00:00:00
I can't wait to talk about lob grow meat
00:00:01
I have been
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trying to get people to
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please
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let's not get canceled
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but the four of us are functional again
00:00:15
we like each other we enjoy we look
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forward to doing the show again
00:00:19
everything styled in is your lab-grown
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meat does it use hormones my lab-grown
00:00:24
meat was a little
00:00:26
let me ask you this about your lab
00:00:28
ground meat do you have
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sage no no no no no
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you know I was told that my lab grow
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meat was a little
00:00:42
I've injected some flavors of tobacco
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black cherry some notes some notes
00:00:48
persimmons
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[Music]
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[Music]
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so let's go to big Tech earnings Google
00:01:12
stock is up five percent after beating
00:01:14
on the top line and bottom line
00:01:15
estimates some high level takeaways
00:01:17
Google announced a 70 billion dollar
00:01:20
stock buyback plan and that their Cloud
00:01:23
unit was profitable for the first time
00:01:24
in its history as we mentioned last week
00:01:27
Sundar officially announced that deep
00:01:28
mind was merging
00:01:30
with brain this is kind of controversial
00:01:33
because it's uh really hard According to
00:01:36
some sources or Sundar to get all his
00:01:39
lieutenants to work together and row in
00:01:41
the right direction Google's Q on search
00:01:43
Revenue up year over year two percent
00:01:45
down five percent quarter of a quarter
00:01:47
just kind of to be expected because
00:01:50
of seasonality and because we're in a
00:01:53
down market right now obviously with the
00:01:55
recession
00:01:56
YouTube down 2.5 year over year down 16
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quarter of a quarter other bets which is
00:02:02
like nest and some other products down
00:02:05
35 year over year
00:02:07
net income 15 billion dollars
00:02:11
any thoughts freeberg on
00:02:14
what is a mixed quarter by Google and I
00:02:17
guess the water macro environment what
00:02:20
was so striking about the earnings call
00:02:22
is not necessarily what was presented
00:02:24
but what was not presented
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which was a stronger voice and a
00:02:27
strategic plan going forward for dealing
00:02:30
with two major issues that the company
00:02:32
one is the operating cost model and the
00:02:34
second is
00:02:35
the AI strategy and the response to this
00:02:37
Evolution and AI I've heard from a lot
00:02:40
of folks that the AI strategy in
00:02:42
particular
00:02:44
it's almost like Google already has this
00:02:46
in the bag but they just haven't kind of
00:02:48
let it out of the bag it's like they've
00:02:49
got a Tasmanian devil and they're
00:02:51
they're ready to go with it and there's
00:02:53
from from my read an incredible amount
00:02:55
of confidence that there's something
00:02:56
that's going to happen
00:02:58
and a set of things that are going to
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happen that are going to be very
00:03:00
profound and Powerful I even heard some
00:03:03
anecdotal stories about hey you know
00:03:06
we don't have this feature in this
00:03:07
product but chat GPT does and then
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people basically showed up to this
00:03:12
meeting and there was all this debate
00:03:13
about well we can't let it out because
00:03:15
we're not sure you know the classic kind
00:03:16
of like we're scared of doing doing
00:03:18
wrong versus leaning forward and taking
00:03:20
risks don't be evil you're referencing
00:03:23
no it was just more about regulatory
00:03:25
concern and getting things wrong and
00:03:26
making a mistake and so there's this
00:03:28
total fear of like again you know
00:03:30
Regulatory and fear so someone kind of
00:03:31
slammed the table and said let's just
00:03:32
put it out the next day they put it out
00:03:34
so there's definitely a cultural change
00:03:35
happening internally is what I've heard
00:03:37
anecdotally but what was really missing
00:03:38
which is what Wall Street needed to hear
00:03:40
what investors and shareholders needed
00:03:41
to hear is what's the strategy there how
00:03:43
are you going to compete how are you
00:03:44
going to resolve what's going to go
00:03:45
forward and secondly what are you going
00:03:46
to do about the cost structure of the
00:03:48
company because everyone else you know
00:03:50
in contrast to meta being up 11 12 after
00:03:52
hours with their cost cutting model and
00:03:54
demonstrating that they're going to
00:03:55
start pulling cash out of this business
00:03:57
Google's top you know kind of top story
00:03:59
was Hey We're stopped serving at peanut
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M M's in the cafeteria or something
00:04:02
ridiculous and you know that doesn't
00:04:05
really address the real structural
00:04:06
question so
00:04:08
I think the stock buyback the 70 billion
00:04:11
stock buyback is an authorization to
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repurchase it's not a plan to repurchase
00:04:14
so it's unclear if when or how that
00:04:17
Capital does actually get deployed in
00:04:19
the market to buy back stock
00:04:21
and so there is also this big kind of
00:04:22
shareholder sentiment of being let down
00:04:25
that there isn't an improvement in
00:04:27
either cash coming out of the business
00:04:28
or in cash being used in a smart way
00:04:31
with the business
00:04:32
and it was the The Silence in the
00:04:34
earnings call that I think really
00:04:36
stunned a lot of people which is why you
00:04:38
didn't see a lot of stock movement
00:04:39
despite the actual business numbers
00:04:41
being better than expected
00:04:43
and so there's a lot that Google I think
00:04:45
still has to catch up to with respect to
00:04:48
their peers both on a product and
00:04:50
strategy point of view but also on
00:04:53
a cost cutting and a communication of
00:04:54
that cost cutting point of view to the
00:04:56
market and to the street otherwise
00:04:58
shareholders are going to start to lose
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faith if they're not already and are
00:05:01
going to start to put their Capital with
00:05:03
other folks who they feel are better
00:05:04
leading and leaning into this new
00:05:06
Evolution of Technology like
00:05:09
Microsoft and Apple and meta which is
00:05:13
really where those big Capital
00:05:14
allocators end up picking stuff to go
00:05:16
one final thing I'll say it's
00:05:18
extraordinarily
00:05:21
important to note that I think Google
00:05:23
has such an incredible AI advantage over
00:05:25
Microsoft
00:05:27
and you know Microsoft is almost solely
00:05:29
dependent on openai this small startup
00:05:31
company and all of Bing chat is powered
00:05:34
by it and Microsoft hasn't built out the
00:05:37
the infrastructure the team the rigor
00:05:39
the depth the models
00:05:41
that Google has and Google made
00:05:44
a few strategic blunders you know they
00:05:46
shouldn't have been as open with the
00:05:48
Transformer work that they did and
00:05:49
shared that publicly it certainly
00:05:50
enabled openai and others to compete
00:05:53
but Google certainly has an incredible
00:05:55
set of tools and capabilities that is
00:05:57
leap years ahead of Microsoft they're in
00:05:59
a position to really compete they just
00:06:01
have to have the will and the leadership
00:06:02
to do it slam the table say here's we're
00:06:05
going to stop wasting money and we're
00:06:06
going to start leading and driving this
00:06:07
this industry forward and this this
00:06:10
could be a quick turnaround story for
00:06:11
the stock and for this company and and I
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I hope it'll happen chamath what are
00:06:15
your thoughts on
00:06:17
Google's leadership specifically is
00:06:19
Sundar the right person to run the
00:06:21
company going forward
00:06:23
does he have the founder authority to
00:06:25
get the ship and to get the lieutenants
00:06:27
all kind of rowing in the same direction
00:06:29
or does it need to be a leadership
00:06:30
change which is the big discussion of
00:06:32
Topic in Silicon Valley right now I
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think he's very capable
00:06:36
that's an amorphous organization of so
00:06:38
many different competing interests
00:06:42
the thing that doesn't add up about the
00:06:43
Google
00:06:45
earnings release but then also what
00:06:48
Freebird just mentioned is
00:06:51
there was this article that kind of
00:06:52
tried to paint sundara as sort of a
00:06:54
caretaker CEO right where Larry was the
00:06:58
actual Shadow CEO well if that's true
00:07:00
you know Larry has more incentive than
00:07:02
anybody else to kind of
00:07:04
Force change
00:07:07
and there was all these kind of like
00:07:08
gripes and complaints that were
00:07:10
articulated and I don't put much stock
00:07:11
in all of this stuff I think that he is
00:07:14
the right person for the job and I think
00:07:17
what they have to do is just do the
00:07:19
simple basic things like it doesn't take
00:07:22
a CEO change
00:07:24
for a board of directors to have the
00:07:26
emotional wherewithal to authorize a 15
00:07:30
or 20 reduction in force
00:07:33
for a company that is so profitable that
00:07:35
clearly is not yet humming on all
00:07:38
cylinders and so you don't need to go
00:07:41
through all of this drastic change to do
00:07:44
these simple obvious things
00:07:46
my takeaway across all of these four big
00:07:49
companies is we are
00:07:51
in a really unique moment to observe
00:07:53
something that may sound controversial
00:07:56
or hurt people's feelings that like
00:07:58
these companies
00:07:59
but I think we're now well past Peak big
00:08:02
Tech
00:08:04
their valuations may still go up because
00:08:06
they generate such an enormous amount of
00:08:08
cash flow
00:08:09
but these are exactly those kinds of
00:08:11
businesses now they are X growth large
00:08:14
cash flow businesses Blue Chip you might
00:08:17
say while they were always Blue Chip but
00:08:19
the way that they grow is not through
00:08:21
Innovation if you look at Google
00:08:23
Facebook Microsoft and Apple and ask
00:08:27
yourself when was the last hugely
00:08:29
disruptive thing that they've created
00:08:31
you're hard-pressed to find something
00:08:33
that was even done in the 2010s yeah
00:08:35
actually that's a good thought I mean
00:08:37
the iPhone for Apple iPhone was 2007.
