Jay Bhattacharya: The Case Against Lockdowns

00:00:00.000 | The following is a conversation with Jay Bhattacharya,

00:00:02.920 | professor of medicine, health policy,

00:00:05.200 | and economics at Stanford University.

00:00:08.180 | Please allow me to say a few words about lockdowns

00:00:11.400 | and the blinding, destructive effects of arrogance

00:00:14.400 | on leadership, especially in the space of policy and politics.

00:00:19.400 | Jay Bhattacharya is the co-author

00:00:21.800 | of the now famous Great Barrington Declaration,

00:00:24.460 | a one-page document that in October 2020

00:00:27.860 | made a case against the effectiveness of lockdowns.

00:00:32.000 | Most of this podcast conversation

00:00:33.600 | is about the ideas related to this document.

00:00:36.360 | And so let me say a few things here about what troubles me.

00:00:40.220 | Those who advocate for lockdowns as a policy

00:00:44.460 | often ignore the quiet suffering of millions

00:00:47.320 | that it results in, which includes economic pain,

00:00:50.160 | loss of jobs that give meaning and pride

00:00:52.560 | in the face of uncertainty,

00:00:54.200 | the increase in suicide and suicidal ideation,

00:00:57.300 | and in general, the fear and anger that arises

00:01:00.840 | from the powerlessness forced onto the populace

00:01:04.180 | by the self-proclaimed elites and experts.

00:01:07.080 | Many folks whose job is unaffected by the lockdowns

00:01:11.600 | talk down to the masses about which path forward is right

00:01:14.840 | and which is wrong.

00:01:16.600 | What troubles me most is this very lack of empathy

00:01:20.040 | among the policy makers for the common man,

00:01:22.880 | and in general, for people unlike themselves.

00:01:26.300 | The landscape of suffering is vast

00:01:28.560 | and must be fully considered

00:01:30.200 | in calculating the response to the pandemic

00:01:33.220 | with humility and with rigorous,

00:01:36.280 | open-minded scientific debate.

00:01:39.300 | Jay and I talk about the email from Francis Collins

00:01:42.080 | to Anthony Fauci that called Jay and his two co-authors

00:01:46.440 | fringe epidemiologists,

00:01:48.760 | and also called for a devastating published takedown

00:01:51.560 | of their ideas.

00:01:53.240 | These words from Francis broke my heart.

00:01:57.140 | I understand them.

00:01:58.600 | I can even steel man them,

00:02:00.740 | but nevertheless, on balance,

00:02:02.940 | they show to me a failure of leadership.

00:02:06.680 | Leadership in a pandemic is hard,

00:02:09.060 | which is why great leaders are remembered by history.

00:02:12.620 | They are rare.

00:02:14.380 | They stand out, and they give me hope.

00:02:18.480 | Also, this whole mess inspires me

00:02:21.580 | at my small individual level to do the right thing

00:02:24.660 | in the face of conformity, despite the long odds.

00:02:28.220 | I talked to Francis Collins.

00:02:31.420 | I talked to Albert Bourla, Pfizer CEO.

00:02:34.180 | I also talked and will continue to talk

00:02:36.540 | with people like Jay and other dissenting voices

00:02:39.820 | that challenge the mainstream narratives

00:02:42.020 | and those in the seats of power.

00:02:43.780 | I hope to highlight both the strengths and weaknesses

00:02:47.900 | in their ideas with respect and empathy,

00:02:50.660 | but also with guts and skill,

00:02:53.180 | the skill part I hope to improve on over time.

00:02:57.820 | And I do believe that conversation

00:03:00.780 | and an open mind is the way out of this.

00:03:03.340 | And finally, as I've said in the past,

00:03:06.980 | I value love and integrity far, far above money,

00:03:11.260 | fame, and power.

00:03:12.960 | Those latter three are all ephemeral.

00:03:16.040 | They slip through the fingers of anyone who tries to hold on

00:03:19.940 | and leave behind an empty shell of a human being.

00:03:23.260 | I prefer to die a man who lived by principles

00:03:27.100 | that nobody could shake

00:03:28.700 | and a man who added a bit of love to the world.

00:03:31.600 | This is the Lex Friedman Podcast.

00:03:34.860 | To support it,

00:03:35.820 | please check out our sponsors in the description.

00:03:38.500 | And now here's my conversation with Jay Barucaria.

00:03:42.200 | To our best understanding today, how deadly is COVID?

00:03:48.500 | Do we have a good measure for this very question?

00:03:52.680 | - So the best evidence for COVID,

00:03:55.180 | the deadliness of COVID comes from a whole series

00:03:58.020 | of seroprevalence studies.

00:04:00.060 | Seroprevalence studies are these studies

00:04:01.580 | of antibody prevalence in the population at large.

