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Speaker 1: This is Wins and Losses with Clay Trevis, play talks

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Speaker 1: with the most entertaining people in sports, entertainment and business.

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Speaker 1: Now here's Clay Trevis. Welcome in Wins and Lost his podcast.

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Speaker 1: I appreciate all of you hanging out with us. I

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Speaker 1: believe we're coming up right on forty of these long

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Speaker 1: form conversations that we have had, and the feedback on

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Speaker 1: them has been phenomenal. If this is the first one

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Speaker 1: that you're listening to, i'd encourage you to go check

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Speaker 1: them out from the world of sports, media, politics, business

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Speaker 1: and also some focus on COVID, which is what we're

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Speaker 1: gonna do again Part two with oh Vic Roy. And

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Speaker 1: before I bring him in, I gotta say it's rare

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Speaker 1: I get praise for anything that I do for my

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Speaker 1: wife in any part of my life at all. But

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Speaker 1: after our first conversation, which we had back in August,

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Speaker 1: she said, I wish everybody in the country could hear

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Speaker 1: him and could hear that conversation because it cut through

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Speaker 1: so much of the north Ways and got to the

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Speaker 1: essence of COVID our response how to balance out going

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Speaker 1: back to school. From the perspective of August, it now

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Speaker 1: has been whatever. It is nearly six months since we

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Speaker 1: last talked. We still are in the throes of much

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Speaker 1: of the COVID related hysteria I would call it. And

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Speaker 1: certainly we're now changing administrations because we're recording, uh the

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Speaker 1: day after the Biden inauguration, and so Ovic again, you're

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Speaker 1: coming at my wife's request for part two of this discussion.

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Speaker 1: So there's high there's high potential here, but also high

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Speaker 1: danger because I know for sure that she'll be listening,

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Speaker 1: and she even sent me a couple of questions that

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Speaker 1: she wanted me to ask. So first of all, thanks

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Speaker 1: for coming with us again. Thanks for being so great

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Speaker 1: in August. If you haven't heard that August conversation, I

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Speaker 1: would encourage you, maybe if you're starting this one, to pause,

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Speaker 1: go back into the podcast listen to that August conversation first,

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Speaker 1: because a lot of the background. I'm not necessarily going

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Speaker 1: to go back over again in because many of you

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Speaker 1: have already heard it and I want to kind of

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Speaker 1: get an update on your thoughts. So thanks for coming

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Speaker 1: on again. People loved our conversation last time, and I

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Speaker 1: hope we can help out a lot of people here

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Speaker 1: and they will enjoy this one. And get informed just

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Speaker 1: as well as they did back in August. Bakeley, what's

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Speaker 1: your wife's name, Laura l A R A And just

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Speaker 1: like you. Uh, she is from Oakland County, so she

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Speaker 1: went to lass Or High School. She grew up in Bloomfield.

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Speaker 1: So I went to the University of Michigan from there

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Speaker 1: and then we met in law school. So so Laura,

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Speaker 1: my wife is definitely listening right now. And uh, and

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Speaker 1: you guys are both fellow Michiganders at least in your youth. Yeah,

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Speaker 1: it's a drive by laws Er High to Detroit Country

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Speaker 1: Day every morning on my way because we talked about

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Speaker 1: we talked about that last time where Chris Webber had

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Speaker 1: gone to Detroit Country Day in Birmingham, Michigan, which is

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Speaker 1: where my wife and I got married back back in

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Speaker 1: the day back in two thousand four. Right, Yeah, well, uh,

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Speaker 1: thank for me, Thank you Laura for for those kind words,

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Speaker 1: and I hope I can live up to it this time.

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Speaker 1: All right, quick background again, I would encourage you if

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Speaker 1: you want a longer form background of how Ovic ended

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Speaker 1: up doing what he does, go listen to our August

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Speaker 1: twenty one conversation. But I just want to reset the

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Speaker 1: table because some people won't do that. You grew up,

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Speaker 1: like you just said, playing basketball with Chris Wheb at

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Speaker 1: h AT in Birmingham, Michigan. Uh. You then went to

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Speaker 1: m I T. And then you went to Yale for

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Speaker 1: medical school. Do you want to give people like a

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Speaker 1: two minute synopsis of what you do in your professional

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Speaker 1: life and have done in your professional life since leaving

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Speaker 1: and finishing your schooling. Yeah? So my my undergraduate major

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Speaker 1: was effectively molecular biology. So the genetics DNA and genetics worked,

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Speaker 1: and how how all that plays into how cells aren't work,

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Speaker 1: how our organs work, our bodies work, how diseases work.

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Speaker 1: And I went to med school, and then after med school,

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Speaker 1: I didn't practice medicine. I ended up joining an investment

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Speaker 1: firm called Being Capital to help them figure out the

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Speaker 1: biotech industry. So I spent a dozen years on Wall

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Speaker 1: Street in Boston and New York, so not not not

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Speaker 1: always in the physical Wall Street, but working as an

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Speaker 1: investor investing in biotech companies, including vaccine companies and companies

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Speaker 1: that developed treatments for various diseases, cancer, things like that,

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Speaker 1: And along the way I got really interested in healthcare policy.

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Speaker 1: Mitt Romney, as many people will know as the founder

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Speaker 1: of bank Capital, and I ended up working for his

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Speaker 1: presidential campaign in twelve. He and his team asked me

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Speaker 1: to help them design their health reform plan for the

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Speaker 1: twelve presidential race, and that led me down the rabbit

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Speaker 1: hole of Obamacare and health reform and public policy in general.

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Speaker 1: And now I run a think tank based in Austin,

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Speaker 1: Texas called the Foundation for Research on Equal Opportunity or

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Speaker 1: free opt dot org online f r e O p

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Speaker 1: P dot org, and we work on ways to expand

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Speaker 1: economic opportunity to those at least have it using free enterprise,

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Speaker 1: individual liberty, technological innovation, and plurals and in in other words, Uh,

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Speaker 1: people like me believe that free enterprise the thing that's

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Speaker 1: lifted people out of poverty all over the world, all

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Speaker 1: over the country, and we need to rededicate ourselves of

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Speaker 1: doing that for the people who are struggling to make

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Speaker 1: it in this incredibly challenging time that we live in now. Amen,

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Speaker 1: you're capitalist exactly, which is like it's like some people

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Speaker 1: are afraid to say they're actually capitalists nowadays. Uh. And so, uh,

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Speaker 1: the data and what I love about it. So I

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Speaker 1: talked about this back in August. But I became aware

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Speaker 1: of you because there was so much noise, uh in

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Speaker 1: this COVID coverage in the media. And I always say,

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Speaker 1: you know, I don't need people to tell me what

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Speaker 1: I should think. I would like to be able to

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Speaker 1: see the numbers myself and make rational decisions. And so

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Speaker 1: I first became aware of you because you were looking

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Speaker 1: at the stratification from an age range perspective of how

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Speaker 1: COVID was impacting different populations, right, because one of the

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Speaker 1: first flaws, I think, uh, the original ends as it were,

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Speaker 1: potentially of our response to COVID has been to treat

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Speaker 1: this as if it is an equal opportunity disease that

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Speaker 1: impacts everybody equally, much like because you hear this analogy

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Speaker 1: all the time the nineteen eighteen flew right, which had

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Speaker 1: a much more consistent impact across all age ranges, and

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Speaker 1: so the decision to shut down schools, for example, was

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Speaker 1: predicated on the idea, Oh, the places that did that

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Speaker 1: in nineteen eighteen had better success. But the problem is

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Speaker 1: that schools and school aged children, unlike in nineteen eighteen,

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Speaker 1: are not primary vectors for the spread of this disease.

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Speaker 1: So the positive impact in terms of lessening the spread

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Speaker 1: is not in any way. We're basically fighting the war

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Speaker 1: with the technology of the last war, when this new

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Speaker 1: war is entirely different, right, And that's what often happens

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Speaker 1: in wars. You try to take the lessons that you

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Speaker 1: learned from the last war. The problem is the situation

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Speaker 1: has changed and this isn't the same thing anymore. Yeah,

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Speaker 1: you know that that war with a hundred and three

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Speaker 1: years ago, and it's a completely different virus. I mean,

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Speaker 1: every virus is different. Influenza virus is the way they

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Speaker 1: behave are different than the way coronaviruses behave in your body,

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Speaker 1: the way they attack you, the kinds of people they attack.

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Speaker 1: We actually have known from from previous coronaviruses that coronaviruses

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Speaker 1: tend to attack older people, tend to be problematic at

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Speaker 1: nursing homes. So people who really looked at the science

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Speaker 1: quote unquote should have known that we really needed to

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Speaker 1: focus on protecting the elderly. But but that's not what

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Speaker 1: we did, and that was tragic. Okay, So I asked

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Speaker 1: you last time, what letter grade would you give our

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Speaker 1: I'm not trying to be partisan our political here I'm

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Speaker 1: just saying, as a policy perspective, what letter grade would

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Speaker 1: you give our response to COVID as a country. Oh boy,

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Speaker 1: that's a tough one. I mean, you know, I would

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Speaker 1: probably say, in fact, you know what here's all do

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Speaker 1: is we actually actually looked at all the advanced economic

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Speaker 1: countries in the world at free ap and be compared like,

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Speaker 1: how's everyone doing, both in terms of just desper capita,

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Speaker 1: in terms of like the actual policy responses, economic restrictions,

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Speaker 1: school closures. And you know, as time has gone on,

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Speaker 1: the grade that we would give the US as declined

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Speaker 1: because as time has gone on, we've been the country

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Speaker 1: that has actually the most lockdowns or some of the

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Speaker 1: most severe lockdowns. Not the most severe in the world

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Speaker 1: right now that goes to Australia New Zealand, but but

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Speaker 1: over the over the eleven month period, if you add

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Speaker 1: it all up, particularly because of California, New York, the

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Speaker 1: Blue or states where they've been much more aggressive on lockdowns,

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Speaker 1: we've had some of the most severe economic restrictions, and

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Speaker 1: yet California is seeing massive spike in cases the lockdowns

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Speaker 1: aren't doing anything right. So you put all that together,

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Speaker 1: the school closures, the lockdowns, and yet the spike in

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Speaker 1: cases and you have to say that that the US

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Speaker 1: is somewhere between a D and F, you know, overall

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Speaker 1: at this point. And it didn't have to be that way,

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Speaker 1: because we were going to have death due to COVID.

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Speaker 1: We were going to have people who are vulnerable who

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Speaker 1: were hit just like in every other country, every other

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Speaker 1: large country that's developed has had that problem. But where

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Speaker 1: we really have UM, what the beds, so to speak,

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Speaker 1: is that we did things that we're provably not working,

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Speaker 1: like keeping schools closed, like keeping the economy shut down

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Speaker 1: in certain states, instead of focusing on the real problem,

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Speaker 1: which was nursing homes and the elderly living in these

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Speaker 1: kind of dorm room like facilities where we need to

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Speaker 1: do more to protective. Finally we started to get the

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Speaker 1: message around that, but by the time we did, UH,

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Speaker 1: the virus had already spreads throughout those communities. All Right,

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Speaker 1: if the country gets somewhere between A D and F,

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Speaker 1: what does the American media get in the way that

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Speaker 1: they have covered uh COVID. In your mind, as a

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Speaker 1: guy who looks at the data, what grade would you

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Speaker 1: give the overall American media? Oh? I mean, f is

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Speaker 1: a generous grade, right, like you, you'd have to give

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Speaker 1: them worse than enough, because I mean the way that

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Speaker 1: media behaved was was almost a sabotage, uh, the way

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Speaker 1: we we responded to COVID. In fact, there's there's probably

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Speaker 1: no institution, if you can call the media an institution,

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Speaker 1: there's no institution that is more responsible for how bad

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Speaker 1: the US COVID response has been than the media. Just

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Speaker 1: to give some examples, So as you talked about Clay,

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Speaker 1: we know from the data that the overwhelming risk in

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Speaker 1: terms of severe illness, hospitalization death from from COVID nineteen

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Speaker 1: is in the elderly. And yet if you actually pull

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Speaker 1: average Americans and ask them, like, what's your what's my

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Speaker 1: perception of my risk from COVID, it's actually young people

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Speaker 1: who are the most scared of dieing of COVID because

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Speaker 1: the media has been telling them day in day out

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Speaker 1: for a year, for for months now that that they're

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Speaker 1: the ones who should be scared witless because they're the

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Speaker 1: ones not going to school, they're the ones on zoom

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Speaker 1: all the time with their teachers or whatever. So that's

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Speaker 1: just wanting example of the incredible malpractice that has gone

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Speaker 1: and you marry that with this part as an environment

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Speaker 1: where there's there's a there was has been such a

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Speaker 1: desire to blame Trump for everything that has gone wrong

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Speaker 1: that people haven't been willing to see or examine where

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Speaker 1: where things really have gone wrong. So the all the

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Speaker 1: things that the Governor Cuomo continues to do to mess

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Speaker 1: up the COVID response in New York for example, or

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Speaker 1: the restrictions in California that aren't working for example, or

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Speaker 1: the fact that you know schools if you you know,

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Speaker 1: have you seen Clay any articles about COVID breakouts and

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Speaker 1: schools for the last four months, and you know that

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Speaker 1: if there was one school in Kansas that had had

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Speaker 1: like people in the hospital because of COVID and because

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Speaker 1: page of the New York Times. So there's basically been

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Speaker 1: no incident of serious COVID problems from reopening schools. But

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Speaker 1: as anyone written any think pieces about wow, we've we've

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Speaker 1: kind of got the school thing wrong. No, it's been

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Speaker 1: this kind of people who moved on to the next

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Speaker 1: drive by thing to complain about. So yeah, I mean, look,

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Speaker 1: the media, the media has been terrible and you can

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Speaker 1: or the New York Times or whatever you want to do.

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Speaker 1: But I try to think about it more in terms of, Okay,

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Speaker 1: the media has been terrible, what is the solution, right,

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Speaker 1: So if we ever have this kind of problem again,

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Speaker 1: of information to everyday people? And that's a harder thing

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Speaker 1: to think about. I mean, I I can't say that, Clay,

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Speaker 1: about the public health establishment, which which comes in alongside

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Speaker 1: the media for a lot of a lot of my criticism,

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Speaker 1: you know, you have the so called leading experts at

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Speaker 1: the leading universities saying the same things at the media,

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Speaker 1: saying that everyone needs to be terrified, hide in their

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Speaker 1: basements and uh and not go out. And that's the

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Speaker 1: only way to solve this problem. And that's ah, that's

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Speaker 1: not a sustainable policy. As we're seeing, like why is

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Speaker 1: it the COVID is COVID case rising today. It's because

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Speaker 1: people cannot sit in their basements for a year. They

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Speaker 1: just can't. And and the public health profession understood that

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Speaker 1: before COVID, the consensus, the conventional wisdom. The excess expert

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Speaker 1: opinion then pre COVID was well, you can't lock down

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Speaker 1: the economy. That never works because people eventually stop listening

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Speaker 1: to you and just go about their business. So you

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Speaker 1: have to have a better strategy than that. That was

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Speaker 1: the conventional wisdom among experts a year ago, and it

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Speaker 1: isn't today. And that's a curious thing so much that

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Speaker 1: I want to unpack from that, and it is. It

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Speaker 1: is incredibly frustrating, I know to a lot of people

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Speaker 1: who are listening out there to see the data right

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Speaker 1: like you do, and to have your background and not

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Speaker 1: be able to convey it to everyone what the data says.

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Speaker 1: And I always say, my wife says, I don't need

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Speaker 1: therapy because I get to say exactly what I think

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Speaker 1: every day, right for better or worse, uh, through my

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Speaker 1: radio show, through my television show. Like I'm fortunate in

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Speaker 1: many ways to be a member of the media, but

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Speaker 1: there are people out there who will come after me

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Speaker 1: on a regular basis because what I am sharing is

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Speaker 1: not the quote unquote conventional wisdom, right, or they'll say, well,

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Speaker 1: you're not a doctor, how in the world are you

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Speaker 1: able to have an opinion on the whether school should

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Speaker 1: be open or not. And my answer is, if you

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Speaker 1: are a reasonably intelligent person, being able to analyze data

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Speaker 1: is one of the most integral assets of any human anywhere.

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Speaker 1: Right risk analysis is arguably the most fundamental trait that

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Speaker 1: has allowed humans to exist and propagate as a species.

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Speaker 1: Right Like, that's innately what we all have to do.

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Speaker 1: But it seems to me that in this social media age, uh,

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Speaker 1: you know, if you said what I've been saying and

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Speaker 1: what you've been saying four months, Hey, elderly people, people

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Speaker 1: with suppressed immune systems, people with major health related concerns

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Speaker 1: are who COVID is attacking. We need to protect those people,

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Speaker 1: but we need to maintain the rest of our economy

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Speaker 1: and let our society function. The immediate response was, Oh,

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Speaker 1: you don't care about Grandma's You want everybody to die.

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Speaker 1: It seems too in many ways have been a fundamentally

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Speaker 1: broken marketplace of ideas because the right ideas haven't won

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Speaker 1: It seems to me, you know, Clay, there's a there's

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Speaker 1: an analogy or a comparison. We can make the sports

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Speaker 1: here because you think about the whole moneyball sports analytics thing. Right,

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Speaker 1: all these people who came in who are sort of

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Speaker 1: nerdy ivy leaguers or whatever, just people who are math nerds,

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Speaker 1: never had played the sport, and they were always clashing

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Speaker 1: with the scouts, who are veterans of the game, you know,

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Speaker 1: using their intuition, their feel for the athletes to have

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Speaker 1: that view of UH. And then they always looked down

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Speaker 1: on the nerds. They said, oh, you know, you don't

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Speaker 1: you don't get it because you've never played the game.

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Speaker 1: You you know, you know, you've never seen anything. But

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Speaker 1: the nerds ultimately have have one that that that debate there, right,

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Speaker 1: and and and and the differences in sports. The right

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Speaker 1: answer wins right, the right answer wins championships, and the

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Speaker 1: right answer puts the best team on the field or

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Speaker 1: on the court. And so you can be vindicated if

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Speaker 1: you if you apply those unconventional UH methods to sports.

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Speaker 1: The difference in public policy is the tenure professors at

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Speaker 1: Harvard and Stanford, who more at Harvard than Stanford, we

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Speaker 1: should say, but but the tenure professors who say we

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Speaker 1: should we should keep schools closed and UH and and

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Speaker 1: terrify all the teenagers and the children. They're still Harvard professors,

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Speaker 1: they're still in position, so there's already some of them

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Speaker 1: are joining the Biden administrations. So in that sense, that's

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Speaker 1: the one thing about public policy is it's not a meritocracy.

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Speaker 1: Wrong ideas, wrong policies can continue to be conveyed and

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Speaker 1: continue to be in force even if they've been proven wrong.

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Speaker 1: That's fascinating and that's well said, and it's true, and

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Speaker 1: that's why I've always argued that sports represents the ultimate

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Speaker 1: foundation of the meritocratic ideal, because everybody's goal is to win,

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Speaker 1: and whoever makes it more likely that you are going

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Speaker 1: to win gets employed, right whether I mean, you can

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Speaker 1: have Antonio Brown, who's got all sorts of different issues

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Speaker 1: off the field, but if the Tampa Bay Buccaneers decide

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Speaker 1: that he makes it more likely they're gonna win a

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Speaker 1: football game, and if they think Tom Brady can work

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Speaker 1: with him, they're gonna find a way to bring him in. Right.

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Speaker 1: A talent ultimately trumps everything. Almost there is a limit

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Speaker 1: where your problems can exceed your talents, but that's relatively rare,

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Speaker 1: and there's a way too immediately vindicated and frankly in

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Speaker 1: the world that you're coming from which is the capitalistic environment,

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Speaker 1: a market based economy over time rewards in theory, the

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Speaker 1: best business so long as they're certain, you know, as

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Speaker 1: long as there's not a monopoly involved, as long as

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Speaker 1: there's not some sort of untoward practice taking place. But

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Speaker 1: that's why capitalism ultimately works so well. Right as you do,

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Speaker 1: much like in sports, get a verdict on whether or

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Speaker 1: not your business made sense totally, I mean, and that's

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Speaker 1: you know, that's uh. You know, there are economists who

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Speaker 1: say it's like, look, you know, if you if you're

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Speaker 1: a business, you don't have your incentive is to be

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Speaker 1: as inclusive as possible, cause you want every customer, you

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Speaker 1: want the employees working for you. And now, obviously it

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Speaker 1: hasn't always worked that way historically, but that's not because

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Speaker 1: the previous system was a free market system. It wasn't

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Speaker 1: because there was the prejudice, there was the segregation, there

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Speaker 1: was a gym crow, there was the stuff going on

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Speaker 1: that really prevented people from taking advantage of the talent

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Speaker 1: that was all around them. And uh, and companies obviously

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Speaker 1: work hard to try to change that. How frustrating is

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Speaker 1: it to you as someone who has been sharing the

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Speaker 1: data from the from the moment this all started. Why

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Speaker 1: school should be open, the stratification of age, range of

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Speaker 1: death and how that can govern our decisions for that

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Speaker 1: not to have been inculcated fully into public policy, and

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Speaker 1: to see us here as we are now into a

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Speaker 1: new administration, not able to for instance, get kids back

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Speaker 1: in school. Because what drives me crazy Ovic and we're

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Speaker 1: talking to O vic Roy. I encourage you to go

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Speaker 1: follow him at ovic a v I K at a

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Speaker 1: v I K on Twitter. Be sure to catch live

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Speaker 1: editions of Outkicked. The coverage with Clay Travis weekdays at

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Speaker 1: six am Eastern three am Pacific is. People who claim

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Speaker 1: that they care about equity the most are propounding now

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Speaker 1: the most inequitable outcome of our lives for the most part,

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Speaker 1: in requiring kids in public schools, very often in cities

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Speaker 1: who don't have WiFi at home, who may not have

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Speaker 1: parents at home, and who don't have access to outside

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Speaker 1: of school education, to be outside of school for a year.