00:08:40
yep Microsoft was in the 1990s Google
00:08:43
was in 1998 with course search maybe
00:08:46
there was maps and Gmail in two in the
00:08:48
2000s Chrome Android they bought some of
00:08:52
that Facebook it was the core service
00:08:53
that we built in the 2000s and then they
00:08:55
acquired brilliantly right so I'm not
00:08:57
saying that they didn't acquire well
00:08:59
yeah my point is that core organic
00:09:02
Innovation hasn't been there for a long
00:09:03
time so this is a moment to just be
00:09:05
reflective of the fact that these are
00:09:07
some incredible companies
00:09:09
with ginormous cash flows
00:09:12
but now you've had this foundational
00:09:14
platform shift which exposes the fact
00:09:17
that they really aren't good at
00:09:19
innovating and at times when they've
00:09:22
tried to organically innovate they've
00:09:24
massively misallocated capital
00:09:27
either TLS would be the example either
00:09:29
through a bloated balance sheet so
00:09:31
someone claim that Google overspends or
00:09:33
through just pure misallocation by
00:09:34
starting projects that just are not
00:09:36
large but consume large amounts of cash
00:09:38
that would be the Facebook VR example
00:09:39
but in all of this I think when you cut
00:09:43
staff and expenses
00:09:47
as a way to meet and beat and Top Line
00:09:50
growth is in the low single digits
00:09:53
it's an important moment to recognize
00:09:54
that these companies have now
00:09:56
transitioned to being cash cows and if
00:09:59
you look at sort of how financial
00:10:01
markets value cash cows they're very
00:10:04
valuable
00:10:06
but it's not where you look for growth
00:10:07
and so in a world where
00:10:10
rates eventually get cut and we start to
00:10:13
come out of a recession
00:10:15
it tends to be that other people get
00:10:17
rewarded so that's an idea that's
00:10:18
getting to your point here they're not
00:10:20
allowed to acquire things Microsoft's
00:10:22
acquisition thing dead dead so they're
00:10:25
not going to be allowed to buy stuff so
00:10:27
then you're right what is the growth
00:10:28
here
00:10:29
they're not able to innovate I think
00:10:31
these companies are X growth which is
00:10:32
why they use their cash flows to do what
00:10:34
borrow money cheaply to buy back stock
00:10:36
to manipulate
00:10:39
their Equity right you can manipulate
00:10:42
and overcome dilution you can manipulate
00:10:45
earnings per share you can manipulate
00:10:46
the number of shares outstanding
00:10:49
and so just by the nature of that whole
00:10:51
game a bunch of passive investors will
00:10:53
end up buying more which helps the
00:10:55
active investors who own that stock so
00:10:57
it's a game so if we're not in the world
00:10:59
financial engineering would be the I
00:11:01
mean the most charitable way to say it
00:11:03
we're in the financial engineering phase
00:11:04
which is fine and by the way you can
00:11:06
make a lot of money Facebook's up 90
00:11:07
percent
00:11:08
so there's a lot of there's a lot of
00:11:10
meaning just this just this year oh yes
00:11:13
so there's a lot of room for financial
00:11:15
engineering but it's not where you need
00:11:18
to look to figure out where
00:11:20
these big improvements and uses of this
00:11:23
next Generation platform technology are
00:11:25
going to come from most likely saxon's
00:11:27
is a fair assessment in your mind
00:11:28
looking at you know the the major tech
00:11:30
companies the fangs yeah I mean their
00:11:32
growth is down to single digits so I
00:11:34
think Microsoft had seven percent
00:11:35
year-over-year Revenue growth Google was
00:11:38
at three percent I think Facebook
00:11:41
was for sales Rose three yeah three
00:11:44
percent from a year earlier but at least
00:11:46
that was an improvement because it's
00:11:47
actually gone down for three straight
00:11:49
quarters so yeah but you're down to you
00:11:52
know single digit year-over-year growth
00:11:53
rates nevertheless most these companies
00:11:57
beat expectations so Microsoft shares
00:11:59
wrote nine percent
00:12:02
meta jump 12 might be up more now
00:12:06
and I guess Google got a little bit of a
00:12:08
bounce and they all gave a pretty upbeat
00:12:11
forecast the only one that wasn't upbeat
00:12:13
was Google where
00:12:15
the CFO Ruth poorat said that the
00:12:18
Outlook remains uncertain but all the
00:12:20
other ones seem to indicate that
00:12:23
things are going to get better
00:12:25
so I think what's interesting about that
00:12:26
is just the mismatch that we have
00:12:28
between how well these companies did in
00:12:30
this quarter versus how uncertain the
00:12:33
rest of the economy is looking right now
00:12:35
and maybe the fed's behavior yeah so
00:12:37
maybe this is the flip side which moth
00:12:39
is saying is they're not growing very
00:12:40
fast but they are profitable machines
00:12:43
generating a lot of earnings and they
00:12:45
seem to be pretty immune from what's
00:12:47
happening in the economy right now or at
00:12:49
least that's what they're saying now
00:12:51
you're right in a parallel track there
00:12:53
was an interesting interview that
00:12:56
Powell did
00:12:58
so uh Jerome Powell gave an interview it
00:13:00
was actually kind of like one of these
00:13:02
hoax calls where a couple of people
00:13:04
pretending to be zelinski engagement and
00:13:07
interview
00:13:07
oh my God that was crazy you want to
00:13:10
explain that reference oh my God
00:13:13
they've done this a number of times
00:13:14
where they've gotten you know major
00:13:16
leaders I think they did this to macron
00:13:17
some other people or they pretend to be
00:13:19
zelinski and they do an interview
00:13:21
it's like Ali G yeah but they played it
00:13:23
straight I don't care that he was fooled
00:13:25
into giving the interview it's like who
00:13:26
cares but some of the things he said
00:13:27
were really interesting I mean number
00:13:29
one
00:13:31
Powell said that the economic outlook
00:13:33
for the year was looking
00:13:35
pretty uncertain and he said the most
00:13:37
likely scenarios were either sub one
00:13:39
percent growth so staying out of
00:13:41
recession but just barely or he said
00:13:44
going into recession so he thought that
00:13:46
was roughly about equally likely he
00:13:48
admitted that we had the worst inflation
00:13:50
of 40 years and that's why interest
00:13:51
rates weren't necessary and he said that
00:13:53
it was necessary to slow the economy in
00:13:55
order to combat inflation and he then
00:13:57
even went further and said that it was
00:13:59
necessary to cool off the labor market
00:14:02
and even to cool off wages specifically
00:14:05
because that's how you combat inflation
00:14:07
that's the only thing we know how to do
00:14:08
in a situation like this so I think this
00:14:12
is certainly a political mistake for
00:14:14
Powell to say that his objective here is
00:14:16
to hurt the wages the American people
00:14:18
and to basically cause a recession but
00:14:21
that is his view apparently and I think
00:14:23
that we are headed for it seems like a
00:14:25
recession I'm a little surprised that
00:14:27
the earnings reports of these tech
00:14:28
companies are so good because or at
00:14:30
least their forecasts are so good well
00:14:32
they can cut spending and we talked
00:14:33
about this last year when we were trying
00:14:34
to reject what would happen I remember
00:14:37
saying well I think jamath and I were
00:14:39
talking about this and I said well
00:14:40
they're saying hey earnings are going to
00:14:41
go down and there's a PE and I said what
00:14:43
if they just stop spending or they make
00:14:44
a lot of cuts well here we are people
00:14:46
are just saying you know what we're
00:14:48
going to cut our way
00:14:50
to and do stock BuyBacks and that's
00:14:53
another way of financial engineering
00:14:55
to Route Around the FED right and to and
00:14:58
to make the stock go up
00:15:01
worked incredible for Facebook I mean my
00:15:04
Lord they were at 91 to share and now
00:15:06
they're over 200.
00:15:08
right but just to bring it back to the
00:15:09
economy so look I think we agree that
00:15:11
these tech companies
00:15:13
seem to be pretty immune they've got a
00:15:15
large cushion in terms of their ability
00:15:16
to continue generating earnings because
00:15:18
of all the bloat that actually gives
00:15:20
them like a margin of error where they
00:15:22
can just keep cutting to prop up
00:15:23
earnings I'm a little surprised that
00:15:25
they think their revenue forecasts are
00:15:26
going to be so positive because again
00:15:28
they were guiding upwards generally so
00:15:30
they seem to think they're not going to
00:15:31
be impacted by the recession and maybe
00:15:33
they won't be again I think what was
00:15:35
interesting
00:15:36
from pal is the way that he seemed to
00:15:39
think that the only thing we know how to
00:15:41
do this is basically what he said the
00:15:42
only thing we know how to do in this
00:15:43
situation with inflation is to kill the
00:15:46
economy is to slow the economy and
00:15:48
specifically to kill jobs and wages
00:15:51
and that was pretty remarkable to me
00:15:53
because there are other things we could
00:15:54
do such as okay well
00:15:57
one thing is that you don't have to
00:15:59
print so much money cut spending
00:16:00
austerity so yes our fiscal policy
00:16:02
remains completely out of whack we're
00:16:04
running two trillion dollar annual
00:16:06
deficits right now pass covid
00:16:08
so he could have said listen we could
00:16:11
get off this Reckless fiscal policy and
00:16:13
be more restrained but he didn't want to
00:16:15
go there
00:16:15
the other thing he could have done was
00:16:17
address the supply side
00:16:19
one of the ways that you can reduce
00:16:20
inflation it's not just to kill demand
00:16:22
you could actually affect Supply chains
00:16:24
so like cost of energy for example
00:16:26
energy is a huge input into the economy
00:16:28
and one of the things that happened at
00:16:30
the beginning of this Administration is
00:16:31
they made it much harder to drill for
00:16:34
oil and gas and I think Biden sort of
00:16:35
reversed course on that at the State of
00:16:37
the Union remember he had that line
00:16:39
where he said we can't get off
00:16:41
oil and gas for 10 years and the
00:16:43
audience started laughing but at any
00:16:44
event the point is just that it's too
00:16:46
little too late they could have done
00:16:47
more on energy to keep costs low and
00:16:51
then there's a whole bunch of other
00:16:51
critical inputs into the economy besides
00:16:53
labor and what you could do is I think
00:16:56
you could go category by category and
00:16:58
say how do we get the price of these key
00:17:01
inputs into our economy down how do we
00:17:02
resolve supply chain bottlenecks how do
00:17:05
we make it you know easier to get access
00:17:07
to whatever the key commodity is and I
00:17:10
think there's things they could do if
00:17:11
they're just willing to work at it maybe
00:17:13
this isn't the fed's job this is more
00:17:14
the administration but what you could do
00:17:17
is say listen we're going to make it
00:17:18
easier for people to produce and create
00:17:20
Supply and if you have a higher supply
00:17:22
of goods and services then
00:17:25
you will start to bring inflation down
00:17:28
because inflation is just the amount of
00:17:30
money in the system divided by the
00:17:33
amount of goods and services and when
00:17:34
the amount of goods and services hasn't
00:17:36
gone up but the money supply has gone up
00:17:38
tremendously you're gonna have inflation
00:17:39
and that's why I think it's a little bit
00:17:41
misplaced to be killing demand the way
00:17:42
they're killing it is because
00:17:43
fundamentally the problem here is they
00:17:46
flooded the economy with money both
00:17:49
through government stimulus and through
00:17:51
quantitative easing and then also they
00:17:53
made it harder on the supply side to
00:17:55
produce certainly with energy so it
00:17:57
seems to me that the approach they're
00:17:59
taking for us to get out of this it's
00:18:00
like taking a meat cleaver to the
00:18:02
economy
00:18:03
or a sledgehammer really and it's the
00:18:06
most violent possible way that they
00:18:08
could solve the problem they previously
00:18:09
created
00:18:11
of too much inflation any other thing
00:18:13
obviously is jobs we're still sitting
00:18:15
here with close to 10 million job
00:18:17
openings and the thing I'm hearing from
00:18:20
the streets
00:18:21
is that unemployment hacking
00:18:24
is become a high art and so labor force
00:18:28
participation remains low it's nowhere
00:18:30
