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this is going to be a feisty episode
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is it
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two of us are on Greenwich Mean Time two
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of us are in Pacific J Cal still would
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sleep in his head I'm good actually you
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good I'm good all right well great to be
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back welcome to the all conspiracy
00:00:16
podcast where we repeat false statements
00:00:18
and help spin them into Tales of
00:00:19
struggling Against The Establishment the
00:00:22
elite and the mainstream media we will
00:00:24
deliver to you the people the revolution
00:00:27
against the powers that be unless it
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offends
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still trying to get my invite for next
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week
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we'll also enjoy sharing with you
00:00:41
fantastic stories of opulence let's be
00:00:44
neutral opulence Leisure and benevolent
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greed here we go joining me today are my
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co-hosts General David sacks commander
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of the fourth Battalion of the internet
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tweet Brigade General welcome
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joining us from a remote location in
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Moscow has there been like an
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establishment takeover of the Pod
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yeah every argument they make against us
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you're basically just conceding is true
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I know exactly from his 12th century
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Mediterranean Castle ill Duce
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welcome to moth how is the Mediterranean
00:01:14
diet treating you
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uh blue blue
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and Emperor Nero calcanus ruler over
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podcasts paid events entrepreneurial
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universities and dental spvs emperor
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thank you for letting me sit in your
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throne today it's good to be here thank
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you Dental sbps shout out to my dentists
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well you say king of STDs
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she says PVS oh spvs Central spvs yeah
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that's the thing with certain STDs where
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once you're working you're going to be
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king for life
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[Music]
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Rain Man David
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we open source it to the fans and
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they've just gone crazy
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[Music]
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the syndicate.com yeah thank you you've
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been doing you said five shows a week
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lately your voice is shot I your voice
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is starting to go so I asked you if
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you'd moderate and you thankfully said
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yes I've got the energy I missed last
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week I enjoyed listening to you guys
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with uh BG great episode sorry I
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couldn't join but let's kick it off
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that's funny I still haven't watched the
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episode that I missed you're not
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interested he's like I'm not on the show
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I'm not interested I've got enough time
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to participate I don't really have time
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to watch it the truth is sax why don't
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you admit to everyone that where you're
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in a show you probably watch it four or
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five times
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all right so let's kick this off
00:02:45
obviously you guys recorded right before
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the Wagner group attempted coup or
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potential coup or theorized coup began
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last week so sax you kind of sent out a
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text saying the show is already stale
00:02:59
because you know kind of missed that uh
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that news cycle by publishing right
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after it started but you recorded right
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before so let's do
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just a quick recap of what happened with
00:03:12
Russia
00:03:13
Ukraine and particularly it's Wagner
00:03:15
Wagner group Rebellion
00:03:17
last Friday the Wagner Group which is a
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Russian paramilitary organization led by
00:03:21
Evgeni pregosian launched what seemed
00:03:25
like an armed Insurrection against
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Russia Wagner had occupied portions of
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rostov vondan a city of over a million
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people a regional capital and
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headquarters of Russia's Southern
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military District before setting off
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towards Moscow and then abruptly
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stopping I think about 200 kilometers
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before reaching Moscow City at that
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point there was supposedly a negotiation
00:03:47
the president of Belarus got involved
00:03:50
and pregosian decided to step down Putin
00:03:54
said I'm not going to persecute or I'm
00:03:55
not going to prosecute you for these
00:03:57
crimes he was given immunity and it was
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announced that all the members of the
00:04:02
Wagner group were given the option of
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returning home or joining the Russian
00:04:06
military and the Wagner group was going
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to be dissolved sax maybe you can kind
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of give us your summary of the events
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that took place
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and then we'll talk a little bit about
00:04:16
the interpretation of what we think this
00:04:18
means for the conflict in Ukraine well
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you're right that this Rebellion took
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place just after we dropped our last
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episode and so everybody both on Twitter
00:04:29
and in the comments was dunking on me
00:04:31
for my take on last week's episode that
00:04:33
the much hyped Ukrainian
00:04:35
counter-offensive was was not succeeding
00:04:38
that it was in fact failing I think
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there's abundant evidence for that which
00:04:42
hasn't changed even CNN had written an
00:04:44
article basically supporting the idea
00:04:46
that the counter-offensive was not
00:04:47
living up to what had been promised and
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so everyone was in the comments saying
00:04:52
that my take had basically aged like
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milk and this armed Rebellion or Mutiny
00:04:57
by fregosian was evidence that the
00:04:59
Russian regime was about to collapse
00:05:00
that Russia was
00:05:02
in fact on the verge of Civil War and
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you saw the exact same people who had
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oversold the counter-offensive now
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overselling this Mutiny as something
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that would bring down the Russian
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regime and and the war and of course
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that that did not happen
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it was certainly a highly unusual event
00:05:21
and I've read takes now from every
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different corner of the internet about
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what it was and what took place you have
00:05:26
people speculating that this is all
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staged I do not believe that I do
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believe that it was an Insurrection or
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Mutiny by progosian I think the trigger
00:05:34
for it was the fact that his Wagner
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organization was being merged in with
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the ministry of defense and the regular
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Russian army and his men were all being
00:05:43
made to sign contracts with the ministry
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of defense that would have resulted in a
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giant loss of income and status for
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pregosian simultaneously for months now
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he's been criticizing the ministry of
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Defense specifically the the minister of
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defense shoigu and the
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Chief general of the general staff
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gerasimov so he's been vocally critical
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of them and I think that this basically
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erupted into a mutiny by him where he
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basically tried to leverage his position
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like you said he marched what they now
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think are about 8 000 men which is about
00:06:20
a quarter of Wagner into rostov on Don
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he then took the ministry headquarters
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and sent about three thousand of his men
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on a convoy to Moscow I think that
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although this is probably best described
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as a mutiny I think that it did have
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coup optionality to it I think that
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pagosian was seeking to find out how
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much support Putin had and who might
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join him and he had put out a number of
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statements that I think from the Russian
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regime's point of view could be
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described as seditious
00:06:51
that morning and he there's a lot of
00:06:54
evidence that he staged this attack on
00:06:56
his base he claimed that there was a
00:06:58
missile attack by the ministry of
00:06:59
defense and that's what launched this
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March for justice and in his comments
00:07:04
he was careful not to criticize Putin
00:07:07
directly but he had a lot to say about
00:07:08
Ministry defense and the overall conduct
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of the war and it was I think harshly
00:07:12
critical
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indirectly of of Putin and I think he
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was looking to see who might support him
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and what happened is that on the way to
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Moscow during this Convoy nobody
00:07:24
supported him in fact all the statements
00:07:26
came out from the other generals
00:07:28
including sir vegan including all the
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regional governors
00:07:32
of members of the Duma and other
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important figures in Russian society and
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there wasn't a single person willing to
00:07:39
publicly support for Goshen and that's
00:07:41
when Putin went on TV called this
00:07:43
basically an act of treason and a stab
00:07:45
in the back and at that point I think
00:07:48
progressions options were pretty limited
00:07:49
and he basically took a deal
00:07:52
that was brokered by lukashenko in which
00:07:54
he would go into Exile in Belarus in
00:07:58
exchange for basically being allowed to
00:08:00
live that was basically the deal that
00:08:02
was ultimately cut and so I think where
00:08:04
things stand today is that although I
00:08:07
think this was an embarrassment and a
00:08:09
black eye for the Russian regime it
00:08:11
never looks good for regime to have any
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kind of mutiny or insurrection
