Latest / UK Column Radio / All Big Events Are Fabricated

Transcript

All Big Events Are Fabricated · UK Column Radio

0:38
Speaker 1: Nick Hudson, thank you for joining me in the trenches.
0:42
Speaker 2: Good to be back again, German. It's been quite a
0:44
Speaker 2: long time, hasn't it.
0:45
Speaker 1: Nick, You are probably one of my most popular guests,
0:50
Speaker 1: going all the way back to twenty twenty.
0:53
Speaker 2: Well, I'm pleased to hear it, and a little bit surprised.
0:57
Speaker 1: Well, it's got nothing to do with what you have
0:58
Speaker 1: to say. It's all to do with your looks, your
1:00
Speaker 1: very good charming books.
1:02
Speaker 2: Nick, can't tell anything about that kind.
1:05
Speaker 3: Of Nick, let's start at the start. What is your background?
1:15
Speaker 4: Well, I mean by profession, I'm a private equity investor.
1:18
Speaker 4: I'd run a private equity fund, and so I've been
1:21
Speaker 4: I've been in the world of finance since I left school,
1:24
Speaker 4: really studying and then working in an insurance environment, and
1:29
Speaker 4: then in corporate finance, and then the last twenty something
1:31
Speaker 4: years in private equity, which I absolutely love and it
1:36
Speaker 4: turns out to be very well suited to me. So
1:39
Speaker 4: I've got no plans to do anything else because I keep
1:42
Speaker 4: on learning. And that's the most important thing in life
1:45
Speaker 4: is to have that feeling that your curiosity is continually
1:48
Speaker 4: provoked by the thing that you're doing. And I'm very
1:51
Speaker 4: pleased to report that after yeah, to more than two
1:54
Speaker 4: decades in private equity, my performance isn't dropping off and
1:57
Speaker 4: my interest isn't dropping off. So that's why I'm going
2:00
Speaker 4: to carry on doing, adding to the sort of range
2:04
Speaker 4: of things that I look at and the range of
2:07
Speaker 4: tools that I deploy. And yeah, that was the setting.
2:12
Speaker 4: I guess you're asking because it's kind of strange that
2:15
Speaker 4: this private equity guy ends up studying COVID. But what
2:20
Speaker 4: private equity is characterized by is that the practitioners are
2:24
Speaker 4: always on the wrong side of a very profound asymmetry
2:27
Speaker 4: of information. When you're looking at buying a business, the
2:31
Speaker 4: private business, the owners of that business typically know a
2:35
Speaker 4: hell of a lot about it, and you know nothing
2:38
Speaker 4: when you walk in the room, and so most of
2:41
Speaker 4: the art is rarely around trying to resolve or make
2:47
Speaker 4: irrelevant that information asymmetry. And so what you should be
2:53
Speaker 4: hearing in all of this is that private equity investors
2:56
Speaker 4: are basically professional skeptics. We're there to detect BS, and
3:06
Speaker 4: when the COVID phenomenon hit, I detected a lot of
3:09
Speaker 4: BS very quickly and scratched. And if you looked at
3:12
Speaker 4: any data at all, you realize that the media narrative
3:15
Speaker 4: was complete and utter nonsense, and that it was propped
3:18
Speaker 4: up by state and security apparatus and this whole pandemic
3:24
Speaker 4: preparedness industry which exists to fleece us on the suspicion
3:29
Speaker 4: of a bug or pathogen doing the arms. And so
3:35
Speaker 4: very quickly I just realized that wall to all, we
3:37
Speaker 4: were dealing with absolute lunacy. To this day now, I
3:43
Speaker 4: don't believe there was a risk additive pathogen spreading at all.
3:47
Speaker 4: All of these stories about gain of function and Wuhan
3:50
Speaker 4: Institute of Virology, it's all nonsense, complete nonsense. There is
3:55
Speaker 4: no such thing as successful gain of function research. I
3:58
Speaker 4: have no doubt that money is thrown at that kind
4:01
Speaker 4: of problem to try and sort of almost create the
4:05
Speaker 4: sense that there might actually be a gainer function going on.
4:08
Speaker 4: But that's complete nonsense. It's even the science that is
4:12
Speaker 4: used supposedly in these gainer function experiments rules out the
4:18
Speaker 4: plausibility of gainer function research.
4:21
Speaker 2: So's we're dealing with.
4:24
Speaker 4: A vast complex of liars in COVID, and the complex
4:28
Speaker 4: of lies, of course extends way beyond COVID.
4:32
Speaker 1: But now let's go back to twenty nineteen. Right, we
4:35
Speaker 1: were all doing our thing. Life was going on as
4:39
Speaker 1: per normal, and suddenly twenty twenty came about, and more
4:44
Speaker 1: specifically March, and then suddenly everything just fell apart. Now
4:48
Speaker 1: the data started coming out. I think it was from
4:51
Speaker 1: Johns Hopkins initially, and the mainstream media, I'm sorry.
4:56
Speaker 2: Faked data from Johns Hopkins Universities.
4:59
Speaker 3: None the list.
5:00
Speaker 1: That was the data that was now being distributed across
5:02
Speaker 1: the mainstream media. I remember stumbling across something called Panda,
5:08
Speaker 1: which is of course how you and I became friends.
5:11
Speaker 1: But you had the same data, right, but you were
5:15
Speaker 1: concluding something different.
5:17
Speaker 3: Take me through that.
5:18
Speaker 4: Well, well, it is I didn't have the same data
5:22
Speaker 4: in the sense that I was looking at far more
5:24
Speaker 4: than just the Johns Hopkins University data. If you had
5:27
Speaker 4: the Johns Hopkins University as the entirety of your universe,
5:31
Speaker 4: you were being misled. I mean, even that data wasn't
5:35
Speaker 4: compatible with a global emergency. But it was really more
5:43
Speaker 4: a case of, yeah, well there's a story of the
5:45
Speaker 4: Diamond Princess. Okay, Well, as soon as the data is available,
5:48
Speaker 4: let's take a look at that. A couple of elderly
5:51
Speaker 4: people were on a cruise ship, and in the ensuing
5:56
Speaker 4: months some of them died. Well, that happens on every crew.
6:00
Speaker 4: You know, cruisers are populated by typically obese and retired people,
6:05
Speaker 4: So nothing surprising there.
6:08
Speaker 2: And in fact, when I look back on it, I
6:11
Speaker 2: made a.
6:11
Speaker 4: Mistake there because it caused me to believe for a
6:14
Speaker 4: while that there might be.
6:15
Speaker 2: Some kind of pathogen involved.
6:18
Speaker 4: That there could just be a you know, circulating virus
6:20
Speaker 4: or something like that, And I no longer believe that
6:22
Speaker 4: to have been the case.
6:25
Speaker 2: But it was care from that.
6:27
Speaker 4: Data that we weren't dealing with anything catastrophic, and very
6:30
Speaker 4: early on, you know, there was just this absence of
6:32
Speaker 4: young people dying, and the people who were dying were
6:35
Speaker 4: being put on ventilators, which is a very non stated,
6:37
Speaker 4: non standard treatment for influenza like illnesses. So basically the
6:42
Speaker 4: hospitals were dispatching them. The hospitals became killing grounds, killing fields,
6:48
Speaker 4: and of course all the catastrophic silliness around cloth masks,
6:53
Speaker 4: and in South Africa we had bands on open toad
6:56
Speaker 4: shoes and selling of hot chicken and all of these
7:00
Speaker 4: this complete silliness emanating from the minds of such catastrophically
7:07
Speaker 4: bigoted and corrupt people like Professor Krim and the National
7:15
Speaker 4: Institute of Communicable Diseases and the South African what were
7:20
Speaker 4: they called s a m D South African Medical Research
7:24
Speaker 4: council s a m r C. All these junked up
7:28
Speaker 4: pseudo scientists pumping out endless streams of nonsense validating this
7:35
Speaker 4: whole construct.
