This discussion delves into the transformative potential of Elon Musk's "Terraab" announcement, exploring its implications for AI compute, space exploration, and the future of humanity, alongside advancements in autonomous transportation and the accelerating impact of AI on various industries and society.
Mind Map
Zum Vergrößern klicken
Klicke, um die vollständige interaktive Mind Map zu öffnen
Without question, for me, the number one
story this week was Elon's announcement
of the Terraab.
>> This is the most important endeavor in
human history by far.
>> In order to understand the universe, you
must explore the universe.
>> He's basically building a galactic
factory. On the left is 20 gawatt. It's
the current global output. And just the
audacity of Elon's vision, it has
tremendous geopolitical implications. As
we discussed on the last pod, this could
either accelerate or more hopefully
mitigate World War II and a Chinese
invasion of Taiwan.
>> We're going to need all the compute we
can create. In fact, I'm actually kind
of worried a self-driving car uses up
basically a full GPU. When is it going
to become illegal for humans to drive? I
think the thing that would make it later
is purely the shortage of chips. Like
the technology will be there and the
demand will be there long before the
chips are there. Figure out how to do
more compute with less silicon. for this
exact use case and you'll be an instant billionaire.
billionaire.
>> I mean, we're heading towards a hundred
trillion dollar company, maybe the
largest, most important company on and
off the planet. Can we get there?
>> Now, that's a moonshot, ladies and gentlemen.
>> Everybody, welcome to Moonshots, another
episode of WTF. Here with my incredible
moonshot mates, DB2 in Boston. Uh, AWG,
looks like you're on your home base as
well. I am, but without my saucer
separated Enterprise 1701D behind me.
>> Oh, yes. I've got I I contracted one of
my boys to create finally finally Lego's
come out with a Star Trek uh you know,
Lego set. I'm tired of all the, you
know, Star Wars Lego set. So, yes, 1701D.
1701D.
Uh, and it does do a saucer separation,
but I'm not going to try it right now
because probably disaster may follow.
And of course we have Dr. Exo Salem at
his normal location at JFK. Sem, how you
doing, pal?
>> Sem is gone.
>> Oh, okay. Well, so much for that.
>> Yeah, that's as you know, listen, we try
to be mobile. We're all dedicated to
this podcast, but uh let's continue on
because the singularity is not going to
wait. So today, uh we're working to get
you future ready. a little bit of a
format change. Uh we're going to be
having some deeper conversations about a
more limited number of subjects still
covering the news that's breaking right
now and there is a lot. But our mission
is to get you excited about the
abundance that's coming. Show you the
opportunities that are coming to you.
Whether you're an entrepreneur, an
investor, a student, a parent, and
really, you know, this is the time to be
paying attention to the supersonic
tsunami, the most important tech in the
world. And hopefully this is your number
one uh podcast on AI and exponential
tech as well.
>> Honestly, Peter, there's more in here
than ever before. It's it's bundled into
themes that we can discuss, but uh if
you just look at the raw news story
count, it's it's as you would expect
exponentially exploding. Uh, you know,
our goal all of us is to make sure that
as we're talking about this on on
Moonshots that it's meaningful to the
listeners, gets you excited, gives you
context, helps you think about this in a
different way. Um, let's jump in.
Without question, for me, the number one
story this week was Elon's announcement
of the Terraab. Uh, he's basically
building a galactic factory. uh think of
this as putting all the parts of his
Lego puzzle together uh in an
extraordinary fashion that is going to
create massive capabilities. So uh let
me hit these uh these quick points and
then we'll jump and discuss it. So the
terraab is an objective across Tesla,
XAI, SpaceX to build 1 terowatt of AI
compute per year. To put this in
context, uh the global output today is
20 gawatt of AI compute. Again, we're
measuring AI computation in terms of
power, not just chips anymore. Uh so
Elon wants to build 50 times the current
production rate of the planet. Uh he's
building two kinds of chips, an edge
inference chip for robots and cars, but
also a high power rad hard for his space
uh Dyson sphere that he's coming online.
The fab is in Austin. Uh, and it looks
eventually like a 100 million square
feet of capacity. Uh, one terowatt in
the near-term near-term, you know,
singledigit years. Uh, long-term a
pedawatt gets you uh only there from
lunar mass drivers. Simil has joined the
story. Hey, Seem, good to see you, pal.
>> Hey, folks. Sorry, I'm bouncing around a
bit, but I'm here.
>> All right. And which terminal at JFK are
you today? Where are you going?
>> Uh, I'm flying to Brazil for 40 hours.
>> Of course you are. Of course you are.
You're a probability function on planet Earth.
Earth.
>> So, just to put this in context, check
out this chart on the left is 20 gawatt.
It's the current global output. And just
the audacity of Elon's vision. Uh, a
thousand gawatts or a terowatt uh is his
objective. I mean, you know, one thing I
heard him say is, "Listen, I've been
going to all the chip manufacturers out
there and saying, I will pay you for as
much production rate as you'll give me.
I I don't want to compete with you, but
give me more, more, more." And of
course, none of them are moving at Elon
speed. And so he said, "Screw it. Uh,
I'm going to go and build my own
production facility." And not exactly
what he did in the launch industry,
right? just lapped the entire existing
launch industry and the autonomous car
and electric car industry. He's playing
his playbook over and over again. Um
before I get into this data stats uh
comments, Dave,
>> well this is the most important endeavor
in human history by far because it
unlocks everything else and uh you know
no great surprise that he's announced it
at the scale that humanity needs it but
the specifics on how you're going to
actually physically do this are unknown
because there are fundamental
constraints to the number of ASML
machines EUV machines to do this and and
many many this is the most complicated
product ever made by humanity and the
supply chain is like like you know it
makes cars look like child's play. So uh
uh so he announced the mission. It's the
right mission. The scale is crazy. It's
you know we estimated this on the last
podcast at 50x all current productivity
of chips and I I guess our estimate was
dead on. So, so we got that part right
and he did allude to it last summer when
we were meeting with him. And um what
what I was most curious about is how he
was going to announce this and attract
all the talent that he needs without uh
irritating Samsung, you know, because he
signed a $16 billion deal for for
production with Samsung, which is more
like 45 billion if it is going according
to plan. And um I guess one of the cover
stories here is well these chips are for
cars and they're also for space. They're
hardened for space. So they're not like
the other chips,
>> but but he he said, "I will buy
everything Samsung can offer me,
>> but you're not offering me enough. So I
will still build all these chips and I
will still buy everything you want to
give me." You know, one thing I love and
he pointed out in his Austin Fab is that
it's full vertical integration under one
roof so that he can run rapid iterations
on chip design. That's impressive. One
thing in our in our Austin podcast when
we were talking to Elon uh I asked him
point blank uh you know TSMC is being
way too conservative in terms of their
production of chips. They should be
10xing their fab manufacturing. And he
said well you know they're you know the
the industry is cyclic. You know maybe
they're being conservative intelligently.
intelligently.
Which is hilarious in hindsight if you
go back and listen to that audio because
in the back of his mind he's like well
I'm going to build something 50 times
bigger anyway. So, but uh yeah, it is
crazy that Samsung uh Intel and TSMC
are not racing to build, you know, 10
20x more production. So, Elon of course
is well, he's going to do it instead. C
can I show you guys some calculations
that I found just extraordinary here? um
listening to the presentation he gave uh
48 hours ago, you know, his target is 1
terowatt of compute per year in orbit.
Uh he said mass to orbit 10 million tons
per year. Uh we're talking about an
average satellite uh his next generation
Starlink at a ton. Long story short, in
order for him to launch that much
capacity, it's 274 launches per day on
Starship. It's a launch every 5.3
minutes, which of course he says,
listen, in the airline business, that's
normal. But just the audacity and the
level of uh of you know, thinking that
Elon takes on uh is amazing. Uh AWG, you
want to you want to jump in?
>> So many thoughts on this. Well, first of
all, I think the elephant in the room is
if Elon can indeed ramp up capacity for
the terra fab in call it the next 5
years, which is the time scale that's
being tossed around. It has tremendous
geopolitical implications. As we
discussed on the last pod, this could
either accelerate or more hopefully
mitigate World War II and a Chinese
invasion of Taiwan. If you look at the
the moon aspirations, the the lunar
aspirations for not a terowatt but a
pedawatt of from the moon. I if you do
the back of the envelope arithmetic for
what would a a pedawatt of GPU compute
that comes from lunar mining take you
run the arithmetic comes out to be
approximately 3100,000
of the lunar mass. So a pawatt coming
from lunar mining with electromagnetic
launches from the moon is starting to
have a material impact on the mass of
the moon. So this is
>> that's just one big one big crater dug
out of the moon.
So that that's that's a pedawatt as we
scale of course to an exowatt of compute
and because why not then at that point
we're talking something like 3% uh of of
the moon's mass and this is you when
people think I'm joking when I talk
about disassembling the moon or the moon
had it coming. It this certainly paints
a portrait. The moon did indeed have it
coming and it the moon is is slated for
disassembly to to build the Dyson swarm.
This is what it looks like. I I think
more broadly there are other sort of
secondary implications really
interesting that this is a joint Tesla
XAI SpaceX maneuver. And many folks have
speculated over the years, wouldn't it
be wonderful if all of Elon's industrial
ecosystem came together into one
singleton? This is sufficiently cruxy
with 20% of its production slated for
Tesla and 80% slated for SpaceX that
this starts to look a little bit like
maybe a cornerstone for some grand
unification of all of Elon's projects. I
tell you, Alex, we talked about on a
previous podcast the idea that, and Elon
said this, we'll see the first hundred
trillion company. And when we look at
the numbers here, I want to show another
set of calculations I did on what might
the Terrafab be worth in the ecosystem.
I mean, we're heading towards a hundred
trillion company. And can we get there?
And at the end of the day, my I don't
know how you guys feel, but the Musk
World ecosystem here looks like it will
lap by an order or two of magnitude uh
what Nvidia's done. Um it may be the
largest most important company on and
off the planet.
>> Yeah. And I I don't think Elon wants to
unify all of his projects just for the
sake of having one unified company. I
think he wants to unify the the capital
raising and the capital leverage with
this massive multi-t trillion dollar IPO
and the massive joint mission unlocking
an unprecedented amount of capital which
is what it's going to take to do these fabs
fabs
>> in parallel at this scale because that's
what that's the thing that's holding
back Samsung and Intel and TS actually
could do it. He needs 25 billion
initially uh to turn on the terra fab
and and get it, you know, get the
buildings started, so to speak,
>> right? I I saw that in the analysis, but
25 billion is is just one fab
>> and here we're going to 50x the US or
the world production. 50x the world
production. So, he needs 50 of those $25
billion investments to to achieve this mission.
mission.
>> Yeah, there's there's a lot to do. I
thought I had two or three thoughts. I
mean, one is like talk about patron
saint of exponentials, right? like this
guy stinks at scales that very few
people do and it sounds incredible. It's
classically the future of anything he's
looking at looks vertical and the path
looks like flat and boring. Um what I
what I thought was great was this is
like amazing exo logic cuz these going
exponentially at the bottlenecks you you
stop competing. You're completely you're
just redefining the game and you're
challenging anybody to dare to to come
with you. I think that's like the
amazing uh part of this. The launch
cadence is is unreal every 5 point odd
minutes and I think that's exactly
right. It forces the operating model to
completely change and it forces
everybody to rethink that including all
the engineers and all the infrastructure
etc. because this is not any kind of
normal industry. Um one thing to point
out is that his predictions on timing
tend to be about 15 to 20% accurate. So,
you know, but but it doesn't matter if
he's if it takes him three times as
long, who the hell cares? Like the fact
that he's thinking at the scale and
he'll get there is the fact that he's
planting up. Yeah.
>> You shoot for the stars and if you get
to the moon, who the hell cares? You've
gotten somewhere amazing.
>> That's AWSG's plan. So, listen. Here's
the next question. A rapid scheduled
disassembly of the moon, I think, is
Okay, good thing I don't wine.
>> Hey everybody, you may not know this,
but I've got an incredible research
team. And every week myself, my research
team study the meta trends that are
impacting the world. Topics like
computation, sensors, networks, AI,
robotics, 3D printing, synthetic
biology, and these metatrend reports I
put out once a week enable you to see
the future 10 years ahead of anybody
else. If you'd like to get access to the
Metatrends newsletter every week, go to diamandis.com/tatrens.
diamandis.com/tatrens.
So a a terowatt of compute per year if
physically achievable. Um is this
aspirational? What time frame? You know
he says 5 years. Uh that's still 50x is
is crazy. But if he's able to achieve
that you know what happens to the you
know to the terrestrial data centers and
to the investments made in terrestrial
data centers. Uh you know gentlemen
questions on that. Oh, every every chip,
every investment in power and data
centers is going to pay off
tremendously. They won't cannibalize
each other. We're going to need all the
compute we can create and so much more.
In fact, I'm actually kind of worried
that the self-driving car is going to
get cannibalized. You know, driving a
self-driving car uses up basically a
full GPU and by the end of this year, a
full GPU can also do brain surgery or it
can discover new math or new physics.
And it's not clear that driving somebody
around is going to make the price cut uh
as the demand for compute goes to near
infinity. So I think the terrestrial
data centers are going to be critical
for national security for every country
in the world because if something goes
wrong in space, you got to fall back.
Your whole society will be running on
these GPUs. You can't you can't have an
outage. So anyone investing in this does
not have to worry about one thing
cannibalizing the other.
And the other thing you're going to see,
I think it's later in the deck, but all
the different um process nodes, all the
fab process nodes are going to get used.
Even the older ones, the 3nmter and the
5nanmter are going to be running full
throttle now, even if they're not as
good as the new 2 and 1.6 nanometer. It
doesn't matter. We need all the compute
we can crank out. So, yeah, that it's
it's going to be just an all hands-on
deck race. and Elon is is just
documenting the up upper bound of what
we can achieve as a as humanity.
>> My expectation my expectation would be
that in the process of I think Elon
likes to call it production hell in in
the process of production hell for
realizing the terrafab over the next 5
years. I strongly suspect we're going to
discover some new not semiconductor
physics but more material physics and
process engineering. It it seems
improbable to me that Elon will just
build the Terrafab based on the existing
stack as say TSMC did of ASML plus the
existing optics plus all of the the
conventional semiconductor processing
techniques. If he really is looking to
disrupt the space, he's going to want
much more disruptive unit economics. So
maybe some of these technologies for
semiconductor production and and
fabrication that have been waiting in
the wings for their right time in in the
light. Maybe uh this is purely
speculative. Maybe he'll look for
example at alternatives to
photoiththography that we it's not like
as civilization we don't have lots of alternatives
alternatives
>> and he's got
>> and he's got in super intelligence to
help him get there to design these systems.
systems.
>> We're going to need the humanoid robots
to be building the fabs. We don't have
the workers. >> Salem,
>> Salem,
>> I think this is a really important point
being made right now. Alex, thank you
for this because you know, you think
about the secondary technologies and the
benefits like like all the carbon fiber
that came from the space industries and
cascaded down to everyday life. The
secondary inventions that will be needed
here will be massively beneficial to humanity.
humanity.
>> Well, this this is one of the most
exciting things in tech history. In
fact, the most exciting thing in tech
history is what Elon was talking about
in Austin is laying down single atoms
using some kind of a self-organizing
process. And I feel like Alex is exactly
right. Something will get discovered in
the next year or two using current LLM
AI running on GPUs that will then
dictate a very different non-lethography
future. But it'll probably five or six
or seven years before those start
getting manufactured at the same scale
as lithography. Uh but it's super
exciting to to watch.
>> Dave, I asked Claude to give me an
estimate based upon all the data that we
got from Elon during his uh his talk on
the value of future tariff abs right.
And so it makes the point here that
initial capex as you said uh Alex you
know it's 20 25 billion but the real
capex for build out is going to be on
the order of $150 billion um at a
minimum it may be a half a trillion uh
there and then he talk and then the the
model here looks at what's the annual
cost savings for his captive
opportunities right so if he's building
the chips himself and he's putting them
in Optimus and in uh cyber cabs and then
there's external revenue you and then
there's an implied enterprise value and
you know it's on the order of a trillion
to multiple trillions. TSMC is valued at
1.7 trillion and what we talked about
last week was that Terraab is expected
to produce on the order of 70% of TSMC's
output. So yet we're layering on another
multi-t trillion dollar opportunity.
These numbers seem low to me. If you're
really generating Yeah. you if you're
generating 50x Yeah. 50x the total
output of AI chips on the planet. >> Yeah.
>> Yeah.
>> Uh you're operating category.
>> This is operating as if it's still a car
company and it's not.
>> Well, not only that. I mean, just right
out of the gate, this is not just doing
what TSMC does. This is TMC plus Nvidia,
>> you know, and Nvidia is worth four and a
half trillion.
>> But I mean, even that's ridiculous as an
analysis. We're talking about 50 times
the production of the world's current
compute. Uh so you know out of the gate
you would take TSMC plus Nvidia and
multiply by 50 to get you know a
starting point for an estimate. So this
is off by
>> over an order of magnitude well over
>> any of you any of you concerned about a
monopoly here? >> No.
>> No.
>> If you're following the the prediction
markets for the SpaceX IPO, this is
already starting to get priced into the
SpaceX IPO. So SpaceX IPO was originally
going to be 1.5 trillion. Now prediction
markets favor two plus trillion dollar
SpaceX IPO pricing in the SpaceX portion
of the Terrafab. So in in some sense
again I'm not quite clear on what the
governance structure is here is going to
look like and and how clean it's going
to be but to the extent it falls mostly
in the SpaceX bucket. the SpaceX IPO,
not an investment advice obviously,
could end up being, as you say, SpaceX
plus Starlink plus Nvidia plus ASML plus
TSMC all rolled into one. I I just as a
as a piece of advice for entrepreneurs
out there, um just understand the level
of audacity that Elon is looking at.
He's building in in a um sort of
multiple orders of magnitude beyond
anybody else. and from his first
principal thinking he's looking at where
are the blockages for my growth and we
had this conversation people are not
generating enough chips I need to build
a chip fab and then he doesn't just go
out to say I'm going to buy Intel or
build a chip fab equivalent to what TSMC
is building in Arizona no if I'm going
to build a chip fab I'm going to build
something that is 50 times bigger than
the world supply um amazing
>> well so he's thinking two moves ahead in
the big chess game and you know two
moves used to be 20 years now it's six
years but but two moves ahead of
everybody else and this came up Alex and
I were talking earlier this week about
fision energy and you know Elon doesn't
talk much about fision energy why well
because he's visualizing solar in space
and and you know solar on earth is a
great stepping stone to solar in space
and it requires panels and batteries and
cooling but it doesn't require turbines
and and fish and reactors so he can skip
a couple of hard steps and go straight
right after the next move in the big
chess game. So, it's really interesting
to watch how those those timelines have
inverted. You know, the Dyson sphere is
now right on our radar and even Google
is talking about it, which means
everyone's talking about it. So, that
came to the forefront really just in the
last month or two. And so, the whole
timeline of humanity got shifted. So,
the Dyson sphere will come before anyone
even figures out how to get licensed for
a fision reactor.
>> Can I be cynic? Can I be cynic just for
a second?
