The Most Important Competitive Advantage is Trust

Why is it that equity returns are at record-highs – and yet beating the market is harder than ever? Josh Wolfe, co-founder and managing partner at Lux Capital, sits down with Mike Green, chief strategist and portfolio manager at Logica Capital Advisers, to explore this question. After Wolfe discusses a few of his recent successful venture exits, the pair discusses how excessive liquidity is making it harder to deploy capital, a process Green calls “alpha-degradation.” Other topics include Tesla, Bloomberg, and the “first-mover disadvantage.” Filmed on January 23, 2020, in New York.

 

MIKE GREEN: Well, you can tell by the smile on my face, I’m happy to be here.
00:04
I’m sitting down with one of my best friends, Josh Wolfe, so excited to be back here in
00:08
New York and chatting with you.
00:09
Welcome back to Real Vision.
00:10
JOSH WOLFE: Symmetrical happiness.
00:12
Always thrilled to be with you.
00:13
MIKE GREEN: Every time you come on the show, we get to talk about a new success.
00:18
Last time, we talked about the magic.
00:19
You were so excited about CTRL-labs, and pushed you on the idea that maybe you were falling
00:23
in love with something and where’s my sales commission?
00:26
JOSH WOLFE: Man, we got to do these interviews more often.
00:29
It’s been a streak of good luck.
00:31
MIKE GREEN: With CTRL-labs, now, you sold it to Facebook.
00:34
Remind people what CTRL-labs is, remind me why you’re begrudging in the sale.
00:40
What do you think Facebook wants to do with something like this?
00:42
JOSH WOLFE: The thesis behind CTRL-labs which led to three nights of sleepless nights in
00:47
pursuit of this entrepreneur, Thomas Reardon and Patrick Kaifosh, his co-founder, was premised
00:53
with this intercepting phenomenon that I call these two arrows, one of inevitability, and
00:59
one of the perception of the impossibility.
01:01
Inevitability is this directional arrow of progress where it doesn’t necessarily tell
01:04
you who the entrepreneur is or what the company is but there’s this inevitable high probability
01:09
that this is the way that technology is trending.
01:12
The impossibility is when everybody else in the field, peer VCs, just don’t see it.
01:16
They think, oh, that’ll never work.
01:18
It’s impossible.
01:19
Impossibility ends up dictating low prices or less competition and inevitability raises
01:23
our confidence and conviction.
01:25
In this case, the arrow of progress, the inevitable, was the idea that something we call the half-life
01:31
of technology intimacy.
01:33
This buzzword that we coined but basically 50 years ago, you had a giant ENIAC computer,
01:37
you physically went up and pulled some plugs and buttons.
01:39
First half-life, 25 years ago, you have a personal computer and you’re tickling the
01:43
keys, you’re touching the monitor, you’re flipping the power switch on the back.
01:46
12 and a quarter years ago, the next half-life, you have a laptop, physically touching your
01:51
thighs, becoming a little bit more intimate with you, you trade the mouse for a trackpad.
01:54
Six and a quarter years ago, now you’ve got your phone cradled in your hand.
01:57
First thing you touch in the morning, last thing you touch tonight, separated from your
02:00
body only by a thin film of fabric.
02:01
Three and a half years ago, your iWatch.
02:03
24 hours a day on your hand or 18 hours a day.
02:06
A year and a half ago, AirPods with a computer inside for voice recognition.
02:09
That directional arrow of progress, the inevitability was computability was becoming more and more
02:14
intimate and close to you.
02:15
We shared that thesis with a lot of people and then we ended up meeting this researcher
02:18
at one of our other companies, Charles Zucker, who’s a PhD neuroscientist.
02:21
He said you got to meet this guy, Reardon.
02:23
Reardon was the inventor of this technology that we use called Internet Explorer when
02:27
he was at Microsoft as a young guy.
02:30
He was one of 17 kids, 10 biological, seven adopted.
02:34
Just insane family situation.
02:35
Bill Gates taps him, he goes and works at Microsoft for a decade from ’90 to 2000.
02:40
He’s also Bill’s right hand guy during the monopoly DOJ trial.
02:43
Then after making a lot of money and being technologically renowned and reasonably wealthy,
02:49
he does what anybody would do in his shoes, he starts another company, Openwave, which
02:52
ends up creating a mobile browser that we all use and then goes back to college and
02:57
gets a degree in Classics in Latin.
02:58
Then spends the next near decade getting a PhD in Neuroscience, where the thesis he was
03:03
working on is this myoelectric response.
03:05
The idea that you could detect from the surface of your skin, the roughly 15,000 neurons,
03:12
that innervate roughly 14 muscles in your hand, which is important because if I’m typing,
03:16
my brain is telling my fingers to do something.
03:18
If I am turning a knob or switch or a lever or doing anything, my brain is subconsciously
03:24
telling my hand to move.
03:25
He figured out how you could take that signal, detect it and map it to the technological
03:30
devices we use.
03:32
Instead of having to type on a keyboard, instead of having to type a switch, instead of having
03:35
to turn a thermostat, I could effectively either do that motion in free space, or, and
03:40
this was the crazy part, think about making that motion and I can control the devices.
03:44
MIKE GREEN: We talked about this last time.
03:46
You highlighted that even if we go back and look at the Tom Cruise film, Minority Report,
03:51
where he has this dynamic and he’s moving this, he’s wearing gloves, he’s making the
03:55
gestures, et cetera, what Thomas Reardon figured out was that that subconscious thought was
04:00
actually sending a signal that they are then restricting and so the process of learning
04:04
how to type on Mavis Beacon is actually just your brain sending those signals to your fingers,
04:09
your fingers then figuring out how to do it, and you’re training that interface back and
04:12
forth.
04:13
CTRL-labs basically shortcuts that process.
04:15
JOSH WOLFE: In fact, they had a maddening demo, which we may have talked about, where
04:19
you try to hit the button before the device knows that you intend to hit the button and
04:25
you can’t do it.
04:26
MIKE GREEN: That’s amazing.
04:27
JOSH WOLFE: It can detect your intention to fire your muscle and move it before you actually
04:31
move it, which makes that, because if you have 1000 neurons that are activating a single
04:35
muscle, if there’s 100 of them, and if you were to do this now and you think about just
04:38
moving a finger, you get the sensation, this feeling of that finger, and it can detect
04:43
that.
04:44
I became obsessed with the entrepreneur, I lost sleep in pursuit of the deal for three
04:48
nights.
04:49
My wife, when she finally met Reardon was like you, you’re responsible for this household
04:53
duress.
04:55
It was an amazing experience, but it was too short.
04:57
It was too short.
04:58
It was about a year and a half, almost two years and Facebook made an entry on the company,
05:01
which we rejected.