00:08:17
and I think that it does raise questions
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that poon's now gonna have to answer to
00:08:22
his various allies and supporters about
00:08:24
how stable his regime is I think
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ultimately Putin has ended up in a place
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of consolidating Russian Society behind
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him like I said there were no power
00:08:34
centers that supported this Mutiny they
00:08:37
all rallied to Putin's defense and the
00:08:39
people of Russia even though Ferguson is
00:08:41
a popular figure being kind of a war
00:08:43
hero from the Battle of bakmut the the
00:08:46
Russian Society supported Putin I think
00:08:48
he's at something like 80 percent poll
00:08:50
numbers so I think where are things
00:08:53
well like Lovato Center which is an
00:08:55
independent polling Agency for example
00:08:58
these are not the Russian regime's own
00:09:00
numbers would you say in a poll that you
00:09:02
don't support Putin
00:09:04
well I don't know how levada Center what
00:09:07
their methodology is but these numbers
00:09:09
when the numbers are bad are cited by
00:09:11
Western sources but there are other
00:09:13
forms of evidence too you may have seen
00:09:15
this video that went viral over the past
00:09:16
few days of this song that's now the
00:09:19
number one chart Buster in in Russia
00:09:23
where it's this very patriotic Russian
00:09:25
song where they're basically singing I
00:09:27
am Russian that's sort of the main
00:09:29
chorus Nick can you pull up the song I'd
00:09:31
like to see this please coming at you
00:09:33
95.5 I love Russia oh my God this is
00:09:37
some serious propaganda
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wow yeah
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[Music]
00:09:45
yeah I mean that looks like a pretty
00:09:47
good party
00:09:50
if you look up the the lyrics to the
00:09:53
song the English translation of the
00:09:55
lyrics the gist of it is I I'm basically
00:09:57
proud to be a Russian and I don't care
00:09:59
who doesn't like it that's basically the
00:10:02
the lyrics of it how do you get invited
00:10:04
to that party
00:10:06
I got so many jokes I'm not gonna make
00:10:08
them I know you gotta be careful be
00:10:10
careful here folks you gotta be careful
00:10:12
here why it looks like a fun party it's
00:10:14
a catchy song
00:10:15
I mean I love to dress up and show up
00:10:18
tell me where to show up Putin I think
00:10:20
the point here is that Russian Society
00:10:22
is United behind the state and wanting
00:10:25
to fight this war and I think that part
00:10:28
of the reason why Ferguson's Mutiny was
00:10:30
so oversold as an imminent coup that
00:10:33
would bring about the collapse of the
00:10:35
Putin regime and of the Russian war
00:10:37
effort and of their front line is
00:10:39
because at least since the beginning of
00:10:41
this war we've had this narrative that
00:10:43
if we applied enough pressure to Russia
00:10:46
that there'd be a palace Intrigue and a
00:10:48
palace coup and that liberal forces
00:10:51
inside of Russia would rise up and
00:10:53
topple the dictator Putin and basically
00:10:55
get them out of this war and I think
00:10:57
what's happened is fairly predictable
00:10:59
but it's the opposite of that which is
00:11:01
the Russian people are rallying around
00:11:02
the flag and rallying around Putin the
00:11:04
war leader and they are a patriotic
00:11:07
people just like the ukrainians and I
00:11:09
think both these countries that both the
00:11:11
Russians and the ukrainians are proud
00:11:13
people and I think they're in a fight to
00:11:15
the death and I think that both
00:11:17
countries okay regardless as existential
00:11:19
and we have basically stuck ourselves in
00:11:22
the middle of this fight to the death
00:11:23
between these two countries and I don't
00:11:25
see this working out very well okay so
00:11:27
look I I will make an admission I
00:11:29
consider myself a modestly well-read
00:11:32
person modestly well-informed I had
00:11:35
never heard of the Wagner group or
00:11:37
pregosian prior to this coup attempt
00:11:39
last week Jay Cal had you guys heard of
00:11:42
this person before
00:11:44
and the Wagner group yeah yeah
00:11:48
oh well maybe I'm an idiot or I'm just
00:11:50
not interested I think what was so
00:11:51
surprising was like how out of the blue
00:11:53
the story seemed to be last week that
00:11:56
there was this disagreement between this
00:11:58
person that commanded this paramilitary
00:12:00
organization who then turned around
00:12:02
against Putin and stood up against him
00:12:04
and marched back towards Moscow and it
00:12:06
felt to me like it came a little bit out
00:12:09
of the blue and was such like a a weird
00:12:11
kind of shocking event did it feel kind
00:12:13
of like that to you guys that there was
00:12:16
surprising instability and the
00:12:18
surprising potential Revolution
00:12:20
happening locally I think stepping back
00:12:22
here and just looking at the news cycle
00:12:24
obviously it I don't think many people
00:12:26
expected this this was a wild card and
00:12:29
so people could be humble in you know
00:12:31
their belief of like how much they
00:12:33
actually understand about what's going
00:12:34
on there
00:12:35
the Russian soldiers are not in favor of
00:12:37
this war this is a war that's very
00:12:39
unpopular in Russia actually and for the
00:12:42
Ukraine having been invaded by Russia
00:12:44
they're fighting for their land and
00:12:45
they're gonna they are much more
00:12:46
motivated I wouldn't believe any of this
00:12:48
propaganda but this is a bit of a raw
00:12:50
shock test everybody on the left got on
00:12:51
Twitter and said this is the end of
00:12:53
Putin everybody's gonna rise up in the
00:12:55
streets and they overplayed that you
00:12:57
know angle of the story and then of
00:12:59
course the right or people who are
00:13:01
pro-russian or anti the West backing
00:13:04
this war are going to take the other
00:13:05
side Freeburg they're going to take the
00:13:06
side of
00:13:08
you know oh everybody loves Russia 80 of
00:13:11
people are voting for this it's
00:13:12
ridiculous to think that anybody in
00:13:14
Russia is going to answer
00:13:15
do you like Putin do you support Putin
00:13:17
on a survey can you imagine a Putin's a
00:13:20
murderous dictator who kills all of his
00:13:22
enemies and he controls through violence
00:13:24
nobody's answering a survey correctly
00:13:26
this you know top song is complete
00:13:29
propaganda Putin has control of the
00:13:31
entire media apparatus there
00:13:33
with this showed actually if you step
00:13:36
back and you look at you go to the party
00:13:37
though if you were invited well I mean
00:13:39
are there going to be potential LPS
00:13:40
there
00:13:42
last week
00:13:44
it's just a joke folks but stepping back
00:13:47
if you look at modern day dictators they
00:13:49
tend to stay in power for about three
00:13:50
decades Putin's in his third
00:13:53
and uh I think we're gonna see in uh the
00:13:57
next 10 years
00:13:58
Putin lose power and he's going to be
00:14:02
out of power
00:14:03
and when we look back on it it's going
00:14:05
to be one of two causes it's going to be
00:14:07
either cancer which you know the
00:14:08
speculation is has had cancer and that's
00:14:10
why he disappears from View
00:14:12
because he might be getting treatment
00:14:14
and we'll look back on us that the end
00:14:17
of his power will be and his control of
00:14:21
Russia which he controlling these
00:14:24
controlling through violence and fear of
00:14:27
violence the threat of violence is
00:14:28
exhausting you have to be paranoid and
00:14:30
that's why it generally doesn't last
00:14:31
that long especially compared to the
00:14:33
west where we have a democracy and
00:14:34
people last about a half decade
00:14:37
he will will look back on his end which
00:14:39
will be in the next 10 years either
00:14:41
through cancer or through his invasion
00:14:44
of Ukraine this is the biggest blunder
00:14:46
he's ever made and and this is a really
00:14:49
crazy sign that somebody would actually
00:14:52
attempt or even float a coup is insane
00:14:56
he's murdered every single person who
00:14:58
has ever even
00:15:00
challenges Authority in a minor way the
00:15:03
fact that his one of his right hand men
00:15:05
this is one of his tight Inner Circle
00:15:08
the fact that one of the people in his
00:15:10
tight Inner Circle would actually start
00:15:13
heading towards Moscow is insane so to
00:15:17
say this wasn't a big deal and Putin's
00:15:19
you know now Consolidated power and
00:15:21
everybody's in the street dancing
00:15:24
this is just simply not true I never
00:15:27
said it wasn't a big deal I wasn't
00:15:28
talking about you I was talking about
00:15:29
the mids on Twitter I never mentioned
00:15:31
your name I think that um your point J
00:15:34
Cal at the beginning that this doesn't
00:15:36
really seem to change anyone's point of
00:15:38
view on the Outlook your point of view
00:15:39
sounds like it's the same as it was a
00:15:41
week ago sacked your point of view is
00:15:43
probably the same as it was a week ago I
00:15:45
think there are a couple of takeaways
00:15:47
here first of all
00:15:48
they've had polling of opinion in Russia
00:15:51
for a long time and like I said when the
00:15:53
polls go the way that the Western
00:15:55
sources want no one questions their
00:15:58
accuracy again I don't know exactly the
00:16:00
methodology but levada Center is an
00:16:02
independent pollster that Western
00:16:04
Publications do trust I hear them repeat
00:16:07
it over and over again and you know by
00:16:10
their methodology which I assume hasn't
00:16:12
changed I think Putin's popularity
00:16:13
before the war is around 65 percent now
00:16:16
they're showing it at about 83 percent
00:16:17
Jason you may not like the war and you
00:16:20
know I certainly don't like them nobody
00:16:23
likes the war but I I think it is simply
00:16:25
a fact that the Russian people have
00:16:27
rallied around the flag and they do
00:16:29
support this war and and Putin as the
00:16:33
leader now I do think he has egg on his
00:16:36
face here from this pagosian Uprising in
00:16:38
terms of did I see this coming no I
00:16:39
didn't have this on my bingo card I
00:16:41
don't think anybody else did either
00:16:42
however did I know who provision is
00:16:44
certainly I mean I've been tracking
00:16:45
progosian statements
00:16:47
since around February he's been vocally
00:16:49
criticizing the ministry of Defense
00:16:51
specifically shoigu and jarasimov in
00:16:54
increasingly insubordinate and you could
00:16:56
argue even seditious ways I'm really
00:16:59
kind of surprised in a way that he
00:17:01
wasn't dealt with before this and I'm
00:17:03
sure that the Kremlin is kicking itself
00:17:04
for probably not dealing with it sooner
00:17:07
but in terms of why he stole alive I
00:17:10
think that Putin had a really tough
00:17:11
decision to make about you quash this
00:17:14
Rebellion completely which would have
00:17:17
led to horrific images of you know
00:17:20
violence potentially in Moscow or
00:17:22
Russians and Russians killing Russians
00:17:25
that might have actually led the the
00:17:27
Russian front to question itself or
00:17:29
collapse so I think he did the expedient
00:17:31
thing which is he cut a deal he got
00:17:33
lukashenko to help broker and he cut a
00:17:35
deal and I think at the end of the day I
00:17:37
think that he made the cool-headed
00:17:39
decision that was in his and in Russian
00:17:43
interest which was to avoid this to
00:17:45
getting to the point of a bloody
00:17:47
Insurrection okay any um any point of
00:17:51
view shift for you coming out of the
00:17:53
pregosian event of the last week on
00:17:56
Russia Ukraine I mean I want to know how
00:17:59
much he got paid
00:18:00
to stop marching towards Moscow yeah I
00:18:03
mean it is like I mean it must have been
00:18:05
it must have been a lot
00:18:08
not bad for a guy that was what Putin's
00:18:11
caterer a few years ago right he started
00:18:13
a catering business I spent nine years
00:18:15
in jail this guy's life been nine years
00:18:17
in jail it's like The Sopranos he went
00:18:19
to jail for nine years for selling
00:18:20
illegal hot dogs or something and all of
00:18:22
a sudden I mean I got just paid billions
00:18:25
of dollars to basically stop his
00:18:27
paramilitary group from taking over one
00:18:29
of the largest countries in the world
00:18:30
it's the largest I mean nuclear Arsenal
00:18:32
in the world yeah
00:18:33
just so you know who this guy is I mean
00:18:36
he he really is the street Thug that
00:18:39
Putin is always accused of being
00:18:41
he was a street dog he did go to jail
00:18:44
he was one of these guys who came up
00:18:47
in Russia as a businessman when to be a
00:18:49
businessman you had to be so tough
00:18:51
businessmen were getting murdered left
00:18:52
and right by gangsters you almost had to
00:18:54
be a gangster yourself apparently he
00:18:57
made some money in the supermarket chain
00:18:59
business and that led him to create a
00:19:01
catering business which brought him to
00:19:03
Putin's attention and he started
00:19:05
catering for the Kremlin he's sometimes
00:19:07
called Putin's Chef I don't think he was
00:19:09
a chef himself he was the guy who owned
00:19:11
he owned the business and then from
00:19:13
there he was given the license to create
00:19:15
this PMC this private uh military
00:19:17
corporation
00:19:19
Bogner group he wasn't the only founder
00:19:21
of it he had a co-founder who was
00:19:23
actually the military man behind it but
00:19:25
Wagner became this this group of
00:19:28
mercenaries who do all sorts of business
00:19:30
in Africa mainly where they are working
00:19:33
on behalf of governments there
00:19:36
to protect mineral resources or oil
00:19:38
wells all sorts of things a Sopranos
00:19:41
Captain who would he be like Phil
00:19:42
leotardo just like 20 years in jail
00:19:44
comes out I think it's sort of like John
00:19:46
Gotti going against Michael Corleone
00:19:47
yeah I think that Putin is sort of the
00:19:50
very cold right rational guy with
00:19:53
everything in his head who's very
00:19:54
calculated it doesn't reveal much right
00:19:57
more like a Michael Corleone whereas I
00:19:59
think that is emotional erratic he's
00:20:03
been saying these statements for months
00:20:04
here which I don't see how they lose
00:20:06
constantly Loose Cannon and the crazy
00:20:08
thing though is is that what you saw on
00:20:10
Twitter and social media was
00:20:12
unrestrained Glee really delirium over
00:20:16
the idea that pagosian my topple Putin
00:20:18
and become the custodian of Russia's
00:20:21
thousands of nuclear weapons yeah and
00:20:23
you know so my comment on this whole
00:20:25
thing is be careful what you wish for
00:20:27
why in the world would Americans want
00:20:30
that we'd be jumping out of the frying
00:20:32
pan Into the Fire you know I've been
00:20:33
saying since the beginning of the war
00:20:34
that this fantasy that Putin is going to
