00:00:00
we're seven minutes in and we've
00:00:01
produced absolutely nothing that will go
00:00:03
in the show
00:00:08
hey burgers
00:00:11
ax I'm actually angry at sax for not
00:00:14
publishing my AMA from the other day
00:00:17
crashed we had such a crowded room we
00:00:20
had some people in the room for like
00:00:22
four hours it was crazy it was like the
00:00:25
original days of Clubhouse everyone I
00:00:27
know that was trying to get in was
00:00:28
texting saying they couldn't get in so
00:00:29
it definitely capped out right I know
00:00:31
well we hit we hit some scalability you
00:00:33
may want to buy an extra server sax
00:00:35
cheap weren't you the same guy who was
00:00:37
responsible for scaring PayPal no that
00:00:39
was somebody else that was eBay
00:00:41
they sold it before it's killed no no
00:00:43
that's not true we had huge scalability
00:00:45
challenges at PayPal too it seems like a
00:00:47
theme yeah the theme is when you have an
00:00:49
app that's breaking out you hit
00:00:51
scalability challenges it's called a
00:00:53
high class problem 2 000 people is not a
00:00:55
high class problem it's a trickle it's
00:00:58
2022. 2 000 people participate in the
00:01:00
conversation is
00:01:02
a child I haven't written code in 20
00:01:04
years here's what you do when you get to
00:01:06
a thousand people coming to the room
00:01:07
that's a lot everybody else is in
00:01:08
passive for you've never written code
00:01:10
ever of course I of course I drive
00:01:12
that's a lie come on be honest oh yeah
00:01:13
it's actually been 25. the last time I
00:01:15
wrote code was Lotus Notes it's true
00:01:19
and let your winners ride
00:01:23
[Music]
00:01:26
we open source it to the fans and
00:01:29
they've just gone crazy
00:01:30
[Music]
00:01:34
so there have been three cheating
00:01:36
scandals across poker chess and even
00:01:38
competitive uh fishing I don't know if
00:01:40
you guys saw the fishing one but they
00:01:42
found weights and fillets during a fish
00:01:44
uh way and that everybody wants us to
00:01:47
check in on the chess and the poker
00:01:48
scandals
00:01:50
chess.com just released their report
00:01:53
that this Grand Master has been
00:01:55
suspended they have evidence he cheated
00:01:58
basically in a bunch of
00:02:02
uh tournaments that were in fact for
00:02:04
money he denied that he had done that
00:02:06
but he had previously cheated as a kid
00:02:07
they now have the statistical proof that
00:02:10
he was playing essentially perfect chess
00:02:13
and uh they've outlined this in like
00:02:15
hundreds of pages in a report Saxy what
00:02:19
are your thoughts on this uh scandal in
00:02:20
chess Magnus Carlson finally came out
00:02:22
and explained why he thought Hans Neiman
00:02:25
was cheating basically he got the strong
00:02:27
perception during the game that Hans
00:02:29
wasn't really putting in a lot of effort
00:02:31
that he wasn't under a lot of stress and
00:02:33
he's he it's his experience that when
00:02:35
he's playing you know the top players
00:02:37
they're intensely concentrating and the
00:02:40
Hans demon didn't seem to be exerting
00:02:41
himself at all so his you know hackles
00:02:44
were raised and got suspicious and then
00:02:46
you know he has had this meteoric rise
00:02:48
the fastest rise in classical chess
00:02:51
rating ever and I guess there were he
00:02:54
had gotten suspended from chess.com in
00:02:56
the past for cheating so on this basis
00:02:58
and maybe other things that Magnus isn't
00:03:00
telling us Magnus basically said that
00:03:01
this guy is cheating I think the maybe
00:03:03
the interesting part of this is that
00:03:05
there's been a lot of analysis now of
00:03:06
Hans Neiman's games and I just think the
00:03:09
methodology is kind of interesting so
00:03:10
what they do is they run all of his
00:03:13
games uh through a computer and they
00:03:16
compare his moves to the best computer
00:03:18
move and they basically assign a
00:03:20
percentage that correlation matches the
00:03:23
correlation matches the computer move
00:03:25
and what they found is there were a
00:03:27
handful of games where it was literally
00:03:28
a hundred percent that's basically
00:03:30
impossible without cheating I mean you
00:03:31
look at the top players who through an
00:03:34
entire career have never had 100 game
00:03:36
that you know chess is so subtle that
00:03:38
the computer can now see so many moves
00:03:41
into the future that nailing the best
00:03:43
move every single time for 40 50 100
00:03:45
moves is just and and and in chess which
00:03:47
a human really can't do that well is
00:03:49
that there are positional sacrifices
00:03:51
that you will make in short lines that
00:03:54
pay off much much later in the future
00:03:55
which is impossible for a human to
00:03:57
calculate and so you know and you saw
00:03:59
this by the way when uh I think it was
00:04:02
the it it was the Google AI the deepmind
00:04:04
AI that also play chess so the idea that
00:04:07
this guy could pay could play absolutely
00:04:09
perfectly according to those lines is
00:04:12
only possible if you're cheating right
00:04:14
exactly so so there were a handful of
00:04:15
games at 100 and then there were
00:04:17
tournaments where his percentages were
00:04:19
in the 70s something plus and so just to
00:04:22
give you some basic comparison Bobby
00:04:24
Fischer during his legendary 20-game
00:04:26
winning streak uh was at 72 percent so
00:04:30
he only matched the computer so for best
00:04:32
move 72 of the time Magnus Carlson
00:04:34
playing at his best is 70 percent uh
00:04:38
Gary Kasparov in his career was 69 and
00:04:41
then the you know the the super GM's
00:04:44
category are typically in the 64 to 68
00:04:46
range so I think it's really interesting
00:04:49
actually how you can now quantify by
00:04:51
comparing the human move to the best
00:04:53
computer move and it's multiple
00:04:55
computers training the best they
00:04:57
actually have it provides a way to
00:04:59
assess who the greatest player ever is
00:05:01
um I actually thought that it was Magnus
00:05:02
but now maybe there's a basis for
00:05:04
believing it was Bobby Fischer because
00:05:06
he was at 72 and Max is only at 70.
00:05:08
however look the idea that Hans Neiman
00:05:10
is in the 70s 80s or 90s during
00:05:13
tournaments would be you know just an
00:05:16
off the charts level of play and if if
00:05:19
he's not cheating then we should expect
00:05:21
over the next couple of years that he
00:05:24
should rapidly become the world's number
00:05:26
one player over the board uh you know
00:05:29
now that they have all this
00:05:30
anti-cheating stuff right so it'll be
00:05:32
interesting to see what happens in his
00:05:33
career now that they've really cracked
00:05:35
down on you know on with with anti-anti
00:05:39
Gene technology I have a general
00:05:40
observation which is these people are
00:05:42
complete losers
00:05:45
the people that cheat in any of these
00:05:47
games don't understand
00:05:49
this basic simple idea which is that
00:05:51
trying is a huge part of The Human
00:05:53
Experience the whole point is to be out
00:05:56
there in the field of play trying and
00:05:59
it's basically taking the wins and the
00:06:01
losses and getting better that is the
00:06:03
path that's what's fun once you actually
00:06:05
win it's actually not that much fun
00:06:08
because then you have this pressure of
00:06:09
maintaining Excellence that's a lot less
00:06:12
enjoyable than the path to getting there
00:06:14
and so the fact that these people don't
00:06:16
understand that makes them slightly
00:06:18
broken in my opinion and then the other
00:06:21
thing is like why is it that we have
00:06:23
this strain of people now that are just
00:06:25
so devoid of any personal responsibility
00:06:28
that they'll just so brazenly take
00:06:30
advantage of this stuff it's really
00:06:32
ridiculous to be they don't it's really
00:06:34
sad these people are pathetic it's
00:06:36
really pathetic this but it is really
00:06:38
interesting how they caught him and
00:06:40
running this against the computer here's
00:06:41
a chart of um his scores in these
00:06:44
tournaments oh well here is this first
00:06:46
chart is how quickly he Advanced which
00:06:49
was off the charts and then the second
00:06:51
chart that's really interesting is
00:06:54
um his chest.com strength so if you
00:06:55
don't know chess.com it has become like
00:06:56
a juggernaut in the Chess World
00:06:57
especially after that HBO series
00:07:01
came out a lot of people subscribe to it
00:07:02
I subscribed to it I like to play chess
00:07:04
there
00:07:05
and man you look at the chest strength
00:07:06
score there
00:07:07
he was just like perfect and then the
00:07:09
number of games he likely cheated in you
00:07:11
can see the last two columns he's
00:07:12
basically cheating in every game
00:07:14
you know Queen's Gambit yeah great show
00:07:16
on Netflix
00:07:18
and he he said he didn't cheat in any uh
00:07:21
of the uh games
00:07:23
where they were live streaming but
00:07:25
they've proven that wrong sax how does
00:07:27
he cheat in person then well that's the
00:07:29
thing no one really knows and I don't
00:07:31
want to overly judge it until they have
00:07:33
hard proof that he was cheating I mean
00:07:36
look here's the thing he was never
00:07:37
caught in the act it's just that the
00:07:39
computer evidence you know seems pretty
00:07:41
damning
00:07:42
and I mean here's the thing is I don't
00:07:44
know how they prove I don't know how
00:07:46
they prove that he was cheating over the
00:07:47
board without actually catching him
00:07:49
doing it and I don't I still don't think
00:07:51
anyone really has a good theory in terms
00:07:53
of how he was able to do that well it's
00:07:55
not just him though like look the the
00:07:56
fishing thing Jason which was crazy I
00:07:58
think Friedberg shared the video this
00:08:00
guy was in a fishing competition and
00:08:02
they basically caught these fish and
00:08:03
then they put these big weighted pellets
00:08:06
inside the fish's body they even put
00:08:07
like a you know chicken breast and
00:08:09
chicken fillets inside of the things
00:08:12
yeah
00:08:14
you know uh then there's no crossover
00:08:17
now in poker everybody's afraid that
00:08:19
there are ways in which you can read the
00:08:22
RFID and some of the cards and some of
00:08:24
these you know televised situations and
00:08:26
and front run what the what the playing
00:08:27
situation is so that you know whether
00:08:29
you're winning or losing and again I
00:08:31
just asked the question like
00:08:33
is it is it is this are things that bad
00:08:35
that this is what it gets to like we all
00:08:38
play poker the idea that we would play
00:08:39
against somebody that would take that
00:08:41
edge
00:08:42
yeah it's gross yeah it's really makes
00:08:45
me really it's sad so disappointing yeah
00:08:48
it's horrible one one observation might
00:08:50
be that across all three because I'm
00:08:52
trying to find some common thread across
00:08:54
these but it could be that there was a
00:08:56
lot of cheating going on for a long time
00:08:59
and maybe the fact that we do have
00:09:02
so much digital imagery that's live on
00:09:06
these things now and so much coverage
00:09:08
and everyone's got a cell phone that
00:09:09
suddenly
00:09:11
our perception of the cheating in
00:09:14
competitive events is becoming
00:09:17
more tuned whereas maybe there's been a
00:09:19
lot of cheating for a long time and it's
00:09:22
just kind of coming to light I mean we
00:09:23
didn't have a lot of live streaming in
00:09:25
poker who knows I mean we could probably
00:09:28
ask Phil this we're kidding but like for
00:09:30
how many years well there was Visions
00:09:32
yeah well there were tons of cheating
00:09:34
and online poker yeah remember like
00:09:37
people are using these like software
00:09:39
programs that would uh track the hand
00:09:42
history yeah of your opposition yeah
00:09:45
exactly so
00:09:46
so it helped you assess whether the
00:09:48
person might be bluffing in that
00:09:49
particular situation yeah like he has
00:09:51
superhuman memory so I don't know if you
00:09:53
guys I don't know if you guys watch
00:09:54
Twitch like video games like fortnite or
00:09:56
whatever but there are like players that
00:09:59
have been accused of using the screen
00:10:01
overlay systems that basically more
00:10:03
accurately show you and drive the mouse
00:10:05
to where an individual is on the screen
00:10:07
so you can more accurately shoot them
00:10:08
and so there's software overlays that
00:10:11
make you a better
00:10:12
you know competitive video I'll tell you
00:10:14
what the through line is and then this
00:10:17
the stuff basically became like so now
00:10:19
what's interesting is now there's eye
00:10:21
tracking software that people are using
00:10:23
on Twitch streams to see if the
00:10:26
individual is actually spotting the
00:10:28
target when they shoot or if the
00:10:30
software is spotting the target yeah
00:10:31
they're called reverse team Bots they're
00:10:33
aimbots yeah yeah and like reverse cheap
00:10:35
the whole thing and I think what's
00:10:36
interesting is just that there's so much
00:10:38
you know Insight now so much more video
00:10:40
streams so much more I mean think about
00:10:42
all those guys
00:10:44
yeah and there's cell phones and they
00:10:46