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Speaker 1: I mean, it makes me want to pull my hair

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Speaker 1: out as a kid who went to public school K

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Speaker 1: through twelve and now is fortunate enough to live in

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Speaker 1: a district where my kids are in school, and I

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Speaker 1: got a kid in private school as well. But if

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Speaker 1: you have advantages, which I do, you can take advantage

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Speaker 1: of those opportunities and give your kids those advantages. But

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Speaker 1: most kids don't have that in this country. It's infuriating

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Speaker 1: to me, you know, totally. I mean the most. I

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Speaker 1: tend not to get frustrated play and just because, like,

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Speaker 1: if you do what I do for a living, which

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Speaker 1: is trying to persuade people of your ideas and things

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Speaker 1: like that, if you're gonna get frustrated when people don't

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Speaker 1: listen to you, this isn't a job for you, right

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Speaker 1: Like you have to be you have to be willing

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Speaker 1: to accept that not everyone's gonna agree with you, and

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Speaker 1: that it's hard work. If you've got a contrarian or

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Speaker 1: dissenting opinion about the way the world should be, or

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Speaker 1: the way policy should be, or the way the law

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Speaker 1: should be, it's your It's up to you to persuade

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Speaker 1: everyone else that you're right, and that's gonna mean talking

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Speaker 1: a lot of people who disagree with you. So that's

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Speaker 1: if you don't have that sort of temperament, then you

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Speaker 1: know you can't really do this kind of thing. So

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Speaker 1: in that sense, I'm I'm a most really fine, but

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Speaker 1: I will say that the one, the one moment or

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Speaker 1: or period of time where I was most my blood

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Speaker 1: pressure was really arising, admittedly was a selfish one where

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Speaker 1: there was a point in time in the in the

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Speaker 1: spring or summer, I can't remember exactly what it was

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Speaker 1: now when the Austin the Travis County, which is the

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Speaker 1: county that contains Austin, Texas, where I live, there was

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Speaker 1: this unelected Travis County Interim Health Authority that basically said

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Speaker 1: all the private schools that have to stay closed along

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Speaker 1: with the public schools. And that was like, you know,

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Speaker 1: for me, because I, like you, I can afford to

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Speaker 1: send my kids to private school, which again is you know,

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Speaker 1: I feel terrible for the people who don't have that luxury.

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Speaker 1: But for me, that was like, wow, this is like

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Speaker 1: the government is going out of a tway to make

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Speaker 1: my life miserable on top of everybody else. That was

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Speaker 1: just sort of at a purely selfish level, something that

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Speaker 1: made me made me, uh, you know, made my blood

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Speaker 1: pressure raise, because go up. But but you're right at

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Speaker 1: that that the incredible unfairness of it that that you

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Speaker 1: and I can still send our kids to school, but

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Speaker 1: so many people cannot. It's just incredible. You know, I

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Speaker 1: testified before Congress, I want to say, seven or eight

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Speaker 1: times last last summer, last you know that sort of spring, summer,

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Speaker 1: fall time last year, and almost every single one of

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Speaker 1: the hearings was about racial inequities that have been exacerbated

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Speaker 1: worse than by COVID um. And the thing that was

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Speaker 1: so surreal or crazy about those hearings is, you know,

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Speaker 1: there was a lot of talking about, oh, you know,

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Speaker 1: it's really terrible that, um, you know, African Americans are

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Speaker 1: getting COVID and and dying of COVID at disproportion of rates,

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Speaker 1: which is true. But you know, it's also true that

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Speaker 1: the economic inequality uh that that has come from government

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Speaker 1: policy has disproportionately harmed minorities who are lower income, who

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Speaker 1: can't afford to go to private schools right or some

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Speaker 1: of their kids to private schools. And that has been,

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Speaker 1: I have to say, like an astoundingly hypocritical thing. You know,

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Speaker 1: you have all these people saying, oh, it's really terrible

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Speaker 1: that that the virus uh, you know has disportuately harmed

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Speaker 1: lower income Americans who are dispportunately non white. Well, yes,

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Speaker 1: the government policies that have taken their jobs away from them,

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00:23:12,160 --> 00:23:14,760
Speaker 1: taking their livelihoods away from them, taking their schools away

431
00:23:14,760 --> 00:23:18,199
Speaker 1: from them, has been incredibly harmful and it's going to

432
00:23:19,080 --> 00:23:23,000
Speaker 1: widen economic inequality of this country. And and you're you're

433
00:23:23,040 --> 00:23:26,080
Speaker 1: absolutely right that, you know, certainly at our organization, at

434
00:23:26,119 --> 00:23:28,399
Speaker 1: Free optote or we've we've worked hard to try to

435
00:23:28,400 --> 00:23:30,840
Speaker 1: make those points. And I think, you know, we've had

436
00:23:30,880 --> 00:23:33,040
Speaker 1: some success with that. I think there are lots of

437
00:23:33,640 --> 00:23:37,640
Speaker 1: um people of both parties, of both you know, ideologies

438
00:23:37,720 --> 00:23:41,400
Speaker 1: or whatever you want to say, progressive, conservative, independent, who

439
00:23:41,520 --> 00:23:44,200
Speaker 1: who realized that schools need to be reopened. The differences

440
00:23:44,400 --> 00:23:48,359
Speaker 1: on the Democratic side, the teachers unions are just such

441
00:23:48,359 --> 00:23:51,119
Speaker 1: a dominant force politically. No one wants to cross the

442
00:23:51,160 --> 00:23:55,160
Speaker 1: teachers unions and that has been the decisive factor. Can

443
00:23:55,200 --> 00:23:58,120
Speaker 1: you say, follow the science and in any way justify

444
00:23:58,200 --> 00:24:00,320
Speaker 1: schools being closed at this point in the United States

445
00:24:00,320 --> 00:24:04,280
Speaker 1: of America. No. And I think one of the things,

446
00:24:04,359 --> 00:24:06,359
Speaker 1: you know, we're gonna we're gonna do a kind of

447
00:24:06,400 --> 00:24:10,760
Speaker 1: an action after action report of the pandemic hope in

448
00:24:10,800 --> 00:24:14,359
Speaker 1: the hope that the pandemic is actually is over in

449
00:24:14,400 --> 00:24:17,439
Speaker 1: the next several months, as people get vaccinated. But I

450
00:24:17,480 --> 00:24:19,639
Speaker 1: think one of the things that's really gonna we're going

451
00:24:19,680 --> 00:24:23,200
Speaker 1: to really focus on in our writing is the absolute

452
00:24:23,280 --> 00:24:27,199
Speaker 1: disgrace of of the or or the gap or or

453
00:24:27,280 --> 00:24:30,600
Speaker 1: discrepancy between the people who use the word science most

454
00:24:30,640 --> 00:24:34,000
Speaker 1: often in their in their speeches or their tweets and

455
00:24:34,080 --> 00:24:38,240
Speaker 1: the actual science, which shows something completely different. And and

456
00:24:38,280 --> 00:24:41,400
Speaker 1: again what's been so troubling is that the people who

457
00:24:41,440 --> 00:24:45,920
Speaker 1: should have the most steak in scientific authority, the Anthony

458
00:24:45,960 --> 00:24:49,120
Speaker 1: Fauci's you know, these people at the universities that I've

459
00:24:49,119 --> 00:24:51,800
Speaker 1: been mentioning, they're the ones who have done the most

460
00:24:51,840 --> 00:24:56,520
Speaker 1: to undermine trust in quote unquote scientific authority. You know,

461
00:24:56,560 --> 00:24:59,760
Speaker 1: Fauci's running around saying, oh, it's so surprising that there

462
00:24:59,760 --> 00:25:03,360
Speaker 1: have have been COVID breakouts in schools. Um no one

463
00:25:03,400 --> 00:25:06,040
Speaker 1: ever expected that. We all expected that that there would

464
00:25:06,040 --> 00:25:08,560
Speaker 1: be massive outbreaks in schools after we were open. So

465
00:25:08,600 --> 00:25:11,080
Speaker 1: that's really quite strange. And I mean, what I'm thinking,

466
00:25:11,320 --> 00:25:14,199
Speaker 1: what bubble is this guy in? But clearly he is

467
00:25:14,240 --> 00:25:17,640
Speaker 1: in one right, and and that is a huge, huge problem,

468
00:25:17,760 --> 00:25:21,399
Speaker 1: And there needs to be a real self assessment in

469
00:25:21,400 --> 00:25:27,800
Speaker 1: the scientific community about about the politicization of basic information

470
00:25:28,280 --> 00:25:31,560
Speaker 1: around who's being impacted by the virus, what kinds of

471
00:25:32,000 --> 00:25:35,640
Speaker 1: UH interventions are working, what kinds of interventions are not working.

472
00:25:36,600 --> 00:25:38,880
Speaker 1: And my hope is that now that Biden is president,

473
00:25:39,840 --> 00:25:41,880
Speaker 1: we can start to have more of an honest conversation

474
00:25:41,880 --> 00:25:44,880
Speaker 1: about that. I feel like, you know, because so many

475
00:25:44,880 --> 00:25:47,040
Speaker 1: people in the academic world are anti Trump. No one

476
00:25:47,080 --> 00:25:50,080
Speaker 1: wanted to say that Trump was doing anything right while

477
00:25:50,119 --> 00:25:52,399
Speaker 1: Trump was in office. But maybe now that he's gone,

478
00:25:52,920 --> 00:25:56,120
Speaker 1: maybe it becomes safer, you know, for that Harvard professor

479
00:25:56,160 --> 00:25:58,600
Speaker 1: to say, like, actually, the Trump administration, they did this thing.

480
00:25:59,160 --> 00:26:00,440
Speaker 1: You know, I don't agree with that thing they did,

481
00:26:00,440 --> 00:26:01,919
Speaker 1: but they did this thing right, or they did that

482
00:26:01,960 --> 00:26:05,600
Speaker 1: thing right. Um, maybe the CDC was wrong in this

483
00:26:05,640 --> 00:26:09,040
Speaker 1: particular case or whatever. Maybe that conversation gets a little

484
00:26:09,080 --> 00:26:12,439
Speaker 1: more deep, deep politicized. Now that that binds an office,

485
00:26:12,480 --> 00:26:15,679
Speaker 1: we can only hope, but but we're going to certainly

486
00:26:15,720 --> 00:26:18,720
Speaker 1: do our part to to contribute to that conversation. I

487
00:26:18,760 --> 00:26:20,520
Speaker 1: can't wait to read that, and I want to make

488
00:26:20,560 --> 00:26:22,680
Speaker 1: sure that I help you distribute it to the best

489
00:26:22,680 --> 00:26:26,600
Speaker 1: way that we can. And in my limited world, certainly

490
00:26:26,880 --> 00:26:28,880
Speaker 1: we have a big audience in the world of sports,

491
00:26:29,480 --> 00:26:32,240
Speaker 1: and I will say. You said, you know, the the

492
00:26:32,320 --> 00:26:35,520
Speaker 1: overall public policy response has been very bad in the

493
00:26:35,560 --> 00:26:39,440
Speaker 1: world of all Republicans, Democrats, independence, whatever you want to say.

494
00:26:39,480 --> 00:26:42,480
Speaker 1: The media, I think in general does deserve a grade

495
00:26:42,520 --> 00:26:45,640
Speaker 1: worse than F, which is what you said. I'm actually

496
00:26:45,720 --> 00:26:49,760
Speaker 1: somewhat encouraged that sports got much of this right, um,

497
00:26:49,960 --> 00:26:52,960
Speaker 1: and it was a battle to get it right. But

498
00:26:53,080 --> 00:26:55,720
Speaker 1: when we talked back on August twenty one, we didn't

499
00:26:55,760 --> 00:26:59,439
Speaker 1: know whether college football was going to happen. We now

500
00:26:59,480 --> 00:27:03,879
Speaker 1: have crowned champion. We did not know whether the NBA

501
00:27:04,000 --> 00:27:05,880
Speaker 1: was gonna be able to finish their season. They did

502
00:27:05,880 --> 00:27:07,639
Speaker 1: in the bubble. Now they're out of the bubble in

503
00:27:07,680 --> 00:27:11,080
Speaker 1: the next season. Major League Baseball finished their season with

504
00:27:11,200 --> 00:27:15,520
Speaker 1: fans present in Texas. The NFL has played their entire

505
00:27:15,560 --> 00:27:17,919
Speaker 1: schedule so far. We're talking in the week of the

506
00:27:17,960 --> 00:27:20,840
Speaker 1: a f C in the NFC Championship games. All of

507
00:27:20,880 --> 00:27:24,720
Speaker 1: those sports, not to mention countless high schools, as well

508
00:27:24,760 --> 00:27:27,320
Speaker 1: as other sports that are not anywhere near as popular

509
00:27:27,359 --> 00:27:30,840
Speaker 1: on a collegiate level or a professional level. Ovic there

510
00:27:30,920 --> 00:27:36,040
Speaker 1: isn't a single death or even serious illness that has

511
00:27:36,040 --> 00:27:39,879
Speaker 1: been connected to coaching or athletics and the coaches are

512
00:27:39,920 --> 00:27:43,760
Speaker 1: obviously older than the players, but that's what the data

513
00:27:43,840 --> 00:27:47,359
Speaker 1: told us was likely to happen. And people are like, oh, wow,

514
00:27:47,400 --> 00:27:50,560
Speaker 1: this actually ended up being possible. Thankfully they took the

515
00:27:50,640 --> 00:27:52,480
Speaker 1: chance and tried to figure out a way to make

516
00:27:52,520 --> 00:27:55,879
Speaker 1: it happen. What letter grade would you give sports leagues

517
00:27:55,960 --> 00:27:59,040
Speaker 1: for their willingness and ability to play once they came

518
00:27:59,040 --> 00:28:03,920
Speaker 1: back certain nascars involved tennis, all these other different sports. Uh.

519
00:28:03,960 --> 00:28:07,200
Speaker 1: And are you at least as appreciative as I am

520
00:28:07,240 --> 00:28:09,760
Speaker 1: that we found a way to get that done and

521
00:28:09,800 --> 00:28:12,560
Speaker 1: that the data showed lo and behold that it was

522
00:28:12,640 --> 00:28:17,040
Speaker 1: safe and it was possible to do. Uh. Definitely appreciative,

523
00:28:17,080 --> 00:28:19,679
Speaker 1: and not just that they did it for for the

524
00:28:19,720 --> 00:28:22,720
Speaker 1: sake of the athletes who obviously worked so hard for

525
00:28:22,800 --> 00:28:25,520
Speaker 1: those opportunities, but for the rest of us, who, you know,

526
00:28:25,760 --> 00:28:29,200
Speaker 1: just as human beings. We needed something that was not political,

527
00:28:29,400 --> 00:28:32,640
Speaker 1: if at least mostly not political. Uh. And and that

528
00:28:32,640 --> 00:28:34,560
Speaker 1: that we could that we could point to and cheer

529
00:28:34,680 --> 00:28:37,480
Speaker 1: about in our lives and in this very challenging year

530
00:28:37,520 --> 00:28:40,440
Speaker 1: we've just had so grateful. I'm grateful to the sports

531
00:28:40,560 --> 00:28:42,600
Speaker 1: leagues that that worked hard to make it happen. We

532
00:28:42,720 --> 00:28:44,560
Speaker 1: and you know, and you've covered it on your show.

533
00:28:45,080 --> 00:28:47,440
Speaker 1: You know, it's not like the sports league said business

534
00:28:47,480 --> 00:28:49,520
Speaker 1: as usual. There's a lot of stuff and a lot

535
00:28:49,520 --> 00:28:51,800
Speaker 1: of work, a lot of testing, a lot of restrictions

536
00:28:51,800 --> 00:28:56,160
Speaker 1: on attendance by the fans that went into keeping sports

537
00:28:56,160 --> 00:29:00,520
Speaker 1: going in a cautious, uh and prudent way. And and

538
00:29:00,560 --> 00:29:02,640
Speaker 1: hopefully they've learned from that to realize, okay, maybe we

539
00:29:02,680 --> 00:29:05,040
Speaker 1: can we can h loosen it up a little bit

540
00:29:05,440 --> 00:29:07,160
Speaker 1: now that we've learned that we can do this safely

541
00:29:07,240 --> 00:29:09,560
Speaker 1: and operate safely. But you know what really comes back

542
00:29:09,560 --> 00:29:12,440
Speaker 1: to in my mind, uh play is what you said

543
00:29:12,480 --> 00:29:17,000
Speaker 1: the beginning, capitalism, right, it's the financial incentive for sports

544
00:29:17,080 --> 00:29:20,080
Speaker 1: leagues to stay open was a big driver of why

545
00:29:20,080 --> 00:29:22,760
Speaker 1: they did stay open. And now at the time, you know,

546
00:29:22,960 --> 00:29:27,040
Speaker 1: last summer, last early fall, August, September, that was you know,

547
00:29:27,360 --> 00:29:29,640
Speaker 1: the sports pundit said, this is so terrible. You know,

548
00:29:30,000 --> 00:29:32,640
Speaker 1: these these leagues, particularly the you know in terms of

549
00:29:32,680 --> 00:29:35,760
Speaker 1: college sports, where you know, there's the conflict between amateurism

550
00:29:35,800 --> 00:29:39,320
Speaker 1: and the money. These leagues are putting money ahead of humanity.

551
00:29:39,360 --> 00:29:42,560
Speaker 1: They're they're they're they're so greedy and so terrible. And

552
00:29:43,080 --> 00:29:46,160
Speaker 1: I look at it in exactly the opposite way. It

553
00:29:46,280 --> 00:29:50,920
Speaker 1: was the the financial or economic incentive which motivated them

554
00:29:50,960 --> 00:29:53,800
Speaker 1: to get it right, to figure out, hey, there's got

555
00:29:53,920 --> 00:29:56,080
Speaker 1: to be a way to do this safely. We're gonna

556
00:29:56,080 --> 00:29:58,080
Speaker 1: lose a lot of money if we don't figure out

557
00:29:58,120 --> 00:30:00,600
Speaker 1: how to do it safely, so let's figure it out.

558
00:30:00,800 --> 00:30:05,080
Speaker 1: And exactly the same dynamic play is true with schools.

559
00:30:05,120 --> 00:30:07,640
Speaker 1: So why is it that private schools around the country

560
00:30:07,640 --> 00:30:10,360
Speaker 1: are open and public schools are not. First of all,

561
00:30:10,400 --> 00:30:12,680
Speaker 1: you don't have teachers unions in private schools. But a

562
00:30:12,720 --> 00:30:15,120
Speaker 1: big part of it is if you're that private school.

563
00:30:15,160 --> 00:30:16,720
Speaker 1: If you're running a private school and you say no,

564
00:30:16,800 --> 00:30:20,080
Speaker 1: we're gonna go to zoom, no one, everyone's gonna disenroll,

565
00:30:20,280 --> 00:30:21,760
Speaker 1: no one's going to show up at that school, and

566
00:30:21,840 --> 00:30:23,760
Speaker 1: your school is going to go broke because you're not

567
00:30:23,760 --> 00:30:26,960
Speaker 1: gonna get any tuition dollars in the door, Whereas in

568
00:30:27,000 --> 00:30:29,760
Speaker 1: the public schools, the money is flowing regardless of what

569
00:30:29,800 --> 00:30:32,320
Speaker 1: you do, So why keep the school open when you're

570
00:30:32,360 --> 00:30:35,600
Speaker 1: gonna get paid either way. So the economic or financial

571
00:30:35,640 --> 00:30:40,680
Speaker 1: incentives were absolutely a critical driver of why public schools

572
00:30:40,680 --> 00:30:44,120
Speaker 1: have been closed, but why sports leagues and private schools

573
00:30:44,120 --> 00:30:49,880
Speaker 1: were open. It's so well said, I mean, is now

574
00:30:50,240 --> 00:30:54,640
Speaker 1: not surprisingly the sports media mostly there are exceptions, you're

575
00:30:54,680 --> 00:30:57,720
Speaker 1: listening to one of them, but the sports media mostly

576
00:30:58,160 --> 00:31:01,600
Speaker 1: followed the lead of the now national media in making

577
00:31:01,600 --> 00:31:04,800
Speaker 1: the arguments there's no way that it's safe to play right.