05:02
Zuckerberg came back and made a more persuasive entry.
05:05
These founders, some of whom, unlike Reardon, had never made money before.
05:09
It was very compelling, the amount of capital that Facebook was going to invest into the
05:14
company on an ongoing basis, as well as the liquidity that people were going to get now.
05:19
I wish we would have held it longer.
05:20
I truly think this would have been a $10 billion business instead of something a little under
05:22
a billion dollars, but it was a great outcome for our investors and a thrill to be part
05:26
of what I think is going to be a historic technology that we will all be using.
05:29
MIKE GREEN: Now, I want to come back to this but this also brings up another topic that
05:33
you and I’ve discussed before, which is basically the concentration of capability inside companies.
05:38
What we have very clearly seen are companies like Google and Facebook, Apple, Microsoft,
05:44
have become very acquisitive.
05:45
They have an extraordinarily low cost of capital and have been able to buy these.
05:50
Do you think for Thomas Reardon as he thinks about going inside Facebook, that that creates
05:56
a limitation or that that changes the trajectory of the technology versus its original vision?
06:00
JOSH WOLFE: I’ll give you two answers because I tried the no answer.
06:04
I tried the moral suasion.
06:06
I tried– and this was at a time when Zuck was in front of Congress being lambasted as
06:11
a poster child of technological excess and election meddling.
06:14
I’m trying to make the case, my God, you are going to take a technology that can capture
06:17
your neural intention and give it to Facebook?
06:20
You’re going to spend the next two years in front of Congressional committees yourself.
06:23
I tried emotional suasion with my kids, saying don’t sell, sending videos to the board.
06:28
We don’t trust Facebook.
06:29
I tried financial suasion to do a secondary in capitalized business.
06:33
In the end, I think Reardon actually had a very rational view of this.
06:36
Again, he worked for Bill Gates at Microsoft when Microsoft was arguably the evil empire.
06:42
His view was back then, Bill Gates was a lot more powerful, and Microsoft was a lot more
06:45
powerful than Facebook is today.
06:48
Bill today is considered a president of the world in many ways.
06:51
He’s curing malaria, he’s taking on poverty, he’s doing big global things in a way that
06:55
many of our other elected leaders are not and there’s no reason that you couldn’t imagine
06:59
a decade hence, as hard as it is, that XOC because of Chan Zuckerberg Initiative might
07:04
find a cure to Alzheimer’s or something and suddenly be in that same position.
07:08
I think the idea that there’s going to be the monopolistic power concentrated, he felt
07:13
was overblown.
07:14
The thing that I think was really appealing to him, when you get bit by the bug of taking
07:19
Internet Explorer from one person to a billion users, the idea that you could take this technology
07:26
and its ability to let humans express themselves and control the world around them, from one
07:31
person or in our case, a few dozen people to 8 billion people, which is his goal, you
07:37
want a platform like a Facebook.
07:41
I think that the world will be better off with this scaling and I think it will unleash
07:46
many technologies in almost this moral imperative case to invent so that genius can get unleashed
07:51
and unlocked, that a lot of genius will get unlocked as people start to use this and discover
07:56
what they can create with it.
07:57
Again, I just wish it was still in my hands for a few more years at a much higher multiple.
08:02
MIKE GREEN: I wish it was to actually, but you didn’t try the physical violence approach,
08:06
which might have been my return.
08:10
Last time we got together, we had a similar discussion.
08:12
This time, it was John– that time, it was Johnson and Johnson acquiring the robotic
08:17
surgical company– JOSH WOLFE: Auris for just under 6 billion.
08:22
MIKE GREEN: Yes.
08:23
Exactly.
08:24
I haven’t had a robot operate on me yet.
08:27
Do you keep tabs on how that progress is developing, how that technology is developing and what
08:32
the next stages are when you exit these vehicles or just the bandwidth that that would consume?
08:37
JOSH WOLFE: No, we continue to track that because again, the thesis is sound and the
08:40
idea that the skill of a human in the operating room is the rate limiting factor to be able
08:46
to scale surgeries, particularly if you are a highly skilled surgeon.
08:49
The ones that make the most money, they’re the most sought after for the most sophisticated
08:53
procedures.
08:54
I think that that’s going to start to go away and the sophistication of the surgeon will
08:57
be embedded in the machine.
08:59
We see that across the history of technology where somebody that has manual dexterity,
09:04
that has precision replicability, rather than that being the implicit knowledge of the surgeon
09:10
through many experiences, why should they not be able to effectively download that into
09:15
a machine so that that can scale and reach many?
09:17
MIKE GREEN: It’s interesting.
09:19
Every time you and I talk, occasionally I get shivers down my back.
09:21
I’m reminded of a paper by Mark Koyama describing the innovations that actually led to the creation
09:27
of the Industrial Revolution.
09:29
One of those innovations was actually the transition from needlepoint in designs and
09:34
fabric to printed fabric can go.
09:37
What they did was they introduced variety into the consumption basket of young women
09:41
being courted by young men and that the young men had to enter into the labor force to obtain
09:45
dollars so that they could actually go buy stuff dramatically changed the work habits
09:49
of the world.
09:51
When you say something like that, and you highlight that type of technological development,
09:55
I can only see the number of opportunities that it expands in terms of the capability
10:00
to lower the costs, increase the ability of people to have the surgeries that they might
10:04
otherwise not have in the United States, the developing world, et cetera.
10:09
Really excited to see how that moves forward.
10:12
My next bet for the one though, that’s going to be acquired, is one you just started talking
10:15
about, which is the Variant Pharma.
10:17
JOSH WOLFE: Yeah, this is early.
10:20
It’s consistent with the theme which we follow, which is the decreasing gap between science
10:23
fiction and science fact.
10:25
In this case, the inspiration really came from X-Men and Professor X puts on this helmet
10:31
called Cerebro and is able, from a call crowd of mutants ridiculously, to spot the one in
10:35
a million person who can shoot lasers out of their eyes and conjure fire from their
10:38
fingers.
10:39
He got us thinking, okay, if there’s a one in a billion chance of some super rare phenotype,
10:43
a trait, which has [indiscernible], seven people walking around that have extreme high
10:48
oxygen saturation at high altitudes, they get into an accident, their bones don’t break,
10:53
they have extremely high metabolism, lots of interesting traits, and you just have to
10:57
go and find them.
10:58
Now, the other interesting thing, and here’s where there’s this arbitrage is the vast majority
11:02
of money and effort and research and talent has gone into sequencing, pale, male, stale
11:07
white Europeans, people like us.
11:09
Maybe not the stale part, but very few people have gone to the outer regions of the world
11:15
because those people don’t have money to find these outlier traits in these outlier regions.
