00:00:00
I don't know if some of you knew I was
00:00:01
an angel investor in some companies.
00:00:05
On the count of three, what's my
00:00:06
favorite angel investment of all time?
00:00:08
One, two, three.
00:00:10
>> Thank you. Give it up. Travis Kalanick.
00:00:21
>> Appreciate you. All right. Wow. Um, on a
00:00:25
big news day, Travis is here on a very
00:00:28
big news day. you spent uh wow I guess
00:00:31
like seven years just in the lab
00:00:34
building last year every year I ask you
00:00:37
hey you want to come to the summit you
00:00:39
want to say no it's like I'm going to
00:00:41
just chill I'm building next year hey
00:00:44
you know just it's always available
00:00:45
>> you understand I'm stealth
00:00:46
>> yeah stealth I'm stealth nobody knows
00:00:49
where I am nobody knows what I'm doing
00:00:50
the employees are not allowed to put the
00:00:52
name of the company on their LinkedIn
00:00:54
>> thousands of employees that weren't
00:00:57
allowed to put the company name on
00:00:58
LinkedIn I mean, incredible. And I'm
00:01:01
like, "Okay." And
00:01:02
>> their parents thought they worked for
00:01:03
the CIA.
00:01:04
>> Yeah. And then he's like, "And by the
00:01:05
way, Jake Al, you can invest. You can't
00:01:07
announce it and you have to sign an you
00:01:09
can't mention you're an investor." It's
00:01:11
like, "Okay, no problem. I'm just happy
00:01:12
to be on the cap table."
00:01:13
>> Is he like kind of like secret saying
00:01:16
what he wasn't supposed to say,
00:01:17
>> right? Now he's doing that just
00:01:20
happened.
00:01:21
>> Well, you know, you're out now. It Let's
00:01:23
go.
00:01:23
>> You're out. It's out. You're You came
00:01:25
out of stealth today.
00:01:26
>> Um,
00:01:27
>> it's so funny. Okay.
00:01:28
>> It's so great. You came out of stealth.
00:01:30
Well, you you talked a little bit. You
00:01:31
came to Allins last year. Is that fair?
00:01:33
You say you're coming out of stealth
00:01:34
today. Is that right?
00:01:35
>> Well, look, let let's just start with
00:01:37
what that meant for our employees
00:01:39
because again, imagine if you're at a
00:01:42
multi,000 person company and every
00:01:45
single employee has stealth on their
00:01:48
LinkedIn,
00:01:50
including salespeople,
00:01:53
okay? Including recruiters.
00:01:56
Like it was they they were they were
00:01:58
living life on hard mode. It
00:02:00
>> was kind of fun too, right? I mean
00:02:01
>> I mean yeah it was like kind of cool
00:02:03
what the what's what is this? Why are
00:02:04
there why is this massive density of
00:02:07
stealth
00:02:08
>> right
00:02:09
>> startup people in Los Angeles? What is
00:02:12
happening over there?
00:02:13
>> Yeah.
00:02:14
>> Yeah.
00:02:14
>> Also technically the name of the company
00:02:17
in different countries was very generic
00:02:20
names of companies. I mean everything
00:02:24
was designed to be stealth,
00:02:25
>> right?
00:02:26
>> So we operate in 30 countries. In the US
00:02:31
the kitchens product is known as cloud
00:02:33
kitchens.
00:02:35
In Korea
00:02:37
it's Kitchen Valley.
00:02:40
In the Middle East it's Nama.
00:02:44
In
00:02:46
Latin America, parts of Latin America
00:02:48
it's Cinaas.
00:02:50
I mean you get the idea. You can't even
00:02:52
remember all the names and all the code
00:02:53
words.
00:02:54
>> Think about it.
00:02:54
>> To think it through. But
00:02:56
>> we have four in China, you know, it's
00:02:57
like all over the place. Yeah.
00:02:59
>> But things have gone really well and
00:03:01
you've been a little inquisitive. So
00:03:03
tell us about the branding today that
00:03:05
you're announcing and then maybe some of
00:03:07
the acquisitions and evolution of the
00:03:10
company. You're not just renting kitchen
00:03:12
space.
00:03:13
>> Those who I mean know how I thought
00:03:16
about things in the Uber day, a lot of
00:03:17
this stuff's not surprising. I I would
00:03:20
often talk about digitizing the physical
00:03:21
world. I think I even did it all at
00:03:23
summit. The quick version of this, I'll
00:03:26
try to do it quickly, but it's like uh
00:03:28
we know the bits world, the computer
00:03:29
world, the one that Michael Dell
00:03:31
essentially invented for us. CPU,
00:03:34
storage, network. These are three core
00:03:35
computing resources. When you go to
00:03:37
computer science class your first day,
00:03:39
three core computer resources. CPU
00:03:42
manipulates the bits. Storage stores the
00:03:43
bits. Network moves bits from point A to
00:03:45
point B. But if you're digitizing the
00:03:47
physical world, you're treating atoms
00:03:49
like bits.
00:03:51
You're building an atomsbased computer.
00:03:53
And I'll explain what I mean to say. I
00:03:55
know this is a little little out there.
00:03:57
CPU manipulates bits. What manipulates
00:03:59
atoms? Manufacturing. Storage stores
00:04:01
bits. What stores atoms? Real estate.
00:04:04
Network moves bits from point A to point
00:04:06
B. What moves atoms? That's
00:04:07
transportation or logistics. So you have
00:04:10
these three core computing resources in
00:04:12
an atomsbased computer. The name of my
00:04:15
company was very obtuse and purposely
00:04:18
designed to be as boring as hell was
00:04:20
called City Storage Systems.
00:04:23
So that's digitized real estate in an
00:04:27
atomsbased computer. Our first computer
00:04:29
being a food computer. What does that
00:04:30
mean? Manufacturing real estate and
00:04:33
logistics for food.
00:04:36
And so you start to get there and the
00:04:38
idea the the mission was infrastructure
00:04:40
for better food. The idea was, can you
00:04:42
get a meal that's prepared and delivered
00:04:44
to you so efficient that it starts to
00:04:47
approach the the the cost of going to
00:04:49
the grocery store? If you can do that,
00:04:52
you do to the kitchen what Uber did to
00:04:54
the car. But in the Uber day, the roads
00:04:57
were there, the cars are unused. You
00:05:00
just had to put an app in the app store.
00:05:01
Wasn't that easy, but kind of that easy.
00:05:04
In this world, you can't do this on a
00:05:06
restaurant. restaurant doesn't have.
00:05:08
When I left Uber, 13% of all San
00:05:12
Francisco miles were Uber miles.
00:05:15
You can't get and that was 10 nine years
00:05:17
ago. You can't get there on food on
00:05:21
restaurants. They they have like 20%
00:05:23
capacity. Uber Eats and Door Dash fill
00:05:25
it, but the infrastructure to do high
00:05:28
capacity, highcale sort of
00:05:31
industrial production is just not there.
00:05:33
And the logistics just not there. It
00:05:35
just doesn't work. That's why on
00:05:36
e-commerce you go through Amazon big ass
00:05:39
warehouses with awesome logistics.
00:05:42
You've got to do the same thing when
00:05:44
food when food goes to e-commerce. That
00:05:46
was a lot. Okay. So bottom line is
00:05:49
>> it's awesome. We do this food
00:05:51
computation stuff. We're doing more
00:05:54
computers now. And so the name of the
00:05:56
company is called atoms and it's let's
00:05:59
say the mission uh is um is uh physical
00:06:05
automation to transform industries and
00:06:07
move the world. And so we have our food
00:06:10
computer I talked about. Then we do
00:06:11
we're doing mining.
00:06:13
>> Mining as in
00:06:15
>> mining data mining.
00:06:16
>> We're talking about atoms guys.
00:06:18
>> Yeah.
00:06:18
>> So well of course you do some mining
00:06:20
data mine too but the point is is
00:06:23
physical mining. So automation of mines
00:06:27
and uh the mission there is
00:06:31
uh more productive minds to power
00:06:34
earth's industries. Right? So it's got
00:06:36
this industrial atoms vibe to it. And
00:06:40
then on the transport side, it's uh
00:06:43
wheelbase for robots because if you're
00:06:45
doing specialized robots, not humanoids,
00:06:49
specialized robots,
00:06:51
you need to be able to move and act in
00:06:53
the physical world. But the minute
00:06:55
you're moving, you got to have a
00:06:57
wheelbase. So, it's just part of the
00:06:59
equation. And a lot of people go, look
00:07:02
at Tesla, it's great. Look at uh look at
00:07:05
Whimo. Awesome. They're cruising around
00:07:06
Austin, of course. But there's so many
00:07:09
things that move. It's not just a ride
00:07:11
sharing thing. Um, and so, um, obviously
00:07:15
including mining equipment that's doing
00:07:17
its thing. So you guys, that's the
00:07:18
general sort of idea. And we acquired a
00:07:21
company on the mining stuff, a company
00:07:23
called Pronto or it's about to close.
00:07:27
It's we're we're inches from closing is
00:07:29
the way to put it.
00:07:30
>> What were they doing? What was their
00:07:31
business? Pronto
00:07:32
>> automating mining equipment.
00:07:34
>> Were they based?
00:07:35
>> Uh, they're based in San Francisco. So,
00:07:37
you and I were starting to talk about
00:07:38
this backstage, but there's some folks I
00:07:40
talked to in the mining industry who
00:07:42
mentioned, you know, like the the big
00:07:43
issue with mining number one is just
00:07:45
surveying, like finding the the
00:07:47
locations, right? Is there an advantage
00:07:49
to be created there? Cuz I know there's
00:07:50
a couple startups that are trying to be
00:07:52
really smart about selecting locations
00:07:53
to get the targets out of the ground.
00:07:55
>> Yeah.
00:07:56
>> And then the other one is like, well,
00:07:57
can you go deep? Because pretty much
00:07:59
anywhere on Earth, you can get whatever
00:08:00
you want if you're willing to go deep
00:08:02
enough, but the cost is is distance
00:08:05
squared, right? So the energy cost is
00:08:07
like how deep are you going to the to to
00:08:09
the second power. So it becomes you know
00:08:11
geometrically more expensive to go
00:08:13
deeper but the deeper you go you're the
00:08:16
more you're able to kind of not worry
00:08:18
about getting the right location. So
00:08:20
does automation unlock that capacity?
00:08:23
>> Automation definitely does. I mean, but
00:08:24
you have I mean, also it's like, man,
00:08:27
does Boring Company have some good stuff
00:08:28
going?
00:08:30
>> Like I hope we we're like we're doing
00:08:32
the mining thing like and Boring Goes
00:08:34
makes, you know, some good tunnels for
00:08:37
for cars to do the thing. But like
00:08:39
there's some kind of boring mechanism,
00:08:41
automated tunneling to do some of this.
00:08:44
But to be honest, there's there's, you
00:08:46
know, they have this this thing is like
00:08:47
rare earths. I don't know why they put
00:08:50
plural rare earths, isn't it? Rar's
00:08:52
earth. I don't know. But the
00:08:55
>> but rare rare
00:08:58
>> rare earth.
00:08:59
>> Yeah. But it's not rare.
00:09:01
>> It's uncommon mineral guys. It's not
00:09:02
rare. It's what you have to do to the
00:09:04
land is aggressive. And what's rare is
00:09:08
the is is where the where are the places
00:09:10
they'll let you do it that you can also
00:09:13
sort of get people to.