00:20:36
be toppled by a palace goo and you're
00:20:38
getting a replacement with navalny or
00:20:39
something like that we're going to get
00:20:41
Gorbachev 2.0 I said that was always
00:20:43
unrealistic and what you're much more
00:20:45
likely to end up with is an even worse
00:20:47
dictator or possible or a hardliner and
00:20:51
I think that is what would happen if
00:20:52
Ferguson had taken over I think would
00:20:54
have been much worse for the West the
00:20:55
final point is what's the takeaway from
00:20:57
here is I think this is going to put
00:20:59
more pressure on Putin to conduct the
00:21:02
war in a more violent way I I know that
00:21:05
people already think that the war is
00:21:06
horrible and violent but Putin has been
00:21:08
criticized by hardliners on his right
00:21:11
for basically making the war a special
00:21:13
military operation instead of an all-out
00:21:15
war and progosian I think expected to
00:21:18
find more support among the sort of
00:21:21
ultra nationalists in Russia and among
00:21:23
the military who have been critical of
00:21:25
Putin for waging the war in what they
00:21:27
consider to be too half-hearted or an
00:21:30
incomplete way they would like to see
00:21:32
this declared to be a war they would
00:21:33
like to see the full mobilization of
00:21:35
Russian society and this is the problem
00:21:38
that I see now is that I think Putin
00:21:40
already knew but this has to underscore
00:21:42
for him that this war is existential for
00:21:44
him personally if he loses it's the end
00:21:47
of not only his regime but probably his
00:21:49
life in Russia and I think he's going to
00:21:51
do whatever it takes to win this war and
00:21:53
I think you could see now over the next
00:21:55
few months a full mobilization in Russia
00:21:57
and I think that this could lead us to
00:21:59
the next point of escalation in this war
00:22:01
that is if this Ukrainian
00:22:02
counteroffensive actually is successful
00:22:04
on some level right now it is it is not
00:22:06
succeeding so there's no reason there's
00:22:08
no reason for Putin to do that but if if
00:22:10
this counter offensive succeeds you will
00:22:12
see the next level of escalation the
00:22:13
fact you did a great job stringing six
00:22:15
points together I think my key takeaway
00:22:17
is which is now 18 points you've made so
00:22:21
you can retire for the rest of the show
00:22:22
my key takeaway from your series of
00:22:25
statements however
00:22:26
is an important one which is to watch
00:22:28
the potential escalation driven by Putin
00:22:30
here
00:22:31
any wrap up otherwise I'm going to move
00:22:33
forward
00:22:34
you know go ahead thinking about this
00:22:36
like
00:22:37
there's a famous Sun Tzu quote the
00:22:39
Supreme Art of War is to subdue the
00:22:41
enemy without fighting
00:22:43
this is a big mistake and we need to
00:22:46
make sure that we don't get into a war
00:22:48
with Taiwan and China and China over
00:22:51
Taiwan so okay we have to avoid these
00:22:53
things and then that's what and I are in
00:22:55
alignment Sun Tzu calacanus we heard it
00:22:57
here first let's uh move forward with
00:23:00
peace I can only hope that the conflict
00:23:01
ends soon as I've always said I realized
00:23:05
over the last week how little I know
00:23:06
about the Russian military conflict with
00:23:09
Ukraine and I appreciate
00:23:12
Sexes contributions super helpful I went
00:23:15
to Cal UC Berkeley
00:23:18
1997 fall in 97 and it was the last year
00:23:22
that Cal had affirmative action
00:23:24
Admissions and I remember at that time
00:23:27
there was a big case a guy named Baki
00:23:30
was rejected by the University of
00:23:31
California Davis Medical School and he
00:23:33
alleged reverse discrimination in 1974
00:23:36
and sued the University of California
00:23:39
and eventually it became a landmark U.S
00:23:41
Supreme Court case Regents of the
00:23:43
University of California versus Baki and
00:23:45
in 1995 the UC Regents voted to
00:23:48
eliminate affirmative action
00:23:50
so the year that I was at Cal I think
00:23:52
was the last year of affirmative action
00:23:54
Admissions and it's obviously been a
00:23:57
pretty hot topic
00:24:00
here in California
00:24:03
for the past you know 25 uh 30 years
00:24:06
this morning the Supreme Court ruled on
00:24:08
two separate cases regarding using race
00:24:12
as an admissions criteria in college
00:24:15
admissions and the votes were six to
00:24:17
three against affirmative action in the
00:24:19
University of North Carolina case in six
00:24:21
to two against affirmative action in the
00:24:23
Harvard case
00:24:24
katanji Brown Jackson recused herself
00:24:26
because she previously served on
00:24:28
Harvard's Board of overseers all the
00:24:30
conservative as their you know kind of
00:24:32
characterized judges voted to strike
00:24:34
down affirmative action and all the as
00:24:36
their characterized liberal judges voted
00:24:38
to keep it both of these cases were
00:24:40
filed in 2014 by a group called students
00:24:42
for fair admissions
00:24:43
and effectively the court said that at
00:24:47
Harvard at UNC the schools were
00:24:49
systematically discriminating against
00:24:51
Asian Americans in violation of civil
00:24:53
rights laws by using uh their race as a
00:24:56
as a system for for profiling excluding
00:24:59
and trying to be more inclusive of a
00:25:01
more diverse and racially diverse
00:25:04
set of applicants so tomorrow I'd love
00:25:07
your read I guess on the surprise or
00:25:10
this was an accept expected case I think
00:25:13
Saxon I mentioned this before but I
00:25:15
think we both expected this to happen
00:25:18
I think it's probably important to maybe
00:25:22
set up a more practical explainer
00:25:26
Friedberg so Nick if you want to just
00:25:28
throw up that
00:25:29
image that I just sent you we can sort
00:25:32
of explain the Genesis of the lawsuit so
00:25:36
what you can see here
00:25:39
is admit rates into Harvard
00:25:42
by race
00:25:44
ethnicity but also by academic decile
00:25:47
yeah and so what it basically shows in a
00:25:49
nutshell is an African-American student
00:25:52
in the 40th percentile of the academic
00:25:55
index is actually more likely to get in
00:25:58
than an Asian student at the 100th
00:26:00
percentile
00:26:02
and so that at the core is sort of sorry
00:26:04
that means that means that the Asian
00:26:07
American student had better scores than
00:26:10
99
00:26:11
of other applicants and still didn't get
00:26:15
in right right
00:26:18
so you have to go back I think to 2003
00:26:23
when essentially what the Supreme Court
00:26:25
said is like look we're going to allow
00:26:27
this affirmative action stuff to last
00:26:29
roughly for another 25 years
00:26:32
but by that point we expect that the
00:26:35
work that needed to be done will have
00:26:36
been done again this is them saying this
00:26:39
not me
00:26:40
and so I think what today does is
00:26:43
actually
00:26:44
quite important not just for what it
00:26:47
means for universities
00:26:49
but also what it means for private
00:26:51
Enterprises so just to take a second on
00:26:53
this I think what happens today
00:26:56
is the pretty obvious stuff which is
00:26:58
that you have to change University
00:27:00
applications you have to change all of
00:27:03
the admissions profiling all of the
00:27:05
stuff that you would normally do
00:27:08
you probably I'm not even sure if you
00:27:09
can even have a box where you can
00:27:11
declare race maybe you can or cannot I
00:27:13
don't even know
00:27:14
but all of that changes today
00:27:16
so then the question is well what's the
00:27:18
first order derivative what changes next
00:27:20
and I called someone who's a pretty well
00:27:23
known
00:27:24
constitutional supreme court lawyer on
00:27:26
this and
00:27:28
The Next Step
00:27:30
is probably going to be around Athletics
00:27:33
based and legacy-based admissions
00:27:35
so Athletics based admissions are pretty
00:27:37
obvious which is you don't really have
00:27:39
great grades
00:27:41
but you're really stupendous at a sport
00:27:44
that's important to that school
00:27:46
so then they let you in because they
00:27:48
want to compete inside sport for
00:27:50
whatever reason
00:27:51
the Legacy one is even more prickly
00:27:53
which is not you're kind of a dummy but
00:27:55
your parents are rich and or went to the
00:27:57
school before
00:27:58
and so then they let you in as well
00:28:01
and
00:28:03
his thought on this is that those things
00:28:07
will go away because if you can't use
00:28:10
race-based admissions to kind of balance
00:28:13
the scales then it'll become pretty
00:28:16
quick where somebody launches a
00:28:17
legacy-based lawsuit or an athletic
00:28:20
based bias lawsuit and wins that as well
00:28:23
so that's the first order derivative so
00:28:26
you know the thought are those because
00:28:27
those aren't constitutionally
00:28:31
protected whereas equality based on race
00:28:35
but it becomes a huge headache for these
00:28:36
schools
00:28:37
right and so you're going to be fighting
00:28:39
these admission standards constantly
00:28:42
changing and so if you're not going to
00:28:44
let you know
00:28:45
a bunch of poor minority black and brown
00:28:48
kids in but you're letting in the Sons
00:28:51
and Daughters of Rich important people I
00:28:55
think that that's going to paint that
00:28:56
school in a very bad light so I think
00:28:58
that's so important white typically
00:29:01
white typically white although one would
00:29:03
say the great thing over the last couple
00:29:06
of decades is there have been a lot of
00:29:07
minorities that have gotten into these
00:29:09
very elite schools which means this
00:29:12
their kids would be the first generation
00:29:14
that's eligible for legacy but you're
00:29:15
gonna wipe that away so I think from a
00:29:18
just a social stigma perspective and I
00:29:21
have a solution for this which I'll get
00:29:22
to at the end for those people but so I
00:29:24
think that's the first order derivative
00:29:25
the second order derivative is now what
00:29:27
lawsuits get launched and what are the
00:29:29
implications for private companies right
00:29:31
so right now this affects any
00:29:34
institution that receives Federal
00:29:35
funding and that includes all the
00:29:37
universities so there's no private or
00:29:39
public university really except for a
00:29:40
handful that don't take this money so
00:29:42
they'll all have to do this
00:29:44
but the really important question after
00:29:46
that will be what happens to companies
00:29:48
like apple or Facebook or Exxon who have
00:29:52
race-based programs to try to attract
00:29:56
African-American Engineers or
00:29:59
Hispanic
00:30:01
chemists whatever whatever the program
00:30:02
is that you want to come up with
00:30:05
will those get challenged and will those
00:30:07
companies have to change and my friends
00:30:10
thoughts on that were that yes that
00:30:11
those would also change and that's going
00:30:14
to have a really important impact on
00:30:16
private Enterprise and how they approach
00:30:17
this stuff and how Dei stuff works and
00:30:20
frankly Downstream how ESG works because
00:30:23
all these ESG check boxes now some of
00:30:25
them will actually become illegal right
00:30:27
so I think the importance of decision
00:30:30
can't be really understated it's going
00:30:32
to the changes will be slow and then
00:30:36
they'll be fast they'll first touch
00:30:38
higher ed but then I think they'll touch
00:30:39
private Enterprise and so I think it was
00:30:41
a it was a very important
00:30:44
decision in America that just happened
00:30:46
what is
00:30:48
what is the right ethics and and values
00:30:51
I mean what do you guys I guess we could
00:30:53
just do this around the table Jacob
00:30:54
maybe you kick it off
00:30:56
should we I mean from your point of view
00:30:58
do you think that values should include
00:31:02
racial diversity in Admissions and
00:31:06
universities yeah this is like the
00:31:08
ultimate or is the values about equality
00:31:10
of opportunity for everyone regardless
00:31:12
of race right you're asking the exact
00:31:14
right question I think that's the
00:31:17
world's greatest moderator but yeah
00:31:18
doing a solid job so far and this
00:31:20
creates a lot of cognitive dissonance
00:31:22
for people right because you you really
00:31:24
want to
00:31:25
believe that the world is a meritocracy
00:31:28
and uh you know if you were to take
00:31:30
other
00:31:31
Pursuits in the world you'd never say
00:31:33
like we should let race gender
00:31:36
age affect people's performance in the
00:31:39
100 yard dash or their their
00:31:41
compensation at a company right all of
00:31:43
that should be based on achievement
00:31:46
and so there is a question on what
00:31:48
achievements should be taken into
00:31:50
account when you apply to a school
00:31:52
and it's pretty obvious the Legacy thing
00:31:55
you know is
00:31:57
you know a back door into these schools
00:31:59
but we want to feel like we're also
00:32:02
making progress because listen the world
00:32:04
has been unfair
00:32:06
the world was built on slavery and our
00:32:10
country has you know it's only 150 years
00:32:12
past that and Civil Rights Act was what
00:32:15
1964 or 65 like we've we've we really
00:32:18
want to see everybody achieve here so I
00:32:20
think you have to pause for a second and
00:32:21
say well if the goal is you want to see
00:32:25
you know black Americans perform better
00:32:27
and I think that's the the underlying
00:32:31
concern here
00:32:33
and it is based on the legacy of America
00:32:35
well how do you do that and I think
00:32:38
we're looking way too far down in the
00:32:41
educational pipeline
00:32:43
the solution here is really Child Care
00:32:45
the solution here is Nursery schools
00:32:46
pre-k
00:32:47
elementary school education and those
00:32:49
things need competition and that's where
00:32:51
people fall behind to be looking at this
00:32:53
at the end of the academic journey is I
00:32:56
think crazy so you know when I'm
00:32:58
president I'm going to have 365 day a
00:33:02
year you know child care and
00:33:05
Pre-K and that's where we should if we
00:33:07
really want to try to make up for some
00:33:09
wrongs in the history of this country
00:33:11
and try to have better outcomes we need
00:33:13
competition in schools
00:33:15
which means probably breaking some of
00:33:16
these unions and giving people vouchers
00:33:18
and choice and then we have to invest
00:33:20
more in the earliest stages of Education
00:33:23
and I think everybody wants to see a
00:33:26
better system here Dei to chamat's point
00:33:29
is
00:33:30
it is illegal to hire people based on
00:33:33
race gender any of those criteria
00:33:35
obviously and the Dai programs are
00:33:38
trying to fill more applicants so their
00:33:41
goal typically in the way they don't
00:33:43
break the law is to just try to in their
00:33:46
best cases find more applicants and but
00:33:51
even that does feel like there's many
00:33:54
times in life when
00:33:56
um people will say things in Corporate
00:33:58
America like we have too many white guys
00:34:00
in this these positions we need we
00:34:02