11:20
I think that there is an absolute genetic goldmine of these people who are quite literally
11:29
mutants whose traits, and it’s really important because the team here, the fourth or fifth
11:36
person that they hired was a computational geneticist but the second and third was a
11:39
cultural anthropologist and an ethicist because they want to get benefit sharing and have
11:44
these people participate in both the economic profits but also the scientific progress that
11:50
comes from finding these unique individuals.
11:52
You will take a minority mutant population and end up helping find cures for the masses.
11:58
I think it’s an area of medicine in genetics that has never been explored.
12:01
We started the company, filled a team, they have now gone and it’s interesting we call
12:05
them at the moment Treks.
12:07
There’s a lot of thought– T-R-E-K, like a trek.
12:13
Which even they don’t really love that, they killed the idea of a mission, because that
12:17
has a connotation of expertise of your [indiscernible].
12:20
Lots of consideration about that even just like how do we approach these populations
12:24
who are rightly skeptical from having been exploited in the past by big companies or
12:28
explorers, or whatever.
12:29
We don’t call them explorations, we don’t call expedition.
12:32
There’s a lot of thought into the etymology of what we call these– MIKE GREEN: Extreme
12:35
wokeness.
12:36
JOSH WOLFE: Yeah.
12:38
They’ve done a handful of different partnerships, the ones that have been publicly announced,
12:42
I’ve been with them, our recent New Zealand who have really interesting metabolic traits.
12:46
Pakistan, which is a genetic island population and that Pakistan is not an island geographically,
12:51
but there’s a lot of interrelated marriages and cousins marrying cousins.
12:55
Because of that, you get interesting traits, which are likely or more likely to have a
13:00
monogenic condition, a similar gene that goes [indiscernible] and does something.
13:04
Then they just went to Nepal and were with the Sherpas and it was absolutely stunning.
13:09
They brought back some video.
13:10
They have a Nat Geo documentary person that’s going around with them filming their treks.
13:15
These Sherpas are going up with hundred pounds on their back and they’re completely not out
13:20
of breath and our team is dying and there is a genetic predisposition for that.
13:25
MIKE GREEN: You skipped one that actually caught my attention, was just the Samoan populations.
13:30
Very quickly, like from reading about the Varian Pharma website, resistance to diabetes
13:36
in the Samoan population run something like 30%.
13:42
As a result, the statistics was to replicate a Variant Pharma study that required only
13:48
10,000 people in Samoa, would require roughly 10 million, if I got those numbers correct,
13:54
in Europe?
13:55
JOSH WOLFE: Yes.
13:56
Because you already have the traits manifest in the people.
13:57
You’re not searching for all this needle in haystack, you have all the needles.
14:01
MIKE GREEN: It’s just absolutely incredible.
14:03
I don’t think people can fully appreciate the revolution that’s in front of us from
14:08
this standpoint.
14:09
I have a number of friends that are in the biotech space.
14:12
One who is going to join us tonight to drink after this event.
14:17
They are highlighting that there’s just this extraordinary advances coming in the biotech
14:22
space as people approach things from a standpoint of how do we change the way we study it, not
14:27
necessarily how do we change the tools?
14:29
The ’80s and the ’90s were largely about innovations in terms of what are the tools?
14:35
JOSH WOLFE: Sequencing.
14:36
MIKE GREEN: Right, exactly.
14:37
Now, you’re talking about the redesign of the actual process of how do you think about
14:41
the problem?
14:42
JOSH WOLFE: Exactly.
14:43
In fact, the way that we think about it is search is really, arguably the first competitive
14:46
advantage because you’re trying to find and identify these populations, some of which
14:48
they’re not publicly disclosing.
14:49
I’ll share one with you, not where it is but what the traits are.
14:53
Sequence, which is relatively as you point out, because the technological curves in this
14:56
are commodity, then you want to go and basically develop and you’re either going to partner
15:01
with Big Pharma, or in some cases, develop your own clinical trials, and that’s a lot
more money.
It’s really the search of how do you partner and develop a competitive advantage.
Arguably, the most important competitive advantage is trust, with the reputation that you have, how you’re contracting with local researchers, how you’re treating the local population,
15:19
how you’re prioritizing them, how you’re deprioritizing them, if that might be the case and what legacy
15:24
you leave.
15:25
One of the populations, South America, nine people were mean.
15:29
This is like a tiny group of people who have extremely high metabolic rate that spikes
15:35
at night.
15:36
Adaptation to the environment, temperature precipitously drops, they almost have like
15:39
a Heat Shock Protein that raises their body temperature.
15:42
Now, if you think about this, if that proved and I don’t know if it is, but if it proved
15:45
to be a monogenic condition, the gene makes the protein that raises the body temperature
15:48
at night and that was a targetable drug, you’re talking about a pill that you take a night–?
15:53
MIKE GREEN: Makes you skinny.
15:54
JOSH WOLFE: I don’t know if it makes you skinny, but you’re definitely burning fat while you’re
15:56
sleeping and with the obesity epidemic in the US, it would be pretty interesting.
15:59
MIKE GREEN: That really is just fascinating, fascinating.
16:03
It is my bet for the next one by the way.
16:05
I don’t know everything in your portfolio, but that is one that strikes me as just an
16:08
instantaneous.
16:09
JOSH WOLFE: Well, if we keep this pattern going, then the next time we sit down– MIKE
16:12
GREEN: I know, who wants to talk about the sale of Variant Pharma?
16:14
You have been begging them not to sell.
16:16
One of the other companies you talked about that got away from you, and I think you actually
16:18
became involved, Zoox.
16:20
This is in the self-driving space.
16:22
There was big announcement from General Motors or Drive more accurately in the past– JOSH
16:26
WOLFE: Cruise.
16:27
MIKE GREEN: Cruise, absolutely.
16:28
Correct.
16:29
Can you talk a little about what’s going on in that space?
16:30
JOSH WOLFE: Cruise was actually the one that got away from us.
16:31
We had offered Kyle $20 million at a 40 million pre-money so 60 million post and somebody
16:40
else did it, another great VC, at 80 million.
16:43
We thought that was double the price that we were in.
16:45
We were being priced disciplined on this.
16:46
Then we introduced Kyle to GM.
16:49
GM bought them nominally $4 billion, a little bit less.
16:53
Yeah, so that would have been 11x in nine months or something.
16:58
That was a big error of omission in hindsight, and that is an amazing team.
17:03
I think that they are serious.
17:04
I think that Aurora, which is another competitor, is serious.
17:07
I think that Zoox is the most serious, obviously biased, we’re invested but we only full stack
17:13
autonomous vehicle driving highway city in San Francisco, in Nevada, elsewhere, actually
17:20
doing robo taxi rides in Vegas.