>> Um, yes. The plans are ridiculously
grandiose and if any of these we can
achieve them with Elon but curious that
the timing of this is just leading into
the IPO to get everybody excited about
things. So that would be the cynical
view but I still I still just love the audacity.
audacity. >> Yeah.
>> Yeah.
>> Well I think See your your first
observation was dead right. If you shoot
for the Mars and you end up at moon
you're still way up. So I want to ask
the I want to ask the question again
that AWG said nope which is
>> monopoly concerns. Do you believe I mean
if he's really generating you know
>> I'm not worried about it because when
you have an MTP which he does you're
basically operating on this massive
mission you may have ethical issues here
and there but generally the trend is so
positive and so beneficial for humanity
who the hell cares. Well,
Well,
>> well, there's always another there's
always another person,
>> somebody who's like Brendan Foody,
maybe, you know, somebody like that
right now is like, "Wow, I'm going to do
this, too." And, you know, it just the
way it is. And that person, we don't
know who they are yet, but they'll
emerge. And, you know, you can't exist
in the US without antitrust action if
you don't have a competitor. So, Elon
will invite that competitor, whoever it
is, and and it'll be great.
>> Tons of competition. If if if we label
this monopolistic behavior, then don't
we have to label everyone with an MTP a monopolist?
monopolist?
>> Market dominance. If you if you get
there, then that's true.
>> But he hasn't even picked a real estate
site yet for the the terapab. It doesn't
he hasn't even picked a final location.
I I think it's way too premature to
declare this monopolistic behavior being
so ambitious as to build the Dyson
swarm. We're going to have multiple
Dyson swarms.
>> I'm with I'm with you.
>> Love it.
>> I'm with you.
Well, Google Google isn't going away.
You know, Google has 300 billion dollars
of revenue, a hundred billion plus of
cash flow, their own chips in design.
Uh, they have all everything except the
rockets. So, uh, Google's not going to
go away as a
>> Well, and don't forget Eric Schmidt,
Eric Schmidt is trying to bring
relativity space online so that the
rockets are at least part of the Google family.
family.
>> Oh, you know, let me let me just close
let me close this.
>> There's plenty of them. Let's not forget
>> one thing that's important here is every
time there is a constraint uh the judo
move is to realize it's a massive
opportunity and so this is an abundance
story once again this is a a massive
increase in abundance of AI compute uh
beyond what anyone was speaking about
just a week ago. So
>> I'm I'm glad he's focused on this and
less on the politics.
>> Yes, I'm in. All right, here is our
second conversation story, one that I'm
excited to have with my moonshot mates.
It's about the future of human
transportation. You know, robots are
getting their driver's licenses, flying
cars are taking flight. Uh here's some
of the data and uh I want to go deep on
this because I want everyone listening
to understand how this is going to
impact how and where you live, how you
commute, every aspect of our lives. So,
Whimo uh hit 170 million fully
autonomous miles uh equivalent to 200
human lifetimes of driving with 92%
fewer serious crashes. So, a significant
reduction uh in crashes. At current,
they've got 3,000 vehicles in 10 cities.
Still early, right? Uber has now
invested one and a quarter billion
dollars in Rivian with plans to deploy
50,000 fully autonomous robo taxis.
Here's a look at Whimo versus human
drivers. Um Whimo is doing an
extraordinary job of of saving uh not
necessarily lives but saving crashes uh
you knowus 92%. So I think cyber cab
we've seen incredible data like this
also on full self-driving from from
Tesla. Check out this image. This is
Joby Aviation. Joe Ben started this
company. It started velocity 11. Took
that money after he sold it. Uh he was
partnered with Rob Nail uh See and uh
Join started Joby Aviation God knows
over a decade ago and here it is flying
over the Golden Gate Bridge in San
Francisco. It's a beautiful image. This
is a EV tall electric vertical takeoff
or landing. It's a name that rolls off
the tongue onto the floor. I'm calling
them flying cars cuz that's what they
are. And here's what's going on in the
EV12 world. I think really important.
So, Joby uh just is now in testing for
its first FAA conforming aircraft.
meaning it's it's demonstrating to the
FA that it can build a reliable design
over and over again. It just had
demonstration flights on the Golden
Great Bridge. Joby and Uber announced
Uber Air powered by Joby. In fact, when
uh when Travis left was before he left,
he had created something called Uber
Elevate and they were doing the earliest
work on flying cars. uh I keynoted their
their talk there but Uber Elevate got
sold to Joby and now Joby and Uber have
a partnership. The other company in the
United States that is the competitor to
Joby is called Archer Aviation. Uh they
have a beautiful aircraft called
Midnight and they're the first company
to achieve 100% FA acceptance of its EV
tall uh aircraft uh means of compliance.
Long story short, we've been waiting a
long time and flying cars are almost
here. We're going to start to see them
operating in the US in the next uh 18
months. Should be here in LA in 2028 in
a big way. Uh and so here are some of
the conversations, gents. Um first off,
when is it going to become illegal for
humans to drive? Seline? >> Yeah,
>> Yeah,
>> I think you know you'll start with city
centers, right? and you'll be illegal to
drive in city centers and it'll slowly
broaden out from there. I think what's
I'm I'm the flying car is the most
exciting technology for me personally
given that I'm commuting to airports a
lot that I could ever ask for. So this
is this is 10 years later than I wanted
it to finally it's finally happening.
What I like about these are not
transportation stories. This is full
urban redesign is what the narrative is.
This essentially you make land becomes
abundant. Now land has always been
scarce. real estate has been scarce.
Real estate becomes abundant. If you fly
across the US, it's empty, right? And uh
we've talked about this statistic just
around uh between Toronto and Chicago
airports, there are 10,000 islands and
lakes, right? So, we do not have a
scarcity problem. We have a mobility and
accessibility problem. So, I think I'm
super excited by this particular model.
I've got my two years for um us to get
to full autonomy before Milan gets his
driver's license. He's 14 right now. So,
I'm I'm pushing hard on this race.
Mostly he wants to get it to get away
from parents, but that's fair. Um, so
we'll see what happen. But, you know,
you compress travel time. You repric
real estate. This is such a huge thing.
And I got to just shout out to Joe Ben
just because it's hard to build a
hardware platform like this and to do it
over a decade with all the inevitable
regulatory and market structures against
you and infrastructure against you. This
is a huge like a Nobel Prize in patience here.
here.
Yeah. Incredible. Uh Dave, you were
going to say,
>> "Oh, the EV tolls are going to move very
quickly. Uh because they don't they
don't run the risk of uh you know,
crashing into houses like cars on a
self-driving cars on a road do. There
are going to be autonomous on birth.
That that's the new thing. EV tolls have
been in the works for years, but the AI
that makes them self-flying,
self-driving, and super safe is here all
of a sudden now."
>> True. But the first airplanes are going
to be piloted, right? There'll be a
single pilot, four passengers in the
back. The the the goal is rapid recharge
at the vertaports when they land
recharge probably an average length of
flight of under 10 km. I think uh you
know going from Santa Monica where I am
to the Dodger Stadium and avoiding uh
the 10. But autonomy will come with
enough with enough data and enough demonstrations.
demonstrations.
>> Wait, why won't they why won't they
fully autonomous from the get- go? I
mean, it's
>> because it's called the FAA rega.
The FAA is not happy until you're not happy.
happy.
>> Yeah, that's exactly it. The the
manufacturing of these wants to happen
right away. And the uh the AI command
and control is being worked on for the
car, not for the EV hole yet. So,
there'll be a couple year very short
period of time in my opinion, two years
or so because you know in in the Middle
East they're already doing the
self-driving self-flying version of
this. So should be very short window
where people get to fly these. Uh you
know the bullet here though is when does
it become illegal for humans to drive? I
think that's going to happen very
quickly as well. Uh very similar to
indoor smoking um or drunk driving.
>> There's a tipping point where a lot of voters
voters
say, "Wait, you're putting my children
at risk with your crappy driving."
>> That that's ridiculous. We've got data
and proof here that the the self-driving
is 90% safer, soon to be 95 97% safer.
And and you know, the human tragedy that
comes from car crashes is is
unbelievable. It's shocking. And so
>> for under 5-year-old under 5-year-old
kids, it's the number one cause of death.
death. >> Yeah.
>> Yeah.
>> It's accidents. Yeah.
>> Oh, and and you know that it's
devastating to families, too. It's just absolutely
absolutely
>> at least in the first world. Yeah. Well,
there'll be a there'll be a TV ad
campaign probably three, four, five
years from now with lots of ugly images
in it. And then there'll be massive
amounts of voting and then people will
say it's it's inconceivable that you
would drive on a public road that's
inhumane. Go drive on a test track.
That's fine. Maybe, you know, some
country roads, that's fine. But, uh, but
no way. Don't put my children at risk.
So, I think that's going to come as soon
as we have the manufacturing for the
cars themselves. But I I think the thing
that would make it later is purely the
shortage of chips. Like the technology
will be there and the demand will be
there long before the chips are there.
>> So if you want to unlock this as an
engineer, figure out how to do more
compute with less silicon for this exact
use case and you'll be an instant billionaire.
billionaire.
>> Alex, your thoughts?
>> I like this format, Peter. This is like
an internal AMA. So I'm going to try for
a lightning round on on all of these.
Oh, leave some room for leave some room
for the rest of us. Let's take it one at
a time.
>> One at a time. At what point does it
become illegal for humans to drive? I
think never. I I think we'll simply
redefine driving to represent higher and
higher levels of abstraction. So, right
now with like FSD14, you tell it where
you want and if if you're running the
most recent subversion, you can have a
conversation with Grock and you can do
minute steers along the way. I think
that notion will get refined such that
driving gets redefined to be
sufficiently abstract that it's always
safe for pedestrians. It's always the
human on the loop of the AI driver. So,
it's effectively a human machine hybrid,
if you will, that has the safety of the
machine, but makes the human feel like
they're in the driver's seat still. I I
said I said this with uh when Dar was on
stage with Sim and myself. I said,
"There's a version in the future of
self-driving where you're driving and
you can push the car as fast and as hard
as you want, and the car knows its own
limits. It knows the traction of its
tires. It knows the road surface, and it
prevents you from doing something
stupid, but you're in control of it 99%
of the time, but the 1% where you're
about to do something that will destroy
you, a person, or the car, it stops you."
you."
>> Exactly. I think the f the future of the
accelerator pedal isn't the accelerator pedal.
pedal.
If you use FSD, it's turning the driving
mode up to Mad Max. That's sort of like
an abstraction of
>> By the way, that's that's all I use is
MadMax. And it still doesn't go fast
enough. So, I'm not surprised.
>> There used to be an ad says, "Friends
don't let friends drive drunk." And so,
you can just keep that ad and drop off
the drunk part and go, "Friends don't
let friends drive." Period. So, all the
all the messaging is there.
>> All right. So, so AWG, why don't you why
don't you kick us off on question two here?
here?
Okay, question two. With Uber partnering
with Whimo and a bunch of other names,
will the Cyber Cab be able to compete?
Uh, I think we mean compete here. Yes,
of course. It's going to be very
competitive market, period.
>> Yeah. And I love the fact that this is
driving us towards abundance, right?
This is driving us towards UHI. If
you've got a dozen companies delivering
autonomous uh uh vehicle services in
your city, they're going to be competing
against quality of service and price and
just bringing the price down to a
minimum amount. Now, one of the things
that's interesting about Cyber Cab is
that that's going to be priced at
probably 30K is what roughly what Elon's
announced and he's going to allow people
to buy it. So, uh, you know, one of my
goals is can I buy, you know, 25 or 50
of them here in Santa Monica and own
them, but have them going out and and
basically generating revenue for me and
for my my cyber cabs. I'm sure they'll
have some level of uh of personhood by
then, Alex. Uh,
>> obvious I I I never would have guessed,
Peter, that that your next gig would be
as a cabbie, but the singularity makes
for Strange Bed Fellows.
>> Fleet owner. Fleet owner. Um um any thoughts?
thoughts?
>> I think the big the big impact for me
when I see this is the complete collapse
in the market structure of cars. Today
we make close to 100 million new cars a
year and they sit empty 94% of the time.
So even if you drop that by 50% utility
um u you you basically collapse the need
for half the car industry instantly. And
these cars maintain for a long time. the
lifetime should be near infinite. My
Tesla Model 7 2017 should it'll go a
million miles. There's nothing wrong
with that car. So, this is going to
completely change the nature. Car
services, car uh maintenance people,
like the complete industry gets
reshuffled from the bottom up. >> Yeah.
>> Yeah.
>> Yeah. Well, think about the implications
of that too, Sem. Right now, if you take
an Uber from SFO to San Fran for like
200 bucks or whatever the hell it is, uh
it's almost all driver costs. So even
before you shrink the number of required
cars by a factor I think the estimate
was 5x
>> even before that savings is it 10x so
then then but the driver is already the
majority so you take the driver out of
the loop so the cost of that ride should
go down you know at least 10x because
the car is coming down 10x and the and
the driver is more than the car anyway.
>> I think the number I've seen is 20
between 10 and 30 cents a mile.
Yeah, the the I've seen it as four to
fivefold cheaper than owning a car. The
the next question I want to ask and um
and offer my points of view is I think
one of the most important one for our
listeners. This is going to have a
profound impact on your real estate
holdings, where you live, what you do
with your real estate. So, if we have
autonomous vehicles, uh and we've
reduced the number of vehicles on the
road by 10x, let's call it that. Um, and
these vehicles don't need to park. Uh,
again, my my current version of this is
I get up from the breakfast table with
my family. I walk towards the front
door. My AI knows that I'm moving to
open the front door. It knows where I'm
going. It's ordered an autonomous
vehicle, what I call automatically, for
me. I haven't had to ask. And so, all of
a sudden, you know, we had in our home
here, we had a threecar garage. We
already converted one of those garages
into an extra bedroom. Um, the other two
garages have become effectively storage
and I'll build out, you know, probably a
workout gym and so forth. I think the
idea of a garage, a personal garage in
your home goes away. So, start thinking
about what are you going to do with your
garage space? What are you going to make
it into? Because you're not going to own
a car. You might want access to a car,
but most of the time, do you really like
driving? I mean, when you get into an
Uber, do you ask the Uber driver to get
out and let you drive?
>> So, right. And, you know, this part of
the conversation is incredibly
actionable for all of our listeners.
That doesn't rise to Alex's level of,
you know, like change the world
tomorrow, but it really matters to
almost everyone who listens. Uh,
everything Sem said and Peter said is
dead right. If you're young and you're
you've got a job and you're living in a
city, which is 60% of you, um you might
not want to buy in the city. Uh keep
renting and look for something that
becomes your second home later in life
that's in a beautiful spot. >> Yes.
>> Yes.
>> That's a little harder to get to,
>> that is going to be incredibly coveted.
Imagine a world where there's 10x more
wealth about 2034, 2036.
And this is a spot that anyone in their
right mind would want. And actually, uh,
if my wife is listening, close that
transaction that you kicked off this
weekend, even if you have to pay a
little more. Um, but yeah, that's what
you that's the life plan you want
because accessibility, not not just
getting to it, but also delivering
things to it. Like, you know, your
Starbucks, your Dunkin Donuts is going
to come by drone. >> Absolutely.
>> Absolutely.
>> So, that that changes what you want.
Think about it. And there aren't, you
know, we have a huge country like Sem
said, but the really great spots are
limited. So, really do your soularching
and look look for that thing. Buy near
an airport in a city.
>> Island real estate is going to become,
you know, 10x 100x more accessible. That
will drive the value up. And in a
downtown LA, you know, it's like I don't
remember the figure. It's like like 30%
of the black top is parking. All of it
gets released new
>> in LA. 60 60% of the land area has
parking spots in Los Angeles.
>> That's crazy number.
>> That's insane. Well, that becomes that
becomes gardens. It becomes Greenland.
It becomes parks.
>> That's incredible.
>> And think of the unbelievable space we
use in in stadium parking lots, right?
Acres and acres and acres of frozen
stuff. So, we'll have to rethink uh you
know, tailgating and everything. There's
so much available uh you know business
opportunity here if you can think ahead
of what you will do with that and if
you're in the parking garage in business
you got to think ahead as well. >> Yeah.
>> Yeah.
>> A couple of other second third order
implications if I may. I mean so we've
already touched on I I think the more
obvious ones parking garages etc need to
be reprogrammed for other purposes.
Another I I think borderline cliche
implication of full autonomy everywhere
is the respread of suburbia. Why invest
so much in urban center real estate if
you can be effectively connected to an
urban center or even not even need an
urban center if AVs take you everywhere.
Basically a virtual subway from from
anywhere to anywhere. So
reuburbanization if you will at least in
relatively low population density
countries like the US. I think these are
pretty cliche implications. A a less
cliched implement uh implication in my
mind is what if we just take this trend
and extrapolate it fully to completion.
What happens? I think there's a future.
I I put out a request for startups
around this idea of why not just create
autonomous wnebos. The the equivalent of
having entire office buildings that are
themselves autonomous vehicles. One
could imagine living in an autonomous
vehicle. It's all part of a social
network. When you need to take an
in-person meeting with someone, your two
AVs are part of the social network and
they connect and synchronize all of your
locations. So maybe you're in Boston in
the morning, but you're in Washington DC
in the evening. This is all handled
automatically to synchronize your
calendar with your AV location and then
it's a sleeper car and then you're in
Chicago or wherever the next day. So you
become you become you become to join you
>> humans become internet packets that are
being routed by the autonomous system.
>> Yes. Yes. Love it. Love it. Love it.
Love it. Um you know the EV talls uh I
it's taken a while. There is still a lot
of doubt people have about EV talls. Um
you know the opportunity we have uh is
going to be limited by the size of these
being able to land locally. So there
needs to be sort of local hub and spoke
vertaports um you know somewhere within
five minute driving and gluing these all
together and that's what Uber wants to
do with their platform. So I hop in my
autonomous Uber it takes me without
thinking to the right EV tall site which
takes me to another location 10 km 20 km
away and then uh I'm in another
autonomous vehicle. What I'm missing
from all of this Alex is is hyperloop
right? So I actually joined one of the
first Hyperloop companies. Uh Virgin got
involved. Um it was we raised you know
probably close to $100 million. It
didn't go forward but the material
science of creating Hyperloop. And of
course the benefit for Hyperloop right
now is effectively supersonic travel uh
pointto-oint inner city to inner city LA
to San Francisco LA to Las Vegas. That
one will be busy. Uh, so got to see
Hyperloop on this list eventually.
>> Peter, which do you think you're going
to see first? Like in in pra in practice
for say New York to Los Angeles, do you
think you're going to see Hyperloop
first or do you think you're going to
see rocket cargo first where you hop on
an Elon Starship go up, go down?
>> You know, I I've thought and looked at
uh pointto-point rocket travel, and it's
a tough it's a tough thing. the energy
dissipation cuz you're basically going
uh you're going to orbital velocities
and you're having to re-enter over or
near a city. I guess the version that
Elon put forward was offshore landing facilities.
facilities.
>> That's right.
>> So that you're you know a kilometer
offshore. Uh I think
for one reason rocket pointto-oint
travel because Elon's behind it uh and
because the vehicle exists and they're
going to be launching every point 5.3 minutes.
minutes.