00:04:04.840 | I was part of the very first set of seroprevalence studies,

00:04:08.940 | one in Santa Clara County, one in LA County,

00:04:10.940 | and one with Major League Baseball around the US.

00:04:14.260 | - If I may just pause you for a second,

00:04:17.200 | if people don't know what serology is in seroprevalence,

00:04:20.720 | it does sound like you say zero prevalence.

00:04:23.080 | It's not, it's sero, and serology is antibodies.

00:04:26.040 | So it's a survey that counts the number of antibodies--

00:04:29.920 | - Specific to COVID, yes.

00:04:31.200 | - People that have antibodies specific to COVID,

00:04:34.880 | which perhaps shows an indication

00:04:37.040 | that they likely have had COVID,

00:04:39.160 | and therefore this is a way to study

00:04:41.680 | how many people in the population

00:04:43.160 | have been exposed to or have had COVID.

00:04:45.440 | - Exactly, yeah, exactly.

00:04:46.840 | So the idea is that we don't know exactly

00:04:51.840 | the number of people with COVID

00:04:53.520 | just by counting the people that present themselves

00:04:56.480 | with symptoms of COVID.

00:04:57.760 | COVID has, it turns out,

00:04:58.960 | a very wide range of symptoms possible,

00:05:01.720 | ranging from no symptoms at all

00:05:04.120 | to this deadly viral pneumonia

00:05:05.920 | that's killed so many people.

00:05:07.480 | And the problem is like,

00:05:09.000 | if you just count the number of cases,

00:05:10.440 | the people who have very few symptoms

00:05:12.320 | often don't show up for testing.

00:05:14.560 | We just don't, they're outside of the can of public health.

00:05:18.160 | And so it's really hard to know the answer to your question

00:05:21.920 | without understanding how many people are infected,

00:05:24.200 | 'cause you can probably tell the number of deaths,

00:05:26.320 | that's even though that there's some controversy over that.

00:05:28.400 | But that, so the numerator is possible,

00:05:31.360 | but the denominator is much harder.

00:05:33.120 | - How much controversy is there about the death?

00:05:35.120 | We're gonna go on a million tangents.

00:05:36.960 | Is that, okay, we're gonna, I have a million questions.

00:05:40.160 | So one, I love data so much,

00:05:43.560 | but I've like almost tuned out

00:05:45.520 | paying attention to COVID data,

00:05:47.440 | 'cause I feel like I'm walking on shaky ground.

00:05:49.960 | I don't know who to trust.

00:05:51.360 | Maybe you can comment on different sources of data,

00:05:54.460 | different kinds of data.

00:05:56.000 | The death one, that seems like a really important one.

00:05:59.180 | Can we trust the reported deaths associated with COVID,

00:06:03.280 | or is it just a giant messy thing that mixed up?

00:06:06.040 | And then there's this kind of stories about hospitals

00:06:09.640 | being incentivized to report a death as COVID death.

00:06:14.320 | - So there's some truth in some of that.

00:06:16.440 | Let me just, so let me just talk about the incentives.

00:06:19.080 | So in the United States, we passed this CARES Act

00:06:23.240 | that was aimed at making sure hospital systems

00:06:26.400 | didn't go bankrupt in the early days of the pandemic.

00:06:29.520 | The couple of things they did,

00:06:30.440 | one was they provided incentives to treat COVID patients,

00:06:34.640 | tens of thousands of dollars extra per COVID patient.

00:06:38.800 | And the other thing they did is they gave a 20% bump

00:06:41.800 | to Medicare payments for elderly patients

00:06:43.600 | who are treated with COVID.

00:06:44.440 | The idea is that there's more expensive to treat them

00:06:46.360 | at, I guess, the early days.

00:06:48.360 | So that did provide an incentive to sort of have a lot

00:06:51.080 | of COVID patients in the hospital,

00:06:52.560 | because your financial success at the hospital,

00:06:55.760 | or at least not the lack of financial ruin,

00:06:57.900 | depended on having many COVID patients.

00:07:00.600 | The other thing on the death certificates

00:07:02.120 | is that reporting of deaths is a separate issue.

00:07:04.080 | I don't know that there's a financial incentive there,

00:07:06.520 | but there is this sort of complicated,

00:07:08.900 | you know, when you fill out a death certificate

00:07:11.240 | for a patient with a lot of conditions,

00:07:13.920 | like let's say a patient has diabetes,

00:07:15.400 | a patient, well, that diabetes could lead to heart failure.

00:07:20.040 | You know, you have a heart attack, heart failure,

00:07:22.080 | your lungs fill up, then you get COVID, and you die.

00:07:26.440 | So what do you write on the death certificate?

00:07:28.200 | Was it COVID that killed you?

00:07:29.520 | Was it the lungs filling up?

