Real Coffee with Scott Adams - Episode 903 Scott Adams： Good News is Breaking Out Everywhere. Swaddle Up and Get Some.

00:00:00.000 Hey everybody, come on in here. It's time for the simultaneous swaddle. It's that bonus

00:00:16.860 entertainment you get, commercial free if you're seeing it live. And it's coming at

00:00:24.800 you with a ferocity matched only by a tiger with coronavirus. Yeah, you need to add a tiger to the

00:00:35.440 coronavirus to get this ferocity. So today I'm going to show you a little bit about reality that

00:00:43.180 some of you may have not been exposed to. You know how I'm always talking about if you have a good

00:00:50.380 talent stack, you can see situations a little more clearly. You can see in more different windows

00:00:56.620 if you have experience in different fields. So I'm going to give you a little glimpse of what I see

00:01:03.360 when I look into this whole coronavirus situation here and see if it matches what you think. Now,

00:01:13.460 for those of you who have similar backgrounds, you're going to say to yourself, I knew that,

00:01:17.980 but you might get a new way to explain it to other people. So stay around for that. And for some of

00:01:25.880 you, you're just going to be sort of surprised. And you're just going to say, I'm not even sure

00:01:33.500 that's real. But we'll find out. But first, some things. In the news happening just recently,

00:01:42.240 did you hear that Japan is going to reportedly, I think I would wait for confirmation on this, but

00:01:49.220 reportedly Japan is going to pay its own firms to leave China? They're going to pay them to leave.

00:01:57.140 Ouch. I think China is going to have some serious problems. Have you noticed that everybody's talking

00:02:03.740 about Kamala Harris for vice president, vice president candidate with Joe Biden? Look how

00:02:13.960 close we are. We're so close. Because even if you don't think she would be the best choice,

00:02:22.720 would you at least agree that the consensus has formed around her? Would you at least agree that

00:02:30.520 other people have sort of decided, because she's leading on predict it? You know, in the betting

00:02:37.160 poll, she's number one for getting the spot. Then Biden has signaled it about as clearly as you

00:02:43.560 possibly could. So you realize that I'm on the verge of the greatest prediction of all time.

00:02:52.040 But it still might not happen, so I don't want to get too cocky. Anything could happen. If it doesn't

00:02:59.980 work out, I'll be the first one to admit it. But it's so close. I don't know if you've noticed,

00:03:09.320 but there are some really hopeful signs. Number one, the number of hospital admittees, I guess,

00:03:17.840 went way down in New York. Number of deaths went up, but I think that's a trailing indicator,

00:03:24.020 meaning those are people who are already in trouble for a while. But if fewer people are going

00:03:29.840 into the hospital, what's that mean? What's it mean? Is it because of hydroxychloroquine? Is it

00:03:36.760 because of social distancing? We don't know, but it's a good sign. And everybody's talking about the

00:03:43.540 curve bending, and maybe things are going to head in the right direction. But there's another good

00:03:50.560 sign, one that we've all been waiting for. My town has toilet paper. I'm getting reports from other

00:04:06.440 people in other places that they too do not have fully stocked shelves, but that at least in some

00:04:15.200 stores, they too have the great white, the great white hope. And so normality is returning. Approximately

00:04:26.720 when I told you it would, a month after the toilet paper disappeared, I said to you, give it one month.

00:04:34.720 It'll be back. It'll be back. And here it is. So let's talk about some other stuff. Oh, poor Drucker

00:04:47.040 died. If you don't know who he is, that won't mean as much to you. Here's a question for you.

00:04:56.900 When we're done, when we're on the other side of this coronavirus, there'll be the heated debate

00:05:01.680 of whether the number of deaths was lower than predicted because we did such a good job

00:05:08.820 or because it was never dangerous in the first place. So in order to anticipate that problem,

00:05:17.280 that we're not going to know why we didn't have a lot of deaths and people will disagree,

00:05:21.920 could we agree in advance who our comparable is? In other words, could we find another country

00:05:29.540 who is sufficiently like us, or maybe a few of them, who are doing something different? Let's say

00:05:38.500 they're not doing social distancing and they're, let's say, not testing because we didn't have that

00:05:45.340 option. And they're not wearing masks, at least all of them, because, you know, we didn't have that

00:05:51.760 option either. So if you're trying to find somebody who is doing something smarter than us,