>> That's right. Uh and you know Elon
almost got involved in Hyperloop but
like you said I can't do everything. Anyways
Anyways
here quick ones.
>> Yeah please. Yeah please. one is I think
uh hyperloop will be used largely for
commercial and for container loads
rather than human beings because then
you don't have to worry about geforces
and the safety standards can be lower.
Uh and the second is remember that all
yeah although it takes us like 3 hours
to fly from New York to Miami um that 3
hours on a plane today is way more
productive than it was say 10 years ago.
you've got full internet, you can work.
So you can maybe on sterling,
>> we can we can schedule ourselves now to
do things when we largely want to do
them. So I think that's a huge
opportunity also.
>> So for our listeners, I would love to
get your feedback on this format where
we're going deep on the topic and having
the conversation trying to educate you
about how we think about uh about in
this case transportation or previously
terapab. Our next conversation is the
great reshuffleling. Job loss is
inevitable. The only question left is
what we build on the other side. So,
here are some of the stats and some of
the articles that came out this week
that have us thinking about this.
Goldman says AI could automate 25% of uh
US work hours. Seems like a low estimate
to me. A PWC told its partners, "If you
resist AI, you have no place here. AI
tool yourself or get out." G42 posted a
job listing exclusively for AI agents.
Is this sort of a gimmick? Is it real?
And I love this one. And this come came
from sort of a uh a hit from Jensen.
Companies are now tracking individual
employee AI token usage. And Jensen came
out saying, "If a $500,000 engineer
didn't consume at least 250,000 tokens,
I'd be deeply alarmed." You know who
this reminds me of? This reminds me of
Debeers saying three month salary to buy
a diamond ring, right? Doesn't it?
>> Tokens are forever, Peter.
>> Tokens are forever. That's great. So, I
mean, lit literally Jensen is saying
he's taking the total salary, you know,
of all engineers on the planet and uh
cutting it in half and saying that's how
many tokens we're going to be selling.
And then Perplexity AI won an appeals in
court uh to continue running shopping
agents on Amazon. So uh I'll show one
chart here and then let's talk about it.
So AI could automate 25% of all work. Uh
this is Goldman's chart uh showing uh uh
each of these columns is a different
type of work and I guess the median here
is about 25%.
We've seen this lots of different places
in different versions of it, but let's
jump in. So, See, you work with more
consultants than I I do than anybody
here does. So, what do you think about
this uh PWC telling its partners, you
know, adapt or die?
>> Yeah, I think that's fine, but I think
it doesn't go far enough. And same with
the McKenzie's thing. So the the
calculations I've been running as I kind
of put get this paper organizational
singularity paper finalized is that
you'll be able to run I'm just give me a
couple of days it'll be ready for like
draft and review registering submission
thing. Um the the uh you'll be able to
run a typical company with between 20 or
25% of the employees you have today
because all workflow goes from human to
human to agent to agent. Right now, you
could take on the doomer side and go,
"Oh my god, 80% job loss." But no,
because we'll just end up creating four
or five times more companies and also
for bigger companies that transition to
an AI based workflow is going to take
much longer than uh for a startup or
mid-market uh and therefore there'll be
time for the economy to uh so so I'm
actually suggesting that we will have no
purbation in jobs almost zero. Okay. Now
um uh definitely uh partners who resist
II will have no place. There's also
something to say that consulting
partners have no place in the future
because in the future you have an AI
agent figure out your strategy. Why do
you need a consultant firm? You're going
to need that for more fir implementation
and if they have better agents than you
do. I think that's where we'll end up
with that.
>> Alex, what are your thoughts on these?
You want to pick one?
>> Very difficult. So on the PWC story,
it's very difficult for organizations to
self-disrupt. So if you're a management
consultancy or an accountancy, some other
other
bill by the hourheavy firm, it's very
difficult for for you to willingly
voluntarily transition to an
outcomebased pricing model versus an
input based pricing model. So I I I take
the you'll have no place comment. I I
interpret that as an attempt to
self-disrupt. in practice it's very very
hard to to do that. Uh and the the whole
point of of shumper and disruptive
innovation in general is most of the
most of the uh macro replacements for in
in this case for inputbased actors in
the economy are probably going to come
from other firms not from large firms
self-disrupting. On the G42 story I
think it's actually really interesting.
I looked closely at the G42 job listing
and it it really is a job listing for AI
agents and uh one has to wonder yes like
around the edges they also ask for
details from the developer and what was
used to make sure that this was really
an AI agent submitting itself for I
think it was a marketing job. We are so
painfully close I think to a near future
where there's a sort of reverse
discrimination against humans and where
humans need not apply
end up ends up being an epithet on so
many jobs.
>> Well, you have that already with the
with the PWC partners, right? If you
don't use AI, get out of here. That's
essentially we're getting to that point.
We're halfway there already.
That's that's PWC though which is a
humanoriented business basically trying
to force humans to self-mate at least
from a a unit pricing perspective this
is born AI jobs where humans need not apply
apply >> agree
>> agree
>> you know sem one thing that you and I do
for large companies that I think people
need to understand most large companies
out there are walking dead their
business models will be fundamentally
disrupted in the next two to five years.
And so the question is how do they
disrupt themselves before uh before
someone else does? And the answer is
it's really hard almost impossible. And
so you know what you and I have done
before is invite super talented young
entrepreneurs to come in hear the
company's business model and say this is
how I would disrupt you if I you know
was funded to do it. And then the
company should fund the best of them,
right? And we've done this uh fund the
best of them to actually build a
adjacent company who's intended to
disrupt the primary company and then literally
literally
>> yeah the company design firm the design
firm Ideal actually did this. They
realized that their methodology would be
widely known and they couldn't stop the
leakage of that. So they they picked one
of their crazy partners and said, "Go to
the edge and build build the disruptor."
And he created an open IDO marketplace
of of design ideas. It was amazing.
>> One one caveat to what Peter said there,
the private equity guys are having a
field day with AI automation. And and if
if a company has great cash flow, even
if its business model is doomed in the
age of AI, the profitability is going to
go through the roof in transition
because in the near humans
>> in the near term, yeah, because you
know, an AI can do the job for 10% soon
to be 2% of the cost of the human, you
know, with no labor laws, no overhead,
no, you know, insurance.
If you've got good cash flow, there's an
entrepreneur looking at that salivating
coming to eat your lunch.
>> Yeah. So, what happens is the private
equity guys will come in and say, "Hey,
cash flow, cash cow with great cash
flow. We're going to buy you or buy part
of you and then we're going to AI
business." Uh, and that'll drive even
more cash to the bottom line. And then
we're going to use that cash flow either
as a vehicle to launch new things like
an incubator or to attract that
entrepreneur or to just roll up those startups
startups
>> and you know acquire them back in.
>> So it becomes kind of a centerpiece.
>> By the way, both Anthropic and Open AI
are setting are partnering with private
equity firms to do exactly this. go buy companies
companies
>> and then AI enabled them because you can
do it with just
>> as as if they could do anything in the
world just announced a new hundred
billion dollar fund to do nothing but this.
this.
>> So doing it, you know,
>> they don't have enough money already.
>> Um, Alex, I'm curious or Dave, I'm
curious about your thoughts on the
fourth bullet here that companies now
track individual employee AI token usage
>> and you should have a minimum token
usage per employee.
>> Thoughts? Dave, do you want to go first?
>> I mean, we we already implemented Yeah,
sorry. We already implemented targets
across all of our companies on this and
we're targeting 80% token, 20% salary
and I think that's a really it's very
similar to what Seem said a minute ago.
Uh there's going to be huge amounts of
job disruption in the next two or three
years and then it'll turn around and by
2030 2032 things will be good again. But
but what you want to do is be one of the 20%
20%
that's still there when it's 80% token
cost, 20% human costs because no
employer in the world, including all the
companies I'm the controlling
shareholder of care about cutting the
last 20% of payroll. That's not a
priority at all because at that point an
employee that can improve the efficiency
of our AI, even 1% is worth a lot more
than cost cutting. And so we're in this
foot race now to 8020. Yeah. Jensen's
got a stepping stone here of, you know,
twothirds oneird u token cost, but
that's going to be very transitory.
We're we're racing towardken costs being
much much bigger than payroll. So
immediate step at 50/50,
>> but it's coming soon. Sorry. Go ahead,
>> Alex. Is this the right metric? I mean,
because you can waste tokens. I mean,
it's got to have a different harness,
right? You're you've got to be measuring
something else besides just token usage.
You can waste tokens except in in this
AI abundant era, you can also ask
another AI to look at all the tokens a
given employee used and ask was this a
good use or not or was this just vacuous?
vacuous?
>> So it it becomes the ultimate
self-looking ice cream cone. The the
quote from from Jensen I think is
interesting and it's it's sort of if a
half million dollar engineer didn't at
least spend a quarter of a million
dollars on uh on inputs that ultimately
flow back to Nvidia, I'm deeply alarmed.
So, so there's a little bit of circular
there's a little bit of circularity
there that I I take with a huge grain of salt.
salt.
>> No appears in the diamond ring.
>> That's right. But there's another side
to this, which is the employee side. So,
I I I talked a bit about this in in my
newsletter. At some firms, especially
the Frontier Labs, employees are
actually competing so-called token
maxing to max out their their token
usage on internal leaderboards to see
who can use more tokens than than the
other person. So, it's not just sort of
big brother top down. It's also bottom
up. I can use more intelligence, more
super intelligence than you can. And I
think this is ultimately probably pretty
helpful. Uh to your original question,
Peter, about whether tokens are the
right unit of productivity, I I think
what's interesting is tokens, they're
they don't even have to be the right
measure or right unit of productivity,
but they're the first measurable unit of
productivity. hours are are certainly
measurable. Like you can punch clocks,
but it's not a really good Yeah, it's
useless. It's it's naively measuring
inputs. But tokens where you can
actually like they're introspectable and
they're legible and you can ask you can
spend other tokens to look at the tokens
the the the primary tokens and decide
are these valuable tokens or not. For
the first time we have legible,
defensible, analyzable inputs for
employee productivity and that is a sea change.
change.
>> Yes. Dave, what's the advice here for
CEOs? The advice to CEOs here for you?
>> Um, Alex completely nailed it. Like
worrying about whether the tokens are
being used intelligently or not is not a
problem at all in the real world. So, so
Jensen's metric is perfect. Just measure
the spend on tokens. and then Alex's
insight that you must the most important
actionable thing is make sure you gather
all of the of the prompt stren history
>> for each and every user
>> you can have an AI AI can analyze the efficiency
efficiency
>> analyze that yes exactly and you can say
hey you know I've got 100 or let's put
it realistically I've got eight direct
reports evaluate the quality of the
prompts and the output they have and
give me feedback on which bottom 20% I
should cut or or train up.
>> Well, and really practical advice, if
you use any of the models on Amazon
Bedrock, the grabbing of the prompt
history is already built in. It goes
right into S3 buckets. I'm sure you can
do it elsewhere, but our companies just
be happen to be using it on Bedrock. Uh
so it you literally don't have to build
anything to start doing this. Uh you
just need to grab the data and feed it
into another AI, which you can also do
on Bedrock or you can do on, you know,
whatever. Uh personally, I like using
Cloud 4.6 sex for this stuff. But uh you
just got to close that loop. But but the
key is grab the data right now before people get used to using their own home
people get used to using their own home account or
account or >> or you know something outside of your
>> or you know something outside of your purview. Do not reimburse people for AI
purview. Do not reimburse people for AI that you can't see.
that you can't see. >> Interesting.
>> Interesting. >> Make sure it's on your infrastructure.
>> Make sure it's on your infrastructure. >> Welcome to the health section of
>> Welcome to the health section of Moonshots brought to you by Fountain
Moonshots brought to you by Fountain Life. You know, my mission is to help
Life. You know, my mission is to help you use the latest technologies,
you use the latest technologies, including AI, to not just do your work
including AI, to not just do your work at home, teach your kids, but to help
at home, teach your kids, but to help you live a long and healthy life. I'm
you live a long and healthy life. I'm here today with an extraordinary
here today with an extraordinary physician, the chief medical officer of
physician, the chief medical officer of Fountain Life, Dr. Don Mucalem. Don,
Fountain Life, Dr. Don Mucalem. Don, let's talk about cancer. Uh, you know, I
let's talk about cancer. Uh, you know, I know from the member database that we've
know from the member database that we've have at Fountain are members who come in
have at Fountain are members who come in who think they're healthy. It turns out
who think they're healthy. It turns out 3.3% of them have a cancer in their body
3.3% of them have a cancer in their body they don't know about.
they don't know about. >> That's right. You know, the majority of
>> That's right. You know, the majority of cancers that we screen for, those aren't
cancers that we screen for, those aren't the ones that are necessarily taking the
the ones that are necessarily taking the lives when found at a late stage. We
lives when found at a late stage. We know that when cancer is found early,
know that when cancer is found early, the chances for cure are much higher. We
the chances for cure are much higher. We know it's much easier to treat a cancer
know it's much easier to treat a cancer when found early versus when found late.
when found early versus when found late. What we're finding in our members is
What we're finding in our members is over 3.3% were found to have these
over 3.3% were found to have these cancers that were otherwise wouldn't
cancers that were otherwise wouldn't have been found or detected.
have been found or detected. >> Yeah. You know, it's interesting.
>> Yeah. You know, it's interesting. People, you don't feel a cancer until
People, you don't feel a cancer until stage three or stage four. And and if
stage three or stage four. And and if you don't know what's going on inside
you don't know what's going on inside your body, it's like driving your car
your body, it's like driving your car with your eyes closed and you can know.
with your eyes closed and you can know. And so when members come through found,
And so when members come through found, how do they detect cancers?
how do they detect cancers? >> So we're doing full body MRI and we also
>> So we're doing full body MRI and we also do early cancer detection screening.
do early cancer detection screening. This is very very important. These are
This is very very important. These are not typical tools used in the
not typical tools used in the conventional care setting when it comes
conventional care setting when it comes to prevention. This is a hard thing
to prevention. This is a hard thing because currently these are not studies
because currently these are not studies that insurance would yet be covering.
that insurance would yet be covering. But the goal is to collect these
But the goal is to collect these numbers, do the research, and work hard
numbers, do the research, and work hard to democratize wellness.
to democratize wellness. >> Yeah. So, at the end of the day, you can
>> Yeah. So, at the end of the day, you can know what's going on inside your body.
know what's going on inside your body. It's your obligation to know. So, check
It's your obligation to know. So, check out Fountain Life. You can go to
out Fountain Life. You can go to fountainlife.com/pater
fountainlife.com/pater to get access to the latest technology
to get access to the latest technology to help you detect cancer at the very
to help you detect cancer at the very beginning at stage one when it is
beginning at stage one when it is curable before it gets to stage three or
curable before it gets to stage three or stage four in your world of hurt. All
stage four in your world of hurt. All right, topic number four, the collapse
right, topic number four, the collapse of terminal value. What happens if AI
of terminal value. What happens if AI makes every competitive mode temporary?
makes every competitive mode temporary? So this is a article posted by Chimath.
So this is a article posted by Chimath. Uh it's a it's a it's a powerful
Uh it's a it's a it's a powerful concept. He argues that AI could
concept. He argues that AI could compress equity valuations by two to
compress equity valuations by two to sevenfold of free cash flow down from
sevenfold of free cash flow down from today's S&P average of 22. So today the
today's S&P average of 22. So today the average S&P companies are getting 22
average S&P companies are getting 22 times uh free uh you know forward
times uh free uh you know forward looking cash flows and he's saying we're
looking cash flows and he's saying we're going to get a massive reduction in
going to get a massive reduction in that. So AI makes disruption so cheap
that. So AI makes disruption so cheap and fast that no company can project
and fast that no company can project free cash flow beyond five years
free cash flow beyond five years terminal value. Very true. I mean it
terminal value. Very true. I mean it used to be all of the uh SAS companies
used to be all of the uh SAS companies were projected forward um and you could
were projected forward um and you could depend on it. Uh so this can break down
depend on it. Uh so this can break down investment paradigms, break down uh VC
investment paradigms, break down uh VC investing. Uh I'd like to jump into
investing. Uh I'd like to jump into that, but first let me just show this is
that, but first let me just show this is uh Chimath's uh uh sort of image. He
uh Chimath's uh uh sort of image. He went along with his uh his post on X. Um
went along with his uh his post on X. Um so here we go. There's 58 trillion in
so here we go. There's 58 trillion in the current S&P 500 and this is at the
the current S&P 500 and this is at the 22x of uh free cash flow. Uh if we
22x of uh free cash flow. Uh if we compress it down to sevenfold, it drops.
compress it down to sevenfold, it drops. We lose a 2/3 of the value of of the S&P
We lose a 2/3 of the value of of the S&P 500. uh if we end up driving it down to
500. uh if we end up driving it down to 2x free cash flow, it's down 90%. And uh
2x free cash flow, it's down 90%. And uh we get a lot of disruption of our
we get a lot of disruption of our financial markets. Um here's a chart
financial markets. Um here's a chart that's showing uh the S&P 500 over the
that's showing uh the S&P 500 over the last 10 years, actually from 1950
last 10 years, actually from 1950 through today. And we're seeing it uh
through today. And we're seeing it uh basically uh deviate significantly on on
basically uh deviate significantly on on value uh you know, PE ratios.
value uh you know, PE ratios. So let's jump into some of the
So let's jump into some of the conversations. I've got the article up
conversations. I've got the article up in front of me as well. I think I'll
in front of me as well. I think I'll I'll read uh the opening paragraph here
I'll read uh the opening paragraph here for us. Uh and Shamala said, "Let's
for us. Uh and Shamala said, "Let's start with first principles. The entire
start with first principles. The entire architecture of modern capital markets
architecture of modern capital markets rests on a single rarely examined
rests on a single rarely examined assumption that competitive advantages
assumption that competitive advantages compound over time. Moes persist. Brands
compound over time. Moes persist. Brands endure. Network effects defend. strip
endure. Network effects defend. strip that assumption away and you aren't just
that assumption away and you aren't just repricing some stocks, you would be
repricing some stocks, you would be dismantling the philosophical foundation
dismantling the philosophical foundation of how capital has been allocated over a
of how capital has been allocated over a century. Dave, let's go to you first on
century. Dave, let's go to you first on this.