00:09:15
>> When you automate, you can go to a lot
00:09:17
of places. Uh well, first is all the
00:09:20
mines that exist are way more
00:09:22
productive. Um and the second is you can
00:09:25
then sort of justify going to places you
00:09:28
wouldn't have been able to go before
00:09:29
because um you don't have as much of a
00:09:32
labor footprint or a safety issue or a
00:09:34
whole bunch of other things that then
00:09:35
>> So if it's inhospitable, if it's
00:09:38
regulated, if it's like I don't want to
00:09:40
live there, it's the end of the earth.
00:09:42
>> Yeah.
00:09:43
>> You can send robots and have people
00:09:46
monitoring them remotely. Yeah.
00:09:48
>> And this is like a a future that feels
00:09:53
like a little bit like science fiction.
00:09:54
>> Look, we're here in Austin. You got to
00:09:56
do the shout out to Tesla and all the
00:09:58
things cuz I like to sort of break down
00:10:00
the physical AI stack
00:10:04
includes not just like, oh yeah,
00:10:06
computation and I've got to have
00:10:07
physical AI models and I got to all the
00:10:10
things you sort of think of. What about
00:10:12
land development? That should be in that
00:10:15
stack. What about chemistry? that needs
00:10:17
to be in the stack. Manufacturing needs
00:10:18
to be in the stack. When you look at the
00:10:20
stack, you're like, damn, Tesla's got
00:10:23
this. They are they are the Google of
00:10:27
this era. Which is what I mean by that
00:10:29
is in the 2000s, if you were doing a
00:10:31
startup in the 2000s, the first question
00:10:33
you would get is uh why isn't why isn't
00:10:36
Google going to kill you or why isn't
00:10:38
Google just going to do it?
00:10:39
>> Why Google?
00:10:40
>> They're not going to know that they
00:10:41
killed you.
00:10:42
>> And before that, Microsoft.
00:10:43
>> And before that was Microsoft late 90s.
00:10:45
Uber had a time 2010.
00:10:47
>> What if Uber puts that in the app?
00:10:48
>> Come on. It's like, dude, this is Uber.
00:10:50
I'm like, but you know, I think in the
00:10:53
physical AI space, that's a that's sort
00:10:54
of a Tesla thing. Um, but there's so
00:10:57
many things to do.
00:10:59
You got to shoot your shot. You got to
00:11:00
do some stuff.
00:11:01
>> And rumors that um, hey, you might not
00:11:04
be done with self-driving, something
00:11:06
that you were very early on. How do you
00:11:08
think about what you're seeing in the
00:11:10
playing field of self-driving? Because
00:11:13
my lord, you know, Whimo's making great
00:11:15
progress. Tesla's making great progress.
00:11:16
>> Pick a winner. Like pick a winner
00:11:18
between Tesla, Tesla, Whimo, Uber,
00:11:21
>> or or like Uber. Uber Uber seems to be
00:11:24
building a network of stuff.
00:11:26
>> Yeah. I mean, the number of
00:11:27
>> pick a winner.
00:11:28
>> The number of players is crazy now,
00:11:30
right? Like
00:11:31
>> Yeah. Look, there's I think there's more
00:11:33
noise. There's more bark than there is
00:11:35
bite right now. Um, look, I think Whimo
00:11:39
obviously is ahead. the existence proof
00:11:41
is there. Their issue is manufacturing
00:11:45
and scale um and urgency and fierceness
00:11:49
like let's
00:11:50
>> come on. Let's go.
00:11:53
>> Yeah.
00:11:53
>> You know, Uber had an autonomy project
00:11:56
back in the day. So, and they have a
00:11:57
different strategy these days. I haven't
00:11:59
been there for a while. So, but the but
00:12:01
the point is is that you so you got
00:12:02
Whimo, then you've got Tesla
00:12:05
fundamentals,
00:12:07
science,
00:12:10
hard mode times 100. And the question
00:12:14
is, do they get there in what time
00:12:17
scale?
00:12:18
If if they and like honestly everybody's
00:12:20
like, could happen tomorrow, could
00:12:22
happen in 5 years.
00:12:25
And I think that it's like when does the
00:12:27
chat GPT moment happen for vision is
00:12:30
basically the thing. Let's call it
00:12:32
vision without other sensors. So super
00:12:34
inspiring, but like what's the timeline
00:12:37
on it?
00:12:38
>> Yeah.
00:12:38
>> Um those are the bas this is basically
00:12:40
the and then there's a lot of other
00:12:41
little guys that don't really have the
00:12:44
stuff I believe yet.
00:12:46
>> There's nobody standing out just yet of
00:12:48
of the others. Do you think we're at a
00:12:51
point now like obviously now that you're
00:12:53
getting into more of these kind of
00:12:56
autonomous systems that move around like
00:12:58
do we have these vision language action
00:13:00
models tuned and ready for prime time?
00:13:03
There's there's been a conversation like
00:13:05
who's going to have the Android the
00:13:07
operating system for vision language
00:13:09
action where I can use my voice tell it
00:13:12
to do something and it knows what I'm
00:13:13
saying and then it identifies the
00:13:15
objects and does the thing in the
00:13:16
physical world. Do those models exist
00:13:18
today or there still work? And is that
00:13:20
like a Google OS or like where does that
00:13:23
OS come from?
00:13:24
>> Look, I think there's a there this is an
00:13:26
area of a lot of energy, a mix of
00:13:29
research and implementation. I think
00:13:30
there's a lot of hope and interesting
00:13:32
stuff. I mean the high level is we all
00:13:35
remember what happened when you used
00:13:36
chat GPT 3.5 and you're like holy.
00:13:41
>> Yeah, it's legit.
00:13:43
>> Whoa. And then it went to four and
00:13:44
you're like, okay, like some stuff just
00:13:47
changed. The world just changed and I
00:13:48
can sort of connect some dots and
00:13:51
getting real.
00:13:54
Is it about to happen? Is it about to
00:13:57
happen for physical AI? And that's what
00:13:59
this is about. Yeah.
00:14:00
>> And the fun part about it is machine
00:14:03
learning, deep learning, this kind of
00:14:04
thing for many years, decades was like
00:14:06
inscrutable. I don't know what the thing
00:14:08
is thinking. It just spits out an answer
00:14:09
and I know it's correct. Well, now you
00:14:11
can have a conversation with it, right?
00:14:14
Like imagine if it's driving your car
00:14:17
and there's different agents and one's
00:14:20
just driving, the other's like, "Yo,
00:14:21
look out over there."
00:14:22
>> Yeah.
00:14:22
>> It's like, "Oh, just like how we roll."
00:14:26
Like somebody does that, you're like,
00:14:27
you're like, "Honey, that's like 200
00:14:30
meters away. We're going to be
00:14:32
>> okay." Yeah.
00:14:33
>> Jason and I don't call each other honey,
00:14:34
but I got you. Yeah.
00:14:36
>> Like,
00:14:37
>> sweetie,
00:14:38
>> you know. Yeah.
00:14:39
>> Okay. So anyways, so that was odd,
00:14:41
wasn't it? Okay. Okay. Okay. I didn't
00:14:43
mean it that way. I didn't mean it.
00:14:45
>> Yeah. You know, I meant it. Okay.
00:14:48
>> Language is a beautiful compression
00:14:50
mechanism that humans use 100 watts of
00:14:55
energy
00:14:57
like and you put that in the scheme of
00:15:00
things of like AI training, AI energy,
00:15:03
the power plants that are built to do
00:15:04
the thing that isn't even at human
00:15:07
strength yet. Okay. The Whimo machine
00:15:11
takes a hundred times more energy to
00:15:14
drive a Whimo than a human does to drive
00:15:18
a Whimo.
00:15:20
So, so language we there are still
00:15:23
things that humans are great at and that
00:15:26
unbeat like the goat. We're still the
00:15:28
goat at certain things. Language is this
00:15:30
epic compression.
00:15:32
And um we need to find ways to compress
00:15:34
cuz like when you think about how how we
00:15:36
first started looking at the physical
00:15:38
world is we saw everything. And you know
00:15:41
what guys and this is sort of obvious
00:15:43
like it doesn't matter what the cloud is
00:15:45
doing if I'm driving.
00:15:48
But like the car doesn't know that. It's
00:15:50
pulling in every freaking data point and
00:15:52
processing everything. And it's it's you
00:15:54
know look they've been about sort of
00:15:55
carving out the things that don't matter
00:15:57
and things like this.
00:15:58
There's ultra awesome versions of this
00:16:00
and you can imagine how you can use
00:16:02
language or things that look like
00:16:04
language to communicate either amongst
00:16:06
agents or sort of safety systems with a
00:16:09
driving system to sort of get very
00:16:13
efficient answers and to identify safety
00:16:16
issues very efficiently.
00:16:18
>> People don't know that you've moved to
00:16:21
Texas as of most people don't know but
00:16:23
it's it's out there.
00:16:25
>> Yeah. You moved here in December, so now
00:16:27
you're a resident of Austin.
00:16:29
>> Yeah, I was I Thank you.
00:16:32
>> It's very exciting for me. We've been
00:16:34
getting to play some back gammon.
00:16:35
>> Back gamon cards. It's
00:16:37
>> cards. We're having a good time.
00:16:38
>> So, I've had a place on Lake Austin
00:16:41
since 2021.
00:16:44
And uh I go there. I'm an avid water
00:16:46
skier. Like,
00:16:48
>> you're impressive at water skiing, I
00:16:50
have to say. Like,
00:16:51
>> so I've had a place in Austin for 5
00:16:53
years.
00:16:54
freaking love it. It's my weekend. I
00:16:56
would go 15 weekends a year.
00:16:58
>> What do you think's going to happen in
00:16:59
California?
00:17:00
>> It's pretty messed up. Look, I I grew up
00:17:02
in Cali. Uh like I grew up in Los
00:17:04
Angeles. Uh my parents were born and
00:17:08
bred in Los Angeles, which basically
00:17:10
makes them the founders of LA. Okay. But
00:17:14
um so I have a lot of heart like my
00:17:16
whole family, everything, you know? It's
00:17:18
pretty
00:17:19
>> it's pretty it's I don't want
00:17:21
>> a lot of us feel that way. I don't want
00:17:22
to get the violin out, but it just But
00:17:24
it's heartbreaking.
00:17:25
>> The plot totally It's just a place you
00:17:27
grew up. It's your home, you know,
00:17:29
>> when you have to leave.
00:17:31
>> Uh but it's getting weird out there
00:17:34
and uh it feels like it's getting
00:17:37
weirder and at some point that's it's
00:17:40
just too weird.
00:17:41
>> It's too weird. Do you think everyone's
00:17:43
going to leave?
00:17:44
>> I mean, it started with Elon and it was
00:17:46
like
00:17:47
>> we don't want Elon here and then he's
00:17:49
like message received,
00:17:51
>> right? And then it kind of worked its
00:17:52
way down the tech industry and in the
00:17:55
kind of it, you know, world of people
00:17:58
building businesses and whatnot. And now
00:18:00
it's kind of gotten so broad in terms of
00:18:03
the
00:18:03
>> Joe Rogan comedy, music, New Yorkers,
00:18:08
restaurant tours. I mean, this place is
00:18:10
>> I'm not even talking about this. I'm
00:18:11
just talking about everyone leaving LA
00:18:13
or sorry leaving California is almost
00:18:16
like working down this path of
00:18:19
>> look my the rest of my team's like we're
00:18:21
when are we moving you know they're like
00:18:24
>> and how are you dealing with that? So
00:18:25
that was the question was like
00:18:26
>> got to buy homes on the lake.