all videoed this thing happening yeah I
00:10:48
think 10 years ago that wouldn't have
00:10:49
been the case and there wouldn't have
00:10:50
been a big story about it and so you
00:10:52
said there was a theme you wanted to uh
00:10:54
there was a threat I I think the theme
00:10:55
is is pretty obvious which is that
00:10:57
there's been an absolute decay
00:11:00
of personal responsibility people don't
00:11:03
feel like there's any
00:11:05
downside to cheating anymore and they're
00:11:07
not willing to take it upon themselves
00:11:09
to take a journey of wins and losses to
00:11:11
get better at something they want the
00:11:13
easy solution
00:11:14
the easy solve the quick answer you know
00:11:17
that gets them to some sort of finish
00:11:20
line that they've imagined for
00:11:21
themselves will solve all their problems
00:11:24
the problem is it doesn't solve any
00:11:26
problems and it just makes them a wholly
00:11:28
corrupt individual yeah okay so let's
00:11:30
talk about this Hustler Casino Live uh
00:11:33
cash game play there's this woman Robbie
00:11:36
who is a new player apparently she's
00:11:38
being staked in a very high stakes game
00:11:40
she's playing as a guy Garrett who is a
00:11:42
very very
00:11:44
um known winning cash game player
00:11:48
and it was a very strange hand on the
00:11:50
turn all the money gets in she says she
00:11:53
has a bluff catcher then she claims that
00:11:54
she had a thought she misread her hand
00:11:56
now people are saying that
00:11:59
the poker world seems to be 70 30 that
00:12:01
she cheated but people keep vacillating
00:12:04
back and forth
00:12:06
there was a lot of weird word salad that
00:12:08
she said that she had a bluff catcher
00:12:10
which would normally be an ace then she
00:12:12
said she thought she had a pair of
00:12:13
Threes And then she immediately said
00:12:15
afterwards that he was giving her too
00:12:17
much credit
00:12:18
they confronted her in the hallway she
00:12:20
gave the money back because she
00:12:21
supposedly loves production so all this
00:12:23
stuff sounds very weird one side says
00:12:25
okay while this is happening because
00:12:27
she's a new player the other side is
00:12:29
saying somebody was signaling her that
00:12:32
she was good and giving her just a
00:12:33
binary you're good because if you were
00:12:35
gonna cheat cheating with Jack high in a
00:12:38
situation where you just put all in for
00:12:39
a 200 quarter million dollar pot seems
00:12:41
very suspect
00:12:43
uh I don't know if you guys watched the
00:12:45
hand breakdown where does everybody
00:12:46
stand on a percentage basis I guess if
00:12:49
they think she was cheating or not
00:12:51
because we this is not definitive
00:12:52
obviously it's not like they cut it open
00:12:54
and found the ball bearings it's not
00:12:55
it's not so obvious in that situation
00:12:57
but I think the way that that line
00:12:59
played made no sense
00:13:03
and I guess in her previous hand she had
00:13:07
a jack three and there was a three on
00:13:09
the board so if she misread her hand was
00:13:12
ten ten nine three no but you would have
00:13:14
but you would have you would have had to
00:13:15
call the Flop so I'm thinking what
00:13:17
yeah no I get it the hand makes no sense
00:13:19
but I'm just trying to find a logical
00:13:21
explanation and that Jack three
00:13:23
explanation somebody kind of fed that to
00:13:26
her and then she changed her story to
00:13:28
that so this changing of the story is
00:13:30
the thing I was sort of keyed on
00:13:31
Friedberg is why does she keep changing
00:13:33
her story is it because she's
00:13:34
embarrassed maybe she's had a couple of
00:13:36
beverages or whatever
00:13:38
or she's just a new player and she's
00:13:40
embarrassed by her play and can't
00:13:42
explain it she can't explain the hand
00:13:43
history all of the things you're saying
00:13:45
are probable
00:13:46
I don't think that yeah I don't think
00:13:48
there's any data for us to have a
00:13:49
strongly held point of view on this I'm
00:13:52
just looking forward to us all playing
00:13:54
live yeah HCL Poker Live October 21st
00:13:57
minus David Sachs unfortunately to mop J
00:14:00
Cal gerthner
00:14:02
Stanley Tang Phil Hellmuth so we're
00:14:04
going to be playing on the same stream
00:14:05
we're gonna be playing on the same
00:14:07
screen same table I figured out how to
00:14:09
hack into the video stream for the
00:14:10
camera I just got my RFID sunglasses as
00:14:13
well I'm gonna read all your shitty
00:14:14
hands jaycal I'm gonna take your money
00:14:16
and I'm gonna buy your kids
00:14:18
for my 40th birthday Sky organized poker
00:14:22
in Tahoe okay and and we we brought in
00:14:25
the team from CBS that was the present
00:14:27
and they they taped it as if it was
00:14:29
being broadcast with whole cards and
00:14:31
commentators and we edited it into a
00:14:33
two-day show it was an incredible
00:14:35
birthday present I I it was it's one of
00:14:37
the greatest things that that anybody's
00:14:39
ever given me I appreciate
00:14:41
there was a one hour block where
00:14:44
somebody at the table said okay guys how
00:14:46
about we do a cheating free-for-all yes
00:14:48
where you could look at each other's
00:14:50
cards and you know you could sort of
00:14:52
help somebody else
00:14:53
switch cards whatever in that one hour
00:14:56
our beautiful home game of friendship
00:14:59
became Lord of the Flies I have never
00:15:02
seen so much hatred Angling
00:15:06
Behavior oh my God it was incredible
00:15:10
all that humans are capable of so I I
00:15:12
hope that we never uh we never we never
00:15:14
see cheating in our game yeah well we'll
00:15:16
see how it goes on October 21st at HCL
00:15:18
Poker Live
00:15:20
I'm excited I can't wait it should be a
00:15:22
lot of fun it should be a lot of fun oh
00:15:23
and uh we're not having any official 100
00:15:26
stuff but the fans uh some of the fans
00:15:28
who were at the um all in Summit
00:15:32
2022 are doing
00:15:34
uh their own 100 episode 100 meetups on
00:15:38
October 15th I think all in meetups.io
00:15:42
so there are fan meetups happening in
00:15:43
Zurich in a bunch of other places I'm
00:15:45
going to FaceTime into some of them and
00:15:46
just say hi to the fans you know it
00:15:47
might be like 10 people in a bar
00:15:49
somewhere
00:15:50
um I think the largest one is like Miami
00:15:52
or San Francisco are going to be like 50
00:15:53
people or something we should all
00:15:55
FaceTime it that'd actually be kind of
00:15:56
fun I I'm basically I told them to send
00:15:59
me an invite and I'll FaceTime in any
00:16:00
time this is next week when is this the
00:16:03
15th I think is this occurring
00:16:06
the October 15th it's a Saturday the
00:16:08
Saturday after the 100th episode people
00:16:09
are doing these all in meetups.io what's
00:16:12
next earlier this week it was reported
00:16:15
that Elon contacted Twitter's board and
00:16:18
suggested that they move forward with
00:16:20
closing the transaction at the original
00:16:21
terms and the original purchase price of
00:16:24
54.20 cents a share in the couple of
00:16:27
days since then and even as of right now
00:16:29
with some news reports coming out here
00:16:31
on Thursday morning
00:16:32
um it's it appears that there are still
00:16:34
some question marks around whether or
00:16:36
not the deal is actually going to move
00:16:38
forward at 5420 a share because Elon as
00:16:40
of right now the report said is still
00:16:43
asking for a financing contingency in
00:16:45
order to close and there's a lot of back
00:16:47
and forth on what the terms are
00:16:48
meanwhile the court case in Delaware is
00:16:50
continuing forward on whether or not
00:16:52
Elon breached his terms of the original
00:16:55
agreement to close and buy Twitter at
00:16:57
54.20 as we know leading up to the
00:17:01
signed deal or a post signing the deal
00:17:04
Elon put together a financing Syndicate
00:17:06
a combination of debt investors as well
00:17:09
as Equity co-investors with him to do
00:17:11
the purchase of Twitter at 54.20 a share
00:17:15
so the 40 some odd billion dollars of
00:17:17
capital that's needed was committed by a
00:17:20
set of investors that were going to
00:17:21
invest debt and equity and there's a big
00:17:23
question mark now on whether or not
00:17:25
those investors want to or would still
00:17:27
consummate the transaction with Elon
00:17:29
given how the markets have turned and
00:17:31
given how debt markets are trading and
00:17:33
Equity markets are trading so chimath
00:17:35
I'd love to hear your point of view on
00:17:37
what um hurdles Does Elon still have in
00:17:39
front of him does he still want to get
00:17:41
this done and is there still a financing
00:17:43
Syndicate that's standing behind him at
00:17:45
the original purchase price to get it
00:17:46
done
00:17:47
that's a great question
00:17:49
um maybe the best way to start is Nick
00:17:50
do you want to queue up what I said in
00:17:53
August 25th the lawsuit really boils
00:17:56
down to one very specific clause which
00:17:58
is the Pinnacle
00:18:00
question at hand which is there is a
00:18:02
specific performance
00:18:05
Clause that Elon signed up to
00:18:08
right which you know his lawyers could
00:18:11
have struck out and either chose not to
00:18:13
or you know couldn't get the deal done
00:18:15
without and that specific performance
00:18:18
Clause says that Twitter can't force him
00:18:21
to close at 5420 a share
00:18:24
and I think the the issue at hand at the
00:18:26
Delaware business court is going to be
00:18:28
that because Twitter is going to point
00:18:30
to all of these you know gotchas and
00:18:33
disclaimers that they have around this
00:18:35
bot issue
00:18:36
as their cover story
00:18:40
and I think that really you know this
00:18:43
kind of again builds more and more
00:18:44
momentum in my mind that the most likely
00:18:48
outcome here is a settlement where
00:18:51
you have to pay
00:18:53
the economic difference between where
00:18:56
the stock is now and 5420 which is more
00:18:58
than a billion dollars
00:19:00
or you close at some number below 54.20
00:19:05
cents a share
00:19:06
and I think that that is like you know
00:19:08
if you had to be a betting person that's
00:19:09
probably and if you look at the
00:19:11
the way the stock is traded and if you
00:19:14
also look at the way the options Market
00:19:15
trades that's what people are assuming
00:19:18
that there's a seven to ten billion
00:19:19
dollar swing
00:19:21
and if you impute that into the stock
00:19:22
price you kind of get into the fifty one
00:19:25
dollars a share kind of a an acquisition
00:19:27
price again I'm not saying that that is
00:19:29
right or should be right that's just
00:19:30
sort of what the market says yeah so so
00:19:33
I it turns out that you know sort of
00:19:35
like that
00:19:36
kind of guessed him it turned out to be
00:19:38
pretty accurate because the stock today
00:19:39
is at 51 this year so I think that the
00:19:42
specific performance thing is exactly
00:19:44
what this thing has always hinged on and
00:19:47
I think that there was a realization
00:19:48
that there were very few outs around how
00:19:51
that contractual term was written and
00:19:53
agreed to so there is an out in the
00:19:56
contract and that out says that I think
00:19:58
it's by April if um if the deal doesn't
00:20:02
get done by April then the banks can
00:20:04
walk away from their commitment to fund
00:20:06
the debt and if the banks walk away then
00:20:09
Elon does have a financing contingency
00:20:11
that allows him to walk away so the
00:20:14
actual set of events that have to happen
00:20:16
is those two things specifically get to
00:20:18
April so the banks can pass and say
00:20:20
we've changed our mind market conditions
00:20:22
are different and then Elon is able to
00:20:24
say well you know the banks just walked
00:20:26
away right now the banks if you look at
00:20:29
all of the debt that they've committed
00:20:30
to
00:20:31
while they committed at a point in time
00:20:34
when the debt markets were much better
00:20:36
than they are today in the last you know
00:20:39
six or seven months since they agreed to
00:20:41
do this the debt markets have been
00:20:43
clobbered and specifically junk bonds
00:20:45
and a bunch of junk bond debt the yields
00:20:48
that you have to pay so the price to get
00:20:49
that kind of debt has skyrocketed so
00:20:52
roughly back of the envelope math would
00:20:55
tell me that right now the banks are
00:20:56
offside between one and two billion
00:20:58
dollars because they're not going to be
00:21:00
able to sell this debt to anybody else
00:21:01
so I think the banks obviously want to
00:21:03
weigh out the problem is their only way
00:21:06
out is to run the shot clock off until
00:21:08
April so I think that's the dance that
00:21:10
they're in right now elon's trying to
00:21:13
find a way to solve you know for the
00:21:15
merger
00:21:16
I think Twitter is going to say we're
00:21:17
not going to give you a financing
00:21:18
contingency you have to bring the banks
00:21:20
in and close right now and then we will
00:21:22
not go to court otherwise we're going to
00:21:25
court
00:21:26
and so I think it's a very delicate
00:21:28
predicament that they're all in but my
00:21:30
estimate is that the equity is probably
00:21:32
20 percent offside so it's not a huge
00:21:35