cannot hire another white guy so the
00:34:04
reality of Dei that I've seen up close
00:34:06
and personal when I was at AOL I've told
00:34:08
the story before
00:34:09
somebody said to me there's no way for
00:34:11
us to make you an EVP you have to stay
00:34:14
at SVP and I said why is that like I'm
00:34:16
doing all this EVP level work and they
00:34:17
said because you're a white guy
00:34:19
and the entire company is white guys at
00:34:21
EVP and we cannot add another white guy
00:34:24
there but we'll just give you the same
00:34:25
bonus compensation so don't worry about
00:34:27
it
00:34:28
and so there's all kinds of games being
00:34:29
played here but I think it's great that
00:34:31
we're having this conversation right
00:34:32
it's a hard conversation for America to
00:34:33
have for me the
00:34:36
I've talked about this in the past I've
00:34:38
always had
00:34:40
concern
00:34:41
when we make the shift away from
00:34:44
equality of opportunity to a quality of
00:34:46
outcome
00:34:47
because we all have this objective that
00:34:49
we want to see everyone have equal
00:34:52
rights to success in some way in the
00:34:55
United States the question is at what
00:34:57
point do you move Beyond opportunity
00:34:59
where everyone is given an equal
00:35:01
opportunity in this country
00:35:04
to
00:35:05
invest themselves in transforming their
00:35:07
own lives versus a
00:35:10
quality of outcome where regardless of
00:35:13
how much you do how much effort or
00:35:17
your trials you were given the same as
00:35:19
everyone else and that ends up looking a
00:35:21
lot like socialism and it's very
00:35:22
concerning because I think it limits
00:35:24
progress and opportunity for everyone
00:35:26
the real challenge with this particular
00:35:28
topic is college admissions
00:35:31
about outcome or is about opportunity
00:35:34
it's outcome in the sense that you spend
00:35:36
12 years going to elementary school and
00:35:39
high school and working hard to get
00:35:41
yourself into college
00:35:42
so it's the outcome of all of that
00:35:44
effort and some people aren't given the
00:35:46
opportunity to have success during those
00:35:49
12 years and you know
00:35:52
and it is an outcome what do you think
00:35:54
we should do and it's an opportunity
00:35:56
because it's about going to college
00:35:57
because without having a great College
00:35:58
cycle you may have a more tougher time
00:36:00
getting into a into the workforce so it
00:36:02
that is why it's a hard value question
00:36:04
for me I I don't have a great answer on
00:36:06
this
00:36:07
but I'm just pointing out it's a lot
00:36:08
like the abortion argument where both
00:36:11
sides have some
00:36:14
value-oriented point of view that feels
00:36:16
like it's negating the other person's
00:36:18
point of view
00:36:19
but at the end of the day they're both
00:36:21
coming from either this is an
00:36:22
opportunity or it's an outcome decision
00:36:24
and that's what makes it so challenging
00:36:25
the National Bureau of economic research
00:36:27
did a study in 2019 that they published
00:36:31
and what they found was that 43 so 4 3
00:36:35
43 of white students admitted to Harvard
00:36:38
were athletes
00:36:41
or Legacy students or children of
00:36:44
faculty and staff or had a relative that
00:36:48
were donors to the school
00:36:50
43 wait it's rigged and then they found
00:36:53
on top of that that 75 of those white
00:36:56
students admitted from those four
00:36:58
categories would have been rejected if
00:37:01
they had been treated as a normal
00:37:02
applicant so I think for all the people
00:37:05
that are looking at all the black and
00:37:07
brown kids that may not get into a place
00:37:09
like Harvard
00:37:10
if you don't look at these other
00:37:12
categories
00:37:13
it is a it's a bit of a gross Injustice
00:37:16
quite honestly so I think that these
00:37:17
institutions have to evolve and if
00:37:19
you're going to be forced to be
00:37:21
meritocratic then actually be
00:37:23
meritocratic and by the way I actually
00:37:25
am fine with legacies and donors but I
00:37:28
think what should happen is you should
00:37:29
just publish a rate card and you should
00:37:31
make it hyper transparent and so I love
00:37:34
it for the rich guy who's got an idiot
00:37:36
son or daughter let's just be upfront
00:37:38
and honest with everybody it costs 50
00:37:40
million dollars to get into Stanford it
00:37:42
costs 80 million dollars to get into
00:37:44
Harvard we all know these numbers so we
00:37:46
should just publish them you should pay
00:37:48
the price and be done with it and for
00:37:50
Harvard and Stanford and Yale and all
00:37:52
these schools
00:37:53
having an extra 10 or 20 dummies but an
00:37:55
extra two or three billion may be a
00:37:57
reasonable trade-off but at least it
00:37:58
would be transparent and fair right this
00:38:01
is an important free market question as
00:38:03
well because these are private
00:38:04
institutions they're privately funded
00:38:06
not if they take federal dollars or not
00:38:08
agreed yes but if they take federal
00:38:09
dollars they're not and then it becomes
00:38:11
a government processing it's government
00:38:13
influence it's a state school all those
00:38:15
is there a separate category here just
00:38:17
like country clubs or any private
00:38:20
membership club where the members of the
00:38:22
club get to decide who they want to
00:38:24
admit to the club and is that
00:38:26
Un-American and should the Supreme Court
00:38:28
and should our Constitution have a role
00:38:30
in defining how private institutions
00:38:33
make decisions about who gets it no
00:38:34
Harvard could absolutely return all the
00:38:36
federal funding the billions of dollars
00:38:37
a year they get that's totally
00:38:39
reasonable then they even decide to just
00:38:40
focus on Legacy admits
00:38:43
that's totally reasonable it's within
00:38:44
their rights
00:38:45
sacks like we got it I know that I know
00:38:48
that you used up your quote your
00:38:49
speaking quota already well yeah I
00:38:51
didn't want to I didn't want to butt in
00:38:52
it give him four on my points you get
00:38:56
four of Jake house minutes go ahead
00:38:58
so yeah two points I guess so on the
00:39:01
Legacy thing I agree with jamas that we
00:39:03
should get rid of it it's not
00:39:04
meritocratic I think that if they did
00:39:07
publish a rate card that would be more
00:39:08
honest but they would be too embarrassed
00:39:10
and ashamed to do that but I think
00:39:12
making that argument exposes the
00:39:13
hypocrisy of it
00:39:15
I've already told my kids I'm not
00:39:16
helping them get into college so they're
00:39:18
going to do it on their own and so look
00:39:20
I think by the way that's the best gift
00:39:22
you can give kids yeah that that
00:39:24
perspective sorry go ahead sex so that's
00:39:27
that's Point number one fully agreed on
00:39:28
on the Legacy thing with respect to the
00:39:30
decision itself that I'm sorry can I
00:39:32
just clarify that do you believe that
00:39:34
the Legacy thing should be like in
00:39:37
federal law I mean is that a government
00:39:38
thing or do you think that that's how
00:39:40
those institutions should behave because
00:39:42
those are different I mean I'm asking
00:39:44
are you suggesting that the law should
00:39:45
be involved that the government should
00:39:46
be involved I don't know if it's a legal
00:39:48
thing because I don't know how to
00:39:49
implement that law but um I think it's
00:39:51
something they should stop doing one way
00:39:52
or another maybe it should be a law but
00:39:54
I think it should stop so I think that's
00:39:55
important Legacy thing for privacy for
00:39:57
private membership so just let me just
00:40:00
double click on that do you think that
00:40:02
should extend to private membership
00:40:03
clubs like country clubs as well that
00:40:04
they shouldn't be allowed to decide who
00:40:06
they let in and don't let in what makes
00:40:08
it different that it's Harvard is it
00:40:09
because it's education versus any other
00:40:11
private membership because it takes
00:40:13
Federal funding
00:40:17
for some person to be able to get into a
00:40:20
school they don't deserve to get into
00:40:21
just because their parent went there or
00:40:24
just because their parents wrote a check
00:40:26
that's unreasonable and if they don't
00:40:28
take Federal funding these schools take
00:40:30
so much Federal funding that they're
00:40:31
quasi-public institutions even the
00:40:33
private ones so that's the distinction
00:40:35
that's the distinction for you just to
00:40:36
be clear yeah and also there is a strong
00:40:40
meritocracy opportunity argument on this
00:40:43
and I think it's why that whole parents
00:40:46
College admissions Scandal was was such
00:40:48
a big deal is that for a lot of people
00:40:50
in this country the ability to have your
00:40:53
kids Advance themselves by being the
00:40:56
first to get into college or going to
00:40:57
college or going to a better college
00:40:59
that is a big part of creating
00:41:01
opportunity in this country so for
00:41:02
people to try and defraud that I think
00:41:04
create a huge backlash so look I think
00:41:08
that the Legacy thing just needs to end
00:41:10
one way or another I don't know exactly
00:41:11
what the right legal
00:41:14
implementation is I have two questions
00:41:17
for the panel number one should you be
00:41:19
able to say by geography Hey listen
00:41:21
we're Harvard over Stanford we want to
00:41:23
have a representation of people from
00:41:25
around the world so we're gonna you know
00:41:27
have the top three students from each
00:41:29
country or you know or by population
00:41:31
however you do it
00:41:33
you know mathematically
00:41:35
come in so a little bit of geography
00:41:37
because I did hear from one of these
00:41:38
coaches that cost like six figures to
00:41:41
get your kids into the college they said
00:41:43
the best thing you can do is like move
00:41:44
to Kentucky
00:41:46
you know and then Harvard and Stafford
00:41:50
are looking to get a certain number of
00:41:51
students from each state I don't know
00:41:52
how true that is but they said that's
00:41:54
like one of the the top ways to do it
00:41:55
and then well do you remember Jason just
00:41:56
to build on your point I don't know if
00:41:58
you guys remember but a few years ago
00:41:59
the the in fashion thing to do was to
00:42:02
learn to play squash and I remember all
00:42:05
these parents telling me that and they
00:42:07
had kids that were older than my kids
00:42:08
and they were they were hiring full-time
00:42:11
squash coaches because apparently squash
00:42:14
was like yeah it was all angle shots
00:42:16
yeah I think like stop with the angle
00:42:19
shooting guys yes the gun should go off
00:42:21
you should run the race and your time is
00:42:24
your time and you should go to whatever
00:42:26
the best school is that you deserve to
00:42:28
get into based on your academic ability
00:42:29
now going back
00:42:31
the big problem and I think Jason you
00:42:33
really nailed it on the head
00:42:35
trying to fix it with affirmative action
00:42:38
at the University
00:42:39
is still quite unfair in the sense that
00:42:42
there are so many black and brown kids I
00:42:44
think with tons of potential that don't
00:42:45
even get there
00:42:47
and so the real question is what are you
00:42:49
doing at the grade school and at the
00:42:50
high school and at the preschool
00:42:53
so that you actually get more of these
00:42:55
kids to the starting line because fixing
00:42:57
it when they're 18 I think is a little
00:43:00
too late yeah right fixing it for 300
00:43:03
three four and five years old that's
00:43:05
when they deserve and need all the help
00:43:07
in the world to yourself that's why we
00:43:09
need school choice we need Charter
00:43:10
Schools we need to break the Monopoly
00:43:12
that the unions have over the schools
00:43:14
running it running for their own benefit
00:43:16
if you Define institutional racism as
00:43:20
conditions that trap people on
00:43:21
conditions of poverty across Generations
00:43:23
I'd say the abysmal quality of our
00:43:26
public schools are number one number one
00:43:29
two and three and the reason is because
00:43:30
there's no competition and the unions
00:43:32
run it for their own benefit how long do
00:43:34
they shut down these schools for in
00:43:35
California because they didn't want to
00:43:37
work because they're afraid don't know
00:43:38
don't see the c word we just got a label
00:43:41
enabled
00:43:45
for the benefit of kids and it wasn't
00:43:46
even medically necessary that was a
00:43:48
benefit they saw for themselves yeah
00:43:49
Jacob you come you come from a family
00:43:51
yep that were members of unions because
00:43:55
they worked for fire for police yeah um
00:43:57
is that right yeah
00:44:00
we speak negatively about the effects of
00:44:02
the teachers unions on our public
00:44:03
education system and it I I think it's
00:44:06
absolutely correct
00:44:08
but how do you share the point of view
00:44:11
from the other side if you're a teacher
00:44:13
and you're a member of the union and the
00:44:14
union takes care of you what's the
00:44:17
argument yeah sure to say this Union is
00:44:20
damaging public education and the
00:44:22
teacher that's working in the union and
00:44:23
a member of the union says this is
00:44:25
necessary for my livelihood to protect
00:44:27
me for my benefit
00:44:29
help us share the point of view because
00:44:31
we all have the strongly held point of
00:44:33
view that the unions are destroying and
00:44:34
eroding public education
00:44:37
people have the right
00:44:39
to form unions
00:44:41
but what we all do is we are forced to
00:44:43
be consumers of one educational product
00:44:46
because of how we pay taxes we pay taxes
00:44:48
in yeah I think in my in California we
00:44:50
each pay sixteen thousand dollars into
00:44:52
the educational system and so if you're
00:44:54
a parent you should get that 16k back
00:44:56
and be able to choose what you do with
00:44:58
it so there's competition so the unions
00:45:00
can have protection but there still
00:45:01
should be competition for these services
00:45:04
and I I think there are two separate
00:45:07
issues yeah there's one other thing
00:45:09
which is
00:45:11
I just want to give a shout out to
00:45:13
a non-profit that I support called smash
00:45:15
Academy smash.org it's done by Mitch and
00:45:18
Freda Kapoor the Mitch Kapoor
00:45:22
founded Lotus one two three if you're uh
00:45:24
under the age of 40 you might not know
00:45:27
and what they do is they realize that a
00:45:29
lot of the students who do get into good
00:45:31
colleges
00:45:32
it turns out a lot of the black and
00:45:33
brown students they get accepted and
00:45:35
they're behind in math and so what smash
00:45:38
has done is they have a three-year
00:45:39
program
00:45:40
and I go speak at it sometimes and I
00:45:42
donate money to it and I encourage you
00:45:44
to do the same they have this intensive
00:45:46
summer program so before you go to
00:45:47
college tomorrow if you were one of
00:45:49
those students
00:45:51
you know you might get into college and
00:45:52