7:36
Speaker 2: And that's just in South Africa.
7:37
Speaker 4: I haven't even got on to the rest of the
7:38
Speaker 4: world and the true story behind the fact.
7:41
Speaker 2: J s U Joe, sorry, j h U data.
7:46
Speaker 4: I think, you know, let's let's not go there, because
7:49
Speaker 4: it's quite a long and detail story.
7:51
Speaker 2: But yeah, it was.
7:52
Speaker 4: And once you've seen through the wall of BS, you
7:56
Speaker 4: can't unsee, you know, And so it gets you into
8:01
Speaker 4: a kind of permanent location.
8:03
Speaker 2: You got.
8:03
Speaker 4: You and I have had a number of conversations about
8:05
Speaker 4: those sort of red pilling moments and the dangers of
8:09
Speaker 4: becoming a black built individual who becomes too cynical the
8:13
Speaker 4: reality of anything, or the validity of any kind of
8:17
Speaker 4: story or theory. But you know, I think and I
8:20
Speaker 4: think we made good points when we had those chats,
8:23
Speaker 4: But the reality is that we are living in a
8:26
Speaker 4: sea of lies.
8:30
Speaker 2: Funny enough, long before COVID.
8:32
Speaker 4: When I was a young corporate finance and analyst in
8:36
Speaker 4: New York on Wall Street, I began to see how
8:42
Speaker 4: complexes of liars can be sustained and be believed by
8:46
Speaker 4: the practice practitioners in In corporate finance, there's often a
8:54
Speaker 4: challenge in assessing the.
8:55
Speaker 2: Value of a company.
8:56
Speaker 4: You know, the corporate finance here is trying to persuade
8:59
Speaker 4: his corporate clas that and a particular acquisition makes sense.
9:02
Speaker 4: If you can get them to agree with that, then
9:04
Speaker 4: the deal happens, and the corporate financier earns enormous fees
9:09
Speaker 4: it can be several percent of the transaction value, and
9:12
Speaker 4: from that their bonuses get paid. And there are a
9:18
Speaker 4: number of sort of trappings of these corporate finance valuation exercises,
9:23
Speaker 4: little methods and techniques that they use to try and
9:27
Speaker 4: create the appearance of the valuation being scientific or methodical
9:36
Speaker 4: or valid in some way. And one of them is
9:40
Speaker 4: this thing called a comparables analysis. And when you do
9:44
Speaker 4: a comparables analysis, what you do is you form what's
9:46
Speaker 4: called a peer set. You go and find companies that,
9:50
Speaker 4: according to you, are similar to the one you're valuing,
9:55
Speaker 4: and you go and build the set and create various
9:59
Speaker 4: metric about the businesses. So for example, it could be
10:03
Speaker 4: their price earnings ratios that you're averaging in this peer set.
10:08
Speaker 4: And round about that time, enormous databases were becoming available
10:13
Speaker 4: that you could crunch through tens of thousands of companies
10:18
Speaker 4: with a quarterly history going back ten years, so you'd
10:21
Speaker 4: have sort of forty financial data points a full price
10:27
Speaker 4: history for ten thousand companies in America and more overseas.
10:32
Speaker 4: And I decided to run a test of this comparable's technique.
10:38
Speaker 4: So what I would do was I would build this
10:40
Speaker 4: entire database of pricing metrics, so it was enormous database,
10:45
Speaker 4: and I would throw in the standard industrial classification code
10:50
Speaker 4: as a variable and see whether it had any value
10:54
Speaker 4: in terms of informing you about the price earnings ratio
11:00
Speaker 4: or the enterprise value to Eberiti ratio. I looked at
11:04
Speaker 4: lots of different evaluation metrics. It was a very carefully
11:07
Speaker 4: conducted exercise, with proper data scrubbing and lots of.
11:13
Speaker 2: Correction of unusual items and all this kind.
11:15
Speaker 4: Of hard work, the gruntwork that goes into such kind
11:18
Speaker 4: of such kinds of analysis. And their answer I came
11:22
Speaker 4: up with was that in the industrial classification of a business,
11:27
Speaker 4: which is the main sort of approach used to characterize
11:30
Speaker 4: a peer set, was information free. There was no there
11:36
Speaker 4: was no value in trying to compare a company to
11:40
Speaker 4: other companies in the same industries if you're trying to
11:42
Speaker 4: conclude that it's undervalued or overvalued, or whatever the case
11:46
Speaker 4: may be. And when I showed this information to corporate
11:51
Speaker 4: finance practitioners that they almost didn't want to look at it.
11:54
Speaker 4: They just didn't didn't want to didn't want that to
11:57
Speaker 4: be the answer because every single pat which book that
12:00
Speaker 4: they'd ever done, we had to have a nice little
12:02
Speaker 4: table or several tables with a lovely little industry based
12:07
Speaker 4: comparables analysis, and the chart's got sexier and sexier over time.
12:12
Speaker 4: They got better ways of representing the data and making
12:16
Speaker 4: it look impressive and so on. I didn't want to
12:19
Speaker 4: hear the answer that this technique was actually codswallop from
12:23
Speaker 4: beginning to end. And I began to see the whole
12:27
Speaker 4: corporate finance industry that way, that there were really hoods
12:31
Speaker 4: trying to persuade their clients to do transactions that they
12:34
Speaker 4: shouldn't be doing, and in the process stripping out enormous
12:40
Speaker 4: amounts of fees. So I guess that was a bit
12:45
Speaker 4: of a framing. And I only recently began to connect
12:49
Speaker 4: that little exercise that I'd done and the kind of
12:52
Speaker 4: skepticism that it built in me with my thinking as
12:57
Speaker 4: it developed over the years, becoming a private equity transact,
13:00
Speaker 4: and then midway well three quarters of the way through
13:03
Speaker 4: that career, being outraged by the Lockdown's.
13:10
Speaker 1: Yeah, but the thing, Nick, is that created the context
13:15
Speaker 1: for where your mind was at in twenty twenty. How
13:19
Speaker 1: you immediately started being suspicious and skeptical of what was
13:22
Speaker 1: going on.
13:23
Speaker 3: Now.
13:23
Speaker 1: I don't have the same background as you, and I
13:26
Speaker 1: mean I effectively talk to people and draw pictures, right,
13:29
Speaker 1: I don't have that analytical mind. But yet, as Matias
13:33
Speaker 1: Desmet said, around thirty percent of people at any given
13:36
Speaker 1: time will go against the sort of mass formation slash convention.
13:44
Speaker 3: Why do you think that is?
13:46
Speaker 4: Yeah, I don't know if it's as high as that
13:47
Speaker 4: I would if I had to hazard a guess, it
13:49
Speaker 4: would be something like ten percent my actual I don't
13:55
Speaker 4: know for sure why it is, but I have a conjecture.
13:58
Speaker 4: I think that if the whole of society was made
14:03
Speaker 4: up of mavericks who push against the grain, life would
14:07
Speaker 4: be chaotic. You need to allow institutions to develop and
14:13
Speaker 4: be stable over time and across geography and so on.
14:17
Speaker 4: You need to allow.
14:21
Speaker 2: Things to recrete.
14:24
Speaker 4: And yet the mavericks have an important function because they
14:27
Speaker 4: are the people who generate innovations, solve problems resulting in improvements.
14:33
Speaker 4: You know, doing the same thing can't solve problems, it
14:37
Speaker 4: can't result in improvement.
14:38
Speaker 2: So you need people who are saying.
14:40
Speaker 4: Hold on a minute, that's there's a better way of
14:42
Speaker 4: doing this, or you guys have been telling me this
14:45
Speaker 4: for fifty years, and it's not.