578
00:31:05,080 --> 00:31:07,360
Speaker 1: CBS Sports, for example, I talked about this a lot

579
00:31:07,440 --> 00:31:10,760
Speaker 1: on my radio program. They had an expert, and you

580
00:31:10,760 --> 00:31:13,680
Speaker 1: know how this works, the experts that say the things

581
00:31:13,680 --> 00:31:16,760
Speaker 1: that don't make headlines. Oh yeah, there's definitely a way

582
00:31:16,800 --> 00:31:20,160
Speaker 1: to play sports that doesn't make the headline. The expert

583
00:31:20,160 --> 00:31:24,080
Speaker 1: who comes out and literally at CBS Sports guaranteed a

584
00:31:24,120 --> 00:31:27,280
Speaker 1: football player would die and predicted there would be at

585
00:31:27,320 --> 00:31:29,840
Speaker 1: least three to seven as if he were Nick, you

586
00:31:29,840 --> 00:31:32,520
Speaker 1: know Joe Namath back in the day. He guaranteed a

587
00:31:32,680 --> 00:31:35,920
Speaker 1: death and said he predicted that there would be three

588
00:31:35,920 --> 00:31:39,480
Speaker 1: to seven. That's a headline at CBS Sports. They finished

589
00:31:39,480 --> 00:31:41,320
Speaker 1: the season in college football, and that's what he was

590
00:31:41,320 --> 00:31:45,360
Speaker 1: specifically making his prediction about. Everybody is fine, there are

591
00:31:45,400 --> 00:31:49,560
Speaker 1: no issues, and the story just disappears right, there's no

592
00:31:49,720 --> 00:31:53,720
Speaker 1: consequence for an expert, and I'm putting that in quotation

593
00:31:53,800 --> 00:31:58,440
Speaker 1: marks being a hundred percent wrong, particularly when those people

594
00:31:58,480 --> 00:32:01,680
Speaker 1: have tenure at university. It's like it's it's impossible for

595
00:32:01,720 --> 00:32:03,680
Speaker 1: them to ever have a consequence. And that probably goes

596
00:32:03,720 --> 00:32:06,880
Speaker 1: back to your point. In a market based economy, if

597
00:32:06,920 --> 00:32:10,840
Speaker 1: you're wrong, you lose your job. In a university setting,

598
00:32:11,000 --> 00:32:14,400
Speaker 1: if you're wrong, you just write a new article explaining

599
00:32:14,440 --> 00:32:17,400
Speaker 1: why you were wrong, and uh, and and and or

600
00:32:17,600 --> 00:32:22,040
Speaker 1: completely ignoring it, there's no and there's no consequence. Yeah,

601
00:32:22,080 --> 00:32:24,680
Speaker 1: you know. In fact, you're reminded me of I can't

602
00:32:24,720 --> 00:32:27,160
Speaker 1: remember which a media organization was, may have been ESPN,

603
00:32:27,160 --> 00:32:30,520
Speaker 1: and may have been Yahoo or CBS. Uh. Lots of

604
00:32:30,520 --> 00:32:33,320
Speaker 1: people there was. There was a Big twelve expert that

605
00:32:33,440 --> 00:32:36,240
Speaker 1: the Big twelve A d. S recruited who said, actually,

606
00:32:36,560 --> 00:32:39,440
Speaker 1: you can operate the league safely and here's how you

607
00:32:39,520 --> 00:32:42,720
Speaker 1: do it. And there was a round of articles criticizing

608
00:32:43,640 --> 00:32:46,160
Speaker 1: and that expert saying, oh, the Big twelve just you know,

609
00:32:46,200 --> 00:32:48,960
Speaker 1: wet doctor shopping and found some idiot off the street

610
00:32:49,040 --> 00:32:51,480
Speaker 1: who who was going to validate what they wanted to

611
00:32:51,520 --> 00:32:54,320
Speaker 1: do and not listen to the science. Right, And that

612
00:32:54,320 --> 00:32:56,400
Speaker 1: guy turned out to be right, and everyone else turned

613
00:32:56,400 --> 00:32:58,040
Speaker 1: out to be wrong, at least the ones that say

614
00:32:58,080 --> 00:33:01,520
Speaker 1: the Big ten was listening to it is I mean

615
00:33:01,640 --> 00:33:03,520
Speaker 1: and and and all of this, you know, and and

616
00:33:03,520 --> 00:33:06,280
Speaker 1: again to kind of relitigate some of this. You remember

617
00:33:06,280 --> 00:33:09,600
Speaker 1: the myocarditis story that flared up. Oh my god, if

618
00:33:09,600 --> 00:33:12,240
Speaker 1: you get a if you get COVID, you're gonna get myocarditis.

619
00:33:12,320 --> 00:33:14,360
Speaker 1: Your heart's gonna be ruined forever. There's no way we

620
00:33:14,360 --> 00:33:18,400
Speaker 1: can play sports. Nobody had myocarditis issues either, but if

621
00:33:18,440 --> 00:33:22,240
Speaker 1: they did, that often happens with viral infections in general.

622
00:33:22,320 --> 00:33:25,920
Speaker 1: It wasn't specific to COVID. And the media what I

623
00:33:26,000 --> 00:33:30,080
Speaker 1: called fear porn governed the day, and and candidly behind

624
00:33:30,120 --> 00:33:33,680
Speaker 1: the scenes, I was having conversations with commissioners as early

625
00:33:33,720 --> 00:33:35,920
Speaker 1: as April, and I said, look, you're used to people

626
00:33:36,360 --> 00:33:39,920
Speaker 1: being in favor of your sport. Everybody in the sports

627
00:33:39,960 --> 00:33:42,760
Speaker 1: media is going to be opposed to you guys playing

628
00:33:42,800 --> 00:33:45,120
Speaker 1: college football this year. They're not going to carry the

629
00:33:45,120 --> 00:33:47,520
Speaker 1: water for the NFL. They're not gonna say, hey, this

630
00:33:47,600 --> 00:33:50,479
Speaker 1: is a brilliant idea. They're all gonna buy into the

631
00:33:50,520 --> 00:33:53,280
Speaker 1: fear and curl up in the fetal position and argue

632
00:33:53,280 --> 00:33:56,479
Speaker 1: that there's no way that should be happening. Well, as

633
00:33:56,760 --> 00:33:58,640
Speaker 1: you know, Clay, I mean it's in a long standing

634
00:33:58,680 --> 00:34:02,000
Speaker 1: dynamic in sports meet is that you know, sports writers,

635
00:34:02,040 --> 00:34:06,560
Speaker 1: sports commentators, they they particularly the ones who work at say,

636
00:34:06,800 --> 00:34:10,560
Speaker 1: major newspapers are major news organizations, right, they feel that

637
00:34:10,719 --> 00:34:14,320
Speaker 1: sort of inferiority complex of we're not the real journalists

638
00:34:14,320 --> 00:34:15,880
Speaker 1: like the people who work on you know, who go

639
00:34:15,960 --> 00:34:19,080
Speaker 1: to Capitol Hill or cover the White House. So they

640
00:34:19,200 --> 00:34:22,160
Speaker 1: feel that that sense of, well, I have to do

641
00:34:22,239 --> 00:34:25,040
Speaker 1: what the the other journalists tell me to do, because

642
00:34:25,080 --> 00:34:26,839
Speaker 1: if I don't, then I'm going to be seen as

643
00:34:26,880 --> 00:34:30,080
Speaker 1: that fluffy sports reporter and not the hard journalists that

644
00:34:30,120 --> 00:34:33,960
Speaker 1: I really am. And so that sort of that sociological

645
00:34:34,080 --> 00:34:38,200
Speaker 1: element of the sports writing or sports media community plays

646
00:34:38,239 --> 00:34:41,919
Speaker 1: a big role in in their deference to to what,

647
00:34:42,400 --> 00:34:45,040
Speaker 1: to what other people are saying that they feel they

648
00:34:45,080 --> 00:34:47,960
Speaker 1: have to defer to. And so it's some of its fears.

649
00:34:47,960 --> 00:34:49,920
Speaker 1: Some of it's that deference. Some of it is just

650
00:34:50,200 --> 00:34:53,040
Speaker 1: genuinely like, you know, being terrified or whatever. It is

651
00:34:53,880 --> 00:34:57,480
Speaker 1: all that to say that, you know, what people like

652
00:34:57,520 --> 00:34:59,279
Speaker 1: you and me and and the people who listen to

653
00:34:59,280 --> 00:35:01,680
Speaker 1: your podcast and other people out there who who who

654
00:35:01,680 --> 00:35:04,080
Speaker 1: have had the same point of view need to do

655
00:35:05,000 --> 00:35:09,040
Speaker 1: is to make sure that uh, as we go through this,

656
00:35:09,120 --> 00:35:11,800
Speaker 1: we're able to assess and and have that after action

657
00:35:11,960 --> 00:35:15,040
Speaker 1: report where we can say, okay, guys, let's learn from this.

658
00:35:15,200 --> 00:35:18,279
Speaker 1: Let's learn about what the so called experts told you

659
00:35:18,320 --> 00:35:20,120
Speaker 1: that was right, and what they told you that was wrong,

660
00:35:20,160 --> 00:35:22,360
Speaker 1: and certain things that were unknown. So to take the

661
00:35:22,400 --> 00:35:25,719
Speaker 1: example of myo karditis, I mean you and I were

662
00:35:26,160 --> 00:35:29,279
Speaker 1: more skeptical that was a serious issue. But you can

663
00:35:29,440 --> 00:35:33,320
Speaker 1: understand risk averse for college president's risk averse a D

664
00:35:33,480 --> 00:35:35,320
Speaker 1: saying you know what, we've we've got to be concerned

665
00:35:35,320 --> 00:35:37,040
Speaker 1: about this because I don't want it. I don't want

666
00:35:37,040 --> 00:35:39,080
Speaker 1: to deal with the litigation if you want to be cynical,

667
00:35:39,160 --> 00:35:40,560
Speaker 1: or I don't want to do with that on my conscience.

668
00:35:40,600 --> 00:35:43,480
Speaker 1: If somebody really gets sick, the kind of the you know,

669
00:35:43,560 --> 00:35:46,960
Speaker 1: the Reggie Lewis type thing, so you know, do the

670
00:35:47,080 --> 00:35:49,920
Speaker 1: m R I s do the testing every you know, UH,

671
00:35:50,040 --> 00:35:55,239
Speaker 1: Power five University certainly has the ability to arrange for

672
00:35:55,280 --> 00:35:58,200
Speaker 1: those tests if you if as someone's COVID positive, you

673
00:35:58,200 --> 00:36:00,160
Speaker 1: can you can look to see if there's information and

674
00:36:00,200 --> 00:36:02,920
Speaker 1: there aren't muscle and and and monitor in which they

675
00:36:02,960 --> 00:36:05,759
Speaker 1: did right. Most of the most of the big conferences

676
00:36:05,840 --> 00:36:09,080
Speaker 1: did that, and that's what allowed them to get that

677
00:36:09,080 --> 00:36:11,200
Speaker 1: that relief that this wasn't a big deal. So I

678
00:36:11,200 --> 00:36:13,880
Speaker 1: don't have a problem with if they're going to be

679
00:36:13,920 --> 00:36:16,759
Speaker 1: really risk averse, invest the extra money, since they make

680
00:36:16,800 --> 00:36:19,560
Speaker 1: so much money off college sports, at least the revenue sports,

681
00:36:19,960 --> 00:36:22,080
Speaker 1: you know, to invest in those tasks to see what's

682
00:36:22,080 --> 00:36:24,320
Speaker 1: going on, make sure that nothing's going wrong. But to

683
00:36:24,400 --> 00:36:27,799
Speaker 1: shut down the season altogether, that's stupid. You know, keep

684
00:36:27,800 --> 00:36:29,719
Speaker 1: an eye on it and if it looks like things

685
00:36:29,719 --> 00:36:31,640
Speaker 1: are going to go wrong, that's one thing. And remember

686
00:36:31,680 --> 00:36:33,880
Speaker 1: there were a lot of sports writers who said about

687
00:36:33,880 --> 00:36:36,440
Speaker 1: the college football season, say, oh, this is so pointless.

688
00:36:36,719 --> 00:36:38,680
Speaker 1: The whole season is gonna get shut down after two

689
00:36:38,760 --> 00:36:42,319
Speaker 1: weeks anyway, you know, why is anybody even bothering? And

690
00:36:42,440 --> 00:36:45,640
Speaker 1: as you said, you know the season basically, yes, there

691
00:36:45,640 --> 00:36:48,160
Speaker 1: were games that were canceled and things like that, but

692
00:36:48,160 --> 00:36:51,080
Speaker 1: but the season was played and and I think most

693
00:36:51,080 --> 00:36:53,719
Speaker 1: people are pretty pretty happy about that. And to go

694
00:36:53,760 --> 00:36:56,560
Speaker 1: to your point on the market based capitalistic economy being

695
00:36:56,560 --> 00:36:59,200
Speaker 1: the most efficient, which by the way, all of world

696
00:36:59,320 --> 00:37:03,800
Speaker 1: history proved, that's a whole another story. But for anybody

697
00:37:03,840 --> 00:37:07,520
Speaker 1: who wants to study the history of of economies UH

698
00:37:07,560 --> 00:37:10,719
Speaker 1: and UH and market based decision making in general. It's

699
00:37:10,719 --> 00:37:13,920
Speaker 1: probably not a surprise if you adopt that line of thinking,

700
00:37:14,360 --> 00:37:17,960
Speaker 1: that the NFL, which had the absolute most money at

701
00:37:18,000 --> 00:37:21,280
Speaker 1: stake and is the biggest business in all of pro sports,

702
00:37:21,719 --> 00:37:25,239
Speaker 1: had the most successful season because not only did they

703
00:37:25,280 --> 00:37:29,120
Speaker 1: play every single game as scheduled, all thirty two NFL

704
00:37:29,160 --> 00:37:32,759
Speaker 1: teams played all sixteen games, but they did it on

705
00:37:33,040 --> 00:37:35,960
Speaker 1: their schedule. They didn't even have so far UH that

706
00:37:36,120 --> 00:37:38,600
Speaker 1: the a f C and NFC championship games are Sunday,

707
00:37:38,640 --> 00:37:40,640
Speaker 1: We're talking in the middle of the week leading into that,

708
00:37:41,080 --> 00:37:42,840
Speaker 1: and then the Super Bowl. They've got two weeks to

709
00:37:42,840 --> 00:37:45,120
Speaker 1: be able to schedule that, but right now it's scheduled

710
00:37:45,160 --> 00:37:47,440
Speaker 1: as it typically is for two weeks after the Sunday

711
00:37:47,480 --> 00:37:50,400
Speaker 1: a f C n NFC championship games, and a lot

712
00:37:50,480 --> 00:37:53,719
Speaker 1: of them had fans present, but every television part of

713
00:37:53,719 --> 00:37:55,719
Speaker 1: their game, which is where the biggest part of their

714
00:37:55,719 --> 00:37:59,520
Speaker 1: revenue comes from. Guess what they did the best job

715
00:37:59,680 --> 00:38:02,480
Speaker 1: big business does, the best job in pro sports with

716
00:38:02,480 --> 00:38:04,759
Speaker 1: putting their product out there for people to watch, and

717
00:38:05,200 --> 00:38:08,800
Speaker 1: it's arguably the most difficult because of all the physical

718
00:38:08,840 --> 00:38:12,320
Speaker 1: contact that goes into football compared to let's say baseball

719
00:38:13,160 --> 00:38:16,680
Speaker 1: or tennis or something like that. Yeah, that's that's a

720
00:38:16,680 --> 00:38:18,200
Speaker 1: great point. You know, as you were talking, I was,

721
00:38:18,239 --> 00:38:22,320
Speaker 1: I was recalling the the European soccer summer soccer season

722
00:38:22,320 --> 00:38:25,239
Speaker 1: from last year. Right, not some of the league's didn't play,

723
00:38:25,280 --> 00:38:27,920
Speaker 1: but the ones that did had no problems. Right, everything

724
00:38:27,960 --> 00:38:30,160
Speaker 1: worked out just fine. Yeah, there were some positive tests here,

725
00:38:30,280 --> 00:38:32,920
Speaker 1: there are things like that, but but but the games

726
00:38:32,960 --> 00:38:36,680
Speaker 1: that were played were played and worked out just fine. Yes,

727
00:38:36,680 --> 00:38:38,439
Speaker 1: there weren't fans in the audience, and they would pump

728
00:38:38,440 --> 00:38:41,000
Speaker 1: in the crowd noise on the broadcast, but but otherwise

729
00:38:41,200 --> 00:38:44,680
Speaker 1: it worked and that was our first indication that actually

730
00:38:44,760 --> 00:38:47,000
Speaker 1: this could be done. Or two that to give us

731
00:38:47,000 --> 00:38:49,480
Speaker 1: the confidence, right, the real world example, so this could

732
00:38:49,520 --> 00:38:52,400
Speaker 1: be done. So so kudos to the NFL. I mean,

733
00:38:52,920 --> 00:38:56,760
Speaker 1: definitely very impressive that that they've managed to have everything

734
00:38:57,120 --> 00:39:01,239
Speaker 1: run on time. And um uh you know, and you

735
00:39:01,280 --> 00:39:03,440
Speaker 1: know part part of it too is you know, one

736
00:39:03,480 --> 00:39:06,040
Speaker 1: thing we we probably should you know take an account

737
00:39:06,080 --> 00:39:10,839
Speaker 1: here is pro athletes, particularly football players, there's so much

738
00:39:10,960 --> 00:39:14,920
Speaker 1: discipline involved, you know in being a pro athlete, you know,

739
00:39:15,000 --> 00:39:17,120
Speaker 1: in the NFL, it's just you know, you can get

740
00:39:17,120 --> 00:39:19,759
Speaker 1: cut so ruthlessly and have your career cut short if

741
00:39:19,800 --> 00:39:23,759
Speaker 1: you make one mistake. Um, and if you make it

742
00:39:23,800 --> 00:39:27,160
Speaker 1: to the pros, you're likely to have that discipline and

743
00:39:27,200 --> 00:39:29,480
Speaker 1: that maturity. And not everyone does. And we've seen some,

744
00:39:29,640 --> 00:39:32,800
Speaker 1: you know, notorious cases that not being the case. But

745
00:39:32,800 --> 00:39:35,319
Speaker 1: but most of the athletes have really stuck to it, right,

746
00:39:35,320 --> 00:39:37,239
Speaker 1: Whereas at the college level it's a little harder. Right,

747
00:39:37,320 --> 00:39:41,320
Speaker 1: these are kids, Um, you know, there's a there's a campus,

748
00:39:41,360 --> 00:39:44,400
Speaker 1: there's parties, there's people who admire them and want to,

749
00:39:44,680 --> 00:39:46,680
Speaker 1: you know, want a party with them. There's a lot

750
00:39:46,719 --> 00:39:50,400
Speaker 1: more temptation when when you're a college student to do

751
00:39:50,480 --> 00:39:54,160
Speaker 1: the wrong thing. And so you know, it's it's impressive

752
00:39:54,200 --> 00:39:56,200
Speaker 1: on both counts, right. It's impressive of the college season

753
00:39:56,400 --> 00:39:58,200
Speaker 1: managed to do as well as it did, even with

754
00:39:58,239 --> 00:40:00,440
Speaker 1: a lot of interruptions. And I'll be see you look

755
00:40:00,480 --> 00:40:03,000
Speaker 1: to the pros and say, hey, uh, you know, hats off.

756
00:40:03,800 --> 00:40:07,400
Speaker 1: We're talking to Ovic Roy. Free op dot org is

757
00:40:07,440 --> 00:40:11,120
Speaker 1: his website. Follow him on Twitter at ovic a v

758
00:40:11,320 --> 00:40:14,640
Speaker 1: I K at a v I k is his Twitter handle. Uh.

759
00:40:14,640 --> 00:40:16,480
Speaker 1: And this is the Winds and the Losses podcast. I

760
00:40:16,520 --> 00:40:21,360
Speaker 1: am Clay Travis, Fox Sports Radio has the best sports

761
00:40:21,360 --> 00:40:24,160
Speaker 1: talk lineup in the nation. Catch all of our shows

762
00:40:24,200 --> 00:40:27,440
Speaker 1: at Fox Sports Radio dot Com and within the I

763
00:40:27,520 --> 00:40:30,480
Speaker 1: Heart Radio apps. Search f s R to listen live.