00:05:58.080 you'd have to find somebody who's not doing much testing and they're also not doing much social

00:06:03.740 distancing and they've already got, you know, a good amount of infection. And then that would be

00:06:10.500 our comparable, right? Am I, am I right? So I don't know which, which country that is, but let me put

00:06:16.900 that out there. That if, if anybody is claiming that they're going to wait until we're on the other

00:06:22.460 side of this and say, see, it's self-evident that it's because of the social distancing or

00:06:29.120 it's self-evident that that made no difference. It was no problem at all. It's not going to be

00:06:36.180 obvious just by looking at it. You're going to have to have some standard to compare it to, to

00:06:41.060 even get a, you know, a hope of understanding what happened. All right. Here's my little whiteboard

00:06:52.240 lesson for today. This is what I call the classic view of reality. This is the view that we think

00:07:01.980 should be the view or that should be the way reality works. If you are young, you may have learned that

00:07:10.300 this is what reality looks like. You've got your experts. They consult with your leaders. Your

00:07:16.340 leaders make some policies. And then those policies rain down on the happy citizens who are happy that

00:07:23.120 experts were part of the process and that leaders took their advice. And what a good world we live in.

00:07:30.900 So that would be the classic view of the world.

00:07:32.920 On the other side of my magic whiteboard that is so incredible, you probably all want one, but

00:07:41.960 you can't have it. It's one of a kind. Here's a more accurate view of the world. It goes like

00:07:54.560 this. You've got your experts. And let's say that there's a situation that comes up that they

00:08:01.260 have not seen before. Let's take the coronavirus situation. Now they've seen other pandemics and

00:08:08.520 other infections, but they haven't seen this one. So I think you'd agree that this was a brand new

00:08:15.060 thing and there was a lot of fog of war and they didn't have good information at first. But they're

00:08:20.840 the experts. Even though they don't have good information, they still have to inform the leaders

00:08:27.580 what to do because doing nothing is also a decision. So the leaders have to make a decision

00:08:34.280 to do nothing or to do something very expensive and aggressive. And they're not going to make the

00:08:39.400 decision on their own. And the leaders aren't going to be able to get away with saying, well, we really

00:08:45.540 don't know. There's not enough information. If you would just let us wait a year, no, we can't wait a

00:08:50.600 year. It might be too late. So the experts are forced to make a decision well before they have

00:08:57.700 anything that would satisfy themselves of what to do. So what do they do? Well, here's what I believe

00:09:03.880 happens in the real world. You've got lots of experts, but they're not all equal. Some experts

00:09:09.560 are more influential. Let's say Dr. Fauci, for example. So there are probably several experts in the field

00:09:16.920 who are recognized as the most important ones. So probably it only takes half a dozen or maybe

00:09:23.760 fewer than 10 of the top experts to agree that they have an instinct for something or they feel

00:09:31.440 there's a problem or this could be really bad. You know, that all of their experience, their pattern

00:09:38.080 recognition, their biases, you know, good and bad. I'm not even saying any of this is bad. I'm saying that,

00:09:43.980 you know, an expert is not just the data. And if they don't have data, at least not reliable data,

00:09:50.820 they're going to take advantage of their pattern recognition and bias. But also very importantly,

00:09:57.060 they don't want to kill anyone. That's going to be job one, right? And that's a really very

00:10:02.820 important bias because they're going to make sure that nobody can blame them for killing anybody in

00:10:09.340 particular. That would be anybody's natural impulse. Now, of course, they're also professionals.

00:10:15.360 So they're going to try to be as independent minded as possible for the greater good.

00:10:20.020 But put yourself in that position. Would you ever recommend something that has a pretty good chance

00:10:26.740 of killing a million people? If you get it wrong, you're going to want to do whatever's the safest

00:10:35.240 in terms of not killing a million people and not taking any chance that you're the one who made 1.00

00:10:42.080 some dumbass recommendation and killed a million people. So professional as they may be, 0.99

00:10:48.160 they're still humans and nobody wants to kill a million people. So you've always got that force.

00:10:54.420 So what do you do if you've got a really strong feeling that that something is bad, but you don't know?