00:18:27
>> There are literally dozens of startup
00:18:29
CEOs of call it successful or growing
00:18:33
companies that I talked to who were like
00:18:36
>> dude I want to leave but I got employees
00:18:37
here. I got an office here. I got a
00:18:39
facility here. build stuff here. How am
00:18:41
I going to leave?
00:18:42
>> Yeah, I totally get it. It's a real
00:18:44
thing. So, look, I think like most
00:18:47
things, uh, sort of when it's time and
00:18:52
it feels painful to do something,
00:18:53
sometimes it's actually not as bad as
00:18:55
you think and you just got to make the
00:18:58
move and lead and do it. Um, and so, uh,
00:19:02
that's kind of what that's kind of the
00:19:04
process, the almost like a morning
00:19:06
process I went through and that's just
00:19:08
what it is. is and you're setting up a
00:19:09
team here.
00:19:10
>> Yeah, of course. Uh and I got that
00:19:12
office right on the lake.
00:19:15
>> Did you get that?
00:19:16
>> Uh it's we are negotiating. No, it's all
00:19:19
good. No, no, it's all good. We're
00:19:20
negotiating right now.
00:19:21
>> But I'm going to jet ski to work.
00:19:23
>> No, literally we is it true story. Last
00:19:27
year we're like driving up the thing and
00:19:28
I was like, "Wow, I wonder who owns
00:19:30
that." He's like, "I will." And I was
00:19:33
like, "Did you look at It's like I
00:19:35
looked at that and I was like, "That's a
00:19:37
that would be a nice one." But the the
00:19:39
truth is, you know, and I had a couple
00:19:41
people move here a couple years ago and
00:19:43
they all had the same reaction. Oh my
00:19:45
god, I'm living in a place that's twice
00:19:47
as big for half as much. The people here
00:19:49
are dope. The food is dope. Everybody
00:19:52
here is got this sense that we're
00:19:54
building the future. And it's just fun
00:19:57
and world positive. And you know, for
00:19:59
me, I got to live New York, LA, San
00:20:01
Francisco. I did three of the great
00:20:03
cities in this country. This one feels
00:20:05
the most like home to me,
00:20:07
>> which is a very strange feeling to me,
00:20:08
but it feels like everybody here wants
00:20:10
to build the future and it's very
00:20:13
diverse, you know, like all these
00:20:15
different industries and people pursuing
00:20:16
stuff. I I think this is the future.
00:20:18
>> Yeah. Here's the thing. Like you go to
00:20:19
San Francisco and I still have a little
00:20:22
nostalgia when I go to San Francisco
00:20:24
just having built Uber there and the
00:20:26
whole thing. Um, I still get the, you
00:20:29
know, the butterflies just I, you know,
00:20:32
but it does have something magical. You,
00:20:33
you just can't take it away. And then
00:20:35
you look at all of these bike lanes and
00:20:38
these bus lanes that never have a bus or
00:20:40
a bike in them
00:20:42
>> and cost $400 million to build one mile
00:20:45
>> and it's literally it's sort of like
00:20:47
this
00:20:49
subconscious desire to choke the city
00:20:51
off. Now remember, I look at things
00:20:53
through roads. That's how I think. So
00:20:55
I'm just like obviously the city is
00:20:58
totally busted.
00:20:59
>> Yeah.
00:21:00
>> No, they they literally took Market
00:21:02
Street and they're like, "What would be
00:21:04
the optimal way to this up and virtue
00:21:06
signal at the same time?" And they're
00:21:08
like, "Yeah, buses." And it's like,
00:21:11
"Nobody's on the bus. Nobody takes the
00:21:13
bus. It's a beautiful small town. San
00:21:16
Francisco. That whole street is empty
00:21:18
and painted red."
00:21:19
>> So, okay. So, we all [Â __Â ] and complain
00:21:21
non-stop on our chat.
00:21:23
>> When are you leaving?
00:21:25
>> I'm number one. I get it. But, when are
00:21:27
you leaving?
00:21:27
>> Okay. Well,
00:21:28
>> on the chat, you're the best, by the
00:21:29
way. I'm like,
00:21:30
>> okay. So, let me just,
00:21:30
>> by the way, there's couple glasses of
00:21:32
wine in
00:21:33
>> public facing Freedberg
00:21:35
>> and he's like, you know, I think there's
00:21:37
a better way to do. And then there's
00:21:38
like Darth Friedberg in group chat. He's
00:21:41
like, these people, these morons are
00:21:44
they're destroying societ. He is like
00:21:47
Darth Friedberg in group chat. Am I
00:21:50
lying?
00:21:51
>> Am I lying? Is he the most
00:21:53
>> correct? Especially after a couple
00:21:55
glasses of wine.
00:21:56
>> Yeah. When I start drinking fall and he
00:21:58
takes pictures, he's like, "Fourth
00:22:00
beverage." And we're like, "Oh, it's
00:22:02
worth staying up on the group chat."
00:22:04
>> Yeah. And then I'm like, I'll go and
00:22:05
attack this congressman on Twitter,
00:22:07
which I realized.
00:22:08
>> And then you go delete the tweet.
00:22:09
>> Yeah.
00:22:10
>> Don't delete the tweet.
00:22:11
>> Yeah. I delete the tweets. Okay. know
00:22:14
there's a group of people trying to
00:22:16
raise $500 million to create like a
00:22:18
tech/ business coalition to go to
00:22:22
Sacramento, which arguably is something
00:22:24
that everyone's left and avoided doing
00:22:26
forever cuz no one wants to spend time
00:22:28
in freaking Sacramento fighting
00:22:29
politicians, but it's almost like we're
00:22:31
all falling off a cliff. It's time to do
00:22:33
something. Do you think there's a
00:22:35
realistic path packed? Do you think the
00:22:37
people can actually get their together
00:22:38
that even if 500 million came in,
00:22:40
there's a way to kind of turn around the
00:22:41
state, fix some of the policies? You
00:22:43
think it's too late?
00:22:44
>> I don't think that. But look, I would go
00:22:46
I look, anybody who's doing anything to
00:22:48
fix things, I'm like, hell yeah, let's
00:22:51
do something. The issue is we all grew
00:22:53
up in the tech world, which was like a
00:22:56
libertarian place where you stay out of
00:22:59
politics and
00:23:01
>> that kind was that kind of vibe. It was
00:23:03
just everybody was like that.
00:23:05
>> Leave me alone. when I want to make
00:23:06
stuff.
00:23:06
>> Yeah. I just I'm not I don't do that.
00:23:08
And that's obviously this not a thing
00:23:11
anymore in California. I think the the
00:23:14
ballot initiatives are very powerful and
00:23:16
there's very clean ways to get something
00:23:18
on the ballot love that. I think that
00:23:21
your DAS who have decided we do not
00:23:24
enforce crime at all anymore. That's
00:23:28
like a sweet spot. Like I believe that I
00:23:31
sort of have this apherism. Truth and
00:23:33
justice are the immune system for
00:23:36
society.
00:23:38
When when the immune system is
00:23:42
suppressed, all the social ills flare
00:23:45
up.
00:23:47
So look for the places where truth and
00:23:50
justice are being deteriorated, are
00:23:52
being degraded, and say, how do we get
00:23:54
at that? Because if you get at that,
00:23:57
everything else downstream will be
00:23:59
better.
00:24:00
So that's kind of how I look at things
00:24:02
and how I also determine whether the
00:24:05
world's getting better or worse. When I
00:24:06
say weird, I'm talking about truth and
00:24:08
justice. That's what I mean when I say,
00:24:11
"Oh man, it's getting weird. It's
00:24:12
getting weirder," which means it's
00:24:13
weird. I'm just talking about truth and
00:24:15
justice.
00:24:15
>> Well, I mean, and you look at the
00:24:17
homeless industrial complex, you look at
00:24:19
Chesuine, which the all-in pod sacks,
00:24:22
myself, and the pod, like we we
00:24:24
literally led the recall of him. And
00:24:27
then you had the same thing going on in
00:24:28
LA where they were just like if somebody
00:24:31
>> Gasone
00:24:31
>> Yeah, Gasone. I mean, we basically lost
00:24:34
the script. You're running the city for
00:24:36
the criminals. It literally is like a
00:24:38
Batman movie. It's like Bane.
00:24:40
>> I mean, you want to arrest the
00:24:42
criminals.
00:24:44
>> Look,
00:24:45
>> I was born in the darkness. I mean,
00:24:46
these guys are lunatics.
00:24:49
>> Yeah. Look, I I know police officers in
00:24:51
Los Angeles who are no longer police
00:24:54
officers, and these are lifelong guys
00:24:56
who protect and serve. That's in their
00:24:58
bloods or DNA. They want to protect
00:25:01
people. They want the bad guys to be
00:25:03
dealt with. And they they almost have
00:25:06
PTSD from
00:25:09
what it is like to want to serve and see
00:25:12
bad things happening and not being
00:25:14
allowed to stop it.
00:25:16
>> Yeah. Nobody's got their back and
00:25:17
they're not allowed to do their job.
00:25:18
It's It's crazy. And I It's getting
00:25:20
weird.
00:25:21
>> Okay. Hey, I want to just go back to AI.
00:25:23
>> Sorry for the darkness.
00:25:24
>> No, no, no. I think it's good.
00:25:25
>> I was trying to induce I'm trying to
00:25:27
induce dark free.
00:25:29
>> Well, I brought it up. I mean, someone
00:25:30
bring me a tequila. I'll get going.
00:25:33
>> Yeah, let's do it. Can we get a couple
00:25:34
tequilas?
00:25:35
>> It was funny. I went on this podcast
00:25:36
yesterday and the guy and the f the guy
00:25:38
was like the first hour was middle of
00:25:39
the road. I was talking about tech and
00:25:40
science and then like politics came up.
00:25:42
He's like, so socialism and he said like
00:25:44
you lost it and then you were like he's
00:25:45
like the energy went 10x and
00:25:49
Yeah. So, it'll come out in a couple
00:25:50
weeks, but I was like, it it got me
00:25:52
going. Okay. I want to talk about
00:25:53
physical AI one more time.
00:25:54
>> Yeah.