14:48
Speaker 2: There's no benefit, there's no result.
14:50
Speaker 4: You know, people calling the bs, but you can't have
14:54
Speaker 4: too many of them. So I actually see it through
14:56
Speaker 4: the kind of social evolutionary length that you can't have
15:04
Speaker 4: the rebels for all of us, but you have to
15:07
Speaker 4: have But what's funny forming people?
15:10
Speaker 1: Yes, But what's funny is that you and I are
15:13
Speaker 1: basically suggesting that we are mavericks. But everybody likes to
15:17
Speaker 1: think that they are mavericks. Right, It's it's never it's
15:20
Speaker 1: it's always them and not the other person. But it's
15:22
Speaker 1: it's obviously quite evident that looking at Panda's data, you
15:27
Speaker 1: were going against the grain. And if I look at
15:29
Speaker 1: my own, my own discussions in early twenty twenty, I
15:33
Speaker 1: was going against what everybody around me was saying, and
15:37
Speaker 1: they thought that I was cuckoo. And I think probably
15:39
Speaker 1: people around you thought you're cuckoo. I mean, if I
15:40
Speaker 1: look at the media and how they were going after you,
15:43
Speaker 1: for sure.
15:44
Speaker 4: Not just the media, the intelligence community, you know it
15:47
Speaker 4: was it was a very widespread attack. Yeah, we had
15:52
Speaker 4: intelligence assets teaming up with South African journalists to write
15:58
Speaker 4: some campaigns based on completely crecated information. And when you
16:03
Speaker 4: notice that kind of thing happening to you, you know
16:05
Speaker 4: again it's it's sort of if this was just a
16:08
Speaker 4: pandemic and people were responding to it, that kind of
16:11
Speaker 4: stuff wouldn't be happening. There's clearly a sye op going on.
16:15
Speaker 4: What is a like a creature likedimah met doing teaming
16:20
Speaker 4: up with complete cret called Rebecca what's.
16:24
Speaker 2: Her name from The Daily Maverick Davis?
16:27
Speaker 4: Yes, Rebecca Davis, Becky young, Becky little Yeah, teams up
16:31
Speaker 4: with like a known intelligence asset to write a complete
16:36
Speaker 4: chunk smell article for the Daily Maverick. Why is the
16:40
Speaker 4: Daily Maverick editorial allowing that to happen. Well, it's because
16:43
Speaker 4: they're not independent, and they are a controlled entity, and
16:47
Speaker 4: they are funded by people who are promoting this nonsense.
16:51
Speaker 4: You know, that's the reality. And when you start seeing
16:54
Speaker 4: through that and you realize, oh well, then pretty much
16:56
Speaker 4: have to be suspicious of anything that they write. And
16:59
Speaker 4: sure enough, the Ukraine exercise comes along and one of
17:02
Speaker 4: their funders gets op ed to write about, you know,
17:06
Speaker 4: George Soros, about the Putin bad you know, basically the
17:12
Speaker 4: sud notion that there was no backdrop to the Ukrainian conflict.
17:16
Speaker 4: One day Putin just woke up on the wrong side
17:18
Speaker 4: of bed and went marching across the border. That's the
17:21
Speaker 4: whole story of Ukraine Soros's article, you know, just completely
17:25
Speaker 4: ignoring them are done, and the long history of NATO
17:30
Speaker 4: and US intervention, the Color Revolution, the top, the deposing
17:35
Speaker 4: of a democratically elected president, and the creation of American
17:41
Speaker 4: citizens as cabinet ministers in Ukraine overnight, like they had
17:46
Speaker 4: them become Ukrainian citizens and install them in the government.
17:50
Speaker 2: You know, things like that.
17:51
Speaker 4: We're just completely left out, And so you realize that
17:55
Speaker 4: these these are not independent media organizations were thinking breathing
17:59
Speaker 4: editors there, these guys are are hacks, and they're they're
18:04
Speaker 4: bribed or or funded into or you know, or controlled
18:09
Speaker 4: by other means even into just writing nonsense about major events.
18:15
Speaker 4: But what I came to realize eventually was actually, if
18:17
Speaker 4: a large noise is being made about anything in the media,
18:20
Speaker 4: then the story is false.
18:21
Speaker 3: Yeah. Yeah, it's funny.
18:24
Speaker 2: Because there are no really big stories.
18:26
Speaker 1: Yes, but what's funny is that you're tapping into a
18:29
Speaker 1: very interesting psychological phenomenon, which Jessica how could recently see
18:33
Speaker 1: them very funnily said on my show. And she said
18:37
Speaker 1: in twenty twenty, she started questioning the COVID event and
18:42
Speaker 1: very quickly thereafter was asking did Man land on the
18:44
Speaker 1: moon for sure?
18:48
Speaker 4: And a hundred other questions.
18:49
Speaker 2: You know, I mean, this morning, I was reading a paper.
18:52
Speaker 4: You know, I tend to get my reading in before
18:55
Speaker 4: everybody else wakes up, And I was reading a paper
18:58
Speaker 4: that had done what's called out of sample testing of
19:01
Speaker 4: climate models. And a comment I've made frequently is that
19:06
Speaker 4: the lack of art of sample testing of climate models
19:08
Speaker 4: actually tells you everything you need to know that the
19:12
Speaker 4: practitioners are either stupid or fraudulent or both, okay, because
19:16
Speaker 4: you don't build models and fail to do art of
19:19
Speaker 4: sample testing. And let me just maybe unpack that a
19:22
Speaker 4: little bit. So you've got some data and you're trying
19:25
Speaker 4: to build a model to describe the future of the
19:28
Speaker 4: trajectory of that data, and the data includes many variables.
19:34
Speaker 4: It will be temperature readings over here, there and everywhere.
19:37
Speaker 4: It will be measures of cloud cover or days of
19:41
Speaker 4: sunlight at a location, it will be humidity. You might
19:45
Speaker 4: have a whole ton of variables that you now must
19:49
Speaker 4: try and fit a model too, with the idea being
19:53
Speaker 4: that your model would then be able to predict the future.
19:57
Speaker 4: And you can make claims about how we all going
19:59
Speaker 4: to die from overheating, and the water is all going
20:02
Speaker 4: to boil, and the askcafs are all going to melt,
20:05
Speaker 4: and the polar bears are going to die, and we
20:07
Speaker 4: can say all those things because we've got our model
20:09
Speaker 4: and it's predicting the future. But when you've got lots
20:14
Speaker 4: of parameters in the model, you can get what's called
20:17
Speaker 4: spurious fit, where statistically the model is a close match
20:23
Speaker 4: to the historical data. The data that you are fitting too,
20:28
Speaker 4: but performs very poorly in what's called out of sample testing,
20:32
Speaker 4: where you take values for those parameters or variables, values
20:40
Speaker 4: for the variables that are not included in the time
20:43
Speaker 4: series that you're using to fit the data, and you
20:46
Speaker 4: test whether the model predicted those values.
20:51
Speaker 2: If you were to.
20:52
Speaker 4: Build a model in a hedge fund setting or something
20:55
Speaker 4: and you fail to do out of sample testing, you
20:57
Speaker 4: would be fired on the spot because employing such a
21:01
Speaker 4: model that hadn't been tested in an art of sample
21:03
Speaker 4: testing fashion is you've got a very real risk of
21:07
Speaker 4: just absolutely drilling it losing tons of money. So you know,
21:13
Speaker 4: where modeling has an economic importance, it's always performed with
21:17
Speaker 4: art of sample testing. And I've just noticed that these
21:20
Speaker 4: climate models don't. They don't do it, and so now
21:22
Speaker 4: somebody's come along and done it. And guess what. The
21:25
Speaker 4: models are hopeless. They're scandalously bad. It's embarrassing and we
21:31
Speaker 4: can see it all around us if you open your
21:33
Speaker 4: eyes and ignore the media headlights. Nobody is getting hot. No,
21:37
Speaker 4: there's no polar melting happening. The weather isn't changing, we're
21:41
Speaker 4: not getting hurricanes and Cape Town, you know.