00:11:01.200 And let me give you a little sort of math example of why they would be confused in the early days of

00:11:09.620 the pandemic. Let's say that the thing that makes a virus powerful is just, I'm oversimplifying a little

00:11:19.640 bit, is how viral it is and how deadly it is if you get it. So two different things, how quickly it

00:11:27.240 spreads. And then if you get it, what are your odds of dying? And so in very rough terms, you could

00:11:33.620 sort of multiply them, multiply them to see how bad it is. So if you had, let's say, a very low

00:11:40.240 spreading, but it was very deadly, well, that wouldn't be the worst thing, because you might be

00:11:46.820 able to stop it, because even though it's deadly, it's not going to spread that far. So multiplying the

00:11:51.980 low virality times the, you know, high death rate gives you some power number. But you can get to

00:12:01.860 that same power number by having something that's super viral and doesn't kill many percentage of

00:12:07.980 people, but it gets so many people that even though it's a low percentage, a lot of them die. So both of

00:12:15.140 them could be equal power, one because it's very viral, and the other because it's very deadly.

00:12:23.240 But what happens if you get something that's super viral, and by the worst turn of fate, super deadly?

00:12:32.620 I don't believe we've had one of those. Maybe the Spanish flu qualifies, but I'm not even sure it was,

00:12:39.200 that one was, you know, the super deadly, super viral. I'm not sure how that gets classified. But

00:12:46.340 in the earliest days of the pandemic, was there enough information that the experts could know

00:12:53.100 if they had something that was very viral, but only a little bit deadly? Or something that was

00:13:01.420 a nine, nine out of 10 in virality and deadly? They didn't know. They were looking at China 0.99

00:13:08.600 and things look bad, right? So what do you do if you're the experts and you've got to advise the

00:13:13.720 policymakers what to do? Either to do nothing or do something, and you don't have any information.

00:13:19.180 Well, you're going to take your best instincts and judgment, your risk management, your not wanting

00:13:25.080 to kill millions of people, and you're going to have to convince leaders who are going to ask you for

00:13:33.300 your data. And what do you do? So the president says, okay, you know, this is important. I get it. 0.97

00:13:40.420 Now show me your numbers. And what do they have? Well, not much of anything, right? Because they just

00:13:46.880 don't have good numbers. They have guesses. They have assumptions or lots of variables. It's really

00:13:51.540 all very complicated. Then what does the president do? How do you convince the president, even though

00:13:57.560 you're the experts, and all you have is hunches and guesses and patterns and things from the past that

00:14:04.180 were similar? You've done some math in the back of an envelope. Can you convince the president? And

00:14:11.420 could you convince the public with something like that? Could you go directly to the public and say,

00:14:16.980 look, we can't prove it, but a whole bunch of us who are experts have a really bad feeling about

00:14:24.400 this thing? And we don't want a million people to die. So could they convince the public? No,

00:14:33.400 because the public would say the same thing. Based on what? Show us your proof that this is so bad.

00:14:39.760 And they would say things like, well, there was this anecdote, and there was a story, and there's

00:14:44.100 this unreliable information. There's this pattern of something that was different. There's this time we

00:14:49.660 thought we were right, that we were wrong. And when they're done, you just say to yourself,

00:14:53.360 I'm not sure I'm convinced any of this is real. Yet, yet we still have to make decisions.

00:15:02.240 So how do you solve this situation in the real world, where it's not that classic view that's

00:15:07.960 on the other side of the whiteboard, where the experts know just what to do? They look at their

00:15:13.020 data, they look at their spreadsheets, it all adds up. They tell the leaders, the leaders say,

00:15:17.760 hey, that looks good to me. Thank you for looking into that. And they make their policies. In the real

00:15:23.160 world, that just doesn't work that way. Even the experts don't know what to do, if they don't have

00:15:30.180 data, and it's the fog of war, and it's a brand new thing. Now, someday, we might understand enough

00:15:36.920 about this, that if we could have traveled back in time, with our future knowledge, well, we maybe we

00:15:43.840 would handle this differently. But even the experts didn't know what was going on. Thank you, China,

00:15:49.960 right? So here's what you do in this situation, where there's something you feel really strongly

00:15:55.540 about, you're pretty sure there's a lot of evidence, but you can't communicate it. You just can't sell it.

00:16:02.260 And so you go to the model makers. And here's where everybody in social media and on the news

00:16:08.980 is making a fundamental misunderstanding. And it's this. You don't use models to give you the answer.

00:16:20.820 Models do not produce information. They're a sales tool. They're for persuasion. How do I know that?