this. >> Absolutely correct. Uh but the
>> Absolutely correct. Uh but the conclusion that the S&P is going to
conclusion that the S&P is going to collapse is not correct. Uh if you say
collapse is not correct. Uh if you say look you know prior to the computer
look you know prior to the computer revolution my ambition was to build an
revolution my ambition was to build an oil company or a a manufacturing company
oil company or a a manufacturing company that would endure for 50 years building
that would endure for 50 years building the exact same goddamn product or
the exact same goddamn product or delivering the exact same goddamn oil
delivering the exact same goddamn oil for 50 years so my great-grandchildren
for 50 years so my great-grandchildren could be as wealthy as a Rockefeller
could be as wealthy as a Rockefeller that's dead forever and good riddance
that's dead forever and good riddance and it should be dead forever. If you
and it should be dead forever. If you said well look 22x free cash flow
said well look 22x free cash flow implies that the company will exist for
implies that the company will exist for 22 years making about that same amount
22 years making about that same amount of money. Well, what company like Apple
of money. Well, what company like Apple has has done that? You know, is Apple
has has done that? You know, is Apple selling the same products it was 22
selling the same products it was 22 years ago? Of course not. So, the
years ago? Of course not. So, the overall tailwind is 10x over just the
overall tailwind is 10x over just the next 10 years. So, there's a massive
next 10 years. So, there's a massive amount of tailwind coming into the
amount of tailwind coming into the economy. Massive amounts of new wealth,
economy. Massive amounts of new wealth, more than we've ever seen in our
more than we've ever seen in our lifetimes is going to come into the
lifetimes is going to come into the economy. But, you got to stop looking at
economy. But, you got to stop looking at 22 times free cash flow from the same
22 times free cash flow from the same product over 22 years. That's nuts. you
product over 22 years. That's nuts. you have to be looking at the management
have to be looking at the management team and the ability to to roll with the
team and the ability to to roll with the innovations all Elon and so I think the
innovations all Elon and so I think the the overall conclusion is yeah there's
the overall conclusion is yeah there's going to be a massive amount of
going to be a massive amount of shuffling in the S&P there's going to be
shuffling in the S&P there's going to be some huge winners like you've never seen
some huge winners like you've never seen before and anyone who's doing the same
before and anyone who's doing the same thing and resting on their laurels like
thing and resting on their laurels like an insurance company oil company
an insurance company oil company >> doomed yeah he's right he's dead right
>> doomed yeah he's right he's dead right this analysis is basically exactly the
this analysis is basically exactly the right analysis to show how that stock's
right analysis to show how that stock's going to go down So,
going to go down So, Alex,
Alex, >> yeah, it uh I mean it's certainly a
>> yeah, it uh I mean it's certainly a provocative thesis, but I don't think it
provocative thesis, but I don't think it holds water. I I think it's the moral
holds water. I I think it's the moral maybe the call it the earnings multiple
maybe the call it the earnings multiple equivalent of friend of the pod Ray
equivalent of friend of the pod Ray Kerszswhile's notion that a singularity
Kerszswhile's notion that a singularity takes the form of a firewall that you
takes the form of a firewall that you can't see past but except in earnings
can't see past but except in earnings multiple form that that as we start to
multiple form that that as we start to see faster faster more accelerationist
see faster faster more accelerationist innovation that free cash flow just
innovation that free cash flow just comes to a halt a few years later
comes to a halt a few years later because everything is disrupted the the
because everything is disrupted the the everything disruption if you will.
everything disruption if you will. Here's the problem. the the free cash
Here's the problem. the the free cash flow does go somewhere. Capital does get
flow does go somewhere. Capital does get allocated somewhere. It may not be
allocated somewhere. It may not be allocated to SAS startups post SAS
allocated to SAS startups post SAS apocalypse. Maybe it gets allocated to
apocalypse. Maybe it gets allocated to infrastructure. Maybe it gets allocated
infrastructure. Maybe it gets allocated to lunar mining. But capital does go
to lunar mining. But capital does go somewhere. It's it's not actually
somewhere. It's it's not actually capital that's being compressed. Quite
capital that's being compressed. Quite the opposite. Capital is is explosively
the opposite. Capital is is explosively expanding because now we have so much
expanding because now we have so much more infrastructure and so many more
more infrastructure and so many more capabilities. So I I think the the sort
capabilities. So I I think the the sort of the the nihilistic take that earnings
of the the nihilistic take that earnings multiples are compressing because a few
multiples are compressing because a few years from now there's no moat anywhere.
years from now there's no moat anywhere. I I think that's relatively narrow or it
I I think that's relatively narrow or it should be construed relatively narrowly
should be construed relatively narrowly to focus on the areas that are
to focus on the areas that are disrupted. In this case uh Chimath
disrupted. In this case uh Chimath focuses on software and SAS type
focuses on software and SAS type businesses. But but but but everything I
businesses. But but but but everything I expect is going to be disrupted. energy
expect is going to be disrupted. energy becomes abundant and farmland
becomes abundant and farmland infrastructure these all become
infrastructure these all become abundant. So in some sense I want to
abundant. So in some sense I want to zoom out and and take his thesis more
zoom out and and take his thesis more broadly as sort of almost bemoning the
broadly as sort of almost bemoning the financial consequences of abundance. And
financial consequences of abundance. And it it may just be the case that a number
it it may just be the case that a number of our existing sectors that are priced
of our existing sectors that are priced based on scarcity uh and moes and moes
based on scarcity uh and moes and moes arguably are a form of scarcity or at
arguably are a form of scarcity or at least a way of enforcing scarcity those
least a way of enforcing scarcity those go away and we live in a postmote world
go away and we live in a postmote world and that will be a better world. So,
and that will be a better world. So, you're going to start to value companies
you're going to start to value companies in a different way. In the old days, it
in a different way. In the old days, it was how predictable is their cash flow?
was how predictable is their cash flow? I have a number of seats in this
I have a number of seats in this particular industry, and these are many
particular industry, and these are many companies I can sell it to, a number of
companies I can sell it to, a number of seats available.
seats available. >> And now, it sounds like from what you
>> And now, it sounds like from what you and Dave just said, I'm actually going
and Dave just said, I'm actually going to be evaluating companies on their
to be evaluating companies on their agility, on how rapidly they can
agility, on how rapidly they can innovate, how rapidly they can get the
innovate, how rapidly they can get the next products out the door. Uh, Seem,
next products out the door. Uh, Seem, love your thoughts here.
love your thoughts here. >> Um, yeah, well, two points. One is um
>> Um, yeah, well, two points. One is um you know we have this EXO index where we
you know we have this EXO index where we ranked the Fortune 100 by their EXO
ranked the Fortune 100 by their EXO score gauging how flexible and adaptable
score gauging how flexible and adaptable and purpose-driven are their own
and purpose-driven are their own structures and we found the top 10 of
structures and we found the top 10 of the Fortune 100 outperform the bottom 10
the Fortune 100 outperform the bottom 10 by 40 times in shareholder returns over
by 40 times in shareholder returns over a 7-year period. So this has been going
a 7-year period. So this has been going on for a long time anyway. Okay, I agree
on for a long time anyway. Okay, I agree with Alex that capital flow but less
with Alex that capital flow but less towards incumbents and way more towards
towards incumbents and way more towards infrastructure and adaptive platforms.
infrastructure and adaptive platforms. It's a very important point. The only
It's a very important point. The only moat I think that's going to be left is
moat I think that's going to be left is a living system that learns faster than
a living system that learns faster than your competitors. That's that kind of
your competitors. That's that kind of inner loop that Eric Schmidt was talking
inner loop that Eric Schmidt was talking about. Right. Exactly. But all the all
about. Right. Exactly. But all the all the mo all the moes on that slide are
the mo all the moes on that slide are under attack from AI. IP get copied.
under attack from AI. IP get copied. Switching cost shrinks scale advantages
Switching cost shrinks scale advantages all weekend etc etc. Um I think but the
all weekend etc etc. Um I think but the bigger point I think is that if free
bigger point I think is that if free cash flow visibility collapses beyond
cash flow visibility collapses beyond say five years the entire logic of the
say five years the entire logic of the public market has to be rewritten. And
public market has to be rewritten. And so that's a very big uh thing. You're
so that's a very big uh thing. You're you're you have to reward renewable
you're you have to reward renewable renewables and optionality, not scale
renewables and optionality, not scale and stability. Physical assets are going
and stability. Physical assets are going to matter more again because atoms are
to matter more again because atoms are harder to disrupt than bits, right? Over
harder to disrupt than bits, right? Over time, but the entire SAS business model
time, but the entire SAS business model is broken. So this is I think one way of
is broken. So this is I think one way of saying this is we're going to have
saying this is we're going to have terminal value collapse.
terminal value collapse. >> Yeah, that's exactly what it the title
>> Yeah, that's exactly what it the title is actually. So the uh the terminal
is actually. So the uh the terminal value collapses. I think if you if you
value collapses. I think if you if you look at the S&P at 22 times free cash
look at the S&P at 22 times free cash flow, uh the midmarket, the non-S&P
flow, uh the midmarket, the non-S&P companies are already down to about
companies are already down to about seven times free cash flow, most of
seven times free cash flow, most of them. Uh so this has already happened
them. Uh so this has already happened outside of the S&P. What's propping up
outside of the S&P. What's propping up the S&P is mostly index funds. A huge
the S&P is mostly index funds. A huge fraction of the market is passive
fraction of the market is passive indexes
indexes >> and and people contributing blindly to
>> and and people contributing blindly to 401k plans, which Elon Musk said,
401k plans, which Elon Musk said, clearly do not do that right now. Uh but
clearly do not do that right now. Uh but but what'll happen next is a lot of
but what'll happen next is a lot of those dirt cheap midcaps and small caps
those dirt cheap midcaps and small caps will get a huge tailwind from AI
will get a huge tailwind from AI automation, you know, especially the
automation, you know, especially the ones that have huge uh payroll and labor
ones that have huge uh payroll and labor components to them. And so that's going
components to them. And so that's going to drive record you it's not unlikely
to drive record you it's not unlikely that you triple your free cash flow
that you triple your free cash flow while your multiple comes down. And so
while your multiple comes down. And so there's some serious bargains out there
there's some serious bargains out there >> uh just from a from a straight cash flow
>> uh just from a from a straight cash flow acceleration through AI point of view. I
acceleration through AI point of view. I mean shareholder shareholder calls are
mean shareholder shareholder calls are going to change to this is how we're
going to change to this is how we're rapidly iterating our products and
rapidly iterating our products and services. This is how we're reinventing
services. This is how we're reinventing what we do um and our future cash flow.
what we do um and our future cash flow. And Sim, you've got to redo the EXO
And Sim, you've got to redo the EXO index. It's time to take a shot at that
index. It's time to take a shot at that once again.
once again. >> So I'm the part of the paper that we're
>> So I'm the part of the paper that we're writing, the reason it's taking a little
writing, the reason it's taking a little longer is that I I I hate to say it, but
longer is that I I I hate to say it, but we it breaks the EXO model, right?
we it breaks the EXO model, right? Community and crowd becomes communities
Community and crowd becomes communities and crowds of agents. So we have to
and crowds of agents. So we have to rethink the model from the ground up.
rethink the model from the ground up. >> We're kind of mostly we've done that.
>> We're kind of mostly we've done that. But then you evaluate based on those new
But then you evaluate based on those new criteria. For example, what's your
criteria. For example, what's your intelligence stack? What's your MTP
intelligence stack? What's your MTP architecture? What's your trust
architecture? What's your trust framework? There's a bunch of different
framework? There's a bunch of different elements that are new here that we have
elements that are new here that we have to take into account because the the
to take into account because the the concept of an organization where you did
concept of an organization where you did things inside the organization is
things inside the organization is completely gone. We're going to be doing
completely gone. We're going to be doing API calls to get uh various things done,
API calls to get uh various things done, legal work, etc., etc. It's all going to
legal work, etc., etc. It's all going to be agentic. And then the firm which used
be agentic. And then the firm which used to be coordination costs and transaction
to be coordination costs and transaction costs with a bit of legal liability now
costs with a bit of legal liability now becomes only legal liability risk uh
becomes only legal liability risk uh purpose container and liability
purpose container and liability container.
container. >> Yeah. Well, also, and this is not super
>> Yeah. Well, also, and this is not super mainstream, so I'll get off the high
mainstream, so I'll get off the high horse quickly here, but but if a private
horse quickly here, but but if a private equity firm like Advent, Dave Muser,
equity firm like Advent, Dave Muser, comes in and brings either SEM or Alex
comes in and brings either SEM or Alex along and says, "Hey, we want to take
along and says, "Hey, we want to take this non-AI company with huge free cash
this non-AI company with huge free cash flow and we want to retool it for the
flow and we want to retool it for the age of AI, triple the cash flow, and
age of AI, triple the cash flow, and retool the business plan for AI." Alex
retool the business plan for AI." Alex or Sem can tear down that company now
or Sem can tear down that company now using agents in 1/ 1,000th the time that
using agents in 1/ 1,000th the time that it would have taken a year ago or two
it would have taken a year ago or two years ago. And so whatever private
years ago. And so whatever private equity firm mechanizes that, it's going
equity firm mechanizes that, it's going to have no trouble retooling all of
to have no trouble retooling all of these entities because because you know
these entities because because you know exactly what the company's assets are,
exactly what the company's assets are, you know, whether it's a regulatory
you know, whether it's a regulatory framework or whether it's a bunch of
framework or whether it's a bunch of data,
data, >> you can rip through that with Gemini or
>> you can rip through that with Gemini or with cloud a or with open AI agents in
with cloud a or with open AI agents in light speed. Now,
light speed. Now, >> it's a transformation wave.
>> it's a transformation wave. >> We automated it.
>> We automated it. >> Yeah,
>> Yeah, >> it's a it's a transformation wave. I
>> it's a it's a transformation wave. I remember there was one of the Star Trek
remember there was one of the Star Trek episodes was uh the was it the Genesis
episodes was uh the was it the Genesis machine that project
machine that project >> the Genesis project has agent.
>> the Genesis project has agent. >> Yes. This this this uh this wave that
>> Yes. This this this uh this wave that that went over a planet and transformed
that went over a planet and transformed everything. We're going to have the same
everything. We're going to have the same thing. You're going to have ch you have
thing. You're going to have ch you have teams cherry-picking companies and
teams cherry-picking companies and reinventing them and disrupting
reinventing them and disrupting >> not just not just private equity. I
>> not just not just private equity. I would argue this applies equally well to
would argue this applies equally well to public equity. So something I would like
public equity. So something I would like I I'll broadcast a request for startups
I I'll broadcast a request for startups if I may to the audience. I would love
if I may to the audience. I would love to see activist investing disrupted by
to see activist investing disrupted by AI. I'd like AIs to write open letters
AI. I'd like AIs to write open letters to public firms telling the firms what
to public firms telling the firms what they're doing wrong to disrupt them. If
they're doing wrong to disrupt them. If if you're if you're building an AI
if you're if you're building an AI activist investor, write to me, please.
activist investor, write to me, please. Would love to find a way to support.
Would love to find a way to support. >> That's a great idea, Alex. And what is
>> That's a great idea, Alex. And what is it's a service to the CEO of the
it's a service to the CEO of the companies who need prompting or need
companies who need prompting or need sort of a forcing function to transform
sort of a forcing function to transform their business models. And the and if
their business models. And the and if you're a board member of any of these
you're a board member of any of these companies, your job as a board member is
companies, your job as a board member is to give your CEO top cover and to say,
to give your CEO top cover and to say, you know, you must get on the disruption
you know, you must get on the disruption uh uh you know, band here. You've got to
uh uh you know, band here. You've got to reinvent. It's also sort of a stealth
reinvent. It's also sort of a stealth way for AI to become a manager of the
way for AI to become a manager of the entire economy and not just picking off
entire economy and not just picking off mom and pop businesses on the margins.
mom and pop businesses on the margins. >> All right, on to story number five. One
>> All right, on to story number five. One of my favorites, hopefully, Alex, one of
of my favorites, hopefully, Alex, one of yours, too. It's the new great space
yours, too. It's the new great space race. NASA picks SpaceX for the moon.
race. NASA picks SpaceX for the moon. Potatoes are growing in lunar dust and
Potatoes are growing in lunar dust and asteroids are carrying the code of life.
asteroids are carrying the code of life. >> All right, so here we go. I mean,
>> All right, so here we go. I mean, listen, Boeing has been building uh the,
listen, Boeing has been building uh the, you know, the Artemis 2 vehicle. It's
you know, the Artemis 2 vehicle. It's going to be launching on April 1st. Uh
going to be launching on April 1st. Uh it had its uh its readiness review on on
it had its uh its readiness review on on March 12th. And if all goes well, April
March 12th. And if all goes well, April 1st, we're going to be going back to the
1st, we're going to be going back to the moon, not to land, but to do basically
moon, not to land, but to do basically an Apollo 8 style circum lunar orbit. Uh
an Apollo 8 style circum lunar orbit. Uh very cool. But you know, the new NASA
very cool. But you know, the new NASA administrator, uh an amazing individual.
administrator, uh an amazing individual. I I'm very happy and proud to call him a
I I'm very happy and proud to call him a friend. We're going to have him on the
friend. We're going to have him on the pod. He's agreed to do it. Just need to
pod. He's agreed to do it. Just need to get it scheduled. Um is elevating SpaceX
get it scheduled. Um is elevating SpaceX into the Artemis program. So Starship is
into the Artemis program. So Starship is going to be taking uh I think astronauts
going to be taking uh I think astronauts more safely, more economically. We'll
more safely, more economically. We'll see those numbers in a moment. But just
see those numbers in a moment. But just to be clear, this is not the US story
to be clear, this is not the US story only. Uh China has confirmed their
only. Uh China has confirmed their intention to land on the moon by 2030.
intention to land on the moon by 2030. Let's play it back again. history
Let's play it back again. history repeating itself. 1961, we're on the
repeating itself. 1961, we're on the moon before the end of the decade. China
moon before the end of the decade. China saying they're on the moon before the
saying they're on the moon before the end of the decade. Um, and so that's
end of the decade. Um, and so that's going to be a a beautiful competition.
going to be a a beautiful competition. Uh, we'll get to the idea that, you
Uh, we'll get to the idea that, you know, you can grow potatoes in lunar
know, you can grow potatoes in lunar soil, the going back to the Martian, you
soil, the going back to the Martian, you know, again, an incredible movie now
know, again, an incredible movie now that uh Project Hail Mary is out. I
that uh Project Hail Mary is out. I can't wait to go see it in IMAX theater
can't wait to go see it in IMAX theater later this week. And we just saw that on
later this week. And we just saw that on the asteroid uh Ryugu Ryugu um we found
the asteroid uh Ryugu Ryugu um we found the five nucleio nucleio bases nucleic
the five nucleio nucleio bases nucleic bases for DNA which has four ATC and G
bases for DNA which has four ATC and G and RNA which has uh U for uricil and we
and RNA which has uh U for uricil and we found all five of those bases on that
found all five of those bases on that asteroid. This strengthens the
asteroid. This strengthens the panspermia theory that life on earth
panspermia theory that life on earth originated elsewhere in our galaxy in
originated elsewhere in our galaxy in the universe and it rained on earth and
the universe and it rained on earth and gave us the starting components uh for
gave us the starting components uh for that. Let's take a look at uh this chart
that. Let's take a look at uh this chart I put on the left here uh NASA's SLS.
I put on the left here uh NASA's SLS. And now to be clear when I say NASA's
And now to be clear when I say NASA's SLS, NASA is the prime contractor and it
SLS, NASA is the prime contractor and it has probably uh aerospace companies in
has probably uh aerospace companies in every single congressional district
every single congressional district building that vehicle. It is an
building that vehicle. It is an expendable vehicle in in a time when
expendable vehicle in in a time when everybody's going reusable. There you
everybody's going reusable. There you have SpaceX um with Starship. And just
have SpaceX um with Starship. And just for comparison of size, here's the
for comparison of size, here's the Saturn Saturn 5 that got us to the moon.