then they drop out or even worse they
00:45:55
switch from a stem degree to a non-stem
00:45:58
degree because they're two years behind
00:46:01
on stem or a year behind on stem and so
00:46:03
the Kapoor has found this like little
00:46:05
opportunity to kind of catch people up
00:46:07
and I think that's what we have to do we
00:46:09
have to address this much earlier and
00:46:11
not put a Band-Aid on it yeah in the
00:46:14
system the ivy league system
00:46:16
um you know needs to well it's not
00:46:19
societies right I'm sure it's not just
00:46:20
pick on Harvard but it's like it's like
00:46:22
the elite institutions which we talk
00:46:24
about institutions okay all institutions
00:46:26
that receive federal funding they need
00:46:28
to take a deep look in the mirror and
00:46:29
say are we doing the best thing for
00:46:31
society the second question I had for
00:46:32
the panel was well I'm not getting the
00:46:34
feeling by the way but yeah but our Pure
00:46:37
academics the best way to accept people
00:46:39
into a college or should there be some
00:46:41
blend of it like putting Sports aside
00:46:43
because that's an obvious one but you
00:46:45
know is there's academics but then
00:46:47
there's also creativity you know if you
00:46:48
you might be terrible on your SATs test
00:46:51
and you might be an incredible virtuoso
00:46:53
pianist so I think what is the criteria
00:46:56
and making that criteria fair is what we
00:46:58
all want and it feels tremendously
00:47:00
unfair my point of view is if the
00:47:04
government is funding these schools then
00:47:06
the government certainly has to have a
00:47:08
point of view on what's the reasonable
00:47:10
model for admissions the government's
00:47:13
not funding the schools I Love A
00:47:15
diversity
00:47:16
in a Marketplace I love having different
00:47:19
schools having different admissions
00:47:21
criteria that allow different people to
00:47:24
find their path
00:47:25
through different institutions to your
00:47:27
point Juilliard does not care perhaps as
00:47:30
much what you did on you know your sat
00:47:34
and chemistry
00:47:36
and art schools do not care as much how
00:47:39
well you did in math and stem schools
00:47:43
don't care whether or not you want an
00:47:44
art competition and I think that that's
00:47:46
the important thing that we need to
00:47:48
preserve we need to preserve optionality
00:47:51
for institutions to Define what sorts of
00:47:54
individuals they want to try and recruit
00:47:56
and progress and train and get ready for
00:47:59
the workforce and the path in life that
00:48:01
they then choose versus trying to create
00:48:03
a cookie cutter model for what the
00:48:05
government says it's fair for everyone
00:48:07
and as much as we can take Government
00:48:09
funding out of these institutions and
00:48:11
out of these systems and give them the
00:48:12
freedom to set their own admissions
00:48:14
criteria and create differential
00:48:16
Educational Systems I think that's going
00:48:19
to create the best diversity of a
00:48:21
Workforce
00:48:22
and I'm I would kind of be more excited
00:48:25
about that sort of
00:48:26
an Institutional system than one that is
00:48:29
standardized by the government you know
00:48:30
why that'll never happen
00:48:33
because the profit motive of these
00:48:35
universities is really to be Shadow
00:48:37
organizations for their endowments
00:48:40
and the thing with endowments is that
00:48:42
the people that work there very much
00:48:44
want to
00:48:46
get paid and behave like profit
00:48:49
generating organizations and I think
00:48:52
the issue is that if their sole job was
00:48:55
to really fund
00:48:57
the
00:49:00
operational expenses of the University
00:49:02
then the endowments would be run very
00:49:04
differently right like take again I just
00:49:06
looked up on the internet but Harvard
00:49:08
has about
00:49:09
the operating expenses are roughly
00:49:12
five and a half billion dollars a year
00:49:14
but the revenues are about five and a
00:49:16
half billion dollars a year right so if
00:49:18
instead you had to basically fund you
00:49:20
know there was essentially no Revenue
00:49:22
per se right there's there is very
00:49:24
little tuition and you didn't take any
00:49:25
federal funding you'd have to come up
00:49:27
with five billion dollars a year so you
00:49:29
just basically take that as a draw
00:49:32
from your endowment the endowment would
00:49:34
be run very differently it would be a
00:49:36
don't lose money endowment
00:49:38
that would generate
00:49:40
very low Vol returns I think the problem
00:49:43
with that is that that's not how the
00:49:45
endowment at Harvard works
00:49:47
they wouldn't necessarily make risk
00:49:49
seeking investments in things like
00:49:51
private equity and hedge funds and
00:49:52
Venture Capital into the extent they did
00:49:54
they would just make much much fewer
00:49:55
much much smaller or both
00:49:57
so I think what you're saying could be
00:49:59
possible
00:50:01
but the problem are probably the
00:50:03
endowments at these universities okay
00:50:05
well 53 billion dollars moving Harvard's
00:50:07
endowment now so yeah it would be ten
00:50:10
percent can anyone tell me who the
00:50:11
largest real estate owner is in San
00:50:13
Francisco
00:50:14
you're not working on the internet oh no
00:50:16
I know I know it's that Art Institute
00:50:19
The Academy of Art University I knew
00:50:21
this because they uh kept buying things
00:50:23
and using them I think she used all the
00:50:26
profit over the years to buy more real
00:50:28
estate and she accumulated the largest
00:50:30
real estate portfolio yeah yeah yeah
00:50:32
sorry who is she the founder I forgot
00:50:34
her name
00:50:35
this is a for-profit university or a
00:50:37
private or I mean a non-profit
00:50:39
University yeah there's a lot of artists
00:50:41
that come out of Academy Art and they
00:50:42
work in a lot of different Industries
00:50:44
including industrial design including
00:50:46
animation is what I'm saying is it like
00:50:49
RISD or is it like actually an art
00:50:51
school oh no it's a great art school
00:50:52
yeah Academy of Art
00:50:55
anyway let's keep going so look speaking
00:50:57
of stem
00:50:58
making a big pivot away from art
00:51:01
to AI a couple big news items you know
00:51:06
the AI frenzy continues here in Silicon
00:51:09
Valley all the way from early to growth
00:51:12
stage funding through to M A events we
00:51:15
saw this week data Brits which is a
00:51:19
privately held data infrastructure
00:51:20
company announced that they were
00:51:21
acquiring Mosaic ml for 1.3 billion
00:51:24
dollars that headline number is based on
00:51:27
a cash and stock purchase price where
00:51:30
the value of the stock that was being
00:51:32
used to acquire Mosaic ml
00:51:35
was based off of the last round's
00:51:36
valuation for data bricks which was 38
00:51:39
billion dollars from a fundraising that
00:51:41
they did in 2021 so arguably the
00:51:43
valuation should be lower and the
00:51:44
overall purchase price could be
00:51:45
considered lower but that's besides the
00:51:47
point Mosaic ml as you guys may remember
00:51:50
is a company I mentioned a number of
00:51:52
episodes ago
00:51:53
led by the founder of Nirvana which was
00:51:57
an early AI business that was acquired
00:52:00
by Intel and then he started Mosaic ML
00:52:02
and he offers
00:52:04
open source models and I shared the
00:52:06
performance data of their most recent
00:52:08
announcement on the show
00:52:10
a few weeks ago
00:52:12
you know there's rumors I don't have any
00:52:14
confirmed reports but there's rumors
00:52:16
that Mosaic ml saw
00:52:18
their ARR grow from 1 million to 20
00:52:21
million dollars since January there was
00:52:23
other rumors that said they were only at
00:52:24
6 million of Revenue regardless
00:52:26
databricks is paying a pretty hefty
00:52:28
premium and I think it begs the question
00:52:30
what do data infrastructure database
00:52:33
companies end up looking like in the
00:52:35
future if AI has to become part of the
00:52:37
core infrastructure of every Enterprise
00:52:39
and this is creating a big shift so Saks
00:52:42
as our Enterprise
00:52:44
software
00:52:46
investor expert maybe you can share with
00:52:49
us what this means for the sector does
00:52:50
this buoy excitement for AI
00:52:53
infrastructure startups does this change
00:52:56
the investing landscape is it just
00:52:59
reinforcing what folks are already doing
00:53:01
I think it's a reinforcement I mean the
00:53:04
space is probably the hottest Spate
00:53:05
where you're talking about like AI
00:53:06
infrastructure for Enterprises I think
00:53:08
it's probably the hottest space right
00:53:10
now in in Venture Land we actually
00:53:12
looked at this deal we had a small
00:53:14
allocation in our next round they had a
00:53:16
term sheet for a series B
00:53:19
emergence was actually an elite it and
00:53:21
this is Mosaic ml yeah yeah I don't know
00:53:25
if I'm supposed to be telling you all
00:53:26
this but yeah
00:53:29
it's not great this is why people see it
00:53:31
in breaking news that a term sheet from
00:53:35
emergence to raise 50 million at 400
00:53:38
posts wow and we yeah and we were gonna
00:53:40
have a small allocation in that and as I
00:53:43
recall
00:53:44
the valuation was somewhere around 30 to
00:53:48
35 times ARR which actually is not that
00:53:50
insane for a very fast scoring company
00:53:52
in a hot space so that implies about 10
00:53:55
million of ARR I don't remember the
00:53:57
exact figure but I think that's sort of
00:53:58
the ballpark but growing very very
00:54:00
quickly I mean up from like one or two
00:54:03
at the beginning of the year right
00:54:05
so I actually understand why someone
00:54:09
would want to acquire or invest in this
00:54:11
company like I said I think we wanted to
00:54:13
invest and while we were sort of you
00:54:15
know trying our best you didn't say
00:54:17
anything when I talked about him on the
00:54:18
show a few weeks ago you were just
00:54:19
sitting there mum's a word or actually I
00:54:21
knew that this deal was was basically in
00:54:23
the works because the founder called us
00:54:26
up and he had already promised us a
00:54:27
small allocation around
00:54:29
Naveen did and he called up and said
00:54:31
actually I've got this deal so we're
00:54:33
putting the round on hold and so I
00:54:36
didn't think I should say anything
00:54:37
because obviously it was still ongoing
00:54:40
but yeah we knew about this deal that
00:54:42
was kind of coming down the pipe I
00:54:44
didn't know for sure that it would
00:54:45
happen but but yeah we we heard it was
00:54:47
in the ballpark of this like 1.2 1.3
00:54:49
billion dollar number which like you
00:54:51
said because databricks is a private
00:54:53
stock maybe it's only
00:54:55
10 or 750 or something like that who
00:54:57
knows it's still a great deal but you
00:54:59
know a lot of people are saying it's a
00:55:00
crazy deal I don't think it's a crazy
00:55:01
deal because before this happened after
00:55:05
Naveen signed the term sheet for the
00:55:07
series B an investor came over the top
00:55:10
to invest at a 700 million dollar
00:55:12
valuation wow so people were kind of
00:55:14
going crazy now I don't think that's
00:55:16
necessarily a rational Behavior I think
00:55:18
that's more of evidence of a Mania going
00:55:21
on but I think that what he got offered
00:55:23
is obviously a fantastic deal and I
00:55:25
think what it's evidence of is that
00:55:27
these big Enterprise infra companies are
00:55:30
going to try and build an end-to-end
00:55:32
tool chain here and I think Mosaic ml
00:55:35
had a very very important part of the
00:55:38
tool set which is training up these
00:55:40
models basically maximizing GPU
00:55:43
efficiency because gpus are basically
00:55:45
the scarce item right now we have a GPU
00:55:47
shortage and it's probably not going to
00:55:49
get better for a year or two if that so
00:55:52
this is a very important part of the of
00:55:54
the stack and I think it's probably a
00:55:57
smart acquisition for both yeah I mean
00:55:59
remember snow snowflake which competes
00:56:01
with data breaks also acquired Neva
00:56:04
which was founded by one of my
00:56:05
colleagues a guy I work with uh and a
00:56:07
new really great guy sweetheart for 150
00:56:10
million dollars last month that deal was
00:56:12
announced and the pattern recognition
00:56:15
that seems to emerge here is that if
00:56:16
you're in the data infrastructure
00:56:19
business it seems like it's becoming
00:56:21
critical to level up that it's not just
00:56:23
about storing and moving and
00:56:25
manipulating data but the interpretation
00:56:28
of data through models and the tooling
00:56:31
to build those models becomes a critical
00:56:33
component think of all of these toolkits
00:56:36
that these software companies have to
00:56:37
provide to their by software companies
00:56:39
and it's a big leveling up that's
00:56:40
necessary which seems to me there's
00:56:42
other companies out there like them that
00:56:44
are also going to need to strap on tools
00:56:47
like this
00:56:48
to make themselves competitive in this
00:56:50
marketscape which means that there are
00:56:52
more Acquisitions still to come yeah
00:56:54
Jacob you guys agree or have a different
00:56:57
point of view looking at it it's a it's
00:57:00
a big number the headline number but I
00:57:02
agree with Saks that the actual numbers
00:57:03
half the number so if it was you know if
00:57:05
you look at the number of Engineers they
00:57:07
had based on LinkedIn data and pitchbook
00:57:09
data probably 60 70 employees 80
00:57:12
employees and then 40 50 of them are
00:57:14
Engineers so that puts it at 30 million
00:57:16
dollars per engineer and that's one way
00:57:18
to look at these acquisitions
00:57:21
and I think you know probably three to
00:57:25
ten million dollars per engineer for
00:57:27
like really high-end Engineers is more
00:57:29
the going price but if this is half that
00:57:31
amount because they bought it with
00:57:33
Monopoly money in other words they're
00:57:34
2021 price for their company it's great
00:57:37
and then they get nailed it um Freeburg
00:57:39
what's happening is
00:57:41
this layer of uh natural language on top
00:57:45
of
00:57:46
any service whether it's something as
00:57:49
simple as Yelp or something as
00:57:50
complicated as a giant financial company
00:57:53
with tons of transaction data being able
00:57:56
to talk to it and understand it and then
00:57:59
have your machine learning team build
00:58:02
tools so business owners don't have to
00:58:05
hire data scientists the actual Business
00:58:06
Leaders can talk to the data and get
00:58:09
back answers or just say hey tell me
00:58:12
about our customers how have they
00:58:13
changed over the year and then hey
00:58:15
that's pretty interesting tell me more
00:58:16
about our customers and you know how are
00:58:19
they reacting to these three new
00:58:22
products
00:58:23
and you will get back in intelligence
00:58:26
that previously was unable
00:58:29
to be accessed and so I was uh I was
00:58:34
just at Sequoia yesterday with uh or two
00:58:36
days ago with the latest seven graduates
00:58:38
from our accelerator we bring them to