00:16:29.420 I know. That's the opposite of what you think, right? You think that the experts are uncertain.

00:16:38.180 So they use a model to become certain and to learn something. That's probably what you thought,

00:16:44.160 right? Ask anybody who's ever done this for a living. That's not what happens. Here's what really

00:16:52.540 happens. The experts say, go make us some models because I got to sell this thing. I need a picture.

00:17:00.040 Give me a graph with a scary looking curve. Give me a few big numbers. Give me a range and make it a

00:17:06.540 big range because I don't want to be wrong. All right? Whatever you do, make it a big range.

00:17:12.900 So the model makers do their magic and they come up with some really scary looking graphs that are

00:17:17.320 really simple that they can sell to scare the public and it's compatible with the experts. Now,

00:17:24.840 what would happen, hypothetically, if the people making the models came up with a prediction

00:17:31.280 that would kill people if it were wrong? What are the experts going to do? So, for example,

00:17:39.100 let's say the experts are looking at the carnage over in Wuhan and they're saying to themselves,

00:17:44.020 I know there's something bad out there. There's definitely something bad out there.

00:17:48.800 This is not normal as far as we can tell. We haven't confirmed it, but we're seeing a lot of

00:17:54.940 stuff that just doesn't look normal, you know, spraying of the streets over there and everything.

00:18:00.860 So what happens if the model makers said, well, we've put in all the variables, we've checked all

00:18:05.060 of our assumptions, and the very best model we've come up with says it's not much of a problem.

00:18:11.040 So it doesn't look like it'll be a problem over here. So what do you do if you're the expert?

00:18:16.820 Because that model would disagree with what you plainly can see. It disagrees with your sense of

00:18:22.500 risk management. It disagrees, you know, with observation, instinct, hunch. It just disagrees with

00:18:29.780 every fiber of your being. Are you going to say to the model maker, oh, thanks, I was worried before.

00:18:36.120 But now that you ran the model, feeling better. No, they discard that model. Because you can't,

00:18:45.000 you can't keep the model that says it might not be a problem. That's the one that could kill people.

00:18:51.640 And remember, job one, job one is don't kill anybody. So the experts can't accept a model

00:18:59.180 that is too, let's teach, too not scary. Because it would put the experts in the position of maybe

00:19:08.860 not warning the public about whatever they're seeing in China, that doesn't look good. Right?

00:19:16.660 So they would have to reject anything that made it look like it's not a problem. Right? So let's say

00:19:23.880 the experts come back and they say, it could be this big range, 2 million, or 100,000. But let's say

00:19:32.680 the first time they come back, they say, you know, it's 2 million, or honestly, it could be 10,000.

00:19:39.180 What if that was the first model that came back? And the experts say, um, we've got a problem here.

00:19:45.060 If it's only 10,000, or even if 10,000 is even in the range, Americans are going to think

00:19:52.400 it's not a problem. Because we're used to having things that are a huge risk, but most of the time

00:19:59.500 you're fine. If you tell the Americans that even within that predicted range is maybe just 10,000,

00:20:07.160 it's like the common cold, basically. How are they going to act? They're going to act in a way

00:20:13.200 that they're not taking it seriously. And then what? And then I've killed all these people

00:20:18.140 because they didn't, you know, potentially, it could be worse than we think. So here's what 0.95

00:20:24.480 happens. Models are simply ways that experts can convince leaders and the public that there's

00:20:32.620 something that needs to be taken seriously. Now you're saying to me, I'm not buying this, Scott.

00:20:37.660 I'm not buying this. Everybody on TV, all the scientists, all the experts, they really sort of act

00:20:44.640 like this is telling you something. Like it's actually giving you a window into the future.

00:20:51.880 That's the whole point of this. We're spending millions of dollars, you know, billions of dollars

00:20:56.400 making them. They're all these models. It's the best, it's the smartest people in the world, Scott.

00:21:00.800 But did you forget you're a cartoonist who has a degree in economics and an MBA? Did you forget

00:21:08.320 that? Because the smartest people in the world, they're not talking like you're talking. No, they're

00:21:14.140 not. But here's my simple argument for why they do not glimpse the future. It goes like this.

