Mark Zuckerberg & Dr. Priscilla Chan: Curing All Human Diseases & the Future of Health & Technology

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.

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Eight Sleep currently ships to the USA,

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Again, that's eightsleep.com/huberman.

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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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.

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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.

ANDREW HUBERMAN: I'd like to take a quick break

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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