21:44
Speaker 1: But you're making a very profound point though, about the
21:48
Speaker 1: uselessness of models. Let's just bring it back to the
21:54
Speaker 1: COVID event, right, Everything was based on models, was it not.
22:00
Speaker 4: Yeah, And they were so easy to pull apart, I mean,
22:03
Speaker 4: and people think there's some kind of mystique.
22:05
Speaker 2: These were very simple models.
22:07
Speaker 4: If you'd done like one year of mathematics and statistics
22:10
Speaker 4: after university, oh sorry, after school.
22:13
Speaker 2: I'm not even sure that would really be necessary. If
22:15
Speaker 2: you had a good school.
22:16
Speaker 4: And you did mathematics, you know, concentrated on mathematics in
22:20
Speaker 4: high school, you'd be able to understand these models.
22:22
Speaker 2: There's no there's no mistique to them.
22:24
Speaker 4: They're called si R models or susceptible infected recovered models
22:29
Speaker 4: s IR.
22:30
Speaker 2: Very simple little.
22:34
Speaker 4: Equations that describe them men but almost impossible to fit
22:39
Speaker 4: to data real time. And so it's very important to
22:45
Speaker 4: check quickly, like and you should be able to check
22:47
Speaker 4: within days that they're performing well against your out of
22:54
Speaker 4: sample data. And none of that was done. And I've
22:57
Speaker 4: had conversations with the people who build these model What
23:00
Speaker 4: you realize is to a man, or in the case
23:04
Speaker 4: of COVID, very often to a woman, they are just
23:08
Speaker 4: exceptionally clueless individuals. They are not practical. They don't live
23:12
Speaker 4: in the real world. Their jobs do not require a
23:16
Speaker 4: tether to reality. There's no market test or environmental test
23:21
Speaker 4: of the quality of the work they do. They do
23:24
Speaker 4: is what Daniel David Graeber called bullshit jobs, and so
23:29
Speaker 4: they become completely disconnected from reality and.
23:32
Speaker 2: Their impulse to.
23:36
Speaker 4: According or corresponding with reality is extremely low.
23:41
Speaker 2: And that's the reality.
23:43
Speaker 4: Most people, I would say, in corporate and bureaucratic jobs
23:47
Speaker 4: are of that nature where their ability to connect to
23:52
Speaker 4: reality has been so profoundly eroded for so long that
23:57
Speaker 4: they become these virtually incompetent human beings. If they lost
24:03
Speaker 4: their jobs and had to do something in the real world,
24:05
Speaker 4: they would die, they would fail. They can only do
24:09
Speaker 4: the bullshit job. And it's completely phenomenal how pervasive that
24:15
Speaker 4: has become.
24:18
Speaker 1: But now there's a philosophical aspect of this, because modeling
24:23
Speaker 1: is something that's very common to say climate climate change science,
24:27
Speaker 1: which is disconnected from reality very obviously, virology is much
24:32
Speaker 1: the same, and I guess there are other fields that
24:35
Speaker 1: probably fall into that same type of critique.
24:39
Speaker 4: But it's not just models, it's theories in general. Because
24:43
Speaker 4: models are like a upset of theories.
24:45
Speaker 1: You know, what I'm trying to understand is, Nick, what
24:48
Speaker 1: I'm trying to understand is why then do you think
24:50
Speaker 1: because someone will always ask the question, yes, but are
24:53
Speaker 1: you saying that those scientists and others working within those
24:58
Speaker 1: model paradigm are they deliberately lying?
25:03
Speaker 2: Let's go.
25:04
Speaker 4: I'll tell you the best way to answer that question
25:05
Speaker 4: is to is to pick up on what I was saying.
25:07
Speaker 2: It's not just models, it's theories.
25:09
Speaker 4: Because you know, in a way we can say, okay,
25:11
Speaker 4: let's look at critical theory. Okay, because that's a sort
25:14
Speaker 4: of that's a model. Let me explain why it's a model.
25:17
Speaker 2: Okay. The model works like this.
25:20
Speaker 4: There are these terrible creatures called white males, and they
25:25
Speaker 4: discriminate against everybody who's not male or not white, and
25:31
Speaker 4: they pay them terrible salaries when they employ them. They
25:34
Speaker 4: discriminate in the workplace, and they don't listen to them,
25:38
Speaker 4: and they don't promote them, and so there's this awful depression.
25:43
Speaker 4: So the model is white male bad.
25:47
Speaker 2: Okay.
25:49
Speaker 4: But you know, in my thirty years of working in
25:54
Speaker 4: and around corporations, I have never seen an instance of
25:59
Speaker 4: somebody being passed over because of their gender or their
26:03
Speaker 4: color of their skin. And I don't expect to see
26:05
Speaker 4: one because I know as a professional shareholder and business
26:09
Speaker 4: owner that when skills are available, you grab them with
26:13
Speaker 4: both hands. You do not care what package they come in,
26:16
Speaker 4: because the art of solving problems in the business world
26:22
Speaker 4: is very difficult and the prize is large. If you
26:26
Speaker 4: can solve those problems, you make lots of money. No
26:29
Speaker 4: saying entrepreneur or businessman is going to give up the
26:33
Speaker 4: chance to solve problems just because he doesn't want women
26:39
Speaker 4: in the workplace or black people in the workplace, or
26:41
Speaker 4: something like that. It's a nonsense constructor does not conform
26:44
Speaker 4: with reality. And sure enough, when you do multi dimensional
26:49
Speaker 4: analysis of women's salaries versus men's, there's no difference. The
26:53
Speaker 4: differences that exist in the simple averages are explained by
26:57
Speaker 4: such phenomena as childbirth, taking women out of the word workforce,
27:00
Speaker 4: that kind of thing. And also it's got a lot
27:02
Speaker 4: to do with choice of work. You know, women don't
27:06
Speaker 4: tend to apply to become garbage collectors and so, and
27:11
Speaker 4: garbage collectors in the collection is an unpleasant job, so
27:14
Speaker 4: it actually gets paid a bit more than other types
27:16
Speaker 4: of manual.
27:16
Speaker 3: Labor, you know, that kind of working in the minds.
27:19
Speaker 4: Yes, mine work is anything that's got risk and danger
27:21
Speaker 4: and unpleasantness to it, you know. So you find that
27:26
Speaker 4: their model, this idea white man bad, is faulty, it
27:31
Speaker 4: doesn't comport with reality. But there's a whole industry around
27:37
Speaker 4: that white man bad model, you know, a critical theory,
27:40
Speaker 4: critical race theory, critical gender theory. It informs the entire
27:44
Speaker 4: garbage construct of DEI diversity, diversity, exclusivity and sorry, what
27:50
Speaker 4: is it? Diversity, equity and inclusivity, you know. And people
27:56
Speaker 4: have bullshit jobs doing DEI type work. They've become experts
28:01
Speaker 4: in the you know, and those are completely useless, incompetent
28:06
Speaker 4: individuals who would not survive in the real world of
28:09
Speaker 4: commerce commerce, or in the real world of the environment
28:14
Speaker 4: for much longer than a couple of days.
28:16
Speaker 2: They would just die basically. No.
28:20
Speaker 1: But what I'm what I'm trying to get to is
28:27
Speaker 1: you'll you'll have let's say, a virologist or you'll have
28:31
Speaker 1: a meteorologist, right, and they've been working in their field
28:35
Speaker 1: for ten, fifteen years whatever, and the virologist might genuinely
28:40
Speaker 1: believe that Sarz CoV two was this deadly pathogen that
28:44
Speaker 1: was spreading around the world and is sitting in a
28:46
Speaker 1: laboratory doing a highly specific, highly specialized line of work.