thing he can make that up because he can
00:21:37
create Equity value like nobody's
00:21:38
business
00:21:39
the debt is way offside by a couple
00:21:41
billion dollars
00:21:43
which is hard to make back but I think
00:21:44
in the end you know given enough time
00:21:47
they can probably make that back
00:21:49
the best off in all of this are the
00:21:50
Twitter shareholders they're getting an
00:21:53
enormous premium to what that company is
00:21:55
worth today in the open market
00:21:57
and so I think this deal is going to
00:21:58
close it's probably going to close in
00:22:00
the next few weeks and had you bought
00:22:02
Twitter when we were talking about it in
00:22:04
August you would have made 25 in six
00:22:07
weeks and you know if the deal closes at
00:22:08
54 you would have made it you know a
00:22:10
third of your money in eight weeks which
00:22:12
is you know very hard to do in a market
00:22:14
if you're a GP at one of the funds like
00:22:17
Andreessen or Sequoia
00:22:19
and you had made this commitment to Elon
00:22:21
or even Larry Ellison a couple months
00:22:24
ago
00:22:25
do you fight against closing at 5420 do
00:22:28
you stick with the deal and support him
00:22:31
I mean what do you do given that the
00:22:32
premium is so much higher than where the
00:22:34
market would trade it at today some
00:22:35
people are saying the stock should be at
00:22:37
like 20 bucks a share or something the
00:22:38
average premium in an m a transaction in
00:22:40
the public markets is about 30 percent
00:22:43
so um and I think the fair value of
00:22:46
Twitter is around 32 to 35 bucks this
00:22:49
year
00:22:49
so you know it's not like he is
00:22:52
massively massively overpaying
00:22:55
and so you know I would just sort of
00:22:56
keep that in the realm of the possible
00:22:59
so like if you take 35 as the midpoint
00:23:02
fair value is really 45.50 so yeah he
00:23:06
paid 20 more than he should have but he
00:23:07
didn't pay a hundred percent more
00:23:09
so it's not as if you can't make that
00:23:11
Equity back as a private company
00:23:13
particularly because there's probably
00:23:15
ten dollars of fat in the stock if you
00:23:17
think about just Opex right in terms of
00:23:19
all the buildings they have maybe they
00:23:21
don't need as many employees maybe they
00:23:23
revisit salaries you know one thing is
00:23:25
when I looked at doing an activist play
00:23:27
at Twitter I think I mentioned this five
00:23:30
or six years ago one of the things that
00:23:31
I found was at that time Twitter was
00:23:33
running their own data centers and you
00:23:36
know the most obvious thing for me at
00:23:37
that time was like we're going to move
00:23:38
everything to AWS now I don't know if
00:23:40
that happened but I'm sure that if it
00:23:42
hasn't just bidding that out to Azure
00:23:45
gcp and AWS can raise you know three or
00:23:48
four billion dollars because I'm sure
00:23:49
those companies would want this kind of
00:23:51
an app on their cloud
00:23:53
so there's all kinds of things that I
00:23:55
think Elon can do as a private company
00:23:56
to make back maybe the small bit that he
00:23:59
overpaid and then he can get to the core
00:24:01
job of rebuilding this company to be
00:24:03
usable uh this product to be usable
00:24:05
because look I'll just speak as a user
00:24:06
right now
00:24:08
it has been decaying at a very very
00:24:11
rapid clip
00:24:12
and I think that his trepidation in
00:24:14
closing the merger
00:24:16
in part also even though he hasn't said
00:24:18
it has to do with the quality of the
00:24:20
experience it's just degraded it's not
00:24:22
as fun to use as it was during the
00:24:25
pandemic
00:24:26
um or even before the pandemic so
00:24:28
something is happening inside that app
00:24:30
that needs to get fixed and if he does
00:24:32
it he'll make a ton of money sort of
00:24:34
like what happened with Friendster and
00:24:35
Myspace and any social networking app
00:24:37
over time the quality degrades if it's
00:24:40
not growing it's shrinking and it gets
00:24:41
if it if it's if it's not growing and
00:24:43
also if the product hygiene isn't
00:24:46
enforced in code and product hygiene in
00:24:48
this case are this you know the spam
00:24:51
Bots you know the trolling it can really
00:24:55
take away from the experience yeah I
00:24:57
mean interestingly like if you think
00:24:59
back to the original the the starting
00:25:01
Days the original days of Twitter I
00:25:02
don't know if you guys remember you
00:25:04
would send in an SMS to do your Tweet
00:25:06
and then it would post up and other
00:25:08
people would get the SMS notification
00:25:10
and um it would crash all the time and
00:25:13
the apps were the app was notoriously
00:25:15
crashing it was poorly architected at
00:25:18
the beginning and some people have
00:25:19
argued that Twitter has had
00:25:22
a cultural technical incompetence from
00:25:25
the earliest days I think that's a
00:25:26
little harsh so I do think look Twitter
00:25:28
was known for what's called a fail whale
00:25:30
you know they used to have these fail
00:25:31
whales constantly
00:25:33
and they did hire people that attempted
00:25:36
to try to fix it I remember the the
00:25:38
funniest part of when I went in there
00:25:40
and said hey here's my plan and here's
00:25:42
what I want to do is literally a day or
00:25:44
two later the head of engineering quit I
00:25:46
can't remember who his name was but he
00:25:48
was just out the door uh but it is a
00:25:54
I think it is a team that has tried its
00:25:56
best
00:25:57
that probably at the edges definitely
00:26:00
made some technical miscalculations like
00:26:02
I said at that time the idea that any
00:26:04
app of that scale would use their own
00:26:06
data centers made note technical sense
00:26:09
whatsoever it made the app laggy it made
00:26:11
it hard to use it made it more prone to
00:26:13
downtime to your point
00:26:15
but that being said I would be shocked
00:26:17
if they haven't made meaningful
00:26:18
improvements because the stack of the
00:26:21
internet has gotten so much better over
00:26:22
the last seven years and so to your
00:26:24
point David if they didn't take
00:26:25
advantage of all these new abstractions
00:26:28
and mechanisms mechanisms to rebuild the
00:26:30
app or to rebuild search or to rebuild
00:26:32
you know how you know all these
00:26:35
infrastructure elements of the app work
00:26:36
I would be really surprised because then
00:26:38
what are they doing over there yeah well
00:26:40
look I mean to the point earlier besides
00:26:42
the product points there was a
00:26:44
a really good
00:26:46
a tweet I liked
00:26:49
that said for what it's worth I think
00:26:51
Elon will show us just how lean the
00:26:53
Silicon Valley Advertising companies can
00:26:54
be run at the very least it'll be an
00:26:56
interesting thought experiment for
00:26:58
spectators
00:27:00
um because if he does go in and actually
00:27:01
does significantly reduce Opex and head
00:27:03
count and the company does turn
00:27:05
profitable and he can grow it well look
00:27:07
it'll really by the way it'll really be
00:27:09
a beacon from the financial big
00:27:11
companies yeah from a financial
00:27:13
perspective there is ten dollars a share
00:27:15
in Opex cuts that he should make right
00:27:17
away just so that he is economically
00:27:19
break even and he looks like every other
00:27:21
m a transaction you know you paid a 30
00:27:23
premium and you bought a company there
00:27:26
is a lot of margin of safety there if
00:27:27
Elon does that so to your point there
00:27:29
probably is and there probably needs to
00:27:31
be a meaningful riff at Twitter I'm not
00:27:33
saying it's right I'm not saying it's
00:27:34
you know and I feel for the people that
00:27:36
may go through it but from a financial
00:27:37
perspective the math makes sense for him
00:27:40
to do that because then he is a break
00:27:43
even proposition on a go in m a
00:27:45
transaction and I think that there's
00:27:46
there's a lot of intelligent Financial
00:27:49
sense so that all the debt holders feel
00:27:51
like he's doing the right thing and all
00:27:53
the equity holders particularly see a
00:27:56
chance for them to make a decent return
00:27:57
here
00:27:58
all right well let's move on
00:28:01
conversation a great conversation
00:28:03
between
00:28:04
Dave Friedberg uh about
00:28:07
the Twitter transaction and now we're
00:28:09
being rejoined by our besties
00:28:13
yeah how was your cappuccino Jayco that
00:28:16
was great I have a I have a nice cold
00:28:18
brew here a nice iced Cobra and a nice
00:28:20
drip coffee I'm working but I'd love to
00:28:23
talk about topics that I'm not uh being
00:28:24
subpoenaed or depositioned about we will
00:28:27
have a lot to say in the coming weeks
00:28:28
I'd love to talk about topics that my
00:28:30
lawyers have advised me not to talk
00:28:31
about how Eerie was our prediction 51
00:28:34
bucks a sharing it is exactly where the
00:28:36
stock is right now that's Yuri
00:28:38
yeah uh all right lots of advances let's
00:28:41
keep going yeah speaking of Elon uh
00:28:43
Tesla AI day
00:28:45
was last week I actually went it was
00:28:47
great uh this is a recruiting event
00:28:49
where what did you do after Fel helmuth
00:28:52
and I went and uh I drove Phil home
00:28:54
youth home uh the end uh no it's a great
00:28:57
event and
00:28:59
it uh is essentially a giant recruiting
00:29:01
event hundreds of AI sorry sorry I'm
00:29:04
sorry I'm sorry can we just talk about
00:29:06
Phil hamley's non-sequitur in the group
00:29:08
chat about Ken Griffin
00:29:10
I mean oh yeah where he's just like I
00:29:14
made a joke about his net worth and what
00:29:16
he's going on what is going on we were
00:29:18
talking about the most serious of topics
00:29:20
and he just comes seven seconds to fill
00:29:22
it's what's going on seven seconds to
00:29:25
fill by the way I uh I I was uh texting
00:29:27
with uh Daniel negranu he did an
00:29:30
incredible podcast if you guys with Lex
00:29:32
Friedman if you haven't listened to it
00:29:34
the Daniel negronu uh pod with Lex is
00:29:37
incredible but I I was joking with
00:29:40
Daniel that there's a section where he's
00:29:41
talking about the greatest poker players
00:29:43
of all time and if you look in the bar
00:29:45
of YouTube it shows where the most
00:29:47
viewership was and it was exactly the 30
00:29:50
seconds he talks about helmuth and I
00:29:52
said to Daniel this must have been Phil
00:29:54
re-watching it over
00:30:05
so anyway the event was um uh super
00:30:08
impressive
00:30:10
Elon only spoke when he showed The
00:30:12
Optimist the new robot he's building a a
00:30:15
general purpose robot that will work in
00:30:17
the factories it's very early days but
00:30:20
they showed two versions of it and um he
00:30:22
said he thinks they could get it down to
00:30:24
twenty thousand dollars it's going to
00:30:25
work in the factory so it's actually got
00:30:26
a purpose
00:30:27
and obviously the factors already have a
00:30:29
ton of robots but this is more of a
00:30:31
robot that uh will benefit from the
00:30:34
general or the the computer vision
00:30:38
and the AI the narrow AI being pursued
00:30:40
by the self-driving team this is like
00:30:42
two and a half hours of really intense
00:30:43
presentations
00:30:45
um the most interesting part for me was
00:30:46
uh they're building their own super
00:30:48
computer
00:30:48
and their chips and the dojo
00:30:52
supercomputer was really impressive
00:30:55
um at how much they can get through uh
00:30:58
scenarios so they're building every
00:31:00
scenario of every self-driving I
00:31:01
actually have the full self-driving beta
00:31:04
on my car I've been using it it's pretty
00:31:06
impressive I have to say
00:31:08
if you haven't used it yet I feel like
00:31:12
AI is moving at a pretty Advanced clip
00:31:15
the past year if you haven't also seen
00:31:17
meta announced a text to video generator
00:31:20
so this is even more impressive than
00:31:22
dolly dolly you put in a couple of words
00:31:24
Friedberg and you get a painting or
00:31:26
whatever
00:31:27
this is put in a couple of words and you
00:31:29
get a short video so they had one of a
00:31:31
teddy bear painting a teddy bear
00:31:33
so it looks like you're going to be able
00:31:35
to
00:31:37
essentially create a whole movie by just
00:31:39
talking to a computer
00:31:41
really impressive where do you think we
00:31:43
are Freeburg in terms of
00:31:46
the compounding nature of these narrow
00:31:48
AI efforts you know obviously we saw
00:31:50
poker chess go Dolly gpt3 self-driving
00:31:55
it feels like this is all compounding at
00:31:57
a faster rate
00:31:59
or am I just you imagining that yeah
00:32:02
look I mean it's interesting when when
00:32:03
people saw the first computer playing
00:32:05
chess they said the same thing I think
00:32:07
any time that you see progress with a
00:32:09
computer that starts to mimic
00:32:11
the predictive capabilities of a human
00:32:14
it it it's uh it's impressive but I will
00:32:17
argue and I just I'll say a few words on
00:32:20
this it's I think this is part of a
00:32:22
60-year cycle that we've been going
00:32:24
through
00:32:25
um fundamentally what humans and human
00:32:28
brains do is we can sense our external
00:32:32
environment then we generate Knowledge
00:32:33
from that sensing and then our brains