00:07:31.220 | Was it the heart failure?

00:07:32.840 | Was it the diabetes?

00:07:34.040 | It's really difficult to like disentangle.

00:07:36.760 | And I think a lot of times what's happened is

00:07:40.600 | people have like erred on the side of signing as COVID.

00:07:44.880 | Now, what's the evidence of this?

00:07:46.120 | There's been a couple of audits of death certificates

00:07:49.640 | in places like Santa Clara County,

00:07:51.040 | where I live in Alameda County, California,

00:07:54.260 | where they carefully went through the death certificate,

00:07:56.560 | said, okay, is this reasonable to say

00:07:57.940 | this was actually COVID, or was COVID incidental?

00:08:00.880 | And they found that about 25%, 20, 25% of the deaths

00:08:03.780 | were more likely incidental than directly due to COVID.

00:08:08.360 | I personally don't get too excited about this.

00:08:10.440 | I mean, it's a philosophical question, right?

00:08:12.240 | Like ultimately, what kills you?

00:08:15.000 | Which is an odd thing to say if you're not in medicine,

00:08:19.360 | but like really, it's almost always multifactorial.

00:08:23.320 | It's not always just the bus hits you.

00:08:25.120 | The bus hits you, you get a brain bleed.

00:08:27.160 | Was the brain bleed that killed you,

00:08:28.680 | would it have burst anyway?

00:08:29.640 | I mean, you know, the bus hits you, killed you, right?

00:08:32.440 | - The way you die is a philosophical question,

00:08:34.120 | but it's also a sociological and psychological question,

00:08:36.820 | 'cause it seems like every single person

00:08:40.360 | who's passed away over the past couple of years,

00:08:43.140 | kind of the first question that comes to mind--

00:08:44.980 | - Was it COVID. - Was it COVID.

00:08:46.580 | Not just because you're trying to be political,

00:08:48.380 | but just in your mind.

00:08:49.380 | - No, I think there's a psychological reason for this,

00:08:51.700 | right, so, you know, we spent the better part

00:08:55.540 | of at least a half century in the United States

00:08:57.700 | not worried too much about infectious diseases.

00:09:01.300 | And the notion was we'd essentially conquered them.

00:09:03.820 | It was something that happens in faraway places

00:09:06.100 | to other people.

00:09:06.980 | And that's true for much of the developed world.

00:09:10.020 | Life expectancy were going up for decades and decades.

00:09:13.960 | And for the first time in living memory,

00:09:16.100 | we have a disease that can kill us.

00:09:18.340 | I mean, I think we're effectively evolved to fear that,

00:09:21.300 | like the panic centers of our brain,

00:09:23.380 | a lizard part of our brain takes over.

00:09:26.000 | And our central focus has been avoiding this one risk.

00:09:29.940 | And so it's not surprising that people,

00:09:32.420 | when they're filling out death certificates

00:09:33.940 | or thinking about what led to the death,

00:09:36.580 | this most salient thing that's in the front

00:09:38.980 | of everyone's brain would jump to the top.

00:09:41.220 | - And we can't ignore this very deep psychological thing

00:09:46.860 | when we consider what people say on the internet,

00:09:50.980 | what people say to each other,

00:09:52.300 | what people write in scientific papers, everything.

00:09:55.740 | It feels like when COVID has been brought onto this world,

00:10:00.740 | everything changed in the way people feel about each other.

00:10:09.340 | Just the way they communicate with each other.

00:10:11.780 | I think the level of emotion involved,

00:10:15.820 | I think in many people, it brought out the worst in them.

00:10:19.420 | For sometimes short periods of time,

00:10:22.220 | and sometimes it was always therapeutic.

00:10:24.200 | Like you were waiting to get out the darkest parts of you.

00:10:27.720 | Just to say, if you're angry at something in this world,

00:10:30.260 | I'm going to say it now.

00:10:31.740 | And I think that's probably talking

00:10:34.020 | to some deep primal thing that fear we have

00:10:38.660 | for maladies of all different kinds.

00:10:41.820 | And then when that fear is aroused

00:10:43.980 | and all the deepest emotions,

00:10:46.020 | it's like a Freudian psychotherapy session,

00:10:49.860 | but across the world.

00:10:51.220 | - It's something that psychologists

00:10:53.060 | are gonna have a field day with for a generation,

00:10:55.200 | trying to understand.

00:10:56.460 | I mean, I think what you say is right,

00:10:59.160 | but piled on top of that is also this sort of,

00:11:02.540 | this impetus to empathy,

00:11:05.960 | to empathize compassion toward others,

00:11:08.640 | essentially militarized.

00:11:10.180 | So I'm protecting you by some actions,

00:11:16.320 | and those actions, if I don't do them,

00:11:20.280 | if you don't do them, well, that must mean

00:11:22.760 | you hate me.