00:21:23.180 If you have a situation that is really fundamentally like other situations, then sometimes yes. Let's say

00:21:29.540 you're into construction and you've built several homes that are comparable, you probably could put

00:21:36.460 together a budget that says, ah, based on these last three homes I built in the same town that are

00:21:41.720 about the same kind, this square footage, yeah, I'll just estimate this next house will be that.

00:21:47.640 That'd be fine. But that doesn't work with a brand new situation. There's no way to estimate it.

00:21:55.440 And if anybody could use any complicated model to predict the future, even statistically better

00:22:03.860 than guessing, they would not be making models. They would be sitting on their trillion dollar

00:22:11.000 yachts. If anybody could do this in actuality, like actually predict the future with some statistical

00:22:21.280 validity whatsoever. They'd do it for stocks, right? They'd do it for knowing what startups

00:22:29.620 to invest in. They could make bets on big events. In other words, they could bet that there'll

00:22:36.920 be a, you know, tragedy or a forest fire, you know, this or that. If anybody could look into the fog

00:22:45.720 of war, which is what was happening in the beginning of the pandemic, if anybody could look into that

00:22:52.240 and build a model on a spreadsheet or with any kind of software that could actually reveal the future

00:22:59.720 better than chance, they would be almost magic. And there's no such thing as magic.

00:23:07.540 So this is, this is me letting you peer behind the screen. Part of the reason I know this is that I did

00:23:14.360 financial projections for a living. And I can tell you that if my, if my model and my prediction did not

00:23:24.400 give management what they felt was the right thing to do for personal reasons, professional reasons,

00:23:30.940 or the good of mankind, I had to go back and tweak it until it did. Because we didn't know what was the

00:23:37.240 right thing to do. But we knew that the model wouldn't tell us. We knew that the variables were,

00:23:43.200 the unknowns were so extreme. For example, one of the things I was trying to do is calculate

00:23:49.400 whether this technology that's, you know, obsolete now, but was new then called ISDN,

00:23:55.760 would have a big future and whether it was economical to put it into the network and sell it to people.

00:24:00.940 And there were just gigantic unknowns about where the technology would go and what would happen

00:24:06.300 with costs. And if you tweaked any one of them, you just get wildly different results. It was either

00:24:11.700 a great idea or a terrible idea. It was my job to calculate that. And so it was, was that in other

00:24:18.440 situations where I learned that you start with the answer. And the answer was, yeah, we're a high tech

00:24:23.860 company. We have to be investing in high tech stuff. Don't bring me back some financials to say we don't do

00:24:30.320 that. Because we're going to do that. We have to do that. It's, you know, it's, you know, our

00:24:36.640 experience, our pattern recognition, our bias, all of our external and internal incentives tell us

00:24:43.320 we're going to invest in the new technology. And it doesn't matter, bean counter, if you come back

00:24:48.120 and tell us it's a bad idea, I'm going to tell you to go back and tweak a variable until it's a good

00:24:52.620 idea, because that's the business we're in. We're in this business. We're not going to be not in that

00:24:56.640 business because your spreadsheet said so. So once you see behind the curtain, you realize that the

00:25:03.220 models are always just an expression of what the experts or the leaders want them to be. And they

00:25:09.400 are sales tools. They are not things that tell you what is true. So for all the people who are not

00:25:16.240 quite seeing behind the curtain, you can see them debating in public. You can see it on TV, you can see

00:25:22.680 it on social media, and they say things like this. Those models were inaccurate.

00:25:30.920 Doesn't mean anything. If you actually understand what the models are for, and what is possible,

00:25:39.100 it is meaningless to say the models turned out not to be accurate, because they were never built to be

00:25:44.340 accurate. You can't be accurate. In the same way that the models were not built to crap bars of gold for

00:25:53.920 all of us. The reason is, it would be great if you could build a model that would crap gold. Wouldn't you 0.74

00:26:01.920 do it? I mean, why not? You're a nice person. You'd give us all a little gold with your algorithm. But you

00:26:09.300 can't really build a model that can predict the future. It's not a thing. So you can't be mad at the people

00:26:16.540 who built the model for not doing a thing that's not a thing, which is predicting the future, from unknowns.

00:26:23.900 Again, if you had a situation where it was very similar to the last one, that's different. But in the fog of war,

00:26:30.860 nobody can predict. It's not a thing. So the people who understand the least, and I would argue people

00:26:39.380 who have never been around financial predicting, people who are not economists, maybe people who

00:26:45.000 are not scientists, maybe journalists, if I may say so. I think the journalists and the artists

00:26:52.040 are more likely to think they don't understand what's going on, because the predictions were so far off

00:26:59.880 what we're actually experiencing. And I think to them, they're saying, well, was this a conspiracy?