764
00:40:32,560 --> 00:40:34,640
Speaker 1: One of the challenges that I see that is the

765
00:40:34,719 --> 00:40:37,040
Speaker 1: largest in the world of sports and other places. And

766
00:40:37,080 --> 00:40:41,120
Speaker 1: I'm curious what you think about this. So much of

767
00:40:41,239 --> 00:40:46,160
Speaker 1: our media is anecdote driven, and the anecdote is used

768
00:40:46,200 --> 00:40:49,279
Speaker 1: to justify the overall story. So, and I'll give you

769
00:40:49,280 --> 00:40:52,680
Speaker 1: an example. If, as you mentioned, I remember, and this

770
00:40:52,719 --> 00:40:56,040
Speaker 1: is unfortunate, and I feel for his family, there was

771
00:40:56,160 --> 00:40:59,760
Speaker 1: a kid who died at Appalachian State University in Boone,

772
00:40:59,760 --> 00:41:04,080
Speaker 1: North Carolina of COVID or with COVID. It's not like

773
00:41:04,120 --> 00:41:08,640
Speaker 1: I've reviewed his medical files to know exactly, but his

774
00:41:08,800 --> 00:41:13,080
Speaker 1: death then becomes a front page New York Times article

775
00:41:13,640 --> 00:41:18,960
Speaker 1: talking about the challenges of going back to college. The

776
00:41:19,000 --> 00:41:21,480
Speaker 1: two million or four million, or whatever the heck number

777
00:41:21,520 --> 00:41:24,479
Speaker 1: it is of college kids that go back and don't

778
00:41:24,520 --> 00:41:27,520
Speaker 1: have any issues at all. It's all about framing. In

779
00:41:27,560 --> 00:41:30,120
Speaker 1: other words, if I wanted to write a story about

780
00:41:30,120 --> 00:41:32,759
Speaker 1: how dangerous it is for kids to drive back to

781
00:41:32,840 --> 00:41:37,200
Speaker 1: college at the end of summer, inevitably every year there

782
00:41:37,200 --> 00:41:40,759
Speaker 1: are kids who die driving back to college campuses. That

783
00:41:40,800 --> 00:41:44,200
Speaker 1: doesn't mean as a general rule that it is incredibly

784
00:41:44,280 --> 00:41:48,600
Speaker 1: dangerous for those kids to be driving back to college campuses. Inevitably,

785
00:41:48,680 --> 00:41:51,960
Speaker 1: every year there are college kids that get the flu

786
00:41:52,080 --> 00:41:55,319
Speaker 1: and die the seasonal flu. That doesn't mean that all

787
00:41:55,440 --> 00:41:58,600
Speaker 1: kids on college campus are in danger of the seasonal flu.

788
00:41:59,239 --> 00:42:03,200
Speaker 1: Outlier occur and as a data guy, outliers can be

789
00:42:03,280 --> 00:42:07,000
Speaker 1: fascinating for you, I'm sure to review, but they are

790
00:42:07,160 --> 00:42:11,120
Speaker 1: just that outliers. How much of our challenge in media

791
00:42:11,160 --> 00:42:17,520
Speaker 1: today is using anecdotal outlier stories to justify a preferred narrative,

792
00:42:17,600 --> 00:42:21,640
Speaker 1: such as sports can't happen because this college kid died,

793
00:42:22,360 --> 00:42:25,440
Speaker 1: even if it's in no way representative of the larger

794
00:42:25,520 --> 00:42:28,319
Speaker 1: data set. That is such a challenge, it seems to me,

795
00:42:28,800 --> 00:42:32,520
Speaker 1: because the story of one death is more overpowering than

796
00:42:32,600 --> 00:42:36,440
Speaker 1: sometimes the story of a million people being fine. You

797
00:42:36,480 --> 00:42:38,840
Speaker 1: know what I'm what I'm thinking about as you go

798
00:42:38,960 --> 00:42:41,960
Speaker 1: through that, and all all Well said is you know

799
00:42:42,000 --> 00:42:46,000
Speaker 1: my my My takeaway from from on that score is

800
00:42:46,680 --> 00:42:50,120
Speaker 1: every high school in America should require that its students

801
00:42:50,160 --> 00:42:54,040
Speaker 1: take a statistics class. Yes, because statistics are the thing

802
00:42:54,280 --> 00:42:57,320
Speaker 1: they drive so much of life nowadays, especially because we

803
00:42:57,360 --> 00:43:00,080
Speaker 1: have all this data being thrown at us because of

804
00:43:00,200 --> 00:43:02,319
Speaker 1: the world we live in, and we just don't know

805
00:43:02,360 --> 00:43:04,440
Speaker 1: how to process it, and we process it wrong. And

806
00:43:04,440 --> 00:43:07,560
Speaker 1: that that affects the way sports, you know, sports get

807
00:43:07,560 --> 00:43:11,520
Speaker 1: to analyze. That, that affects the way lawsuits happened, particularly

808
00:43:11,640 --> 00:43:15,320
Speaker 1: the class action type lawsuits. It affects government policy, obviously,

809
00:43:15,320 --> 00:43:19,160
Speaker 1: affects medicine, affects so many different things in our world. Uh.

810
00:43:19,200 --> 00:43:22,520
Speaker 1: And if people had that set, that understanding of statistics

811
00:43:22,520 --> 00:43:27,799
Speaker 1: and how to separate anecdotes from the overall uh context

812
00:43:28,239 --> 00:43:31,640
Speaker 1: of those of those anecdotes, you know, that would be

813
00:43:31,800 --> 00:43:33,480
Speaker 1: an important service that would do a lot to just

814
00:43:33,600 --> 00:43:36,879
Speaker 1: calm everyone down. I hope you know. Maybe that's ah,

815
00:43:37,480 --> 00:43:41,040
Speaker 1: that's uh pollyannas or naive of made to feel that way,

816
00:43:41,080 --> 00:43:43,960
Speaker 1: But I really do believe that if we could have

817
00:43:44,040 --> 00:43:48,800
Speaker 1: a country where people were more journalists, in particular, more numerous,

818
00:43:49,000 --> 00:43:52,280
Speaker 1: more affluent in statistics, it would be so much better.

819
00:43:52,320 --> 00:43:55,040
Speaker 1: I mean that the story that really crystallizes to me

820
00:43:55,719 --> 00:43:58,680
Speaker 1: everything that went wrong with the media coverage last year

821
00:43:59,719 --> 00:44:02,920
Speaker 1: was was the huge story. And I talked about it

822
00:44:02,960 --> 00:44:05,080
Speaker 1: the last time when I was on with You in

823
00:44:05,120 --> 00:44:07,880
Speaker 1: the in the New York Times, where it was claimed

824
00:44:07,880 --> 00:44:10,680
Speaker 1: that there was a South Korean study that was purported

825
00:44:10,719 --> 00:44:14,960
Speaker 1: to show that kids were infectious and it was dangerous

826
00:44:15,000 --> 00:44:18,160
Speaker 1: to reopen schools. And this was plastered all over the

827
00:44:18,200 --> 00:44:21,200
Speaker 1: New York Time. It was circulated to every school district

828
00:44:21,280 --> 00:44:24,160
Speaker 1: in the country, and there was reporting afterwards that said

829
00:44:24,200 --> 00:44:26,759
Speaker 1: that that basically there were a lot of you know,

830
00:44:26,880 --> 00:44:31,279
Speaker 1: school principles superintendents who wanted to open schools. They read

831
00:44:31,280 --> 00:44:32,920
Speaker 1: that article in The New York Times and said no,

832
00:44:33,080 --> 00:44:35,360
Speaker 1: we're not going to do it, obviously egged on by

833
00:44:35,400 --> 00:44:38,640
Speaker 1: the teachers unions. And it turned out that the that

834
00:44:38,760 --> 00:44:42,480
Speaker 1: the article totally misrepresented the data, and that once the

835
00:44:42,480 --> 00:44:44,360
Speaker 1: full data set came out, it was pretty clear that

836
00:44:44,400 --> 00:44:47,480
Speaker 1: in fact, kids were not infectious in South Korea, just

837
00:44:47,520 --> 00:44:50,440
Speaker 1: like kids were not infectious anywhere else, that schools had

838
00:44:50,480 --> 00:44:52,920
Speaker 1: been open and everything had been fine. But did the

839
00:44:52,920 --> 00:44:56,080
Speaker 1: New York Times retract their story. No, did the New

840
00:44:56,160 --> 00:44:58,959
Speaker 1: York Times run another story saying, hey, we got South

841
00:44:59,040 --> 00:45:02,160
Speaker 1: Korea were not really I mean, they did write another

842
00:45:02,280 --> 00:45:04,440
Speaker 1: article about South Korea, but it was a very mealy

843
00:45:04,480 --> 00:45:08,040
Speaker 1: mouth and no reader who didn't already know what's going

844
00:45:08,080 --> 00:45:10,160
Speaker 1: on would be able to know from that article at

845
00:45:10,200 --> 00:45:12,560
Speaker 1: the New York Times it made a terrible mistake. But

846
00:45:12,680 --> 00:45:17,759
Speaker 1: that's one journalist at one influential newspaper who's misunderstanding of

847
00:45:17,800 --> 00:45:21,840
Speaker 1: scientific data lead to something that impacted the lives of

848
00:45:21,920 --> 00:45:25,839
Speaker 1: tens of millions of kids all over the country. And uh,

849
00:45:26,040 --> 00:45:29,640
Speaker 1: that's that's something that just should not happen. And I

850
00:45:29,680 --> 00:45:32,200
Speaker 1: hope we can we can have a world in which

851
00:45:32,719 --> 00:45:36,160
Speaker 1: statistics are more a part of the conversation, where when

852
00:45:36,160 --> 00:45:40,120
Speaker 1: you encounter a fact or a uh, you know, a

853
00:45:40,200 --> 00:45:43,759
Speaker 1: journalistic assertion, we could do more to have statistics back

854
00:45:43,760 --> 00:45:46,200
Speaker 1: it up. Now, that alone won't solve the problem, because

855
00:45:46,239 --> 00:45:49,080
Speaker 1: you know, the old line from Benjamin Disraeli, the old

856
00:45:49,360 --> 00:45:52,719
Speaker 1: nineteenth century Prime Minister of of of Great Britain, was

857
00:45:53,160 --> 00:45:55,600
Speaker 1: there's lives, damn lives and statistics. We all know from

858
00:45:55,600 --> 00:45:58,120
Speaker 1: sports that that you can come up with lots of

859
00:45:58,160 --> 00:46:02,200
Speaker 1: different statistics to justify, uh, you know, anything that you

860
00:46:02,520 --> 00:46:04,360
Speaker 1: and anything that you want to believe or anything that

861
00:46:04,480 --> 00:46:06,000
Speaker 1: that are your prior So you have to go one

862
00:46:06,080 --> 00:46:10,360
Speaker 1: level below that and really understand which statistics accurately measure

863
00:46:10,400 --> 00:46:13,320
Speaker 1: things in which statistics don't. But just having that basic

864
00:46:13,400 --> 00:46:17,359
Speaker 1: understanding of you know, one anecdote is not, you know,

865
00:46:18,040 --> 00:46:20,120
Speaker 1: reflective of everything else. If one person dies in a

866
00:46:20,160 --> 00:46:22,960
Speaker 1: car accident, it doesn't mean you should hide in your garage.

867
00:46:23,280 --> 00:46:25,600
Speaker 1: If one person dies of COVID, it doesn't mean the

868
00:46:25,600 --> 00:46:28,480
Speaker 1: world's gonna end um. You know. That would obviously be

869
00:46:29,000 --> 00:46:32,680
Speaker 1: a welcome development. One way that I try to combat

870
00:46:32,760 --> 00:46:35,600
Speaker 1: that is in addition to talking to people like you

871
00:46:35,640 --> 00:46:40,360
Speaker 1: and hopefully sharing your worldview with my audience, is throughout

872
00:46:40,480 --> 00:46:43,680
Speaker 1: this entire COVID mess on my radio show, I've been

873
00:46:43,800 --> 00:46:46,080
Speaker 1: very transparent with the choices that I'm making in my

874
00:46:46,160 --> 00:46:49,399
Speaker 1: own life because people can say, oh, you're saying that,

875
00:46:49,920 --> 00:46:51,960
Speaker 1: But I think for most people out there who are

876
00:46:51,960 --> 00:46:55,080
Speaker 1: parents like you and me, our children are our most

877
00:46:55,160 --> 00:46:59,000
Speaker 1: prized possessions. My children, my oldest is in private school.

878
00:46:59,000 --> 00:47:01,960
Speaker 1: My two youngest go to school every day. I have

879
00:47:02,040 --> 00:47:05,279
Speaker 1: traveled with them on airplanes. I have taken them to

880
00:47:05,280 --> 00:47:08,959
Speaker 1: go watch NFL games. We have allowed them to play

881
00:47:09,000 --> 00:47:11,480
Speaker 1: in addition to going to school, all of their different

882
00:47:11,480 --> 00:47:14,839
Speaker 1: sports leagues in our neighborhood where the sports leagues are

883
00:47:14,840 --> 00:47:19,000
Speaker 1: going on, and I hope to be sharing that is anecdotal, right,

884
00:47:19,080 --> 00:47:22,480
Speaker 1: but it's me trying to live up to the data

885
00:47:22,680 --> 00:47:25,400
Speaker 1: under which I am telling them that the things that

886
00:47:25,440 --> 00:47:27,800
Speaker 1: I believe should happen, sports should be played, for example.

887
00:47:28,080 --> 00:47:30,600
Speaker 1: I'm living my life by that data too. I'm not

888
00:47:30,640 --> 00:47:33,880
Speaker 1: telling you to do one thing and then doing the opposite,

889
00:47:33,880 --> 00:47:38,400
Speaker 1: which frankly has been I think probably the most destructive

890
00:47:38,719 --> 00:47:41,400
Speaker 1: thing that public policy officials have have done. Whether it's

891
00:47:41,440 --> 00:47:46,200
Speaker 1: Gavin Newsom eating at the French Laundry restaurant, or so

892
00:47:46,320 --> 00:47:48,839
Speaker 1: many different politicians out there who tend to be more

893
00:47:48,880 --> 00:47:52,080
Speaker 1: affluent than the average person they represent, having their own

894
00:47:52,160 --> 00:47:55,359
Speaker 1: kids in private school going to class, while they are

895
00:47:55,400 --> 00:47:57,799
Speaker 1: allowing all of the public school kids who don't have

896
00:47:57,840 --> 00:48:02,000
Speaker 1: the same ability of resources as their own kids to

897
00:48:02,080 --> 00:48:05,560
Speaker 1: not be in school, right, so that hypocrisy. I'm trying

898
00:48:05,600 --> 00:48:07,399
Speaker 1: to live my life in the way that I would

899
00:48:07,440 --> 00:48:10,400
Speaker 1: say the data reflects I should. And that goes to

900
00:48:10,760 --> 00:48:12,759
Speaker 1: what I think is is a really big story here.

901
00:48:12,760 --> 00:48:14,840
Speaker 1: And I know this is basically what you do for

902
00:48:14,880 --> 00:48:18,080
Speaker 1: a living. You're talking about analyzing probability and statistics, which

903
00:48:18,080 --> 00:48:21,480
Speaker 1: I think Americans as a group do poorly, but also

904
00:48:21,760 --> 00:48:25,160
Speaker 1: success or failure to me in many parts of life

905
00:48:25,560 --> 00:48:30,120
Speaker 1: seems to be predicated on your ability to analyze risk

906
00:48:30,600 --> 00:48:33,360
Speaker 1: in this country, whether it's what you invest in, whether

907
00:48:33,400 --> 00:48:35,560
Speaker 1: it's what you do on a day to day basis,

908
00:48:35,960 --> 00:48:38,560
Speaker 1: your risk barometer. I would bet if there was a

909
00:48:38,560 --> 00:48:41,120
Speaker 1: way to study it, the people who are the best

910
00:48:41,200 --> 00:48:44,719
Speaker 1: at risk barometer basis are probably the most successful in

911
00:48:44,719 --> 00:48:46,879
Speaker 1: the country. Would you buy into that idea as well?

912
00:48:49,040 --> 00:48:51,319
Speaker 1: You know, that's that's such a great point, you know,

913
00:48:51,480 --> 00:48:55,759
Speaker 1: I it's not just what your ability to analyze risk,

914
00:48:56,200 --> 00:48:58,840
Speaker 1: but it's your attitude toward risk. I mean, one of

915
00:48:58,840 --> 00:49:01,160
Speaker 1: the one of the things that led to the to

916
00:49:01,200 --> 00:49:03,160
Speaker 1: the rise of Donald Trump in a sense is this

917
00:49:03,239 --> 00:49:08,880
Speaker 1: divide between blue collar America and you know, college college,

918
00:49:08,920 --> 00:49:12,920
Speaker 1: e book smart America. And we're seeing that in the

919
00:49:12,920 --> 00:49:16,359
Speaker 1: electoral results. Like if you look at the electoral election returns, Uh,

920
00:49:16,640 --> 00:49:18,400
Speaker 1: if you and you look at who's voting for whom,

921
00:49:18,520 --> 00:49:21,200
Speaker 1: what really is the driver more than race, more than income,

922
00:49:21,239 --> 00:49:24,239
Speaker 1: more than any other factor. Is Uh, do you have

923
00:49:24,280 --> 00:49:26,680
Speaker 1: a college degree or not? Uh? And if you do,

924
00:49:26,840 --> 00:49:28,359
Speaker 1: you tend to vote one way, and if you don't,

925
00:49:28,400 --> 00:49:30,040
Speaker 1: you tend to vote another way. That that's the most

926
00:49:30,080 --> 00:49:33,640
Speaker 1: powerful thing out there. And and you know, people who

927
00:49:33,719 --> 00:49:35,439
Speaker 1: are on the elite side, so Oh, that just means

928
00:49:35,440 --> 00:49:38,399
Speaker 1: that we the educated, smart people, you know what's best

929
00:49:38,440 --> 00:49:39,799
Speaker 1: for you all, and you're all you all, the rest

930
00:49:39,840 --> 00:49:41,719
Speaker 1: of you are dumb and ignorant. I look at it

931
00:49:41,760 --> 00:49:44,440
Speaker 1: a different way, which is now, I'm looking out my

932
00:49:44,440 --> 00:49:47,120
Speaker 1: window right now in downtown Austin, and there's a you know,

933
00:49:47,160 --> 00:49:49,960
Speaker 1: a twenty story building under construction right right across the

934
00:49:50,000 --> 00:49:53,520
Speaker 1: street here, and there are people climbing up on the scaffolds,

935
00:49:53,560 --> 00:49:56,080
Speaker 1: you know, cleaning the windows and laying down the insulation.

936
00:49:56,160 --> 00:49:59,239
Speaker 1: And those people understand risk, right, because if if they

937
00:49:59,280 --> 00:50:03,359
Speaker 1: don't strap themselves in and they don't act carefully, they're

938
00:50:03,360 --> 00:50:06,680
Speaker 1: gonna fall off of that platform and and and die

939
00:50:06,840 --> 00:50:10,560
Speaker 1: literally die. Right. They understand the risk of of of

940
00:50:10,600 --> 00:50:13,800
Speaker 1: their jobs and the challenge for a lot of sports writers,

941
00:50:13,840 --> 00:50:17,520
Speaker 1: a lot of academics, people who basically live lives with

942
00:50:17,560 --> 00:50:19,759
Speaker 1: no risk. And frankly, I'm in that crowd, right, Like

943
00:50:19,800 --> 00:50:21,840
Speaker 1: I have a white collar job, I have a good income.

944
00:50:22,200 --> 00:50:24,320
Speaker 1: You know, we've we've talked about how my life is

945
00:50:24,360 --> 00:50:26,719
Speaker 1: pretty cushy compared to the people who can't send their

946
00:50:26,760 --> 00:50:30,239
Speaker 1: kids to school, et cetera. Like people who have that

947
00:50:30,520 --> 00:50:34,279
Speaker 1: sociological background or socioeconomic background they tend to be more

948
00:50:34,400 --> 00:50:36,759
Speaker 1: risk averse, right, because they're not used to dealing with

949
00:50:36,800 --> 00:50:38,879
Speaker 1: the world in which like if they're on if they're

950
00:50:38,920 --> 00:50:42,400
Speaker 1: careless about something, things can go badly wrong. Whereas blue

951
00:50:42,400 --> 00:50:46,160
Speaker 1: collar America, you're people are used to things going wrong.