00:58:40
meet with sax and his team and bring
00:58:41
them to meet with Sequoia and when we
00:58:43
were at Sequoia
00:58:45
I realized that of the seven companies
00:58:47
four of them would not have been
00:58:49
possible before these machine learning
00:58:52
apis were available and open AI is but
00:58:55
one there are now in the companies I'm
00:58:57
talking to they're they're trialing
00:58:59
Freeburg on average six seven eight
00:59:01
language models before they pick one and
00:59:03
they're not picking open AI every time
00:59:05
putting that aside these businesses were
00:59:07
not possible before this technology was
00:59:10
introduced and available via API in the
00:59:12
last six to 12 months
00:59:14
and I think there's a bunch of
00:59:15
businesses that economically would not
00:59:17
work that now work I can give one
00:59:20
example
00:59:22
there are countless meetings that are
00:59:25
recorded over uh Zoom right
00:59:27
think like a local school board
00:59:30
well nobody could ever make a database
00:59:32
of all the discussions going on at local
00:59:34
school boards and then analyze all that
00:59:37
but now because all of those are saved
00:59:40
uh on zoom and they occur on zoom and
00:59:42
they're available for public record you
00:59:44
can ingest every single one of those and
00:59:46
then build a Bloomberg terminal of every
00:59:48
discussion happening at every school
00:59:50
board everywhere in the United States
00:59:52
and do that with chat GPT or any of the
00:59:56
language models and then get really
00:59:57
great insights from it that would be too
01:00:00
costly to transcribe at you know a
01:00:02
hundred dollars every hour to a
01:00:04
normalize let alone to analyze and so
01:00:07
I'm looking at
01:00:09
businesses as an investor what I'm
01:00:12
looking at right now is businesses that
01:00:13
were previously not economically viable
01:00:16
before this technology and then that are
01:00:19
now economically viable if that makes
01:00:21
sense and um I'm just looking at each
01:00:23
company under that lens right now and
01:00:26
I'm finding a lot of interesting
01:00:27
startups they seem to have that in
01:00:28
common similar news supporting this very
01:00:32
quick Evolution further up the value
01:00:34
stack so we were just talking about
01:00:36
these companies that are providing
01:00:38
effectively tooling as infrastructure
01:00:41
a little bit more up the value stack is
01:00:44
inflection AI which was started by
01:00:46
Mustafa Suleiman and Reed Hoffman uh who
01:00:50
was a co-founder while mistafa was
01:00:52
working with him as a
01:00:55
Venture partner at Greylock but Mustafa
01:00:57
as you guys know was the co-founder of
01:01:00
deepmind which Google bought for 400
01:01:01
million dollars really created the core
01:01:03
of Google's AI capability and is
01:01:06
considered one of the kind of preeminent
01:01:08
thought leaders and entrepreneurs built
01:01:10
that has built in this space he started
01:01:12
inflection
01:01:14
and the business just announced today
01:01:16
that they've just closed a 1.3 billion
01:01:18
dollar funding round LED by Microsoft
01:01:19
Reed Hoffman Bill Gates Eric Schmidt
01:01:22
Nvidia uh with an intention
01:01:25
of building the largest AI cluster in
01:01:27
the world 22 000 Nvidia h100s as part of
01:01:31
the build out but I think you've shared
01:01:34
chamoth historically that these big
01:01:36
funding rounds for these AI businesses
01:01:37
that haven't necessarily been launched
01:01:39
the product yet
01:01:40
don't make sense is this still you know
01:01:43
kind of reinforce your point and you
01:01:46
know what's your read on on the
01:01:47
inflection funding well the list price
01:01:50
of an h100 is about 30 odd thousand but
01:01:53
the street price so it's very hard to
01:01:55
get it so if you go to the if you go to
01:01:56
like eBay and try to buy an h100 it's
01:01:58
like 40 or 45
01:02:00
000. so if you have a 22 000 cluster of
01:02:04
h100s that's about 900 million dollars
01:02:08
of capex just that
01:02:10
and then you know all the sundry stuff
01:02:12
around it call it roughly plus or minus
01:02:15
a billion dollars
01:02:17
and so of the one and a half billion
01:02:19
they've raised let's say a billion goes
01:02:21
into building this 22 000 node cluster
01:02:24
you have 500 million for SG a and so
01:02:27
what that leaves behind is basically two
01:02:29
and a half billion of Enterprise value
01:02:31
for their chatbot
01:02:33
so I don't know I mean I've never used
01:02:35
Pi has anybody any of you guys used it
01:02:37
do you guys know if it's good Jason I'm
01:02:40
sure you've experimented with it have
01:02:41
you experimented with it or not really
01:02:42
which one
01:02:43
pie
01:02:45
that's what they're that's what their
01:02:47
chat bot is called pipe yeah I think
01:02:49
it's like api.com or something I think I
01:02:52
did try it once
01:02:53
and it was
01:02:55
not memorable Oh you mean po no not po
01:02:59
no no no no
01:03:01
personal one where you talk to it and I
01:03:04
say hey how are you doing and their
01:03:05
concept is like you have this one
01:03:07
relationship so it's like one chat
01:03:09
thread it's not kind of how I like to
01:03:11
work with the I use threads and I share
01:03:14
threads with my team so I'm not a fan of
01:03:16
this like you have one relationship with
01:03:17
one assistant I think the thing is it's
01:03:19
interesting to note that
01:03:21
very rarely when you invest money in the
01:03:24
billions of dollars
01:03:26
does the capex or purchase of one
01:03:29
specific form of equipment take up
01:03:31
literally
01:03:33
25 of the Enterprise Value that's
01:03:36
atypical
01:03:38
at least for a startup
01:03:40
if you're if you're buying a fixed plant
01:03:41
of a slow cash flow generating business
01:03:43
then maybe you know a bunch of that has
01:03:46
some value so that's what stood out to
01:03:48
me and all of these things Freeburg is
01:03:49
again increasingly this is all just a
01:03:52
pass through to Nvidia
01:03:55
it's probably in some ways a pass
01:03:57
through to the big cloud providers so
01:03:59
whenever I see a chip maker and a cloud
01:04:01
provider come together to put in a lot
01:04:02
of money it's essentially round-tripping
01:04:04
cash they're giving the money which then
01:04:06
they use to buy their services and then
01:04:08
you know you just you're just pumping
01:04:09
Revenue so I hope it works wishing the
01:04:13
best but you know that's the that's what
01:04:15
we just talked about where the
01:04:16
infrastructure companies that are
01:04:18
increasingly
01:04:20
looking like more commodity service
01:04:22
providers if they don't up level with AI
01:04:25
tooling
01:04:26
acquiring Mosaic ML and acquiring
01:04:31
Neva
01:04:32
do you think that that's an indication
01:04:34
of more emana to come and if so doesn't
01:04:38
that justify The increased funding The
01:04:41
increased valuation and the activity
01:04:43
that we're seeing in the early stage
01:04:44
with some of these businesses I think
01:04:45
for sure there's going to be more M A
01:04:47
and I think the valuations will be high
01:04:49
not because these companies have a lot
01:04:50
of Revenue yet but because it's very
01:04:52
strategic for these big infra companies
01:04:54
to assemble the end to end
01:04:56
tool chain I think we should explain to
01:04:58
folks what that means enterprise
01:05:00
software companies provide software to
01:05:03
businesses that are not traditionally
01:05:04
technology companies they also provide
01:05:06
software to other businesses to help
01:05:08
them build new tools to help them build
01:05:10
out their business so an Enterprise
01:05:12
software company can sell to United
01:05:13
Airlines or consult a Visa or consult a
01:05:15
Ford and that software can then be used
01:05:18
by that company to build tools that are
01:05:20
powered by the database or powered by
01:05:23
the data analytics or increasingly
01:05:25
powered by AI tools and so they can
01:05:28
build AI applications and AI
01:05:30
capabilities into their business whether
01:05:32
it's United Airlines or Expedia or Visa
01:05:35
or whomever and that's why this these
01:05:37
companies are so critical in terms of
01:05:40
enabling the transformation of
01:05:41
Industries with AI tooling and and why
01:05:44
you know getting AI tooling into their
01:05:46
capability set is so critical right now
01:05:47
it's important to note though guys that
01:05:49
whenever you have the emergence of a new
01:05:51
sector
01:05:52
Sox I think you are right that M A goes
01:05:54
up
01:05:55
but it tends to be that the valuations
01:05:57
go down Peak M A froth happens at the
01:06:00
beginning of the cycle when hype is at
01:06:02
maximum and facts are at the minimum
01:06:06
and that's okay you know that's good for
01:06:08
the startup it's marginally negative for
01:06:11
the existing shareholder of a large
01:06:13
company and then over time it gets
01:06:15
itself sorted out when the facts are
01:06:17
more obvious so yeah it's kind of like
01:06:20
you guys remember when the optical
01:06:21
networking craze oh my God had these
01:06:24
multi-billion dollar Acquisitions and
01:06:26
where did they go they went they went
01:06:27
nowhere they just disappeared we
01:06:30
actually have like a market map that we
01:06:31
did that I think can explain this
01:06:33
concept of a end-to-end tool chain so
01:06:35
this is a slide that our growth team
01:06:39
shout out to Mike Robinson and chemical
01:06:41
Borough they put this together in
01:06:43
preparation and part of the investment
01:06:44
memo for Mosaic and I think to explain
01:06:48
the point you were kind of making
01:06:49
Freeburg like why do Enterprises need
01:06:53
the services one really simple way of
01:06:56
thinking about it right now is that
01:06:57
every Enterprise would like to roll out
01:06:59
its own chat GPT they would all like to
01:07:01
have their own internal version of chat
01:07:03
GPT where their employees for example
01:07:05
could ask questions and get answers
01:07:08
that's where all the action is right now
01:07:09
every Enterprise would buy that tomorrow
01:07:11
if it existed in the way
01:07:15
sex yeah give an example the idea would
01:07:17
be that you know any employee in the
01:07:19
company could ask questions to the AI
01:07:21
model the way you can ask chat GPT
01:07:23
questions and it would have all the
01:07:24
Enterprises data and it would also
01:07:26
understand their permissions and have
01:07:27
all the security settings so that only
01:07:30
there I feel could get the right
01:07:31
information that's the kind of
01:07:32
intelligence they want to unlock I mean
01:07:34
there are lots of other use cases but
01:07:36
that's a really simple one a corporate
01:07:38
Oracle so I can if I'm in HR I can ask
01:07:40
hey tell me about our you know
01:07:42
composition yeah totally co-pilot for
01:07:45
the CEO I think there's gonna be lots of
01:07:46
these I think the sales team is gonna
01:07:48
have their own co-pilot I think the
01:07:49
marketing team's gonna have their own
01:07:50
co-pilot and customer support has it
01:07:52
already yeah this customer support will
01:07:54
have it
01:07:56
there's gonna be a lot of these but I
01:07:58
think Enterprises want one at the level
01:08:00
of the call it the company intranet
01:08:02
where employees could just ask it
01:08:03
questions and but they do not want to
01:08:06
share their data with open AI That's
01:08:08
like very clear they want to roll out
01:08:09
their own models
01:08:10
so the question is well how do you roll
01:08:12
out your own model and what this shows
01:08:13
here is the different pieces of the
01:08:15
stack that you have to have so first you
01:08:16
capture all of your data you got to
01:08:18
label it to be classified right for the
01:08:20
model you've got to store it somewhere
01:08:22
then you need to get one of these open
01:08:24
source AI models off the shelf and
01:08:26
there's a probably the most prominent
01:08:28
site for this called a hugging face
01:08:30
which already has something like I think
01:08:32
a two billion dollar valuation that's
01:08:34
another like really crazy valuation to
01:08:37
ARR multiple but hugging face has kind
01:08:40
of all the open source models it's very
01:08:42
active and so people grab the latest
01:08:44
open source model that's the best fit
01:08:46
for them and then they need to train
01:08:48
that model and that's really where
01:08:50
Mosaic played and there's a ton of
01:08:51
activity right now in this last mile
01:08:54
problem of how do you customize a model
01:08:56
to make it suitable for your use whether
01:08:58
you're an Enterprise whether you're a
01:09:00
customer service team whether you're a
01:09:03
SAS app that wants to incorporate AI
01:09:05
capabilities into your app that's where
01:09:08
all the action is right now is
01:09:09
customizing these open source models
01:09:10
models that then leads to basically
01:09:13
being able to get the right inferences
01:09:15
and there's sort of a separate category
01:09:17
around Hardware that is you know we
01:09:19
don't play there but this is kind of the
01:09:21
end-to-end tool chain and I think these
01:09:22
big tech companies are going to be
01:09:24
racing to put this whole thing together
01:09:25
to fill it out yeah yeah and that's so
01:09:28
there'll be more M A is your prediction
01:09:29
which means more startup valuation
01:09:31
booming more Capital deploying there was
01:09:33
a uh
01:09:35
a couple of Articles this week and
01:09:37
tomorrow this is your red meat as much
01:09:39
as Ukraine is Saks is
01:09:41
because you've talked about this at
01:09:43
length social messaging startup IRL is
01:09:46
shutting down after a board
01:09:47
investigation found 95 percent of its
01:09:49
claimed 20 million users were actually
01:09:51
fake this is a company that in June of
01:09:53
2021 raised 170 million dollar series C
01:09:57
at evaluation of over a billion dollars
01:09:58
making it one of the many proclaimed
01:10:00
unicorns of Silicon Valley led by
01:10:02
softbanks Vision fund the investor who
01:10:04
was sitting on the board said that they
01:10:06
didn't know if we've ever given an
01:10:08
investment term sheet to a startup
01:10:10
faster than SoftBank gave to IRL at the
01:10:12
time at the same time different story
01:10:14
but in the same week a company called
01:10:17
baiju which you have talked about in the
01:10:19
past was once valued at 22 billion
01:10:21
dollars and claimed to be India's most
01:10:24
valuable startup is in turmoil as
01:10:25
shareholders and creditors are seeking
01:10:27
to dilute the founder and he's rushing
01:10:30
to find Capital and raise a billion
01:10:32
dollars to try and buoy the company
01:10:34
process one of the investors Mark the
01:10:36
company's valuation down to 5.1 billion
01:10:38
so down 75 percent
01:10:40
I guess the question is overall are we
01:10:43
still seeing this kind of turmoil in
01:10:46
Silicon Valley from the zerp era funding
01:10:49
of startups in Stark juxtaposition to
01:10:52
the excitement and the frenzy around AI
01:10:53
it's a characteristic of the exact same
01:10:55
thing meaning if you replaced AI with
01:10:59
crypto
01:11:00
it's the exact same thing if you're
