's
a travel expert that
can help you make travel
plans or give you ideas.
But the key thing
about all of these
is they're not modeled
off of existing people.
So they don't have to have
100% fidelity to making sure
that they never say something
that a real person who they're
modeled after would never say
because they're just made up
characters.
So I think that that's a
somewhat easier problem.
And we actually got a bunch
of different well-known people
to play those characters
because we thought
that would make it more fun.
So there's like Snoop Dogg
is the dungeon master.
So you can drop
him into a thread
and play text-based games.
And I do this with my daughter
when I tuck her in at night.
And she just loves storytelling.
And it's like Snoop Dogg,
as the dungeon master,
will come up with here's
what's happening next.
And she's like, OK, I
turn into a mermaid.
And then I like
swim across the bay.
And I go and find the
treasure chest and unlock it.
And it's like, and then
Snoop Dogg just always
will have a next
version of the--
a next iteration on the story.
So I mean, it's
stuff that's fun.
But it's not
actually Snoop Dogg.
He's just the actor who's
playing the dungeon master,
which makes it more fun.
So I think that's probably
the right place to start,
is you can build versions
of these characters
that people can interact
with doing different things.
But I think where
you want to get over
time is to the place where any
creator or any small business
can very easily just create an
AI assistant that can represent
them and interact with your
community or customers,
if you're a business,
and basically just help
you grow your enterprise.
So I think that's
going to be cool.
It's a long-term project.
I think we'll have more progress
on it to report on next year.
But I think that's coming.
ANDREW HUBERMAN: I'm
super excited about it
because we hear a lot
about the downsides of AI.
I mean, I think people are now
coming around to the reality
that AI is neither good nor bad.
It can be used for good or bad.
And there are a lot of
life-enhancing spaces
that it's going to show
up and really, really
improve the way that we engage
socially, what we learn,
and that mental health
and physical health
don't have to
suffer and, in fact,
can be enhanced by the
sorts of technologies
we've been talking about.
So I know you're extremely busy.
I so appreciate the
large amount of time
you've given me today to sort
through all these things.
MARK ZUCKERBERG:
Yeah, it's been fun.
ANDREW HUBERMAN: And to
talk with you and Priscilla
and to hear what's happening
and where things are headed,
the future certainly is bright.
I share in your optimism.
And it's been only strengthened
by today's conversation.
So thank you so much.
And keep doing
what you're doing.
And on behalf of myself
and everyone listening,
thank you because, regardless
of what people say,
we all use these
platforms excitedly.
And it's clear that
there's a ton of intention,
and care, and thought about what
could be in the positive sense.
And that's really
worth highlighting.
MARK ZUCKERBERG:
Awesome, thank you.
I appreciate it.
ANDREW HUBERMAN: Thank
you for joining me
for today's discussion with Mark
Zuckerberg and Dr. Priscilla
Chan.
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