17:22
Tesla, as you know, if you follow me on Twitter at all, that is mostly BS when it comes to
17:28
autopilot.
17:29
It’s actually dangerous that this is even on the road but the level of sophistication
17:33
that you have on everything from solid state LiDAR to the software simulation to being
17:37
able to navigate double parked cars, pedestrians, right hand turn, left hand turns, multi-coordination,
17:43
intersections, it’s really complex.
17:45
It’s still going to take a very long time for all of these things to see the light of
17:48
day, billions of dollars will be invested.
17:50
My hand to my heart, I actually think that the first real application of this, which
17:54
is another interesting phenomenon and trend that I think is going to play into cities
17:58
in a big way and it’s going to touch everything from Amazon to the smart home, I think you
18:03
will see self-driving cars first manifest in right hand turn lanes in certain city districts
18:08
where just like bike lanes, you are making multiple rights and doing a traveling salesman
18:14
problem trying to figure out how you navigate from neighborhood to neighborhood, 24 hours
18:18
a day delivering things, not people but things.
18:21
Even Zoox is focused on people and Cruise is focused on people.
18:25
There are some others that I think are thinking about commerce and goods.
18:28
Now, if you think about just the trend, again in a directional arrow progress, we are used
18:32
to our phone as a remote control where you press a button, you get your stuff.
18:36
Amazon Prime has primed us for one-hour delivery or two-hour delivery.
18:40
You press your button, something comes from a warehouse in New Jersey, using New York
18:43
as an example.
18:44
There is an autonomous vehicle that runs a route, gets to New York, has a human in there
18:49
to do the last mile delivery, which eventually might see robots that are people that are
18:52
trying to do that but I think it’s too many variables situations coming out of a vehicle
18:56
into apartment buildings and others that you’ll see that but human will come out like a FedEx
19:00
Delivery person.
19:01
Then the next thing that they will need in this value chain is access control.
19:05
I actually think that you’re going to see a whole suite of industrial blocks and cameras,
19:09
some of which you’re seeing early incarnations of, we have one called Latch Access, Amazon
19:13
bought a camera company called Ring.
19:16
There’s going to be many others in the space, but the ability to give trusted access to
complete strangers to enter your home and treat your cupboard, your medicine cabinet,
your fridge or closets in the same way you might give somebody trusted access to access
or deposit a file into a box or Dropbox or Google Drive.
This idea of access control, I think it’s this next phase.
19:38
From pressing a button on your remote control for the thing you want, to an autonomous vehicle
19:42
delivering at 24 hours a day, to a human entering your home, because you’ve given the trusted
19:46
access.
19:47
Again, this is almost like if I would say 10 years ago, you’re going to get in cars
19:51
with strangers, you’d be like, no way.
19:53
Today, because you mostly trust the brand, and the accountability and the choke point
19:57
of an Uber or Lyft, you get into stranger’s cars.
20:00
I think you’re going to be letting strangers into your home to do this last quarter mile
of cars.
MIKE GREEN: I actually very much agree with that vision, that we are ultimately moving
to an environment in which trust becomes the underlying dynamic.
We’ve talked about this occasionally in the dynamic of crypto or various other things
that trust is becoming a feature that is embedded into the application layer.
JOSH WOLFE: It’s actually the one feature that I’ve joked with Facebook Portal, is totally
absent.
I always said that.
Facebook Portal just got this great design, but it’s missing the one feature which is
trust.
MIKE GREEN: Also my pushback on companies like Uber and Lyft actually has been the days
are going to suffer from a first mover disadvantage.
They have had to address the issue of how do I transport people by “hiring” millions
of people?
The process of shedding those employees is actually going to be far more difficult than
20:49
they think.
20:50
That actually sets up a dynamic in which a company like Zoox or and others who has built
20:55
themselves purposely, not to establish an app and get the app installed on the phone,
20:59
which is actually remarkably easy, although the trust layer becomes an important component
21:02
of it.
21:04
They’ve cut out the labor component that the separation there was going to create a bunch
21:08
of social anxiety and potentially lead to far more enforced regulations.
21:12
We’re already seeing this in California, where they’re being forced to treat them as employees
21:16
as compared to contractors.
21:17
JOSH WOLFE: They’re trying to say, look, we are just layered to match a driver and rider
21:21
and we don’t want to employ or be responsible, but you’re right.
21:24
The regulatory aspect of this is going to apply pressure to labor.
21:26
MIKE GREEN: Yeah, I think that’s ultimately right.
21:28
Now, you mentioned this idea that we’re going to take things, so I understand what you’re
21:32
saying.
21:33
I wonder if the challenge there is the person who has to be there to take the delivery.
21:38
JOSH WOLFE: I think that there will be a designated– and I’ve actually seen privately some of the
21:45
apps that some of these companies have, that are almost like an augmented reality thing
21:49
that when, let’s say a UPS delivery person, or if it was an Amazon Prime delivery person,
21:54
they look at their phone, they’re given a provision code to enter the apartment.
21:59
It takes a picture so it knows who’s there, knows what they’ve entered with.
22:03
They enter and they see this augmented reality thing of where they should– it might literally
22:07
be X marks the spot that they’re looking on the phone, put this here, or might be when
22:12
they go over the fridge, put this here.
22:14
They literally use that as a layer, which itself is another interesting thing I want
22:18
to talk about but the simulation layer to place things in certain places.
22:22
It may not be that you’re trusting them to come into your bedroom yet, for your bathroom
22:25
yet.
22:26
People will trust an Amazon Prime to come in and load their fridge and put away all
22:30
their groceries.
22:31
We get fresh, direct delivered on a weekly basis, and what do they do?
22:34
They come into our home, and they lay down all the bags.
22:37
Then my wife and I and the kids put everything away.
22:40
There’s no reason that I wouldn’t pay another $5 during that delivery fee to have them put
22:44
everything away for us in a consistent predictable way.
22:46
MIKE GREEN: Yeah, that consistent predictable way is actually a great distinction.
22:50
We have people who help maintain our home and when they unload the dishwasher, I’m constantly
22:55
saying, where the hell did they put this?
22:58
The ability to actually have that enforced in a consistent manner, I completely agree
23:01
with you, and I actually share your– JOSH WOLFE: We’ve read your head style, everything
23:03
in its right place.
23:04
MIKE GREEN: Everything in its right place, which sounds terrible in a lot of ways.
23:08
We all see those homes with the– I think the condo stuff is what it’s called, Marie
23:13
Kondo, where everything’s labeled and it’s got its own specific place.
23:16
I think you and I look at that, like, oh my God.
23:18
JOSH WOLFE: That stresses me out.