near the historic highs we have been
00:18:33
very permissive during covet for good
00:18:35
reasons to give people very extended
00:18:37
benefits people have now learned
00:18:39
is my understanding and this is a
00:18:41
something that's happening on a regional
00:18:43
level state by state level
00:18:45
people are learning how to hack
00:18:47
unemployment and not going to work and
00:18:49
people are just not taking the jobs that
00:18:50
are open which are service industry jobs
00:18:52
Americans don't want to work them we
00:18:54
don't want to let people into the
00:18:55
country we've got record low people
00:18:56
coming into the country
00:18:58
it seems to me that would be a much more
00:18:59
productive way to do this right yes I
00:19:02
think it's an excellent point because
00:19:03
exactly what you're doing there is
00:19:05
addressing the supply side which is
00:19:06
you're unlocking the supply of a keep
00:19:08
input into the economy which is all this
00:19:10
unused labor it's all these people
00:19:12
aren't working you're right the labor
00:19:13
force participation rate is still much
00:19:15
lower than it could be so if you get
00:19:17
more people into the economy then that
00:19:19
helps alleviate the cost of Labor it
00:19:22
helps fill these jobs but it doesn't
00:19:24
kill the economy
00:19:25
so it would be a much more positive way
00:19:28
to address this so I just think it
00:19:29
showed a lack of creativity for him to
00:19:31
say that the only thing we can do in
00:19:33
this situation is not just to raise
00:19:36
rates you did say that but but to go
00:19:38
further
00:19:39
and cool off the job market increase
00:19:43
unemployment and cool off wages
00:19:46
I mean that's gonna be a very unpopular
00:19:48
thing to say I think because what you're
00:19:50
basically saying is you're going to hurt
00:19:51
the wages of the American people who
00:19:53
wants that here's the chart for job
00:19:55
openings we thought this would collapse
00:19:56
it went down certainly as we you know I
00:19:59
don't want to obsess over macro stuff
00:20:01
but it's still way up there and so the
00:20:04
fact that we can't get people to take
00:20:06
these jobs I don't know chamoth what do
00:20:08
you think about the employment
00:20:09
and participation situation and how that
00:20:12
might unlock things I also wanted to
00:20:14
know from YouTube off this concept of
00:20:16
the FED is reacting to data just so
00:20:20
slowly and then you have these companies
00:20:22
that maybe are more Nimble and they have
00:20:24
better data than the FED I think that
00:20:26
that's a truism and I don't think
00:20:28
anything about that is going to change I
00:20:30
mean I think we talked about how these
00:20:32
folks calculate non-farm payrolls or how
00:20:34
they calculate CPI
00:20:37
it's incredibly outdated right it's
00:20:39
people with clipboards walking around
00:20:41
talking to people and checking boxes and
00:20:43
filling out forms
00:20:45
can that change probably it could will
00:20:48
it change it probably won't
00:20:51
and so they're going to focus on the
00:20:54
most simple but most powerful measure
00:20:56
that they have which is controlling the
00:20:57
money supply kind of what sax talked
00:20:59
about so they're going to manipulate the
00:21:01
money supply to either put more
00:21:04
liquidity in the system in which case
00:21:06
markets go up and asset prices go up but
00:21:08
then inflation goes up or constrained
00:21:10
liquidity which then causes markets to
00:21:13
go down asset prices to go down and
00:21:15
inflation eventually to go down
00:21:17
the thing that we're facing today when
00:21:19
you look at this labor market chart is a
00:21:20
couple of things that I think we've
00:21:22
talked about before and I just want to
00:21:23
reiterate them which is
00:21:25
you have to remember that we are in this
00:21:28
new world order which is the
00:21:30
ex-china World Order
00:21:33
and in that there is no more unitary
00:21:35
economy that can do things cheaper
00:21:37
faster and better globally around the
00:21:39
world
00:21:40
right so we're going to near shore or
00:21:42
onshore all kinds of things that used to
00:21:44
be done by the Chinese they'll sit in
00:21:46
Mexico or they'll sit in Central America
00:21:49
maybe in some cases they'll sit in
00:21:51
Canada and all of that will feed into
00:21:52
the United States
00:21:54
the problem with all of that is that
00:21:56
that will keep costs higher because
00:21:58
it'll be naturally more inefficient
00:22:01
it will naturally take more money
00:22:05
and that will naturally cause the prices
00:22:07
of those things to be higher which means
00:22:09
that terminal inflation I think is just
00:22:11
roughly higher
00:22:12
as a result
00:22:14
I think that more
00:22:17
power if you will
00:22:19
goes to labor so in this constant
00:22:22
tension that we have in an economy
00:22:23
between labor and capital the people
00:22:25
that own the factories or the businesses
00:22:27
and the people that run them and work
00:22:29
inside of them we've been in this
00:22:30
position where the pendulum has swung so
00:22:32
far towards capitals the owners the
00:22:35
shareholders that all this financial
00:22:37
engineering has tremendous upside right
00:22:39
that's why companies engage in it
00:22:42
but when you show that chart Jason what
00:22:44
it means is it's just really hard to
00:22:45
find people and so the only way you're
00:22:47
going to get people off their butt to go
00:22:49
into work to sit in a chair to do a job
00:22:51
that you need them to do is to pay them
00:22:52
more and in finding that wages will have
00:22:56
to go up
00:22:58
the counterbalance of that is what AI
00:23:00
will do which I think I have to say that
00:23:03
is the key yeah which is massively
00:23:05
deflationary so that is going to be the
00:23:08
tension that we're in now for a really
00:23:09
long time as we explore this I don't
00:23:10
know if you guys saw today but Sequoia
00:23:12
led a 20 million dollar round in this
00:23:14
thing called harvey.ai the legal uh yeah
00:23:17
which is like a legal super wizard for
00:23:19
law firms yeah we knew that was coming
00:23:21
and my partners and I were debating it
00:23:23
and
00:23:25
what we thought of was well how much do
00:23:27
you pay out of the 800 or thousand
00:23:30
dollars an hour that you charge
00:23:31
to harvey.i maybe you're willing to pay
00:23:33
five percent or ten percent
00:23:35
but then the reality is that one of the
00:23:38
most powerful things it does is it's
00:23:39
able to go into Westlaw find all these
00:23:43
cases and say yeah this is germane to
00:23:45
the thing that you're working on right
00:23:47
now that's a very useful thing
00:23:49
but the N plus First Law Firm will also
00:23:51
use that tool and instead of charging
00:23:52
800 an hour they'll say well we'll
00:23:54
charge 600 bucks an hour and we're still
00:23:56
willing to give you five or ten percent
00:23:58
so I just don't see a world where on the
00:24:01
one hand physical labor will continue to
00:24:03
be more expensive
00:24:05
they'll demand more and more money to do
00:24:07
the job that they're asked to do
00:24:10
and then knowledge work will become
00:24:12
increasingly more deflationary because
00:24:13
so much of it will be automated by AI
00:24:15
that those folks will charge less and
00:24:17
less and there's going to be attention
00:24:19
there and I don't exactly know what's
00:24:20
going to happen I did a couple of
00:24:21
experiments uh this week I've been
00:24:24
rolling up my sleeves and playing with
00:24:26
these tools it's pretty amazing and I've
00:24:28
been trying to use them
00:24:30
for actual tasks in our companies what
00:24:33
have you learned what did you do and
00:24:34
what have you learned so I got on the
00:24:37
openai plugins Greg thank you I sent him
00:24:40
my email and he got me on to that and
00:24:43
you can connect it to zapier so I have
00:24:46
two projects I'm working on currently
00:24:48
one of them I was since I'm raised so
00:24:50
raising launch Run 4 and I'm actually
00:24:51
going out to people not just taking
00:24:52
inbound I was like hey can I get the
00:24:55
names of all the major LPS and start
00:24:57
doing some research there put in a table
00:24:58
stuff that sax did when he does blog
00:25:00
posts
00:25:01
but then I started connecting it
00:25:03
with finding people's Twitter handles
00:25:06
finding their LinkedIn profiles and then
00:25:09
the next piece I'm working on is
00:25:11
automatically following them dming them
00:25:13
on Twitter let's say or following them
00:25:16
on and doing an in message saying hey we
00:25:18
haven't met here's the deal memo for my
00:25:20
next fund would love to you know get
00:25:22
together this is sent from Jason's AI
00:25:24
script I was gonna like actually tell
00:25:26
them but here's my real email if after
00:25:28
you read the summary of the next fund
00:25:30
you want to meet and then I was I'm
00:25:32
going to pair that and then this is a
00:25:34
piece I'm going to probably need a
00:25:35
developer to do with our internal LP
00:25:36
database to not email people who are
00:25:38
already duplicates
00:25:40
and then up inside with newsletters I
00:25:42
have it
00:25:43
building a database of every newsletter
00:25:46
we've ever sent the writing style and
00:25:48
that I'm having it go find in real time
00:25:51
news stories that we should be including
00:25:53
in the newsletters which I think will
00:25:54
make the writers right now a third more
00:25:57
productive
00:25:58
but these are things that would cost 40
00:26:00
50 bucks an hour 30 bucks an hour for
00:26:03
you know college-educated Americans and
00:26:05
Canadians
00:26:06
and I have already figured out and I'm
00:26:09
not a developer anymore I had to script
00:26:10
them and I'm actually thinking about
00:26:12
learning to code again just so I can do
00:26:14
this myself
00:26:16
and so on Saturday I'm going to do a
00:26:17
little coding with a friend of mine and
00:26:19
get back up to speed on that
00:26:21
I think about 30 percent of what
00:26:23
knowledge workers do right now is
00:26:26
possible so I put every single person at
00:26:28
both companies on chatgpt4 and the San
00:26:32
uh the playground
00:26:33
about 30 percent of what knowledge
00:26:35
workers at both firms can do currently
00:26:40
is doable if you can figure out and this
00:26:42
stuff is not perfectly scripted yet so
00:26:44
I've been doing some stuff in travel as
00:26:46
well playing with the kayak
00:26:48
interface
00:26:49
Expedia interface Etc
00:26:52
to look at travel planning and it's
00:26:53
pretty good as well
00:26:55
so it's
00:26:56
it it's this is the real deal folks I I
00:27:00
think by the end of this year 30 of
00:27:02
knowledge work could be done by this and
00:27:05
then additionally on Monday I went back
00:27:07
to work in person
00:27:09
and I went to
00:27:11
I hosted our accelerator in person and
00:27:14
then I hosted founder University in
00:27:15
person in the city the city was
00:27:17
absolutely dead
00:27:19
but we had a hundred people fly in from
00:27:21
around the world for our founding
00:27:22
University and a lot of them were
00:27:23
working on AI projects
00:27:25
and what's very interesting is like
00:27:27
there's this big debate going on
00:27:29
Friedberg between
00:27:31
is this going to be built into chat gpt4
00:27:33
or Bard or you know Poe or whatever it
00:27:35
is or should I even bother so should I
00:27:38
bother building
00:27:40
you know a verticalized app and it turns
00:27:42
out like I think you should do the
00:27:43
verticalized app
00:27:45
and you're going to be able to put
00:27:48
together multiple of these AIS that have
00:27:51
different Specialties
00:27:52
um so I'm super stoked about it but I do
00:27:55
think if you're not using this if you
00:27:57
hear my voice right now and you're a
00:27:58
white collar worker a knowledge worker
00:28:00
and you're not using this
00:28:02
this year and getting up to speed on it
00:28:04
I think you'll be out of a job within
00:28:05
the next two
00:28:06
jeez wow I just don't think you'll
00:28:09
compete it would be like trying to
00:28:11
compete without knowing how to use
00:28:12
Microsoft Office 20 years ago right like
00:28:15
could you work and not know email
00:28:17
remember when we came into the workforce
00:28:19
30 years ago and some people knew office
00:28:21
and email
00:28:23
and web research and then other people
00:28:25
didn't those other people retired they
00:28:27
were phased out if you didn't know how
00:28:28
to use a computer and type and use an
00:28:30
Excel spreadsheet or do a PowerPoint you
00:28:32
were done I think there's two possible
00:28:34