00:25:54
>> So, one of now that now that you're
00:25:56
doing this, I saw a presentation the
00:25:58
other day. Someone showed like a video
00:25:59
of a squirrel jumping from one tree to
00:26:01
another tree. And they're like a tenth
00:26:03
of a watt or something like like the the
00:26:06
biology is tuned and it's so perfect in
00:26:09
terms of its efficiency of energy
00:26:11
utilization to do physical things. And
00:26:13
we're taking these like big things of
00:26:15
metal and motors and like actuators and
00:26:19
if you add up or you compound all of the
00:26:21
inefficiencies in the system, it's like
00:26:23
1,200 watts to get the robot to walk 4
00:26:26
foot. Like like break apart not just the
00:26:30
software but the hardware layer and
00:26:32
where are we at in evolving things like
00:26:35
actuators and the materials and
00:26:38
everything else that's going to make
00:26:39
physical AI work and scale. Oh, look, a
00:26:42
lot with the questions you're asking are
00:26:45
going down humanoid lane, which is like
00:26:48
this thing and and everybody talks about
00:26:50
how do you do the hand? It's almost like
00:26:51
Terminator 2 type obsession with the
00:26:53
hand, which is fair. Like, it's a very
00:26:56
critical part of it. I mean, look at the
00:26:58
I like to look at the Achilles the quote
00:27:00
unquote Achilles tendon of any of these
00:27:01
machines and you're like, that's where
00:27:03
the action is. This this there's a
00:27:06
couple other places. Um,
00:27:09
look, I'm in the nonhumanoid space. I
00:27:12
mean, but mechanical engineers have been
00:27:14
dealing with actuators and, you know,
00:27:17
all the sort of electromechanical sort
00:27:19
of interactions that make machines do
00:27:22
certain things, but like I'm in the food
00:27:24
machine space, so I can tell you how to
00:27:27
open a paper bag and put a put a a bowl
00:27:32
in a paper bag without tearing the paper
00:27:34
bag. But I am less into the I forget the
00:27:39
name pero. They're they're the the
00:27:41
senses to understand awareness and
00:27:44
touch. Um I'm not in that game. Um so
00:27:49
when you're mining you're like you're
00:27:51
not like you know you know
00:27:53
>> you're not threating
00:27:54
>> you're not playing tennis. Um certain
00:27:58
things may be equivalent to tennis. So,
00:27:59
look, the bottom line is we're seeing
00:28:01
obviously all you have to do is go
00:28:03
online and look at where the humanoids
00:28:05
are going over time and how much better
00:28:08
they're getting. Um, it's wild and it's
00:28:12
happening so freaking fast. But any
00:28:15
humanoid demo starts with dancing and
00:28:19
martial arts.
00:28:20
>> Yeah.
00:28:21
>> And we're sort of down specialized robot
00:28:25
lane, which is gainfully employed
00:28:27
robots.
00:28:28
>> Yeah. So, I know I didn't totally answer
00:28:30
the question, the technology piece, but
00:28:33
>> I just like do you agree that there's
00:28:34
probably like a big opportunity for
00:28:36
venture money and like research to go
00:28:38
into material science extra? Yeah,
00:28:40
>> for sure.
00:28:41
>> Sure. Because if the physical AI stack
00:28:43
manipulation and all of the related
00:28:47
things around it
00:28:49
>> is massive.
00:28:50
>> And so if you get the software working,
00:28:52
it's almost like the hardware has to
00:28:53
catch up. Yes,
00:28:54
>> we got a lot of
00:28:56
>> investor.
00:28:58
>> Well, actually was it's good that you
00:28:59
bring this up. You know, one of the
00:29:00
things you pioneered um at Uber was um
00:29:05
capital as a weapon and you were very
00:29:07
thoughtful about hey if we can take this
00:29:10
capital off the table then that's going
00:29:12
to let's call it what it is. It's going
00:29:14
to be an advantage versus the
00:29:15
competitors and these other competitors
00:29:17
couldn't get that capital. That's now I
00:29:20
think people have seen that playbook and
00:29:22
they're like hm Samman's like that was
00:29:24
smart let me try and it's at a different
00:29:27
scale now that you've come out of
00:29:28
stealth
00:29:29
>> now that you've got and and people are
00:29:31
starting to understand just starting
00:29:33
today how big your vision is
00:29:35
>> capital as a weapon this is I guess in
00:29:38
your plan yeah
00:29:39
>> well I mean here's the thing right so
00:29:41
capital as a strategic weapon for its
00:29:44
own sake is not a thing but when it is
00:29:47
actually a strategic weapon And then it
00:29:49
is a thing. And what I mean by that is
00:29:50
like in the Uber world early days if you
00:29:54
didn't have capital didn't matter how
00:29:56
good your app was because MASA is going
00:29:58
to put a billion dollars into your
00:29:59
competitor and you're going to lose 20%
00:30:01
market share tomorrow. So a critical
00:30:04
competency in fact your worldass
00:30:06
competencies one of them has to be
00:30:08
raising capital and you need to do it
00:30:10
better than everybody else and if you
00:30:11
don't you are going to lose.
00:30:13
>> Let me ask one follow up to that. Sorry
00:30:14
you go ahead. Um but the Middle East
00:30:16
>> Yeah. I've heard theories the last
00:30:19
couple days that big capital seekers are
00:30:21
kind of right now because of what's
00:30:23
going on in the Middle East with the
00:30:24
Iran war. Dubai, Qatar, Saudis are kind
00:30:29
of going to close up the capital flowing
00:30:31
to the US right now. And is that real? I
00:30:34
mean, do you think that's a real threat?
00:30:35
So look, our Middle East business was
00:30:36
supposed to go public in January and the
00:30:41
Saudi market was went down 20% over like
00:30:45
a two-month period and that was like a
00:30:48
massive damper on the situation. Now
00:30:51
part of it's part of that was because
00:30:53
the oil prices had gone down so
00:30:56
dramatically
00:30:57
>> and if you went into KSA, you went to
00:30:59
the kingdom, everybody's like, we need
00:31:01
we need oil prices to go up. uh you know
00:31:05
that's the other side of the equation.
00:31:07
So I don't know what h like I don't I'm
00:31:09
not in the market raising money right at
00:31:12
this moment and this is a twoe old thing
00:31:14
that I you know look I see the news just
00:31:16
like everybody else and I'm not out
00:31:18
there calling while a war is going on
00:31:20
and saying hey guys you got some money.
00:31:23
Um, so I don't know exactly what's going
00:31:25
to happen. But if you are an optimist
00:31:27
and you're like, "Okay, this isn't this
00:31:29
is not going on forever." Just like the
00:31:31
tariffs,
00:31:33
it was the end of the world and then it
00:31:35
wasn't very quickly.
00:31:37
>> If you're an optimist about this
00:31:38
situation and it won't be the end of the
00:31:40
world, maybe even a better world, then
00:31:44
we get to a better place. And I think
00:31:46
>> progress, abundance, the golden age
00:31:49
happens. And a lot of it is about all
00:31:51
the things that are happening in AI in
00:31:53
physical AI and and and just the
00:31:57
productivity gains that are coming in
00:31:59
very massive ways. Yeah.
00:32:00
>> Yeah. And I mean it it was shock shock
00:32:03
and awe and then hey now we've got a
00:32:06
steady state and let's hope that's what
00:32:07
happens in Iran is that we can depose
00:32:10
these evil dictators, replace it with
00:32:13
something a little more stable.
00:32:14
>> And related to this before we wrap,
00:32:16
they're going to China. There's big
00:32:17
trade deal being negotiated. What do you
00:32:19
hope comes out of this Chinese thing?
00:32:21
>> And what did you learn in China?
00:32:22
>> What would Yeah. What would you learn?
00:32:23
And and what do you think would be great
00:32:25
for America? Like what would you like to
00:32:26
see and be like, man, that's going to
00:32:27
set us all up. No more.
00:32:29
>> Look, here's the thing. If you go to
00:32:30
China right now and you go and just take
00:32:32
a tour of the manufacturing that's going
00:32:34
on there, just the manufacturing base,
00:32:38
the cities, especially if you've gone to
00:32:40
China for a couple decades in a row,
00:32:43
you're like, damn. Yeah. You So, let's
00:32:46
just do two things. You go to Shenzen,
00:32:49
which before felt like Kansas City, but
00:32:52
50 years ago and really humid, which I
00:32:55
guess Kansas City sometimes, but you go
00:32:59
there now and it's like oneupping
00:33:01
Singapore,
00:33:03
right? Or so that's the city view.
00:33:06
You're just experiencing a very awesome.
00:33:09
You're like, this is advanced and you
00:33:12
just get the vibe and it's everywhere.
00:33:14
And then you go and you start seeing the
00:33:16
manufacturing base and you see what like
00:33:19
Xiai is doing or any of the other
00:33:22
there's so many scrappy guys, badass
00:33:24
guys everywhere and you're like
00:33:27
f
00:33:28
>> they're hungry.
00:33:29
>> So does anybody remember the 2008
00:33:32
Olympics in Beijing? Anybody? Does
00:33:34
anybody This is a little bit you're down
00:33:36
a rabbit hole. Does anybody remember the
00:33:38
opening ceremony?
00:33:40
>> And you're like these mofos are taking
00:33:43
over. At least that's what they want to
00:33:45
do. That shit's happening.
00:33:48
So, I don't have any issue or this is
00:33:52
not negativity for me. I'm like, these
00:33:53
guys are killing it. The best idea is
00:33:55
winning. They're fiercely They're
00:33:58
fiercely going after truth and progress
00:34:00
and they're making [Â __Â ] happen. Let's
00:34:02
step up our game. Okay. But we can also
00:34:05
have a friendly game. Like, we don't
00:34:07
have to like be like the Detroit Pistons
00:34:09
in the '90s, you know?
00:34:10
>> Yes. We can we there there's a way
00:34:12
>> go into the stands.
00:34:14
>> Yeah. Yeah. You know, there's a way to
00:34:16
do this, right? And there's a way to do
00:34:18
it like adults. I hope that's where we
00:34:20
would end up. Um I have a employee who
00:34:24
because we we we for for a long time
00:34:27
we're the largest build kitchen builders
00:34:29
in China. I have an employee in China
00:34:32
has an American wife. Okay. They both
00:34:35
live in China. They're both from China
00:34:38
originally. Okay. but want him to it
00:34:42
would be great for him to work here on
00:34:43
some things I'm doing. It's very hard to
00:34:46
make that happen right now. Now, that's
00:34:48
selfish like I like maybe selfish like
00:34:51
I'm like there's a person I've been
00:34:52
working with for over a decade. I'd love
00:34:54
to continue here. Maybe there's other
00:34:57
bigger picture items that I'm not
00:34:59
dealing with. I'm not the geopolitical
00:35:01
guy, but I'd love for there to be sort
00:35:04
of
00:35:05
good
00:35:07
relations and good like like if you have
00:35:11
a significant other in who is an
00:35:13
American citizen like do we have to make
00:35:16
that hard as an example
00:35:18
>> some normaly would
00:35:19
>> something you know I'm just saying now I
00:35:21
agree like
00:35:23
>> there are ways to do immigration
00:35:25
properly like we effed it up super bad
00:35:28
don't to get me started. But there's
00:35:31
also there's good migration too. Like a
00:35:33
lot of great innovators
00:35:36
all over the place came from other
00:35:38
places for their own version of the
00:35:40
American dream. God bless
00:35:42
>> Freeberg.
00:35:43
>> And we don't have to that doesn't have
00:35:45
to be a negative thing. And so I'd like
00:35:48
to see more of that. And um yeah,
00:35:51
China's China's wild. So let's uh let's
00:35:53
keep our eye on the ball and let's let's
00:35:54
give them a run for their money, too.
00:35:56
>> Give it up for TK.
00:35:58
All right. Well done, brother. Thank
00:35:59
you. That was good. Good to see you,
00:36:02
brother. Wow. Michael Dell.
00:36:07
My lord. Texas native.
00:36:10
>> Michael.
00:36:11
>> Yes. Born born born in Houston.