00:32:36
build a model that predicts an outcome
00:32:38
and then that that predicted outcome is
00:32:40
what drives our actions and our Behavior
00:32:42
we observe the sunrise every morning
00:32:44
then we observe that it sets and you see
00:32:46
that enough times and you build a
00:32:47
predictive model from that data that's
00:32:49
been generated in your brain
00:32:51
that I predict that the sun has risen it
00:32:53
will therefore set it has said it will
00:32:55
therefore rise and I think that the
00:32:57
Computing approach is very similar it's
00:32:59
all about
00:33:00
sensing or generating data and then
00:33:02
creating a predictive model and then you
00:33:03
can drive action and initially the first
00:33:07
approach was just basic algorithms and
00:33:09
these are deterministic models that are
00:33:11
built it's a piece of code that says
00:33:13
here's an input here's an output and
00:33:15
that that model is really built by a
00:33:17
human and a human designed to design
00:33:18
that algorithmic model and said this is
00:33:20
what the uh the predictive potential of
00:33:24
the software is then there was this term
00:33:26
called data science so as data
00:33:28
generation began to proliferate meaning
00:33:30
there was far more sensors in the world
00:33:32
it was really cheap to to create Digital
00:33:34
Data from the physical world really
00:33:36
cheap to transmit it really cheap to
00:33:37
store it and really cheap to compute
00:33:39
with it data science became the hot term
00:33:41
in Silicon Valley for a while and these
00:33:43
models were not just a basic algorithm
00:33:45
written by a human but it became an
00:33:48
algorithm that was a similar
00:33:49
deterministic model
00:33:51
that had parameters and the parameters
00:33:54
were ultimately resolved by the data
00:33:56
that was being generated and so these
00:33:58
models became much more complex and much
00:34:00
more predictive finer granularities uh
00:34:02
finer range then we used this term
00:34:04
machine learning and in the data science
00:34:06
era it was still like hey there's a
00:34:09
model and you would solve it statically
00:34:12
you would get a bunch of data you would
00:34:13
statically solve for the parameters and
00:34:15
that would be your model and it would
00:34:16
run machine learning then allowed those
00:34:18
parameters to become Dynamic so the
00:34:21
model was static but generally speaking
00:34:23
the parameters
00:34:24
that drove the model became dynamic as
00:34:27
more data came into the system and they
00:34:29
were dynamically updated and then this
00:34:31
era of AI became and that's the new
00:34:33
catch word and what AI is realizing is
00:34:36
that there's so much data that rather
00:34:38
than just resolve the parameters of the
00:34:40
model you can actually resolve a model
00:34:42
itself the algorithm can be written by
00:34:45
the data the algorithm can be written by
00:34:46
the software and so with a with AI
00:34:49
example so poker plug an Adaptive model
00:34:52
so people uh so you're playing poker and
00:34:55
the software begins to recognize
00:34:56
behavior and it builds a predictive
00:34:58
model that says here's how you're
00:34:59
playing and then over time it actually
00:35:01
changes not just the parameters of the
00:35:03
model but the model itself the algorithm
00:35:05
itself and so Ai and then it eventually
00:35:07
gets to a point where the algorithm is
00:35:09
so much more complex that a human would
00:35:11
have never written it and suddenly the
00:35:13
AI has built its own intelligence its
00:35:15
own ability to be predictive in a way
00:35:16
that a human algorithmic programmer
00:35:18
would have never done and and this is
00:35:21
all driven by statistics so none of this
00:35:23
is new science person say there's new
00:35:25
techniques that all on their underlying
00:35:28
use statistics as their basis and then
00:35:30
there's these techniques that allow us
00:35:31
to build these new systems of model
00:35:33
development like neural Nets and so on
00:35:35
and those statistics build those neural
00:35:37
Nets they they solve those parameters
00:35:38
and so on but fundamentally there is an
00:35:41
um geometric increase in data and a
00:35:44
geometric decline in the cost to
00:35:46
generate data from sensors because the
00:35:48
cost of sensors is coming down with
00:35:49
Moore's Law transmit that data because
00:35:52
the cost of moving data has come down
00:35:53
with Broadband Communications the cost
00:35:55
of storing data because the cost of dram
00:35:57
and and solid-state hard drives has come
00:36:00
down with Moore's Law and now the
00:36:02
ability to actually have enough data to
00:36:04
do this AI driven where people are
00:36:06
calling AI but it really is the same
00:36:07
it's part of the spectrum of things that
00:36:09
have been going on for 60 years to
00:36:11
actually drive predictions in the in the
00:36:13
world is really being realized in a
00:36:15
bunch of areas that we would have
00:36:16
historically been really challenged and
00:36:18
surprised to see and so my argument is
00:36:21
at this point Big Data played a big role
00:36:23
yeah yeah we've over the last decade
00:36:25
we've reached this Tipping Point in
00:36:27
terms of data generation storage and
00:36:28
computation that's allowed these
00:36:30
statistical models to resolve
00:36:32
dynamically and as a result they are far
00:36:35
more predictive and as a result we see
00:36:37
far more human-like behavior in the
00:36:39
predictive systems both physics both
00:36:41
those that are you know like a like a
00:36:43
robot is the same as one that existed 20
00:36:45
years ago but the way that it's run is
00:36:47
using the software that is driven by
00:36:49
this Dynamic model and that data is for
00:36:52
a better answer
00:36:55
okay I have two things to say but one
00:36:57
the first one is a total non-sequitur so
00:36:59
use the term data scientist do you know
00:37:01
where the term data scientist came from
00:37:04
as classically used in Silicon Valley it
00:37:07
came from Facebook and it came from my
00:37:09
team in a critical moment this was in
00:37:11
2007 I was trying to 2008 I was trying
00:37:13
to build the growth team
00:37:15
this is the team that became very famous
00:37:17
for getting 2 billion users and you know
00:37:20
building a lot of these algorithmic
00:37:21
insights and I was trying to recruit a
00:37:25
person from Google and he was like a PhD
00:37:28
in some crazy thing like astrophysics or
00:37:30
particle physics or something and we
00:37:33
gave him an offer
00:37:34
has a data analyst because this is what
00:37:36
I needed at the time this is what I
00:37:37
thought I needed an analyst you know to
00:37:39
analyze data and he said absolutely not
00:37:43
I'm offended by the job title and I
00:37:45
remember talking to my my HR you know
00:37:47
business process partner and I asked her
00:37:49
like I don't understand what is this
00:37:51
where is this coming from and she said
00:37:52
he Fashions himself a scientist and I
00:37:55
said well then call him a data scientist
00:37:57
so we wrote in the offer for the first
00:37:59
time data scientist and at the time
00:38:01
people internally were like this is a
00:38:04
dumb title what does this mean anyways
00:38:06
we hired the guy he was a star and uh
00:38:09
and that title just took off internally
00:38:11
so Rob it's funny because parallel we
00:38:13
started climate Corp in 2006 and the
00:38:15
original the first guy I hired was a
00:38:17
buddy of mine who was a 4.0 for in
00:38:18
applied math from Cal and then everyone
00:38:20
we hired on with him we called them the
00:38:23
math team and they were all applied math
00:38:25
and statistics phds and we called them
00:38:27
the math team and it was really cool to
00:38:29
be part of the math team but then we
00:38:31
switched the team name to data scientist
00:38:33
and then it obviously created this much
00:38:35
more kind of impressive role impressive
00:38:38
title Central function to the
00:38:39
organization that was more than just a
00:38:42
math person or data analyst as I think
00:38:44
it may have been classically treated
00:38:45
because they really were building the
00:38:47
algorithms that drove the models that
00:38:49
made the product work right Peter Thiel
00:38:51
is a very funny observation not funny
00:38:53
but you know observation which is you
00:38:55
should always be wary of any science
00:38:57
that actually has science in the name
00:38:59
political science social science I guess
00:39:02
maybe data scientists you know because
00:39:04
the real Sciences don't need to qualify
00:39:05
themselves physics chemistry biology
00:39:07
anyways that's so here's what I wanted
00:39:09
to talk about with respect to AI two
00:39:12
very important observations that I think
00:39:14
is useful for people to know the first
00:39:17
one Nick if you throw it up here is just
00:39:18
a baselining of you know when we have
00:39:21
thought about intelligence and compute
00:39:23
capability we've always talked about
00:39:24
Moore's Law and Moore's Law essentially
00:39:27
this idea that there is a fixed amount
00:39:28
of time where the density of transistors
00:39:32
inside of a chip would double and
00:39:34
roughly that period for many many years
00:39:36
was around two years and it was largely
00:39:38
led by Intel and we used to equate this
00:39:41
to intelligence meaning the more density
00:39:43
there was in a chip the more things
00:39:46
could be learned and understood and we
00:39:49
used to think about that as the
00:39:50
progression of how Computing
00:39:53
intelligence would grow and eventually
00:39:55
Ai and artificial intelligence would
00:39:57
would get to mass Market
00:39:59
well what we are now at is a place where
00:40:02
many people have said Moore's law has
00:40:04
broken why it's because we cannot cram
00:40:08
any more transistors into a fixed amount
00:40:10
of area we are at the boundaries of
00:40:12
physics
00:40:14
and so people think well does that mean
00:40:16
that our ability to compute
00:40:18
will essentially come to an end and stop
00:40:20
and the answer is no and that's what's
00:40:22
demonstrated
00:40:23
on this next chart just to make it
00:40:25
simple which is that what you really see
00:40:28
is that if you think about you know
00:40:30
super computing power so the ability to
00:40:33
get to an answer that has actually
00:40:36
continued unabated and if you look at
00:40:39
this chart the reason why this is
00:40:41
possible is entirely because we've
00:40:43
shifted from CPUs to these things called
00:40:46
gpus so you may have heard companies
00:40:48
like Nvidia why is companies like Nvidia
00:40:50
done so well it's because they said they
00:40:53
raised their hand and said we can take
00:40:54
on the work
00:40:55
and by taking on the work away from a
00:40:58
traditional CPU you were able to do a
00:41:00
lot of what Freeburg said is get into
00:41:02
these very complicated models so this is
00:41:05
just an observation that I think that we
00:41:08
are continuing to compound knowledge and
00:41:11
intelligence effectively at the same
00:41:14
rate as Moore's Law and we will continue
00:41:17
to be able to do that because this makes
00:41:20
it a problem of power and a problem of
00:41:23
money so as long as you can buy enough
00:41:26
gpus from Nvidia or build your own
00:41:29
and as long as you can get access to
00:41:31
enough power to run those computers
00:41:34
there really isn't many problems you
00:41:36
can't solve and that's what's so
00:41:38
fascinating and interesting and this is
00:41:40
what companies like open AI
00:41:42
are really proving you know when they
00:41:43
raise the billion dollars what they did
00:41:46
was they attacked this problem because
00:41:48
they realize that by Shifting the
00:41:49
problem to gpus it left all these
00:41:52
amazing opportunities for them to
00:41:55
uncover and that's effectively what they
00:41:56
have the second thing that I'll say very
00:41:59
quickly is that
00:42:00
it's been really hard
00:42:02
for us as a society
00:42:04
to build intelligence
00:42:06
in a multi-modal way like our brain
00:42:09
works so think about how our brain works
00:42:10
our brain works in a multimodal way we
00:42:13
can process imagery we can process words
00:42:16
and sounds we can process all of these
00:42:19
different modes text
00:42:22
into one system and then Intuit some
00:42:26
intelligence from it and make a decision
00:42:27
right so you know we could be watching
00:42:30
this YouTube video there's going to be
00:42:31
transcription there's video voice audio
00:42:33
everything all at once
00:42:35
and we are moving to a place very
00:42:37
quickly where computers will have that
00:42:40
same ability as well today we go to very
00:42:42
specific models and kind of balkanized
00:42:45
silos correct to solve different kinds
00:42:47
of problems but those are now quickly
00:42:49
merging again because of what I just
00:42:51
said about gpus
00:42:52
so I think what's really important about
00:42:55
AI for everybody to understand is the
00:42:58
marginal cost of intelligence is going
00:43:00
to go to zero
00:43:01
and this is where I'm just going to put
00:43:03
out another prediction of my own when
00:43:06
that happens
00:43:07
it's going to be incredibly important
00:43:10
for humans to differentiate themselves
00:43:12
from computers and I think the best way
00:43:14