952
00:50:46,200 --> 00:50:48,000
Speaker 1: People are used to having to be careful, they're used

953
00:50:48,040 --> 00:50:51,640
Speaker 1: to physical danger. And athletes to write you you you're

954
00:50:51,680 --> 00:50:53,880
Speaker 1: not You're careless about the way you train, You're careless

955
00:50:53,880 --> 00:50:56,080
Speaker 1: about the way you stretch, You're careless about the way

956
00:50:56,080 --> 00:50:58,600
Speaker 1: you run the football. You're gonna get injured, right, And

957
00:50:58,640 --> 00:51:03,080
Speaker 1: athletes are very aware of that um And so people

958
00:51:03,120 --> 00:51:07,840
Speaker 1: who deal with physical risk every day are much more

959
00:51:07,880 --> 00:51:10,000
Speaker 1: likely to look at something like COVID and say, you

960
00:51:10,000 --> 00:51:13,120
Speaker 1: know what, I can handle that, whereas it's the people

961
00:51:13,120 --> 00:51:15,120
Speaker 1: who sit on in front of a computer all day

962
00:51:15,880 --> 00:51:19,200
Speaker 1: who are terrified. It's also what I always say, is

963
00:51:19,280 --> 00:51:21,560
Speaker 1: like going to public school and going and I went

964
00:51:21,600 --> 00:51:24,520
Speaker 1: to some public schools that weren't very good, But there

965
00:51:24,600 --> 00:51:27,319
Speaker 1: is a benefit to knowing that you might get your

966
00:51:27,360 --> 00:51:31,000
Speaker 1: ass kicked at school, you know, like having that fear

967
00:51:31,880 --> 00:51:35,200
Speaker 1: where you know that you're not a hundred percent safe

968
00:51:35,440 --> 00:51:37,960
Speaker 1: and you have to carry yourself in a way that

969
00:51:38,040 --> 00:51:41,319
Speaker 1: analyzes risk. Maybe I shouldn't say that to that guy

970
00:51:41,480 --> 00:51:45,080
Speaker 1: right now, right because he might beat my ass, right,

971
00:51:45,160 --> 00:51:47,520
Speaker 1: And I feel like we have and and look at

972
00:51:47,640 --> 00:51:53,080
Speaker 1: every generation is getting safer progressively in the United States, right, So, uh, this,

973
00:51:53,080 --> 00:51:54,919
Speaker 1: this and that's been going back in time, the data

974
00:51:54,960 --> 00:51:57,880
Speaker 1: would reflect from the moment people got on ships and

975
00:51:57,960 --> 00:52:02,359
Speaker 1: came to our shores. Life life links are growing like

976
00:52:02,560 --> 00:52:06,319
Speaker 1: we are living in the least dangerous time in the

977
00:52:06,360 --> 00:52:09,279
Speaker 1: world that anyone could ever live in. Right, Um, all

978
00:52:09,320 --> 00:52:12,359
Speaker 1: the data reflects that, But it seems to me that

979
00:52:12,440 --> 00:52:17,000
Speaker 1: our fear meters are so much more attuned to danger

980
00:52:17,120 --> 00:52:20,000
Speaker 1: than they ever have been before. And people who are

981
00:52:20,040 --> 00:52:22,480
Speaker 1: in COVID is a metaphor for this. People who are

982
00:52:22,560 --> 00:52:24,799
Speaker 1: not at risk, as you said when we started this conversation,

983
00:52:24,880 --> 00:52:28,359
Speaker 1: young people, they feel terrified, right. They think they're gonna

984
00:52:28,400 --> 00:52:30,200
Speaker 1: get and this isn't just COVID. They think they're gonna

985
00:52:30,239 --> 00:52:33,319
Speaker 1: get kidnapped, they think they're gonna get murdered. They think

986
00:52:33,440 --> 00:52:37,080
Speaker 1: something bad is going to happen to them, when statistically

987
00:52:37,200 --> 00:52:40,080
Speaker 1: most people have never been safer. If you're living in

988
00:52:40,080 --> 00:52:44,120
Speaker 1: America right now than any time in human civilization than

989
00:52:44,160 --> 00:52:49,000
Speaker 1: this exact moment. Yeah, you know that that's another great point, Clay,

990
00:52:49,040 --> 00:52:53,279
Speaker 1: that that there's a negativity and you know we've been

991
00:52:53,280 --> 00:52:55,680
Speaker 1: complaining a lot on on this interview, but you know,

992
00:52:55,719 --> 00:53:00,799
Speaker 1: like there's a negativity to uh, to journalism today that

993
00:53:01,200 --> 00:53:04,839
Speaker 1: something good happening typically isn't news, right, Like if something

994
00:53:04,880 --> 00:53:08,279
Speaker 1: bad happens, if a train derails, that's news. If a

995
00:53:08,440 --> 00:53:11,760
Speaker 1: train goes and stays on its tracks, which is almost

996
00:53:11,800 --> 00:53:15,160
Speaker 1: always what happens with every train, it's not news. Right.

997
00:53:15,200 --> 00:53:18,919
Speaker 1: A plane crashing is news. A billion plane flights going

998
00:53:18,960 --> 00:53:21,800
Speaker 1: off and taken in landing is not news. So news

999
00:53:21,840 --> 00:53:25,680
Speaker 1: in and of itself is better easily able to spread now.

1000
00:53:26,120 --> 00:53:29,920
Speaker 1: But there is I think a natural negativity bias because

1001
00:53:29,960 --> 00:53:34,000
Speaker 1: good news happens far more frequently and therefore isn't news.

1002
00:53:34,280 --> 00:53:36,960
Speaker 1: The negative tends to dictate and scare. Again, it goes

1003
00:53:37,000 --> 00:53:40,200
Speaker 1: to your point on probability and statistics and analysis and

1004
00:53:40,280 --> 00:53:44,840
Speaker 1: being able to contextualize what I was saying risk. You know,

1005
00:53:45,760 --> 00:53:48,439
Speaker 1: that's absolutely right, And and the one thing I'm I'm

1006
00:53:48,440 --> 00:53:52,040
Speaker 1: thinking about as you say that is how does technology

1007
00:53:52,120 --> 00:53:55,279
Speaker 1: digital media change all that are our conventional wisdom, which

1008
00:53:55,320 --> 00:53:59,640
Speaker 1: is obviously has some truth to it is uh, social media, Facebook, Twitter,

1009
00:54:00,000 --> 00:54:03,480
Speaker 1: able news Uh, exacerbate or worse than those problems because

1010
00:54:03,560 --> 00:54:05,880
Speaker 1: one of the things that people want to get motivated

1011
00:54:05,880 --> 00:54:07,840
Speaker 1: and get angry about and share with their friends and

1012
00:54:07,840 --> 00:54:09,799
Speaker 1: the stuff that they're mad about about the world. Right,

1013
00:54:10,000 --> 00:54:13,319
Speaker 1: And that's certainly true. But it's also true that on

1014
00:54:13,640 --> 00:54:18,120
Speaker 1: UM on digital platforms, you see people share inspiring stories. Yeah,

1015
00:54:18,280 --> 00:54:19,840
Speaker 1: a lot of times. If you look at the stories

1016
00:54:19,840 --> 00:54:22,920
Speaker 1: that are getting the most traffic, it's like, uh, and

1017
00:54:22,960 --> 00:54:25,879
Speaker 1: I think you shared it. Actually, the story about Tom

1018
00:54:25,920 --> 00:54:28,840
Speaker 1: Brady throwing the touchdown to Drew Brees a son beautiful

1019
00:54:29,800 --> 00:54:33,160
Speaker 1: last weekend, right, like that got enormous traffic. So people

1020
00:54:33,239 --> 00:54:38,200
Speaker 1: hunger for for good news too. And I guess the

1021
00:54:38,239 --> 00:54:40,399
Speaker 1: thing is, can we think about again? I'm always trying

1022
00:54:40,400 --> 00:54:42,160
Speaker 1: to think about what's the solution here? How do we

1023
00:54:42,200 --> 00:54:45,000
Speaker 1: move beyond this and try to make things better? And

1024
00:54:45,040 --> 00:54:48,160
Speaker 1: I feel like we've we've gotta think more, and media

1025
00:54:48,239 --> 00:54:50,719
Speaker 1: organizations that that have an economic incentive to do so,

1026
00:54:50,880 --> 00:54:54,280
Speaker 1: just think more about how do I share those inspiring stories,

1027
00:54:54,280 --> 00:54:57,880
Speaker 1: the good news that the kindness, the sportsmanship, the things

1028
00:54:57,920 --> 00:54:59,879
Speaker 1: that that we could show to our kids and say,

1029
00:55:00,040 --> 00:55:02,440
Speaker 1: know what, be more like that? Guy, be more like

1030
00:55:02,520 --> 00:55:04,919
Speaker 1: Tom Brady and du Brees after a hard fought game,

1031
00:55:05,280 --> 00:55:07,759
Speaker 1: don't be like the sore loser or whatever. You know.

1032
00:55:08,000 --> 00:55:10,560
Speaker 1: I think there's an opportunity in there. It's interesting to

1033
00:55:10,600 --> 00:55:13,359
Speaker 1: me because if you think about let's take a step

1034
00:55:13,360 --> 00:55:15,600
Speaker 1: back and just think about it from a capitalistic perspective,

1035
00:55:16,280 --> 00:55:20,520
Speaker 1: there is big incentive to get financial stories right, such

1036
00:55:20,640 --> 00:55:24,640
Speaker 1: that people will pay huge amounts of money to you know,

1037
00:55:24,719 --> 00:55:27,440
Speaker 1: get a Wall Street journal or a Bloomberg article or

1038
00:55:27,480 --> 00:55:30,799
Speaker 1: wherever it is to them first right. And getting that

1039
00:55:30,880 --> 00:55:35,680
Speaker 1: news right from a financial perspective is incredibly important. It

1040
00:55:35,760 --> 00:55:38,560
Speaker 1: seems to me that there, and so the quality I

1041
00:55:38,600 --> 00:55:40,520
Speaker 1: would say. You may disagree. I'm not an expert in

1042
00:55:40,560 --> 00:55:43,960
Speaker 1: finance journalism, but it seems to me the quality of

1043
00:55:44,120 --> 00:55:48,279
Speaker 1: finance journalism is higher than the quality of many other

1044
00:55:48,360 --> 00:55:52,440
Speaker 1: types of journalism because what pays in many other types

1045
00:55:52,480 --> 00:55:57,560
Speaker 1: of journalism is not nuance or analysis or intelligence. Necessarily,

1046
00:55:57,960 --> 00:56:01,239
Speaker 1: it's emotion. And the emotion can be good, Oh look

1047
00:56:01,280 --> 00:56:03,799
Speaker 1: how great Tom Brady is and Drew Brees after that

1048
00:56:03,840 --> 00:56:07,040
Speaker 1: game they're throwing a pass. But the emotion can also

1049
00:56:07,080 --> 00:56:09,760
Speaker 1: be hyper negative, which is why I say, look, Trump

1050
00:56:09,880 --> 00:56:13,200
Speaker 1: is a symptom of the industry and universe in which

1051
00:56:13,239 --> 00:56:15,680
Speaker 1: we live, not the cause of it. He is an

1052
00:56:15,680 --> 00:56:19,040
Speaker 1: inarticulate voice in many ways for a conversation that needs

1053
00:56:19,080 --> 00:56:21,799
Speaker 1: to happen. What always friend Trump is a whole different story.

1054
00:56:21,800 --> 00:56:24,600
Speaker 1: But always frustrated me about Donald Trump was I just

1055
00:56:24,680 --> 00:56:27,280
Speaker 1: wish somebody had been making some of the same arguments

1056
00:56:27,320 --> 00:56:30,880
Speaker 1: that he was making with a factual foundation as opposed

1057
00:56:30,920 --> 00:56:33,799
Speaker 1: to a gut foundation, which I think was very often

1058
00:56:33,840 --> 00:56:39,320
Speaker 1: the way he was responding. Yeah, I mean that that's uh,

1059
00:56:39,360 --> 00:56:41,239
Speaker 1: that's what I certainly hope for the same thing. I

1060
00:56:41,320 --> 00:56:44,839
Speaker 1: hope that we can we can draw the lessons of

1061
00:56:44,040 --> 00:56:47,920
Speaker 1: the criticisms of America that that Trump that that what

1062
00:56:47,960 --> 00:56:52,280
Speaker 1: Trump was right about without the other aspects of Trump's

1063
00:56:52,880 --> 00:56:55,520
Speaker 1: approach the life, that that we wouldn't want to teach

1064
00:56:55,520 --> 00:56:57,560
Speaker 1: our kids or we wouldn't want to in terms of

1065
00:56:57,560 --> 00:57:02,480
Speaker 1: the way we treat each other. Okay, all thatsolutely go ahead. No, Well,

1066
00:57:02,520 --> 00:57:05,080
Speaker 1: I was gonna catch I had a big question here.

1067
00:57:05,120 --> 00:57:08,160
Speaker 1: I was gonna try to hit you with. But all

1068
00:57:08,239 --> 00:57:11,319
Speaker 1: this conversation we just had, um is, people are gonna

1069
00:57:11,360 --> 00:57:13,920
Speaker 1: love it and fantastic, But you said you're working on

1070
00:57:13,960 --> 00:57:16,680
Speaker 1: basically a retrospective to look back at the way the

1071
00:57:16,760 --> 00:57:19,280
Speaker 1: society responded, to look back at the decision to shut

1072
00:57:19,320 --> 00:57:22,560
Speaker 1: down schools? When is that going to come out? And

1073
00:57:22,760 --> 00:57:25,520
Speaker 1: it's a cliche because it is true, especially if you

1074
00:57:25,600 --> 00:57:29,440
Speaker 1: like history. Hindsight is right. We find out the errors

1075
00:57:29,480 --> 00:57:32,360
Speaker 1: that we made and hopefully learn from them going forward

1076
00:57:32,360 --> 00:57:35,480
Speaker 1: into the future. Who knows when the next pandemic might happen.

1077
00:57:36,000 --> 00:57:37,720
Speaker 1: If you had been able to look at the data

1078
00:57:37,760 --> 00:57:41,160
Speaker 1: set that you have right now, you're reviewing all everything

1079
00:57:41,200 --> 00:57:44,640
Speaker 1: that has happened with COVID. What would have gotten us

1080
00:57:44,840 --> 00:57:48,400
Speaker 1: in a in public policy? What would have gotten the

1081
00:57:48,520 --> 00:57:52,360
Speaker 1: media and a in coverage? What would have been the

1082
00:57:52,400 --> 00:57:55,160
Speaker 1: best response that we could have had. Let's pretend that

1083
00:57:55,240 --> 00:57:59,520
Speaker 1: you and I are able to implement American policy, or

1084
00:57:59,560 --> 00:58:02,320
Speaker 1: maybe not me at all. You back in March, when

1085
00:58:02,320 --> 00:58:05,280
Speaker 1: this virus is just arriving on our shores. Probably it

1086
00:58:05,320 --> 00:58:07,360
Speaker 1: was here in December or whatever else, but March, when

1087
00:58:07,400 --> 00:58:11,400
Speaker 1: we really start responding to it. What was the right response?

1088
00:58:11,760 --> 00:58:15,160
Speaker 1: What should we have done to have the most effective

1089
00:58:15,240 --> 00:58:19,640
Speaker 1: possible American response to COVID? We Well, first of all,

1090
00:58:19,920 --> 00:58:22,600
Speaker 1: I love that you're like you're now my editor and

1091
00:58:22,640 --> 00:58:25,320
Speaker 1: you gave me a deadline, or you give us a

1092
00:58:25,320 --> 00:58:27,520
Speaker 1: deadline when your your articles out, So I'm gonna I'm

1093
00:58:27,520 --> 00:58:29,000
Speaker 1: gonna do it. I'm gonna say, Okay, we'll get this

1094
00:58:29,040 --> 00:58:32,640
Speaker 1: out by the end of February, so I can't wait. Yes,

1095
00:58:32,720 --> 00:58:35,360
Speaker 1: I'll get it out of the end of February. And

1096
00:58:35,600 --> 00:58:37,320
Speaker 1: you know, I'll say a couple of things that you

1097
00:58:37,320 --> 00:58:39,080
Speaker 1: know obviously this is this is we can have a

1098
00:58:39,120 --> 00:58:41,000
Speaker 1: we have a whole hour and a half conversation about

1099
00:58:41,040 --> 00:58:43,840
Speaker 1: that question. But I'll say maybe we will when your

1100
00:58:43,880 --> 00:58:45,600
Speaker 1: whole paper comes out, because I'd love for you to

1101
00:58:45,640 --> 00:58:47,320
Speaker 1: come back after I have a chance to read it

1102
00:58:47,360 --> 00:58:49,520
Speaker 1: and digest it, for you to be able to talk

1103
00:58:49,560 --> 00:58:51,560
Speaker 1: about it with us, because I think my audience would

1104
00:58:51,560 --> 00:58:54,680
Speaker 1: love it. But uh, okay, dive in broad picture question

1105
00:58:54,720 --> 00:58:58,000
Speaker 1: that I just asked. Yeah, and look, there's a lot

1106
00:58:58,040 --> 00:58:59,600
Speaker 1: to say about this topic. And you know, in fact,

1107
00:58:59,680 --> 00:59:04,760
Speaker 1: I just interviewed uh the now former HHS Secretary Alex

1108
00:59:04,800 --> 00:59:07,520
Speaker 1: asar on a lot of this stuff last week. You

1109
00:59:07,560 --> 00:59:09,400
Speaker 1: can find it on YouTube if you google my name

1110
00:59:09,440 --> 00:59:12,960
Speaker 1: and his um. More to say about that, But I'll

1111
00:59:13,000 --> 00:59:15,080
Speaker 1: say a couple of things to to to what the

1112
00:59:15,120 --> 00:59:18,680
Speaker 1: appetite of you and your listeners. First is if you

1113
00:59:18,720 --> 00:59:21,840
Speaker 1: actually look at which countries have performed well this time

1114
00:59:21,880 --> 00:59:25,640
Speaker 1: around with COVID, it was mostly the countries of the

1115
00:59:25,680 --> 00:59:29,840
Speaker 1: Pacific Rim East Asia. And why is that. It's because

1116
00:59:29,840 --> 00:59:34,200
Speaker 1: the countries of the Pacific Rim have encountered the coronavirus before.

1117
00:59:35,000 --> 00:59:39,640
Speaker 1: They had encountered Stars Kobe one in two thousand three,

1118
00:59:40,040 --> 00:59:44,120
Speaker 1: and it was because of their experience with that first

1119
00:59:44,600 --> 00:59:47,640
Speaker 1: Stars code coronavirus, or at least the one that we

1120
00:59:47,720 --> 00:59:52,080
Speaker 1: call the first Stars coronavirus that led them to when

1121
00:59:52,160 --> 00:59:54,760
Speaker 1: this one came around, they took it seriously from from

1122
00:59:54,840 --> 00:59:58,240
Speaker 1: day one. They did the social distancing and the other

1123
00:59:58,320 --> 01:00:00,800
Speaker 1: kinds of things to be careful, but they didn't shut

1124
01:00:00,800 --> 01:00:04,920
Speaker 1: down their societies. They didn't have to because their citizenry

1125
01:00:05,120 --> 01:00:08,880
Speaker 1: knew how to behave their governments knew what steps to

1126
01:00:08,920 --> 01:00:11,600
Speaker 1: take to get the testing going and everything else. So

1127
01:00:12,200 --> 01:00:17,040
Speaker 1: my hope, my optimism is that the experience of this

1128
01:00:17,120 --> 01:00:20,800
Speaker 1: pandemic will lead us to be smarter in general about

1129
01:00:20,880 --> 01:00:24,240
Speaker 1: both the way everyday people respond to the crisis and

1130
01:00:24,280 --> 01:00:27,160
Speaker 1: have the government response. Maybe that's too optimistic, but I

1131
01:00:27,240 --> 01:00:29,480
Speaker 1: think there's reason to be hopeful of that. If we

1132
01:00:29,520 --> 01:00:32,040
Speaker 1: look at the example of Asia. The second thing I'll

1133
01:00:32,080 --> 01:00:36,120
Speaker 1: mention is the vaccine. So one of the things that's

1134
01:00:36,160 --> 01:00:39,680
Speaker 1: come out of this past twelve months or ten months

1135
01:00:39,720 --> 01:00:43,520
Speaker 1: that's been remarkable is the development of these of these

1136
01:00:43,520 --> 01:00:46,360
Speaker 1: coronavirus vaccines. As I think I talked about with you

1137
01:00:46,360 --> 01:00:50,000
Speaker 1: and your last show that we did together, the previous

1138
01:00:50,080 --> 01:00:53,240
Speaker 1: world record for developing a vaccine for a novel virus

1139
01:00:53,640 --> 01:00:57,720
Speaker 1: was five years for the ebolavirus five years. We shattered

1140
01:00:57,760 --> 01:01:01,080
Speaker 1: that record. Two different biotech come and these one American

1141
01:01:01,160 --> 01:01:06,160
Speaker 1: Maderna on another German bio Intact basically developed these mRNA

1142
01:01:06,360 --> 01:01:08,800
Speaker 1: based vaccines. M RNA is a is a type of

1143
01:01:08,840 --> 01:01:13,880
Speaker 1: genetic code material like DNA. They developed these mRNA vaccines

1144
01:01:14,440 --> 01:01:16,720
Speaker 1: and turn them around incredibly quickly, and we got them

1145
01:01:16,760 --> 01:01:20,240
Speaker 1: on the market in an incredible record time. And what's

1146
01:01:20,360 --> 01:01:25,640
Speaker 1: really really encouraging about that is that these mRNA vaccines

1147
01:01:25,640 --> 01:01:28,840
Speaker 1: are actually really easy to manufacture. They're really easy to develop.

1148
01:01:28,920 --> 01:01:32,120
Speaker 1: It's almost like software. You type into genetic code, you

1149
01:01:32,160 --> 01:01:36,000
Speaker 1: PLoP out the vaccine and it's ready. The Maderna they

1150
01:01:36,040 --> 01:01:39,000
Speaker 1: had their vaccine, their first batch that they developed for

1151
01:01:39,080 --> 01:01:42,240
Speaker 1: testing that was ready to go in January February of

1152
01:01:42,360 --> 01:01:45,720
Speaker 1: last year, almost a year ago. So think about this.

1153
01:01:45,840 --> 01:01:49,600
Speaker 1: If we have another coronavirus or another virus that is

1154
01:01:49,680 --> 01:01:53,480
Speaker 1: amenable to that kind of technology, you could develop a

1155
01:01:53,560 --> 01:01:56,840
Speaker 1: vaccine much sooner. Once the genetic sequence of that virus

1156
01:01:56,880 --> 01:02:00,320
Speaker 1: is published, you can develop the vaccine right away. And

1157
01:02:00,400 --> 01:02:04,920
Speaker 1: for those high risk individuals, frontline workers, nursing home residents,

1158
01:02:05,080 --> 01:02:07,600
Speaker 1: the people who are particularly vulnerable, you can get them

1159
01:02:07,680 --> 01:02:10,840
Speaker 1: vaccinated within a month of the pandemic or two months

1160
01:02:10,880 --> 01:02:13,720
Speaker 1: of the pandemic, instead of waiting for almost a year

1161
01:02:14,080 --> 01:02:16,320
Speaker 1: to get that vaccine out. And if you can do that,

1162
01:02:17,120 --> 01:02:20,120
Speaker 1: you can stem a lot of the damage that comes

1163
01:02:20,200 --> 01:02:23,840
Speaker 1: from the serious illness from from a novel pandemic. Hopefully

1164
01:02:23,920 --> 01:02:25,720
Speaker 1: this is a situation we don't have to encounter for

1165
01:02:25,760 --> 01:02:29,520
Speaker 1: a while. But to me, that technological advance is one

1166
01:02:29,520 --> 01:02:31,360
Speaker 1: of the things that a lot of people aren't talking

1167
01:02:31,400 --> 01:02:35,120
Speaker 1: about that we can bring to the next crisis that

1168
01:02:35,160 --> 01:02:37,480
Speaker 1: we have if we are so unlucky as to have one.