00:36:12
>> Well, I missed the the opening. We we
00:36:14
jumped to the music, but you started
00:36:16
Dell Computer here in Austin with a
00:36:19
thousand bucks
00:36:20
>> 42 years ago in my dorm room at Dolby at
00:36:25
UT.
00:36:26
uh about 10 days before I finished my
00:36:29
freshman semester.
00:36:31
>> Amazing. And it's been working out
00:36:35
pretty good.
00:36:37
>> Yeah, it's been some bumps in the road,
00:36:39
but yeah, it's generally generally
00:36:41
worked out okay. Yeah, we'll have about
00:36:43
140 billion in revenue this year. So,
00:36:47
>> yeah, it's okay. It compounds over time,
00:36:50
doesn't it?
00:36:50
>> Yeah.
00:36:51
>> Yeah.
00:36:51
>> You know, you start small and just keep
00:36:53
adding and
00:36:54
>> there you That's how it goes. It's just
00:36:56
that easy.
00:36:59
>> But why Texas? Like, I think this is an
00:37:02
important thing. We're in Austin. Jason
00:37:04
lives here. David Sax lives here now.
00:37:07
More people are moving from California
00:37:09
to Austin. Why Austin? Why Texas? Why is
00:37:12
it work here? And is it getting better?
00:37:14
Is it just always worked? You know, I
00:37:16
think Texas has had a
00:37:20
uh, you know, low tax, progrowth,
00:37:25
uh, environment for a long time and pro,
00:37:28
you know, sort of progressive business
00:37:31
climate. And you know, if you sort of
00:37:33
look at the the growth of the Texas
00:37:36
economy relative to the rest of the
00:37:38
United States without Texas, you know,
00:37:42
Texas just kind of looks like a better
00:37:43
version of the US economy.
00:37:46
And uh you know, you now you've got uh
00:37:49
Austin is sort of just about in the top
00:37:52
10 cities in in the United States. So
00:37:56
you've got uh when that happens, you'll
00:37:59
have four of the 10 largest cities in
00:38:01
America in Texas. One out of 10 children
00:38:05
born in the United States born in Texas.
00:38:08
Uh more New York Stock Exchange
00:38:10
companies in Texas than in New York or
00:38:13
anywhere else. And uh you know, you've
00:38:16
got the University of Texas here in
00:38:18
Austin, which I always think of as kind
00:38:21
of the wellspring for a lot of the
00:38:24
companies that are here. certainly ours
00:38:27
and um you know a long history of of uh
00:38:33
innovative pioneering spirit and
00:38:36
entrepreneurship and um it's been a
00:38:40
fantastic place for us
00:38:42
>> and part of this I think Freeberg and
00:38:44
Michael is what's happened in the other
00:38:47
great cities or what were once great
00:38:49
cities my hometown New York I got to
00:38:52
spend 10 years in LA and the last 12 in
00:38:54
um the Bay Area and what's happening
00:38:56
there is incredibly unamerican uh and
00:39:01
they're decelerating when compared and I
00:39:03
think maybe the gap maybe and the
00:39:05
disparity from these um two locations
00:39:09
has gotten greater. Yeah. And you're
00:39:11
seeing a lot more people say life there
00:39:14
here in Austin seems a lot better than
00:39:16
the life I'm living in New York, LA or
00:39:20
in in the Bay Area.
00:39:21
>> Yeah. Well, I've got a lot of new
00:39:23
friends and neighbors, you know, that
00:39:24
that have that have that have come. And
00:39:26
certainly, I mean, if if you look at the
00:39:27
the the migration statistics, Texas has
00:39:32
attracted an enormous number of people
00:39:35
and and look, I mean, when you when you
00:39:37
when you look at the environment here
00:39:40
and compare it to the other kind of
00:39:42
situations that are going on, uh it's
00:39:45
it's it's very attractive. But, you
00:39:47
know, it's it's kind of been great for a
00:39:50
long time. So, uh, it's not really new
00:39:54
news to us that have been here a while.
00:39:56
>> Yeah. Elon had a great experience when
00:39:58
he was building the the Gigafactory over
00:40:01
here.
00:40:01
>> They let you do stuff here basically.
00:40:04
You know,
00:40:04
>> they let him build it
00:40:06
>> which he said was like an incredible
00:40:08
experience for him because in California
00:40:10
they didn't let him build, you know,
00:40:12
these factories. And in fact, the Tesla
00:40:14
factories that in Fremont was just an
00:40:16
old ancient factory that he was able to
00:40:18
retrofit. So, uh, there's something
00:40:21
going on here as well with the data
00:40:22
centers and and that's actually, I
00:40:23
think, very close to what you're working
00:40:25
on at Dell. Maybe you could talk a
00:40:26
little bit about the data center boom
00:40:28
that's going on in Texas that maybe
00:40:29
people aren't paying attention to.
00:40:31
>> Sure. Well, there's, you know, obviously
00:40:34
been enormous buildout of AI
00:40:36
infrastructure
00:40:37
and that requires, you know, lots of new
00:40:40
data centers, lots of power. Texas, you
00:40:43
know, has an enormous uh advantage there
00:40:47
relative to other states. Lot of power,
00:40:50
a lot of land, and it's and and you can
00:40:54
build stuff, right? So, there's there's
00:40:55
been a massive buildout particularly in
00:40:58
some of the cities in and towns in West
00:41:01
Texas where there's not a lot of
00:41:02
population and so they're not really too
00:41:05
opposed to having data centers out in
00:41:08
the middle of nowhere where there's land
00:41:10
and power. And so um yeah, I mean the
00:41:14
the the
00:41:16
demand for tokens is enormous. You know,
00:41:18
we've been building these AI data
00:41:22
centers not just here in Texas, but
00:41:24
around the world. And you know, the
00:41:27
growth in that has been tremendous.
00:41:30
You know, we we introduced the the first
00:41:32
uh H100 server. It was literally a
00:41:35
couple weeks before Chat GPT was
00:41:37
announced. And you know uh the
00:41:41
progression of our business in that area
00:41:43
has sort of gone from like 2 billion to
00:41:47
10 billion to 25 billion to this year
00:41:50
it'll be like 50 billion. So so
00:41:52
tremendous growth and when when you
00:41:55
think about what these models are
00:41:57
creating
00:42:00
there's this phase change that's
00:42:01
happened in computing right we we we had
00:42:04
60 years of calculating computing. Now
00:42:06
we have machines that are thinking and
00:42:08
helping us think. And so the demand for
00:42:11
that kind of intelligence and you know
00:42:13
the models are amazing but they're also
00:42:16
the worst they'll ever be and they
00:42:17
continue to improve. And so we just see
00:42:22
uh a lot more demand than supply. And
00:42:26
it's happening not just in the
00:42:28
hyperscalers and the cloud service
00:42:31
providers. It's happening in 4,000
00:42:34
enterprises where we building these
00:42:36
these Dell AI factories. It's happening
00:42:39
in sovereign AI, you know, like with
00:42:41
Palunteer and, you know, people want to
00:42:43
protect their data but also use AI on
00:42:46
it. They want to bring the AI to where
00:42:48
their data is. And you know when this
00:42:50
kind of started a few years ago, we had
00:42:52
some
00:42:54
really sophisticated
00:42:56
uh large companies think of like Fortune
00:42:59
100 and they started you know buying
00:43:02
these AI servers from us and and they
00:43:06
kind of knew what they were doing right
00:43:07
you know and and uh we said what what
00:43:09
are you doing and and they they were
00:43:12
they were kind of taking uh building
00:43:14
their own models they were taking open-
00:43:17
source models they were running them
00:43:18
some of them were were algorithmic
00:43:20
traders or you know derivatives of
00:43:23
machine learning and of course um they
00:43:27
needed a lot of help in in doing that
00:43:29
cuz it it it was sort of a complicated
00:43:32
thing. So about two years ago, we we put
00:43:34
together this product that we we we
00:43:39
called the Deli factory and now we've
00:43:42
got 4,000 plus of these and it's kind of
00:43:46
running rampant across enterprises.
00:43:49
>> How do you think about
00:43:51
the payback time on the investment
00:43:54
that's being made? The administration
00:43:56
put in place this accelerated
00:43:58
depreciation rule. the cont.
00:44:00
>> Yeah, that's very helpful actually.
00:44:02
>> So just for folks to understand that a
00:44:03
little bit like if you spend a hundred
00:44:06
billion dollars this year building and
00:44:08
data centers and buying infrastructure
00:44:10
for those data centers, you get to write
00:44:12
off 100% of that this year. Correct.
00:44:15
>> To deduct it. So you don't pay taxes.
00:44:16
You pay way much fewer tax
00:44:18
>> and that's in place for 10 years. I
00:44:20
think
00:44:20
>> that's a 10ear deal. So it's
00:44:22
accelerating the investment. How much is
00:44:25
that helping versus how are you seeing
00:44:28
folks rationalize the investment
00:44:30
relative to the return they're going to
00:44:32
make and over what time scale? This is
00:44:34
still the big question. Is the money
00:44:35
really there? The hyperscalers maybe
00:44:37
they're starting to come up but end
00:44:39
usage end states are we kind of hey wait
00:44:43
and see we don't know yet or folks are
00:44:45
getting 20% ROIC starting in year one
00:44:49
after they've made the investment. You
00:44:51
know, I I I can tell you in in our
00:44:53
business in in our company, we
00:44:55
definitely see plenty of use cases where
00:44:57
the ROI or the improvement in
00:45:01
productivity efficiency is 20% or or
00:45:05
greater
00:45:06
>> right away. It gets there.
00:45:07
>> I mean it, you know, it's not like you
00:45:10
just hit a button and you get 20%.
00:45:11
Right? There's there's work required in
00:45:15
thinking through the processes and maybe
00:45:17
it's worth a little bit describing that.
00:45:19
So you know when you have a a any
00:45:22
company
00:45:24
its processes and tools and technology
00:45:26
are a function of what was available at
00:45:29
the time it created those things. And so
00:45:31
what you sort of have to do is step back
00:45:33
and say all right what's the trajectory
00:45:36
of the improvement of the tools? What
00:45:39
outcome are we trying to create? And now
00:45:43
let's simplify and standardize the
00:45:45
processes. Get all the tools together.
00:45:48
get all the data together and then apply
00:45:51
the technology and this really has to be
00:45:55
done in kind of a tops down way. Uh you
00:45:58
can't sort of do it spontane in you know
00:46:00
in in silos are not going to
00:46:02
spontaneously improve themselves and
00:46:04
often that means that you're completely
00:46:07
changing the way the organization works.
00:46:10
>> It's like a wholesale rearchitecture.
00:46:12
>> It's a it's a reimagining of the way a
00:46:15
company works. And you know, I mean, the
00:46:17
way I described this to our team about 3
00:46:20
years ago is,
00:46:22
you know, uh, we were going to have a
00:46:24
new competitor 5 years from now, that
00:46:26
would be two years from now, you know,
00:46:28
that was in every business that we're in
00:46:30
except they were going to be faster and
00:46:32
more innovative and more successful and
00:46:35
lower cost and they were going to put us
00:46:37
out of business. And the only way we
00:46:39
were going to prevent that is is we're
00:46:40
going to become that company. And here's
00:46:42
how we're going to do it. And you know,
00:46:44
it excited some people, it scared some
00:46:47
people. And but I actually believe that
00:46:49
that's
00:46:51
what's going to happen. And and so we've
00:46:54
been dramatically changing our business.