for humans to differentiate ourselves is
00:43:17
to be more human it's to be less compute
00:43:20
intensive it's to be more empathetic
00:43:23
it's to be more emotional a lot less
00:43:25
emotional because those differentiators
00:43:27
are very difficult for Brute Force
00:43:29
compute to solve be careful the
00:43:31
replicants on this call are getting a
00:43:33
little nervous here they're not
00:43:35
processing that that was an emotional
00:43:36
statement do not want to process that
00:43:38
one well I to your point uh during this
00:43:41
AI day
00:43:42
um they were showing in self-driving as
00:43:45
you're talking about this balkanization
00:43:46
and trying to
00:43:48
um make decisions across many different
00:43:50
uh decision trees you know they're
00:43:52
looking at Lane changes they're looking
00:43:54
at other cars and pedestrians they're
00:43:56
looking at road conditions like fog and
00:43:58
rain and then they're using all this big
00:44:01
data to your point Freeburg to run uh
00:44:04
tons of different simulations so they're
00:44:05
building like this virtual uh World on
00:44:09
Market Street and then they will throw
00:44:11
people dogs cars people about that into
00:44:15
the simulation it's such a wonderful
00:44:16
example imagine that system hears a horn
00:44:20
yeah well you hear a horn so clearly
00:44:23
there's some auditory expression of risk
00:44:26
right there's something risky
00:44:28
and now you have to scan your visual
00:44:30
field you have to probabilistically
00:44:32
decide what it could be what the evasive
00:44:35
maneuver if anything should be so that's
00:44:38
a multimodal set of intelligence that
00:44:41
today isn't really available yeah but we
00:44:44
have to get there if we're going to have
00:44:45
real full self-driving so that's a
00:44:47
perfect example Jason a real world
00:44:48
example of how hard the problem is but
00:44:50
it'll get solved because we can brute
00:44:52
force it now with with chips and with
00:44:54
compute I think that's going to be the
00:44:55
very interesting thing with the robots
00:44:57
as well is all of these decisions
00:44:59
they're making moving cars through roads
00:45:03
all of a sudden we're going to see that
00:45:04
with vetoles vertical takeoff and
00:45:06
Landing
00:45:08
you know aircraft and we're going to see
00:45:10
it with this General robot and everybody
00:45:12
wanted to ask Elon about General AI you
00:45:14
know the Terminator kind of stuff
00:45:16
and his position is I think if we solve
00:45:19
enough of these problems Friedberg it'll
00:45:20
be an emergent
00:45:22
Behavior or an emergent phenomenon I
00:45:25
guess would be a better word based on
00:45:27
each of these cities crumbling you know
00:45:29
each of these tasks getting solved by
00:45:31
groups of people you have any thoughts
00:45:33
as we wrap up here on the discussion
00:45:34
about a general Ai and the timeline for
00:45:37
that because obviously we're going to
00:45:38
solve every vertical AI problem in short
00:45:40
order I spoke about this a little bit on
00:45:43
um the ask AMA on call in on Tuesday
00:45:46
night um once sax gets it out you can
00:45:49
listen to it but I really have this
00:45:51
strong belief that the servers crashes
00:45:55
this episode drops okay yeah you guys
00:45:59
can try to download the app but it might
00:46:00
crash so just be careful so so here's
00:46:02
here's my my course that you were 10
00:46:06
times more popular than jaycal so it's
00:46:08
unexpected levels of traffic
00:46:10
well you had you did have an account
00:46:11
with 11 000 followers I mean it might
00:46:14
look like you're right we'll put you on
00:46:16
that account next time yeah please yeah
00:46:18
I'm starting from zero yeah that's fair
00:46:21
that's fair yeah look my core thesis is
00:46:23
I think humans transition from being
00:46:26
um let's call it you know passive in
00:46:29
this system on Earth to being laborers
00:46:32
and then we transition from being
00:46:34
laborers to being creators and I think
00:46:36
our next transition with AI is to
00:46:38
transition from being creators to being
00:46:40
narrators and what I mean by that is as
00:46:42
as we started to do work on Earth and
00:46:45
engineer the world around us we did
00:46:47
labor to do that we literally plowed the
00:46:49
fields we walked distances we built
00:46:52
things and over time we built machines
00:46:55
that automated a lot of that labor
00:46:57
you know everything from a plow to a
00:47:00
tractor to a caterpillar equipment to a
00:47:03
microwave that cooks for US Labor became
00:47:05
less we became less dependent on our
00:47:07
labor abilities and then we got to
00:47:09
switch our time and spend our time as
00:47:10
creators as knowledge workers and a vast
00:47:13
majority of the developed world now
00:47:15
primarily spends their time as knowledge
00:47:18
workers creating and we create stuff on
00:47:20
computers we do stuff on computers but
00:47:22
we're not doing physical labor anymore
00:47:24
as a lot of the knowledge work gets
00:47:26
supplanted by AI as it's being termed
00:47:29
now but really gets supplanted by
00:47:31
software the role of the human I think
00:47:33
transitions to being one of a narrator
00:47:35
where instead of having to create the
00:47:37
the blueprint for a house you narrate
00:47:40
the house you want and the software
00:47:42
creates the blueprint for you dictate
00:47:43
and instead of yeah and instead of
00:47:45
creating the movie and not spending 100
00:47:47
million dollars producing a movie you
00:47:49
dictate or you narrate the movie you
00:47:51
want to see and you iterate with the
00:47:52
computer and the computer renders the
00:47:54
entire film for you because those films
00:47:56
are shown digitally anyway so you can
00:47:57
have a computer render it instead of
00:47:59
creating a new piece of content you
00:48:03
narrate the content you want to
00:48:04
experience you create your own video
00:48:06
game you create your own Movie
00:48:07
experience and I think that there's a
00:48:09
whole Evolution that happens and if you
00:48:10
look Steve pinker's book Enlightenment
00:48:12
now has a great statistic a set of
00:48:14
statistics on this but the amount of
00:48:16
time that humans are spending on leisure
00:48:18
activities per week has climbed
00:48:20
extraordinarily over the past couple of
00:48:22
decades we spend more time enjoying
00:48:24
ourselves and exploring our creative
00:48:25
interests than we ever did in the in the
00:48:28
past in human history because we were
00:48:29
burdened by all the labor and all the
00:48:31
creative and knowledge work we had to do
00:48:32
and now things are much more accessible
00:48:35
to us and I think that AI allows us to
00:48:37
transition into an era that we never
00:48:39
really thought possible or realized
00:48:40
where the limits are really our
00:48:42
imagination of what we can do with the
00:48:44
world around us and the software
00:48:45
resolves to the um and automation
00:48:48
resolves to make those things possible
00:48:50
and that's making a really exciting kind
00:48:51
of vision for the future that I think AI
00:48:53
enables Star Trek had this right people
00:48:55
didn't have to work and they could
00:48:56
pursue things in the Holodeck or
00:48:58
whatever that they felt was rewarding to
00:49:01
them but speaking of jobs uh the job
00:49:04
reports for August came in we we talked
00:49:06
about this we were trimming 300 000 jobs
00:49:09
a month and we're wondering if the other
00:49:11
shoe would drop but boy did it drop 100
00:49:13
over a million jobs burned off in August
00:49:16
so without getting into the macro talk
00:49:18
it does feel like what the FED is doing
00:49:20
and companies
00:49:22
doing hiring freezes and cuts is finally
00:49:25
finally having an impact if we start
00:49:26
losing a million as we predicted could
00:49:28
happen here on the show
00:49:31
people might actually go back to work
00:49:33
and lift and Uber are reporting that the
00:49:36
driver shortages are over they no longer
00:49:38
have to pay people spiffs and stuff like
00:49:40
that to get people to come back to work
00:49:41
so at least here in America feels like
00:49:43
we're turning a corner do we want to go
00:49:45
can we let's talk about the marijuana
00:49:49
yeah yeah I was about to say we got a
00:49:51
couple of things we really want to get
00:49:52
to here uh Ukraine section 230 and then
00:49:55
this breaking news uh we'll pull it up
00:49:57
here on the screen
00:49:58
while we're recording the show President
00:50:00
Biden says and I'm just going to quote
00:50:02
here
00:50:04
first
00:50:06
I'm pardoning all prior Federal offenses
00:50:08
of simple marijuana possession there are
00:50:10
thousands of people who are were
00:50:12
previously convicted of simple
00:50:13
possession who may be denied employment
00:50:15
housing or educational opportunities
00:50:16
there's not my pardon will remove this
00:50:18
burden that's big news second I'm
00:50:20
calling on Governors
00:50:21
to part in simple State marijuana
00:50:24
possession offenses just as no one
00:50:26
should be in a federal prison solely for
00:50:28
possessing marijuana nobody should be in
00:50:29
a local jail or state prison for that
00:50:31
reason either finally this is happening
00:50:33
third and this is an important one we
00:50:36
classify the marijuana at the same level
00:50:38
as heroin and even and more serious than
00:50:40
fentanyl makes no sense I'm asking
00:50:43
secretary bakara and the Attorney
00:50:46
General to initiate the process of
00:50:48
reviewing how marijuana is scheduled
00:50:49
under federal law
00:50:51
I'd also like to note that as federal
00:50:53
and state Regulators change we still
00:50:56
need important limitations on
00:50:57
trafficking marketing and under eight
00:50:58
hours of marijuana
00:51:00
thoughts on this breaking news is this
00:51:03
giving the the timing on this is kind of
00:51:06
midterm
00:51:07
related it just seems is this is this I
00:51:10
guess is this a politically popular
00:51:11
decision to do
00:51:13
I think so I mean look I support it so
00:51:16
buying finally did something I like
00:51:18
great I mean I thought that we should
00:51:21
decriminalize marijuana for a long time
00:51:24
or specifically you know I agree with
00:51:27
this idea of descheduling it it does not
00:51:29
make sense to treat marijuana the same
00:51:31
as heroin as a schedule one narcotic
00:51:34
just doesn't make any sense it should be
00:51:35
regulated separately and differently
00:51:38
obviously you want to keep it out of the
00:51:40
hands of minors but no one should be
00:51:42
going to jail I think for simple
00:51:43
possession so I do agree with this and I
00:51:45
think the thing they need to do I don't
00:51:48
see it mentioned here is they should
00:51:49
pass a federal law that would allow for
00:51:53
the normalization of let's call it legal
00:51:57
legal
00:51:58
um you know cannabis companies so so
00:52:01
companies that are allowed to operate
00:52:03
under state laws like in California
00:52:06
should have access to the banking system
00:52:08
should have access to payment rails
00:52:09
because right now the reason why the
00:52:12
legal cannabis industry isn't working at
00:52:14
all in California is because they can't
00:52:16
bank they can't take payments so so it's
00:52:19
this weird all-cash business that makes
00:52:20
no sense so so listen if we're not going
00:52:23
to criminalize it as a drug like heroin
00:52:25
if we're going to allow states to make
00:52:28
it legal then allow it to be a more
00:52:31
normal business where the state can tax
00:52:33
it and it can operate in a more above
00:52:36
board way
00:52:38
is what you're saying the federal medic
00:52:40
I think it could still be regulated on a
00:52:42
state-by-state basis but I think you
00:52:44
need the feds to bless the idea that
00:52:47
Banks and payment companies can take on
00:52:49
those clients which states have already
00:52:52
said are legally operating companies and
00:52:54
right now they can't and it's a huge gap
00:52:57
in the laws so maybe that's the one
00:52:58
thing I would add to this but I don't
00:53:00
have any complaints about this right now
00:53:01
based on what we know from this tweet
00:53:03
storm and I would say this by the way
00:53:06
was an about face this was an about
00:53:07
faced by Biden yeah do you know what the
00:53:09
polling data says I mean is is there I'm
00:53:12
assuming there's big support in kind of
00:53:14
the independence and the middle
00:53:16
uh for this it was 70 at one point yeah
00:53:19
yeah
00:53:20
so so look this to me this is the kind
00:53:22
of thing that Biden should be doing with
00:53:24
the 50 50 Senate finding these sorts of
00:53:26
bipartisan compromises right
00:53:29
so yeah I look this is good news for us
00:53:31
why hasn't this happened in the past
00:53:33
like what's been the political reason
00:53:35
that other presidents Obama even
00:53:40
didn't that that have there was a
00:53:42
similar ideology like why why but why
00:53:44
does anyone know why this hasn't been
00:53:45
done in the past there was rumors he was
00:53:47
going to do in the second term
00:53:49
they just didn't have the political
00:53:50
Capital why to do it why I don't I don't
00:53:53
well it's hard ones doesn't require yeah
00:53:55
the pardon doesn't require political
00:53:57
capital I think it's probably the
00:53:58