1169
01:02:37,960 --> 01:02:40,200
Speaker 1: A couple more questions. Though I know how busy you are,

1170
01:02:41,000 --> 01:02:43,960
Speaker 1: you hear a lot about the death rate from COVID,

1171
01:02:44,080 --> 01:02:46,440
Speaker 1: and I always say, like nobody, I always say on

1172
01:02:46,440 --> 01:02:49,120
Speaker 1: my radio show nobody hates death more than me, right like,

1173
01:02:49,240 --> 01:02:50,920
Speaker 1: so I am, I want to make it clear here

1174
01:02:51,000 --> 01:02:53,800
Speaker 1: that I am adamantly opposed to death. I wish we

1175
01:02:53,840 --> 01:02:56,959
Speaker 1: could live almost forever. I wish nobody's grandma ever died.

1176
01:02:57,640 --> 01:03:01,000
Speaker 1: All those things the folk us again going back to

1177
01:03:01,040 --> 01:03:05,560
Speaker 1: the statistical analysis, the age of death from COVID, and

1178
01:03:05,840 --> 01:03:08,520
Speaker 1: you might need to say with COVID, but however you

1179
01:03:08,560 --> 01:03:12,440
Speaker 1: want to phrase that is either around the same age

1180
01:03:12,520 --> 01:03:15,240
Speaker 1: as the average age of death in the country as

1181
01:03:15,240 --> 01:03:19,680
Speaker 1: a as a whole, or maybe a little bit older, right, uh.

1182
01:03:19,720 --> 01:03:21,960
Speaker 1: And you can speak to that data better than I can.

1183
01:03:22,080 --> 01:03:24,880
Speaker 1: So am I correct roughly in the in the average

1184
01:03:24,880 --> 01:03:27,960
Speaker 1: age of death from somebody who is being classified as

1185
01:03:28,000 --> 01:03:31,560
Speaker 1: a COVID death is not much different than the average

1186
01:03:31,560 --> 01:03:36,480
Speaker 1: age of death overall in the United States. That's uh,

1187
01:03:36,480 --> 01:03:39,000
Speaker 1: that's generally true. You know. Obviously it's older people who

1188
01:03:39,000 --> 01:03:42,040
Speaker 1: are typically dying of COVID. It's older people who died generally, yes,

1189
01:03:42,320 --> 01:03:45,040
Speaker 1: um uh. And in fact, as you know, we we've

1190
01:03:45,040 --> 01:03:49,320
Speaker 1: published some analyzes that show that the real UH bulge

1191
01:03:49,320 --> 01:03:52,800
Speaker 1: are differential in in who's dying of COVID in United

1192
01:03:52,840 --> 01:03:57,479
Speaker 1: States relative to pre existing normal quote unquote death rates

1193
01:03:57,520 --> 01:04:00,640
Speaker 1: by age, Brackett, is that sort of upp upper middle

1194
01:04:00,680 --> 01:04:03,640
Speaker 1: age bracket rather than the elderly, because the elderly, as

1195
01:04:03,640 --> 01:04:05,160
Speaker 1: you say, are dying anyway. And this is this is

1196
01:04:05,200 --> 01:04:07,720
Speaker 1: something I think there's gonna be and I think this

1197
01:04:07,840 --> 01:04:10,560
Speaker 1: may be the thing you're getting at. We may find

1198
01:04:10,600 --> 01:04:13,560
Speaker 1: as we sift through the data that the death rates

1199
01:04:13,880 --> 01:04:18,360
Speaker 1: of the elderly versus the death rates of the elderly

1200
01:04:18,400 --> 01:04:22,240
Speaker 1: in a normal year we're not that different. And or

1201
01:04:22,400 --> 01:04:25,880
Speaker 1: that the the the age of death is only you know,

1202
01:04:25,920 --> 01:04:28,400
Speaker 1: maybe a couple of months before the life expectancy for

1203
01:04:28,800 --> 01:04:31,120
Speaker 1: say an eighty five year old. Maybe that eighty five

1204
01:04:31,200 --> 01:04:33,440
Speaker 1: year old would have lasted until lady six, maybe it

1205
01:04:33,440 --> 01:04:36,120
Speaker 1: would have last till Lady seven. And COVID, you know,

1206
01:04:36,320 --> 01:04:40,080
Speaker 1: pushed that a little earlier, but not by a dramatic amount.

1207
01:04:40,160 --> 01:04:42,200
Speaker 1: We don't know. I think those are some debates that

1208
01:04:42,240 --> 01:04:45,840
Speaker 1: are going on in the statistical community right now. But

1209
01:04:45,920 --> 01:04:48,280
Speaker 1: we'll start to learn about that. And another thing that

1210
01:04:48,320 --> 01:04:50,720
Speaker 1: we're going to have to study, Clay, is how many

1211
01:04:50,760 --> 01:04:55,680
Speaker 1: people died not because of COVID, but because they were

1212
01:04:55,720 --> 01:05:00,040
Speaker 1: locked in their rooms or they the average age of

1213
01:05:00,080 --> 01:05:02,600
Speaker 1: those people is going to be much younger, which is

1214
01:05:02,600 --> 01:05:06,320
Speaker 1: where I was gonna go years lost of life. We

1215
01:05:06,400 --> 01:05:10,720
Speaker 1: talk a lot about death, but really, I think everybody

1216
01:05:10,720 --> 01:05:12,120
Speaker 1: out there when you take a step back and think

1217
01:05:12,120 --> 01:05:15,080
Speaker 1: about it from an analytical perspective, uh, and also the

1218
01:05:15,080 --> 01:05:17,440
Speaker 1: in factor in a little bit of emotion. The reason

1219
01:05:17,480 --> 01:05:19,800
Speaker 1: why when a five year old dies we feel so

1220
01:05:19,880 --> 01:05:22,280
Speaker 1: much worse than when an eighty five year old dies

1221
01:05:22,960 --> 01:05:25,920
Speaker 1: is because the five year old had so many lives,

1222
01:05:25,960 --> 01:05:29,000
Speaker 1: so much of their life left, so many years to

1223
01:05:29,160 --> 01:05:32,880
Speaker 1: live compared to the eighty five year old. And one

1224
01:05:32,920 --> 01:05:34,200
Speaker 1: of the things I've said it to the extent that

1225
01:05:34,240 --> 01:05:36,520
Speaker 1: there is a gift at all. Can you imagine if

1226
01:05:36,560 --> 01:05:40,240
Speaker 1: we had COVID except it had taken all young people

1227
01:05:40,840 --> 01:05:44,720
Speaker 1: instead of primarily been old people. That's a totally different dynamic,

1228
01:05:44,720 --> 01:05:47,160
Speaker 1: which goes to your point about the vaccine. I mean,

1229
01:05:47,200 --> 01:05:49,000
Speaker 1: I've got young kids. I mean I would have been

1230
01:05:49,160 --> 01:05:51,040
Speaker 1: curled up in the basement right like I would have

1231
01:05:51,080 --> 01:05:54,560
Speaker 1: been terrified for them. And so when we talk about

1232
01:05:54,560 --> 01:05:56,520
Speaker 1: the number of deaths that we have. The other thing

1233
01:05:56,560 --> 01:05:58,920
Speaker 1: that I don't here discussed very much is from an

1234
01:05:58,920 --> 01:06:04,000
Speaker 1: analyticaltistical perspective, in theory, if the people who are dying,

1235
01:06:04,560 --> 01:06:08,480
Speaker 1: are dying not necessarily with tons of years left on

1236
01:06:08,520 --> 01:06:12,520
Speaker 1: their life, right, they have comorbidity ease, they are otherwise unhealthy.

1237
01:06:12,640 --> 01:06:15,560
Speaker 1: People are talking about how this is an unprecedented death,

1238
01:06:15,760 --> 01:06:20,760
Speaker 1: and I understand that. But in two, and in tree

1239
01:06:21,000 --> 01:06:25,200
Speaker 1: and four, and maybe even in one, if the vaccine

1240
01:06:25,200 --> 01:06:28,360
Speaker 1: gets distributed, well, isn't it likely that we would see

1241
01:06:28,360 --> 01:06:32,920
Speaker 1: a substantial decline in deaths? In other words, focusing on

1242
01:06:33,000 --> 01:06:36,480
Speaker 1: how many people are dying this year, to me, is

1243
01:06:36,560 --> 01:06:39,800
Speaker 1: missing that a lot less people would theoretically die in

1244
01:06:39,840 --> 01:06:43,240
Speaker 1: the next couple of years ahead, and not just rationalizing

1245
01:06:43,240 --> 01:06:47,680
Speaker 1: and recognizing we're not stopping death right, like the average

1246
01:06:47,720 --> 01:06:50,400
Speaker 1: age of death is going to still be what it is.

1247
01:06:50,440 --> 01:06:53,600
Speaker 1: I hope we can continue to raise it. But every day,

1248
01:06:53,640 --> 01:06:56,320
Speaker 1: I think in this country, around eight thousand people die,

1249
01:06:56,680 --> 01:07:00,000
Speaker 1: and the overall understanding of that seems to be very

1250
01:07:00,200 --> 01:07:03,200
Speaker 1: limited in the media and the analysis and discussion of

1251
01:07:03,240 --> 01:07:07,280
Speaker 1: this issue. Yeah, you know, it's it's one of those

1252
01:07:07,280 --> 01:07:09,520
Speaker 1: things that's going to be hard to tease that from

1253
01:07:09,600 --> 01:07:12,440
Speaker 1: just last year's dat because, as you said, who died

1254
01:07:12,520 --> 01:07:15,800
Speaker 1: with COVID, who died of COVID, we literally are not

1255
01:07:15,920 --> 01:07:20,280
Speaker 1: recording that information because the hospitals don't have an incentive too,

1256
01:07:20,360 --> 01:07:22,880
Speaker 1: So so that's gonna be a hard thing to to

1257
01:07:22,920 --> 01:07:27,000
Speaker 1: look at next year, I mean this year in and understand.

1258
01:07:27,000 --> 01:07:29,520
Speaker 1: But you know, what you're what you're bringing up is

1259
01:07:29,560 --> 01:07:32,520
Speaker 1: that after several years, let's say we look at the

1260
01:07:32,560 --> 01:07:40,040
Speaker 1: period from five and say, okay, over that five year period,

1261
01:07:40,840 --> 01:07:43,640
Speaker 1: how many people in a particular age bracket died versus

1262
01:07:43,680 --> 01:07:46,600
Speaker 1: what we'd see in a non pandemic period. That's going

1263
01:07:46,640 --> 01:07:48,640
Speaker 1: to give you the answer that you're talking about in

1264
01:07:48,720 --> 01:07:51,080
Speaker 1: terms of it was there. It may not even be

1265
01:07:51,200 --> 01:07:53,680
Speaker 1: noticeable over ten years. It's probably not going to be

1266
01:07:53,720 --> 01:07:56,840
Speaker 1: noticeable at all if you average it out over ten years. Right.

1267
01:07:57,200 --> 01:07:59,680
Speaker 1: In other words, we're all so much of social media,

1268
01:07:59,760 --> 01:08:03,120
Speaker 1: and much of media today is about reacting instantaneously to

1269
01:08:03,240 --> 01:08:06,959
Speaker 1: what's occurring at this exact point. But when you sort

1270
01:08:06,960 --> 01:08:11,320
Speaker 1: of expand your horizon, a lot of public policy decisions,

1271
01:08:11,360 --> 01:08:13,680
Speaker 1: it seems to me, are are based on trying to

1272
01:08:13,680 --> 01:08:16,479
Speaker 1: do something in this week or this month that doesn't

1273
01:08:16,479 --> 01:08:18,599
Speaker 1: necessarily make sense. And look, I mean, you can say,

1274
01:08:18,640 --> 01:08:21,120
Speaker 1: even you know, broadening the perspective, you hear a lot

1275
01:08:21,160 --> 01:08:24,599
Speaker 1: of people say, once their businesses go public, oh, We've

1276
01:08:24,600 --> 01:08:26,879
Speaker 1: got to make sure that we make our quarterly numbers.

1277
01:08:27,360 --> 01:08:29,799
Speaker 1: But are you making the right decision in that quarter

1278
01:08:29,880 --> 01:08:32,000
Speaker 1: for the next ten years or you just trying to

1279
01:08:32,040 --> 01:08:35,160
Speaker 1: clear that hurdle right now. There's a difference between managing

1280
01:08:35,200 --> 01:08:37,240
Speaker 1: for the future and managing for right now. I guess

1281
01:08:37,240 --> 01:08:38,439
Speaker 1: it is one of the things that I'm trying to

1282
01:08:38,479 --> 01:08:42,120
Speaker 1: get to. Well. Well, the thing that you're you're stimulating

1283
01:08:42,120 --> 01:08:44,040
Speaker 1: in my mind in terms of what to to mention

1284
01:08:44,200 --> 01:08:47,679
Speaker 1: as you say that, is something we haven't talked about yet,

1285
01:08:48,080 --> 01:08:53,600
Speaker 1: and that is the profound fiscal and economic changes that

1286
01:08:53,680 --> 01:08:56,879
Speaker 1: have tap have taken place over the last twelve months.

1287
01:08:56,960 --> 01:09:00,880
Speaker 1: We've increased the federal debt from twenty trillion dollars to

1288
01:09:00,960 --> 01:09:03,360
Speaker 1: twenty eight trillion dollars and bidens trying to add another

1289
01:09:03,360 --> 01:09:07,200
Speaker 1: two trillion of that. The Federal Reserve increased the supply

1290
01:09:07,760 --> 01:09:11,240
Speaker 1: of US dollars, the effective supply of US dollars in

1291
01:09:11,280 --> 01:09:15,679
Speaker 1: the economy, by which, in theory, all else being equal,

1292
01:09:15,680 --> 01:09:18,960
Speaker 1: means that the dollars in your wallet are worth four

1293
01:09:19,080 --> 01:09:22,360
Speaker 1: fifths of what they were worth before, because literally the

1294
01:09:22,400 --> 01:09:25,640
Speaker 1: Federal Reserve just printed more dollars and flooded them into

1295
01:09:25,640 --> 01:09:28,479
Speaker 1: the economy, which went to the banks, which went to

1296
01:09:28,520 --> 01:09:31,480
Speaker 1: the wealthy, which went to the people who owned stocks

1297
01:09:31,560 --> 01:09:33,800
Speaker 1: and uh and could benefit from all that extra cash

1298
01:09:33,800 --> 01:09:37,200
Speaker 1: flowing around, didn't go to ordinary people. And and those

1299
01:09:37,240 --> 01:09:39,920
Speaker 1: problems are gonna come back to haunt us. One of

1300
01:09:39,920 --> 01:09:42,760
Speaker 1: the things that I really worry about, I'm I'm I'm

1301
01:09:42,800 --> 01:09:45,760
Speaker 1: optimistic about our ability to handle a future pandemic for

1302
01:09:45,800 --> 01:09:50,080
Speaker 1: the reasons I mentioned. I'm a lot more concerned about

1303
01:09:50,240 --> 01:09:53,559
Speaker 1: what increasing the debt by eight trillion dollars and increasing

1304
01:09:53,560 --> 01:09:57,519
Speaker 1: the money supply by is going to do to push

1305
01:09:57,600 --> 01:10:01,240
Speaker 1: us into a long term fiscal crisis that we're not

1306
01:10:01,320 --> 01:10:03,360
Speaker 1: going to be able to deal with. And people, you know,

1307
01:10:03,800 --> 01:10:07,360
Speaker 1: America has been such a stable and generally prosperous country

1308
01:10:07,400 --> 01:10:11,040
Speaker 1: for so long, people have forgotten what it's like to

1309
01:10:11,160 --> 01:10:15,040
Speaker 1: be in an environment where we really have a fundamentally

1310
01:10:15,600 --> 01:10:20,160
Speaker 1: unstable economy, and by fundamentally unstable, I'm talking vim our Germany,

1311
01:10:20,360 --> 01:10:23,920
Speaker 1: great depression, that kind of instability. And we are well

1312
01:10:23,960 --> 01:10:26,080
Speaker 1: on our way. We are well on our way to

1313
01:10:26,120 --> 01:10:30,320
Speaker 1: having basically the monetary policy of vim our Germany, and

1314
01:10:30,439 --> 01:10:33,200
Speaker 1: look out if that ever comes to pass. And and

1315
01:10:33,240 --> 01:10:35,840
Speaker 1: there are a lot of scenarios I could, I could

1316
01:10:35,840 --> 01:10:38,360
Speaker 1: bore you with or terrify you with that that could

1317
01:10:38,360 --> 01:10:40,880
Speaker 1: take place over the next ten twenty years in that

1318
01:10:40,960 --> 01:10:44,439
Speaker 1: regard and not to me, that's the biggest mess that

1319
01:10:44,479 --> 01:10:47,240
Speaker 1: we're gonna have to clean up from the last twelve months.

1320
01:10:47,280 --> 01:10:50,360
Speaker 1: How do we get our our fiscal and economic picture

1321
01:10:50,400 --> 01:10:54,120
Speaker 1: back in line because if we don't, the rising generations

1322
01:10:54,120 --> 01:10:56,600
Speaker 1: are never gonna know what it's like to have to

1323
01:10:56,640 --> 01:10:59,800
Speaker 1: have had that that success, in that prosperity that that

1324
01:11:00,000 --> 01:11:02,360
Speaker 1: people that are my age and your age take for granted.

1325
01:11:02,800 --> 01:11:04,960
Speaker 1: Be sure to catch live editions of Out Kicked the

1326
01:11:05,000 --> 01:11:08,320
Speaker 1: coverage with Clay Travis weekdays at six am Eastern three

1327
01:11:08,320 --> 01:11:11,840
Speaker 1: am Pacific. We're talking to Ovic Roy. He works at

1328
01:11:11,960 --> 01:11:15,000
Speaker 1: free opt dot org. He is at a v I

1329
01:11:15,120 --> 01:11:17,439
Speaker 1: k thank him for talking with us. I keep saying

1330
01:11:17,439 --> 01:11:19,400
Speaker 1: I have two more questions, but I do. I think

1331
01:11:19,439 --> 01:11:22,360
Speaker 1: have only two more questions now. One of those questions,

1332
01:11:22,600 --> 01:11:25,200
Speaker 1: uh that is out there is one that my wife

1333
01:11:25,240 --> 01:11:29,560
Speaker 1: asked me to ask you specifically, what is this vaccine

1334
01:11:29,640 --> 01:11:33,560
Speaker 1: going to do? Presuming that everybody starts to get the vaccine,

1335
01:11:34,160 --> 01:11:36,559
Speaker 1: when do you think things can get back to quote

1336
01:11:36,600 --> 01:11:41,880
Speaker 1: unquote normalcy and walk us through because she was like, hey,

1337
01:11:41,920 --> 01:11:43,720
Speaker 1: they're saying that you're still gonna have to wear a

1338
01:11:43,760 --> 01:11:47,160
Speaker 1: mask after you're vaccinated because you might still then be

1339
01:11:47,320 --> 01:11:52,200
Speaker 1: asymptomatic and able to spread it even after vaccination, which

1340
01:11:52,240 --> 01:11:55,200
Speaker 1: her concern is if that's true, then how do we

1341
01:11:55,280 --> 01:11:59,280
Speaker 1: get back to normalcy? Uh? And can you break down

1342
01:11:59,640 --> 01:12:03,719
Speaker 1: the vaccination process and what it looks like in means

1343
01:12:04,000 --> 01:12:07,920
Speaker 1: to the average person out there? That was her big question, um,

1344
01:12:07,960 --> 01:12:10,719
Speaker 1: because she doesn't think there's an in depth discussion enough

1345
01:12:11,080 --> 01:12:16,479
Speaker 1: about what the vaccination actually means in terms of our lives. Yeah,

1346
01:12:16,600 --> 01:12:19,640
Speaker 1: great question, Laura. So first of all, we should we

1347
01:12:19,640 --> 01:12:23,320
Speaker 1: should mentioned there's multiple vaccines. They're not identical. The MODERNA

1348
01:12:23,400 --> 01:12:25,920
Speaker 1: vaccine and the bio Intech vaccine and the new Johnson

1349
01:12:25,920 --> 01:12:29,280
Speaker 1: and Johnson vaccine. They're all a little different, um, and

1350
01:12:29,400 --> 01:12:31,160
Speaker 1: they all seem to work, which is the good news.