ways you can interpret what you're
00:28:35
saying so in terms of the economic
00:28:37
impact so one is that you could say well
00:28:41
AI is going to do 30 of the knowledge
00:28:42
work therefore 30 of the knowledge
00:28:44
workers are going to be put out of work
00:28:46
I think that a different way to put it
00:28:48
would be every knowledge worker can get
00:28:50
30 more work done
00:28:52
correct so if that's the case then
00:28:55
they're more productive and we're just
00:28:57
talking about the problem of how do you
00:28:58
increase real wages in the economy
00:29:00
without having inflation well the way to
00:29:03
do that is for every worker to be more
00:29:04
productive so if every worker is 30 more
00:29:06
productive in theory their wages should
00:29:08
be able to go up by up to 30 percent
00:29:10
that's how you get wage growth now maybe
00:29:13
there will be some companies that don't
00:29:15
need all those employees because now
00:29:18
they're able to get you know whatever a
00:29:19
third more done but there will be other
00:29:21
companies who can hire them they can go
00:29:22
off and do other jobs for other
00:29:24
companies especially when you've got
00:29:25
this backlog of like you said eight or
00:29:27
ten million new you know jobs that are
00:29:29
unfilled those jobs are all service
00:29:30
though you know they're not you're
00:29:32
actually you're gonna have this big
00:29:34
group of knowledge workers there's just
00:29:36
nothing for them to do oh no I just
00:29:38
don't I agree with you but I think
00:29:39
there's going to be a group of knowledge
00:29:40
workers who do not Embrace this and do
00:29:42
not make the transition because it is
00:29:44
it's going to require an upskilling like
00:29:47
I think you're actually going to need to
00:29:48
know how to do some basic programming
00:29:50
and coding to really take advantage of
00:29:52
the at least like scripting levels up I
00:29:55
don't know it's pretty easy to use I
00:29:57
agree with you there have been writing
00:29:58
blog posts but the date the example I
00:30:00
gave of like taking the lp database
00:30:01
sorting it you know it's not quite sure
00:30:05
it will be this is like a chatbot
00:30:07
but it is like I think it takes like
00:30:09
level two programming skills no it
00:30:11
doesn't no you don't have to know how to
00:30:13
program you just have to know how to
00:30:14
prompt it in natural language it's the
00:30:16
opposite of need to learn how to code
00:30:18
the thing about the thing that makes
00:30:19
coding hard is that you have to learn
00:30:21
the specific commands it's like its own
00:30:23
language you have to learn a new
00:30:24
language with this you don't in fact one
00:30:26
of the cool things about some of these
00:30:29
uh open AI apis is that you just tell it
00:30:32
what you want it to do there's not even
00:30:34
like a scripting language a lot of it's
00:30:35
in natural language and that makes it
00:30:38
incredibly easy to use even for
00:30:40
Developers
00:30:41
so I don't think this is a hard
00:30:43
technology to use I agree with you there
00:30:45
may be people are resistant to it
00:30:46
because there's always people who are
00:30:48
resistant to change and new technology
00:30:50
and you're right if they don't adapt
00:30:51
they're going to be dinosaurs but I
00:30:53
don't think this is a hard technology to
00:30:55
to rock how to use and get benefit from
00:30:57
you might be right I mean I right now
00:30:59
it's so new that the glue between
00:31:02
systems is just not there yet and maybe
00:31:04
you'll be able to talk to chat gpt4 and
00:31:08
it'll connect your database on notion it
00:31:10
will take a type form and a survey
00:31:12
monkey and put it all together and
00:31:14
figure that all out for you in the game
00:31:16
right now is still connecting all these
00:31:17
things and that and that's what I'm
00:31:19
talking about and like it that's kind of
00:31:21
not there yet but in the auto GPT stuff
00:31:23
you need a developer right now but
00:31:25
anyway I'm deep in it and I am more
00:31:28
excited right now this feels to me like
00:31:30
2005 to
00:31:32
2012 period when you just saw Ajax and
00:31:36
the web and speed just all coming
00:31:39
together so quickly and the rapid
00:31:41
iteration and is just unbelievable I I
00:31:45
every day I find a new use for it I have
00:31:47
made my default web page opening like
00:31:50
when I open a new page on my PC it just
00:31:53
opens chatgpt4 now just so I'm forcing
00:31:55
myself to use it for every possible task
00:31:58
and the people who work for me some of
00:31:59
them are doing it's most of them are not
00:32:01
and I'm just trying to drag everybody
00:32:03
along
00:32:05
and then you have at the same time
00:32:07
that this um
00:32:09
a remote work thing happening where
00:32:12
salaries I'm finding are starting to
00:32:14
normalize not across cities but across
00:32:17
countries
00:32:18
so
00:32:20
you know hiring somebody in Canada
00:32:22
Estonia Sao Paulo and then you add this
00:32:25
AI to it
00:32:26
the cost to do things is this is like I
00:32:30
don't know I think everything's going to
00:32:31
cost about 10 all this knowledge work is
00:32:33
going to be 10 as expensive to do
00:32:36
I don't think it's 10 less chamoth or
00:32:39
the you know I think it's like 90
00:32:40
percent
00:32:41
10 cents on the dollar
00:32:43
I agree I agree and I it's not this is
00:32:46
not a five-year 10-year prediction this
00:32:48
is like five quarter ten by the way we
00:32:51
said that the first organizations to use
00:32:53
this like the canary in the coal mine
00:32:55
would be the Consulting organizations
00:32:57
and today when Harvey got announced one
00:32:59
of the things that that right on the
00:33:00
heels of that pricewaterhousecoopers
00:33:02
announced like a billion dollar
00:33:03
investment into AI which makes sense
00:33:06
because as a Consulting organization
00:33:07
full of lawyers and accountants and I.T
00:33:10
folks those are the services jobs that
00:33:12
you get tremendous Leverage
00:33:14
if you were to use these tools free to
00:33:17
bring any thoughts I don't know I mean I
00:33:18
think we kind of beat this horse to
00:33:20
death right we've talked about it for a
00:33:22
couple of months and I think we just
00:33:24
keep repeating ourselves are you doing
00:33:25
anything when you're first hand are you
00:33:27
playing with it yourself yeah look tell
00:33:29
us about that by the way one thing I
00:33:31
will say we all talk about cost
00:33:33
reduction and then oh you know knowledge
00:33:35
work is dead and we're gonna save money
00:33:37
and all this stuff
00:33:38
what what that is always the first
00:33:40
reaction to any new point of Leverage
00:33:43
realized from some novel technology the
00:33:46
second is suddenly people start doing
00:33:48
things that
00:33:50
use that leverage to do things that they
00:33:52
couldn't have done before so it's not
00:33:54
just about dropping costs it's about
00:33:56
enabling new things that does a hundred
00:33:59
times more or unimaginable things prior
00:34:02
and I think the next phase of this AI
00:34:05
shock wave that that kind of hit us and
00:34:07
hit the world and
00:34:08
you know kind of hit Enterprises is
00:34:11
going to be the evolution of integrating
00:34:13
those tools in a very unique way with
00:34:16
other tools to drive very novel things
00:34:19
forward to create new things new
00:34:21
projects new progress that was
00:34:23
unfathomable before so it's not just
00:34:25
about cost savings it's going to be
00:34:26
about new stuff I shared a link on
00:34:29
Twitter yesterday there's some guy I
00:34:31
want to quote him
00:34:34
correctly his name is McKay Wrigley so
00:34:37
shout out to McKay on his Twitter page
00:34:40
it says that he didn't know how to code
00:34:44
in 2019 he learned how to code for the
00:34:46
first time he taught himself
00:34:48
and he put together an object
00:34:51
recognition tool with chat GPT
00:34:56
I saw this video it's crazy with his
00:34:58
webcam and basically he holds up like a
00:35:01
Diet Coke and he's like you know tell me
00:35:03
how many calories what is this and how
00:35:04
many calories are in it and it's like oh
00:35:06
there's no calories in it it's a Diet
00:35:08
Coke and he does this three different
00:35:10
times with three different objects and
00:35:11
he hacked this thing together in a
00:35:13
couple of hours that is a product that
00:35:16
was like theoretically unfeasible or you
00:35:19
know kind of very very difficult to kind
00:35:21
of see how you would put that piece
00:35:23
together quickly and easily with one
00:35:25
person in a room in a few hours
00:35:27
a year ago and here you see a demo of of
00:35:30
this person who didn't know how to code
00:35:32
not too long ago putting it together and
00:35:34
creating this product that would have
00:35:35
been such a profound startup imagine if
00:35:38
you went to VCS 18 months ago and were
00:35:39
like look I've got this thing and I hold
00:35:41
stuff up in front of it it tells me all
00:35:42
about it and it talks to me and I
00:35:44
literally use my voice to talk to it and
00:35:46
he basically strung together
00:35:48
a text-to-speech chat GPT an object
00:35:51
recognition tool all of this stuff
00:35:53
completely open source and a a plug-in
00:35:55
that does web browsing and the whole
00:35:57
thing
00:35:58
is basically like your own interactive
00:36:00
visual robot it's it's an incredible
00:36:03
product demo and I thought it was so
00:36:04
amazing and profound I'm sure it's a
00:36:06
prototype and it's kind of janky but it
00:36:08
was done in a few hours on almost a no
00:36:10
code basis it's incredible so what's
00:36:13
going to come from that is a whole set
00:36:15
of new products and ideas and things
00:36:18
that we are certainly not thinking about
00:36:19
today but in six months is going to
00:36:22
become almost Mainstay and many new
00:36:24
categories of products many new
00:36:26
Industries many new businesses are going
00:36:27
to emerge that we're not even thinking
00:36:29
about so the Luddite argument of oh this
00:36:31
is going to destroy jobs and destroy the
00:36:33
economy and drop costs by 90 lawyers are
00:36:35
going to get cheaper et cetera et cetera
00:36:37
I think that doesn't even matter it's
00:36:38
the tip of the iceberg what's more
00:36:40
exciting is all the new evolutionary
00:36:42
stuff that's going to hit the market
00:36:43
that's really going to transform the
00:36:45
things that we can do and that we didn't
00:36:47
realize we could do there's gonna be
00:36:49
incredible analogy for this because what
00:36:51
you're really talking about is more
00:36:52
people being able to use tools and be
00:36:56
creators and what happened in the 80 is
00:36:59
the 90s when the NBA started playing
00:37:01
exhibition games around the world
00:37:03
was more people around the world started
00:37:05
playing basketball and then you started
00:37:07
seeing people like Luca or before him
00:37:09
Yao Ming
00:37:10
Mutombo you start to have people from
00:37:12
around the world who had never been
00:37:14
exposed to basketball just incredible
00:37:16
porzingis incredible talents
00:37:18
emerged because you just had more people
00:37:21
playing with the basketball I think
00:37:22
you're going to have more people playing
00:37:23
with code
00:37:24
and Building Products so you're going to
00:37:26
have incredible amounts of creativity
00:37:29
from people who maybe you didn't expect
00:37:32
because they didn't go to school for
00:37:34
coding or have that opportunity hey um
00:37:38
I mentioned I was in fidai
00:37:41
and I was at fenwick's office and then
00:37:43
Wilson cincini's offices to law firms
00:37:46
being the law firms in the financial
00:37:49
district in the Embarcadero it was an
00:37:50
absolute ghost town and when I say ghost
00:37:52
town I mean like serious ghost town like
00:37:55
weird like this is uh still like being
00:37:58
in some dystopian science fiction they
00:38:00
were the last man on Earth and then we
00:38:03
saw in the group chat today 350
00:38:05
California Street
00:38:06
was worth 300 million dollars four years
00:38:09
old it's a 20 two-story glass and Stone
00:38:12
Tower it's a picture of it
00:38:14
it's going up for sale and they believe
00:38:17
according to the Wall Street Journal
00:38:18
that bids will come in at 60 million
00:38:20
dollars and 80 percent Decline and we
00:38:23
talked about this commercial real estate
00:38:25
uh would have this moment a lot of the
00:38:28
banks uh the smaller Regional Banks own
00:38:30
this debt
00:38:31
Saks what do you think is going to
00:38:34
happen here who is the person
00:38:36
who would buy an office tower