00:46:57
I would say the biggest benefit by far
00:47:00
is speed. We're much faster at being
00:47:03
able to apply innovations. And so, you
00:47:06
know, you look at our at our
00:47:08
infrastructure business last quarter
00:47:09
grew 73%. Well, that's kind of unusual
00:47:13
for a business of this size.
00:47:17
>> And uh you know, this quarter we guided
00:47:21
that it would grow even faster, like
00:47:22
100%. So,
00:47:24
>> you've lived through a couple of
00:47:25
paradigm shifts here. The PC revolution
00:47:27
obviously you led that. Um and then you
00:47:29
of course had you know client server,
00:47:32
the network revolution, online uh
00:47:35
internet cloud, mobile.
00:47:38
>> Yeah. So each one of those we saw
00:47:40
massive disruption. We were talking in
00:47:42
in the green room about hey we used to
00:47:44
have a typing pool. There was a mail
00:47:45
room. All these things got abstracted
00:47:47
away by the PC andorked PC revolution.
00:47:51
Um but it took a decade or two and this
00:47:54
one's happening a lot faster. Yeah.
00:47:56
>> Yeah. This one I think it's it's like um
00:47:59
you know a quarter is like a year maybe
00:48:02
it's five times faster or something like
00:48:04
that. But but back to your question, I I
00:48:06
I would say maybe 10 or 15% of large
00:48:10
companies have really figured this out
00:48:12
and the rest of them are kind of
00:48:13
fumbling around. And you know, there's a
00:48:15
tendency when when you hear about a new
00:48:18
technology to like, oh, let's just let's
00:48:21
just go do it. You know, show the boss,
00:48:22
hey, we did AI. You know,
00:48:24
>> the board said we got to do AI. We got
00:48:26
to do AI, guys.
00:48:27
>> We need AI. Are you proud of me, boss?
00:48:29
You know,
00:48:30
>> um and
00:48:31
>> look at what I made. Exactly. And I also
00:48:35
think you know there's an important
00:48:36
point about about uh this which is
00:48:40
>> you know the barrier to technology
00:48:42
adoption is is not technology it's
00:48:46
culture and leadership and courage right
00:48:50
and and so
00:48:51
>> willingness to change and
00:48:53
>> and you know if you if you're in a
00:48:55
business that you don't think is
00:48:56
changing very much or you know hard
00:48:58
change is really hard right you you have
00:49:01
to it can be very uncomfortable you're
00:49:02
like, well, we're going to stop doing
00:49:04
that. Well, we don't need this anymore.
00:49:06
>> Particularly if your bonus is dependent
00:49:08
on not messing things up. But let's go,
00:49:12
let's use the internet as an analogy,
00:49:14
which you saw up close. There were
00:49:17
businesses that were internet transition
00:49:21
successful. They made the transition.
00:49:23
Maybe Macy's.com versus Sears Robuck,
00:49:27
right? Like maybe Macy's did a better
00:49:29
job of taking advantage of the internet
00:49:31
than Sears. But then there was internet
00:49:33
native businesses that seemed to blow
00:49:35
them all out and maybe Amazon's a good
00:49:38
example or um CSN stores whatever they
00:49:41
be Wayfair etc. What's the right way to
00:49:44
think about this evolution in industries
00:49:47
generally? Are we going to have
00:49:50
their businesses are going to transition
00:49:52
successfully and those that aren't and
00:49:53
they're going to die? And is this really
00:49:55
going to are we going to see AI native
00:49:57
businesses in every industry come in and
00:50:00
just disrupt everything? I believe we
00:50:02
will and certainly you know when you
00:50:04
talk to the Collison brothers at Stripe
00:50:07
they'll tell you that the rate of growth
00:50:10
of the 2025 cohort companies is about
00:50:14
four times faster than the 2018
00:50:16
companies and so every year comp the new
00:50:20
batch of companies are growing faster
00:50:21
and faster because they're starting with
00:50:23
all these new tools that you know
00:50:24
>> because they see all the new companies
00:50:25
on their platform.
00:50:27
>> Exactly. And so when when you think
00:50:29
about uh an incumbent company, okay,
00:50:33
that already exists, it has, let's say,
00:50:35
it's got brands, it's got balance
00:50:37
sheets, it's got, you know, customer
00:50:39
relationships, whatever stuff, right?
00:50:41
Okay. Um, but that's sort of like those
00:50:45
are expiring value assets. If it doesn't
00:50:47
change quickly and get onto the other
00:50:50
side of this, I think it will go out of
00:50:52
business. And which is exactly the
00:50:54
speech I gave to our team, you know,
00:50:56
three three years ago. And
00:50:58
uh I think, you know, you you you you
00:51:02
you have to be uh bold and you got to go
00:51:06
make those changes to to
00:51:09
not only survive this, but but to but to
00:51:11
thrive. And you know, I think about it
00:51:14
is how do we prepare our company to be
00:51:16
ready for the 2030s,
00:51:17
>> right? Isn't it like it's much more
00:51:20
it's kind of the story line. There's
00:51:22
more to do than there ever was. It's
00:51:24
like when the internet arrive kind of
00:51:26
came around, Sears doesn't just need to
00:51:28
sell locally. They can sell to the
00:51:30
world.
00:51:31
>> Well, sure. I mean, this is the point
00:51:35
when when when we have better tools, we
00:51:37
can do way more things, right? And and
00:51:39
uh you know, when you know, when I hear
00:51:41
people say, "Oh, you know, um maybe
00:51:46
we're just going to have all these great
00:51:48
tools and we we won't do more things.
00:51:50
we'll just do the same things with fewer
00:51:52
people. It doesn't sound right to me. I
00:51:55
I mean, there'll be some of that, but I
00:51:56
think most of it will be we're just
00:51:58
going to do a whole lot more things.
00:51:59
We're going to solve a lot more
00:52:00
problems. We're going to accelerate
00:52:02
scientific discovery.
00:52:04
We're we're we're going to invent all
00:52:06
sorts of new things. We're going to
00:52:07
solve all sorts of problems that haven't
00:52:09
been solved.
00:52:11
>> And you know, that's that's super
00:52:13
exciting.
00:52:14
>> What do we have wrong on infrastructure?
00:52:16
So the original build cycle looked a lot
00:52:19
like everything's in a data center.
00:52:21
Everything's got to sit there. That's
00:52:23
where all the intelligence. It'll all be
00:52:25
in these kind of hosted proprietary
00:52:27
cloud models. Do you think that it's
00:52:29
open source? Is it distributed on the
00:52:31
edge? Where does the intelligence where
00:52:33
does the inference sit? And how does
00:52:35
that really change or kind of
00:52:37
rearchitect the industry? Do you think
00:52:39
>> it's really all the above? I mean it
00:52:41
it's not like there's one answer. I mean
00:52:44
certainly if you go to
00:52:47
uh any industrial company or natural
00:52:50
resources company advanced manufacturing
00:52:54
retail logistics there's tons of
00:52:57
inference at the edge and that's growing
00:52:59
very very fast and uh you know we make a
00:53:03
lot of that embedded equipment certainly
00:53:06
you know telos are doing that too I mean
00:53:08
it's pretty much every every industry
00:53:10
think about wherever data is being
00:53:12
created you want the AI I infrastructure
00:53:14
and the inference you know close to the
00:53:17
data. Um
00:53:20
you know there there there has been this
00:53:22
sort of rebalancing as companies have
00:53:25
figured out you know sort of everybody
00:53:27
loves the public cloud right until they
00:53:30
get the bill right they get the bill
00:53:32
they're like wait this is supposed to
00:53:33
save us money yes
00:53:34
>> costs quite a bit more. So, you know,
00:53:37
the the the lowest cost token is going
00:53:39
to be the one that's generated right
00:53:41
where the data is on the device. You're
00:53:44
going to have, you know, tokens being
00:53:46
generated on your phone, on your PC, in
00:53:49
every embedded piece of equipment. And
00:53:52
and look, we have an interesting
00:53:53
perspective on this business because we
00:53:54
have 10,000 customers where they embed
00:53:58
our product in their product. This is
00:54:00
you know think medical devices,
00:54:02
security, all sorts of things in
00:54:05
hospitals and industrial plants and you
00:54:08
know any any kind of uh you know uh
00:54:13
datadriven activity right requires some
00:54:16
kind of computing network storage
00:54:18
infrastructure.
00:54:19
>> Yeah. So when you um look at the desktop
00:54:22
where you started, it's coming full
00:54:24
circle and this must be at least very
00:54:27
interesting or intriguing to you that
00:54:29
you see this open claw movement.
00:54:31
Everybody trying to buy the most
00:54:33
powerful desktop they can and all these
00:54:35
hobbyists who were your customers who
00:54:37
were calling you up and ordering from,
00:54:39
you know, Dell, their their bespoke PC.
00:54:43
Now they're
00:54:43
>> Dell.com.
00:54:45
>> What did I say? Dell.com. Yeah,
00:54:47
>> you said ordering from Dell. Calling us
00:54:48
up. They they they order online usually.
00:54:50
>> They order online now. Yes.
00:54:52
>> We have this thing called the internet.
00:54:53
>> They do. It works out pretty well. Um
00:54:56
but this is incredible that they're like
00:54:59
all stacking computers and and running,
00:55:03
you know, uh local models. I was just
00:55:06
thinking back to how much the first
00:55:07
couple of computers I owned cost $4,000
00:55:10
in 1980.
00:55:12
And then the prices came down. You can
00:55:14
buy a Dell for 500 bucks, 800 bucks,
00:55:16
like really nice laptops for that price.
00:55:19
Um, use the promo code allin. Um, it's
00:55:23
not a sponsor. It's a joke. Um, but do
00:55:26
you think there's a world where we're
00:55:27
going to start to see the desktop
00:55:30
because people want to protect that
00:55:32
data, they want to protect the skills
00:55:34
they're building. They don't want to
00:55:35
give it to Sam Alman, put it in a cloud
00:55:37
somewhere. They don't want to give it to
00:55:39
Google, whoever it happens to be. Um,
00:55:41
and that the desktop revolution comes
00:55:42
back and everybody's got a $10,000
00:55:44
desktop. Is that coming?
00:55:46
>> I don't know if everyone will have
00:55:48
$10,000 desktop, but that would be
00:55:49
great. I mean, you know, uh
00:55:53
um you know, so we have this
00:55:56
Dell uh portal on hugging face and we
00:55:59
have all these open models and we
00:56:01
qualified them on every kind of machine
00:56:04
we have and you know there's been
00:56:06
enormous progress in the open source
00:56:08
models. You know, Google has these Gemma
00:56:11
models, GMMA, and they work really,
00:56:13
really well on small machines. You know,
00:56:16
OpenAI has their open- source models.
00:56:18
You got the Nvidia Neotron models.
00:56:21
You've got, you know, enormous
00:56:24
uh ecosystem of open- source that is,
00:56:28
you know, thriving and certainly open
00:56:31
claw and, you know, there'll be some
00:56:33
good discussion about that.
00:56:35
>> How many people have set up OpenClaw?
00:56:37
Raise your hand. Oh my lord. That's
00:56:39
about what a 20% of the audience here.