perception that this is soft on crime in
00:54:01
some way or there wasn't enough
00:54:03
broad-based support as David said I mean
00:54:05
I think the the United States population
00:54:07
has moved pretty meaningfully in the
00:54:09
last 20 years look at the chart here
00:54:12
um you know we were talking about 2000
00:54:14
it was only 31 percent uh and then you
00:54:17
look at 2018 it's up at 60 plus percent
00:54:20
so when people saw the states doing it
00:54:22
and they saw absolutely no problem you
00:54:25
know in every state and I think what
00:54:26
people will see next I think that's a
00:54:28
Gallup poll that's a Gallup pollution
00:54:29
yeah yeah so I mean it's increased
00:54:31
dramatically MDMA psilocybum and some of
00:54:34
these other plant-based medicines
00:54:35
Ayahuasca are next and they're doing
00:54:37
studies on them now I don't want to take
00:54:39
away from how important this is for uh
00:54:41
all the people for whom this will
00:54:43
positively impact I just want to talk
00:54:45
about the schedule change for marijuana
00:54:47
as a parent one of the things that I'm
00:54:50
really really concerned about is that
00:54:52
through this process of legalization
00:54:55
getting access to marijuana has frankly
00:54:57
become too easy particularly for kids
00:55:00
at the same time I saw a lot of really
00:55:03
alarming evidence that the the intensity
00:55:06
of these marijuana-based products have
00:55:09
gone you know I think it's like five or
00:55:11
six times more intense than I don't know
00:55:13
50 or 100 tomorrow much higher right so
00:55:16
so it's no longer you know this kind of
00:55:19
like you know Do no harm drug that it
00:55:22
was 20 years ago this is this could be
00:55:24
actually David the way that it's
00:55:27
productized today as bad as some of
00:55:30
these other
00:55:31
you know uh narcotics so
00:55:34
in June of this year
00:55:36
the Biden Administration basically made
00:55:38
this press release that said the FDA is
00:55:41
going to come out with regulations that
00:55:43
would cap the amount of nicotine in
00:55:45
cigarettes and I think that was a really
00:55:47
smart move because it basically set the
00:55:49
stage to taper nicotine out of uh out of
00:55:53
cigarettes which would essentially you
00:55:55
know decapitate it as a an addictive
00:55:58
product and I think by
00:56:01
thinking about how it's how it's dealt
00:56:03
with what I really hope the
00:56:05
administration does is it empowers the
00:56:08
FDA if you're going to legalize it you
00:56:11
need to have expectations around what
00:56:14
the intensity of these drugs are because
00:56:16
if you're delivering drugs OTC and now
00:56:18
any kid can go in at 18 years old and
00:56:20
buy them which means that 18 year olds
00:56:23
are going to buy them for 16 year olds
00:56:24
16 year olds are going to get fake IDs
00:56:26
to buy them for themselves you need to
00:56:28
do a better job so the parents you're
00:56:30
helping parents do our job here's what
00:56:32
you need
00:56:33
one like alcohol
00:56:36
if alcohol is 21 then of course yeah
00:56:39
fine but even alcohol David you know
00:56:40
that there are there are we know what
00:56:42
the intensity of these are their labels
00:56:43
and there's warnings and you know the
00:56:45
difference between they're they're
00:56:46
getting wine versus hard alcohol but let
00:56:49
me just give you some statistics here
00:56:50
chamoth if you think about the the
00:56:52
Cannabis in the 90s uh and prior to that
00:56:55
uh they've been very you've been a ton
00:56:57
of studies on this in Colorado it was uh
00:56:59
the THC content was less than two
00:57:01
percent
00:57:02
and then in 2017
00:57:04
we were talking about you know things
00:57:06
going up to uh 17 to 28 for a specific
00:57:11
strain so they have been building
00:57:12
strains like Girl Scout cookies Etc that
00:57:15
have just increased and increased and
00:57:16
then there are things like shards and
00:57:18
obviously Edibles you can create
00:57:20
whatever intensity you want so you have
00:57:22
this incredible there you know
00:57:24
variation you could have an edible
00:57:26
that's you know got one milligram of THC
00:57:29
one that has a hundred or you could have
00:57:31
a pack of Edibles and you see this
00:57:33
happen in the news all the time some kid
00:57:34
gets their parents pack or somebody
00:57:36
gives one and the kids don't know um and
00:57:38
this dabbing phenomenon combined with
00:57:40
dabbing is like the shards like this
00:57:42
really intense uh stuff
00:57:45
combined with the Edibles is really the
00:57:46
issue and the labeling of them so you
00:57:49
got to be incredibly careful with this
00:57:50
it's not good for kids it screws up
00:57:52
their brains and so
00:57:53
yeah be very careful I have a I have a
00:57:56
zero tolerance policy on this stuff I
00:57:57
don't care if it's legal illegal like I
00:57:59
don't want my kids touching any of this
00:58:00
stuff until it's not for kids obviously
00:58:02
yeah but we also should not be over
00:58:03
there until they're 35 or 40 and even
00:58:05
then I hope they never do it but but I
00:58:07
need some help
00:58:09
and I'm not sure I'm the only parent
00:58:10
that's asking you can't have this stuff
00:58:12
be available effectively sold like in a
00:58:14
convenience store no no that's not going
00:58:17
to happen where there isn't even
00:58:18
labeling at least like cigarettes are
00:58:19
labeled it's very clear how bad this
00:58:22
stuff is for you oh do you guys have any
00:58:23
feedback on the job report or anything
00:58:25
they're all going away when when the
00:58:26
when the AI wins
00:58:28
well that's why I brought it up it's
00:58:30
like we're now going to see a potential
00:58:32
you know a situation where Jobs go away
00:58:35
and a lot of the stuff like even
00:58:36
developers right don't you think
00:58:37
Freiburg developers are going to start
00:58:39
development tasks
00:58:42
everyone assumes a static lump of work I
00:58:45
think what happens particularly in
00:58:47
things like developer tools is the
00:58:49
developer can do so much more and then
00:58:52
we generate so much more output and so
00:58:54
the overall productivity goes up not
00:58:55
down
00:58:56
um so it's pretty exciting as these and
00:58:59
remember like like we were talking on
00:59:01
the AMA the other night Adobe Photoshop
00:59:03
was a tool for photographers so you
00:59:05
didn't have to take the perfect
00:59:06
photograph and then print it you could
00:59:08
you know you could use the software to
00:59:10
improve the quality of your photograph
00:59:11
and I think that that's what we see
00:59:13
happening with all software
00:59:15
um in the creative process is it helps
00:59:17
people do more than they realized they
00:59:18
could do before and that's pretty
00:59:20
powerful and it opens up all these new
00:59:21
avenues of interest and things we're not
00:59:23
even imagining today all right so scotus
00:59:25
is going to hear two uh cases for
00:59:27
Section 230
00:59:28
the family of nohima Gonzalez a 23 year
00:59:31
old American college student who was
00:59:33
killed in an Isis terrorist attack in
00:59:35
Paris back in 2015. you remember those
00:59:37
horrible attacks is claiming that
00:59:39
YouTube helped and aided and abetted
00:59:41
Isis uh the family's argument is
00:59:43
YouTube's algorithm was recommending
00:59:46
videos that make it that makes it a
00:59:48
publisher of content as you know it's
00:59:49
section 230 common carrier now if you
00:59:52
make editorial decisions if you promote
00:59:53
certain content you lose your 230
00:59:55
protections uh in court papers filed in
00:59:58
2016 they said the company quote
01:00:00
knowingly permitted Isis to post on
01:00:02
YouTube hundreds of radicalizing videos
01:00:04
inciting violence which helped the group
01:00:06
recruit including some who were actually
01:00:09
involved in the terrorist attack so they
01:00:10
have made that connection well look
01:00:12
let's let's be honest we can we can we
01:00:13
can put a pin in this thing because I
01:00:15
think it would be shocking to me if this
01:00:18
current scotus
01:00:19
uh all of a sudden founded in the
01:00:22
cockles of their heart to protect big
01:00:23
Tech I mean they've dismantled
01:00:27
um a lot of other stuff that I think is
01:00:31
a lot more controversial than this
01:00:34
um and so you know we've we've basically
01:00:37
looked at gun laws we've looked at
01:00:39
affirmative action we've looked at
01:00:41
abortion rights sorry well I mean I
01:00:44
think as as we've said I think we all
01:00:46
know where that dye is unfortunately
01:00:48
going to get cast
01:00:49
um so to me it just seems like this
01:00:52
could be an interesting case where it's
01:00:53
actually nine zero
01:00:55
in favor for complete for completely
01:00:58
different sets of reasons I mean if you
01:00:59
think of the liberal left part of the
01:01:01
Court they have their own reasons for
01:01:03
saying that there aren't 230 protections
01:01:04
for big Tech and if you look at the the
01:01:06
far right or the right-leaning Parts
01:01:09
members of this of scotus they have they
01:01:11
have another set of different do you
01:01:12
think you're gonna make a political
01:01:13
decision not illegal no but even even in
01:01:15
their politics they actually end up in
01:01:17
the same place they
01:01:19
have protections but for different
01:01:20
reasons
01:01:21
so there there's a reasonable outcome
01:01:23
here where you know uh Roberts is going
01:01:26
to have a really interesting time trying
01:01:27
to pick who writes the majority opinion
01:01:28
there was a related case in the fifth
01:01:30
circuit in Texas where do you guys see
01:01:33
this fist circuit decision where
01:01:36
Texas passed a law imposing common
01:01:39
carrier restrictions on social media
01:01:41
companies the idea being that social
01:01:44
media companies need to operate like
01:01:45
phone companies and they can't just
01:01:47
arbitrarily deny you service or deny you
01:01:49
access to the platform and the argument
01:01:53
why previously that had been viewed
01:01:56
actually is unconstitutional was this
01:01:58
idea of compelled speech that you can't
01:02:00
compel a corporation to support speech
01:02:03
that they don't want to because that was
01:02:04
a violation of their own First Amendment
01:02:06
rights and with the first the fifth
01:02:09
circuit said is no that doesn't make any
01:02:10
sense Facebook or Twitter can still
01:02:12
advocate for whatever speech they want
01:02:14
as a corporation but as a platform they
01:02:17
if Texas requires them to not
01:02:20
discriminate against people on the base
01:02:22
of their Viewpoint then Texas has the
01:02:24
right to to impose that because that
01:02:26
doesn't it their quote was that does not
01:02:28
chill speech if anything it chills
01:02:30
censorship so what's the right legal
01:02:32
decision here in your mind putting aside
01:02:34
politics if you can for a moment but on
01:02:37
your legal hat what is the right thing
01:02:38
for society what is the right legal
01:02:40
issue around section 230 specifically in
01:02:43
the YouTube case and just generally
01:02:45
should we look at YouTube should we look
01:02:47
at a blogging platform like medium or
01:02:49
blogger Twitter should we look at those
01:02:52
as common carrier
01:02:54
and they're not responsible for what you
01:02:56
publish on them obviously they have to
01:02:58
take stuff down if it breaks their terms
01:02:59
of service Etc or if it's illegal I've
01:03:01
made the case before that I I do think
01:03:03
that common carrier requirements should
01:03:05
apply on some level of the stack to
01:03:07
protect the rights of ordinary Americans
01:03:10
to have their speech in the face of
01:03:12
these giant monopolies which could
01:03:14
otherwise de-platform them for arbitrary
01:03:16
reasons just to you know just explain
01:03:18
this a little bit so
01:03:20
historically there was always a debate
01:03:22
between so-called positive rights and
01:03:25
negative rights so where the United
01:03:28
States start off as a country was with
01:03:30
this idea of negative rights that what a
01:03:32
right meant is that you'd be protected
01:03:34
from the government taking some action
01:03:36
against you and if you look at the Bill
01:03:37
of Rights you know the original rights
01:03:39
are all about protecting the citizen
01:03:42
against intrusion on their Liberty by by
01:03:45
a state or by the federal government in
01:03:46
other words Congress shall make no law
01:03:48
it was always a restriction so the right
01:03:50
was negative it wasn't sort of
01:03:52
positively a force and then with the
01:03:54
Progressive Era you started seeing you
01:03:56
know more uh Progressive rights like for
01:03:59
example uh American citizens should have
01:04:01
the right to health care right that's
01:04:03
not protecting you from the government
01:04:04
that's saying that the government can be
01:04:06
used to give you a right that you didn't
01:04:09
otherwise have and so that was sort of
01:04:11
the big Progressive Revolution my take
01:04:13
on it is I actually think that the
01:04:15
problem we have in our society right now
01:04:17
is that free speech is only a negative
01:04:19
right it's not a positive right I think
01:04:21
it actually needs to be a positive right
01:04:23
I'm embracing a more Progressive version
01:04:26