00:11:24.400 | It's created this social tension

00:11:26.400 | that I've never seen before.

00:11:27.240 | And we have started, we looked at each other

00:11:30.960 | as if we were just simply sources of germs,

00:11:35.620 | rather than people to get to know,

00:11:37.840 | people to enjoy, people to learn from.

00:11:41.180 | It colored basically almost every human interaction

00:11:44.840 | for every human on the planet.

00:11:47.200 | - Yeah, the basic common humanity.

00:11:49.600 | It's like you can wear a mask, you can stand far away,

00:11:52.680 | but the love you have for each other

00:11:54.720 | when you're looking into each other's eyes,

00:11:56.200 | that was dissipating, and by region, too.

00:11:59.920 | I've experienced, having traveled quite a bit

00:12:01.960 | throughout this time, it was really sad.

00:12:06.320 | Even people that are really close together,

00:12:08.360 | just the way they stood, the way they looked at each other.

00:12:11.560 | And it made me feel for a moment

00:12:14.280 | that the fabric that connects all of us

00:12:17.400 | is more fragile than I thought.

00:12:20.000 | - I mean, if you walk down the street,

00:12:21.280 | or if you did this during COVID,

00:12:23.200 | I'm sure you had this experience

00:12:24.400 | where you walk down the street,

00:12:25.440 | if you're not wearing a mask, or even if you are,

00:12:27.720 | people will jump off the sidewalk

00:12:30.240 | that you walk past them as if you're poison.

00:12:33.460 | Even though the data are that COVID spreads

00:12:37.920 | indifferently outdoors, or if at all, really, outdoors.

00:12:41.400 | But it's not simply a biological

00:12:43.480 | or infectious disease phenomenon, epidemiological,

00:12:46.360 | it is a change in the way humans treat each other.

00:12:50.680 | I hope temporary.

00:12:52.840 | - I do wanna say on the flip side of that,

00:12:54.820 | so I was mostly in Boston, Massachusetts

00:12:58.240 | when the pandemic broke out.

00:12:59.480 | I think that's where I was, yeah.

00:13:01.440 | And then I came here to Austin, Texas

00:13:04.340 | to visit my now good friend, Joe Rogan,

00:13:07.560 | and he was the first person, without pause,

00:13:11.000 | this wasn't a political statement, this was anything,

00:13:13.420 | just walked toward me and gave me a big hug

00:13:16.560 | and said, "It's great to see you."

00:13:18.360 | And I can't tell you how great that felt

00:13:20.360 | because I, in that moment, realized the absence

00:13:22.760 | of that connection back in Boston

00:13:24.240 | over just a couple of months.

00:13:26.880 | And we'll talk about it more,

00:13:30.200 | but it's tragic to think about that distancing,

00:13:34.200 | that dissolution of common humanity at scale,

00:13:36.880 | what kind of impact it has on society.

00:13:39.400 | Just across the board, political division,

00:13:43.220 | and just in the quiet of your own mind,

00:13:45.380 | in the privacy of your own home, the depression,

00:13:47.560 | the sadness, the loneliness that leads to suicide,

00:13:51.200 | and forget suicide, just low-key suffering.

00:13:55.800 | - Yeah, no, I think that's the suffering,

00:13:58.400 | that isolation, we're not meant to live alone.

00:14:00.940 | We're not meant to live apart from one another.

00:14:02.400 | I mean, that's, of course, the ideology of lockdown

00:14:04.440 | is to make people live apart, alone, isolated,

00:14:07.920 | so that we don't spread diseases to each other, right?

00:14:10.360 | But we're not actually designed as a species

00:14:12.280 | to live that way.

00:14:13.180 | And that, what you're describing, I think,

00:14:16.620 | if everyone's honest with themselves,

00:14:18.080 | have felt, especially in places where lockdowns

00:14:21.400 | have been sort of very militantly enforced,

00:14:23.960 | has felt deep into their core.

00:14:26.400 | - Well, if I could just return to the question of deaths,

00:14:30.060 | you said that the data isn't perfect,

00:14:31.580 | because we need these kind of seroprevalence surveys

00:14:35.480 | to understand how many cases there were

00:14:37.940 | to determine the rate of deaths.

00:14:39.960 | And we need to have a strong footing

00:14:42.100 | in the number of deaths.

00:14:42.940 | But if we assume that the number of deaths

00:14:45.800 | is approximately correct, what's your sense,

00:14:49.280 | what kind of statements can we say

00:14:51.720 | about the deadliness of COVID across different demographics,

00:14:55.920 | maybe not in a political way or in the current way,

00:14:58.840 | but when history looks back at this moment of time,

00:15:03.380 | 50 years from now, 100 years from now,

00:15:05.240 | the way we look at the pandemic 100 years ago,

00:15:09.560 | what will they say about the deadliness of COVID?