23:19
MIKE GREEN: That would drive me insane.
23:21
There is a component of predictability that you want to life hack, expend the minimal
23:26
amount of energy saying, hey, where’s the rolling pin today?
23:29
Where is the measuring cup?
23:31
When I think about that question that we started to address in terms of this self-driving capability
23:38
and you referred to the Tesla solution as being dangerous, which I share your concerns.
23:46
The challenge of self-driving, as I understand it, there’s certainly as it is presented is
23:51
this idea of miles on the ground.
23:53
How many miles do you have to travel to solve every possible permutation?
23:59
That seems like such a flawed model to me it, what’s your reaction to that?
24:04
JOSH WOLFE: I think it’s going to be a combination.
24:05
It’s going to be a combination of simulation where you’re trying to predict every scenario
24:08
from a human walking out, three humans walking out, old person, young person, ball coming
24:12
across, horse, dogs, different weather situations, potholes.
24:16
Why?
24:17
Because in any model, including what I would argue in human consciousness, you have this
24:20
prediction, memory prediction framework.
24:22
The computer basically has a memory based on either simulation or reality of what the
24:26
thing ought to be.
24:28
Then it experiences in real time what that reality is and maps it.
24:31
If it confirms to what the memory is, then the prediction, there’s no surprise.
24:35
This is the same thing I think that we experience in human consciousness.
24:38
I see you, I see if you see my funky shoes, you predict, hey, that’s Josh.
24:43
If you were looking at somebody else, and you saw this funky shoes and you– hey, that’s
24:47
Josh, but then it was Steve, you’d be like, oh, surprised.
24:49
Then you have this emotional salience that updates your prior, updates your model.
24:53
Computers are the same way.
24:54
These simulations in the self-driving cars and robots are the same way.
24:57
There’s a prior, whether that is through experience for programming, and the programming could
25:02
be from simulation.
25:03
Then there’s the actual experience.
25:05
Then when those map and conform, there’s no surprise, you don’t have to update the model.
25:09
If you think about all of the permutations that occur in reality, it’s infinitely complex.
25:14
You’re going to need a mix of models that are mapping onto the real world, and then
25:18
the ability to quickly discern.
25:20
In Zoox’s case, when you watch some of these videos online of the situations that they’re
25:25
able to navigate, in many of the cases, there’s no programming of those situations.
25:29
Having a double parked car, followed by a biker coming out of nowhere and a pedestrian,
25:34
every one of those things has to be almost consciously recognizing objects, and then
25:38
classifying those objects as humans, as bikes, as cars, as static objects.
25:43
Then intuiting what an intention might be and making a prediction about that.
25:47
It’s super complex, it’s going to be years of iteration.
25:50
I do think that these things are still very dangerous.
25:53
The idea of putting cars out on the road and calling them autopilot and giving people this
25:57
false sense of confidence is super dangerous.
26:00
It’s irresponsible.
26:01
It’s an accounting trick being used to book revenue and pull it forward, but this will
26:09
happen.
26:10
We will be in autonomous vehicles.
26:11
MIKE GREEN: It’s interesting, actually, because what you described as a very complex system
26:18
has features that I think are actually that overlay with some of the work that I’m doing
26:22
and I think you know this but I’m involved in some– my first machine learning projects
26:25
and there’s this issue of tractability, what can actually program.
26:30
Ironically, the transition to self-driving is the most difficult.
26:35
Because you have the unpredictability of human beings that may or may not conform to the
26:39
laws, that may or may not conform to these components.
26:41
Balls will always be there, children are always run out into the street.
26:46
The car driving itself, somebody double parking and behaving in a manner that’s not consistent,
26:50
having no mechanism to communicate that to you other than the very rudimentary signals
26:54
that come from brake lights, hazard signals, turn signals, et cetera, that’s ultimately
26:59
going to give way to a much more tractable problem as you have more and more machine-driven
27:05
vehicles on the road.
27:06
JOSH WOLFE: Well, especially as vehicle to vehicle protocols start to communicate the
27:10
intentions with each other.
27:12
Humans have this where if you and I are walking on the sidewalk in New York, and we come into
27:15
each other, you have that awkward Larry David like moment where you’re going left, I’m going
27:19
right then we make a mistake and the coordination problem.
27:23
Coordination is a function of both prediction and communication.
27:25
I do agree with you that you will have all kinds of layers of protocols where self-driving
27:30
cars and other robot systems, autonomous systems will have this coordination communication
27:35
protocol.
27:36
MIKE GREEN: Well, and we tend to take for granted the human’s capability to do that.
27:42
We all have the experience of making eye contact with a pedestrian crossing the crosswalk and–
27:47
JOSH WOLFE: You do a little dance and– MIKE GREEN: Well, even a car driver.
27:50
It’s just all it requires is that eye contact that allows people to be aware that you’ve
27:53
actually seen them then you can proceed under conditions.
27:57
It would be the rare assumption that you would make eye contact with the driver, enter the
28:01
crosswalk, and they would run you over.
28:04
We are very much programmed.
28:06
It’s built into our capabilities to understand when somebody has actually seen us.
28:10
That flash of recognition of this is a human being like it’s very much built in there.
28:14
We tend to take that for granted.
28:16
Machines don’t have that capability yet, or they’re developing it, as you’re highlighting
28:20
the Zoox, but once they have, then they’ll have their own native protocol as well that
28:24
makes this problem so much easier.
28:26
JOSH WOLFE: That, by the way, is one of the hallmarks just generally of human intelligence
28:29
and relevant entirely markets, which is I know that you know that I know.
28:33
Then it’s how many layers is that?
28:36
One of my kids I think, is very savvy.
28:38
She knows that I know that she knows that she’s like four layers whereas one of my other
28:41
kids is like, I know.
28:43
MIKE GREEN: Well, since we’re now crossing over to the virtual world, you introduce your
28:46
Twitter handle.
28:48
My character is the Vizzini from The Princess Bride.
28:51
I always focus on that– JOSH WOLFE: Inconceivable.
28:54
MIKE GREEN: Inconceivable, but the most important part for me of that character is actually
28:57
the iocane powder, where it’s a game being played but people are actually not aware with
29:01
it.
29:02
He believes he’s outsmarting somebody, but he doesn’t actually know the game that’s being
29:05
played involves poison in both cups.
29:07
It’s like immunity condition.
29:09
Which brings us to actually a discussion of a game that I’ve had with a number of people,
29:12
and one of our mutual friends, Mike Mobizen.
29:16
We’re going to transition into discussing public markets for a second here and Mike
29:19
has written several books and he’s talked often about the dynamic of skill development
29:24
in markets and how markets are becoming more challenging.