00:38:39
in downtown even at an 80 discount
00:38:42
knowing that you have to pay all those
00:38:44
carrying costs
00:38:45
and there's
00:38:48
so much fake in office space and it's
00:38:50
only increasing right what's your who
00:38:53
buys this it's called land banking okay
00:38:55
explain so in other words okay what I
00:38:58
mean is you're right there's 30 vacancy
00:39:00
in San Francisco right now maybe going
00:39:01
up even more in the next few years as
00:39:03
Lisa's role and people take less space
00:39:08
you may have a countervailing effect in
00:39:10
terms of new companies moving back
00:39:12
because of AI or expanding so it's
00:39:15
possible you start to see some growth in
00:39:17
the office market in San Francisco but
00:39:18
the bottom line is 30 plus vacancy is
00:39:22
going to take years and years of growth
00:39:25
in order to absorb so you're right this
00:39:27
building they can slash it to rent but
00:39:30
they still probably can't fill it I mean
00:39:31
there's just no there's just no demand
00:39:33
so you're going to be sitting on that
00:39:35
property for five years ten years before
00:39:37
the market comes back the way that you
00:39:39
need it to but there's no value right oh
00:39:42
it's going to trade way below its
00:39:44
replacement cost right if you were to
00:39:45
build that building today it would cost
00:39:47
you many times what they're going to pay
00:39:49
for it the problem is you can't finance
00:39:51
that purchase with debt because if
00:39:53
Billy's not going to generate enough
00:39:54
Revenue so that's what I mean by land
00:39:56
banking it's going to have to be an
00:39:58
equity investor who's willing to think
00:40:00
long term and say I'm going to buy this
00:40:02
at a super distressed price and I'm just
00:40:04
going to sit there and hold it and wait
00:40:07
carry it like you said bury the carrying
00:40:09
costs until the market comes back but
00:40:11
Jay Cal I want to say something I think
00:40:13
it's a great analogy because public
00:40:15
growth stocks have declined 70 plus
00:40:17
percent right since the uh the market
00:40:19
started to decline
00:40:21
and we've talked a lot about the
00:40:24
statistic that I've shared a bunch
00:40:25
publicly on how 70 percent of publicly
00:40:28
traded companies that have gone public
00:40:30
since 2020 are trading below their total
00:40:33
cash invested since since founding which
00:40:35
should translate to an estimate that
00:40:37
call it somewhere on the order of 70 of
00:40:39
private companies are probably worth
00:40:40
less than their preference stack
00:40:42
and so they're not worthless companies
00:40:45
they just have a capital structure that
00:40:47
is upside down those companies are
00:40:48
making products for customers those
00:40:50
products those customers are paying
00:40:51
money for those products there's value
00:40:53
there there's real value there the
00:40:55
value's just been reset and so it's
00:40:57
interesting it's not just the asset
00:40:59
class of growth stocks and the asset
00:41:00
class of private companies or private
00:41:02
Tech it's also you know in commercial
00:41:04
real estate we we try and treat each of
00:41:07
these as if they were in isolation but
00:41:09
the problem is many of these assets were
00:41:13
funded with some degree of Leverage
00:41:15
preferred stock is leveraged and
00:41:19
you know it is a form of death because
00:41:20
it has a preference over the
00:41:22
shareholders there the common
00:41:22
shareholders the equity holders and the
00:41:25
same is true with this commercial real
00:41:27
estate market that there was a certain
00:41:28
amount of debt so the availability of
00:41:30
low-cost Capital
00:41:32
um securitized against some asset in the
00:41:33
form of debt or in the form of preferred
00:41:35
stock in a private company has the same
00:41:37
effect which it allowed the valuations
00:41:39
to balloon on the equity and now that
00:41:42
the market has re-rationalized the
00:41:43
prices down 70 plus percent across all
00:41:46
three of these connected but you know
00:41:48
somewhat disparate asset classes you're
00:41:49
kind of having this big reset moment and
00:41:51
funny enough the other statistic is the
00:41:54
cell phone traffic down 70 in downtown
00:41:55
SF right so it's funny all four of these
00:41:58
numbers are pretty much on track
00:42:00
yeah there's this chart that's crazy
00:42:02
it's literally like you have some cities
00:42:04
that have more cell phone traffic than
00:42:07
they did last year or a couple years ago
00:42:09
and this is downtown by the way yeah and
00:42:13
Sam I mean The Wider Bay Area is is I
00:42:15
don't say booming but it's vibrant yeah
00:42:18
I said on last week's show I was looking
00:42:19
for a place to host the accelerator in
00:42:22
San Mateo area I got dozens of people
00:42:25
contacting me hundreds of locations and
00:42:28
offers at 25 of what their carrying cost
00:42:33
is or like not the carrying costs the
00:42:34
the rent was and people offering the
00:42:37
major companies offering me free space
00:42:40
just because they would like to have
00:42:41
Founders hanging around and there was
00:42:43
one project that I really liked the
00:42:45
person was like I'll give it to you for
00:42:46
whatever just because I want to get more
00:42:48
people to downtown San Mateo uh so that
00:42:51
that does sort of prove the point that
00:42:52
there is a
00:42:54
what I and I saw this in New York City
00:42:56
during the the 90s when things were so
00:42:58
cheap people just got creative with
00:43:00
space it inspired people to say I'm
00:43:02
going to create an art gallery I'm going
00:43:04
to create a performance space and I
00:43:06
don't know when that happens in San
00:43:08
Francisco with these spaces but feels
00:43:10
like it's going to be a while
00:43:12
I don't know what you when do you think
00:43:14
there would be demand for this SpaceX
00:43:17
if you had to pick a year over and give
00:43:19
us an over under
00:43:20
I mean
00:43:22
five years plus I mean just to give you
00:43:25
some numbers I think a healthy vacancy
00:43:27
rate and a office Market is five to ten
00:43:30
percent a high vacancy rate in a city
00:43:33
was considered like 15 like you wouldn't
00:43:35
want to be an office investor in a
00:43:38
market that have 15 vacancy five to ten
00:43:40
percent was sort of the normal range if
00:43:42
you were under five percent it was a
00:43:43
super hot market and then 10 to 15 was
00:43:45
sort of a
00:43:46
not great Market from an investor
00:43:48
standpoint
00:43:49
so they're at 30 plus
00:43:51
and like I said it could get worse
00:43:54
before it gets better because it's
00:43:55
Lisa's role people are going to shed
00:43:57
more space that that they might not
00:43:58
already be subleasing
00:44:00
so the real number might be like 40
00:44:02
percent
00:44:05
I think it's like yeah it doesn't seem
00:44:07
like a decade it's a decade assuming
00:44:10
that San Francisco gets his house in
00:44:12
order and companies come back speaking
00:44:15
of that new companies are created and
00:44:17
they don't completely wreck it it's not
00:44:18
clear to me that like things will go in
00:44:19
the right direction
00:44:21
I mean speaking of that do we want to
00:44:23
bring up this horrific uh bear spray
00:44:25
attack now you want to cue that up sax I
00:44:28
mean we're like in full-on Gotham City
00:44:31
now now we have vigilantes
00:44:33
there was a story of a fire commissioner
00:44:36
named uh don carmignani who was beaten
00:44:38
with a metal pipe by a gang of homeless
00:44:42
addicts who were encamped in front of
00:44:45
his mother's house and apparently they
00:44:47
were harassing her and they were doing
00:44:49
drugs smoking drugs or whatever right in
00:44:51
front of not not pot it was like
00:44:53
professional whatever fentanyl or meth
00:44:56
or crack something like a hard drug and
00:44:59
um so what we know is he went down there
00:45:01
had words with them
00:45:04
Boop you know and they bashed them
00:45:07
upside the head with a pipe and uh now
00:45:11
it turns out that he was accused by the
00:45:14
defendant's lawyer the one who assaulted
00:45:16
him so we don't really know what's true
00:45:17
here of using bear spray on them first
00:45:20
so the da dropped charges the lawyer for
00:45:23
the defendant in that case is saying
00:45:24
that he apparently was the perpetrator
00:45:27
of these bear spray attacks on
00:45:30
on homeless people going back a number
00:45:32
of years I guess there's a like you said
00:45:34
pretty gnarly video of of yeah
00:45:41
but obviously the D.A thought something
00:45:43
was kind of hinky because they dropped
00:45:44
charges against the the guy who
00:45:46
assaulted him we shouldn't the person
00:45:48
who sprays the bear spray and the person
00:45:50
who beat somebody with a pipe shouldn't
00:45:52
both people yeah yeah yeah
00:45:55
of course listen there's video of
00:45:57
somebody bear spraying homeless people
00:45:59
and that's clearly wrong however that
00:46:01
was from a couple years ago the one that
00:46:02
was released is from 2021 we have video
00:46:05
from the night that carbignani was
00:46:08
assaulted
00:46:10
that they were chasing him down they're
00:46:12
chasing him down yes with the metal pipe
00:46:14
and even even if they were acting in
00:46:16
self-defense you can't go chasing the
00:46:18
guy dude that's not self-defense more
00:46:20
damage on exactly that's Vengeance
00:46:22
that's not self-defense yes so they took
00:46:24
it out of that zone of self-defense and
00:46:26
they were chasing after him and if you
00:46:28
saw what he looked like after the attack
00:46:30
but they were using deadly force he
00:46:32
could have been killed and you know if
00:46:35
Donna gotten killed by the metal pipe I
00:46:37
don't think it'd be a defense that he
00:46:39
bear sprayed them first
00:46:41
it would have been an excessive use of
00:46:43
force so yeah but in any event I mean
00:46:45
where the D.A ended up on this it was
00:46:47
just a drop charges from that night but
00:46:50
you know that they're going to drop
00:46:52
those charges I think that that's going
00:46:53
to be untenable you know they already
00:46:55
dropped the charges they have to I mean
00:46:58
justice has to be
00:46:59
blind correct I mean you're you're
00:47:01
you're a trained lawyer here we have to
00:47:04
apply the law equally to the sadistic
00:47:06
insane person who wait a second they
00:47:09
arrested the guy who hosed the person
00:47:10
down didn't they arrest them as well I
00:47:12
remember seeing a perp walk we talked
00:47:14
about that on the future anyway it's
00:47:15
Gotham City folks this has gone to Pure
00:47:17
yeah this proves anything I mean again
00:47:20
what they're trying to say now is that
00:47:22
because of of the actions that Don took
00:47:24
that San Francisco is safe and there's
00:47:26
nothing to worry about and these addicts
00:47:28
people who are encamped on the sidewalks
00:47:30
doing drugs doing hard drugs there's
00:47:32
nothing to worry about because somehow
00:47:34
they were provoked by karmagnani and I
00:47:37
just think I agree with you that this is
00:47:38
part of an overall pattern of chaos and
00:47:41
lawlessness in the city it is like
00:47:43
Gotham City so
00:47:45
you know it doesn't make me feel a lot
00:47:47
better about what's happening on the
00:47:48
streets it's nuts
00:47:50
Shabbat you wanted to add something I
00:47:52
want Freebird too oh Riff on lab meat uh
00:47:56
yes well there was actually a story
00:47:57
about this I guess there's two types of
00:48:00
lab there's two types of mock Meats I've
00:48:02
Had The Impossible Burger
00:48:04
yeah I've never craved an impossible
00:48:07
Burger there's so many great Burgers you
00:48:08
can get out there Shake Shack Five Guys
00:48:09
in and out why would I go to get this
00:48:11
impossible Burger unless I was doing it
00:48:12
like vegan stuff but then there was also
00:48:14
supposed to be 3D printed meets and this
00:48:16
stuff seems to be taking forever where
00:48:18
is this at because there was a story in
00:48:19
the Wall Street Journal about how poorly
00:48:22
this is apparently going so there's
00:48:25
three
00:48:27
categories of
00:48:29
these alternative proteins to
00:48:31
traditional animal protein the first is
00:48:34
these
00:48:35
call it alternative
00:48:38
proteins where you use things like soy
00:48:40
protein
00:48:42
or pea protein Beyond Burger is a good
00:48:44
example they have a pea protein based
00:48:45
burger and so
00:48:47
that category
00:48:49
was kind of hot for a minute where
00:48:51
everyone was like oh it's an
00:48:52
eco-conscious decision people will make
00:48:54
the shift