00:15:12.480 | - I mean, I think the deadliness of COVID

00:15:14.160 | depends on not just the virus itself, but who it infects.

00:15:18.480 | So probably the most important thing about it,

00:15:20.360 | about the deadliness of COVID is this steep age gradient

00:15:24.400 | in the mortality rate.

00:15:26.260 | So according to these seroprevalence studies

00:15:28.880 | that have been done, now hundreds of them,

00:15:31.680 | mostly from before vaccination,

00:15:34.400 | 'cause vaccination also reduces the mortality risk of COVID,

00:15:37.760 | the seroprevalence studies suggest that the risk of death,

00:15:42.460 | if you're say over the age of 70 is very high,

00:15:47.460 | 5% if you get COVID, if you're under the age of 70,

00:15:52.280 | it's lower, 0.05, but there's not a single sharp cutoff.

00:15:56.360 | It's more like, I have a rule of thumb that I use.

00:15:59.520 | So if you're 50, say, the infection fatality rate

00:16:03.400 | from COVID is 0.2%, according to the seroprevalence data,

00:16:07.520 | that means 99.8% survival if you're 50.

00:16:11.460 | And for every seven years of age above that, double it.

00:16:14.600 | Every seven years of age below that, have it.

00:16:17.040 | So a 57-year-old would have a 0.4%,

00:16:20.920 | mortality, a 64-year-old would have a 0.8% and so on.

00:16:24.680 | And if you have a severe chronic disease like diabetes

00:16:27.640 | or if you're morbidly obese,

00:16:29.520 | it's like adding seven years to your life.

00:16:32.440 | - And this is for unvaccinated folks.

00:16:35.080 | - This is unvaccinated before Delta also.

00:16:38.960 | - Are there a lot of people

00:16:40.160 | that would be listening to this with PhDs

00:16:42.580 | at the end of their name that would disagree

00:16:44.960 | with the 99.8, would you say?

00:16:47.480 | - So I think there's some disagreement over this.

00:16:49.960 | And the disagreement is about the quality

00:16:52.980 | of the seroprevalence studies that were conducted.

00:16:56.280 | So as I said earlier, I was a senior investigator

00:16:58.920 | in three different seroprevalence studies

00:17:00.640 | very early in the epidemic.

00:17:02.000 | I view them as very high-quality studies.

00:17:05.080 | In Santa Clara County, what we did

00:17:08.600 | is we used a test kit that we obtained

00:17:13.600 | from someone who works in Major League Baseball, actually.

00:17:18.040 | He'd ordered these test kits very early, March 2020,

00:17:21.320 | that very accurately measures antibodies

00:17:24.880 | in the bloodstream.

00:17:28.040 | These test kits were approved by the,

00:17:31.680 | had a EUA, Emergency Use Authorization by the FDA

00:17:36.000 | sort of shortly after we did this.

00:17:37.760 | And it had a very low false positive rate.

00:17:40.240 | False positive means if you don't have

00:17:43.480 | these COVID antibodies in your bloodstream,

00:17:46.080 | the kit shows up positive anyways.

00:17:48.320 | That turns out to happen about 0.5% of the time.

00:17:51.420 | And based on studies, a very large number of studies

00:17:56.040 | looking at blood from 2018, you try it against this kit,

00:18:00.520 | and 0.5% of the time, 2018,

00:18:03.320 | there shouldn't be antibodies there to COVID.

00:18:05.600 | So if it turns positive, it's a false positive.

00:18:07.400 | It's 0.5% of the time.

00:18:08.780 | And then, like a false negative rate,

00:18:12.080 | about 10%, 12%, something like that.

00:18:15.440 | I don't remember the exact number.

00:18:16.760 | But the false positive rate's the important thing there.

00:18:18.960 | So you have a population in March 2020 or April 2020

00:18:22.400 | with very low fraction of patients

00:18:25.320 | having been exposed to COVID.

00:18:26.560 | You don't know how much, but low.

00:18:28.760 | Even a small false positive rate

00:18:30.380 | could end up biasing your study quite a bit.

00:18:33.440 | But there's a formula to adjust for that.

00:18:36.000 | You can adjust for the false positive rate,

00:18:37.520 | false negative rate.

00:18:38.360 | We did that adjustment.

00:18:40.160 | And those studies found in a community population,

00:18:43.440 | so leaving aside people in nursing homes

00:18:45.120 | who have a higher death rate from COVID,

00:18:47.080 | that the death rate was 0.2% in Santa Clara County

00:18:52.080 | and in LA County.

00:18:53.440 | - Across all age groups in the community,

00:18:56.080 | community meaning just like regular folks.