of Rights but on behalf of sort of this
01:04:29
original negative right so and the
01:04:31
reason is because the Town Square got
01:04:33
privatized right I mean you used to be
01:04:35
able to go anywhere in this country
01:04:36
there'd be a multiplicity of town
01:04:38
squares anyone could pull out their
01:04:39
soapbox draw a crowd they could listen
01:04:41
that's not how speech occurs anymore
01:04:43
it's not on public land or public spaces
01:04:46
the way that speech political speech
01:04:48
especially occurs today is in these
01:04:50
giant social networks that are that have
01:04:52
giant Network effects and are basically
01:04:54
monopolies so if you don't protect the
01:04:57
right to free speech in a positive way
01:04:58
it no longer exists so you not only
01:05:01
believe wow that YouTube should keep it
01:05:04
section 230 you believe they YouTube
01:05:06
shouldn't be able to de-platform as a
01:05:09
private company
01:05:10
you know uh Alex Jones as but one
01:05:12
example they should have their free
01:05:14
speech rights and we should lean on that
01:05:16
side of forcing YouTube to put Alex
01:05:18
Jones or Twitter to put Trump back on
01:05:21
the platform besides your position
01:05:23
I'm not saying that the constitution
01:05:25
requires YouTube to do anything uh what
01:05:28
I'm saying is that if a state like Texas
01:05:31
or if the federal government wants to
01:05:33
pass a law saying that YouTube If you
01:05:37
are say of a certain size you're a
01:05:38
social network of a certain size you
01:05:39
have Monopoly Network effects I wouldn't
01:05:41
necessarily apply this to all the little
01:05:43
guys but for those big monopolies we
01:05:45
know who they are if the if the federal
01:05:48
government or state wanted to say that
01:05:50
they are required to be a common carrier
01:05:53
and they cannot discriminate against
01:05:54
certain viewpoints I think the
01:05:56
government should be allowed to do that
01:05:57
because it furthers a positive right
01:05:59
historically they have not been able to
01:06:02
do that because of this idea because
01:06:04
this idea of compelled speech meaning
01:06:06
that it would infringe on YouTube's
01:06:08
speech rights I don't think it would I
01:06:10
mean Google and YouTube can advocate for
01:06:11
whatever positions they want they can
01:06:13
produce whatever content they want yeah
01:06:15
but but the point that and I think
01:06:17
section 230 kind of makes this point as
01:06:18
well is that they are platforms they're
01:06:20
distribution platforms they're not
01:06:22
Publishers so if they don't once so
01:06:24
especially if they want section 230
01:06:25
protection they should not be engaging
01:06:27
in Viewpoint discrimination okay so now
01:06:29
there is a rub here wait can I just say
01:06:30
can I just say go ahead your explanation
01:06:32
David your explanation that you just
01:06:33
gave before was so excellent thank you
01:06:36
that it allows me to understand it even
01:06:39
more clearly that was really so tremoff
01:06:41
do you think the algorithm is an act of
01:06:44
editorialization yes yes yes and so then
01:06:48
should YouTube look at the end of the
01:06:50
day let me let me break down an
01:06:52
algorithm for you okay effectively it is
01:06:54
a mathematical equation of variables and
01:06:57
weights
01:06:59
an editor 50 years ago was somebody who
01:07:03
had that equation of variables and
01:07:06
weights in his or her mind
01:07:08
okay and so all we did was we translated
01:07:11
again this multimodal model that was in
01:07:14
somebody's brain
01:07:16
into a model that's mathematical that
01:07:19
sits in code you're talking about the
01:07:21
front page and the New York Times yeah
01:07:23
and I think it's a fig Leaf to say that
01:07:25
because there is not an individual
01:07:27
person who writes 0.2 in front of this
01:07:29
one variable and 0.8 in front of the
01:07:31
other
01:07:32
that all of a sudden that this isn't
01:07:34
editorial decision making is wrong we
01:07:36
need to understand the current moment in
01:07:39
which we live which is that these
01:07:41
computers are thinking actively for us
01:07:45
they're providing this you know
01:07:47
computationally intensive
01:07:50
decision making and reasoning
01:07:53
and I think it's
01:07:55
it's pretty ridiculous to assume that
01:07:57
that isn't true that's why when you go
01:07:59
to Google and you search for you know
01:08:01
Michael Jordan we know what the right
01:08:04
Michael Jordan is because it's reasoned
01:08:06
there is an algorithm that is doing that
01:08:09
it's making an editorial decision around
01:08:11
what the right answer is they have
01:08:12
deemed it to be right and that is just
01:08:15
true and so I think we need to
01:08:16
acknowledge that because I think it
01:08:18
allows us at least to be in a position
01:08:20
to rewrite these laws through the lens
01:08:23
of the 21st century
01:08:25
we need to update our understanding for
01:08:28
how the world works today and you know
01:08:30
jamaat there's such an easy way to do
01:08:32
this if you're Tick Tock if you're
01:08:34
YouTube if you want section 230 if you
01:08:36
want to have common carrier and not be
01:08:38
responsible if it's there when a user
01:08:40
signs up it should give them the option
01:08:42
would you like to turn on an algorithm
01:08:44
here are a series of algorithms which
01:08:46
you could turn on you could bring your
01:08:48
own algorithm you could write your own
01:08:50
algorithm with a bunch of Sliders or
01:08:52
here are ones that other users and
01:08:54
services provide like like an App Store
01:08:55
so YouTube moth could pick one for your
01:08:58
family your kids that would be I want
01:08:59
one that's leaning towards education and
01:09:02
takes out conspiracy theories takes out
01:09:03
cannabis use takes out this one it's a
01:09:06
wonderful what you're saying is so
01:09:07
wonderful because for example like you
01:09:08
know this organization Common Sense
01:09:10
Media yes I love that website every time
01:09:12
I put in a movie I put Common Sense
01:09:14
Media decide if we should watch it or
01:09:16
like an I use it a lot for apps because
01:09:18
they're pretty good at just telling you
01:09:19
which which apps are reasonable and
01:09:21
unreasonable but you know if Common
01:09:23
Sense Media could raise a little bit
01:09:25
more money and and create an algorithm
01:09:26
that would help filter
01:09:28
stories in Tick Tock for my kids I'd be
01:09:32
more likely to give my kids tick tock
01:09:33
when they turn 14. right now I know that
01:09:36
they're going to sneak it by going to
01:09:37
YouTube and looking at YouTube shorts
01:09:39
and all these other things because I
01:09:41
cannot control that algorithm and it
01:09:43
does worry me what kind of content that
01:09:46
they're getting access to and you could
01:09:48
do this by the way chamath on the
01:09:49
operating system level or on the router
01:09:51
level in your house you could say I want
01:09:53
the common sense algorithm I will pay 25
01:09:55
a month a hundred dollars a year
01:09:59
and then any IP that goes through it
01:10:01
would be programmed properly I want less
01:10:03
violence I want less sex you know
01:10:05
whatever I think we are as a society
01:10:07
sophisticated enough now yes um to have
01:10:10
these controls and so I think we need
01:10:12
them and so I think we do need to have
01:10:15
the right observation of the current
01:10:18
state of play Friedberg
01:10:21
where do you sit on this do you think
01:10:22
the algorithm should be I don't I don't
01:10:24
I don't know right out of 230. yeah I I
01:10:27
don't fully agree with Saks on
01:10:30
um the monopolistic assumption I I think
01:10:33
that there are I think there are other
01:10:35
places to access content and I think
01:10:37
that there is still a free market to
01:10:39
compete
01:10:40
and it is possible to compete I think
01:10:43
that we saw this happen with Tick Tock
01:10:44
we saw it happen with Instagram we saw
01:10:46
it happen with YouTube uh competing
01:10:49
against Google video and Microsoft video
01:10:51
prior to that there has been a very
01:10:53
significant battle for the attention of
01:10:57
kind of being the next gen of media
01:10:59
businesses and we have seen Spotify
01:11:00
compete and we're seeing Spotify
01:11:01
continue to be challenged by emerging
01:11:04
competitors
01:11:05
so I don't buy the assumption that these
01:11:09
are built-in monopolies and therefore it
01:11:11
allows
01:11:12
some regulatory process to come in and
01:11:15
say hey Free Speech needs to be actively
01:11:17
enforced because they're monopolies this
01:11:18
isn't like when utilities laid power
01:11:21
lines and Sewer lines and and trains
01:11:24
across the country and they had a
01:11:26
physical Monopoly on being able to
01:11:28
access and move goods and services the
01:11:30
internet is still thank God knock on
01:11:32
wood open and the ability for anyone to
01:11:34
build a competing service is still
01:11:36
possible and there is a lot of money
01:11:38
that would love to disrupt these
01:11:40
businesses that is actively doing it and
01:11:42
I think every day look at how big Tick
01:11:44
Tock has gotten it is bigger than
01:11:45
YouTube almost or will be soon
01:11:48
and there is a competition that happens
01:11:50
and because of that competition I think
01:11:52
that the the market will ultimately
01:11:54
choose where they want to get their
01:11:56
content from and how they want to
01:11:57
consume it and I don't think that the
01:11:58
government should play a role Zach's
01:12:00
rebuttal to that you buy that well so
01:12:02
not all these companies are monopolies
01:12:05
but I think they act in a monopolistic
01:12:07
way with respect to restricting Free
01:12:09
Speech which is they act as a cartel
01:12:11
they all share like best practices with
01:12:13
each other on how to restrict speech and
01:12:16
we saw the The Watershed here was
01:12:18
remember when Trump was thrown off first
01:12:20
Twitter made the decision you know Jack
01:12:22
I don't even know if it was Jack but
01:12:24
basically the company Jack said it
01:12:26
wasn't him actually he said it was the
01:12:27
woman who was running it specifically
01:12:29
that's it for that yeah yeah Jack
01:12:31
actually said it was a mistake but any
01:12:32
event Twitter did it first and then all
01:12:34
the other companies followed suit I mean
01:12:36
even like Pinterest and OCTA and
01:12:39
Snapchat like officially YouTube
01:12:42
everybody yeah but Trump was actually on
01:12:44
Facebook he wasn't on all these other
01:12:45
companies they still threw him off so
01:12:47
they all copy each other and Jack
01:12:49
actually said that in his comments where
01:12:51
he said it was a mistake he said he
01:12:53
didn't realize the way in which
01:12:55
Twitter's action would actually Cascade
01:12:58
he said that he thought originally that
01:13:01
the action was okay because it was just
01:13:03
Twitter deciding to take away Trump's
01:13:05
right to free speech but he could still
01:13:07
go to all these other companies and then
01:13:08
all these other companies basically you
01:13:10
know they are all subject to the same
01:13:13
political forces the the leadership of
01:13:15
these companies are all sort of they all
01:13:17
drink from the same monocultural found
01:13:19
they all have the same political biases
01:13:21
the polls show this so the problem of
01:13:23
Freeburg is yeah I agree a bunch of
01:13:24
these companies aren't quite monopolies
01:13:26
but they all act the same way but I
01:13:28
agree with you I'm a pretty Collective
01:13:30
effect is of a speech cartel so the
01:13:33
question is how do you protect the
01:13:34
rights of Americans to free speech in
01:13:37
the face of a speech cartel that wants
01:13:39
to basically block them go ahead
01:13:40
Freeburg respond here's my argument my
01:13:42
argument is that these are not public
01:13:43
service providers they're private
01:13:45
service providers and the market is
01:13:47
telling them what to do the market is
01:13:49
saying and I think I don't think so I
01:13:51
think that the pressure that was felt by
01:13:53
these folks was that so many consumers
01:13:55
were pissed off that they were letting
01:13:57
Trump rail on or they were pissed off
01:13:58
about Jam 6 or pissed off about whatever
01:14:01
whatever the current fat is the trend is
01:14:04
they respond to the market and they say
01:14:06
you know what this has crossed the line
01:14:08
and this was the case on public
01:14:09
television when nudity came out and
01:14:11
they're like okay you know what we need
01:14:13
to take that off the TV we need to
01:14:14
because the market is telling us they're
01:14:16
going to boycott us and I think that
01:14:18
there's a market pressure here that
01:14:19
we're ignoring that is actually pretty
01:14:21
pretty relevant that as a private
01:14:23
service provider if they're going to
01:14:24
lose half Their audience because people
01:14:26
are pissed about one or two pieces of
01:14:28
content showing up that they're acting
01:14:30
in the best interest of their
01:14:32
shareholders and in the best interest of
01:14:33
their platform they're not acting as a
01:14:35
public service look I love Market forces
01:14:37
as much as the next libertarian but I
01:14:40
just think that fundamentally that's
01:14:42
just not what's going on here this has
01:14:43