28:52
Speaker 1: And or you might have the meteorologist who genuinely thinks
28:55
Speaker 1: that we're all going to drown, you know, because of
28:58
Speaker 1: ice caps melting. Now are they are they just genuinely
29:05
Speaker 1: decent people doing their best, or are they deliberately lying?
29:08
Speaker 3: That's kind of what I'm trying to get to.
29:10
Speaker 4: Well, I suppose that, you know, let's say they're average decency.
29:14
Speaker 2: Let's give them, Just give them that.
29:16
Speaker 4: But the problem is that in their fields, there's no
29:19
Speaker 4: hard feedback loop. See in my field, if I get
29:23
Speaker 4: something wrong, I lose my investors' money and my fund
29:26
Speaker 4: gets closed. Simple, it happens very quickly. I've got the
29:30
Speaker 4: gap between making a couple of major errors and no
29:34
Speaker 4: longer having a career is short. It's a few years,
29:36
Speaker 4: you know, and then I'm unemployable. So I've got a
29:40
Speaker 4: real hard feedback loop. I'm either good at this or
29:44
Speaker 4: I'm not, and if I'm not, I'm not allowed to
29:46
Speaker 4: do it. But if you're a climate scientist and you
29:50
Speaker 4: come up with a codswallop forecast.
29:52
Speaker 2: There's no feedback loop.
29:54
Speaker 4: People don't start losing money and firing you if you
29:57
Speaker 4: get the temperature prediction for twenty twenty seven wrong. Nobody's
30:02
Speaker 4: even going to remember that you made it. And so
30:05
Speaker 4: it's the absence of feedback. And it's the same with virology.
30:10
Speaker 4: You know, these guys, we've analyzed the genetic tree of
30:13
Speaker 4: these viruses and the omega variant is closely related to
30:18
Speaker 4: the beta two point one variant. Well, if that turns
30:22
Speaker 4: out to be wrong, what's the consequence? You know, the guys,
30:24
Speaker 4: you loses money. You don't have investors beating down your
30:29
Speaker 4: door to throw you out the top floor window. It's
30:34
Speaker 4: just as they live in these highly research kind of
30:39
Speaker 4: artificial worlds where tethering to reality is just simply not
30:45
Speaker 4: part of the job requirement.
30:47
Speaker 3: Yeah.
30:47
Speaker 1: So as nasim tleb are calls that they don't have
30:51
Speaker 1: skin in the game.
30:54
Speaker 4: Yes, that's one way of putting it, skin interpreter's money. Yeah, skin,
30:58
Speaker 4: there's no skin in the game. They unsuffer the ironic.
31:00
Speaker 3: He got it very wrong, didn't he.
31:02
Speaker 2: Weeah, he got it terribly wrong.
31:03
Speaker 4: And that in itself is interesting because I went and
31:06
Speaker 4: the scratched around in the background to see where he
31:10
Speaker 4: went wrong.
31:10
Speaker 2: And it's quite interesting.
31:12
Speaker 4: There's quite a difnite story about how these public intellectuals
31:16
Speaker 4: were actually primed for years prior to the COVID event
31:20
Speaker 4: to see epidemics as a material risk or pandemics as
31:24
Speaker 4: a threat. It was a sophisticated operation, very expensive operation,
31:29
Speaker 4: with a man by the name of John Brockman at
31:32
Speaker 4: the center of it, and some very creepy characters hanging
31:36
Speaker 4: around the edges, by the way, pedophiles and strange, strange.
31:40
Speaker 1: Very strange nick Listening to you now, it makes one
31:46
Speaker 1: very nervous about the future of science and medicine and
31:54
Speaker 1: everything else because of the inherent flaw of modeling. If
32:01
Speaker 1: you zoom out a little bit, what does this mean?
32:03
Speaker 1: It is sort of on a on a on a
32:06
Speaker 1: on a what is on on an existential level?
32:14
Speaker 4: Look, good science will be done under conditions of freedom, always,
32:21
Speaker 4: because better technologies afford the possibility of making money and
32:28
Speaker 4: doing doing more with less. So as long as there
32:32
Speaker 4: is freedom, science will be fine. It just just, you know,
32:36
Speaker 4: I don't know why people expected to take place in
32:38
Speaker 4: universities where there's no skin in the game. And the
32:41
Speaker 4: incentive structures all messed up and so on. It always
32:44
Speaker 4: kind of surprises me that people have, you know, but universities,
32:47
Speaker 4: that's where science has done.
32:48
Speaker 2: No.
32:48
Speaker 4: Actually, I think most of the universe, most of the
32:50
Speaker 4: science has done in small corporations trying to innovate, trying
32:56
Speaker 4: to come up with a new gadget or gizmo, or
32:58
Speaker 4: to solve some engineering or data problem. Those people are
33:05
Speaker 4: highly incentivized and motivated to get the solution, and that's
33:08
Speaker 4: where the science is done. Professors don't really do science,
33:12
Speaker 4: you know, they do, they also have bullshit jobs, and
33:18
Speaker 4: so there this kind of mystique around the university, which
33:20
Speaker 4: I don't think is well deserved. And I'm not at
33:25
Speaker 4: all surprised that the I don't look at it and
33:28
Speaker 4: say the enterprise of science at university is deteriorated. I'd
33:31
Speaker 4: look at it and said, probably never was much. So
33:36
Speaker 4: I don't think that that presents an existential threat. Bad
33:39
Speaker 4: bad thinking, bad theories, bad models have.
33:42
Speaker 2: Always been with us.
33:44
Speaker 4: It's just when you allow non commercial organizations and institutions
33:51
Speaker 4: to become so large, you know, so these massive organizations
33:54
Speaker 4: with no skin in the game and no feedback loop.
33:56
Speaker 4: Government is an example institute of viral you would be
34:00
Speaker 4: an example that anybody doing climate sciences. And these are
34:04
Speaker 4: all examples of mega institutions that are not tethered to reality.
34:11
Speaker 2: And that's the dangerous thing is the centralization and.
34:16
Speaker 4: Concentration of resources in these untethered organizations.
34:22
Speaker 2: So what you want to do is smash them.
34:24
Speaker 4: People that some people say to me, oh, you need
34:27
Speaker 4: to reform these institutions. No, the problem is the centralized structure.
34:32
Speaker 4: There is no such thing as reforming a centralized organization.
34:35
Speaker 4: You need to dismantle it. You need to absolutely obliterate
34:38
Speaker 4: it and put safeguards into prevent it from happening.
34:41
Speaker 2: Again. You need to have.
34:43
Speaker 4: Ironclad laws that prevent governments from becoming bigger than five
34:46
Speaker 4: percent of GDP, never mind fifty five percent. You need
34:51
Speaker 4: to prevent the ownership of money or the control of
34:55
Speaker 4: money by the state. That shouldn't happen. You need to
34:59
Speaker 4: build in freedom into the sort of the fabric of reality.
35:04
Speaker 4: That that's the defense, that's the technology that's required. And
35:07
Speaker 4: as long as you do that, you'll have a good
35:11
Speaker 4: science and bad science, and the good science will tend
35:13
Speaker 4: to survive instead of the bad science. What we have
35:16
Speaker 4: is a world where bad science can persist because the
35:19
Speaker 4: institutions aren't teted from reality. I have no need to
35:23
Speaker 4: test their theories against the real world.