00:18:58.080 | - Yeah, so that's actually a real important question too.

00:19:00.180 | So the Santa Clara study,

00:19:02.200 | we did this Facebook sampling scheme,

00:19:05.800 | which is, I mean, not the ideal thing,

00:19:08.040 | but it was very difficult to get a random sample

00:19:11.160 | during lockdown, where we put out an ad on Facebook

00:19:16.160 | soliciting people to volunteer for the study,

00:19:19.880 | randomly selected set of people.

00:19:22.240 | We were hoping to get a random selection of people

00:19:24.120 | from Santa Clara County, but it tended to,

00:19:26.320 | the people who tended to volunteer

00:19:27.580 | were from the richer parts of the county.

00:19:28.960 | Like I had Stanford professors writing,

00:19:31.320 | begging to be in the study

00:19:32.280 | 'cause they wanted to know their antibody levels.

00:19:34.200 | So we did some adjustment for that.

00:19:36.200 | In LA County, we hired a firm that had a preexisting

00:19:40.760 | representative sample of LA County.

00:19:42.520 | But it didn't include nursing homes,

00:19:46.000 | it didn't include people in jail, things like that,

00:19:48.160 | didn't include the homeless populations.

00:19:49.720 | So it's representative of a community dwelling population,

00:19:53.800 | both of those.

00:19:54.920 | And there we found that both in LA County

00:19:57.880 | and Santa Clara County in April, 2020,

00:20:00.320 | something like 40 to 50 times more infections

00:20:04.240 | than cases in both places.

00:20:06.760 | So for every case that had been reported

00:20:09.200 | to the public health authorities,

00:20:10.920 | we found 40 or 50 other infections,

00:20:15.320 | people with antibodies in their blood

00:20:17.920 | that suggested that they'd had COVID and recovered.

00:20:20.240 | - So people were not reporting,

00:20:21.560 | or severe, at least in those days, under-reporting.

00:20:25.000 | - Yeah, I mean, there was, you know, there's testing,

00:20:26.880 | I mean, there weren't so many tests available.

00:20:29.000 | People didn't know, a lot of them,

00:20:31.440 | we asked a set of questions about the symptoms they'd faced,

00:20:34.840 | and most of them said they faced no symptoms,

00:20:36.320 | or at the most, 30, 40% of them said

00:20:38.280 | they faced no symptoms.

00:20:39.400 | - And I mean, even these days,

00:20:42.200 | how many people report that they get COVID

00:20:44.240 | when they get COVID?

00:20:45.080 | Okay, have those numbers, that 0.2%,

00:20:49.120 | has that approximately held up over time?

00:20:51.400 | - That is, so if Professor Johnny Anides,

00:20:53.400 | who's a colleague of mine at Stanford,

00:20:55.240 | is a world expert in meta-analysis,

00:20:56.920 | one of the most cited scientists on Earth, I think,

00:21:00.080 | at least living, he did a meta-analysis

00:21:03.240 | of now 100 or more of these seroprevalence studies.

00:21:06.480 | And what he found was that that 0.2%

00:21:11.560 | is roughly the worldwide number.

00:21:13.800 | I mean, in fact, I think he cites a lower number, 0.15%,

00:21:17.360 | as the median infection fatality rate worldwide.

00:21:20.600 | So we did these studies,

00:21:21.920 | and it generated an enormous amount of blowback

00:21:25.240 | by people who thought that the infection

00:21:26.680 | fatality rate is much higher.

00:21:28.480 | And there's some controversy over the quality

00:21:30.440 | of some of the other studies that are done.

00:21:32.400 | And so there are some people who look at this

00:21:34.720 | same literature and say, well,

00:21:36.400 | the lower quality studies tend to have lower IFRs.

00:21:39.800 | The higher quality studies-- - IFR?

00:21:41.200 | - Oh, infection fatality, right?

00:21:42.520 | I apologize.

00:21:43.360 | I do this in lectures, too.

00:21:44.720 | - And I'm going to rudely interrupt you

00:21:47.600 | and ask for the basics sometimes, if it's okay.

00:21:51.680 | - No, of course.

00:21:52.560 | So these higher quality studies,

00:21:54.440 | they say, tend to produce higher IR.

00:21:56.640 | But the problem is that if you want a global

00:21:59.800 | infection fatality rate, you need to get

00:22:01.840 | seroprevalence studies from everywhere,

00:22:04.480 | even in places that don't necessarily have

00:22:06.120 | the infrastructure set up to produce

00:22:07.560 | very, very high quality studies.

00:22:09.640 | And in poor places in the world,

00:22:11.980 | places like Africa, the infection fatality rate

00:22:16.880 | is incredibly low.