00:27:06.900 Was everybody stupid? And I don't think it was a conspiracy. And I don't think anybody involved 0.99

00:27:13.840 was stupid. And I don't think anybody involved was acting with bad intent. I think everybody involved 0.57

00:27:23.260 used the only tools they could to get a result they thought would protect people the most,

00:27:30.480 and maybe with a little bias toward making sure they were not the ones who killed a million people

00:27:34.780 personally. So I think everybody had good intentions and used the tools they had. But don't be fooled

00:27:40.440 into thinking that the models tell you what the future is. That's just not a thing. But they are good

00:27:48.220 for persuasion. And you can see how well they work. Now, having observed this situation, and I think

00:27:55.320 I succeeded in at least convincing some of you that the way it works is not that models tell you

00:28:02.480 something. It's pretty much the experts tell the models what the models need to say in order to get

00:28:07.920 to the next level, if you will. You can see why some people have a problem with the climate

00:28:14.780 models. And I don't want to get into that. I just want to alert you that it's hard to form an opinion

00:28:23.400 on one of these things without extending it to, well, does that apply to climate change? And so here's

00:28:31.580 what I would ask you. The climate change models, again, would be accurate if they are similar to

00:28:40.660 things that have worked before. In other words, if we were good at modeling climates, and this was

00:28:47.380 just another one, maybe we'd be good at it. But we've never modeled a climate 80 years in advance

00:28:55.680 using the tools that we're using. We've never done it. We think we're doing it now, but there's nothing

00:29:01.700 to, you know, to test it against. I would suggest that the best way to understand the climate change models

00:29:10.520 is this way as well, which is that the scientists genuinely believe, based on everything they've seen,

00:29:19.480 every scientific test, every paper, their pattern recognition, the people they've talked to, just the whole

00:29:26.320 weight of their experience, I think, is screaming to them, we're in trouble, the planet's going to warm

00:29:32.300 up. But they don't have the ability, because it's impossible, it's not because they're bad at it,

00:29:38.160 it's just impossible, to communicate all the things they know, you know, the full weight of their

00:29:43.040 knowledge, to a leader, or to the public. Because it just doesn't transmit, it would just sound like

00:29:50.340 gobbledygook, and we'd say, I don't know, I'm not even sure you know what you're talking about.

00:29:53.700 And so, the scientists do the same thing that you're seeing here, which is they go to a model,

00:30:00.260 for them the model is really persuasion, but the public thinks the model is actually information.

00:30:06.680 The public thinks it's an actual prediction. Now, it might be a prediction that the temperature

00:30:11.280 is going up, and, you know, there's a risk, but the actual model, we'll see. And by the way,

00:30:20.200 the model can be right, if they make the range big enough, if you make the range big enough,

00:30:25.460 there's a pretty good chance it's going to fall in that range. So, that is my lesson for today.

00:30:30.920 I hope that was useful. Was it? I would like to see your feedback. I'll stay just for a minute to

00:30:39.540 look at that. Is Bill Gates in here? I don't know what you're talking about. Who wins? Big business?

00:30:52.160 Well, I don't know if anybody's winning. Very few people are winning.

00:30:57.340 So, I see more people asking about Dr. Shiva. Yeah. So, I'm still waiting for somebody to make a statement.

00:31:08.100 Oh, good. I'm getting some good feedback here.

00:31:11.020 I'm waiting for somebody to make a statement that they think they would like my opinion on

00:31:16.540 that is something that Dr. Shiva says. So, narrow it down to me. Not just like a whole topic,

00:31:26.540 but like, is there a statement or a fact? Just narrow it down, and I'll give you an opinion.

00:31:33.800 Post your whiteboard on Twitter. Yeah, I'll take a picture of it as soon as we get off here.

00:31:39.760 Oh, good. Actually, the feedback is better than I thought.

00:31:42.280 I didn't know if that was going to work as well as it did. How delightful.

00:31:48.280 All right.

00:31:50.900 So, it doesn't seem to me that you treat climate models like this.

00:31:55.100 Should they be treated like this? Well, I just did.

00:31:59.860 Maybe you just signed on or something.