29:27
The alpha degradation that we’re seeing in public markets he attributes to an increase
29:32
in skill that is being accumulated in the market.
29:35
I think Michael actually misunderstands the game that’s being played.
29:39
He uses the poker analogy.
29:41
He says, we saw this online, there was a game of poker.
29:45
As poker moved online, there’s an explosion of players.
29:48
Initially, they were a bunch of Patsy’s that decided that they had been good at their local
29:52
games, got online and the pros were able to basically fleece these players and take their
29:56
money away.
29:57
Eventually, you’re left to the game in which only pros are playing pros.
30:00
JOSH WOLFE: Skill level has leveled up and a lot of the variance is more attributed to
30:06
luck.
30:07
MIKE GREEN: Correct.
30:08
The stock market is the extension of that analogy for him.
30:12
I think it’s a flawed analogy, and I wanted to get your reaction to that.
30:16
The way I look at it is poker is a fixed game.
30:20
It’s ergodic in nature.
30:21
We know at every point that the number of cards is going to be unchanged, there’s the
30:24
probability of a hand is going to be unchanged.
30:26
The configuration of the river or what you have in your hand can influence your perception
30:30
of those probabilities, but the odds really don’t change.
30:34
Stock markets or any form of market for that matter, is nonergodic.
30:40
We have no knowledge about what the distribution of the possible configurations are in the
30:43
future.
30:45
I actually think that he’s improperly framing the question, I think he’s using an ergodic
30:49
game to make an analogy to a non-ergodic game, in which the idea of skill development really
30:55
can’t exist.
30:56
JOSH WOLFE: I think Michael would agree that markets are complex adaptive systems.
31:01
There’s punctuated periods where there is a game, there’s a recognition of how that
31:05
game is played, then people level up to that game.
31:08
Then at some point, they may not be aware that the game is changing, but I think during
31:11
the period where people understand what the game is, the skill level is rising and so
31:17
the variation between investors is increasingly attributed to luck, but then, like you say,
31:22
the undulating landscape changes and suddenly the game that you thought you were playing,
31:26
you’re no longer playing.
31:27
You see this all the time.
31:29
Hedge fund guys before ’07 didn’t care about macro at all.
31:33
They were just bottoms up stock pickers, long short equity, short always overvalued, be
31:38
long, it was undervalued.
31:39
All of a sudden, everybody came, all the quarter letters, while the top value guys were suddenly
31:44
talking about macro.
31:45
They were pledging, oh, well, we didn’t because the game changed, macro mattered.
31:50
I think that at any given point in time, now you could argue it’s people that are getting
31:53
smart to the structure of the market as you are about passive indexation and inflows and
31:58
incremental flows and how that is changing the game.
32:03
I think Michael’s point is markets are complex adaptive systems, people can get wise to what
32:09
the game is.
32:11
They may not realize that the game has changed, but as long as there’s a general agreement
32:14
about the game, skill level rises and variance is more attributed to luck.
32:17
MIKE GREEN: That’s fascinating insight in terms of the way I’ve been thinking about
32:21
it, because it resonates with me, a discussion I had recently with a legendary investor from
32:25
the 2000s who I’m not going to name, said to me, Mike, I was meant to invest in the
32:30
2000s.
32:31
The game that is being played today, I don’t understand.
32:35
I’m at this point too old and too rich to try to figure it out entirely.
32:39
It’s really interesting to think about it in that context.
32:42
Because it becomes a question of are those who have been so successful and accumulated
32:46
the– JOSH WOLFE: Half listening and half thinking about who I think it is, and I think
32:48
I know who I think it is.
32:50
MIKE GREEN: Do you think you know what I think I know?
32:53
JOSH WOLFE: Inconceivable.
32:55
MIKE GREEN: Exactly.
32:56
That actually becomes a really interesting question, though, because it then raises the
33:01
issue of have we allowed that concentration of wealth, have we allowed that to blind ourselves
33:05
to the potential that the game has completely changed, which certainly what my research
33:08
would lead, that the market is no longer the market as people think about it?
33:12
There are exploitable phenomenon, but it requires a complete rethinking of how you approach
33:16
the problems.
33:18
As phrased in those terms, I completely agree.
33:21
I think that will still lead me to say that it’s actually not skill development.
33:25
That would be a cyclical phenomenon that would show up slightly differently, the tools that
33:30
were developed for how we manage markets, how we think about them were largely created
33:34
in that time period.
33:36
The assumptions that we make in the use of those tools, things like alpha, beta, Sharpe,
33:41
et cetera, I think are actually improperly suited for the current environment but that
33:45
brings us then into the general discussion of public markets, which is, let’s talk about
33:49
how you see the world of public market’s valuations, and how you think about how that is either
33:54
influencing or being influenced by the private markets as you primarily participate in.
33:58
JOSH WOLFE: I just had a dinner with also a very prominent and maybe the most prominent
34:02
CIO in the endowment world.
34:04
I asked him, do you see risks about liquidity and illiquidity in both public markets and
34:11
private markets?
34:12
In the public markets, is it a function of passive indexation and inflows and whether
34:17
it’s Fed, algos, momentum, whatever it is, dollar and by everything rising, what happens
34:22
if there are withdrawals and everything comes down?
34:25
His view on that was with passive roughly 20% of market structure today?
34:31
MIKE GREEN: It’s about 35%.
34:32
JOSH WOLFE: Okay with but I think you’ve made the point that something like 80% or 90% of
34:36
the incremental dollars are going into passive?
34:37
MIKE GREEN: Far more than 100%.
34:39
JOSH WOLFE: Okay.
34:40
His view was when it got to like 90%, he would be worried and I recalled and actually raised
34:45
you as an example, I said, I have a smart friend who mathematically has shown actually
34:48
when it gets around where we are now, 35%, I thought it was for, that’s when you get
34:52
the structural runaway risk on liquidity side of passive indexation.
34:57
That was on the public side.
34:58
On the private side, he has done something interesting, which was, he never wants his
35:04
illiquid portfolio to be more than 50% of the endowment.
35:06
What he’s done because of who he is, has gone to the underlying GPs and said, give me your
35:10
hand to the heart mark of what you think this is worth, not the fast 157 mark based on accounting
35:17
basis.
35:18
Historically, when he did this, in 2000 and in 2007, or ’08, both saving them from substantial
35:25
drawdowns during the crisis.
35:27
It was somewhere between 25% and 30% discount to what any given company that ended up exiting
35:32
in that year, proved to exit at.
35:35
There was a level of conservatism that the managers expressed because they valued the
35:39
relationship with this particular CIO.
35:41
They said they were going to be super honest and ethical about what their hand to the heart
35:44
was because they wanted to continue to be hired as a manager.