00:22:18.240 | And in some richer places, like New York City,

00:22:23.520 | the infection fatality rate is much higher.

00:22:25.820 | There's a range of IFRs, not a single number.

00:22:30.760 | This sometimes surprises people,

00:22:32.640 | because they think, well, it's a virus.

00:22:34.160 | It should have the same properties no matter where it goes.

00:22:36.800 | But the virus kills or infects or hurts

00:22:41.800 | in interaction with the host.

00:22:44.760 | And the properties of both the host and the virus

00:22:47.840 | combine to produce the outcome.

00:22:50.160 | - But you also mentioned the environment, too?

00:22:53.040 | - Well, I'm thinking mainly just about the person.

00:22:55.600 | Like, if I'm gonna think about it,

00:22:56.920 | the most simplest way to think about it is age.

00:22:58.840 | Age is the single most important risk factor.

00:23:01.240 | So older places are going to have a higher IFR

00:23:06.000 | than younger places.

00:23:07.040 | Africa, 3% of Africa is over 65.

00:23:10.160 | So in some sense, it's not surprising

00:23:12.660 | that they have a low infection fatality rate.

00:23:15.400 | - So that's one way you would explain the difference

00:23:17.320 | between Africa and New York City,

00:23:19.000 | in terms of the fatality rate, is the age, the average age?

00:23:22.040 | - Yeah, and especially in the early days of the epidemic

00:23:24.840 | in New York City, the older populations

00:23:29.760 | living in nursing homes were differentially infected,

00:23:33.400 | based on, because of policies that were adopted, right?

00:23:35.880 | To send COVID-infected patients back to nursing homes

00:23:39.100 | to keep hospitals empty.

00:23:40.320 | - What do you mean by differentially infected?

00:23:42.880 | - The policy that you adopt determines who is most exposed.

00:23:47.400 | - Right, okay.

00:23:48.480 | - So that's what I mean by differentially.

00:23:49.320 | - It's the policy, it's the person that matters.

00:23:52.800 | I mean, it's not like the virus just kind of doesn't care.

00:23:56.120 | I mean, the policy determines the nature of the interaction.

00:23:59.320 | And there's also, I mean, there is some contribution

00:24:01.640 | from the environment.

00:24:04.020 | Different regions have different proximity,

00:24:06.020 | maybe, of people interacting,

00:24:08.280 | or the dynamics of the way they interact.

00:24:10.120 | - The heterogeneity, I'm like, if you have situations

00:24:12.720 | where there's lots of intergenerational interactions,

00:24:17.260 | then you have a very different risk profile

00:24:19.480 | than if you have societies where generations

00:24:22.900 | are more separate from one another.

00:24:24.700 | Okay, so let me just finish, we're real fast about this.

00:24:27.620 | So you have, in New York, you have a population

00:24:32.620 | that was infected in the early days

00:24:34.180 | that was very likely going to die,

00:24:36.980 | had a much higher likelihood of dying if infected.

00:24:40.140 | And so New York City had a higher IFR,

00:24:43.220 | especially in the early days, than Africa has had.

00:24:48.220 | The other thing is treatment, right?

00:24:50.880 | So the treatments that we adopted

00:24:52.900 | in the early days of the epidemic,

00:24:54.000 | I think actually may have exacerbated the risk of death.

00:24:58.200 | - Which treatments?

00:25:00.080 | - Using ventilators, like the over-reliance on ventilators

00:25:03.140 | is what I'm primarily thinking of,

00:25:04.540 | but I can think of other things.

00:25:06.480 | But that, also, we've learned over time

00:25:09.560 | how better to manage patients with the disease.

00:25:12.680 | So you have all those things combined.

00:25:14.680 | So that's where the controversy over this number is.

00:25:18.080 | - I mean, New York City also is a central hub

00:25:23.080 | for those who tweet and those who write powerful stories

00:25:28.640 | and narratives in article form.

00:25:31.720 | And I remember there was quite dramatic stories

00:25:34.680 | about doctors in the hospitals and these kinds of things.

00:25:37.360 | I mean, there's very serious, very dramatic,

00:25:40.480 | very tragic deaths going on, always, in hospitals.

00:25:44.840 | Those stories, loved ones losing each other on a deathbed,

00:25:49.840 | that's always tragic, and you can always write

00:25:53.200 | a hell of a good story about that, and you should,

00:25:56.320 | about the loss of loved ones.

00:25:58.200 | But they were doing it pretty well, I would say,

00:26:01.820 | over this kind of dramatic deaths.

00:26:04.040 | And so, in response to that, it's very unpleasant to hear,

00:26:09.240 | even to consider the possibility that the death rate

00:26:13.080 | is not as high as you might feel.