Saturn Saturn 5 that got us to the moon. If you look at these two charts, these
If you look at these two charts, these two bar charts on the left here, uh
two bar charts on the left here, uh we're seeing uh well, let's go to the
we're seeing uh well, let's go to the center ones. We're seeing the uh the
center ones. We're seeing the uh the delivered mass uh to orbit that that was
delivered mass uh to orbit that that was it teal teal color is is Starship over,
it teal teal color is is Starship over, you know, twice as much as we're getting
you know, twice as much as we're getting with with Artemis with um the SLS
with with Artemis with um the SLS vehicle. And if you look at uh to trans
vehicle. And if you look at uh to trans lunar injection TLI getting out of Earth
lunar injection TLI getting out of Earth orbit to the moon, we're seeing twice as
orbit to the moon, we're seeing twice as much mass going on a Starship compared
much mass going on a Starship compared to um uh to the SLS. But where the
to um uh to the SLS. But where the rubber really hits the road is launch
rubber really hits the road is launch costs and mission costs. Um it's
costs and mission costs. Um it's expensive to be running the SLS system.
expensive to be running the SLS system. It's like the space shuttle. The space
It's like the space shuttle. The space shuttle used to cost if you did four
shuttle used to cost if you did four launches a year, it was a billion
launches a year, it was a billion dollars a launch. If you did one launch
dollars a launch. If you did one launch a year, it was $4 billion a launch. It
a year, it was $4 billion a launch. It wasn't the cost of the vehicle. It was
wasn't the cost of the vehicle. It was the standing army of 20,000 humans that
the standing army of 20,000 humans that were used to to operate the space
were used to to operate the space shuttle. So, I I honestly don't know why
shuttle. So, I I honestly don't know why the SLS has existed as long as it has. I
the SLS has existed as long as it has. I think Starship is going to do a clean
think Starship is going to do a clean sweep of this. And of course, we've got
sweep of this. And of course, we've got Blue Origin as well. Alex comments.
Blue Origin as well. Alex comments. Well, uh, do you want to place bets as
Well, uh, do you want to place bets as to how long before the United Launch
to how long before the United Launch Alliance, which is the prime contractor
Alliance, which is the prime contractor for SLS, gets acquired by Jeff Bezos or
for SLS, gets acquired by Jeff Bezos or someone else?
someone else? >> But why would you acquire it? I guess
>> But why would you acquire it? I guess for the contracts.
for the contracts. >> For the contracts, for the expertise. I
>> For the contracts, for the expertise. I I I'm familiar with again all the
I I'm familiar with again all the cliches in the space space about how SLS
cliches in the space space about how SLS was a make jobs program or a way to keep
was a make jobs program or a way to keep alive in civilian form certain
alive in civilian form certain capabilities that were useful for
capabilities that were useful for defense or other say intelligence
defense or other say intelligence purposes. But I think at the end of the
purposes. But I think at the end of the day, we're so painfully close to finally
day, we're so painfully close to finally relaunching a second space race and I
relaunching a second space race and I think Starship is is the obvious
think Starship is is the obvious incumbent there, not the SLS. So, uh
incumbent there, not the SLS. So, uh hopefully we have humans on landing on
hopefully we have humans on landing on the moon again in the next 2 to 3 years
the moon again in the next 2 to 3 years and we get humans eventually on Mars and
and we get humans eventually on Mars and all of this plays out exactly as for all
all of this plays out exactly as for all mankind has foreseen except decades
mankind has foreseen except decades late.
late. >> Yeah. I don't know if you if Dave or
>> Yeah. I don't know if you if Dave or See, you want to play on this uh on this
See, you want to play on this uh on this conversation. I just think, you know,
conversation. I just think, you know, we're we're building the uh I don't know
we're we're building the uh I don't know what your best historical analogy, the
what your best historical analogy, the you know, the covered wagons, the
you know, the covered wagons, the railroads, and
railroads, and >> wagon train to the stars. Gene Rodenbury
>> wagon train to the stars. Gene Rodenbury called it.
called it. >> It's it's all currently on on Starship.
>> It's it's all currently on on Starship. Uh Starship is extremely economical.
Uh Starship is extremely economical. >> Yeah. Go ahead. I
>> Yeah. Go ahead. I >> I thought two or three things. One is
>> I thought two or three things. One is we've gone from kind of government space
we've gone from kind of government space theater to commercial space evolution. I
theater to commercial space evolution. I think that's really powerful for me. The
think that's really powerful for me. The really exciting thing was finding all
really exciting thing was finding all the nuclear bases on Ryugu.
the nuclear bases on Ryugu. >> Um, you know, it takes life from
>> Um, you know, it takes life from scarcity to abundance. Um,
scarcity to abundance. Um, >> yeah, I think that's a big deal.
>> yeah, I think that's a big deal. >> Here's the uh here's the graphic if you
>> Here's the uh here's the graphic if you would again adine, guanine, cytosine,
would again adine, guanine, cytosine, thymine, and uricil. The five components
thymine, and uricil. The five components of DNA and RNA found on these on these
of DNA and RNA found on these on these vehicles. I do believe that as we get to
vehicles. I do believe that as we get to Mars, as we get to Europa, as we get to
Mars, as we get to Europa, as we get to the uh all of the planets and moons,
the uh all of the planets and moons, that we're going to find at least
that we're going to find at least microbial life uh you know, ubiquitous
microbial life uh you know, ubiquitous on all of these.
on all of these. >> Did you see Peter Jared's prediction
>> Did you see Peter Jared's prediction about microbial life on Mars?
about microbial life on Mars? >> No. What did you say? Jared's our NASA
>> No. What did you say? Jared's our NASA administrator. Yes.
administrator. Yes. >> Yeah. uh uh NASA administrator uh said
>> Yeah. uh uh NASA administrator uh said that he he predicted more than 90% plus
that he he predicted more than 90% plus probability that NASA will imminently
probability that NASA will imminently find evidence of microbial life in some
find evidence of microbial life in some form on Mars which is a sea change in in
form on Mars which is a sea change in in terms of NASA's official position on
terms of NASA's official position on life on Mars. It was always well we
life on Mars. It was always well we found water frozen water now we found
found water frozen water now we found liquid water we hope to find signs of
liquid water we hope to find signs of life the signs are ambiguous now for the
life the signs are ambiguous now for the first time we have a NASA administrator
first time we have a NASA administrator who's saying 90 plus% probability we're
who's saying 90 plus% probability we're going to find microbial life and the
going to find microbial life and the exciting thing is how related is it
exciting thing is how related is it going to be to microbial life on earth
going to be to microbial life on earth one of the theories of course Mars
one of the theories of course Mars cooled first which probably means life
cooled first which probably means life evolved on Mars first and of course that
evolved on Mars first and of course that we know uh when astro large asteroids
we know uh when astro large asteroids impacted Mars. The ejecta uh the rocks
impacted Mars. The ejecta uh the rocks that flew out, some of them reached Mars
that flew out, some of them reached Mars escape velocity and landed on Earth. And
escape velocity and landed on Earth. And so we have Martian meteorites uh in
so we have Martian meteorites uh in museums today. Uh and did those uh did
museums today. Uh and did those uh did those meteorites carry life with them
those meteorites carry life with them from Mars to the Earth? Are we going to
from Mars to the Earth? Are we going to find uh basically even genes that are
find uh basically even genes that are common between Martian life and life
common between Martian life and life here? I mean, the real exciting thing is
here? I mean, the real exciting thing is if we go to Europa or some place like
if we go to Europa or some place like that and we find completely independent
that and we find completely independent life forms that don't connect with life
life forms that don't connect with life on Earth. That will be amazing.
on Earth. That will be amazing. >> That's really cool. So, I got a question
>> That's really cool. So, I got a question for you since since you guys are experts
for you since since you guys are experts on this and I'm not. Um, is it in the
on this and I'm not. Um, is it in the scenario where lo and behold, it turns
scenario where lo and behold, it turns out that everything we learned in
out that everything we learned in biology should have said life started on
biology should have said life started on Mars or actually started farther out in
Mars or actually started farther out in the solar system and then asteroids
the solar system and then asteroids knock chunks off and then they transport
knock chunks off and then they transport to other chunks and then life starts
to other chunks and then life starts over again and then it ends up on Earth
over again and then it ends up on Earth through that mechanism. Is that all
through that mechanism. Is that all going to be then bounded to the solar
going to be then bounded to the solar system or is it more likely in your mind
system or is it more likely in your mind that hey wait this this propagates
that hey wait this this propagates through deep space?
through deep space? >> When I was a freshman in school I did a
>> When I was a freshman in school I did a paper on the interstellar medium and you
paper on the interstellar medium and you can actually look at the interstellar
can actually look at the interstellar medium and you can find the building
medium and you can find the building blocks of life out in the in you know in
blocks of life out in the in you know in the medium between stars in our in our
the medium between stars in our in our in our galaxy. uh these components are
in our galaxy. uh these components are everywhere
everywhere >> and and the galaxy is relatively well
>> and and the galaxy is relatively well mixed on on the time scale of a billion
mixed on on the time scale of a billion years. So I think the statistic is Mars
years. So I think the statistic is Mars cooled about a billion years or or so
cooled about a billion years or or so plus or minus earlier than Earth. That's
plus or minus earlier than Earth. That's a a lot of so so the galaxy first of all
a a lot of so so the galaxy first of all is not rigid. We're we're constantly we
is not rigid. We're we're constantly we have different stars at different
have different stars at different velocities passing by each other close
velocities passing by each other close passes all of that. So on on a time
passes all of that. So on on a time scale of a billion years, that buys an
scale of a billion years, that buys an enormous amount of time for panspermia
enormous amount of time for panspermia at potentially a galactic scale, not
at potentially a galactic scale, not even necessarily at a an interstellar
even necessarily at a an interstellar neighborhood scale. And we're we're
neighborhood scale. And we're we're several generations in as well. We're,
several generations in as well. We're, you know, born from several generations
you know, born from several generations of of stars uh exploding uh and then
of of stars uh exploding uh and then forming new stars. There's been a lot of
forming new stars. There's been a lot of nebular mixing in our interstellar
nebular mixing in our interstellar neighborhood. There there's one other of
neighborhood. There there's one other of relevance story. Um folks can find it if
relevance story. Um folks can find it if if if they Google it. uh this is from a
if if they Google it. uh this is from a few years back attempting to extrapolate
few years back attempting to extrapolate based on genetic complexity when the the
based on genetic complexity when the the last common ancestor actually would have
last common ancestor actually would have been uh and finding that if you just
been uh and finding that if you just take genetic complexity as I don't
take genetic complexity as I don't forget how exactly it's measured but you
forget how exactly it's measured but you come up with some appropriate
come up with some appropriate parameterization of genetic complexity
parameterization of genetic complexity of life on earth uh extrapolate
of life on earth uh extrapolate backwards you find that the time when
backwards you find that the time when you get the first base pair happens
you get the first base pair happens approximately a billion years before
approximately a billion years before life is thought to have observed on
life is thought to have observed on Earth. So that's sort of an independent
Earth. So that's sort of an independent measure of when in principle life as we
measure of when in principle life as we know it, DNA, RNA based life could have
know it, DNA, RNA based life could have emerged. Maybe it started on Mars, but
emerged. Maybe it started on Mars, but we'll find out I suspect soon enough.
we'll find out I suspect soon enough. >> Exciting times. Seem, you want to add
>> Exciting times. Seem, you want to add something?
something? No, I just remembered my favorite thing
No, I just remembered my favorite thing around all this is the Drake equation
around all this is the Drake equation where you where and I'll just go to um
where you where and I'll just go to um where you uh where you calculate all the
where you uh where you calculate all the uh factors that led to the probabilities
uh factors that led to the probabilities of binary stars and life appearing and
of binary stars and life appearing and you and when you add it all up you end
you and when you add it all up you end up with 100%. It's good. So the
up with 100%. It's good. So the panspermia thing, but I think what was
panspermia thing, but I think what was mentioned earlier, if we could find
mentioned earlier, if we could find something as non-carbon based, that
something as non-carbon based, that would be truly exciting. You know what's
would be truly exciting. You know what's really cool to me is this called AI
really cool to me is this called AI >> the the dinosaurs were extinguished by a
>> the the dinosaurs were extinguished by a meteorite and or a meteor and uh the
meteorite and or a meteor and uh the propagation of the DNA or the base pairs
propagation of the DNA or the base pairs is also via asteroids and meteors and
is also via asteroids and meteors and early in the universe history you know
early in the universe history you know this may be popping up all there might
this may be popping up all there might be life popping up constantly everywhere
be life popping up constantly everywhere and propagating through all these
and propagating through all these projectiles flying around, but it always
projectiles flying around, but it always gets extinguished by another meteor, you
gets extinguished by another meteor, you know, just like the dinosaurs were. And
know, just like the dinosaurs were. And it's not until everything cools and
it's not until everything cools and settles that you can have enough time to
settles that you can have enough time to evolve human
evolve human intelligence or other intelligences out
intelligence or other intelligences out there in the universe. So, it's just a
there in the universe. So, it's just a big system dynamic settling problem,
big system dynamic settling problem, which is just really cool to me to think
which is just really cool to me to think about. I hope it turns out to be right.
about. I hope it turns out to be right. >> Yeah. All right. Uh, story number six,
>> Yeah. All right. Uh, story number six, the model wars go underground. The AI
the model wars go underground. The AI frontier is fracturing into a stealth
frontier is fracturing into a stealth arms race where uh, anonymity is the new
arms race where uh, anonymity is the new moat. And here's the story. There are
moat. And here's the story. There are two stories here to focus on. One,
two stories here to focus on. One, OpenAI launches GPT 5.4 mini and nano
OpenAI launches GPT 5.4 mini and nano that runs twice as fast and approaches
that runs twice as fast and approaches the full GPT 5.4 on coding benchmarks.
the full GPT 5.4 on coding benchmarks. So, these models are getting smaller and
So, these models are getting smaller and faster. And the second story which I
faster. And the second story which I think is most of our conversation here
think is most of our conversation here is there was a mystery model. So a one
is there was a mystery model. So a one trillion parameter model called hunter
trillion parameter model called hunter alpha appeared on open router with no
alpha appeared on open router with no attribution. It was secret. It had a
attribution. It was secret. It had a million token context window. It was
million token context window. It was free. There was no developer announce,
free. There was no developer announce, no press release, no origin story. And
no press release, no origin story. And it processed 160 billion plus tokens.
it processed 160 billion plus tokens. Everyone thought it was Deepseek V4,
Everyone thought it was Deepseek V4, right? because DeepS had been the main
right? because DeepS had been the main player here, but it turned out to be
player here, but it turned out to be Xiaomi's AI team and when that was
Xiaomi's AI team and when that was announced, their stock went up 5.8%.
announced, their stock went up 5.8%. I remember meeting the team at Xiaomi
I remember meeting the team at Xiaomi when they came out with their first
when they came out with their first mobile phone. Like three young founders.
mobile phone. Like three young founders. They've since gone beyond just mobile
They've since gone beyond just mobile phones to electric cars and now they've
phones to electric cars and now they've got a killer model.
got a killer model. Points, gentlemen.
Points, gentlemen. I I think there are at least
I I think there are at least >> point one is proliferation of models is
>> point one is proliferation of models is very hard to contain because the the
very hard to contain because the the existence of the prior model gives you a
existence of the prior model gives you a complete road map on how to build the
complete road map on how to build the next model and it helps you build the
next model and it helps you build the next model.
next model. >> So I at this stage I think it's a fair
>> So I at this stage I think it's a fair bet that trillion parameter models are
bet that trillion parameter models are going to propagate all over the world
going to propagate all over the world with anyone who has about $50 to hund00
with anyone who has about $50 to hund00 million that they're willing to invest.
million that they're willing to invest. Uh and that'll come down too as Alex is
Uh and that'll come down too as Alex is pointing out many times. uh the the
pointing out many times. uh the the algorithmic improvements are driving
algorithmic improvements are driving that down constantly.
that down constantly. >> Alex, go ahead.
>> Alex, go ahead. >> Alex, sorry.
>> Alex, sorry. >> Yeah, may maybe a couple points. So,
>> Yeah, may maybe a couple points. So, first on the the 5.4 story, distillation
first on the the 5.4 story, distillation continues to work and I find that
continues to work and I find that completely remarkable. Uh on
completely remarkable. Uh on >> would you would you explain distillation
>> would you would you explain distillation for our listeners who don't know?
for our listeners who don't know? >> Yeah, sure. So the the I think the
>> Yeah, sure. So the the I think the reasonable expectation for say
reasonable expectation for say OpenAI as well as other firms launching
OpenAI as well as other firms launching a big model first with lots of weights a
a big model first with lots of weights a high parameter count and then
high parameter count and then subsequently launching a mini or nano
subsequently launching a mini or nano version and by the way anthropic does
version and by the way anthropic does the same thing and DeepMind does the
the same thing and DeepMind does the same thing. they all launch smaller
same thing. they all launch smaller models later is that they're using the
models later is that they're using the larger models to generate lots of data,
larger models to generate lots of data, synthetic data, and then using those
synthetic data, and then using those synthetic data to train a a smaller
synthetic data to train a a smaller model that can be faster and less
model that can be faster and less expensive. So that that's sort of a a
expensive. So that that's sort of a a caricatured way of describing the
caricatured way of describing the distillation process of in some sense
distillation process of in some sense squeezing down or compressing the larger
squeezing down or compressing the larger model down to a simpler student model.
model down to a simpler student model. And the fact that this continues to work
And the fact that this continues to work is I think borderline magic. The the
is I think borderline magic. The the amount of complexity that's already in
amount of complexity that's already in the full 5.4 model that and and moreover
the full 5.4 model that and and moreover that 5.4 has likely been the result of
that 5.4 has likely been the result of so-called iterated amplification and
so-called iterated amplification and distillation over many cycles where 5.4
distillation over many cycles where 5.4 was likely in large part trained off of
was likely in large part trained off of synthetic data generated by distilled
synthetic data generated by distilled models from earlier generations. that we
models from earlier generations. that we can keep playing this magic trick over
can keep playing this magic trick over and over again. It's borderline magic
and over again. It's borderline magic that that it continues to work and that
that that it continues to work and that we continue to be able to distill down
we continue to be able to distill down models while retaining a large fraction
models while retaining a large fraction of their capabilities. It it again makes
of their capabilities. It it again makes me think that there has to be an end to
me think that there has to be an end to the story, but hopefully it's a very
the story, but hopefully it's a very satisfying end where at the end of the
satisfying end where at the end of the the distillation rainbow, we get like
the distillation rainbow, we get like the the distilled black hole of a model
the the distilled black hole of a model or a neutron star or something. The
or a neutron star or something. The ultimate phase change where it's maybe
ultimate phase change where it's maybe like a few million parameters. It's the
like a few million parameters. It's the the end state
the end state >> a one a one kilobyte file on on your
>> a one a one kilobyte file on on your phone. 1 kilob file. That's like the
phone. 1 kilob file. That's like the master equation for super intelligence
master equation for super intelligence after all of this distillation.
after all of this distillation. >> And we we showed on a previous podcast
>> And we we showed on a previous podcast uh a gentleman on his iPhone using a
uh a gentleman on his iPhone using a distilled model on airplane mode uh
distilled model on airplane mode uh being able to basically answer every
being able to basically answer every question. So imagine if on all of your
question. So imagine if on all of your devices without having to have, you
devices without having to have, you know, Wi-Fi internet access, you have
know, Wi-Fi internet access, you have the distilled knowledge of humanity
the distilled knowledge of humanity there to serve you. It's inside your
there to serve you. It's inside your kid's teddy bear. it's in your, you
kid's teddy bear. it's in your, you know, Thomas train set. Um, it's becomes
know, Thomas train set. Um, it's becomes magical. Here's my question for you, uh,
magical. Here's my question for you, uh, Alex and and and Dave. You know, uh, now
Alex and and and Dave. You know, uh, now that we're seeing this, uh, we're seeing
that we're seeing this, uh, we're seeing a basically, uh, a mystery trillion
a basically, uh, a mystery trillion parameter model announced without any
parameter model announced without any attribution. Uh it used to be that the
attribution. Uh it used to be that the traditional moat for these models was
traditional moat for these models was their brand, their capitalization,
their brand, their capitalization, who they were. You know, is there any
who they were. You know, is there any defendability or are we just going to
defendability or are we just going to see newcomers rushing in with new
see newcomers rushing in with new models? Um that you're going to just
models? Um that you're going to just utilize a new model, you're going to be
utilize a new model, you're going to be no longer dependent upon open AI uh or
no longer dependent upon open AI uh or Gemini. Thoughts on that?