01:11:02
placed
01:11:03
AI with
01:11:05
co-working if you replaced AI with
01:11:10
I don't know synthetic biology if you
01:11:13
replaced AI with SAS this has all
01:11:17
happened before I think it's important
01:11:18
to identify what this is
01:11:21
what this is is that there aren't enough
01:11:23
checks and balances and there are
01:11:25
fundamentally people
01:11:27
who are deeply inexperienced who are in
01:11:30
the wrong job
01:11:32
and in the few key moments
01:11:34
where the venture capitalist is supposed
01:11:37
to add value that person is ill-equipped
01:11:40
and unprepared why because they were the
01:11:42
VP of X Y and Z at some startup
01:11:45
and they got hired through no fault of
01:11:47
their own
01:11:48
into a dynamic because these Venture
01:11:50
funds wanted to raise larger and larger
01:11:52
amounts of money
01:11:54
so what happens you don't even know how
01:11:57
to ask the basic questions or even more
01:12:00
insidiously you don't have the courage
01:12:01
to say
01:12:03
the hard thing
01:12:04
and so these things happen that are
01:12:08
frankly inexcusable
01:12:10
right so in the case of one of these
01:12:13
companies and I've mentioned this before
01:12:15
they approached us for financing and
01:12:18
when we asked for a data room we got a
01:12:21
Google doc link to a spreadsheet
01:12:24
now there's no reasonable World in which
01:12:26
a company is that unsophisticated when
01:12:29
it comes to understanding their business
01:12:31
right a data room should include an
01:12:34
enormous amount of operational and
01:12:36
financial metrics that you can use to
01:12:38
come to your own conclusion so that you
01:12:40
can present it transparently to the
01:12:42
investor
01:12:43
the idea that boards wouldn't even hold
01:12:46
these companies accountable is just a
01:12:48
sign that the board members themselves
01:12:50
are pretty fundamentally inexperienced
01:12:52
people
01:12:53
and I think the thing that we do which
01:12:55
is a mistake is we say oh well X Y and Z
01:12:57
firm LED this deal yeah that may be true
01:13:01
but really what it means is that firm
01:13:05
in a grab to get the money
01:13:07
hired some person that checked some
01:13:10
boxes put them in the job that person
01:13:12
LED around and there just wasn't any
01:13:15
infrastructure to either teach that
01:13:17
person or then that person to have the
01:13:20
courage to hold the founder responsible
01:13:21
that will play out in AI as well it's
01:13:24
just that we're at the beginning of the
01:13:25
hype cycle
01:13:26
right because we replace AI again as I
01:13:30
said with any of these other things we
01:13:31
sat here
01:13:32
a little bit hand-wringing when we saw
01:13:34
these crazy valuations for these nft
01:13:36
projects where are those now
01:13:38
right so you name it zero this is about
01:13:41
fundamentally inexperienced people doing
01:13:44
a job that seems pretty easy from the
01:13:46
outside
01:13:47
but in Practical reality there are only
01:13:49
a few Legends in our business most
01:13:51
people and I think it was shy Goldman
01:13:53
that did the math on this most people do
01:13:55
not know how to run these businesses
01:13:57
well Nick will find that tweet you can
01:13:59
show
01:14:00
I think it's like two and a half percent
01:14:02
of all of the funds that are in
01:14:05
pitchbook so over 800 of them have ever
01:14:07
generated more than 3x and two funds
01:14:10
so this is a hard business it turns out
01:14:12
you can't just wake up and be an
01:14:13
investor it turns out and
01:14:16
that's what we're finding so I don't
01:14:18
know it's not very surprising in the end
01:14:19
none of this is surprising
01:14:21
had a really good point in the middle
01:14:23
there is that there is a generation of
01:14:25
venture capitalists who were added
01:14:26
during the boom who were operators but
01:14:28
they've never been taught to have the
01:14:30
discipline of capital allocators and one
01:14:33
of those key pieces of discipline is
01:14:35
asking uncomfortable questions and doing
01:14:37
uncomfortable
01:14:39
diligence and you can trust people but
01:14:42
you need to verify that is a key part of
01:14:45
the job you can trust the founders but
01:14:47
you have to verify that the data you
01:14:49
have is correct
01:14:50
the fact that SoftBank did is that
01:14:52
incredible valuation and the person who
01:14:54
did this deal never checked that the
01:14:58
customers were real
01:15:00
is uh makes you unfit to serve in the
01:15:03
job and I will do diligence and during
01:15:06
that time period Freeburg I had many
01:15:08
Founders say to me you're asking for
01:15:10
more diligence than the lead and this
01:15:13
deal is closing and we are over
01:15:15
subscribed and I said okay and they said
01:15:18
okay so you're not gonna you're not
01:15:19
going to require assistance I said oh no
01:15:21
we require this diligence we want to see
01:15:22
your you know very basic stuff your bank
01:15:25
statements your p l we want to talk to
01:15:27
why don't you give us a list of your
01:15:28
first thousand give us a list of 500
01:15:31
customers from last month we'll give you
01:15:32
five numbers we're going to talk to five
01:15:34
random customers like people did not
01:15:35
want to do this stuff we do that this
01:15:37
work at our firm when we start to own
01:15:39
five ten percent of this and we train
01:15:40
our Founders to do to be ready for
01:15:42
proper diligence all that diligence is
01:15:44
happening now now in the early stages
01:15:46
there's not much to go on but you can
01:15:48
check stuff
01:15:49
during this period people Founders used
01:15:52
the hot Market to not
01:15:55
participate in the due diligence process
01:15:57
and when you look at companies
01:15:59
a lot of times people will suspend
01:16:01
disbelief you know this company uh buy
01:16:03
you I don't know a ton about it but it
01:16:05
seems to have
01:16:07
you know like an educational app like a
01:16:09
company brilliant.org that chamoth and I
01:16:11
are um early investors in and Jama
01:16:13
incubated
01:16:15
great great business
01:16:17
um and but then their business and their
01:16:19
revenue seems to be based on a series of
01:16:21
like Kumon like in-person instruction oh
01:16:24
that's not a high gross margin business
01:16:25
sorry if you have to have a storefront
01:16:28
you're not a software business anymore
01:16:30
and so people started giving valuations
01:16:33
and this is the second part and I'll
01:16:34
just wrap on this people started giving
01:16:35
valuations to these companies that were
01:16:37
real world businesses that were low
01:16:38
margin businesses direct to Consumer
01:16:41
whatever it was they suspended disbelief
01:16:43
and they gave them valuations for high
01:16:46
growth
01:16:47
high gross margin businesses and that
01:16:49
was another mistake and you put those
01:16:51
two things together
01:16:52
not doing diligence and then just
01:16:55
misvaluing of actual assets that's the
01:16:58
cleanup work that's been that's going on
01:17:00
right now and it takes years I mean it
01:17:01
took decades for them to pinch Bernie
01:17:04
Madoff it can take 10 years for these
01:17:06
frauds to come out there was a guy who
01:17:08
kept telling the SEC about Bernie Madoff
01:17:10
I think he was like nine years since the
01:17:12
first time he he let them know
01:17:14
that the you know perfect returns were
01:17:17
just not possible statistically
01:17:19
so it it takes time but they're they're
01:17:21
picking these uh Folks up everywhere
01:17:24
dokwan got picked up at Montenegro you
01:17:26
know the guy from Luna
01:17:27
and it's going to take a decade to clean
01:17:29
up all of the fraud in our space I think
01:17:32
this was sort of mentioned by chamoth
01:17:34
but I think it needs to be a bigger
01:17:35
point which is the influence of fund
01:17:37
size on these decisions
01:17:39
I mean crash funds are in the six to
01:17:42
seven hundred million range so when we
01:17:44
write a check it's usually 10 15 20
01:17:46
million dollar check in a series a
01:17:49
company
01:17:50
that's like a big check for us we're
01:17:52
really going to sweat that decision for
01:17:54
SoftBank at 10 to 20 million dollar
01:17:56
check in a hundred billion dollar fund
01:17:58
which is what they had
01:18:00
it doesn't even make sense it's a waste
01:18:01
of their time it's not even a rounding
01:18:03
error for them to basically make a ten
01:18:06
thousand dollar check for you yeah
01:18:08
exactly so for them they had to write
01:18:10
200 million dollar checks if to justify
01:18:13
their time managing 100 billion dollars
01:18:15
and so the mistake when they make a
01:18:17
mistake is 20 times bigger than it
01:18:19
should be that should have been maybe a
01:18:21
10 million mistake not a 200 million
01:18:24
dollar mistake but their Fun Size force
01:18:26
them to basically write these gigantic
01:18:28
checks and they're writing them into
01:18:29
companies that were effectively seed
01:18:32
stage or series a companies if it was
01:18:34
into a growth stage company I think
01:18:36
that's fine there's a lot more data and
01:18:38
there's a lot more customer references
01:18:40
that you can check at a later Stage
01:18:41
Company by the way the number one part
01:18:43
of diligence I'd say for us other than
01:18:45
looking at metrics which is anyone can
01:18:47
do is the offsheet references talking to
01:18:50
customers from a list that you figured
01:18:53
out yourself not from the company itself
01:18:55
is probably the single most important
01:18:57
qualitative part of of diligence
01:19:00
so I don't know what happened here but
01:19:02
it's not stated explicitly but I think
01:19:04
it's important David you have
01:19:05
credibility So when you say something
01:19:08
sucks people listen because you have
01:19:10
Bona fides that are undeniable
01:19:12
same thing with jcal same thing with you
01:19:14
Freeburg I think you know and this may
01:19:16
sound mean but it's like most of these
01:19:17
people
01:19:18
are just XYZ mid-level VPS from a
01:19:22
startup and that's a great thing
01:19:24
but it's not necessarily going to give
01:19:27
you the gravitas especially if that's
01:19:29
not what you were forced to do to help
01:19:31
build that company and when push comes
01:19:33
to shove inside of a boardroom or in the
01:19:36
middle of diligence
01:19:38
there has to be conflict I think it's a
01:19:40
necessary feature of good decisions and
01:19:43
that conflict arises internally within
01:19:45
your investment team but it also has to
01:19:46
come externally with the executives of
01:19:49
the startup and with the CEO themselves
01:19:51
because when you're Prosecuting a good
01:19:53
decision it's unbelievable that you
01:19:54
agree on 100 of things and there has to
01:19:56
be certain things that are controversial
01:19:58
otherwise by definition that company
01:19:59
isn't really even pushing the boundary
01:20:01
so I just think that these are all
01:20:04
skills that are poorly taught
01:20:07
while you are building a business it is
01:20:09
not the reason why you should have been
01:20:11
in charge of allocating 50 and 100
01:20:13
million dollar checks into companies
01:20:14
that is just crazy town
01:20:16
I love your point sacks though about Fun
01:20:19
Size Dynamics Fun Size Dynamics our
01:20:21
destiny right it really is and and the
01:20:24
optimal Fun Size for Venture is
01:20:26
somewhere between 250 and 600 million
01:20:30
according to everybody who's been doing
01:20:32
this for you know more than a decade or
01:20:34
two and who's successful whether it's
01:20:36
when the costs are coming down so as the
01:20:37
input costs come down whether it's for
01:20:39
engineers and co-pilots and and hardware
01:20:41
and abstraction layers then
01:20:43
theoretically
01:20:44
greater outcomes should be generated
01:20:46
with fewer dollars in which would again
01:20:48
tell you that fund sizes should actually
01:20:50
go down not up that the reason they go
01:20:52
up is because you get paid an annual
01:20:54
management fee and so obviously the way
01:20:56
to wait the way to make more money is to
01:20:59
get two percent on a larger fixed number
01:21:01
every year versus two percent on a
01:21:02
smaller number or you know for example
01:21:05
what we did was we were like we're gonna
01:21:07
go and hit grand slams and so I traded
01:21:10
off management fee in return for 30
01:21:12
carry and that turned out to be
01:21:14
literally a multi-billion dollar smart
01:21:18
decision because I gave up tens of
01:21:19
millions of dollars up front for back
01:21:21
end now the back end could have gone to
01:21:23
zero and maybe it still can so who knows
01:21:25
but you know most folks wouldn't do that
01:21:28
most folks take the sort of
01:21:30
risk-adjusted bet and say you know what
01:21:32
I'll just take the two percent and I'll
01:21:33
raise a 200 million then a 500 million
01:21:35
then a billion then a two billion
01:21:37
dollars
01:21:38
yeah and they stack them all and they
01:21:40
get the two percent and all of a sudden
01:21:41
the profits don't matter which means the
01:21:43
outcomes don't matter which means the
01:21:45
diligence is perfunctory and it becomes
01:21:47
a theatrical expose that you can use you
01:21:51
know this sort of thin fig Leaf you can
01:21:53
point to LPS and say we did our work
01:21:56
here give us more money for this next
01:21:58
fund that's the rat race that the
01:22:00
Venture Community is in and it's going
01:22:02
to get played out in companies like IRL
01:22:04
and baijus and a lot of these AI
01:22:06
companies quite honestly well and it the
01:22:08
chickens have come home tours for all
01:22:09
the questions is not different is I
01:22:11
think what I'm trying to say this time
01:22:12
is not different did you guys see there
01:22:14
was some article that reported that
01:22:17
fundraising for late stage funds is just
01:22:19
like cratered dead so Insight was trying
01:22:22
to raise a 10 billion dollar fund and
01:22:23
they've only been able to raise two
01:22:24
according to this article no no it was
01:22:26
it was 22 down to 10 and of which
01:22:28
they've raised two okay so 10 and then
01:22:31
tiger was trying to raise 12 they cut it
01:22:34
to six and then they can only raise two
01:22:36
makes sense yeah so basically that's
01:22:39
like a whatever uh 80 to 90 reduction in
01:22:43
the size of these funds yeah meanwhile
01:22:45
the sovereigns where they were going for
01:22:47
this money or buying sports teams
01:22:48
they're like you know what instead of
01:22:49
tech let's just buy sports teams and
01:22:51
they're buying series a and they're
01:22:53
buying Manchester United and they're
01:22:55
buying distressed portfolios yeah and
01:22:57
the but there's a sovereign crunch
01:22:58
there's a huge we've talked about it
01:23:00
before but there's a huge crunch in late
01:23:01
stage financing it's only going to get
01:23:02
worse over the next 18 months I asked
01:23:05
Brad last week like how many of these
01:23:07
zombie courts do you think there are
01:23:09
these are the 1400 he said 30 to 40
01:23:11
percent yeah I think he might be you
01:23:13
know it could be look out of 1400 I
01:23:16
think it could be 700 or zombie coins I
01:23:17