35:27
Speaker 1: But what you're kind of alluding to also is the
35:32
Speaker 1: cascade of false dichotomies, because the COVID event has led
35:37
Speaker 1: me to question philosophical aspects or ideological aspects of how
35:41
Speaker 1: we view the world. You and I've spoken about, for example,
35:43
Speaker 1: communism being a city term, capitalism being a silly term
35:46
Speaker 1: left right.
35:47
Speaker 3: All these terms are very empty.
35:50
Speaker 2: Very much so.
35:52
Speaker 4: Yes, And you know they also they create this sort
35:55
Speaker 4: of this notion of a kind of pure model that
36:00
Speaker 4: that can we can consider as being plausibly applied in
36:05
Speaker 4: the real world, you know, the ownership of the commanding
36:10
Speaker 4: heights by the proletariat, the blue collar workers or whatever,
36:15
Speaker 4: or in capitalism, the unfettered free market. And of course
36:22
Speaker 4: none of these things can ever exist. You're always going
36:24
Speaker 4: to have mixed models, and ideally, I would say you
36:28
Speaker 4: want to have multiple sort of structures being tested at
36:31
Speaker 4: different levels of annularity all the time. Also, an idea
36:37
Speaker 4: filtered from TALEB was this thing that is quite reasonable
36:41
Speaker 4: to be a communist at home and a radical libertarian.
36:45
Speaker 4: At the level of the state, the size of the
36:48
Speaker 4: organizing unit is very very important consideration when it gets
36:53
Speaker 4: down to working out how to govern it. So you know,
36:56
Speaker 4: my house is proudly communist. Everybody in the house gets
36:59
Speaker 4: the same level of health care. Even though I produce
37:03
Speaker 4: or one hundred percent of the income, I get the
37:05
Speaker 4: same health care as everybody else.
37:07
Speaker 2: So the house is communist. But when I step out
37:12
Speaker 2: of my house, I lose my.
37:13
Speaker 4: Communism very quickly because I'm dealing with a bigger level
37:16
Speaker 4: of scale, and then I become a libertarian quite fast.
37:19
Speaker 4: I don't like it when I get told that I
37:21
Speaker 4: need to say certain things or do certain things. I
37:25
Speaker 4: recently retired, resigned in protest from the Institutent Faculty of
37:29
Speaker 4: Actuaries in the UK. After there, I went through a
37:33
Speaker 4: five year Kangaroo court disciplinary process and I got the
37:38
Speaker 4: expected result, which is a very poorly reasoned legal judgment
37:41
Speaker 4: totally unconnected to the original charges that were brought against me,
37:45
Speaker 4: which were misinformation and killing people by telling them not
37:48
Speaker 4: to get vaccinated and things like that.
37:50
Speaker 2: Quite childish altogether, and.
37:52
Speaker 4: It's surely sure enough after five years they eventually found
37:55
Speaker 4: me guilty of offensive speech for calling Professor Karimer a
38:02
Speaker 4: bigoted nut job, you know, her public figure. So so
38:06
Speaker 4: much for freedom of speech these days, And yeah, isn't
38:09
Speaker 4: that just so pathetic? And guess what the lead the
38:13
Speaker 4: lead tribunal of the chair of the tribunal was, was
38:19
Speaker 4: an expert in de I that was called. So she
38:25
Speaker 4: saw me coming and she gave the game away several
38:27
Speaker 4: times where she you know that she'd made a presumption
38:30
Speaker 4: of guilt. It was very clear they were going to
38:32
Speaker 4: they were going to get me on something, no matter
38:34
Speaker 4: how silly and frivolous.
38:35
Speaker 2: It was.
38:37
Speaker 1: An undercurrent of your commentary over the years which I
38:42
Speaker 1: have very much enjoyed is the idea of complex models.
38:48
Speaker 1: The world is highly nuanced and complex. And why I'm
38:51
Speaker 1: bringing that up is because the polar opposite would be
38:56
Speaker 1: somebody like David and Ike, who I like, but he
38:59
Speaker 1: simplifies every thing down to there's a small group of
39:02
Speaker 1: people right at the top and everything is well oiled
39:04
Speaker 1: and slick. And your position is no, because we are
39:09
Speaker 1: we are human beings, we are complex, and we are
39:11
Speaker 1: competing with one another all the time.
39:14
Speaker 4: Yes, I would say that there are competing elites everywhere,
39:18
Speaker 4: and this idea that there's like a Council of three
39:21
Speaker 4: hundred or thirty, or an Illuminati, or a Jesuit a
39:24
Speaker 4: High Court or Free Masonic whatever.
39:28
Speaker 2: These may all be.
39:31
Speaker 4: Real structures that are being spoken of, but they're they're there.
39:35
Speaker 2: The idea that.
39:36
Speaker 4: One community of dark lords is in the shadows quietly
39:43
Speaker 4: manipulating the entire planet in all societies, I think is false.
39:48
Speaker 4: That's not to say that there isn't a great deal
39:50
Speaker 4: of a manipulation that takes place. I mean it's it's
39:53
Speaker 4: become really clear to me right now in South Africa
39:56
Speaker 4: that there's a third force trying to stoke up racial animosity. Yes,
40:02
Speaker 4: you see these bots and you become very sensitive to bots,
40:06
Speaker 4: like you detect them quickly after you've been through four
40:09
Speaker 4: years of facing COVID bots. And I can just see
40:14
Speaker 4: that somebody is paying somebody full time work to come
40:20
Speaker 4: up and provoke racial animosity on social media accounts, and
40:25
Speaker 4: they're very active accounts. These are not people who have
40:27
Speaker 4: real lives and jobs. These are hired individuals, and I
40:32
Speaker 4: think the money for that comes from outside the country.
40:35
Speaker 4: It's just all kind of disturbing, you know why. What's
40:38
Speaker 4: the interest. Why are we trying to divide South Africa
40:42
Speaker 4: and fan the flames of racist legislation brought out by
40:47
Speaker 4: the ANC and so on?
40:49
Speaker 2: Why is this suddenly happening?
40:50
Speaker 1: You know, you're reminded me now of I think it
40:55
Speaker 1: was a CIA that funded something like sixty or one
40:58
Speaker 1: hundred thousand people to push an anti China COVID wuhan
41:04
Speaker 1: narrative already back in twenty twenty already. So, I mean,
41:07
Speaker 1: these sorts of shadowy people do exist, but they don't
41:12
Speaker 1: necessarily control everything from the top.
41:16
Speaker 2: Yes, So who was Michael Sanger's handler? You know, he
41:20
Speaker 2: wrote that book what is it called again?
41:22
Speaker 4: But basically trying to pin the entire COVID phenomenon on China,
41:25
Speaker 4: which was nonsense.
41:26
Speaker 3: You know that's also a red flag.
41:29
Speaker 2: Yeah, yes it is. That's a red flag. Good pun.
41:32
Speaker 4: But in one conversation with as he said to him,
41:36
Speaker 4: you're pretty good for a spook, and he just chuckled,
41:38
Speaker 4: he laughed, He kind of admitted that he was a spook.
41:41
Speaker 2: Amuses me.
41:42
Speaker 4: You know, yeah, there are a couple of people who
41:44
Speaker 4: considered themselves skeptics and someone who who there you know,
41:47
Speaker 4: who then fall for make America great again and Donald Trump,
41:51
Speaker 4: or maybe they loved Putin. They become big fans of Putin,
41:53
Speaker 4: you know, because he's giving a stick to NATO. He's
41:56
Speaker 4: a hero, you know that they just fell to see that. No,
41:59
Speaker 4: these us all somewhere between thugs and actors, you know,
42:06
Speaker 4: or sometimes a mixture of the two. Donald Trump's an actor,
42:11
Speaker 4: is not a real statesman, you know, And I'm not.