00:48:55
and you know beyond Mead had this
00:48:57
massive IPO and the stock went crazy and
00:48:59
I someone said it was the biggest return
00:49:00
ever for Kleiner Perkins but it really
00:49:02
was just taking plant protein processing
00:49:05
it and trying to make it sort of mimic
00:49:06
the texture and flavor and taste of
00:49:08
animal protein
00:49:10
and it's more expensive so I've
00:49:12
generally been fairly negative on
00:49:14
whether that really moves the needle
00:49:15
right the the needle for me is can you
00:49:17
replace animal proteins traditionally
00:49:19
and stop using all this land and putting
00:49:21
all this carbon into the atmosphere and
00:49:23
all this water and all these resources
00:49:24
that we use to make all these animal
00:49:26
proteins which I think is both kind of
00:49:28
ethically incorrect but also
00:49:29
extraordinarily environmentally costly
00:49:31
sorry can I ask a question qualifying
00:49:33
question do you think it's also
00:49:34
important for it to not
00:49:36
just replace natural products despite
00:49:39
all of those externalities you talked
00:49:41
about with artificial products with
00:49:42
chemicals and sugar
00:49:44
so first of all everything is a chemical
00:49:46
so that the you know I think the
00:49:49
the categorization of you know all
00:49:51
chemicals are bad and silly because
00:49:52
everything is made of chemicals I think
00:49:55
it's a question of are there bad things
00:49:56
that are being put in there that's not
00:49:58
good for your health
00:49:59
to make it flavorful or whatever and
00:50:01
that that may or may not be the case
00:50:02
it's really product dependent I don't
00:50:03
think it's a good generalization
00:50:05
but do you so you think when I eat a
00:50:07
salad I'm just eating chemicals
00:50:09
it is chemicals yeah got it but coffee
00:50:12
ones right healthy chemicals there's
00:50:14
good in there or bad yeah for sure and
00:50:16
then bad chemicals are in like sugary
00:50:18
cereal yeah like refined sugar is bad
00:50:20
for sure right that's a bad chemical and
00:50:22
no I'm just I just want to understand
00:50:23
how you just viewed as a spectrum of
00:50:25
chemicals some good some bad yeah
00:50:27
there's things that are good for you
00:50:28
there's good fats there's bad fats
00:50:30
there's there's you know and even in the
00:50:32
category of sugar some people say all
00:50:33
sugars are bad some people say some
00:50:35
sugars are better than were others as
00:50:37
measured by the glycemic index all you
00:50:39
know there's a lot of ways to kind of
00:50:40
look at this stuff is beyond meat and
00:50:41
these P ones uh they're all processed
00:50:44
highly processed they got a lot of salt
00:50:47
they got a lot of fat right they're
00:50:48
they're not good for you so the way that
00:50:50
Beyond and impossible and others have
00:50:52
tried to make it taste good for people
00:50:54
is they've added a lot of you know
00:50:56
saturated fats which is a way to drive
00:50:58
the mouth feel and make it taste good
00:51:00
but then a lot of doctors at the
00:51:02
American Heart Association came out and
00:51:04
said that those fats are really bad for
00:51:05
your heart and you should meet them
00:51:07
and also there's been a general kind of
00:51:09
consumer sentiment shift so a couple
00:51:12
years ago these were the hottest
00:51:13
products it was like all the food
00:51:15
ingredient companies were shifting to
00:51:17
plant-based proteins and they were
00:51:18
building plant-based protein business
00:51:20
categories and it was this big hot thing
00:51:22
and then they came out and they're like
00:51:23
wait a second this isn't going as we
00:51:25
thought what happens is people try them
00:51:27
out and they're like yeah that's a cool
00:51:29
thing I want to do good for the planet
00:51:30
but would I rather pay five bucks for a
00:51:32
do good for the Planet Burger that kind
00:51:34
of doesn't taste that good or would I
00:51:36
rather pay three bucks for a burger that
00:51:38
tastes really good and what happens is B
00:51:41
B I choose option b yeah and so do most
00:51:43
people right and so almost all people
00:51:45
and that's the point of view I've always
00:51:47
shared I said it's just it's not going
00:51:48
to win the hearts and minds of the world
00:51:50
unless it's cheaper and it tastes better
00:51:52
and healthier and it's identical yeah
00:51:53
and doesn't damage your health doesn't
00:51:55
make you worse exactly so the more
00:51:57
challenging technical solution is the
00:51:59
other two categories the second category
00:52:01
is can you synthesize animal proteins
00:52:04
using recombinant DNA so this is where
00:52:07
you take the DNA that codes for the
00:52:09
protein whether it's the milk protein or
00:52:11
the egg protein or the cheese protein
00:52:13
and you put it in a bacterial cell or a
00:52:15
yeast cell that are used to ferment that
00:52:17
we use to make wine that we use to make
00:52:19
beer and they eat sugar and then they
00:52:21
spit out a product and in the case of
00:52:23
wine and beer they eat sugar from grapes
00:52:25
or for from malt or whatever and they
00:52:27
spit out alcohol ethanol and Genentech
00:52:30
was the first company to really Pioneer
00:52:32
recombinant DNA at a mass scale they
00:52:35
basically use recombinant DNA to make
00:52:36
insulin so they took the DNA from humans
00:52:39
that that codes for insulin the gene for
00:52:41
insulin they put in E coli bacteria and
00:52:43
then they put the E coli bacteria in a
00:52:45
big tank and the E coli start to
00:52:47
duplicate and they make all this insulin
00:52:49
and that's how we make all the world's
00:52:50
insulin today it's using that
00:52:51
biomanufacturing process and it's how we
00:52:54
make all of biologic drugs all antibody
00:52:56
drugs are made this way it's a 300
00:52:58
billion year Market just in biologic
00:53:00
drugs so when crispr kind of came about
00:53:02
in 2012 suddenly the toolkit to go in
00:53:06
and do a much better job and a much
00:53:08
cheaper job of editing the genomes of
00:53:10
those little microbes to make them more
00:53:11
efficient at making these proteins
00:53:12
became standard and everyone said let's
00:53:15
go use this new category of what's being
00:53:17
called synthetic biology or synbio to
00:53:20
make all these animal proteins that we
00:53:22
use animals to get today so can I just
00:53:24
ask a question is the idea that if you
00:53:26
use recombinant DNA in this process it
00:53:28
would taste better and be healthier in
00:53:30
all this chemically identical so it's
00:53:32
the exact same protein as you can get
00:53:34
and just I understand it'd be the exact
00:53:36
same under a microscope or whatever but
00:53:38
would it taste the same tastes exact
00:53:40
same totally exact same so that's the
00:53:42
whole point do we know that or was that
00:53:44
that was the guess no we know that it's
00:53:46
the same protein shamat so whether you
00:53:47
get the protein from the cow or you get
00:53:49
the protein from the yeast cell so
00:53:51
what's the issue it's too expensive to
00:53:52
do this process because so the key
00:53:54
metric in that second category is
00:53:57
productivity grams per liter per day how
00:54:00
well can you get that little
00:54:01
microorganism to make that protein the
00:54:04
more protein it makes per day the
00:54:05
cheaper the price per protein and we're
00:54:07
still a far ways off from getting this
00:54:09
to be price competitive so that's a a
00:54:11
challenged category right now there's a
00:54:14
lot of I'm invested in a couple of
00:54:15
companies in this space where we're
00:54:17
trying to make it faster and cheaper to
00:54:18
do that strain engineering to edit the
00:54:20
genome up front and make them make those
00:54:22
little cells more productive to bring
00:54:24
the price per gram down and hopefully
00:54:25
make it compete ultimately with the
00:54:27
traditional market for eggs cheese milk
00:54:29
Etc but what is the constraint is it an
00:54:31
energy constraint or is it an actual
00:54:33
biological incapability no so the great
00:54:36
thing is our first principles basis the
00:54:38
biophysics indicates that this should
00:54:39
make proteins cheaper and that is good
00:54:42
for the planet it's good for human
00:54:43
health it's good for everything we
00:54:45
should be able to make eggs cheese milk
00:54:47
all this stuff exactly the same as what
00:54:49
you get from an animal without the
00:54:50
animal because the biophysics of a
00:54:52
single cell making it is better than the
00:54:54
biophysics of a whole animal think about
00:54:55
a chicken it grows feathers it bucks it
00:54:58
walks around it has energy it makes heat
00:55:00
so the chicken as a system is not that
00:55:02
energy efficient but a little cell that
00:55:04
just eats sugar and it's programmed to
00:55:06
do one thing and one thing only eat
00:55:08
sugar make protein each sugar make
00:55:09
protein and spit as much of it out you
00:55:11
Theory officially can make it way more
00:55:12
efficient exactly now we're making great
00:55:15
progress but we're not there yet we're
00:55:16
not a commodity price point why I'm
00:55:18
trying to ask why where's the failure
00:55:20
Point there's two failure points sorry I
00:55:22
should I should say there's three the
00:55:24
first is strain engineering which is you
00:55:25
want to shuffle all the other genes in
00:55:27
the organism to stop doing things like
00:55:29
growing a bigger cell wall or you know
00:55:32
taking your time to duplicate you want
00:55:34
to change the Genome of the cell to get
00:55:35
it to do stuff faster the second stage
00:55:37
is process engineering when you put that
00:55:39
cell in a tank you're changing the sugar
00:55:42
the methanol the CO2 the oxygen the pH
00:55:45
everything about that tank and the
00:55:46
condition of the tank has to be adjusted
00:55:48
so there's about 60 variables and those
00:55:50
60 variables all need to be tuned and
00:55:52
tweaked before you optimize the
00:55:54
performance of production
00:55:55
the third category is the hardest which
00:55:57
is scale Manufacturing there's about a
00:56:00
hundred million liters of
00:56:01
biomanufacturing capacity on Earth today
00:56:04
95 million liters it's owned and
00:56:06
operated by companies that use and when
00:56:08
I say buy a manufacturing capacity I'm
00:56:09
talking about big stainless steel tanks
00:56:11
you pour water you pour sugar you pour
00:56:13
your cells in they make hobbies and they
00:56:15
make your stuff of that 100 million
00:56:18
liters 95 million is owned and operated
00:56:19
by companies 5 million is available for
00:56:22
rent of that 5 million liters 4 million
00:56:25
is rented for its entire lifespan by
00:56:27
some company usually a biologic Drug
00:56:28
Company because very little of this is
00:56:30
being done in food today so there's only
00:56:31
a million liters left to rent and
00:56:33
there's 200 syn bio startups trying to
00:56:35
make animal proteins and they've all
00:56:37
competed for this this capacity so the
00:56:39
capacity cost has gone up by about
00:56:40
fourfold but it sounds like the the
00:56:43
latter two you can overcome with capital
00:56:45
but the first one is really bounded by
00:56:47
science it's more engineering because
00:56:49
we're
00:56:50
back is kind of What's called the
00:56:51
tighter curve which is grams per liter
00:56:53
and the more experiment you do the
00:56:55
higher that number goes and so if you
00:56:57
can increase your experimental rate and
00:56:59
the few the few grams that it does
00:57:00
produce today when when a normal person
00:57:02
tastes it they're like Yep this tastes
00:57:04
the same as a a wagyu rib eye no so
00:57:09
remember I'm talking about proteins
00:57:10
right now I'm not talking about cellular
00:57:12
meat I want to talk about cellular meat
00:57:14
last which is the hardest category which
00:57:16
is what you're talking about I'm talking
00:57:18
about taking that protein and then using
00:57:19
it to make a product like a like a
00:57:21
cheese or you know using it as an egg
00:57:23
replacer that kind of stuff it's the
00:57:24
same protein as what you would get from
00:57:26
eggs or milk or what have you this all
00:57:27
just sounds so hard well it's a big
00:57:30
problem and it's a lot of money so is
00:57:31
that a problem eggs alone or 200 billion
00:57:34
dollars a year I mean the methane
00:57:36
released from cows is one of the largest
00:57:37
contributors to global warming it's a
00:57:39
it's a real
00:57:40
problem also we're going to need to
00:57:42
solve this Jamaica there's a lot of
00:57:43
resources we're going to colonize Uranus
00:57:46