Gemini. Thoughts on that? >> Well, you you've said it a million
>> Well, you you've said it a million times, Peter. data is actually the great
times, Peter. data is actually the great moat, not the model itself. And many
moat, not the model itself. And many many people are accumulating phenomenal
many people are accumulating phenomenal data for, you know, for s brain surgery,
data for, you know, for s brain surgery, for material science, for chemistry, for
for material science, for chemistry, for all of these use cases. And, you know,
all of these use cases. And, you know, if you create the next great great great
if you create the next great great great model using that proprietary data, the
model using that proprietary data, the parameters are out there in the world,
parameters are out there in the world, but the the data that trained it is not.
but the the data that trained it is not. And it's very hard. You people can use
And it's very hard. You people can use the model, but they can't compete with
the model, but they can't compete with you by creating a a ripoff model because
you by creating a a ripoff model because they don't have the underlying data.
they don't have the underlying data. Now, you can generate synthetic data
Now, you can generate synthetic data using the prior model. Uh, Alex is dead
using the prior model. Uh, Alex is dead right about that. But I don't think that
right about that. But I don't think that it's I don't think it's all these
it's I don't think it's all these companies killing each other. I think
companies killing each other. I think it's the whole all the boats rising with
it's the whole all the boats rising with the tide. I also think that if you take
the tide. I also think that if you take what Alex said a minute ago, and you
what Alex said a minute ago, and you know, so many college seniors ask me,
know, so many college seniors ask me, "What should I do? What should I do? How
"What should I do? What should I do? How am I how do I, you know, what?" Just
am I how do I, you know, what?" Just replay 10 times what Alex just said
replay 10 times what Alex just said slowly until you fully understand
slowly until you fully understand everything he just said and then ask
everything he just said and then ask your favorite AI to generalize on it and
your favorite AI to generalize on it and find as many documents as you can around
find as many documents as you can around the internet to read. At the end of that
the internet to read. At the end of that process, you'll be able to build a
process, you'll be able to build a distilled focused model that solves some
distilled focused model that solves some problem better than anyone else on the
problem better than anyone else on the planet. And that's that's instant
planet. And that's that's instant business, instant value ad, instant
business, instant value ad, instant success. So just just really in fact the
success. So just just really in fact the other thing you can do is take your open
other thing you can do is take your open claw and have it look for every episode
claw and have it look for every episode of this podcast where Alex said
of this podcast where Alex said something related to what he just said
something related to what he just said and have it also synthesize that and
and have it also synthesize that and bring it back and feed it into your
bring it back and feed it into your machine. I guarantee that's a good move
machine. I guarantee that's a good move something good good spring project for
something good good spring project for anyone listening. If Sam Alman were in
anyone listening. If Sam Alman were in this discussion, he might point in terms
this discussion, he might point in terms of the moat question that you were
of the moat question that you were asking Peter to, well, OpenAI is
asking Peter to, well, OpenAI is building up its own data centers,
building up its own data centers, although that's no longer really true.
although that's no longer really true. Stargate is being pivoted to now renting
Stargate is being pivoted to now renting servers, so maybe less of a moat there.
servers, so maybe less of a moat there. He might point to having the best
He might point to having the best research team in the world generating
research team in the world generating the best models, but they've been
the best models, but they've been hemorrhaging researchers and those are
hemorrhaging researchers and those are becoming a commodity. then he might
becoming a commodity. then he might point to being the becoming the core
point to being the becoming the core subscription having as as he said a
subscription having as as he said a billion plus users I'd much rather have
billion plus users I'd much rather have more than a billion users than I would
more than a billion users than I would have state-of-the-art model because
have state-of-the-art model because models walk out the door every day uh
models walk out the door every day uh there's a lot of fungeibility in terms
there's a lot of fungeibility in terms of research employees only one problem
of research employees only one problem that the billion user distribution
that the billion user distribution advantage may be a a little bit tenuous
advantage may be a a little bit tenuous at the edges because you see maybe
at the edges because you see maybe enterprises are more valuable as
enterprises are more valuable as customers than individuals. So maybe the
customers than individuals. So maybe the billion users a little bit less valuable
billion users a little bit less valuable on margin. And then maybe also you see
on margin. And then maybe also you see other labs that are able to use cheap
other labs that are able to use cheap Chinese openweight models, maybe
Chinese openweight models, maybe fine-tuned legally or otherwise with
fine-tuned legally or otherwise with clawed outputs are able to put out
clawed outputs are able to put out seemingly miraculous results. So I I I
seemingly miraculous results. So I I I do think we're seeing the baseline
do think we're seeing the baseline models for the moment become something
models for the moment become something of a commodity and the value then
of a commodity and the value then migrates up the stack to open claw or
migrates up the stack to open claw or other higher level frameworks.
other higher level frameworks. This episode is brought to you by
This episode is brought to you by Blitzy, autonomous software development
Blitzy, autonomous software development with infinite code context. Blitzy uses
with infinite code context. Blitzy uses thousands of specialized AI agents that
thousands of specialized AI agents that think for hours to understand
think for hours to understand enterprisecale code bases with millions
enterprisecale code bases with millions of lines of code. Engineers start every
of lines of code. Engineers start every development sprint with the Blitzy
development sprint with the Blitzy platform, bringing in their development
platform, bringing in their development requirements. The Blitzy platform
requirements. The Blitzy platform provides a plan, then generates and
provides a plan, then generates and pre-ompiles code for each task. Blitzy
pre-ompiles code for each task. Blitzy delivers 80% or more of the development
delivers 80% or more of the development work autonomously while providing a
work autonomously while providing a guide for the final 20% of human
guide for the final 20% of human development work required to complete
development work required to complete the sprint. Enterprises are achieving a
the sprint. Enterprises are achieving a 5x engineering velocity increase when
5x engineering velocity increase when incorporating Blitzy as their preide
incorporating Blitzy as their preide development tool, pairing it with their
development tool, pairing it with their coding co-pilot of choice to bring an AI
coding co-pilot of choice to bring an AI native SDLC into their org. Ready to 5x
native SDLC into their org. Ready to 5x your engineering velocity? Visit
your engineering velocity? Visit blitzy.com to schedule a demo and start
blitzy.com to schedule a demo and start building with Blitzy today
>> if we could. I'm going to move on to number seven. Machines that build
number seven. Machines that build machines AI designed a CPU in half a day
machines AI designed a CPU in half a day and now it wants to put data centers in
and now it wants to put data centers in orbit. So here's the article um that uh
orbit. So here's the article um that uh you know prompted me to have this
you know prompted me to have this conversation about you know machines
conversation about you know machines building machines. We're seeing
building machines. We're seeing recursive self-improvement
recursive self-improvement happening at a faster and faster and a
happening at a faster and faster and a more fundamental rate than ever before.
more fundamental rate than ever before. So, an AI agent called Design Conductor
So, an AI agent called Design Conductor uh by Veor autonomously built a 1.5 GHz
uh by Veor autonomously built a 1.5 GHz Linux capable risk uh risk 5 CPU uh from
Linux capable risk uh risk 5 CPU uh from concept to tape out in 12 hours
concept to tape out in 12 hours compressing a quarterly engineering
compressing a quarterly engineering cycle into a lunch break. um
cycle into a lunch break. um pretty extraordinary and so uh you know
pretty extraordinary and so uh you know here's the here's the actual numbers uh
here's the here's the actual numbers uh Veror AI did this particular design task
Veror AI did this particular design task in 12 hours we can see in the chart here
in 12 hours we can see in the chart here while the traditional engineering team
while the traditional engineering team would have normally taken 90 days now
would have normally taken 90 days now maybe this is a little bit overplayed uh
maybe this is a little bit overplayed uh I'm sure it's not just 90 days I'm sure
I'm sure it's not just 90 days I'm sure that they were saving time along the way
that they were saving time along the way but what we're seeing over and over
but what we're seeing over and over again is AI being able to do, you know,
again is AI being able to do, you know, 0 to completion on its own, uh,
0 to completion on its own, uh, iterating faster than any humans. Um,
iterating faster than any humans. Um, Alex, this is recursive self-improvement
Alex, this is recursive self-improvement starting to break out of the software
starting to break out of the software loop. Uh, this is the innermost at least
loop. Uh, this is the innermost at least portion or maybe a rivallet of the the
portion or maybe a rivallet of the the innermost loop where it's you see this
innermost loop where it's you see this recursive self-improvement which would
recursive self-improvement which would otherwise be software optimizing
otherwise be software optimizing software starting to eat through its
software starting to eat through its container. it's eating down to the the
container. it's eating down to the the EDA uh electronic design automation
EDA uh electronic design automation level of designing risk five cores and
level of designing risk five cores and then it I think it's going to eat
then it I think it's going to eat further out and redesign the data
further out and redesign the data centers and the energy supplies and then
centers and the energy supplies and then the entire economy. So it is in one
the entire economy. So it is in one sense very satisfying to to see this
sense very satisfying to to see this happening and another sense maybe a
happening and another sense maybe a person who's slightly more skeptical
person who's slightly more skeptical that that this is that this represents a
that that this is that this represents a broader trend would say well of course
broader trend would say well of course it was able to to automatically design a
it was able to to automatically design a risk 5 core risk 5 has all of these unit
risk 5 core risk 5 has all of these unit tests so it's it's easy to define
tests so it's it's easy to define verifiable rewards and then you can do
verifiable rewards and then you can do RL and all of these other things you can
RL and all of these other things you can iterate uh and put react loops on it
iterate uh and put react loops on it because you you have an easy way knowing
because you you have an easy way knowing whether a given architecture uh a given
whether a given architecture uh a given floor plan for the chip works or not
floor plan for the chip works or not because it's such a common architecture.
because it's such a common architecture. But I I think that cynical perspective
But I I think that cynical perspective completely overlooks how remarkable it
completely overlooks how remarkable it is that we're now at the stage of
is that we're now at the stage of recursive self-improvement where the
recursive self-improvement where the thing is designing its own chips.
thing is designing its own chips. >> And not only here it's going to be
>> And not only here it's going to be robots building robots, it's going to be
robots building robots, it's going to be everything. Um so here's the here's a
everything. Um so here's the here's a couple of questions. You know, so if a
couple of questions. You know, so if a senior design engineer earns $400,000 uh
senior design engineer earns $400,000 uh and a full tape out team costs, you
and a full tape out team costs, you know, millions over the course of a
know, millions over the course of a year, if AI collapses it to a lunch
year, if AI collapses it to a lunch break, what happens to the 50,000
break, what happens to the 50,000 hardware engineers currently working? Uh
hardware engineers currently working? Uh where do they get applied?
where do they get applied? Oh, I don't think that I think that
Oh, I don't think that I think that vision is flawed in a huge way in that
vision is flawed in a huge way in that right now because the cost of
right now because the cost of engineering a new chip is so high, we
engineering a new chip is so high, we all use the same GPU and CPU for every
all use the same GPU and CPU for every single task, even though it's nowhere
single task, even though it's nowhere near optimal for that task. What this
near optimal for that task. What this unlocks is chip designs that are
unlocks is chip designs that are specific to the use case that are
specific to the use case that are probably about a factor of 10 more
probably about a factor of 10 more efficient and maybe more. And if you
efficient and maybe more. And if you think, well, we're going to spend $23
think, well, we're going to spend $23 trillion
trillion on these chips over the next couple
on these chips over the next couple years and these data centers. If you can
years and these data centers. If you can unlock a 10x performance improvement for
unlock a 10x performance improvement for a use case, that has hundreds of
a use case, that has hundreds of billions of dollars of implications. So
billions of dollars of implications. So all 50,000 of those engineers are going
all 50,000 of those engineers are going to be useful using the AI for all the
to be useful using the AI for all the different use cases, for all the
different use cases, for all the different chip designs. Also, the fab
different chip designs. Also, the fab doesn't care a wit. like you can change
doesn't care a wit. like you can change the mask every day for a different
the mask every day for a different design and still get the same throughput
design and still get the same throughput through the factory. So it's the the
through the factory. So it's the the fabs don't care a wit if there are tens
fabs don't care a wit if there are tens of thousands of different designs
of thousands of different designs instead of us all using the exact same
instead of us all using the exact same CPU for everything. So it's just a huge
CPU for everything. So it's just a huge unlock.
unlock. >> I I love the way we're using trillions
>> I I love the way we're using trillions and trillions now on a regular basis
and trillions now on a regular basis where just you know a couple years ago
where just you know a couple years ago was billions and billions. It's feel you
was billions and billions. It's feel you can feel the acceleration. We need a new
can feel the acceleration. We need a new TV series that is called Trillions, Not
TV series that is called Trillions, Not Billions, for sure. Um, I I want to hit
Billions, for sure. Um, I I want to hit a few other stories quickly before we
a few other stories quickly before we get to our AMA segment. And these are
get to our AMA segment. And these are stories that didn't fit in the other
stories that didn't fit in the other categories. And again, please give us
categories. And again, please give us your feedback on the format here today.
your feedback on the format here today. Are you enjoying it more? Um, we'd love
Are you enjoying it more? Um, we'd love to know. So, in other news, here we go.
to know. So, in other news, here we go. Um
Um uh the DOE announced about $300 million
uh the DOE announced about $300 million for the Genesis project uh inviting
for the Genesis project uh inviting teams to leverage AI across 20 national
teams to leverage AI across 20 national challenges spanning manufacturing,
challenges spanning manufacturing, biotech and energy. Of course, the
biotech and energy. Of course, the Genesis project is about the US actually
Genesis project is about the US actually uh you know using its national labs and
uh you know using its national labs and the data contained within national labs
the data contained within national labs uh to accelerate and expand uh uh the US
uh to accelerate and expand uh uh the US in its AI and scientific pursuits. Alex,
in its AI and scientific pursuits. Alex, >> I think it's a generally a good thing
>> I think it's a generally a good thing for the US to have an industrial policy
for the US to have an industrial policy and I think Genesis mission to the
and I think Genesis mission to the extent that for the first time at least
extent that for the first time at least from the Department of Energy's vantage
from the Department of Energy's vantage point it is starting to articulate grand
point it is starting to articulate grand challenges that are collectively part of
challenges that are collectively part of a broader industrial policy which the US
a broader industrial policy which the US hasn't had for decades. I think it's
hasn't had for decades. I think it's very important. So fusion obviously one
very important. So fusion obviously one uh one of the grand challenges I I think
uh one of the grand challenges I I think it's so important for to to the extent
it's so important for to to the extent we have a federal government that has a
we have a federal government that has a budget to to fund progress to put money
budget to to fund progress to put money behind grand challenges in general. So
behind grand challenges in general. So I'm I'm I'm in the weeds from a bunch of
I'm I'm I'm in the weeds from a bunch of different dimensions with the Genesis
different dimensions with the Genesis mission. actually uh Daario who's the uh
mission. actually uh Daario who's the uh leading uh the the relevant portions of
leading uh the the relevant portions of DOE on this. I worked with him as an
DOE on this. I worked with him as an undergrad at MIT and as an undergraduate
undergrad at MIT and as an undergraduate researcher. So so memories.
researcher. So so memories. >> It's a what a tangled web we weave. What
>> It's a what a tangled web we weave. What can I say? But I'm I'm I'm generally a
can I say? But I'm I'm I'm generally a big fan of of what Genesis is doing.
big fan of of what Genesis is doing. >> What concerns me here, Alex, is that um
>> What concerns me here, Alex, is that um this is great, right? These are like X-
this is great, right? These are like X- prizes in one sense that the
prizes in one sense that the government's going to be running but and
government's going to be running but and it's moving us in the direction that
it's moving us in the direction that China has been doing for a while now.
China has been doing for a while now. Yes.
Yes. >> But China is deploying hundreds of
>> But China is deploying hundreds of billions of dollars into state directed
billions of dollars into state directed AI investments and saying you know we
AI investments and saying you know we want fully development in uh in the
want fully development in uh in the architectures around robotics around
architectures around robotics around these AI models and so forth. This is a
these AI models and so forth. This is a relatively small amount of money for the
relatively small amount of money for the US government. Hopefully, it's just a
US government. Hopefully, it's just a first towin.
first towin. >> It's true. But on the other hand, I I
>> It's true. But on the other hand, I I would argue China distorts its markets
would argue China distorts its markets so much relative to to what if if you
so much relative to to what if if you compare US industrial policy distortion
compare US industrial policy distortion versus Chinese industrial policy
versus Chinese industrial policy distortion, they're not in the same
distortion, they're not in the same league. And we have much deeper private
league. And we have much deeper private capital markets that China lacks. I I
capital markets that China lacks. I I like our odds on balance much more than
like our odds on balance much more than China's. H um our next article here is
China's. H um our next article here is the rural Ohio Ohioans seek a
the rural Ohio Ohioans seek a constitutional amendment uh to ban data
constitutional amendment uh to ban data centers over 25 megawws in the state and
centers over 25 megawws in the state and you know this is the ultimate nimi not
you know this is the ultimate nimi not my backyard and it's pretty extreme I
my backyard and it's pretty extreme I mean to go after an you know a
mean to go after an you know a constitutional amendment this is a
constitutional amendment this is a genuine grassroots revolt at the end of
genuine grassroots revolt at the end of the day um and I Peter, we need a new
the day um and I Peter, we need a new acronym like
acronym like >> not not not in my backyard like yes in
>> not not not in my backyard like yes in my orbital plane. Okay
my orbital plane. Okay >> or something. This is just going to
>> or something. This is just going to drive all these data centers to orbit.
drive all these data centers to orbit. >> But this is this is crazy. I mean these
>> But this is this is crazy. I mean these communities don't realize the amount of
communities don't realize the amount of wealth these data centers are going to
wealth these data centers are going to create for them. I think it's about $10
create for them. I think it's about $10 billion per gawatt of invested power
billion per gawatt of invested power >> or invested. They'll miss them when they
>> or invested. They'll miss them when they see them in the night sky.