35:48
Today, he says it’s between zero and 10%, so elevated valuations on the private equity
35:54
side.
35:55
If you look at the total amount of PE money today– MIKE GREEN: Well, just to be clear
35:57
what you’re saying.
35:58
What they are saying is they see no discount to where they’ve marked it in the event that
36:02
they would have to sell under distress type conditions?
36:04
JOSH WOLFE: Correct.
36:05
MIKE GREEN: That’s astonishing.
36:06
JOSH WOLFE: $1.5 trillion of PE assets are sitting on the sidelines right now, so there’s
36:10
an enormous amount of dry powder.
36:12
Now if you’re a public market investor, maybe that’s a positive thing.
36:14
MIKE GREEN: Well, wait a second.
36:15
Again, I want to be clear, when you say PE assets are sitting on the sideline, this is
36:19
the cash that has been raised but not yet deployed?
36:22
JOSH WOLFE: Correct, by buyout funds and venture funds.
36:24
Venture is a mouse to the elephant here, but 1.5 trillion globally, 800 billion of that
36:30
is North America.
36:31
That’s about two times the level of what it was 10, 11 years ago going into even then
36:36
a PE crisis 2007, ’08, ’09.
36:38
VC itself has raised about 50 billion across 250 funds in each of the last two years, which
36:44
is four times what it was 10 years ago.
36:46
Again, we’ve talked about this in the past, but the number one thing that is predictive
36:49
of returns is not the BCG McKinsey, whatever.
36:53
It’s the amount of capital that’s flooding in.
36:54
The amount of capital that’s flooding in is undeniably high.
36:57
You look at some of the surveys for LPs, they will say 80% of them feel unequipped in a
37:02
downturn that they’re well-positioned, yet two thirds of them are continuing to increase
37:06
their allocation to PE notwithstanding the numbers that I just gave you.
37:11
When there was a downturn, and you had this denominator effect, again, 10 years ago, two
37:16
thirds of those LPs were not making any new commitments to new funds on a private equity
37:22
side.
37:23
They anticipate that they’re not quite there, but they can’t help but continue to allocate
37:26
and I think that’s setting up a problem.
37:28
You had public markets to your point up 32%, 33%?
37:31
MIKE GREEN: 31% last year.
37:33
JOSH WOLFE: Denominator effect.
37:35
If that were to continue, great, everybody’s portfolio looks good.
37:37
You got high marks on these private equity for the other people that are not doing this
37:40
more conservatively.
37:41
If the public markets were to decline, you have a denominator effect, what are people
37:45
going to do with this PE portfolio?
37:48
There’s going to be a race for secondaries and liquidity.
37:50
I think the secondary guys in the next few years are going to be really well-poised,
37:53
they might be sitting on cash for longer than people expect.
37:57
On the public market side, there’s really interesting thing that Jim Grant had recently
38:04
shown, PE on the S&P; is 21, 22.
38:07
The PE is of course market cap weighted on the S&P; 500, but if you market cap weight
38:14
the E part, instead of just aggregating and averaging as it is, you actually have a 32
38:18
times multiple.
38:20
MIKE GREEN: Is the difference– the way it’s calculated on the public indices is what’s
38:23
called the harmonic median.
38:24
Effectively, you are going through and it’s almost like ignoring the outliers.
38:29
JOSH WOLFE: Because each of the cases aren’t you’re taking a multiple where you’re taking
38:33
the PE of Apple times the weighting of Apple and the PE of GE and the weighting of GE and
38:37
just basically aggregating that.
38:39
MIKE GREEN: Not quite.
38:40
The details, we can walk through another point, so it’s the calculation is actually what’s
38:46
called the harmonic median.
38:47
If actually, you’re going through the 50th percentile type dynamic.
38:50
You’re 100% right.
38:53
The other point that I would raise is that we’ve never seen a larger gap between GAAP,
38:58
G-A-A-P, and the “operating earnings” that make up that 21, 22 PE that you’re referring
39:03
to.
39:04
JOSH WOLFE: Well, and on top of this, you have something like 95% of companies that
39:05
are now reporting non-GAAP earnings.
They’re making up funny metrics.
39:10
Now, we saw this in WeWork on the private side, when you had community adjusted EBITDA.
39:14
Tesla is like ground zero of like ridiculous terms like, what are delivery sales?
39:19
What does that actually mean?
39:21
There’s a lot of companies that are just using funny language because in a bull market, people
39:23
are less scrutinizing.
39:25
I think that that’s really a ripe situation where you have lots of non-GAAP accounting
terms that are signifiers of risk.
You have S&P; growing revenue 3%, 4%, 2%, 3%, 4%?
MIKE GREEN: Somewhere in that range, yeah.
39:36
On per share basis, slightly higher, but yeah.
39:40
JOSH WOLFE: Most of the 31%, 32% return over the past year was mostly for multiple expansion
39:44
because I’ve had– MIKE GREEN: More than 100% actually flat to slightly negative earnings.
39:47
JOSH WOLFE: For the past four quarters.
39:49
People are paying higher multiples for lower or negative growth.
39:52
One interesting thing and this is a forming hypothesis that is a little bit more wishful
39:56
thinking from the venture side.
39:59
If we are at peak earnings, and people have been talking about peak earnings forever,
40:02
but if we’re at peak earnings, and corporates are looking and saying, okay, how do I actually
40:06
maintain margins at a time where 60% of COGS is labor, I think that there will be an increasing
40:12
turn to technology.
40:14
Now, I don’t know the timeframe.
40:15
That’s not going to be like, okay, let’s quickly implement the system and lay off a bunch of
40:18
people and maintain our margins again.
40:20
I do think that some of the things that we’re investing in, whether it’s metal 3D printing
40:23
or certain technological systems for efficiency, you have the opportunity for at least margin
40:30
stability against a situation where revenues are declining, prices are coming down.
40:36
There’s another question about what happens to input costs?
40:39
Well, a lot of smart people are– and I don’t know if you agree with this or not, but weak
40:44
dollar, long commodities, long gold, higher input prices, smaller margins.
40:49
MIKE GREEN: Not on that account.
40:50
JOSH WOLFE: You’re in the higher dollar camp?
40:52
MIKE GREEN: I tend to think that we’re going to have a higher dollar simply because the
40:56
global system is ultimately set up on a collateral basis and everything we’re describing in terms
41:02
of high valuations and increasing risk is actually touching that collateral dynamic.
41:07
We’re concerned about the risks that the collateral contracts.
41:10
If the collateral contracts and the debt actually becomes increasingly due, which means the
41:13
dollar is under demand.
41:16
I fall into the higher dollar camp, but– JOSH WOLFE: Do you have a view on margin pressure?