we're going to need to get food there I
00:57:48
just asked the question like if you if
00:57:50
you go after the high emission
00:57:51
categories first do you give yourself
00:57:53
room to leave these things because
00:57:55
you're doing so much already
00:57:57
just a question animal agriculture
00:57:59
emissions are one of the largest and
00:58:02
unfortunately one of the biggest drivers
00:58:04
because it's people's GDP per capita
00:58:06
increments the first thing they spend
00:58:08
money on is probably no I get it I'm
00:58:09
saying something different which is if
00:58:10
we just invented better heat pumps you'd
00:58:12
have industrial Heating and Cooling
00:58:14
which represents like almost a third of
00:58:15
all greenhouse gas emissions you get
00:58:17
that off and you give yourself a lot of
00:58:20
time and space and room and maybe you
00:58:22
let the cows Roam and Belch and burp
00:58:24
because the tape the meat just tastes
00:58:26
better and you don't have to spend a
00:58:27
bunch of time and effort I don't think
00:58:28
it's an ore I think it's an antimoff I
00:58:30
think we should be doing all these
00:58:31
things and I think that I I'm a big
00:58:34
believer as you guys know in markets so
00:58:36
I'm not a believer in transition for the
00:58:39
sake of you know Carbon saving because
00:58:41
people aren't going to pay a premium as
00:58:42
we've seen with the kind of alternative
00:58:44
Meat Market fifteen dollars people want
00:58:47
some cheaper hamburgers that's one
00:58:48
cheaper cheaper cheaper so if you can
00:58:50
make proteins cheaper it's also a great
00:58:52
Roi you can make money doing this
00:58:54
and the market will buy it because it's
00:58:56
cheaper protein sorry I just want to I
00:58:59
just want to hit on this because we keep
00:59:00
sidetracked a little bit the third
00:59:01
category is the one that the article was
00:59:03
about which is cellular meat so cellular
00:59:05
meat is where you're trying to make your
00:59:06
wagyu or your shrimp or your fish you're
00:59:09
trying to make cells not just proteins
00:59:10
but entire cells and those cells stick
00:59:13
together and they look and cook and feel
00:59:15
and taste like cellular meat like like
00:59:18
muscle like what you eat when you eat
00:59:19
fish or beef or whatever
00:59:22
and the problem there is you're trying
00:59:24
to take a cell and cells normally grow
00:59:27
on you know bones and on tissue and so
00:59:29
there's scaffolding and all these
00:59:31
systems that hold all the cells together
00:59:32
and so to get cells to grow in a tank
00:59:35
and stick together and replicate without
00:59:37
other cells signaling them turns out to
00:59:40
be really expensive there was an
00:59:41
executive at Merck I spoke to a few
00:59:42
months ago and he said we're going to
00:59:44
sell fetal bovine serum which is
00:59:46
basically like this liquid that they get
00:59:49
from the fetuses of cows and this is how
00:59:51
cellular meat started they took a cell
00:59:53
from a cow and they put it in a tank
00:59:55
with fetal bovine serum and the cell
00:59:57
started to replicate and duplicate and
00:59:59
then they could take those cells and try
01:00:00
and turn them into a beef into a burger
01:00:02
and sell it or try it that was the
01:00:04
million dollar burger if you remember
01:00:05
that a couple years ago and Fetal bovine
01:00:07
serum Market has gone through the roof
01:00:09
because so many companies are trying to
01:00:10
make cellular meat and the Merc exact
01:00:12
was like we're going to sell a billion
01:00:13
dollars of fetal bovine serum and then
01:00:15
we're going to sell zero because No
01:00:16
One's Gonna Be able to make money doing
01:00:17
this it's just impossible you're not
01:00:19
gonna sell 500 Burgers so the technical
01:00:21
challenge there is do you have to edit
01:00:23
the cells to get them to duplicate you
01:00:25
have to get them to grow in suspension
01:00:27
meaning in a tank instead of growing on
01:00:28
Bones and growing next to each other and
01:00:30
Scaffolding and then you have to change
01:00:32
the feedstock so that you're creating
01:00:34
all these other proteins and signaling
01:00:35
factors and hormones that you pour into
01:00:38
the tank that trigger those cells to
01:00:39
grow is there any chance that after all
01:00:41
this it's it actually just tastes
01:00:42
slightly different or better it may yeah
01:00:45
it may but likely not I mean let's be
01:00:48
honest these are you're taking a cow
01:00:49
cell or a salmon cell now the reason I
01:00:52
say this is that I don't know if you I
01:00:54
mean you don't eat meat so maybe you
01:00:55
don't know this but depending on where
01:00:58
the water that they drink the actual
01:01:01
grass that they eat the meat does taste
01:01:03
different
01:01:04
and that's part of the whether the cow
01:01:07
is massage
01:01:08
I mean look at the acorn fed cows the
01:01:11
beef that we used to have at poker
01:01:13
before austerity measures life was so
01:01:15
good in 2021 well that's that no we
01:01:18
can't get it anymore yeah I know we're
01:01:20
on a budget I get it no not us we can't
01:01:22
get it anymore because they sell it
01:01:24
through one channel
01:01:25
um but yeah like it's so good yeah the
01:01:28
variation you're talking about is
01:01:29
obviously at an Echelon and a class of
01:01:32
eating chamoth it's probably not
01:01:33
Mainstay like you know the thirty
01:01:36
thousand dollar a year McDonald's burger
01:01:37
and chicken nugget eater is probably
01:01:39
happy to take no I disagree with you
01:01:41
chicken nugget that tastes I disagree
01:01:42
with you because if you go into Whole
01:01:43
Foods and you actually buy like a USDA
01:01:46
top sirloin there's a certain taste that
01:01:48
it has that things that are not USDA
01:01:51
don't have so even even at like the most
01:01:54
basic layer of the food pyramid
01:01:56
you can differentiate on taste based on
01:01:59
the same this is why I'm saying I think
01:02:00
it's just a very complicated long
01:02:03
drawn-out process and I just wonder if
01:02:04
the people that are in these businesses
01:02:06
if they actually love food or not I
01:02:09
understand why they love the science I
01:02:10
get it and why they would love to save
01:02:12
the planet I get that too
01:02:14
but unless some of these people are are
01:02:16
also food lovers they're gonna miss I
01:02:19
think the thing where it all dies
01:02:20
anyways I just want to restate again
01:02:22
for the final time these are these are
01:02:25
identical cells and identical proteins
01:02:27
to what you're getting from the animal
01:02:29
so they are not like what we talked
01:02:31
about in that first category where
01:02:32
you're trying to get other stuff to sort
01:02:34
of taste like meat you're literally
01:02:36
trying to create the meat and create the
01:02:37
protein using these systems
01:02:41
I'm just trying to tell you that salmon
01:02:43
two pieces of salmon can taste totally
01:02:45
different depending on where it swam
01:02:46
right I and I guess what I could say is
01:02:48
the same protein yeah you could probably
01:02:50
adjust the conditions in the tank if
01:02:52
needed to change the the characteristic
01:02:54
this is my point like you don't even
01:02:55
know where to start how is it the
01:02:57
[ __ ] kelp in the Atlantic Ocean like
01:03:00
what are you changing look I don't know
01:03:01
what kelp effects on the salmon I don't
01:03:03
know if salmon but this is my point
01:03:05
Nobody Does the Atlantic Ocean this is
01:03:07
why we pay so much for the acorn fed
01:03:09
beef I get it but most beef is not uh
01:03:11
kelp from the Atlantic Ocean fed salmon
01:03:14
it's animals grown in very large
01:03:16
feedlots fed corn and water that's it
01:03:18
let me just say that again 90 95 of
01:03:21
animal protein consumed is cows pigs and
01:03:25
chickens grown in feedlots fed corn and
01:03:27
soybeans and water and that's it right
01:03:29
but if you if you go to different
01:03:31
countries and taste the meat that's fed
01:03:32
in that exact same way it tastes
01:03:34
different so for example if you go to
01:03:36
Argentina I appreciate what you're
01:03:38
saying but the point I'm trying to
01:03:40
imagine you can recreate whatever the
01:03:42
system is that you're talking about so I
01:03:43
want to just get back to the unit
01:03:44
economics the cost per kilogram or the
01:03:46
cost per gram of the protein we are
01:03:49
still many orders of magnitude away on
01:03:51
cellular meat so the problems you're
01:03:52
laying out are really down the road
01:03:54
problems of optimization right now we've
01:03:56
got more fundamental problems on how do
01:03:58
you actually get this stuff to be cost
01:03:59
competitive now fortunately the tools of
01:04:01
crispr and since crispr cast 9 came out
01:04:03
10 years ago there are now hundreds of
01:04:05
variants that are open source IP free
01:04:07
royalty free and used very broadly and
01:04:10
generally and DNA sequencing costs
01:04:11
continue to decline those are the two
01:04:13
basic tools that are being used by
01:04:15
biochemists and Engineers to do rapid
01:04:18
evolutionary iteration needed to produce
01:04:21
the recombinant production of proteins
01:04:23
to produce the new cells to produce the
01:04:24
feedstock for those tanks and there's a
01:04:27
cost curve that we're trying to get over
01:04:28
it's not happening overnight hundreds of
01:04:31
millions of dollars and in several cases
01:04:32
billions of dollars have gone into these
01:04:34
systems and it's very likely that these
01:04:36
companies may need several more years
01:04:38
and several billion dollars we are going
01:04:40
to get there the technology is
01:04:41
progressing the rate of progress is a
01:04:44
little slower and it's a little more
01:04:45
challenged I think than the first round
01:04:48
of investors had hoped but I do think
01:04:50
that scientifically in first principles
01:04:52
it is absolutely feasible it's a
01:04:54
function of engineering our way there to
01:04:55
giving to moth and everyone else that
01:04:57
eats burgers and chicken nuggets
01:04:59
everything that they want hopefully at a
01:05:00
lower price if you put it on the curve
01:05:02
of uh self-driving cars you know we have
01:05:04
Crews doing some automated taxis in like
01:05:07
a very constrained area in San Francisco
01:05:09
but we don't have it everywhere where do
01:05:11
you put this on the curve it's a great
01:05:13
question so so what's happened by the
01:05:15
way as we've gotten down the cost curve
01:05:17
we are unlocking new markets so new
01:05:20
products are being produced existing
01:05:22
proteins that are come from animals
01:05:24
there's a good example of a product
01:05:25
called pepsin it's uh it's extracted
01:05:28
from pigs today it's very expensive
01:05:29
similar to how we used to make insulin
01:05:31
and we're replacing the sourcing of that
01:05:33
product we replaced insulin which we
01:05:35
used to get from pigsplains we now make
01:05:38
it recombinantly we're now replacing
01:05:39
pepsin we're replacing the the rennet
01:05:42
that's used to make cheese so as we move
01:05:44
down the cost curve these Hive what are
01:05:46
called high value proteins are the first
01:05:48
to fall those markets collapse because
01:05:50
we now make them recombinantly they were
01:05:52
sorry they collapse in price
01:05:53
because they're now cheaper using
01:05:55
recombinant systems instead of taking
01:05:56
them from animals and eventually we'll
01:05:58
get to that cost curve where they're
01:05:59
ubiquitous for all proteins or for all
01:06:01
types of cells in the meantime they're
01:06:03
pretty sizable markets to go after these
01:06:05
are multi-billion dollar markets that
01:06:06
are getting knocked down we don't talk
01:06:08
about it every day it doesn't show up in
01:06:09
the news but it's really profound and
01:06:11
interesting to see that this technology
01:06:13
is working it's overturning
01:06:15
multi-billion dollar markets it's making
01:06:17
progress and you know hopefully it'll
01:06:19
it'll get to the point that you know
01:06:21
everything from the chicken nugget to
01:06:23
the kelp fed salmon can have you guys
01:06:26
tried a Beyond Burger or an impossible
01:06:28
Burger I've had it I've tried them a
01:06:30
long time ago but I've not tried them
01:06:32
recently they're like it's like eating
01:06:35
something mushy that's 60 percent of
01:06:38
average hamburger it's not worth paying
01:06:41
double for certainly
01:06:42
for somebody who's a hamburger eater
01:06:45
so while we were talking by the way
01:06:48
Amazon's results came out they crushed
01:06:50
it
01:06:51
earnings per share of 31 cents versus
01:06:54
11.