1351
01:12:31,560 --> 01:12:34,040
Speaker 1: They're all a little different and so, uh So there'll

1352
01:12:34,040 --> 01:12:35,559
Speaker 1: be a lot of different vaccines that are that are

1353
01:12:35,560 --> 01:12:37,479
Speaker 1: out out there that you can get access to, just

1354
01:12:37,520 --> 01:12:38,960
Speaker 1: like there are a lot of different COVID tests that

1355
01:12:39,000 --> 01:12:41,799
Speaker 1: you can get access to, but they work and that's reassuring.

1356
01:12:41,840 --> 01:12:43,840
Speaker 1: So for the people out there, who are who are

1357
01:12:44,040 --> 01:12:46,800
Speaker 1: have been skeptical of whether the vaccines work or not,

1358
01:12:46,960 --> 01:12:50,360
Speaker 1: or whether it's some you know, government plot. Um, I'm

1359
01:12:50,400 --> 01:12:53,040
Speaker 1: pretty competent that the vaccines have been studied well as

1360
01:12:53,040 --> 01:12:56,200
Speaker 1: they do work and that they are affective. So, uh,

1361
01:12:56,600 --> 01:12:58,040
Speaker 1: will you be in line to get it? Will you

1362
01:12:58,080 --> 01:12:59,479
Speaker 1: be in line to get a vaccine? Like? Is that

1363
01:12:59,600 --> 01:13:02,320
Speaker 1: something that you care about or your kids as opposed

1364
01:13:02,320 --> 01:13:04,800
Speaker 1: to your parents who may want to get a vaccine? Like,

1365
01:13:04,840 --> 01:13:08,599
Speaker 1: what would your personal decision be? Yeah, I've I've signed

1366
01:13:08,680 --> 01:13:10,880
Speaker 1: up on the Austin website. Now I'm you know, I'm

1367
01:13:10,920 --> 01:13:13,160
Speaker 1: forty eight years old, and I'm you know, I don't

1368
01:13:13,200 --> 01:13:16,320
Speaker 1: I don't have any serious serious illnesses, so I'm not

1369
01:13:16,360 --> 01:13:18,040
Speaker 1: I'm not going to be the front of the line

1370
01:13:18,040 --> 01:13:19,680
Speaker 1: eye idem. I wouldn't want to cut in line. I

1371
01:13:19,720 --> 01:13:24,160
Speaker 1: want the greatest risk to get it first. But but yeah,

1372
01:13:24,160 --> 01:13:26,200
Speaker 1: when it comes out, I'll definitely get When I'm able

1373
01:13:26,200 --> 01:13:27,840
Speaker 1: to get it, I'll definitely get it. And the good

1374
01:13:27,840 --> 01:13:31,240
Speaker 1: news is, you know, again, for all the catterwauling in

1375
01:13:31,280 --> 01:13:33,920
Speaker 1: the press and and from from people with a partisan

1376
01:13:33,960 --> 01:13:37,320
Speaker 1: point of view, the fact is, uh, we've we've started

1377
01:13:37,360 --> 01:13:40,720
Speaker 1: to to overcome some of the stupidity, particularly at the

1378
01:13:40,720 --> 01:13:44,360
Speaker 1: state level, in terms of blocking people from getting the vaccine.

1379
01:13:44,960 --> 01:13:47,880
Speaker 1: The rules that that really uh Florida Dennis excuse me,

1380
01:13:47,960 --> 01:13:50,519
Speaker 1: Roder Santas and Florida Uh innovator, where you just give

1381
01:13:50,520 --> 01:13:52,400
Speaker 1: it to everyone over sixty five. You know, let's just

1382
01:13:52,439 --> 01:13:56,120
Speaker 1: do that. Show him your driver's license, boom boom, straightforward,

1383
01:13:56,720 --> 01:13:58,920
Speaker 1: get all the over sixty five people the vaccines, then

1384
01:13:58,960 --> 01:14:01,840
Speaker 1: go work down from there. That's the right way to go.

1385
01:14:01,920 --> 01:14:04,679
Speaker 1: And that was really bungled by by a number of people,

1386
01:14:04,880 --> 01:14:07,160
Speaker 1: as the Santis done the best job almost of any

1387
01:14:07,160 --> 01:14:09,280
Speaker 1: governor in the country, despite the fact that he's been

1388
01:14:09,280 --> 01:14:13,760
Speaker 1: criticized rapidly by many people in the media. Absolutely, you know,

1389
01:14:13,800 --> 01:14:16,320
Speaker 1: I mean this vaccine thing is only reinforced what you

1390
01:14:16,360 --> 01:14:18,920
Speaker 1: and I talked about with the nursing homes uh in

1391
01:14:19,000 --> 01:14:21,280
Speaker 1: back in August. I mean, it was the Santis who

1392
01:14:21,360 --> 01:14:22,920
Speaker 1: did the right thing, which is, let's just open it

1393
01:14:23,000 --> 01:14:26,000
Speaker 1: up to everybody over sixty five. We're not gonna grill

1394
01:14:26,080 --> 01:14:28,439
Speaker 1: you on exactly what you're medical history. We're gonna look

1395
01:14:28,479 --> 01:14:31,040
Speaker 1: at your driver's license boom, and if there's extra vaccine

1396
01:14:31,040 --> 01:14:33,040
Speaker 1: at the end of the day, boom. You know, stick

1397
01:14:33,080 --> 01:14:35,639
Speaker 1: it in the arm of the pizza guy right Whereas

1398
01:14:35,920 --> 01:14:39,160
Speaker 1: Andrew Cuomo New York is literally saying to clinics, if

1399
01:14:39,200 --> 01:14:41,240
Speaker 1: you give the vaccine to someone who isn't in the

1400
01:14:41,360 --> 01:14:44,680
Speaker 1: right subgroup that I've dictated, I will find you a

1401
01:14:44,760 --> 01:14:47,320
Speaker 1: million dollars. And what does that do that He's a

1402
01:14:47,320 --> 01:14:50,000
Speaker 1: lot of vaccine is getting wasted because at the end

1403
01:14:50,000 --> 01:14:51,559
Speaker 1: of the day they run out of people who were

1404
01:14:51,600 --> 01:14:53,320
Speaker 1: at the clinic to give the vaccine to and they

1405
01:14:53,320 --> 01:14:56,160
Speaker 1: literally have to throw out the remaining doses. It's just

1406
01:14:56,680 --> 01:15:00,400
Speaker 1: profoundly idiotic. And it just goes to show, oh again,

1407
01:15:00,439 --> 01:15:03,320
Speaker 1: it's like consistent with what happened before. You have one,

1408
01:15:03,640 --> 01:15:05,920
Speaker 1: uh one governor in particular, who stands out as a

1409
01:15:06,000 --> 01:15:08,920
Speaker 1: data driven guy who's always doing the right thing and

1410
01:15:09,040 --> 01:15:10,960
Speaker 1: based on the science, and they have another guy who's

1411
01:15:11,040 --> 01:15:15,600
Speaker 1: just operating from ego and instinct and and messing up NonStop. So,

1412
01:15:15,720 --> 01:15:18,200
Speaker 1: by the way, also the one that gets criticized is

1413
01:15:18,240 --> 01:15:20,880
Speaker 1: the one who's actually looking at the data. That's what's

1414
01:15:20,880 --> 01:15:22,960
Speaker 1: so frustrating to me from that perspective. There are a

1415
01:15:23,000 --> 01:15:25,000
Speaker 1: ton of people listening to us right now that are like, wait,

1416
01:15:25,479 --> 01:15:27,760
Speaker 1: Florida Governor Rhonda Santis has done one of the best

1417
01:15:27,840 --> 01:15:30,200
Speaker 1: jobs in the country. I saw on CNN that there

1418
01:15:30,200 --> 01:15:33,040
Speaker 1: were people at the beach in Florida and that everybody

1419
01:15:33,120 --> 01:15:35,240
Speaker 1: was gonna die in Florida, right, and that they're opening

1420
01:15:35,280 --> 01:15:37,639
Speaker 1: bars and that their restaurants are open, and that schools

1421
01:15:37,640 --> 01:15:40,840
Speaker 1: are open. It's it's that's what's so frustrating to me,

1422
01:15:41,040 --> 01:15:44,720
Speaker 1: is the media is actually selling us something that's fundamentally

1423
01:15:44,720 --> 01:15:46,519
Speaker 1: not true. And I saw, by the way, on Florida

1424
01:15:46,880 --> 01:15:50,040
Speaker 1: data yesterday, sixty eight percent of all people that have

1425
01:15:50,120 --> 01:15:53,520
Speaker 1: gotten the vaccine in Florida, more than anybody in the country,

1426
01:15:53,560 --> 01:15:57,160
Speaker 1: are sixty five or over. And so they're specifically focusing

1427
01:15:57,160 --> 01:16:00,320
Speaker 1: on the people who are dying of COVID. Yeah, they've

1428
01:16:00,320 --> 01:16:02,800
Speaker 1: have been very smart about it, again compared to other

1429
01:16:02,840 --> 01:16:05,080
Speaker 1: places where say, well, you have to be sixty five

1430
01:16:05,160 --> 01:16:06,720
Speaker 1: and live in a nursing him and have a pre

1431
01:16:06,800 --> 01:16:10,080
Speaker 1: existing condition, and then maybe we'll get the vaccine to you.

1432
01:16:10,160 --> 01:16:11,920
Speaker 1: But if you're not, then you have to wait until

1433
01:16:11,960 --> 01:16:13,840
Speaker 1: we're through with all those people first. I mean, it's

1434
01:16:13,840 --> 01:16:17,479
Speaker 1: just totally dumb, just logistically and uh. And credit to

1435
01:16:17,600 --> 01:16:19,719
Speaker 1: the Santists for seeing through that, and and the CDC,

1436
01:16:20,960 --> 01:16:23,360
Speaker 1: the so called you know, gold standard at the CC.

1437
01:16:23,400 --> 01:16:25,720
Speaker 1: All the bureaucrats of the CDC, they contributed to this

1438
01:16:25,760 --> 01:16:29,320
Speaker 1: problem by creating a very unwieldy the kind of thing

1439
01:16:29,320 --> 01:16:31,880
Speaker 1: that bureaucrats would do, not based on real world how

1440
01:16:31,920 --> 01:16:35,640
Speaker 1: things work, how things get distributed. So, uh, kudos to

1441
01:16:35,680 --> 01:16:39,479
Speaker 1: the santists. And then the Trump administration in its waning days,

1442
01:16:39,960 --> 01:16:41,640
Speaker 1: UH saw that and said, hey, this is stupid. That

1443
01:16:41,640 --> 01:16:43,519
Speaker 1: is what the CDC put out doesn't make any sense.

1444
01:16:43,840 --> 01:16:46,240
Speaker 1: Let's uh, let's overrule them. Of course, there were a

1445
01:16:46,240 --> 01:16:47,960
Speaker 1: lots of people to go, oh, you're overruling the CDC,

1446
01:16:48,120 --> 01:16:50,080
Speaker 1: but no, they did the right thing there. And you

1447
01:16:50,080 --> 01:16:52,639
Speaker 1: know you have Biden now saying well, and I want

1448
01:16:52,680 --> 01:16:54,800
Speaker 1: to answer a lar's question about this, do you have

1449
01:16:54,840 --> 01:16:58,040
Speaker 1: Biden out? They're saying, you know, well, are are big plan.

1450
01:16:58,160 --> 01:16:59,880
Speaker 1: The thing we're gonna do that's different from Trump is

1451
01:17:00,000 --> 01:17:02,799
Speaker 1: we're going to make sure that we deliver a hundred

1452
01:17:03,040 --> 01:17:07,120
Speaker 1: million doses of COVID over the next hundred days. Well,

1453
01:17:07,160 --> 01:17:09,600
Speaker 1: do you know what the run rate is of vaccines

1454
01:17:09,760 --> 01:17:11,520
Speaker 1: in the last couple of days of the Trump administration

1455
01:17:11,640 --> 01:17:14,280
Speaker 1: was to one point five million a day, meaning that

1456
01:17:14,479 --> 01:17:17,760
Speaker 1: if Biden does nothing, just lets the Trump administration plan

1457
01:17:17,880 --> 01:17:20,920
Speaker 1: play out, they'll have delivered a hundred fifty million doses

1458
01:17:21,240 --> 01:17:24,439
Speaker 1: over the next hundred days at least. So you know,

1459
01:17:24,479 --> 01:17:26,360
Speaker 1: there's a lot of like Biden put out this big

1460
01:17:26,360 --> 01:17:27,960
Speaker 1: press release the other day saying, Oh, here's all the

1461
01:17:27,960 --> 01:17:30,280
Speaker 1: things I'm gonna do. I'm gonna make people produce pp

1462
01:17:30,520 --> 01:17:32,960
Speaker 1: I'm gonna deliver a hundred million dolls of cod It's like,

1463
01:17:33,000 --> 01:17:35,519
Speaker 1: this is all common sense stuff that's already being done.

1464
01:17:36,400 --> 01:17:39,680
Speaker 1: And the good news is again there there's obviously been

1465
01:17:39,720 --> 01:17:41,920
Speaker 1: a lot of snappy there's a lot of things that

1466
01:17:41,920 --> 01:17:44,200
Speaker 1: have gotten messed up in the early going here, but

1467
01:17:44,280 --> 01:17:46,880
Speaker 1: the good news is we're learning from that in real time.

1468
01:17:47,600 --> 01:17:50,479
Speaker 1: I do think we're gonna get easily through a hundred

1469
01:17:50,520 --> 01:17:53,320
Speaker 1: million doses in the first hundred days. We should have

1470
01:17:53,640 --> 01:17:56,439
Speaker 1: all the at risk populations of people who actually want

1471
01:17:56,479 --> 01:17:57,920
Speaker 1: to take the vaccinumously. There are a lot of people

1472
01:17:57,920 --> 01:17:59,559
Speaker 1: who are scared of it or don't want to take

1473
01:17:59,560 --> 01:18:01,479
Speaker 1: it for pill sophical reasons. But the people who want

1474
01:18:01,479 --> 01:18:03,679
Speaker 1: to take the vaccine who are over sixty five should

1475
01:18:03,720 --> 01:18:07,640
Speaker 1: all get it by March uh if you know, if

1476
01:18:07,680 --> 01:18:10,360
Speaker 1: they want to, then we start going to the general populations.

1477
01:18:10,680 --> 01:18:14,160
Speaker 1: And my hope is that let's call it, let's call

1478
01:18:14,200 --> 01:18:18,200
Speaker 1: it July. Uh. We you know, the the vast majority

1479
01:18:18,240 --> 01:18:20,920
Speaker 1: of people who want to get vaccinated should be well

1480
01:18:20,960 --> 01:18:23,160
Speaker 1: on their way to getting vaccine at least the first

1481
01:18:23,160 --> 01:18:26,200
Speaker 1: shot and hopefully the second. And that means that from

1482
01:18:26,200 --> 01:18:29,200
Speaker 1: a standpoint of the way viral transmission works, the virus

1483
01:18:29,240 --> 01:18:30,760
Speaker 1: is not going to be a problem. Right if you've

1484
01:18:30,800 --> 01:18:34,680
Speaker 1: got that much immunity in society, the virus is not

1485
01:18:34,720 --> 01:18:36,479
Speaker 1: going to really be able to get the traction to

1486
01:18:36,520 --> 01:18:39,639
Speaker 1: continue to spread even if not everybody has gotten the vaccine.

1487
01:18:39,680 --> 01:18:42,519
Speaker 1: Think about the measles vaccine. Not everybody gets the measle vaccine.

1488
01:18:42,520 --> 01:18:44,679
Speaker 1: Not everybody gets the flu shot every winter, and yet

1489
01:18:45,280 --> 01:18:48,879
Speaker 1: enough people do that that that we don't have influenza pandemic.

1490
01:18:48,960 --> 01:18:51,960
Speaker 1: So similarly, here, if enough people get the vaccine, we

1491
01:18:51,960 --> 01:18:54,600
Speaker 1: should be able to return to normal life. So I

1492
01:18:54,880 --> 01:18:57,760
Speaker 1: don't agree with the people are saying no, we have

1493
01:18:57,880 --> 01:19:01,240
Speaker 1: to behave as if we're still in law down for

1494
01:19:01,240 --> 01:19:03,360
Speaker 1: for most of this year. I think for the first

1495
01:19:03,439 --> 01:19:06,360
Speaker 1: quarter is still going to be tough sledding. But but

1496
01:19:06,520 --> 01:19:09,759
Speaker 1: once we get to to the April May June time frame,

1497
01:19:09,840 --> 01:19:13,120
Speaker 1: I do think things should start to subside. Hopefully the

1498
01:19:13,160 --> 01:19:16,360
Speaker 1: stacks on the cases and the hospitalization starts to subside

1499
01:19:16,360 --> 01:19:19,120
Speaker 1: to and that'll be the thing that hopefully turns around

1500
01:19:19,160 --> 01:19:23,599
Speaker 1: that allows us to build more momentum for for reopening schools,

1501
01:19:23,600 --> 01:19:28,000
Speaker 1: reopening the economy, etcetera. Fox Sports Radio has the best

1502
01:19:28,080 --> 01:19:30,920
Speaker 1: sports talk lineup in the nation. Catch all of our

1503
01:19:30,920 --> 01:19:34,439
Speaker 1: shows at Fox sports Radio dot com and within the

1504
01:19:34,439 --> 01:19:37,599
Speaker 1: I Heart Radio app search f s R to listen live.

1505
01:19:39,200 --> 01:19:41,400
Speaker 1: I'm Clay Travis. This is Wins and Loss as you're

1506
01:19:41,400 --> 01:19:44,200
Speaker 1: hearing from O vicroy legit last question for you, and

1507
01:19:44,200 --> 01:19:45,880
Speaker 1: I think we could talk all day, by the way,

1508
01:19:45,920 --> 01:19:47,840
Speaker 1: I could just I could just keep unpacking so much

1509
01:19:47,840 --> 01:19:50,320
Speaker 1: of what you're saying and continue this conversation, and I

1510
01:19:50,320 --> 01:19:53,559
Speaker 1: hope people have enjoyed it. I said, legit last question.

1511
01:19:53,600 --> 01:19:54,920
Speaker 1: But I do want to ask you this. How is

1512
01:19:54,960 --> 01:19:57,720
Speaker 1: free ap dot org funded. If people are listening to

1513
01:19:57,760 --> 01:20:00,360
Speaker 1: this right now and they love what you're saying and

1514
01:20:00,360 --> 01:20:02,120
Speaker 1: they're like, man, I want to go check out more

1515
01:20:02,160 --> 01:20:05,439
Speaker 1: of the work they're doing their capitalists or do you

1516
01:20:05,439 --> 01:20:08,600
Speaker 1: guys raise money? Are you privately funded? What is the

1517
01:20:08,840 --> 01:20:11,040
Speaker 1: method by which you are able to do the work

1518
01:20:11,040 --> 01:20:14,599
Speaker 1: that you do. Well, thank you for asking that, Clay.

1519
01:20:14,680 --> 01:20:16,760
Speaker 1: Because we are a nonprofit of five oh one C

1520
01:20:16,960 --> 01:20:21,960
Speaker 1: three tax exept, nonprofit, nonpartisan, and we basically survive our donations.