00:32:05.520 I wouldn't watch you if I didn't like your take on things. That's true.

00:32:09.140 Are you ruining models forever?

00:32:13.840 Well, I'll tell you, models were ruined for me when I became the person who made them.

00:32:21.840 Nothing will disillusion you more than being the person who's doing that thing.

00:32:26.420 If you want to be unimpressed with something that you used to be impressed at,

00:32:32.560 try being that person. Let me give you a concrete example.

00:32:36.360 When I became a cartoonist, I immediately set my sights on winning the top award in cartooning.

00:32:44.760 Because I thought, you know, if you're an actor, you probably daydream of getting the Academy Award, right?

00:32:50.600 That would be natural.

00:32:51.780 So, whatever business you're in, you probably spend a little time daydreaming about winning the Super Bowl

00:32:58.240 or getting the gold medal or something like that.

00:33:00.640 So, I wanted to win this top award in cartooning.

00:33:05.440 It was called the Rubin, named after Rube Goldberg.

00:33:10.260 And every year, the cartoonists get together with sort of an Academy Awards,

00:33:14.000 and there's a vote, and they give the top award to a cartoonist.

00:33:17.380 So, I, of course, wanted that.

00:33:18.920 And years go by, something like 10 years, and I'm not even nominated.

00:33:23.300 And Dilbert just takes off and gets financially successful.

00:33:29.820 So, suddenly, Dilbert is like one of the biggest things in the country.

00:33:35.220 And then I got nominated.

00:33:38.040 And I said to myself, wait a minute.

00:33:42.140 I don't think the comic is that much better than it was a year ago,

00:33:46.120 or two years ago, or three years ago.

00:33:48.720 But what is different is that events and society sort of propelled me in front of a lot of people,

00:33:54.980 and then the comic took off.

00:33:56.900 So, the thing that was different is I was making a lot of money and getting a lot of attention.

00:34:01.240 And I thought to myself, what kind of award for cartooning depends on how much PR I'm getting?

00:34:10.620 And so, I actually ended up winning the two top awards in cartooning.

00:34:16.580 One is the overall award, you know, the biggest one you can get.

00:34:19.920 And then the top award in my category, which was cartoon strips.

00:34:24.840 And so, I won them both in the same year.

00:34:26.920 And, of course, I was, you know, terribly honored and everything.

00:34:31.680 But almost as soon as I won them and got home,

00:34:34.800 the entire thing seemed like a farce and didn't mean anything to me.

00:34:38.600 Because once I got on the inside, and I was the actual winner of it,

00:34:43.560 I just realized it wasn't so much because of my cartooning.

00:34:47.560 It's because I had become a name brand.

00:34:50.980 And the organization that puts on the event likes to have name brands.

00:34:56.340 So, it makes everybody else want to go.

00:34:58.780 It just makes the event better if you've got, you know, some famous people there.

00:35:01.640 So, I had won the award simply as a way to get me to attend the event.

00:35:08.620 It had nothing to do with anything.

00:35:10.160 And I spent years of my career, like, lusting after this award

00:35:14.800 that when I won it, I realized was completely meaningless.

00:35:20.000 And if there's anybody out there who has won that award, sorry.

00:35:26.900 Sorry about that.

00:35:28.000 But my college, my undergraduate college did the same thing.

00:35:33.160 When I got famous, they made me alumni of the year or something

00:35:36.740 and invited me to go back and give a talk and everything.

00:35:40.440 And I thought, my God, this is what an honor.

00:35:42.480 I'm alumni of the year of the little college I went to.

00:35:46.940 I was like, man, of all those people, won't they envy me?

00:35:50.740 My old classmates, when they see that I'm the alumni of the year.

00:35:56.360 And then you go back and you realize it's just a fundraising thing.

00:36:00.980 You know, once you get famous for doing something,

00:36:03.920 you're alumni of the year so that they can have more access to you for fundraising.

00:36:08.420 Anyway, that's more than you wanted to know.

00:36:11.320 And I will talk to you in the morning.

00:36:13.840 You know when.

00:36:14.560 Let's do that.

00:36:15.760 Let's do it.

00:36:24.680 Let's do it.

Real Coffee with Scott Adams - April 10, 2020

Episode 903 Scott Adams： Good News is Breaking Out Everywhere. Swaddle Up and Get Some.

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