00:56:42
>> Yeah. So, you know, autonomous agents,
00:56:45
um, big deal. And certainly inside
00:56:47
companies,
00:56:49
there's going to be a lot more
00:56:50
autonomous agents. There are significant
00:56:53
security requirements that need to go
00:56:54
with that. We need to we need to be able
00:56:56
to authenticate and validate who these
00:56:59
agents are and what they're doing and,
00:57:01
you know, have the right controls and
00:57:03
and and that sort of thing.
00:57:05
>> Yeah. and uh your take on uh AGI and
00:57:11
when we're going to hit it like do you
00:57:13
actually think about super intelligence
00:57:14
and AGI and the the the two sets of
00:57:18
problems that could solve there and do
00:57:20
you have a personal definition that you
00:57:22
like to use for those when you're
00:57:23
talking internally with your team of how
00:57:26
things are moving?
00:57:28
>> I I I don't really know Jason. Um, you
00:57:31
know, I I think if it feels like with
00:57:34
the latest releases, we were talking
00:57:36
about this backstage, uh, you know, the
00:57:40
Gemini 3.1,
00:57:42
the Opus 4.6, the OpenAI 5.4, it feels
00:57:46
like we sort of u hit some kind of
00:57:49
threshold where the just the quality of
00:57:52
the the models are are just tremendous.
00:57:54
and and and and you know when I listen
00:57:56
to what our teams are able to accomplish
00:57:58
in a day or two weeks that would have
00:58:01
taken them you know a few months or nine
00:58:03
months time you know it's it's just
00:58:06
amazing the speed of of innovation.
00:58:09
>> Yeah. And so
00:58:12
it it seems to be continuing and we get
00:58:15
all the reinforcement learning and
00:58:18
there's also tons of private dark data
00:58:22
that these models haven't been applied
00:58:24
to and that's sort of what's happening
00:58:26
with these
00:58:27
>> I think the auto research is the that's
00:58:29
the the key with auto research the
00:58:31
capacity to take a standard model and
00:58:34
then retrain it on your private data and
00:58:36
keep it private and build an advantage
00:58:39
for your organization based on the
00:58:40
history of your data that no one else
00:58:42
has. That seems to be what a lot of
00:58:44
folks are thinking about that have the
00:58:46
capacity. But if you were to start a
00:58:48
company today that was not in computing
00:58:50
and you were to build a business from
00:58:52
the ground up, how would you architect
00:58:54
your people and your organizational
00:58:56
principles as an AI kind of first
00:59:00
knowing what you know about computing
00:59:02
and where things are headed? Are you
00:59:04
hiring people? Are you hiring a bunch of
00:59:06
people to run a bunch of agents? How do
00:59:08
you think about architecting a new
00:59:09
business today?
00:59:10
>> It's a great question. I don't really
00:59:12
spend a lot of time thinking about that.
00:59:14
You know, I'm thinking about how do how
00:59:15
do I run
00:59:16
>> what the rest of us are thinking about.
00:59:17
>> How do I how do I run our company? I
00:59:20
mean, that's hard enough. So,
00:59:22
>> yeah, everyone I talk to, that's the
00:59:24
question. They're like, everyone goes to
00:59:25
these off sites and they're like, I'm
00:59:28
actually doing this with my management
00:59:29
team on Monday. We're doing like a tear
00:59:30
down be like, "Hey, how would we build
00:59:32
the business differently today?" Knowing
00:59:34
>> I mean what we've been thinking a lot
00:59:36
about is
00:59:38
it's sort of this this reimagining
00:59:40
question. Yeah.
00:59:41
>> You know, sort of all right, we know the
00:59:44
trajectory of the tools. What are the
00:59:46
tools going to be in 27 28 29 and how do
00:59:51
we
00:59:52
accelerate you know our path to that?
00:59:55
How worried are you about
00:59:58
social issues? So AI recently ranked as
01:00:01
the most unfavorable term of a list of
01:00:04
terms including president.
01:00:06
>> It was somewhere between ISIS, the
01:00:08
Democrats, ICE, ISIS, and the Democrats.
01:00:11
>> ICE was better than AI.
01:00:13
>> People liked ICE mass agents
01:00:16
>> more than they like AI.
01:00:17
>> Yeah. Well, I think I think part of the
01:00:19
problem is it's been it's been uh you
01:00:23
know maybe sold as
01:00:26
>> as you know it it it sort of presents
01:00:28
itself like a human would.
01:00:30
>> Yeah.
01:00:30
>> Right. And you know maybe if we called
01:00:32
it linear algebra
01:00:35
>> matrix multiplication
01:00:36
>> and statistics instead
01:00:38
>> matrix multiplier maybe that would be
01:00:40
more friendly. I don't know you know.
01:00:42
>> Yeah.
01:00:42
>> But do you think do you think we're
01:00:43
going to have No, I think you're right.
01:00:45
the positioning is wrong and then we're
01:00:47
not communicating to people, hey, this
01:00:49
could help healthare, this could make
01:00:51
you live longer, this could help your
01:00:52
kids get educated more, that this could
01:00:55
help with housing costs, this could help
01:00:56
with food cost. Messaging aside, I mean,
01:00:59
how much do you actually worry
01:01:01
>> about disruption or dislocation in
01:01:03
employment, about acceleration of
01:01:06
earnings for some people and
01:01:08
deceleration for other people in society
01:01:10
that feel left behind? And that starts
01:01:13
to fuel more of the kind of social
01:01:15
concerns and politicians saying, "Hey,
01:01:18
we got to stop building all the data
01:01:19
centers, you know, like that kind of
01:01:21
stuff." And and how much are you really
01:01:23
>> I I tend to be, you know, more
01:01:25
optimistic and and um you know, I I do I
01:01:30
do think that in all technology cycles,
01:01:33
you get sort of these network effects
01:01:36
and
01:01:37
uh that's kind of inevitable. But I also
01:01:41
think, you know, uh we're going to do
01:01:44
more with the tools. You you do have
01:01:46
this acceleration of all sorts of uh
01:01:50
great things. I mean, education can
01:01:52
dramatically improve, scientific
01:01:54
discovery, healthcare, energy, you know,
01:01:57
all sort of the the unsolved problems
01:01:59
can can be accelerated. Ultimately, I
01:02:01
think it's it's amplification of of
01:02:04
human potential and capability
01:02:06
>> and
01:02:07
>> extending the frontier, too. And and and
01:02:09
and by the way, we should also remember
01:02:11
that basically what we're talking about
01:02:13
here beyond sort of some of the advanced
01:02:15
semiconductors in the you know big data
01:02:18
centers, we're talking about software,
01:02:20
right?
01:02:21
>> Yeah.
01:02:22
>> Right. It's like software that runs on
01:02:24
your computer.
01:02:26
So you know if somebody says well we
01:02:28
don't we don't want that. It's like how
01:02:30
do you stop software I mean
01:02:32
>> yeah how are you going to how you going
01:02:33
to stop someone putting an open source
01:02:35
model on their computer at home and
01:02:36
asking it for medical advice? You know,
01:02:38
New York just passed a law saying AI
01:02:39
models can no longer give
01:02:41
>> medical advice. Passed, right? It's
01:02:43
being proposed.
01:02:44
>> Oh, proposed. It's being proposed. You
01:02:46
can't give legal and health advice.
01:02:47
>> We're anti-s software. It's like,
01:02:49
>> yeah. Well, we're also anti books and
01:02:50
advice. So, if you were going to look it
01:02:52
up in a book, but were you dropping into
01:02:54
your Bernie Sanders right there?
01:02:56
>> That was my Bernie Sanders.
01:02:57
>> Michael D. 1% of the 1% that you're
01:03:01
enabling with your data centers.
01:03:03
Why are you doing this to the people of
01:03:06
our great nation while you give your
01:03:08
money to children in the Invest America
01:03:11
accounts?
01:03:12
>> Yeah,
01:03:12
>> this is a good one you're doing.
01:03:14
>> Yeah. Can we talk about Invest America?
01:03:15
>> I think he might have even criticized
01:03:17
that, but but you know,
01:03:18
>> well, that's the problem. The
01:03:20
billionaires are giving our children
01:03:21
money and they're not asking us
01:03:23
permission and then those kids are going
01:03:24
to buy things that their parents never
01:03:26
asked for.
01:03:29
>> Well, they they don't they don't
01:03:30
actually get the money till they're 18
01:03:32
years old. So that's that's
01:03:34
>> But what gives you the right to give our
01:03:36
children an education? What is this
01:03:38
philanthropy? It makes no sense. No, I
01:03:41
mean honestly.
01:03:42
>> Well, I see I see my great friend Brad
01:03:44
Gersonner here.
01:03:45
>> Brad's here.
01:03:47
>> There he is.
01:03:48
>> Brad, come up for this little uh segment
01:03:49
here. Sit sit for a second. Let's talk
01:03:51
about America. We got 5 minutes left
01:03:52
here.
01:03:53
>> So, you know, I heard
01:03:54
>> everybody the fifth bestie. Give him a
01:03:55
round.
01:03:56
>> I didn't know he's going to be here.
01:04:00
>> All right. I
01:04:02
>> how did this go down, Michael?
01:04:03
>> I I heard about this idea in 2021 from
01:04:07
Brad
01:04:08
>> and I thought, you know, that's a that's
01:04:10
just a great idea. That's an awesome
01:04:12
idea. And, you know, I think there were
01:04:15
some discussions with the prior
01:04:16
administration,
01:04:18
but they didn't uh do do anything about
01:04:20
that, unfortunately.
01:04:23
And um you know, here we are, you know,
01:04:26
a miracle. you know, uh the the the
01:04:29
Invest America Act was was passed and um
01:04:33
you know, now we have thousands of
01:04:34
companies that are joining in and
01:04:38
matching the government's contribution
01:04:40
and u you know, Susan and I made a big
01:04:43
announcement uh giving $250 to 25
01:04:47
million children in uh zip codes where
01:04:51
the median income is
01:04:54
>> I mean, Michael, let's just let's pause
01:04:56
for a second
01:04:58
This is one of the greatest
01:05:01
>> What do you think, Bernie? Do you
01:05:02
approve?
01:05:04
>> I'm going to go with Jake All this. I
01:05:06
just want to pause on this because it is
01:05:08
one of the greatest philanthropic gifts
01:05:10
in the history of humanity. And I I
01:05:13
people have just kind of glossed over to
01:05:15
it because there's a lot of big numbers
01:05:16
in the world, but we're talking about
01:05:18
you. You personally, you and Susan sat
01:05:20
down and said, "We're going to give a
01:05:22
number." And that number was five, six,
01:05:25
seven billion dollars or this is
01:05:27
>> well it's it's $250
01:05:30
to 25 million children ages 2 to 10 in
01:05:34
zip codes where the median income is
01:05:36
$150,000 or lession. It's $6.25 billion.
01:05:40
>> I mean
01:05:42
and I just want to say something. You
01:05:44
know, we live at a time where
01:05:48
>> but they have to sign up to claim the
01:05:50
accounts. Yes,
01:05:51
>> they they have accounts, but they have
01:05:52
to sign up to claim the accounts. You
01:05:54
know, I think we're getting 100,000 plus
01:05:56
kids now a day signing up.
01:05:59
>> Yeah.