42:15
Speaker 4: I've got no gun in the fight. I think it
42:18
Speaker 4: is better that we've got Trump. Then then Kamala Harris,
42:23
Speaker 4: I mean, what a flake. But I mean she's an
42:25
Speaker 4: actor too, right, an actress. So you know, they're all
42:30
Speaker 4: it's all nonsense. It's all stage managed. The Overton windows
42:33
Speaker 4: tightly controlled the available candidates for election or control. It's
42:38
Speaker 4: establishment people or establishment friendly candidates. Even the destruction they
42:43
Speaker 4: causes is that it must be the right type of destruction,
42:47
Speaker 4: you know.
42:49
Speaker 2: And so you now I have this. Yeah, some of
42:51
Speaker 2: these people are such patent.
42:54
Speaker 4: Assets. Like Connie in Canada. I've been watching that guy
43:00
Speaker 4: for years. You didn't have a very high profile. But
43:03
Speaker 4: if you ever want to see a person who's simply
43:07
Speaker 4: marching to the narrative and has become so schooled in
43:12
Speaker 4: the narrative that he almost can't do anything else. He's
43:15
Speaker 4: kind of got a robotic nature to him. Then you
43:18
Speaker 4: look no further than Connie, you know, and you you
43:21
Speaker 4: mentioned elegive.
43:23
Speaker 1: Sorry, no, I was going to say, you mentioned the
43:25
Speaker 1: Overton window. And I'm reminded of I think it was
43:27
Speaker 1: Nam Chomsky who said that a great way to control
43:31
Speaker 1: narratives is by encouraging very aggressive debate, but within certain parameters.
43:39
Speaker 2: That's it was a Chomsky line of thinking.
43:42
Speaker 4: Yes, that you want to create the appearance of a
43:44
Speaker 4: vigorous social discourse, So you allow people to have a
43:48
Speaker 4: debate about, for example, COVID natural origin versus lab origin. Yes,
43:53
Speaker 4: you don't allow me to enter the room saying bullshit,
43:57
Speaker 4: there was no risk addtive pathogen. So this whole discussion
44:01
Speaker 4: is irrelevant. There meaningless to talk about the origin of
44:05
Speaker 4: a thing that didn't exist. And you will have a
44:08
Speaker 4: smear campaign. There will be a whole bunch of people
44:10
Speaker 4: who jump on you. You're you're a virus denier and
44:13
Speaker 4: and so on, and they will shut you down and
44:15
Speaker 4: they will not allow you into the debate. And that's happened.
44:19
Speaker 4: You know, the Overton window moves around. So at the beginning,
44:21
Speaker 4: to make the claim that COVID presented no risk to children,
44:25
Speaker 4: that was COVID denier. You're not allowed to say that.
44:28
Speaker 4: Now you're allowed to say that, there's not even a
44:29
Speaker 4: debated issue. But at the time when they were trying
44:33
Speaker 4: to close schools, they wanted to close schools, they wanted
44:35
Speaker 4: to close society. They didn't want to hear that children
44:40
Speaker 4: were not at risk of COVID, and you know, they
44:42
Speaker 4: would come up with elaborate ruses like that children infect their.
44:46
Speaker 2: Grandparents, you know, that kind of thing. And so that's
44:49
Speaker 2: irrational that.
44:52
Speaker 4: When the when the windows started opening, the reasoning becomes different.
44:56
Speaker 4: It's no longer that the children at risk trying to
44:58
Speaker 4: scare the parents into to school closures. It's now that
45:02
Speaker 4: no granny zippisk from the child, and that's why we
45:04
Speaker 4: did it. So there's a rationale provided after the initial
45:07
Speaker 4: narrative has collapsed.
45:10
Speaker 3: Yeah, another way of thinking about it is, since we're
45:13
Speaker 3: talking about it, it's.
45:14
Speaker 1: Come to my mind that you'll have let's say in
45:17
Speaker 1: the States, you'll have the the Biden camp that will say, well,
45:21
Speaker 1: there was a pandemic, but it came from a wet
45:24
Speaker 1: market in China, and then you'll have the Trump camp
45:27
Speaker 1: camp that will say, well, no, it came from a
45:31
Speaker 1: lab in China. Both camps are saying that there was
45:33
Speaker 1: a pandemic, right, So they're both arguing the same thing.
45:36
Speaker 1: Or you might have the Obama camp saying well, human
45:40
Speaker 1: activity is warming up Earth, and then you'll have the
45:43
Speaker 1: Trump camp saying, well, no, it's not coming from our cause,
45:48
Speaker 1: but it's coming from geoengineering. They're changing the climate. So
45:51
Speaker 1: both camps again are arguing that humans are warming Earth.
45:55
Speaker 1: Whereas the third option, which as you pointed out, you're
45:58
Speaker 1: not allowed to say that perhaps humans are not powerful
46:00
Speaker 1: enough to change earth temperature and perhaps there was no pandemic.
46:04
Speaker 2: Yeah, exactly. Yeah.
46:06
Speaker 4: Look, I think I think a lot of people are
46:07
Speaker 4: coming around to those ideas. When I say a lot,
46:10
Speaker 4: I mean on a comparative based But the man in
46:13
Speaker 4: the street, well, what does he think? Well, I think
46:16
Speaker 4: I think actually, you're you know, most people who have
46:20
Speaker 4: non bullshit jobs were never taken in They maybe haven't
46:24
Speaker 4: got a platform to say anything, or the time of
46:27
Speaker 4: day to spend themselves in indulging thoughts about the grand
46:32
Speaker 4: narratives of the time.
46:34
Speaker 2: But certainly uber drivers were people who.
46:36
Speaker 4: Are found so straight through the COVID phenomenon and don't
46:39
Speaker 4: give a rats about them.
46:41
Speaker 2: It's phenomenon, but.
46:45
Speaker 4: We've got you know, when it gets when you get
46:47
Speaker 4: to the laptop classes, it's a different story. And those
46:49
Speaker 4: people glass over when you when you confront them with
46:53
Speaker 4: evidence of a scam, of a grand narrative that is
46:58
Speaker 4: entirely false, of a of a artificial construct like climate change,
47:03
Speaker 4: clibate crisis. But yes, the lack of humility is a
47:07
Speaker 4: fundamental characteristic of the people who go in for these ideas.
47:11
Speaker 4: They do have this sense that because they believe in
47:15
Speaker 4: centralized control as a plausibly beneficial or generative kind of polity.
47:23
Speaker 2: You know, it's just the same the same intellectual error allows.
47:28
Speaker 4: Them to conclude that, yeah, humans can control climates of
47:32
Speaker 4: the planet.
47:33
Speaker 2: That's that's that's definitely viable.
47:35
Speaker 4: You know that the net force of this what it's
47:39
Speaker 4: Stephen Hawkins called it, the thin chemicals scum on the
47:43
Speaker 4: surface of planet. You know, that's kind of how these
47:44
Speaker 4: guys think that that that thin chemical scum could overcome
47:49
Speaker 4: the science and sorry, the sun and clouds and you know,
47:55
Speaker 4: the mass of the atmosphere and all sorts of other
47:58
Speaker 4: variables on which humanity has no bearing. Yeah, it's that
48:04
Speaker 4: that that sort of arrogance, the epistemic humility is lacking.
48:08
Speaker 1: I was wanting to ask you earlier, just quickly. You've
48:10
Speaker 1: spoken a couple of times about centralized control. Let's just
48:13
Speaker 1: unpack that, just very briefly, because I've been asked about
48:16
Speaker 1: what that means, because in your home there's a degree
48:19
Speaker 1: of central control.
48:20
Speaker 3: Yes, so how does this scale?
48:23
Speaker 2: Well? The problem.
48:24
Speaker 4: The problem is that as you scale things, complexity increases.
48:28
Speaker 4: And I mean, and let's just let's be care what
48:32
Speaker 4: we're talking about in a domain. Okay, So if you
48:35
Speaker 4: scale the political unit, the complexity of.