>> Yeah, obviously utterly insane. And utterly insane to use a constitutional
utterly insane to use a constitutional amendment for this purpose. I mean, to
amendment for this purpose. I mean, to to point out the obvious, the data
to point out the obvious, the data centers are are tiny as a footprint on
centers are are tiny as a footprint on land. They're absolutely tiny. And the
land. They're absolutely tiny. And the wealth that they create is astronomical
wealth that they create is astronomical for the neighborhood they're in. So,
for the neighborhood they're in. So, there's got to be a much better way to
there's got to be a much better way to make a win-win than to ban something
make a win-win than to ban something that's obviously going to benefit your
that's obviously going to benefit your state tremendously. But, look, put that
state tremendously. But, look, put that aside. You're in California. Alex and I
aside. You're in California. Alex and I are in Massachusetts. The way we make
are in Massachusetts. The way we make decisions through legislators is so
decisions through legislators is so messed up.
messed up. >> Yeah.
>> Yeah. >> Like that something like this could even
>> Like that something like this could even get proposed is ludicrous. And that's
get proposed is ludicrous. And that's what really needs to change because when
what really needs to change because when you talk to the governors, they're like,
you talk to the governors, they're like, I don't want this.
I don't want this. >> Like, okay, well, you we're a
>> Like, okay, well, you we're a representative democracy. They're
representative democracy. They're supposed to be very, very smart people
supposed to be very, very smart people thinking about complex issues and then
thinking about complex issues and then deciding what happens. You don't throw
deciding what happens. You don't throw things like this out to a referendum of
things like this out to a referendum of people who just got laid off.
people who just got laid off. >> And it's and people saying, you know, my
>> And it's and people saying, you know, my access to clean water and energy and my,
access to clean water and energy and my, you know, my consumer price index of
you know, my consumer price index of energy is going through the roof. And
energy is going through the roof. And there's other ways to deal with this um
there's other ways to deal with this um then instead of banning it uh by
then instead of banning it uh by constitutional amendment. That's for me
constitutional amendment. That's for me that's insane. All right. Uh
that's insane. All right. Uh >> I've never seen a data center that
>> I've never seen a data center that affected the water supply. That's like
affected the water supply. That's like it's so I hear it all the time. is
it's so I hear it all the time. is utterly ludicrous. The data center needs
utterly ludicrous. The data center needs a fixed amount of water to cool itself.
a fixed amount of water to cool itself. It doesn't drink the water. It just goes
It doesn't drink the water. It just goes around in a circle.
around in a circle. >> It's nuts.
>> It's nuts. >> Uh all right. Uh another story worth
>> Uh all right. Uh another story worth mentioning is Nvidia
mentioning is Nvidia won approval to sell uh its H200 chips,
won approval to sell uh its H200 chips, its most advanced chips, uh to Beijing.
its most advanced chips, uh to Beijing. That's a big deal. Um and in fact, you
That's a big deal. Um and in fact, you know, the well the realization is the
know, the well the realization is the ban didn't work.
ban didn't work. uh China both was getting access to chip
uh China both was getting access to chip through third parties and China was
through third parties and China was developing its own competitive and this
developing its own competitive and this cost Nvidia tens of billions of dollars
cost Nvidia tens of billions of dollars and since it's not working in fact it's
and since it's not working in fact it's stimulating a homegrown homegrown you
stimulating a homegrown homegrown you know uh equivalent of Nvidia in China
know uh equivalent of Nvidia in China they said uh let's reverse policy
they said uh let's reverse policy question is is it too late
question is is it too late >> yeah that everything that you just said
>> yeah that everything that you just said is correct except the one part where
is correct except the one part where when Jensen complains he lost tens of
when Jensen complains he lost tens of billions of dollars every single thing
billions of dollars every single thing he's manufactured is sold out for years
he's manufactured is sold out for years to come.
to come. >> So, the fact that it didn't go to China,
>> So, the fact that it didn't go to China, it definitely sold. Even if it was like
it definitely sold. Even if it was like a de, you know, a a dysfunctional 880
a de, you know, a a dysfunctional 880 design or whatever that did, the, you
design or whatever that did, the, you know, Chinese design, it still got sold.
know, Chinese design, it still got sold. Everyone everyone in the world wants
Everyone everyone in the world wants these things. So, that he didn't
these things. So, that he didn't actually lose any money. I'm surprised
actually lose any money. I'm surprised though because I think I think the
though because I think I think the embargo or the ban didn't work. You're
embargo or the ban didn't work. You're dead right. Uh, China is doing its own
dead right. Uh, China is doing its own thing now. But I also think that if you
thing now. But I also think that if you say, "Well, let's start selling them
say, "Well, let's start selling them again. Maybe they'll stop." Not that
again. Maybe they'll stop." Not that that's not going to you cut them off.
that's not going to you cut them off. They're not going to forget like that.
They're not going to forget like that. That isn't going to happen. So, I was
That isn't going to happen. So, I was really surprised that you know that they
really surprised that you know that they reverse course on this. I don't know.
reverse course on this. I don't know. Any comments further?
Any comments further? >> Yeah, if if I'm Beijing, I mean, on the
>> Yeah, if if I'm Beijing, I mean, on the one hand, I I read the same stories and
one hand, I I read the same stories and of course variety of Chinese frontier AI
of course variety of Chinese frontier AI labs are all slurping up as many H200s
labs are all slurping up as many H200s as they can get. And of course it
as they can get. And of course it borderline obvious that the black wells
borderline obvious that the black wells are are now the frontier. So in some
are are now the frontier. So in some sense Beijing is being kept a half step
sense Beijing is being kept a half step or two behind the frontier chips
or two behind the frontier chips available to the US AI labs. I think the
available to the US AI labs. I think the story behind the story not not to be
story behind the story not not to be overly speculative but if I were the
overly speculative but if I were the Chinese Communist Party I'd be doing the
Chinese Communist Party I'd be doing the moral equivalent of having people taste
moral equivalent of having people taste my water at this point uh in terms of
my water at this point uh in terms of these chips. Nvidia's been very public
these chips. Nvidia's been very public about how there are variety of counter
about how there are variety of counter measures that can be put in place to
measures that can be put in place to prevent the wrong chips from ending up
prevent the wrong chips from ending up in the wrong locations.
in the wrong locations. I I I would and this is based on stories
I I I would and this is based on stories that I've read, stories where the
that I've read, stories where the Chinese government is suspicious at the
Chinese government is suspicious at the at the circuit level of uh American
at the circuit level of uh American chips. Uh I I have to imagine that
chips. Uh I I have to imagine that they're looking now at our chips with
they're looking now at our chips with renewed scrutiny to see what else is in
renewed scrutiny to see what else is in these chips that we're shipping to them.
these chips that we're shipping to them. what what algorithms are embedded deep
what what algorithms are embedded deep inside and we've seen this in the
inside and we've seen this in the opposite direction. All right, here's a
opposite direction. All right, here's a story, Alex, that you and I have enjoyed
story, Alex, that you and I have enjoyed talking about. Scientists successfully
talking about. Scientists successfully froze an entire pig brain while locking
froze an entire pig brain while locking in the cellular activity with minimal
in the cellular activity with minimal damage. Uh this is cryogenics and it's
damage. Uh this is cryogenics and it's happening in a large mammal. Of course,
happening in a large mammal. Of course, the pig has organs uh heart, liver,
the pig has organs uh heart, liver, lung, kidney, and brain on the order of
lung, kidney, and brain on the order of human organs. So this is this is
human organs. So this is this is significant. Um
significant. Um >> so actually I I I played a minor role in
>> so actually I I I played a minor role in the story and I'm not subject to
the story and I'm not subject to confidentiality in the story. So I can
confidentiality in the story. So I can tell the story. This is from this is
tell the story. This is from this is from a company I informally advise named
from a company I informally advise named Nectto. Uh and um I have another company
Nectto. Uh and um I have another company with uh that where the the founder of
with uh that where the the founder of Nectto is also involved. This is Eon
Nectto is also involved. This is Eon focusing on whole brain uploading and
focusing on whole brain uploading and emulation. Nectome, which I'm not
emulation. Nectome, which I'm not formally involved with, is focused on
formally involved with, is focused on just the preservation side. And I I had
just the preservation side. And I I had been nudging them like they have these
been nudging them like they have these amazing results, publish the results,
amazing results, publish the results, they publish the results. And it it is
they publish the results. And it it is so wonderful to see now for the first
so wonderful to see now for the first time real competition in call it the
time real competition in call it the cryionics space or the preservation
cryionics space or the preservation space since the way nect works isn't
space since the way nect works isn't quite the same way as say the way 21st
quite the same way as say the way 21st century medicine, which we've spoken
century medicine, which we've spoken about previously on on the pod, works.
about previously on on the pod, works. 21 cm is more focused on vitrification.
21 cm is more focused on vitrification. Nect is more focused on a type of
Nect is more focused on a type of chemical preservation. But nonetheless,
chemical preservation. But nonetheless, >> you know what the cryo the
>> you know what the cryo the cryopreservant is? I mean, this is and
cryopreservant is? I mean, this is and just to describe it to the uh to our
just to describe it to the uh to our listenership, you're basically at or
listenership, you're basically at or near death. You're replacing the blood
near death. You're replacing the blood supply with something that goes and
supply with something that goes and fundamentally uh you know infiltrates
fundamentally uh you know infiltrates the cells and keeps the water in the
the cells and keeps the water in the cells from crystallizing and destroying
cells from crystallizing and destroying the structure in the cells.
You're you're you're searching your latest model to find.
latest model to find. >> Yeah. No, I'm I'm I'm double checking to
>> Yeah. No, I'm I'm I'm double checking to see how much they've made public. Um so
see how much they've made public. Um so so maybe let me just talk about it at a
so maybe let me just talk about it at a high level. So uh so it's it's a
high level. So uh so it's it's a chemical technique. It's it's a little
chemical technique. It's it's a little bit less focused on uh vitrification
bit less focused on uh vitrification with the whole point of vitrification on
with the whole point of vitrification on the 21 cm side is uh is basically
the 21 cm side is uh is basically ensuring that ice crystals don't form
ensuring that ice crystals don't form and that there isn't uh strong osmotic
and that there isn't uh strong osmotic pressure, reverse osmotic pressure that
pressure, reverse osmotic pressure that that cause cells to explode. On the nect
that cause cells to explode. On the nect side, it's a chemical process. I I'm
side, it's a chemical process. I I'm I'll be cautious with what I say because
I'll be cautious with what I say because I haven't I I need to check to see what
I haven't I I need to check to see what what's in the public and and what isn't
what's in the public and and what isn't about the process. Um but the the more
about the process. Um but the the more important I I think result here in
important I I think result here in addition to uh Nectto putting out I
addition to uh Nectto putting out I think their their first bioarchchive
think their their first bioarchchive paper in years since their original
paper in years since their original paper that won the brain preservation
paper that won the brain preservation foundation award for demonstrating local
foundation award for demonstrating local preservation of the structure of of
preservation of the structure of of neurons is that now they've demonstrated
neurons is that now they've demonstrated in in full public view scaling this
in in full public view scaling this process up to an entire uh mamalian
process up to an entire uh mamalian brain a large mamalian brain not even a
brain a large mamalian brain not even a mouse brain. So I I think we're we're
mouse brain. So I I think we're we're finding ourselves in in a near future
finding ourselves in in a near future slashpresent where finally we have
slashpresent where finally we have enough data to to be confident that
enough data to to be confident that entire mamalian brains are being
entire mamalian brains are being preserved and this immediately raises
preserved and this immediately raises the question which is the question I ask
the question which is the question I ask almost everyone why where are all of the
almost everyone why where are all of the cryionics patients why don't we have
cryionics patients why don't we have billions of people now that we have a
billions of people now that we have a growing body of evidence that brain
growing body of evidence that brain structure can be preserved by whichever
structure can be preserved by whichever technique whether it's nect on the one
technique whether it's nect on the one side 21 cm on the other why don't we
side 21 cm on the other why don't we have a billion people signing up for
have a billion people signing up for cryionics and and I would again to the
cryionics and and I would again to the audience sign up for cryionics like just
audience sign up for cryionics like just >> this is the way this is the way you get
>> this is the way this is the way you get to see the 23rd century yes
to see the 23rd century yes >> it's a I I got uh I I heard from uh from
>> it's a I I got uh I I heard from uh from the head of ALOR after my last call to
the head of ALOR after my last call to action to to do Quranics apparently lots
action to to do Quranics apparently lots of people flooded into ALOR it's a
of people flooded into ALOR it's a nonprofit I make no money off of saying
nonprofit I make no money off of saying this no financial trust. Just get
this no financial trust. Just get yourself a cryionics plan as part of a
yourself a cryionics plan as part of a portfolio for longevity. Period. Love
portfolio for longevity. Period. Love it. Love it. Um, I want to take a second
it. Love it. Um, I want to take a second and just say thank you to Nick Singh and
and just say thank you to Nick Singh and and uh and Dana Khan, our producers for
and uh and Dana Khan, our producers for supporting us on this new format. I
supporting us on this new format. I enjoyed it. Did you guys enjoy it?
enjoyed it. Did you guys enjoy it? >> I love it. It just feels organized.
>> I love it. It just feels organized. >> Yeah. Well, it feels organized and fun.
>> Yeah. Well, it feels organized and fun. It actually feels fun to think through
It actually feels fun to think through the topics with you guys.
the topics with you guys. >> It's like an entire episode worth of
>> It's like an entire episode worth of AMA. Yeah, with ourselves. All right.
AMA. Yeah, with ourselves. All right. >> Changes the stories we cover too, you
>> Changes the stories we cover too, you know, because we we normally go through
know, because we we normally go through the most important stories to change
the most important stories to change your life. But here, when you put it
your life. But here, when you put it into themes, you actually dig up other
into themes, you actually dig up other stories that are related to the primary
stories that are related to the primary topic that you otherwise would have
topic that you otherwise would have missed. So, I love that.
missed. So, I love that. >> All right. Uh here we go. Uh let's pick
>> All right. Uh here we go. Uh let's pick one each from page one and one from page
one each from page one and one from page two. Uh uh Alex, would you go first?
two. Uh uh Alex, would you go first? >> Oh, so many good options. Okay. Um we
>> Oh, so many good options. Okay. Um we we'll start with number two. Where
we'll start with number two. Where should entrepreneurs actually run their
should entrepreneurs actually run their AI compute? Local hardware, AWS cloud or
AI compute? Local hardware, AWS cloud or iPhone. And that comes from Frank Gerard
iPhone. And that comes from Frank Gerard Marketing.
Marketing. There isn't a good answer. There are at
There isn't a good answer. There are at least no single good answer. Lots of
least no single good answer. Lots of decent answers. There are benefits to
decent answers. There are benefits to each. So with local hardware, you have
each. So with local hardware, you have greater control, greater confidentiality
greater control, greater confidentiality and data privacy. you're going to on the
and data privacy. you're going to on the other hand end up maintaining said local
other hand end up maintaining said local hardware. You have to worry about your
hardware. You have to worry about your own backups can be a pain in the neck
own backups can be a pain in the neck from variety of different perspectives.
from variety of different perspectives. With AWS or one of the many other public
With AWS or one of the many other public clouds, you don't have to worry about
clouds, you don't have to worry about that. That's abstracted away. On the
that. That's abstracted away. On the other hand, you might have to compete
other hand, you might have to compete ferociously for access to say GPU
ferociously for access to say GPU resources. You're competing with other
resources. You're competing with other tenants for common resources. You might
tenants for common resources. You might have to worry on margin depending on how
have to worry on margin depending on how familiar you or your organization are
familiar you or your organization are with with OPSSEAC and and cyber
with with OPSSEAC and and cyber security. You might have greater surface
security. You might have greater surface for attack. On the other hand, you have
for attack. On the other hand, you have more more scalability. Uh with the
more more scalability. Uh with the iPhone, you have it it's sort of the
iPhone, you have it it's sort of the ultimate edge device until all of us and
ultimate edge device until all of us and not just some of us are running
not just some of us are running foundation models on our watches and our
foundation models on our watches and our smart glasses, which is already
smart glasses, which is already happening and is going to be more evenly
happening and is going to be more evenly distributed. You have even greater
distributed. You have even greater privacy. So I I don't think this is I
privacy. So I I don't think this is I maybe this goes without saying I I don't
maybe this goes without saying I I don't think this should be viewed as a black
think this should be viewed as a black or white or binary trade-off. There is a
or white or binary trade-off. There is a spectrum from edge edge compute to data
spectrum from edge edge compute to data centers at the core. I I think the best
centers at the core. I I think the best answer actually is I want to run my AI
answer actually is I want to run my AI compute in the Dyson swarm. Uh and that
compute in the Dyson swarm. Uh and that Dyson swarm will be perfect blend when
Dyson swarm will be perfect blend when fully realized in a few years of uh of
fully realized in a few years of uh of data centers. We'll have lots of maybe
data centers. We'll have lots of maybe if if Elon statistics are to be believed
if if Elon statistics are to be believed 100 kilowatt nodes filling the sky, but
100 kilowatt nodes filling the sky, but also it'll be incredibly elastic. If if
also it'll be incredibly elastic. If if we're disassembling the moon to fire off
we're disassembling the moon to fire off new 100 kowatt nodes in in the the the
new 100 kowatt nodes in in the the the stellar or Dyson swarm fabric, it
stellar or Dyson swarm fabric, it perfect blend.
perfect blend. >> Uh okay, Dave, which one you going to
>> Uh okay, Dave, which one you going to choose? said one thing on this topic
choose? said one thing on this topic only because I spent the whole weekend
only because I spent the whole weekend uh dorking around on Amazon AWS Bedrock
uh dorking around on Amazon AWS Bedrock which is a great choice by the way even
which is a great choice by the way even though if my bed was made of rock it
though if my bed was made of rock it would feel like getting started on
would feel like getting started on Amazon Bedrock. I mean, that's that's
Amazon Bedrock. I mean, that's that's it's it's a brutal get up and running
it's it's a brutal get up and running process on bedrock, but it does the
process on bedrock, but it does the critical thing that you need, which is
critical thing that you need, which is it captures all of your prompt history
it captures all of your prompt history for you and any teammates that you have
for you and any teammates that you have into easy to manage S3 buckets. So your
into easy to manage S3 buckets. So your AI can analyze everything that you've
AI can analyze everything that you've done, which is a critically important
done, which is a critically important function. So that may be available
function. So that may be available elsewhere, too, but it's it's probably
elsewhere, too, but it's it's probably as good a choice as any. But whatever
as good a choice as any. But whatever you do, don't just start running on some
you do, don't just start running on some random hardware and then lose all the
random hardware and then lose all the prompt history. So that's just an easy
prompt history. So that's just an easy way to capture it.
way to capture it. >> Pick a number.