01:05:59
>> First, thanks for having me up. I mean,
01:06:01
what what a national hero and national
01:06:04
asset that my friend Michael Dell is,
01:06:06
but he understates this
01:06:08
>> because I've been working on this for
01:06:10
four years. We've been talking about it
01:06:12
on the All-In Pod. We had a lot of
01:06:14
momentum, but behind the scenes, Trump
01:06:18
gets elected. Um, and so it's April that
01:06:22
we're in the middle of the tariff
01:06:24
strife. April 25, we realize there's
01:06:27
only going to be one piece of
01:06:28
legislation that gets passed during
01:06:30
Trump's first two years. It'll be the,
01:06:32
you know, this big beautiful bill, the
01:06:34
reconciliation bill. And so I I call up
01:06:37
Michael and I said, "Michael, we got to
01:06:39
go. We've got five days. We have the it
01:06:42
it's drafted in the Senate. We have
01:06:44
bipartisan support, but we have a window
01:06:48
and like we ha I have to get in the Oval
01:06:50
Office. We have to get in the Oval
01:06:52
Office. And you know, Michael said, you
01:06:55
know, what what should the text say? And
01:06:58
you and I had a conversation and you you
01:07:01
know uh you know the text to DJT.
01:07:04
>> Yes. I I'm not talking out of school.
01:07:06
Listen, Biden, where wherever you sit on
01:07:09
the political divide, I will say I've
01:07:10
said this, Trump seeks out ideas from
01:07:14
business leaders and he has deep respect
01:07:16
for business leaders like Michael Dell.
01:07:18
Like you wherever your politics are,
01:07:20
that's just the truth. And the last
01:07:21
administration didn't and you know, if
01:07:24
it may have done the same thing
01:07:26
>> and and this and I just have to say
01:07:28
this, Invest America, it's not a red
01:07:30
idea or a blue idea. It's a red, white,
01:07:32
and blue idea, right? It's it's
01:07:34
>> so into the prior conversation when
01:07:38
Michael and I first talked about it um
01:07:42
you know it was this is the right thing
01:07:45
to do right like we have to reconnect
01:07:48
the 70% of people who feel left out and
01:07:50
left behind to the American dream right
01:07:52
but this isn't our self-interest this is
01:07:54
about defending
01:07:55
the ownership society and capitalism
01:07:58
that for 250 years created the greatest
01:08:01
experiment in the history of the world
01:08:03
but that's at at risk
01:08:05
less than half of people under the age
01:08:06
of 40 have a favorable view of
01:08:08
capitalism. So when I talked to you
01:08:09
about it the first time, Michael
01:08:11
understood both sides of it. It's the
01:08:13
right thing to do and it's the right
01:08:15
thing for the country. And uh so at any
01:08:17
rate, Michael Dell, tremendous American
01:08:21
I have one just one one punch up the
01:08:26
name invest America Trump accounts. What
01:08:29
do you think?
01:08:30
>> Were were you considering this in the
01:08:32
context of other philanthropy? I mean,
01:08:34
how do you how do you kind of put this
01:08:36
together in the spectrum of how you
01:08:37
think about giving back?
01:08:38
>> Yeah, great question. So, so you know,
01:08:40
we have a foundation that's very focused
01:08:43
on children in urban poverty. That's
01:08:47
basically the central focus of the
01:08:49
foundation, although folks in uh central
01:08:52
Texas would know that we do a few other
01:08:54
things here in our local community. Um,
01:08:57
and
01:08:59
you know, when I heard about this idea,
01:09:01
one of my thoughts was, "Wow, this is
01:09:03
like a platform for directly giving to
01:09:07
the people that we're targeting, right?"
01:09:10
And, you know, we actually thought about
01:09:13
doing it just in Texas first. And, um,
01:09:17
you know, things have gone pretty well
01:09:19
with the company and all that. So, you
01:09:21
know,
01:09:23
we thought we just go bigger. And and
01:09:26
what happens, Brad, if
01:09:28
you know 10 more Michael Dells show up
01:09:31
and and there are dozens of
01:09:33
>> That's not a lot of Michael Dells, let's
01:09:35
be honest. But
01:09:36
>> there there's a number of folks who
01:09:37
could make an equal size or even greater
01:09:40
gift. Um there are people who, you know,
01:09:43
many hands makes for light work. There
01:09:45
are a thousand people who can make a
01:09:46
gift of significance. What if this
01:09:49
actually becomes a movement and we
01:09:51
>> I think I think it actually is becoming
01:09:54
a movement instead of a moment and and
01:09:56
and we've you know we've got a lot of
01:09:58
that queued up. Brad, why don't you
01:10:00
>> Wait, have you called anybody? Michael,
01:10:02
have you did you call?
01:10:04
>> Michael and I chair the Invest America
01:10:06
Giving Committee and we're ambitious
01:10:08
guys.
01:10:09
>> You're knocking on doors.
01:10:10
>> We we've had a few conversations.
01:10:12
>> You're texting people.
01:10:13
>> Yeah.
01:10:14
>> And so so so just there was a question
01:10:16
earlier. First, it's really important to
01:10:18
understand and for you guys to spread
01:10:19
the word. Every child under the age of
01:10:21
18, every child under the age of 18 is
01:10:24
eligible to claim their account. Number
01:10:27
one. Number two, you've heard this like,
01:10:30
oh, kids born between 25 and 28. No,
01:10:34
this is forever more. The legislation
01:10:36
creates this account forever more. Every
01:10:38
child born in America starting January
01:10:41
1st, 2027 will automatically get an will
01:10:44
get a trump account right at birth
01:10:47
stapled to their social security card.
01:10:49
The $1,000 has to be reauthorized every
01:10:52
four years. Okay? But the accounts
01:10:54
don't. So every kid, this is social
01:10:57
security 2.0. This is the biggest change
01:10:59
to the social contract in America in in
01:11:03
50 years. 3.7 million kids a year will
01:11:06
get an account that can compound. a 401k
01:11:09
from birth. And yes, we're going to have
01:11:11
a lot of announcements, but it's not
01:11:14
just billionaires. It's going to be
01:11:16
companies that are donating stock on
01:11:18
their IPOs into these accounts. It's
01:11:20
going to be wealthy people. It's going
01:11:22
to be states. It's going to be moms and
01:11:24
dads. It's going to be corporations. And
01:11:26
the estimate is over 15 years, we can
01:11:29
move $5 trillion
01:11:32
into the pockets of families that would
01:11:34
have otherwise had zero. $5 trillion,
01:11:39
right? And so to me, the leadership that
01:11:41
Michael showed not only in helping me
01:11:43
get the meeting that ultimately got this
01:11:46
passed into law. And it does take people
01:11:48
like those moments either happen or they
01:11:51
don't happen. And if they don't happen,
01:11:53
there's no law. And this doesn't change
01:11:55
kids lives.
01:11:56
>> By the way, two things on this.
01:11:58
>> If this $5 trillion moved through
01:12:01
government programs, it would get
01:12:03
incinerated.
01:12:03
>> Exactly.
01:12:04
>> That's what we see happen. there's just
01:12:06
a million crony structures that take it
01:12:09
away and destroy it. So to give it
01:12:10
directly into the accounts is the
01:12:12
circumstance. The second thing is it
01:12:14
makes a lot of sense that you guys can I
01:12:17
I'll I'll be the lead but can we replace
01:12:20
social security in this country with a
01:12:22
defined benefit or defined contribution
01:12:24
like this and eventually everyone has a
01:12:27
Trump account or whatever you call it
01:12:29
and we don't have to have this fake
01:12:31
Ponzi scheme that we call social do we
01:12:34
can do it. Well, they they they have a
01:12:35
defined benefit program, but I'm saying
01:12:37
like everyone has an account and they
01:12:39
all own a piece of their future. And
01:12:40
every time you get a payroll to tax
01:12:41
deduction, instead of it getting
01:12:42
inviscerated and destroyed and
01:12:45
vaporized, that money actually goes into
01:12:47
an account and you buy a piece of a
01:12:49
company and maybe you can direct it.
01:12:50
>> Freeberg's getting
01:12:51
>> on July 4th of this year.
01:12:53
>> You're getting me wound up. So for all
01:12:54
the there are 4.5 million kids who've
01:12:57
claimed their account almost $150,000 a
01:13:00
day will have on the trajectory we're on
01:13:02
10 million by July 4th our 250th
01:13:05
anniversary of the country. Every one of
01:13:07
those kids accounts the parents and the
01:13:10
kids on July 4th they'll see an app on
01:13:12
their phone that looks a lot like a
01:13:14
Robin Hood app that'll see them owning.
01:13:16
It'll say you've received your $1,000 or
01:13:19
your $250. you're and it will show a
01:13:22
little bit of Nvidia, a little bit of
01:13:23
Walmart, a little bit of Dell. We
01:13:25
decompose the S&P 500 which they own
01:13:28
into the constituent parts so they can
01:13:30
get excited about being an owner in the
01:13:34
upside of America. And when moms and
01:13:35
dads doubleclick and Apple pay 510 bucks
01:13:38
into the account, right? When they send
01:13:40
their QR codes to their friends on their
01:13:43
birthday and now their friends all add
01:13:45
to the account or on Christmas or bar
01:13:46
mitzvah and they add to the accounts.
01:13:49
When companies add to the accounts, all
01:13:51
of this, they see it growing and it
01:13:53
unlocks the human potential. It's not
01:13:56
just the money. It's that I'm in the
01:13:59
game. I have a shot. Which to David's
01:14:03
point, I think the biggest crime of
01:14:05
social security. And we made very clear,
01:14:07
social security is a sacred promise. We
01:14:10
we refused and many people tried to get
01:14:12
us to to to take on the broader
01:14:14
struggle. And we didn't do it because we
01:14:17
knew it it would kill this program. But
01:14:19
but let's be clear about this. Our
01:14:21
government requires all of us to give
01:14:23
10% of what we earn into social
01:14:26
security, right? It was the social
01:14:28
contract evolution in the industrial
01:14:30
revolution that kept kept the country
01:14:32
together. The only problem is it goes
01:14:34
into a black hole. Nobody sees it.
01:14:36
Nobody knows what's there. But it is
01:14:38
your savings. Now imagine if that same
01:14:40
money was required to, you know,
01:14:42
government took it away, but it was in
01:14:44
an account with your name on it. You
01:14:46
could see it grow. you knew exactly what
01:14:48
was there. You could get excited and
01:14:50
say, "Hey, I'm going to add a little bit
01:14:51
more to that, right?" And and and you
01:14:53
had a little bit of choice. That to me
01:14:55
is uh is the possibility. And I think we
01:14:57
will end up there some
01:14:58
>> and and and Brad, thank you for
01:15:06
just going to say, you know, Brad Brad
01:15:08
also adopted his home state of Indiana.
01:15:11
We have Ray and Barbara Dal Dalio
01:15:14
adopted their home state of Connecticut.
01:15:16
and many many more to come.
01:15:19
>> And and and look, it's going to be super
01:15:20
easy for anybody to add a 100 kids in
01:15:24
your neighborhood, adopt a zip code,
01:15:26
adopt a school district, adopt a town.
01:15:28
>> It's going to be amazing. Give it up for
01:15:30
one of the great entrepreneurs of our
01:15:32
time and an incredible pledge.
01:15:35
I'm going all in.
01:15:51
I'm going all in.