01:06:56
and uh stock is about 10 10 off hours
01:06:59
and it was up four percent today
01:07:02
for The Insider Traders exactly how do
01:07:04
you feel about your recession prediction
01:07:07
I'm sticking by it I think we're still
01:07:09
going to have a recession but it is an
01:07:10
interesting Paradox here so I think
01:07:13
there's only a couple possibilities
01:07:14
either
01:07:15
Tech is sort of immune or they forecast
01:07:19
down so much they were so conservative
01:07:21
in their forecast thinking we're going
01:07:23
to be in a recession that it was easy to
01:07:25
beat
01:07:26
or look I could be wrong about the
01:07:28
recession but Powell is saying it
01:07:31
and pal is saying if it's not a
01:07:32
recession it's gonna be less than one
01:07:33
percent growth it's gonna be a thousand
01:07:34
year to recession so it's not credible
01:07:37
so I'm not revising my forecast
01:07:40
well I I think pal is credible when he's
01:07:42
giving us bad news because their
01:07:43
incentive is always to fluff it up and
01:07:46
make it sound better than it is so when
01:07:47
he's telling you things look bad maybe
01:07:49
they're looking really bad
01:07:52
I don't know man but look it's A Tale of
01:07:55
Two Cities right now I mean the big tech
01:07:56
companies seem to be doing really well
01:07:57
so it's it's definitely a paradox
01:08:01
yeah all right everybody well the whole
01:08:03
RFK thing
01:08:04
okay that's a good topic yeah great
01:08:06
topic go ahead I think we should tell
01:08:07
people like what he's about where are
01:08:10
all ears I think he gave a terrific
01:08:12
announcement speech okay and just give
01:08:14
you some background for the younger
01:08:15
viewers who may not know
01:08:17
so Robert F Kennedy his father ran for
01:08:21
the Democratic nomination in 1968 after
01:08:23
his brother John F Kennedy had been
01:08:24
president as assassinated as we know in
01:08:27
the early 1960s what happened is at this
01:08:30
time before the 1968 election Lyndon B
01:08:33
Johnson was the incumbent Democratic
01:08:35
president and everyone thought that he'd
01:08:37
be the party's nominee and he was going
01:08:39
to get reelected and he was brought down
01:08:41
by an extremely unpopular War the
01:08:42
Vietnam War and it was RFK Jr's father
01:08:46
who was a great critic of the Vietnam
01:08:48
War and he ran for the Democratic
01:08:50
nomination and I think that he very
01:08:52
likely would have gotten it on the night
01:08:53
that he won the California primary he
01:08:55
was assassinated by Sirhan Sir Henry
01:08:58
yeah if you go back and look at the
01:09:00
things that he was saying in that
01:09:02
campaign he really was saying a lot of
01:09:04
beautiful things that are in his son's
01:09:07
add that I think would be worth playing
01:09:09
here but I I think you have maybe the
01:09:12
setup for a similar situation here
01:09:13
you've got an incumbent Democratic
01:09:15
president who is sort of not that
01:09:18
popular he's sort of old and out of it
01:09:20
and incoherent he's presiding over a war
01:09:22
that is rapidly becoming a debacle you
01:09:25
don't hear so much about the spring
01:09:26
counter offensive anymore these new
01:09:28
Pentagon papers that were leaks show
01:09:30
that the Ukrainian casualties are at
01:09:33
least five times greater than have been
01:09:34
publicly admitting it looks like Russia
01:09:37
is certainly not losing the war the way
01:09:39
they used to be they've captured 90 of
01:09:40
Bak mud which has been the most violent
01:09:42
bloody Battle of the war and Biden at
01:09:45
this point has no strategy to bring that
01:09:46
to an end in fact he's rejected multiple
01:09:49
attempts at a peace deal and so now it
01:09:52
looks like it's the Chinese who are in
01:09:54
the driver's seat potentially putting
01:09:56
together some sort of diplomatic
01:09:57
settlement so I think listen if the
01:10:00
economy ends up going into recession and
01:10:02
this war ends up becoming the Fiasco
01:10:03
that's increasingly looking like you
01:10:05
could have a setup like 1968 where
01:10:08
people are wondering why the hell is
01:10:09
this guy our nominee and let me tell you
01:10:12
RFK Juniors already pulling at 19 which
01:10:14
I think is pretty good considering he
01:10:16
just came out of the gate and people
01:10:18
don't even know the substance of his
01:10:19
campaign yet Marion Williamson's at nine
01:10:22
percent so if she dropped out you'd be
01:10:24
at 28 for the alternative and I think he
01:10:27
could go up from here and I think if you
01:10:29
if you watch the speech he gave I
01:10:31
thought there was a lot of really
01:10:33
beautiful sentiments in there it's very
01:10:36
good he said that Biden has made Ukraine
01:10:38
a pawn and a geopolitical battle that
01:10:40
has put the flower of Ukraine's youth
01:10:42
into an avatar of death in order to
01:10:44
exhaust Russia he channeled America's
01:10:47
anti-war Traditions he quoted John
01:10:49
Quincy Adams that America should not go
01:10:51
abroad in search of monsters to destroy
01:10:53
he quoted Martin Luther King Jr there's
01:10:55
a direct link between poverty and
01:10:57
violence and oppression at home and War
01:11:00
abroad he talked about the role of the
01:11:03
CIA during
01:11:05
his uncle's Administration where he said
01:11:07
that John F Kennedy eventually realized
01:11:11
that the purpose the CIA had become to
01:11:14
create a steady pipeline of Wars to feed
01:11:16
the military-industrial complex and he
01:11:18
talks about how JFK came to distrust the
01:11:21
CIA and realized that it was lying to
01:11:24
him and the biggest Applause of his
01:11:26
speech was when he quoted JFK
01:11:28
approvingly saying that he wanted to
01:11:30
take the CIA and shatter it into a
01:11:32
thousand pieces and Scattered to the
01:11:33
winds and this very same week that he
01:11:35
gave the speech we found out that five
01:11:37
former
01:11:38
CIA directors have participated in a
01:11:41
giant hoax on the American people by
01:11:43
claiming that this Hunter Biden's story
01:11:45
was Russian disinformation they knew it
01:11:47
was not they knew it was not the
01:11:50
information on the hard drive was real
01:11:51
it showed that Hunter Biden received
01:11:54
multi-million dollar payments from
01:11:56
foreign governments including China and
01:11:58
Ukraine okay and regardless of what you
01:12:02
think of that story it should not have
01:12:03
been suppressed by social media and it
01:12:05
certainly should not have been
01:12:06
suppressed in a psyop by 51 former
01:12:10
intelligence officials including five
01:12:13
former directors of the CIA and if
01:12:15
that's the way they're going to behave
01:12:16
if they're going to meddle in American
01:12:17
politics that way I think we do need to
01:12:19
start over we do need to ask what's
01:12:21
going on with the security state they're
01:12:22
not supposed to be meddling in American
01:12:23
politics that way
01:12:25
so I think if this is the way they're
01:12:27
going to act I say shatter away scatter
01:12:29
that thing into a thousand pieces hey
01:12:32
it's Catholic I'll vote for him
01:12:34
and he's called out the insanity of
01:12:37
covid lockdowns and man that's the thing
01:12:40
that he's I guess that's the big
01:12:42
controversy is he's anti he's an
01:12:44
anti-vaxxer I guess that's the one thing
01:12:46
they're trying to position him as and he
01:12:48
does conspiracy theories so so listen if
01:12:52
you go back and look at his record he
01:12:53
was an environmental activist for most
01:12:55
of his people here he did the worship
01:12:56
Project New York where they basically
01:12:58
bought the land along the Hudson I
01:13:00
remember I was at some events for it
01:13:01
they wanted to clean the Hudson up and
01:13:04
they just bought the land and didn't
01:13:05
people donated the land and they bought
01:13:07
it they raised money and uh the Hudson
01:13:09
today has like
01:13:11
you know it's it's flourishing amazingly
01:13:14
and he's directly responsible for that
01:13:16
he was a big critic of the way that
01:13:17
corporate greed could lead certain big
01:13:19
companies to engage in environmental
01:13:21
pollution and at certain point he
01:13:23
realized that big Pharma had a similar
01:13:25
incentive now I don't know if he was
01:13:26
right about those vaccines but I do know
01:13:28
that he's right in the case of covid
01:13:29
they had an incentive to push this
01:13:31
dubious
01:13:33
RNA shot on us so they would get boosted
01:13:36
a zillion times and he's right about
01:13:38
that he was right about the fact that
01:13:39
this should never have been mandated we
01:13:41
should not have the lockdowns and you
01:13:42
know what in his nomination speech or
01:13:45
his declaration the word vaccine was
01:13:47
only mentioned once so this is not what
01:13:49
his campaign is about I look forward to
01:13:51
having him on
01:13:52
yeah and to be honest I mean look at all
01:13:55
the other things that were deemed to be
01:13:56
conspiracy theories that ended up being
01:13:58
true oh yeah Monster
01:14:01
not that you go either way or it could
01:14:05
be embarrassing let me ask you this ax
01:14:07
if it was him versus Trump who do you
01:14:08
vote for
01:14:09
well I'm gonna I'm gonna Reserve okay
01:14:11
versus Trump sex I'm not going to take a
01:14:14
position on the General yet but but in
01:14:16
in the Democratic primary I'm definitely
01:14:17
endorsing RFK Jr in the narcotic primary
01:14:27
all right everybody uh to all the
01:14:29
amazing people who got together for the
01:14:31
episode
01:14:32
unbelievable over 40 or 50 of them Ray
01:14:35
great job shout out to Ray shout out to
01:14:37
Ray I dialed into a bunch of them in I
01:14:40
think
01:14:41
oh that's great Europe and I don't know
01:14:44
all over the place no one got robbed or
01:14:46
mugged or bearish but hopefully I don't
01:14:47
know if they did any in San Francisco
01:14:48
there's no bear spray incense so that's
01:14:50
good
01:14:54
all right
01:14:56
four of the Sultan of science the
01:14:57
dictator himself and
01:15:00
the
01:15:01
mouth feel
01:15:03
and Rain Man I am the world's greatest
01:15:07
moderator we'll see you next time
01:15:08
everybody love you boys
01:15:12
let your winners ride
01:15:15
Rain Man Davidson
01:15:19
we open source it to the fans and
01:15:22
they've just gone crazy
01:15:23
[Music]
01:15:46
it's like this like sexual tension that
01:15:48
they just need to release
01:15:50
[Music]
01:15:56
where did you get Mercies
01:16:01
[Music]