think it's at least 700 I think I think
01:23:19
it's probably 60 yeah it's and then the
01:23:22
other 40 let's say how many of them have
01:23:25
a Down Round coming I think 60 go to
01:23:28
zero
01:23:29
of the remaining 40 half of them
01:23:32
probably return money
01:23:34
and then of the remaining half half of
01:23:37
those maybe get one and a half X
01:23:40
and then you get a geometric
01:23:41
distribution from there which means the
01:23:43
Blended return of that entire stream of
01:23:45
unicorns will be about 1.1 x but it'll
01:23:48
be very massively distributed I think
01:23:49
that is exactly right tomorrow I I would
01:23:51
yeah everybody's getting their money
01:23:53
back except if you don't have
01:23:55
diversification yeah it's it's the I
01:23:57
think the market is sending a very clear
01:23:59
message which is these are usage
01:24:02
everybody's getting no no no most people
01:24:04
won't get their money most people get
01:24:05
their money back but on average it's
01:24:07
going to be one extra turn yeah so it's
01:24:08
not going to be evenly distributed
01:24:09
correct
01:24:10
well I'm glad that the term zombie corn
01:24:12
has held guys um we are coming up on our
01:24:15
time do you guys want to do science
01:24:16
corner or do you want to
01:24:18
um
01:24:19
yeah the three of us need to use the
01:24:20
bathroom break so go ahead and just
01:24:24
if you could just go do it we're gonna
01:24:25
go and take a leak and we'll come back
01:24:27
and make a Uranus joke
01:24:31
so look it's been great it's been great
01:24:33
being your host we crashed yourself give
01:24:35
us the science quitter give us you don't
01:24:37
hug me you don't embrace me you know
01:24:38
you're gonna love my contribution free
01:24:40
book I actually I posted a clip of RFK
01:24:43
talking about vaccines I'd love for you
01:24:45
to listen to it and actually give us the
01:24:47
critique
01:24:48
yes I will
01:24:50
that's going viral right now he did such
01:24:52
a good job explaining his position on
01:24:54
that he did such a good job yeah look do
01:24:57
you guys read the offit piece I forward
01:24:58
it to you and I said please read this
01:25:01
if you guys read that piece I'll watch
01:25:03
his clip and let's talk about it next
01:25:04
week is that cool
01:25:07
yeah he is a vaccine scientist who RFK
01:25:10
Jr references often as someone that he
01:25:13
met with and spoke with and says I
01:25:14
caught him in a lie and often basically
01:25:16
said here's exactly what happened here's
01:25:18
the conversation here's the data here's
01:25:20
the fact here's the science
01:25:22
and I would really really encourage you
01:25:25
guys to read that please
01:25:27
and then I'll watch his clip and like
01:25:28
let's have a real kind of analytical
01:25:30
conversation about statements that are
01:25:33
good questions to ask and good things to
01:25:35
interrogate and things that are being
01:25:37
said that maybe aren't factually correct
01:25:40
and I think that we need to kind of
01:25:41
really as a service to ourselves and to
01:25:43
people that listen to us really do that
01:25:45
work
01:25:46
so let's do that and come back and talk
01:25:47
about it next week if that's cool
01:25:49
but I'd encourage everyone to read often
01:25:51
he put it on substack Nick we'll put the
01:25:53
link in the the notes Here continue
01:25:56
dialogue continue exactly I think that's
01:25:58
the most important you want to talk
01:25:59
about that certain Senator that all of a
01:26:01
sudden just basically gave us the
01:26:03
Heisman no we've had a lot of those by
01:26:05
the way so let me just be clear that's
01:26:06
not the only Heisman we've received on
01:26:08
the all in Summit I will say that the
01:26:10
speaker list for the all-in summit it's
01:26:13
looking fantastic
01:26:16
we're gonna have a great time
01:26:19
and I'm really excited for the
01:26:20
conversation but you're saying some
01:26:22
folks Heisman does because of our
01:26:24
support of RFK
01:26:26
that did happen and specifically the
01:26:29
fact that that's really open-minded yeah
01:26:31
and then there were other folks who were
01:26:33
insulted by things or said about them by
01:26:36
people on the show So Soft you guys want
01:26:38
to hear about the the nanograph release
01:26:41
that came out yesterday okay I'll cover
01:26:42
this one real quick what about this like
01:26:44
slow Hummer that we're all getting what
01:26:46
is that that's it it's a slow Hummer
01:26:48
exactly okay are you talking about like
01:26:51
the new H3 EV but it only goes up to 50
01:26:53
miles per hour
01:26:55
what is that the new Hummer the new HV
01:26:57
you know they have an EV they're coming
01:26:59
out with an EV event oh you're kidding
01:27:01
it's hilarious yeah it's like a great
01:27:03
troll I remember when Schwarzenegger got
01:27:05
the Hummer back in the 90s and
01:27:06
everyone's like oh my God it's amazing
01:27:09
and then the Hummer was the coolest car
01:27:11
to drive yeah
01:27:16
Okay so yesterday a paper was published
01:27:20
by an international
01:27:22
um scientific Consortium this group is
01:27:24
called nanograph
01:27:26
and they've been using a series of
01:27:27
instruments to measure pulsars including
01:27:31
a 500 meter radio telescope array
01:27:34
um which allows them to see what's so
01:27:37
funny no I just had like three Uranus
01:27:40
jumps one sentence and I had to stop
01:27:42
myself
01:27:44
the nanograph data that was released is
01:27:47
15 years of data from pulsars and
01:27:49
pulsars are neutron stars which are
01:27:52
stars that have collapsed on themselves
01:27:53
and are basically super dense and start
01:27:55
spinning and then these pulsars you know
01:27:58
you basically like a lighthouse you can
01:27:59
see the light so it looks like a almost
01:28:01
like a strobe light and we can see
01:28:03
thousands of these Across the Universe
01:28:05
and we can observe them and the rate at
01:28:07
which the pulsing is coming out of these
01:28:09
pulsars tells us a lot about what is
01:28:12
happening in the space between Earth and
01:28:16
those pulsars and when you collect
01:28:18
enough data over a long enough period of
01:28:20
time which is what these folks have just
01:28:21
released as 15 years worth of this data
01:28:23
you can start to see really interesting
01:28:27
patterns in the data that support the
01:28:31
theory that space time itself is slowly
01:28:36
vibrating being stretched being
01:28:38
compressed being pulled apart being
01:28:40
pushed back together because of very
01:28:42
large gravitational events happening
01:28:45
around the universe and what that means
01:28:47
is you guys have all seen
01:28:49
you know that kind of two-dimensional
01:28:50
image of a black hole and Nick if you
01:28:52
could find one online and pull it up
01:28:54
where it looks like hey at the middle of
01:28:55
a black hole space itself collapses in
01:28:57
and it collapses down and what happens
01:28:59
is space and time gets significantly
01:29:02
elongated when they're really close to
01:29:04
gravity gravity actually pulls space
01:29:06
sucks it in sucks in time and it becomes
01:29:09
distorted and so when you have large
01:29:12
black holes around the universe spinning
01:29:14
and running past each other they're
01:29:16
actually pulling and stretching space
01:29:18
time itself and that sends out ripples
01:29:20
throughout the Universe ripples that are
01:29:23
slowly undulating space and time itself
01:29:27
so by observing all of these pulsars
01:29:29
around the universe and the rate at
01:29:31
which these pulsars are pulsing and
01:29:33
seeing slight variations we can start to
01:29:37
measure and actually see those waves
01:29:39
those very slow waves of space time
01:29:43
itself undulating and being pushed and
01:29:45
compressed and so it supports Einstein's
01:29:48
general theory of relativity which
01:29:50
indicated that space-time itself can be
01:29:52
warped by gravity and it provides a
01:29:54
really interesting picture on the
01:29:56
universe itself that all around us we
01:29:59
have large masses that are many many
01:30:01
millions or billions of light years away
01:30:03
that are creating waves in space and
01:30:06
time itself that we as humans will never
01:30:08
kind of observe realize or feel
01:30:10
ourselves but as part of the fabric and
01:30:13
the underlying nature of our universe
01:30:15
with space and time being slowly warped
01:30:18
and slowly elongated slowly compressed
01:30:20
and it's a really fascinating picture of
01:30:22
the universe over time as scientists
01:30:24
gather more and more of this data
01:30:26
it will provide insights into where in
01:30:29
the universe these massive black hole
01:30:31
events May Be occurring and also provide
01:30:33
insights into the early picture and the
01:30:35
large-scale structure of the universe
01:30:37
which helps us better understand how
01:30:39
everything started and where we're all
01:30:41
coming from so it was a really
01:30:42
fascinating data release I think it's a
01:30:45
really kind of profound thing if you
01:30:46
take a little time and think about it
01:30:48
it's super exciting it's getting a lot
01:30:50
of press coverage today and
01:30:52
encourage us all to pull our heads out
01:30:54
of the Ukraine war and Silicon Valley
01:30:56
and money and all this stuff and realize
01:30:58
that there are things of extraordinary
01:30:59
scale and structure that are that are
01:31:01
happening around us let me ask you two
01:31:02
questions
01:31:03
number one why does it matter
01:31:06
and number two any theories here of what
01:31:09
we might discover you know if this you
01:31:12
know goes 10x or 100x in terms of the
01:31:15
the information we're getting many years
01:31:17
ago it was theorized that there's what's
01:31:19
called a cosmic microwave background
01:31:21
radiation the CMB and what that is it's
01:31:25
the leftover heat from the formation of
01:31:28
the Universe from the universe when the
01:31:29
Big Bang happened yep and these
01:31:31
scientists figured out how to create
01:31:32
really sensitive radio telescopes and um
01:31:35
put them in the the uh in orbit and they
01:31:38
started to observe the background
01:31:40
radiation and what that showed us was
01:31:42
the fingerprint of the universe the
01:31:44
original structure of the universe that
01:31:46
created ultimately all the galaxies
01:31:48
super galaxies and then ultimately all
01:31:50
the stars and then the planets and
01:31:51
everything that that came from that
01:31:53
this could be the beginning of seeing a
01:31:55
gravitational background of the universe
01:31:58
where we could actually start to see
01:32:00
perhaps the fingerprint of the
01:32:02
space-time
01:32:03
of the whole universe of what the actual
01:32:06
structure of space itself and time
01:32:08
itself looks like throughout the
01:32:09
Universe with the perturbations being
01:32:11
driven by some very large massive
01:32:13
supermassive black holes there was a
01:32:15
black hole discovered this week that's
01:32:16
30 billion times the mass of our sun
01:32:20
there are these massive objects out
01:32:22
there that are actively distorting
01:32:24
space-time and we're going to start to
01:32:26
get a fingerprint of that with this sort
01:32:28
of data and over time that just gives us
01:32:30
a better sense of what the overall
01:32:32
structure of the universe looks like not
01:32:34
just from the heat energy that we're
01:32:35
collecting but also the gravitational
01:32:37
waves that we're now able to observe
01:32:39
through the inference of this data
01:32:40
collection let's just make deepens our
01:32:42
understanding of the universe but
01:32:43
there's no structure of the universe
01:32:44
yeah which is amazing and interesting
01:32:47
but and and it validates and proves the
01:32:50
general theory of relativity which if
01:32:51
you think about the application of that
01:32:53
down the road that may lead to things
01:32:55
like close to or as fast as like travel
01:32:58
or things related to time travel or you
01:33:01
know there's a lot of things that people
01:33:02
have theorized for decades about
01:33:05
you know black holes and the warping of
01:33:07
space-time itself I'm not saying that
01:33:09
any of this stuff is did you see the
01:33:11
three body problem trailer
01:33:13
oh yeah it looks amazing it looks
01:33:15
amazing it's amazing what a great what a
01:33:17
great can't believe we have to wait so
01:33:19
long I hate it when they put out
01:33:20
trailers so how long when is it coming
01:33:22
out next year oh wow yeah oh I still
01:33:26
haven't seen your movie the guys your
01:33:28
movie that you wanted me to see
01:33:30
which one better try oh everything
01:33:32
everywhere all at once I got a better
01:33:33
movie for you I got a great pull for you
01:33:35
uh it's on Pay-per-view right now the
01:33:38
movie call about BlackBerry
01:33:40
it tells the story of black oh I heard
01:33:42
it's like an independent film it is
01:33:44
awesome
01:33:45
I just reviewed it on this week in
01:33:47
startups it's called just on his
01:33:49
BlackBerry called Blackberry yeah I
01:33:52
think okay well guys this has been
01:33:53
episodes amazing 135 of the all-in Pod I
01:33:57
really appreciate it
01:33:58
are oh my God my time together just
01:34:01
enough time to get you back to your
01:34:03
Nirvana concert
01:34:04
Leo
01:34:06
what's the background on this one is
01:34:07
that a rival what is that what's that
01:34:09
background what movie that's a black
01:34:12
hole that's a black hole just a black
01:34:14
hole okay
01:34:15
no I think that that's from
01:34:18
might be event might be a matter with
01:34:20
Sax's hair because he looks like did you
01:34:21
get it cut sacks he got a cut did you
01:34:24
cut it no you broke I got a mild
01:34:27
sloughing shower show us the floor let's
01:34:30
go let's see
01:34:36
just keep growing it out man Jacob you
01:34:39
want to take us out you do a better
01:34:40
outro all right everybody for David
01:34:42
sacks the architect coming at you Z100
01:34:46
boarding Zoo
01:34:47
for Tuesdays Tears for Fears coming up
01:34:50
and free bird with the science project
01:34:52
all right traveling back in time with
01:34:55
David Friedberg two for Tuesday Jackson
01:34:58
Browne the lowdown here we go
01:35:04
the greatest moderator
01:35:07
I'm the world's greatest modern I can do
01:35:09
my MPR voice if you like dude all right
01:35:12
closing us out here episode 135 pcrw
01:35:16
92.3 The Sound of Santa Monica this
01:35:19
Sunday at the Venice uh Farmers Market
01:35:22
two for one on the organic milk go check
01:35:25
it out and uh we'll see you all later on
01:35:28
the politics of culture David sacks uh
01:35:32
coming in on the Republican GOP position
01:35:34
which we did consider Friedberg deeply
01:35:37
going into science
01:35:39
on wealth disparity for everybody I am
01:35:44
your host here at KCRW
01:35:46
is the world's most moderate moderator
01:35:49
we'll see you next time KCRW
01:35:52
I can do any of these radio bits
01:35:56
love you guys
01:35:58
we'll let your winners ride
01:36:02
[Music]
01:36:15
besties
01:36:17
[Music]
01:36:32
it's like this like sexual tension that
01:36:35
we just need to release somehow
01:36:40
[Music]
01:36:47
[Music]
01:36:51
I'm going all in