nothing to do with Market forces has
01:14:44
everything to do with political forces
01:14:46
that's what's driving this look do you
01:14:48
think the average consumer the average
01:14:50
user of PayPal is demanding that they
01:14:53
engage in all these restrictive policies
01:14:55
throwing off all these accounts who have
01:14:57
the wrong viewpoints no that's nothing
01:14:58
to do with it it has to do with the
01:15:00
vocal mind
01:15:04
who work at these companies and create
01:15:07
pressure from below it's also the you
01:15:09
know the the people from outside the
01:15:10
actors who create these boycott
01:15:12
campaigns and pressure from outside and
01:15:14
then it's basically people on Capitol
01:15:16
Hill who have the same ideology who
01:15:18
basically create threats from above so
01:15:20
these companies are under enormous
01:15:21
pressure from above below and sideways
01:15:24
that and it's 100 political hold on it's
01:15:26
not it's not about maximizing profits I
01:15:29
think it's about maximizing you know
01:15:32
political outcomes yeah I don't know
01:15:33
that is that is what the American people
01:15:35
need to be protected from now I will I
01:15:37
will add one Nuance to my theory though
01:15:40
which is
01:15:41
I'm not sure what level of the stack we
01:15:44
should declare to be common carrier so
01:15:46
in other words you may be right actually
01:15:49
that at the level of YouTube or Twitter
01:15:52
or Facebook maybe we shouldn't make them
01:15:54
commentary and I'll tell you why because
01:15:56
just to take the other side of the
01:15:57
argument for a second which is you know
01:15:59
if you don't because those companies do
01:16:02
have legitimate reasons to take down
01:16:04
some content I don't like the way they
01:16:06
do it but I do not want to see bots on
01:16:08
there I do not want to see fake accounts
01:16:10
and I actually don't want to see like
01:16:12
truly hateful speech or harassment and
01:16:15
the problem is I do worry that if you
01:16:18
subject them to common carrier they
01:16:19
won't actually be able to engage in
01:16:21
let's say legitimate curation of their
01:16:24
social networks yeah right however so so
01:16:27
there's a real debate to be had there
01:16:28
and it's going to be messy but I think
01:16:31
there's one level of the stack below
01:16:32
that which is at the level of pipes like
01:16:34
in AWS like a cloud flare like a PayPal
01:16:37
like the the isps like the banks they
01:16:40
are not doing any content moderation or
01:16:43
they have no legitimate reason to be
01:16:44
doing content moderation none of those
01:16:45
companies should be able allow to engage
01:16:47
in Viewpoint discrimination we have a
01:16:48
problem right now where American
01:16:50
citizens are being denied access to
01:16:52
payment rails into the banking system
01:16:54
you're saying because of their view AWS
01:16:56
shouldn't be able to deny service to the
01:16:58
Ku Klux Klan or some hate speech group I
01:17:00
think that they should be under the same
01:17:02
requirements the phone companies under
01:17:05
okay
01:17:08
you know the question is like look I
01:17:10
could frame the same question to you
01:17:12
should you know something such a
01:17:14
horrible group should such and such
01:17:15
horrible group be able to get a phone a
01:17:17
phone account right yeah no no and you'd
01:17:19
say no they shouldn't get anything but
01:17:20
they have that right that has been
01:17:22
litigated and that's been pretty much
01:17:24
protected by the Supreme Court you know
01:17:26
even if it's a government-conferred
01:17:28
monopoly the Supreme Court has said okay
01:17:30
listen like it's not violating one's
01:17:32
constitutional right for example if your
01:17:34
water bill gets terminated without you
01:17:36
getting due process
01:17:38
and the the inverse is also true so
01:17:41
for what whether we like it or not that
01:17:43
Jason that issue has been litigated I
01:17:45
think
01:17:47
I think I think for me again just like
01:17:49
practically speaking for the functioning
01:17:52
of Civil Society I think it's very
01:17:54
important for us
01:17:55
to now introduce this idea of
01:17:57
algorithmic choice
01:17:59
and I don't think that that will happen
01:18:01
in the absence of us rewriting section
01:18:04
230 in a more intelligent way I don't
01:18:06
know I don't know whether this specific
01:18:08
case
01:18:10
create an upstanding for us to do all of
01:18:12
that
01:18:14
but I think it's an important thing that
01:18:17
we have to revisit as a society because
01:18:19
Jason what you described
01:18:21
as having a breadth of algorithmic
01:18:23
choices over time where there are
01:18:25
purveyors and sellers could you imagine
01:18:27
that's not a job or a company that the
01:18:29
four of us would ever have imagined
01:18:31
could be possible five years ago but
01:18:33
maybe there should be an economy of
01:18:35
algorithms and there are these really
01:18:36
great algorithms
01:18:38
that one would want to pay a
01:18:40
subscription for because One Believes In
01:18:42
the quality of what it gives you
01:18:44
we should have that choice and I think
01:18:46
it's an important set of choices that
01:18:47
will allow actually YouTube as an
01:18:50
example to operate more safely as a
01:18:52
platform because it can say listen I've
01:18:54
created this set of abstractions you can
01:18:56
plug in all sorts of algorithms there's
01:18:58
a default algorithm that works but then
01:19:00
there's a Marketplace of algorithms just
01:19:02
like there's a Marketplace of ideas I
01:19:04
don't discriminate and let people choose
01:19:06
this is the key thing with this model
01:19:08
like if if it was on a blockchain if all
01:19:11
the videos all the video content was
01:19:12
uploaded to a public blockchain and then
01:19:15
distributed on distributed computing
01:19:16
system then your ability to search and
01:19:19
use that media would be a function of a
01:19:22
service provider you're willing to pay
01:19:23
for that provides the best service
01:19:24
experience and by the way this is also
01:19:26
why I think over time to sax to to kind
01:19:29
of sax and I are both arguing both sides
01:19:31
a little bit but I think that what will
01:19:33
happen
01:19:34
I don't think that the government should
01:19:35
come in and regulate these guys and tell
01:19:37
them that they can't take stuff down and
01:19:39
whatnot I really don't like the
01:19:40
precedent it sets period I also think
01:19:43
that it's a terrible idea for YouTube
01:19:45
and Twitter to take stuff down
01:19:48
um and I think that there's an
01:19:49
incredibly difficult uh balance that
01:19:52
they're gonna have to find because if
01:19:53
they do this as we're seeing right now
01:19:54
the quality of the experience for a set
01:19:57
of users declines and they will find
01:19:58
somewhere else any Market will develop
01:20:00
for something else to compete
01:20:01
effectively against them and so I that's
01:20:03
why I don't like the government
01:20:04
intervening because I want to see a
01:20:06
better product emerge when the big
01:20:08
company makes some stupid mistake and
01:20:10
does a bad job and then the market will
01:20:12
find a better outcome and it just it's
01:20:14
messy in the middle and as soon as you
01:20:16
do government intervention on these
01:20:18
things and tell them what they can and
01:20:19
can't take down
01:20:21
I really do think that over time you
01:20:23
will limit the user experience to what
01:20:24
is possible if you allow the free market
01:20:26
and this is where the the industry needs
01:20:29
to police itself if you look at the
01:20:30
movie industry with the MPAA and Indiana
01:20:33
Jones and the the Temple of Doom they
01:20:35
came out with the PG-13 rating
01:20:37
specifically for things that were a
01:20:38
little too edgy for PG this is where our
01:20:41
industry could get ahead of this they
01:20:43
could give algorithmic Choice an
01:20:45
algorithmic app store and if you look at
01:20:48
the original sin it was these lifetime
01:20:49
bands like Trump should not have been
01:20:51
given a lifetime van they should have
01:20:52
given them a one-year ban they should
01:20:54
have had a process they overreached we
01:20:57
wouldn't be in this position
01:20:59
when you talk about like having a
01:21:02
industry Consortium like the MPAA what
01:21:04
you're doing is formalizing the
01:21:05
communication that's already taking
01:21:07
place is already happening between these
01:21:08
companies and what is the result of that
01:21:10
communication they all standardize on
01:21:12
overly restrictive policies because they
01:21:13
all know there is no free the same
01:21:14
political bias no but if they did it
01:21:16
correctly it's all in the execution
01:21:18
sacks it has to be executed properly
01:21:20
like the movie industry it doesn't
01:21:21
matter you'll end up with the same
01:21:23
problem as having the government
01:21:24
intervene if you have the government
01:21:25
intervene or private body intervene any
01:21:27
sort of set standard intervention that
01:21:29
prevents the market from company I just
01:21:31
I disagree with you I think you can
01:21:33
create more competition if the
01:21:34
government says
01:21:35
um okay folks you can have the standard
01:21:37
algorithm but you need to make a a
01:21:39
simple
01:21:41
abstracted way for somebody else to
01:21:43
write some other filtering mechanism and
01:21:45
to basically you so that users can pick
01:21:46
the power users yes
01:21:48
what the mpia did was I don't know why
01:21:52
isn't that more choice because that's a
01:21:54
product that's a product company I don't
01:21:56
want to be told how to make my product
01:21:57
right if you're not on YouTube you have
01:21:59
you have an algo you're now saying that
01:22:01
there is this distinction of the algo
01:22:02
from the ux from the data and my choice
01:22:04
might be to create different content
01:22:06
libraries for example YouTube has
01:22:08
YouTube kids and it's a different
01:22:09
Content Library and it's a different
01:22:11
user interface and it's a different
01:22:12
algorithm and you're trying to create an
01:22:14
abstraction that may not necessarily be
01:22:15
natural for the evolution of the product
01:22:17
set of that company I would much rather
01:22:19
see them figure it out that's not a good
01:22:21
argument that again if you were not a
01:22:23
monopoly I would be more sympathetic but
01:22:25
because like somebody somebody's
01:22:27
feelings would get hurt a product
01:22:29
manager's feelings will get hurt inside
01:22:30
of Google feelings
01:22:32
reason to not protect free speech I
01:22:34
think you're unnaturally disrupting the
01:22:35
product Evolution and I tough luck
01:22:37
that's what that's what happens when
01:22:38
you're worth two trillion dollars when
01:22:40
you impact a billion people on the
01:22:42
planet when you start having massive
01:22:44
impact in society you have to take some
01:22:46
responsibility those companies are not
01:22:48
taking responsibility if you're not
01:22:50
super super successful if this is not
01:22:52
going to affect you so you don't have
01:22:53
nothing to worry about you'll see you'll
01:22:55
see apps offshore and you'll see Tick
01:22:57
Tock and other things compete because
01:22:58
they'll have a better product experience
01:23:00
no no nobody's going to create a new
01:23:03
Google because they're downranking one
01:23:07
to ten percent of the search results
01:23:09
agreed some accountability hold on in
01:23:12
the ideal World Companies like Google
01:23:13
and so forth would not take sides in
01:23:15
political debates to be politically
01:23:17
neutral but they're not you look at all
01:23:19
the data around the political leanings
01:23:20
the people running these companies and
01:23:22
then you look at the actual actions of
01:23:23
these companies and they have become
01:23:25
fully political and they've waded into
01:23:26
all these political debates with the
01:23:28
result that the American people's rights
01:23:30
to speech into earn have been reduced
01:23:33
you have companies like PayPal which are
01:23:35
just engaging in retaliation basically
01:23:37
Financial retaliation purely on based on
01:23:41
what political viewpoints they have why
01:23:43
it's not like face it's not like PayPal
01:23:45
needs to be in the business content
01:23:47
creation let's continue this
01:23:48
conversation
01:23:49
call in ama uh if they can get some
01:23:53
servers over that I don't know maybe so
01:23:55
you got to raise some money stacks for
01:23:56
this app and get some more service all
01:23:58
right listen for the dictator who needs
01:24:00
to hit the Lou to do a number two yes I
01:24:04
am the world's greatest moderator
01:24:06
Friedberg is the Sultan of Science and
01:24:08
David Sachs is the prince of
01:24:12
peace see you all next week on the
01:24:16
episode wait wait is this 98 or 99 no
01:24:19
it's 99 it's 99. only one episode left
01:24:21
Wayne Gretzky get it well less enjoy it
01:24:24
well less that we're wrapping it up here
01:24:25
all right we'll see you all next time
01:24:27
have a great movement bye bye
01:24:30
we'll let your winners ride
01:24:34
[Music]
01:24:38
we open source it to the fans and
01:24:41
they've just gone crazy with it
01:24:44
[Music]
01:24:47
somehow
01:24:50
[Music]
01:25:14
we need to get Mercies
01:25:19
foreign
01:25:22
[Music]