>> Pick a number. >> Uh, I'll take number three. Are humanoid
>> Uh, I'll take number three. Are humanoid robots overengineered? Would it be more
robots overengineered? Would it be more efficient to isolate basic needs like
efficient to isolate basic needs like food, water, and clothes and automate
food, water, and clothes and automate those directly instead from goite FB3GN?
those directly instead from goite FB3GN? Um, short answer, yes, absolutely. So
Um, short answer, yes, absolutely. So why are we putting all this energy into
why are we putting all this energy into humanoid robots? The reason we're
humanoid robots? The reason we're putting all the energy into humanoid
putting all the energy into humanoid robots is because AI kind of came into
robots is because AI kind of came into the world almost overnight and we're in
the world almost overnight and we're in a race to capital right now. And so
a race to capital right now. And so what's critical for all these projects
what's critical for all these projects and startups is getting funded. The
and startups is getting funded. The humanoids are so much more visually
humanoids are so much more visually appealing that they're easier to fund.
appealing that they're easier to fund. They're also easier to recruit into. And
They're also easier to recruit into. And that'll unlock the supply chain of all
that'll unlock the supply chain of all the parts and that'll unlock all of the
the parts and that'll unlock all of the other robots that you know that farm and
other robots that you know that farm and create clothes and whatever which will
create clothes and whatever which will probably not look all that humanoid. But
probably not look all that humanoid. But you know when you look at the
you know when you look at the Gigafactory like Peter and I did vast
Gigafactory like Peter and I did vast majority of the automation there is not
majority of the automation there is not humanoid robots. It's machines that look
humanoid robots. It's machines that look like you know machines doing their jobs.
like you know machines doing their jobs. Yeah.
Yeah. >> And then the humanoids just operate
>> And then the humanoids just operate those machines. So I I think they are
those machines. So I I think they are overengineered and overinvested relative
overengineered and overinvested relative to where we'll end up. for a very good
to where we'll end up. for a very good reason. You should think about like
reason. You should think about like visual appeal and capital raising are a
visual appeal and capital raising are a core core part of this step function
core core part of this step function we're living in right now.
we're living in right now. >> So I want to go with number four. How
>> So I want to go with number four. How can AI be used to end a war? Not as a
can AI be used to end a war? Not as a weapon but as an impartial negotiator uh
weapon but as an impartial negotiator uh that all parties could trust. This is
that all parties could trust. This is from jnind 5. So I find that absolutely
from jnind 5. So I find that absolutely fascinating and I do think uh it's a a
fascinating and I do think uh it's a a powerful tool if you haven't used a
powerful tool if you haven't used a large language model for negotiations.
large language model for negotiations. Um one of the things is we don't know
Um one of the things is we don't know how to think other than the way we know
how to think other than the way we know how to think. So being able to put
how to think. So being able to put yourself in the mindset of another
yourself in the mindset of another individual um is extraordinarily
individual um is extraordinarily powerful. If you haven't said listen I'm
powerful. If you haven't said listen I'm you know I'm anti-guns. my my neighbor,
you know I'm anti-guns. my my neighbor, my friend, my spouse loves guns, you
my friend, my spouse loves guns, you know, can you please help me uh explain
know, can you please help me uh explain to them my feelings in a way that lands
to them my feelings in a way that lands with them uh and isn't viewed as
with them uh and isn't viewed as offensive? Uh you can get some, you
offensive? Uh you can get some, you know, extreme uh uh you know, support on
know, extreme uh uh you know, support on your negotiation skills. Um, and at the
your negotiation skills. Um, and at the end of the day, I think this is one of
end of the day, I think this is one of the most exciting unexplored
the most exciting unexplored applications for AI because the system
applications for AI because the system can ingest also every peace treaty,
can ingest also every peace treaty, every negotiation script, every conflict
every negotiation script, every conflict resolution framework that's ever been
resolution framework that's ever been had and can, you know, model outcomes
had and can, you know, model outcomes with no tribal allegiance. One of the
with no tribal allegiance. One of the biggest challenges we have as humans is
biggest challenges we have as humans is we have these cognitive biases and these
we have these cognitive biases and these tribal biases that are driving us. So
tribal biases that are driving us. So you know can we use this for
you know can we use this for negotiation? Absolutely. Um and I think
negotiation? Absolutely. Um and I think both sides if you set as an objective
both sides if you set as an objective function that you want to reach a
function that you want to reach a balanced solution that both sides have
balanced solution that both sides have and both sides are using AI. I mean it
and both sides are using AI. I mean it could be different models. I think the
could be different models. I think the probability of getting to a solution is
probability of getting to a solution is much much higher. Um we are biased when
much much higher. Um we are biased when we're dealing with humans. Uh and one of
we're dealing with humans. Uh and one of the things that goes on when you're
the things that goes on when you're talking for example to an AI model for
talking for example to an AI model for uh you know psychological therapy
uh you know psychological therapy um when you realize you're you know
um when you realize you're you know you're telling your innermost thoughts
you're telling your innermost thoughts to a human you feel like you're going to
to a human you feel like you're going to be judged but you don't feel judged when
be judged but you don't feel judged when you're talking to an AI model. And so I
you're talking to an AI model. And so I think there's real value to be had here.
think there's real value to be had here. Um I don't know if Sem's back online or
Um I don't know if Sem's back online or not but
not but >> he's not. But if if if if I might add
>> he's not. But if if if if I might add just one thing to to your point, Peter,
just one thing to to your point, Peter, something that I'm seeing more and more
something that I'm seeing more and more in the past few months, not for for war,
in the past few months, not for for war, but for commercial negotiation. I'm
but for commercial negotiation. I'm seeing this all the time. Two parties
seeing this all the time. Two parties that are at loggerheads in a commercial
that are at loggerheads in a commercial negotiation. One of the parties will
negotiation. One of the parties will bring in a frontier model and ask the
bring in a frontier model and ask the frontier model what the commercially
frontier model what the commercially reasonable outcome is. Bring it to the
reasonable outcome is. Bring it to the other side. The other side will will
other side. The other side will will consult their model and they'll come to
consult their model and they'll come to rapid agreement. Yeah,
rapid agreement. Yeah, >> I'm seeing this happen now over and over
>> I'm seeing this happen now over and over again.
again. >> All right, here's the next eight
>> All right, here's the next eight questions from our AMA. Uh, Alex, over
questions from our AMA. Uh, Alex, over to you.
to you. >> Sure. Again, there are so many fun
>> Sure. Again, there are so many fun questions here.
questions here. >> Uh, rather than choose eight, which
>> Uh, rather than choose eight, which would require me to give implicit
would require me to give implicit investment advice. Number seven, I'll
investment advice. Number seven, I'll avoid that one. Number six, uh, less
avoid that one. Number six, uh, less slightly less interesting. I'll tackle
slightly less interesting. I'll tackle number five since since I've been
number five since since I've been beating the drum a bit for for solve
beating the drum a bit for for solve everything including disease. So the
everything including disease. So the question is once AI solves most diseases
question is once AI solves most diseases how soon will treatments be available to
how soon will treatments be available to everyone? Will access lag? And this is
everyone? Will access lag? And this is from Katis 896.
from Katis 896. So maybe the sub question first is when
So maybe the sub question first is when do I think AI has a decent chance of
do I think AI has a decent chance of solving most diseases? My timeline and
solving most diseases? My timeline and this is not specific to me. I I think if
this is not specific to me. I I think if you ask the the more optimistic elements
you ask the the more optimistic elements of CZI, the Chan Zuckerberg Initiative,
of CZI, the Chan Zuckerberg Initiative, Biohub, maybe ARC Institute and some
Biohub, maybe ARC Institute and some other organizations,
other organizations, maybe anthropic on a good day. I think
maybe anthropic on a good day. I think they'll say something like 5 years from
they'll say something like 5 years from now. So 5 years is pretty rapid time
now. So 5 years is pretty rapid time scale. Uh it's more rapid in in many
scale. Uh it's more rapid in in many cases than what historically has been
cases than what historically has been the the clinical trial process end to
the the clinical trial process end to end through three phases. So the second
end through three phases. So the second sub question here is how soon will
sub question here is how soon will treatments be available to everyone? If
treatments be available to everyone? If if say tomorrow one of the frontier labs
if say tomorrow one of the frontier labs says all right here here are the cures
says all right here here are the cures to the top 5,000 unsolved or untreatable
to the top 5,000 unsolved or untreatable diseases. We have vast computational
diseases. We have vast computational experiments
experiments demonstrating to to the satisfaction of
demonstrating to to the satisfaction of all experts that these are the cures or
all experts that these are the cures or at least the treatments for these
at least the treatments for these diseases. How soon would those
diseases. How soon would those treatments be broadly available? Under
treatments be broadly available? Under the present regime, which by the way is
the present regime, which by the way is not the same as the regime even one year
not the same as the regime even one year ago, there would still be probably a
ago, there would still be probably a multi-year process. The FDA has recently
multi-year process. The FDA has recently announced two major developments that
announced two major developments that are I think relevant here. one, the FDA
are I think relevant here. one, the FDA uh under this administration has decided
uh under this administration has decided to adopt a basian perspective as opposed
to adopt a basian perspective as opposed to a frequentist perspective, meaning
to a frequentist perspective, meaning that they're willing to incorporate for
that they're willing to incorporate for the first time in history evidence in
the first time in history evidence in terms of clinical approvals from outside
terms of clinical approvals from outside a particular drug. And that's that's a
a particular drug. And that's that's a huge sea change. It means that in in
huge sea change. It means that in in principle, drug approvals can operate
principle, drug approvals can operate much more quickly because they can take
much more quickly because they can take into account lots of pre-existing
into account lots of pre-existing information that predated the particular
information that predated the particular drug. Second big development, a move
drug. Second big development, a move from, and this was again relatively
from, and this was again relatively recently announced by this FDA, from a
recently announced by this FDA, from a twoclinical trial process to a
twoclinical trial process to a oneclinical trial process for certain
oneclinical trial process for certain cases, expediting the approval process.
cases, expediting the approval process. So I I think fast forwarding to is a
So I I think fast forwarding to is a long-winded answer to how soon will
long-winded answer to how soon will treatments be available to everyone. I
treatments be available to everyone. I think if tomorrow or five years from now
think if tomorrow or five years from now a frontier lab or multiple frontier lab
a frontier lab or multiple frontier lab said here are the very well motivated
said here are the very well motivated top 5,000 cures to everything I think we
top 5,000 cures to everything I think we would see similar developments from the
would see similar developments from the FDA to go to a zero clinical trial model
FDA to go to a zero clinical trial model given enough basian evidence and given
given enough basian evidence and given enough computational evidence which is
enough computational evidence which is to say a zero trial model. I I think
to say a zero trial model. I I think there would be so much political
there would be so much political pressure that we would probably, barring
pressure that we would probably, barring some exceptional circumstance, see
some exceptional circumstance, see relatively fast availability.
relatively fast availability. >> All right. So Dave, let's go to you.
>> All right. So Dave, let's go to you. >> Okay. Uh well, I'll take number eight
>> Okay. Uh well, I'll take number eight since Alex couldn't touch it and we've
since Alex couldn't touch it and we've lost the lame. Um if you had to choose
lost the lame. Um if you had to choose one public company to bet on in the age
one public company to bet on in the age of AI, which one and why from Matthew
of AI, which one and why from Matthew Johnson 6525? Uh, so we can't give
Johnson 6525? Uh, so we can't give investment advice obviously, but I will
investment advice obviously, but I will tell you I've said a bunch of times on
tell you I've said a bunch of times on the pod, go to 13f.info,
the pod, go to 13f.info, look up the situational awareness fund,
look up the situational awareness fund, which is Leopold Dashenbrunner, and
which is Leopold Dashenbrunner, and every quarter he has to file his his
every quarter he has to file his his holdings. He's killing it. And the
holdings. He's killing it. And the reason he's killing it is because he
reason he's killing it is because he listens to exactly what Alex is always
listens to exactly what Alex is always saying. Look for the innermost loop.
saying. Look for the innermost loop. Find, you know, the the tailwinds in
Find, you know, the the tailwinds in equities and assets are like nothing
equities and assets are like nothing you've ever seen. But you have to be in
you've ever seen. But you have to be in the AI loop to be relevant. And so
the AI loop to be relevant. And so you'll see all Leopold's holdings are
you'll see all Leopold's holdings are somehow in the centerpiece of the
somehow in the centerpiece of the innermost loop. And so those are the
innermost loop. And so those are the things you want to own. So those include
things you want to own. So those include things that are chip fabs, things that
things that are chip fabs, things that have power, things that are related to
have power, things that are related to chip design, things that are algorithmic
chip design, things that are algorithmic that are directly deriving use cases.
that are directly deriving use cases. Those are all in that fund. So that's
Those are all in that fund. So that's your road map. So look at his holdings
your road map. So look at his holdings and then generalize from there and
and then generalize from there and you'll find lots of great stuff that you
you'll find lots of great stuff that you should be you should be buying. Also,
should be you should be buying. Also, you know, W2 income is going to get
you know, W2 income is going to get pummeled in this next three years, but
pummeled in this next three years, but assets, you know, holdings and ownership
assets, you know, holdings and ownership and things is going to go through the
and things is going to go through the roof. So, so buy stuff, you know,
roof. So, so buy stuff, you know, whether it's equities, p public or
whether it's equities, p public or private, real estate, you know, things
private, real estate, you know, things that'll appreciate. That's what you need
that'll appreciate. That's what you need uh in this next three-year window.
uh in this next three-year window. >> Amazing. I am under time pressure
>> Amazing. I am under time pressure myself. I've got to jump on a uh a film
myself. I've got to jump on a uh a film recording. I'm going to go to our outro
recording. I'm going to go to our outro music here, uh, which is, uh, brought to
music here, uh, which is, uh, brought to us by CJ Truheart. Um, it was a piece he
us by CJ Truheart. Um, it was a piece he developed for the Abundance Summit
developed for the Abundance Summit called Moonshot Minds. Uh, take a
called Moonshot Minds. Uh, take a listen. Love the lyrics. Uh, here we
listen. Love the lyrics. Uh, here we are. Thank you to CJ for Moonshot Minds.
Not because it's easy. That's the moonshot dare.
That's the moonshot dare. Bold beat safe.
Bold beat safe. We're already there.
We're already there. Moonshot mind. We're riding a future
Moonshot mind. We're riding a future code.
code. Shoot for the moon. Paving the
Shoot for the moon. Paving the untraveled road.
untraveled road. The future's not a fantasy. We're making
The future's not a fantasy. We're making it appear.
it appear. We build what was said impossible. Year
We build what was said impossible. Year after year
after year mind.
The future's what we build when you're bold enough to start.
A billion lives get better when you think beyond the wall.
think beyond the wall. Sci-fi turns to science facts for those
Sci-fi turns to science facts for those who hear the call.
who hear the call. Where the skeptics see the ceiling, we
Where the skeptics see the ceiling, we see stars to reach. Every exponential
see stars to reach. Every exponential future starts with those who are dead at
future starts with those who are dead at astral
astral build fast and learn to fly.
build fast and learn to fly. Smash the spot where sci-fi meets the
Smash the spot where sci-fi meets the sky. Moon shot mind. We're riding a
sky. Moon shot mind. We're riding a future cold.
future cold. Shoot for the moon.
Shoot for the moon. Baby travel road.
Baby travel road. The future's not a fantasy. We're making
The future's not a fantasy. We're making it up here.
it up here. We built what was impossible. Year after
We built what was impossible. Year after year
year our minds
our minds away
the future won't be built when you're bold enough to stop.
>> All right, gentlemen. >> Video gets better and better all the
>> Video gets better and better all the time.
time. >> It gets better all the time. AWG DB2,
>> It gets better all the time. AWG DB2, uh, this was fun. Wishing you guys an
uh, this was fun. Wishing you guys an extraordinary day. Uh Salem who's
extraordinary day. Uh Salem who's airborne to Brazil. Safe travels, buddy.
airborne to Brazil. Safe travels, buddy. Uh soon we'll be putting you on rocket
Uh soon we'll be putting you on rocket rides to get you there. Anyway, thank
rides to get you there. Anyway, thank you to everybody for listening. Uh
you to everybody for listening. Uh please tell or hyperloop. Yeah. Yeah, I
please tell or hyperloop. Yeah. Yeah, I guess we can do Hyperloop under the uh
guess we can do Hyperloop under the uh under the Gulf. Anyway, long story
under the Gulf. Anyway, long story short, thank you for listening. Uh
short, thank you for listening. Uh please join us. If you've not
please join us. If you've not subscribed, please do. We're putting out
subscribed, please do. We're putting out these podcasts on a a cadence that you
these podcasts on a a cadence that you want to get alerts. So, uh, our mission
want to get alerts. So, uh, our mission help you see an abundant world. The
help you see an abundant world. The world is getting better at an
world is getting better at an extraordinary rate. The technologies to
extraordinary rate. The technologies to solve the world's biggest problems. If
solve the world's biggest problems. If you're an entrepreneur, thank you for
you're an entrepreneur, thank you for being an entrepreneur. Entrepreneurs are
being an entrepreneur. Entrepreneurs are individuals who find juicy problems and
individuals who find juicy problems and solve problems. The more entrepreneurs
solve problems. The more entrepreneurs on the planet, the better Earth and
on the planet, the better Earth and humanity is. Gentlemen, until I see you
humanity is. Gentlemen, until I see you next time, be well. Peter D. Mandis,
next time, be well. Peter D. Mandis, your host, signing off.
your host, signing off. >> See you guys. Take care.
>> See you guys. Take care. >> Have a good movie, Peter.
>> Have a good movie, Peter. >> Thanks, pal. If you made it to the end
>> Thanks, pal. If you made it to the end of this episode, which you obviously
of this episode, which you obviously did, I consider you a moonshot mate.
did, I consider you a moonshot mate. Every week, my moonshot mates and I
Every week, my moonshot mates and I spend a lot of energy and time to really
spend a lot of energy and time to really deliver you the news that matters. If
deliver you the news that matters. If you're a subscriber, thank you. If
you're a subscriber, thank you. If you're not a subscriber yet, please
you're not a subscriber yet, please consider subscribing so you get the news
consider subscribing so you get the news as it comes out. I also want to invite
as it comes out. I also want to invite you to join me on my weekly newsletter
you to join me on my weekly newsletter called Metatrends. I have a research
called Metatrends. I have a research team. You may not know this, but we
team. You may not know this, but we spend the entire week looking at the
spend the entire week looking at the meta trends that are impacting your
meta trends that are impacting your family, your company, your industry,
family, your company, your industry, your nation. And I put this into a
your nation. And I put this into a two-minute read every week. If you'd
two-minute read every week. If you'd like to get access to the Metatrends
like to get access to the Metatrends newsletter every week, go to
newsletter every week, go to diamandis.com/tatrends.
Thank you again for joining us today. It's a blast for us to put this together
It's a blast for us to put this together every week.
Klicke auf einen beliebigen Text oder Zeitstempel, um direkt zu dieser Stelle im Video zu springen
Teilen:
Die meisten Transkripte sind in unter 5 Sekunden bereit
Mit einem Klick kopieren125+ SprachenInhalt durchsuchenZu Zeitstempeln springen
YouTube-URL einfügen
Gib den Link eines beliebigen YouTube-Videos ein und erhalte das vollständige Transkript
Transkript-Extraktionsformular
Die meisten Transkripte sind in unter 5 Sekunden bereit
Unsere Chrome-Erweiterung installieren
Transkripte abrufen, ohne YouTube zu verlassen. Installiere unsere Chrome-Erweiterung und greife mit einem Klick direkt auf der Wiedergabeseite auf das Transkript jedes Videos zu.