1521
01:20:22,000 --> 01:20:25,280
Speaker 1: So we get donations from people like you, people who

1522
01:20:25,320 --> 01:20:28,760
Speaker 1: are listening to this uh podcast or radio show, and

1523
01:20:28,760 --> 01:20:32,960
Speaker 1: where we we get donations from also charitable foundations that

1524
01:20:33,000 --> 01:20:36,080
Speaker 1: we apply to grants from and and so we basically

1525
01:20:36,479 --> 01:20:38,479
Speaker 1: you hit up as many people as we'll We'll take

1526
01:20:38,520 --> 01:20:40,960
Speaker 1: the calls, take the meetings, and give them our our

1527
01:20:41,000 --> 01:20:42,920
Speaker 1: pitch about what we're doing and say, hey, look, if

1528
01:20:42,920 --> 01:20:45,600
Speaker 1: you're if you're looking for a set of ideas that

1529
01:20:45,600 --> 01:20:48,760
Speaker 1: can bring Republicans and Democrats together to make the country better,

1530
01:20:48,840 --> 01:20:52,439
Speaker 1: to expand freedom and expand prosperity, particularly for the little

1531
01:20:52,439 --> 01:20:55,479
Speaker 1: guy who's struggling in this day and age, take a

1532
01:20:55,479 --> 01:20:58,280
Speaker 1: lookal what we're doing and and help support our scholars

1533
01:20:58,400 --> 01:21:00,200
Speaker 1: and and you know, to take the example of our

1534
01:21:00,000 --> 01:21:03,439
Speaker 1: our COVID work. Right. So it wasn't just me. You know,

1535
01:21:03,479 --> 01:21:05,800
Speaker 1: you're you're having me on your show, and I appreciate that,

1536
01:21:05,840 --> 01:21:08,040
Speaker 1: appreciate the chance to share what we work on. But

1537
01:21:08,120 --> 01:21:09,960
Speaker 1: it was a whole team of people who put together

1538
01:21:10,280 --> 01:21:13,160
Speaker 1: our work, like on on reopening schools. Yes, we had

1539
01:21:13,160 --> 01:21:17,000
Speaker 1: our healthcare people talking about the COVID piece of it,

1540
01:21:17,080 --> 01:21:18,920
Speaker 1: the virus piece of it, right, but we also had

1541
01:21:18,960 --> 01:21:22,240
Speaker 1: our education experts, people like Dan lips for who's our

1542
01:21:22,520 --> 01:21:25,320
Speaker 1: expert on case through twelve education, Preston Cooper, who's our

1543
01:21:25,360 --> 01:21:28,320
Speaker 1: expert on on college and vocational school and how to

1544
01:21:28,320 --> 01:21:31,280
Speaker 1: reopen those schools. And so we had a plan that

1545
01:21:31,360 --> 01:21:34,120
Speaker 1: went from how to reopen preschools to grade schools, the

1546
01:21:34,240 --> 01:21:37,599
Speaker 1: high schools, the colleges to trade schools. We went through

1547
01:21:37,640 --> 01:21:40,400
Speaker 1: it all, and that's because we're able to leverage our

1548
01:21:40,400 --> 01:21:44,240
Speaker 1: whole team of scholars, not just in in biotech and healthcare,

1549
01:21:44,240 --> 01:21:47,559
Speaker 1: but also in education and economics and housing and other

1550
01:21:47,600 --> 01:21:50,439
Speaker 1: areas to do this kind of work. So and we

1551
01:21:50,439 --> 01:21:52,200
Speaker 1: wouldn't be able to do that if it weren't for

1552
01:21:52,200 --> 01:21:54,800
Speaker 1: for the donations of of the people like the people

1553
01:21:54,800 --> 01:21:57,120
Speaker 1: who are listening to this podcast. So if you're interested

1554
01:21:57,160 --> 01:21:59,599
Speaker 1: in supporting our work, whether it's a ten dollar check

1555
01:21:59,680 --> 01:22:01,400
Speaker 1: or if if you're Clay, if you have Clay Travis

1556
01:22:01,400 --> 01:22:04,439
Speaker 1: money a bigger chest than that, you can click on

1557
01:22:04,479 --> 01:22:07,599
Speaker 1: the donate tab on our website and I legitimately am

1558
01:22:07,640 --> 01:22:09,800
Speaker 1: going to donate today. I'm meant to ask this the

1559
01:22:09,880 --> 01:22:12,519
Speaker 1: last time, so free op dot org. I mean, I'm

1560
01:22:12,560 --> 01:22:14,679
Speaker 1: just I'm just so impressed with the work that you're doing,

1561
01:22:14,720 --> 01:22:17,280
Speaker 1: and I think we need more work like this, and

1562
01:22:17,360 --> 01:22:19,880
Speaker 1: so I'm going to head uh straight there. So, if

1563
01:22:19,920 --> 01:22:23,640
Speaker 1: you are also enjoying this conversation and you want to

1564
01:22:23,640 --> 01:22:26,240
Speaker 1: to support free op dot org, is where you go? Okay,

1565
01:22:26,479 --> 01:22:30,640
Speaker 1: last question for you, so, and the reason why I

1566
01:22:30,680 --> 01:22:33,599
Speaker 1: would use Vietnam as an example is Vietnam is almost

1567
01:22:33,680 --> 01:22:37,879
Speaker 1: universally decided to have been the biggest failure of American

1568
01:22:37,960 --> 01:22:40,559
Speaker 1: public policy for most of the last fifty years. Right,

1569
01:22:40,640 --> 01:22:43,200
Speaker 1: let's go all the way back to to Vietnam. The

1570
01:22:43,320 --> 01:22:47,960
Speaker 1: smartest people got it all wrong on Vietnam. In the

1571
01:22:48,080 --> 01:22:51,960
Speaker 1: years that have ensued since Vietnam finished, that has become

1572
01:22:52,000 --> 01:22:55,479
Speaker 1: the consensus opinion. We got it wrong. We didn't foment

1573
01:22:55,640 --> 01:22:59,280
Speaker 1: the right public policy, we wasted a lot of lives,

1574
01:22:59,400 --> 01:23:02,040
Speaker 1: we didn't do what would have been best for the country.

1575
01:23:02,120 --> 01:23:04,679
Speaker 1: I would imagine almost everybody out there listening right now,

1576
01:23:05,120 --> 01:23:07,160
Speaker 1: there's very few people who are like in the camp

1577
01:23:07,240 --> 01:23:10,759
Speaker 1: of Vietnam was expertly executed by the United States government.

1578
01:23:12,200 --> 01:23:14,760
Speaker 1: Will we reach the point I know you said, you've

1579
01:23:14,800 --> 01:23:17,439
Speaker 1: got your report coming out at the end of February

1580
01:23:17,680 --> 01:23:25,440
Speaker 1: where masses of American population recognized that lockdowns, that shut downs,

1581
01:23:25,520 --> 01:23:30,439
Speaker 1: that schools being closed was a failure of policy. Or

1582
01:23:31,200 --> 01:23:34,479
Speaker 1: are so many people committed to what their opinion was

1583
01:23:34,520 --> 01:23:38,559
Speaker 1: in real time through social media and everything else, that

1584
01:23:38,680 --> 01:23:42,839
Speaker 1: people will be unwilling to recognize what the data tells

1585
01:23:42,920 --> 01:23:46,000
Speaker 1: them because it conflicts with the emotions they felt in

1586
01:23:46,040 --> 01:23:49,559
Speaker 1: that moment. And I at asked that question because I

1587
01:23:49,600 --> 01:23:53,519
Speaker 1: think it's significant and important that we learn from the

1588
01:23:53,560 --> 01:23:56,480
Speaker 1: mistakes that we make in public policy in our country.

1589
01:23:57,040 --> 01:23:58,800
Speaker 1: Will we end up because I think you would agree

1590
01:23:58,840 --> 01:24:01,519
Speaker 1: with me right now that the data is almost uniform

1591
01:24:01,600 --> 01:24:04,679
Speaker 1: that lockdowns don't make sense, and you can use Fortunately,

1592
01:24:04,680 --> 01:24:07,880
Speaker 1: because of federalism, we've got all these fifty different states

1593
01:24:07,920 --> 01:24:11,040
Speaker 1: that may have implemented a little bit different policy. And

1594
01:24:11,080 --> 01:24:14,240
Speaker 1: I think it's clear that California hasn't had some radically

1595
01:24:14,320 --> 01:24:18,960
Speaker 1: better result than Texas, or that certainly New York hasn't

1596
01:24:19,000 --> 01:24:21,840
Speaker 1: been better than Florida. In fact, it's far worse. I

1597
01:24:21,920 --> 01:24:24,760
Speaker 1: use those four states because they're the most populous. In

1598
01:24:24,800 --> 01:24:28,160
Speaker 1: other words, the virus was gonna virus, right, like we

1599
01:24:28,160 --> 01:24:30,559
Speaker 1: weren't going to be able to escape it based on

1600
01:24:30,600 --> 01:24:36,320
Speaker 1: a public policy decision exclusively, Will we reach that consensus?

1601
01:24:37,080 --> 01:24:40,360
Speaker 1: When does that consensus come? If we are ever going

1602
01:24:40,400 --> 01:24:43,640
Speaker 1: to reach it, I think it's going to take a

1603
01:24:43,680 --> 01:24:47,400
Speaker 1: long time, Clay, because you know, the people who were

1604
01:24:47,439 --> 01:24:49,280
Speaker 1: involved as debate, people like you and me and the

1605
01:24:49,280 --> 01:24:53,080
Speaker 1: people who we disagreed with, pretty invested in their point

1606
01:24:53,080 --> 01:24:55,000
Speaker 1: of view at this point, right, Nobody wants to admit

1607
01:24:55,040 --> 01:24:58,320
Speaker 1: they're wrong. Nobody is going to be inclined to admit

1608
01:24:58,320 --> 01:25:00,559
Speaker 1: the wrong, even if they secretly believe they're Some people

1609
01:25:00,560 --> 01:25:02,080
Speaker 1: just don't believe they're wrong because they're not gonna look

1610
01:25:02,080 --> 01:25:05,880
Speaker 1: at the data that doesn't confirm their own preconceptions, right.

1611
01:25:05,960 --> 01:25:09,479
Speaker 1: So I think it's gonna take some time for that

1612
01:25:09,520 --> 01:25:13,920
Speaker 1: to happen. But that's where organizations like free Up hopefully

1613
01:25:14,080 --> 01:25:16,160
Speaker 1: can play a role, along with obviously guys like you,

1614
01:25:16,479 --> 01:25:19,559
Speaker 1: in terms of doing the research, doing the analyzes that

1615
01:25:19,600 --> 01:25:23,479
Speaker 1: we can then circulate uh in the media, circulate with

1616
01:25:23,479 --> 01:25:26,800
Speaker 1: with our our peers and colleagues that show UH that

1617
01:25:26,800 --> 01:25:28,920
Speaker 1: that's the case. Right. So it's up to the people

1618
01:25:29,000 --> 01:25:31,640
Speaker 1: like us who have the views that we have or

1619
01:25:31,680 --> 01:25:34,160
Speaker 1: the hypotheses or whatever you want to call it, to

1620
01:25:34,200 --> 01:25:36,920
Speaker 1: actually do the research, do the work to show that actually,

1621
01:25:36,960 --> 01:25:38,879
Speaker 1: if you look at California and you look at Texas,

1622
01:25:39,160 --> 01:25:41,639
Speaker 1: and you look at the economic restrictions that they put

1623
01:25:41,680 --> 01:25:44,840
Speaker 1: in or didn't put in, here was how that affected

1624
01:25:45,320 --> 01:25:48,360
Speaker 1: the rate of COVID infections and their hospitalizations in debt.

1625
01:25:48,720 --> 01:25:51,720
Speaker 1: And it's it's pretty clear that that that nothing really

1626
01:25:51,760 --> 01:25:53,560
Speaker 1: happened there and and or that that that that that

1627
01:25:53,840 --> 01:25:56,520
Speaker 1: the Texas or Florida model was vindicated. I think that's

1628
01:25:56,560 --> 01:25:58,080
Speaker 1: that's going to be some of the work that that

1629
01:25:58,280 --> 01:26:00,479
Speaker 1: that people at Free Up and L we're going to

1630
01:26:00,560 --> 01:26:04,200
Speaker 1: have to do to make that point clear. So it's

1631
01:26:04,280 --> 01:26:07,439
Speaker 1: up to researchers who want to who want to test

1632
01:26:07,479 --> 01:26:11,240
Speaker 1: that hypothesis or or or or or prove it to

1633
01:26:11,360 --> 01:26:14,640
Speaker 1: do that work. And and that's where organizations like us

1634
01:26:14,680 --> 01:26:16,360
Speaker 1: really make a difference. Through the whole reason we started

1635
01:26:16,360 --> 01:26:18,640
Speaker 1: Free Up. Free Up is only five years old, four

1636
01:26:18,680 --> 01:26:20,160
Speaker 1: and a half years old, and the whole reason we

1637
01:26:20,200 --> 01:26:22,880
Speaker 1: started is because even though this country is so big,

1638
01:26:22,920 --> 01:26:26,840
Speaker 1: three foundered thirty million people, there was literally nobody doing

1639
01:26:26,840 --> 01:26:28,320
Speaker 1: this kind of work. If we didn't do it, which

1640
01:26:28,360 --> 01:26:30,360
Speaker 1: sounds crazy, Like I look around them, like, how is

1641
01:26:30,400 --> 01:26:33,120
Speaker 1: it possible that we're the only ones writing these you know,

1642
01:26:33,280 --> 01:26:36,080
Speaker 1: long arctics. I say that in sports every day. How

1643
01:26:36,120 --> 01:26:38,280
Speaker 1: is it possible that OutKick is the only place doing

1644
01:26:38,320 --> 01:26:43,000
Speaker 1: what we do. It's it's scary, honestly. Yeah. And so

1645
01:26:43,439 --> 01:26:45,519
Speaker 1: it just, uh, you know, puts a little more pressure

1646
01:26:45,560 --> 01:26:48,040
Speaker 1: on us maybe to work harder and get that stuff

1647
01:26:48,080 --> 01:26:51,679
Speaker 1: out there. And and we certainly take that responsibility seriously

1648
01:26:51,680 --> 01:26:53,720
Speaker 1: and are are going to continue to do that. So

1649
01:26:53,720 --> 01:26:55,840
Speaker 1: so keep an eye on on our Twitter account, on

1650
01:26:55,840 --> 01:26:58,200
Speaker 1: our website and uh and hold us accountle if we

1651
01:26:58,240 --> 01:27:00,679
Speaker 1: don't get it done, and asked us to when when

1652
01:27:00,680 --> 01:27:02,559
Speaker 1: that work is going to come out, because it's important

1653
01:27:02,560 --> 01:27:04,479
Speaker 1: to get it done. It's not only important to get

1654
01:27:04,479 --> 01:27:08,080
Speaker 1: it done, it's also important for the very scientific method itself,

1655
01:27:08,120 --> 01:27:12,560
Speaker 1: because the idea that experts know everything to me is

1656
01:27:12,600 --> 01:27:15,800
Speaker 1: one of the lasting legacies of COVID that is going

1657
01:27:15,840 --> 01:27:19,479
Speaker 1: to be the most destructive, because the scientific method is

1658
01:27:19,520 --> 01:27:24,440
Speaker 1: predicated on coming up with hypotheses, testing them, and always

1659
01:27:24,520 --> 01:27:28,200
Speaker 1: expecting that you may be wrong, whereas it seems to

1660
01:27:28,240 --> 01:27:32,080
Speaker 1: me that social media is predicated almost entirely on never

1661
01:27:32,160 --> 01:27:35,880
Speaker 1: admitting you were wrong about anything. Yeah, you know, I

1662
01:27:35,880 --> 01:27:38,760
Speaker 1: mean you mentioned Vietnam. We we we talked about it

1663
01:27:38,800 --> 01:27:41,360
Speaker 1: on the last interview as well. You know, we obviously

1664
01:27:41,400 --> 01:27:43,559
Speaker 1: talked about COVID. Think about the housing crisis in two

1665
01:27:43,600 --> 01:27:47,320
Speaker 1: thousand eight. Right, all the experts said, housing prices only

1666
01:27:47,360 --> 01:27:50,280
Speaker 1: go up, they never go down, because that's what historical

1667
01:27:50,800 --> 01:27:54,320
Speaker 1: charts show. But of course, you know, every trend is

1668
01:27:54,360 --> 01:27:56,639
Speaker 1: made to be broken. And you have a housing bubble,

1669
01:27:56,640 --> 01:27:59,839
Speaker 1: you have a financial bubble, you have uh institutions behaving recklessly,

1670
01:27:59,840 --> 01:28:03,080
Speaker 1: people behaving recklessly, over leveraging their their their equity in

1671
01:28:03,120 --> 01:28:05,040
Speaker 1: their homes, and boom, you have a crash. Right, And

1672
01:28:05,040 --> 01:28:07,600
Speaker 1: that's what happened. And there were the people that that

1673
01:28:07,680 --> 01:28:09,960
Speaker 1: Michael Lewis wrote about in The Big Short, which was

1674
01:28:10,040 --> 01:28:13,519
Speaker 1: a great book and a great you know, I mean,

1675
01:28:13,600 --> 01:28:16,360
Speaker 1: and obviously the author of Moneyball as well, and The Blindside,

1676
01:28:16,720 --> 01:28:19,920
Speaker 1: incredible writer. Like what, what's the running theme of all

1677
01:28:19,920 --> 01:28:22,559
Speaker 1: those all those books, all those movies. It's that the

1678
01:28:22,680 --> 01:28:25,760
Speaker 1: experts didn't get it right, and there was some random

1679
01:28:26,000 --> 01:28:28,679
Speaker 1: creative nerd out there who was right where the experts

1680
01:28:28,680 --> 01:28:32,280
Speaker 1: were wrong. And so that's a you know, there's a balance, right.

1681
01:28:32,520 --> 01:28:36,200
Speaker 1: We don't want to say science doesn't matter that you know,

1682
01:28:36,280 --> 01:28:39,040
Speaker 1: you should just ignore everything that a scientist says because

1683
01:28:39,080 --> 01:28:41,680
Speaker 1: the scientists are always wrong. That's not true. But it's

1684
01:28:41,720 --> 01:28:44,680
Speaker 1: also true that experts, particularly experts who have a political

1685
01:28:44,720 --> 01:28:48,479
Speaker 1: point of view, often uh uh, you know, aren't willing

1686
01:28:48,479 --> 01:28:52,360
Speaker 1: to see countervailing or contradictory evidence that that conflicts with

1687
01:28:52,360 --> 01:28:54,479
Speaker 1: their world viewing. So the balances somewhere in the middle.

1688
01:28:54,760 --> 01:28:59,280
Speaker 1: The balances have a healthy skepticism of of what you

1689
01:28:59,280 --> 01:29:02,360
Speaker 1: hear from the so called experts. Don't automatically assume they're

1690
01:29:02,360 --> 01:29:04,880
Speaker 1: wrong either, but have that healthy skip to skepticism. Do

1691
01:29:04,960 --> 01:29:08,519
Speaker 1: your own work, do your own checking, ask intelligent questions.

1692
01:29:08,800 --> 01:29:10,240
Speaker 1: That's what we should have done in two thousand and

1693
01:29:10,240 --> 01:29:12,160
Speaker 1: eight with the natural crisis. That's what we should have

1694
01:29:12,200 --> 01:29:14,720
Speaker 1: done with the Vietnam war or the Iraq war. That's

1695
01:29:14,720 --> 01:29:16,840
Speaker 1: what we should do with COVID and everything else that

1696
01:29:16,880 --> 01:29:18,360
Speaker 1: comes along along the way. And if we do that,

1697
01:29:18,680 --> 01:29:22,320
Speaker 1: we'll have a much more healthy society uh and hopefully

1698
01:29:22,360 --> 01:29:25,160
Speaker 1: better response to the challenges that come before us in

1699
01:29:25,200 --> 01:29:28,240
Speaker 1: the future. I'm donating to him free op dot org.

1700
01:29:28,320 --> 01:29:30,320
Speaker 1: Got encourage you to do it as well. I'd also

1701
01:29:30,439 --> 01:29:33,599
Speaker 1: encourage you to go follow uh oh vic Roy at

1702
01:29:33,640 --> 01:29:35,639
Speaker 1: a v I K. You can thank him for coming

1703
01:29:35,680 --> 01:29:38,080
Speaker 1: on and talking to our OutKick audience here, and when

1704
01:29:38,160 --> 01:29:41,599
Speaker 1: you publish, hopefully in late February, since you've now established

1705
01:29:41,640 --> 01:29:43,760
Speaker 1: a date. When you published that and I have a

1706
01:29:43,840 --> 01:29:46,559
Speaker 1: chance to read it, we will get you on again.

1707
01:29:47,040 --> 01:29:50,800
Speaker 1: I appreciate you answering my wife's questions uh and uh,

1708
01:29:50,840 --> 01:29:53,759
Speaker 1: and again give her credit because she loved this interview

1709
01:29:53,760 --> 01:29:55,160
Speaker 1: and she said, you've got to get him on again,

1710
01:29:55,200 --> 01:29:56,519
Speaker 1: and you've got to talk to him again and get

1711
01:29:56,520 --> 01:29:59,000
Speaker 1: an update. So I appreciate everything that you're doing at

1712
01:29:59,000 --> 01:30:01,800
Speaker 1: your organization, and I appreciate time you gave us today.

1713
01:30:02,280 --> 01:30:04,960
Speaker 1: Hey Sam do you thanks for being a voice for

1714
01:30:04,960 --> 01:30:07,280
Speaker 1: for for the real the real truths out there on

1715
01:30:07,280 --> 01:30:10,639
Speaker 1: these issues really really important. Your your audience. You've you've

1716
01:30:10,680 --> 01:30:13,920
Speaker 1: grown such a big audience and you have, uh you know,

1717
01:30:13,960 --> 01:30:16,000
Speaker 1: the trust of so many people, and you've used it,

1718
01:30:16,439 --> 01:30:19,519
Speaker 1: uh for for social good to to get people the

1719
01:30:19,560 --> 01:30:21,880
Speaker 1: information they need. So people like me are grateful to

1720
01:30:21,960 --> 01:30:24,400
Speaker 1: you for that. Oh vic Roy, go follow him at

1721
01:30:24,439 --> 01:30:26,400
Speaker 1: A V I K. I am Clay Travis. This is

1722
01:30:26,400 --> 01:30:28,160
Speaker 1: Wins and Losses. I think. This is the first time

1723
01:30:28,200 --> 01:30:29,720
Speaker 1: we've ever had a guest on twice, so you know

1724
01:30:29,760 --> 01:30:31,800
Speaker 1: how highly I think of him. Go donate free op

1725
01:30:31,880 --> 01:30:34,000
Speaker 1: dot org. Appreciate all of you. Go check out the

1726
01:30:34,000 --> 01:30:37,200
Speaker 1: rest of our Wins and Losses conversations, including Ovic and

1727
01:30:37,280 --> 01:30:40,920
Speaker 1: Eyes first conversation, which is up from August one. Thank you,

1728
01:30:40,960 --> 01:30:42,680
Speaker 1: my man, Thanks to all you, and I hope you

1729
01:30:42,680 --> 01:30:44,800
Speaker 1: guys enjoy it, share with your friends. This has been

1730
01:30:44,960 --> 01:30:46,000
Speaker 1: Wins and Losses