41:21
MIKE GREEN: I think the margin pressure is likely to come actually from a couple of different
41:25
areas.
41:26
We’ve seen unequivocally the margin pressure.
41:29
We’re allowing the system to increasingly run with tight labor, whether that shows up
41:35
in wages or not is heavily influenced by the composition.
41:40
When you have lots of old people, wages don’t go up all that much because they tend not
41:45
to ask for raises and that tends to conceal the relatively rapid wage gains that we’re
41:50
seeing in the younger generation.
41:51
There’s a couple of good reports that I could send you on that stuff.
41:54
JOSH WOLFE: I do wonder if the wage gains are happening taking into account the amount
41:58
of new company formation.
42:00
When you have a flood of capital into any sector, if there’s a lot of company formation,
42:04
those companies are competing with each other for talent and so wages are rising.
42:08
I do wonder if some of that capital inflow starts to abate, that you would actually see
42:13
more people consolidating, more supply of talent going into fewer companies, and wage
42:17
suppression.
42:18
MIKE GREEN: What we’re seeing is actually more on the opposite side.
42:20
The rates cut– while you’re very active in the process of business formation, I actually
42:24
would suggest that many of the statistics that we receive from the Bureau of Labor Statistics,
42:28
the BLS, are inflated by the assumptions around business formation.
42:33
Actually, the data suggests that business formation has fallen dramatically, not your
42:37
type of business– JOSH WOLFE: The mom and pop shops and independent contractors.
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MIKE GREEN: That type of business formation has taken an extraordinary hit.
42:46
That, in turn, actually weirdly increases the potential for this to behave in a convex
42:54
fashion because what you’re beginning to see, and you’re seeing this very clearly in the
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data is as the economy has slowed in this last cycle, we have seen overtime hours decline,
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we have seen weekly hours decline, which has pressured some of the headline numbers in
43:07
terms of the average weekly compensation that people are receiving, you’re beginning to
43:10
see this show up and stress in terms of credit cards, et cetera.
43:13
The early signs of some weakness are there.
43:16
The primary dynamic that we’re actually seeing is this issue of hoarding of labor.
43:22
Companies are seeing decreased utilization of their labor but because of the headline,
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finding new employees is so hard, they’re resisting with every fiber of their being
43:32
letting go employees that they currently have.
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We haven’t yet seen that turn and we may not.
43:37
It’s very hard to know how that plays, but the data actually suggested it’s heading in
43:41
the opposite direction of the way that your hypothesis– JOSH WOLFE: That wages will continue
43:44
to rise.
43:45
MIKE GREEN: We are at an inflection point in which that could continue to tighten.
43:48
That’s one of the risks that the Federal Reserve may have created with reinforcing the cycle
43:53
with the interest rate cuts.
43:55
Only the future can actually tell us what actually ends up happening.
43:58
JOSH WOLFE: Demographics.
44:00
Let me ask you, because I always love your views.
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MIKE GREEN: I’m interviewing you.
44:02
JOSH WOLFE: But your answers inform me.
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MIKE GREEN: I understand that.
44:07
Nobody is interested in what I have to say on this topic.
44:09
We’ll talk offline on the demographics.
44:11
I want to touch though on a topic that demographics does influence that you and I both care fairly
44:15
passionately about, which is politics, the election that’s approaching.
44:19
You and I have publicly sparred, you have supported Bloomberg as a candidate, he wouldn’t
44:25
be among my last choices.
44:28
I’m interested to hear how you’re thinking about it.
44:30
I’m sure– JOSH WOLFE: Mine is very simple.
44:32
These are debates that I used to get into with Lauren, my wife, that I never really
44:38
thought the president matter.
44:39
I thought that all you needed was a good figurehead, who mostly was the better looking person that
44:44
conveyed all the evolutionary psychology appeals of symmetry and dominance and that stuff.
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I think, in this case, I want the candidate and this is something that Bill Gates who
44:56
I serve on a board with said to my friend, Andrew Sorkin at the [indiscernible] conference
45:00
earlier this year, I just want the most professional person that really resonated with me, I just
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want the most professional person.
45:09
The rancor that I see, the debasement of the office that I see with the current individual,
45:16
maybe I have this false nostalgia of pining for somebody that can set a level of behavior
45:22
and that is presidential, one that I want my kids to look up to and say like that is
45:27
the way to behave.
45:28
That is the way to make decisions.
45:29
That’s the way under pressure or criticism to react.
45:33
My preference for Bloomberg is really in actually thinking that unlike Trump, he’s actually
a billionaire and he can’t be bought and that the appeal that he has is more about legacy
than short term gratification.
45:45
I find him to be the most professional and the most rational, but tell me your counter
45:53
thesis.
45:54
MIKE GREEN: My counter thesis would be almost saying exactly what you’re saying, which is
he perceives himself as the most professional but doesn’t perceive himself as a statesman.
Someone who’s meant to represent.
You can actually see it in what he is describing is his approach to the central office.
He’s going to open it up, turn it into a bullpen, he’s going to manage it.
He’s going to manage the US economy like he’s managed Bloomberg.
That, unfortunately, is not the job of the president.
My fear is, is that he very clearly doesn’t know that.
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JOSH WOLFE: Do you think with the management of New York, which is a vibrant, complex,
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diverse economy, that he did a bad job?
46:31
MIKE GREEN: I don’t I think that he did a bad job, but I think that he was handed a
46:34
gift.
46:36
The inflation that we saw through the 1990s created a revenue stream.
46:39
We, unfortunately, are going to run out of time here.
46:42
We didn’t get to talk about China, which you’ve also become very vocal on.
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You and I are both involved there.
46:47
Let’s treat that for another time.
46:49
JOSH WOLFE: I will say, to your credit, this was something I was hyper bullish on in the
46:53
idea that there were two Chinas, an old China and a new China.
46:56
You would say, Josh, you’re wrong, you’re missing this.
46:59
I got to tell you, you changed my mind because I’ve come to see the evils and the skepticism
47:04
and there’s an idealistic view about what China could be and there’s a realist view
47:09
of what it is today, and I become much more in your camp.
47:12
It’s a great example of something I’ve changed my mind on because of you.
47:14
MIKE GREEN: To your credit, you absolutely have done that.
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I’m very excited to see that.
47:19
My guess is we’ll get the same way with Bloomberg.
47:20
Hope we don’t actually see the need for that to happen once he’s in office.
47:24
Josh, as always, such an amazing time spending with you.
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The time flies by and we’ve run out of it now.
47:29
Look forward to seeing you again.
47:32
Hopefully within a year.
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JOSH WOLFE: Thank you, Mike.
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Always good.
47:35
MIKE GREEN: Take care, Josh.