48:38
Speaker 2: That unit increases. Right.
48:42
Speaker 4: If I have gas, a single gas in a jar,
48:45
Speaker 4: and I make the job bigger, my model doesn't really
48:48
Speaker 4: change for the behavior of the gas. Uh. You know,
48:51
Speaker 4: complexity is not really increasing in that kind of setting.
48:55
Speaker 4: So we're not talking about this kind of idealized models
49:00
Speaker 4: and so on. We're talking about the real world. When
49:02
Speaker 4: you increase scale, when you increase complexity, and complexity very
49:07
Speaker 4: quickly becomes overwhelming your theories about what will happen if
49:12
Speaker 4: I do this, If I introduce this policy, if I
49:15
Speaker 4: build this product or provide the service, the predictive ability
49:18
Speaker 4: becomes rapidly eroded. And the only way to make progress
49:23
Speaker 4: in the face of complexity is through trial and error,
49:28
Speaker 4: conjecture and criticism.
49:30
Speaker 2: And what.
49:33
Speaker 4: Large qualities centralized organizations necessarily do otherwise they can't become
49:39
Speaker 4: centralized organizations, is they rule out conjecture and criticism. If
49:44
Speaker 4: you're going to operate an organization from a central point,
49:48
Speaker 4: it must have rules and inhibitions of ideas and freedoms
49:54
Speaker 4: and independence. That is the same thing as that inhibition
49:59
Speaker 4: is the the same thing as a centralized organization. So
50:03
Speaker 4: centralization stamps out the only mechanism for solving problems in
50:08
Speaker 4: the face of complexity, and therefore it stamps out progress
50:11
Speaker 4: and human flourishing.
50:13
Speaker 2: It's very simple.
50:16
Speaker 1: So there isn't a obvious way in which to gauge
50:21
Speaker 1: that metric. You just kind of feel it as you
50:22
Speaker 1: go along with with that scalability.
50:28
Speaker 4: Well, so yes, are you sort of saying that you
50:32
Speaker 4: have to operate with rules of thumb? You know that
50:35
Speaker 4: when a corporation gets beyond a certain number of employees,
50:38
Speaker 4: you should start considering selling off some business units.
50:41
Speaker 1: Or yeah, I mean for example, yeah, So for example,
50:44
Speaker 1: we both will agree that a centralized political entity like
50:49
Speaker 1: the government is a bad idea. It's better off if
50:53
Speaker 1: it's more decentralized. But then, for example, a community like
50:57
Speaker 1: let's say a little town can still be very successful
51:02
Speaker 1: if it's centralized.
51:04
Speaker 2: Yeah. Yeah, small small units can. Yeah, they need you.
51:08
Speaker 4: Look, centralization is not a universally bad thing at all
51:10
Speaker 4: levels of scale. That was the point of my analysis
51:13
Speaker 4: communism at home. The scale is important, And the really
51:18
Speaker 4: important thing though, is you know you want you don't
51:21
Speaker 4: want to have this kind of cookie cutter imposed keen
51:26
Speaker 4: spreadsheet based a sort of determination of the units of scale,
51:30
Speaker 4: you know, dividing of the world into nations and then
51:34
Speaker 4: into provinces and then into municipalities. That's the kind of
51:37
Speaker 4: way in which a centralized thinker operates. There's nothing wrong
51:40
Speaker 4: with having fluid organizational units where the scale of the
51:45
Speaker 4: unit is spontaneously arrived at, and where in different places,
51:50
Speaker 4: different scales are attempted and it could be reflecting different
51:55
Speaker 4: underlying realities. So if your community is rather homogeneous, Dan, yeah,
52:00
Speaker 4: you can probably have quite a large scale organization making
52:04
Speaker 4: local decisions. But if it's very heterogeneous, and their pockets
52:07
Speaker 4: of people with different mindsets and different wealth and income
52:11
Speaker 4: and different problems and their lives, different size families, all
52:14
Speaker 4: sorts of parameters might differ. Then something smaller might be
52:19
Speaker 4: better because you allowed differentiation and attention gets directed at
52:25
Speaker 4: solving different sets of problems.
52:28
Speaker 1: Which actually feeds into what you said earlier about complexity.
52:32
Speaker 1: It gives it gives us an idea as to why,
52:34
Speaker 1: for example, the US is culturally collapsing in Why China,
52:37
Speaker 1: for example, isn't.
52:39
Speaker 4: Yeah, yeah, it's very hard to gauge what's going on
52:43
Speaker 4: in China. I mean there's a very homogeneous country. Hand
52:48
Speaker 4: Chinese constitute. You know, most of the people in any
52:53
Speaker 4: kind of political office and so on, so there are
52:56
Speaker 4: more and they speak one language for the most part.
52:59
Speaker 4: I know that it's a huge country and there are
53:02
Speaker 4: other cultures embedded, but for the most part, you've got
53:06
Speaker 4: this very homogenous story. Whereas yes, America is significantly more
53:13
Speaker 4: diverous if you spend anything. Japan's very homogeneous as well.
53:17
Speaker 4: They actually do it with tight control of immigration policy.
53:23
Speaker 4: I don't think the Chinese have to prevent a whole
53:24
Speaker 4: lot of people from trying to move there. But the
53:29
Speaker 4: Japanese have for many years run very tight culture culture
53:34
Speaker 4: confining policies. Korea another one, and America has had this
53:40
Speaker 4: fluidity and profound regional differences. The culture in Texas is
53:44
Speaker 4: very different from the culture in New Hampshire, and well
53:47
Speaker 4: that's about example from Maine or something like that. The
53:51
Speaker 4: culture in California is very different from the culture in Florida,
53:54
Speaker 4: you know.
53:56
Speaker 2: And that's been a pronounced feature of America.
54:00
Speaker 4: Yeah, and actually, you know, the centralization in America, the
54:06
Speaker 4: erosion of states rights and independence has been a very
54:10
Speaker 4: long story and a very tragic story. In my opinion,
54:13
Speaker 4: America would be in a much better position if federal
54:17
Speaker 4: government had never been.
54:18
Speaker 2: Allowed to swell.
54:20
Speaker 4: If that confined it to like one percent of GDP
54:23
Speaker 4: or something very low, America would be a wonderful and
54:26
Speaker 4: poor place.
54:28
Speaker 1: Nick, I know your time's tight, so let's come in
54:30
Speaker 1: for landing. Give me a nugget of wisdom.
54:34
Speaker 2: But they've been twenty there, haven't they been? Have more
54:38
Speaker 2: children there? You go.
54:39
Speaker 4: I've just had a three months old at home, and
54:42
Speaker 4: it's a wonderful experience out there. You're fifty one to
54:47
Speaker 4: be having a kid in the home again. I'm totally
54:49
Speaker 4: enjoying it.
54:50
Speaker 1: I'm also enjoying it. So our little boys must play
54:52
Speaker 1: soon together?
54:53
Speaker 2: Yes they must?
54:54
Speaker 3: Yeah, okay quickly? How can I follow you? Nick?
54:59
Speaker 2: I as active as I used to be.
55:01
Speaker 4: I'm sort of doing an interview a week on some
55:04
Speaker 4: or every couple of weeks on a platform somewhere, and
55:06
Speaker 4: I have lightweight communication on Twitter. My handle is Nick
55:10
Speaker 4: Hutson CT and that's about it for now, because my
55:15
Speaker 4: concentration is on doing the deep thinking and trying to
55:18
Speaker 4: understand things a bit better before I shoot my mouth off,
55:21
Speaker 4: and also on learning what I can from a very
55:24
Speaker 4: interesting business.
55:26
Speaker 3: Nick Kutson, thank you for joining me in the trenches.
55:28
Speaker 2: Yes,