00:26:17.520 | - Yeah, I was surprised by the reaction,

00:26:20.600 | both by regular people and also the scientific community

00:26:23.840 | in response to those studies,

00:26:25.660 | those early studies in April of 2020.

00:26:28.220 | To me, they were studies.

00:26:31.600 | I mean, they're the kinds of, not exactly the kinds

00:26:34.480 | of work I've worked on all my life,

00:26:35.600 | but kind of like the kind of, you write a paper,

00:26:39.720 | and you get responses from your fellow scientists,

00:26:42.840 | and you change the paper to improve it,

00:26:45.160 | and you hopefully learn something from it.

00:26:47.240 | - Well, but to push back, it's just a study.

00:26:50.480 | But there's some studies, and this is kind of interesting,

00:26:53.240 | 'cause I've received similar pushback on other topics.

00:26:57.200 | There's some studies that, if wrong,

00:27:04.400 | might have wide-ranging detrimental effects on society.

00:27:09.400 | So that's the way they would perceive the studies.

00:27:12.000 | If you say the death rate is lower,

00:27:13.920 | and you end up, as you often do in science,

00:27:16.480 | realizing that, nope, that was a flaw

00:27:19.920 | in the way the study was conducted,

00:27:21.120 | or we're just not representative of a broader population,

00:27:23.880 | and then you realize the death rate is much higher,

00:27:26.020 | that might be very damaging in people's view.

00:27:29.600 | So that's probably where the scientific community

00:27:33.520 | sort of, to steel man the kind of response,

00:27:36.640 | is that's where they felt like, you know,

00:27:40.040 | there's some findings where you better be damn sure

00:27:43.440 | before you kind of report them.

00:27:45.880 | - Yeah, I mean, we were pretty sure we were right,

00:27:47.400 | and it turns out we were right.

00:27:48.520 | So like, when we, so we released the Santa Clara study

00:27:53.520 | via this open science process,

00:27:56.800 | and this server called MedArchive.

00:27:59.240 | It's designed for releasing studies

00:28:02.220 | that have not yet been peer reviewed

00:28:03.880 | in order to garner comment from the scientists

00:28:06.520 | before peer review.

00:28:07.580 | The LA County study, we went through

00:28:10.320 | this traditional peer review process,

00:28:12.400 | and got it published in the Journal

00:28:13.640 | of American Medical Association sometime in like July,

00:28:16.600 | I think, I forget the date, of 2020.

00:28:19.040 | The Santa Clara study released in April of 2020

00:28:22.000 | in this sort of working paper archive.

00:28:25.080 | The reason was that we felt we had an obligation,

00:28:28.180 | we had a result that we thought was quite important,

00:28:31.960 | and we wanted to tell the scientific community about it,

00:28:35.040 | and also tell the world about it.

00:28:36.720 | And we wanted to get feedback.

00:28:38.120 | I mean, that's part of the purpose

00:28:39.840 | of sending it to these kinds of places.

00:28:42.280 | I think a lot of the problem is that

00:28:45.480 | when people think about published science,

00:28:47.860 | they think of it as automatically true.

00:28:50.360 | And if it goes through peer review, it's automatically true.

00:28:52.320 | If it hasn't gone through peer review,

00:28:53.300 | it's not automatically true.

00:28:54.920 | And especially in medicine,

00:28:56.520 | when we're not used to having this access

00:28:59.880 | to pre-peer reviewed work,

00:29:03.400 | I mean, in economics, actually, that's quite normal.

00:29:05.880 | You, it takes years to get something published,

00:29:07.960 | so there's a very active debate over,

00:29:10.640 | or discussion about papers before they're peer reviewed

00:29:13.480 | in this sort of working paper way.

00:29:16.280 | Much less normal, or much newer in medicine.

00:29:20.440 | And so I think part of that,

00:29:21.520 | the perception about what those,

00:29:23.920 | what process happens in open science

00:29:26.080 | when you release a study, that got people confused.

00:29:29.380 | And you're right, it was a very important result.

00:29:31.480 | 'Cause we had just locked the world down

00:29:33.760 | in middle of March, with, I think, catastrophic results.

00:29:38.720 | And if that study was right, if our study was right,

00:29:41.960 | that meant we'd made a mistake.

00:29:44.040 | And not because the death rate was low.

00:29:45.840 | That's actually not the key thing there.

00:29:47.800 | The key thing is that we had adopted these policies,

00:29:51.600 | these test and trace policies, these policies,

00:29:54.240 | these lockdown policies aimed at suppressing

00:29:57.000 | the virus level to close to zero.

00:30:00.060 | That was essentially the idea.

00:30:01.900 | If we can just get the virus to go away,

00:30:04.820 | we won't have to ever worry about it again.

00:30:07.420 | The main problem with our result,

00:30:09.380 | as far as that strategy was concerned,

00:30:10.740 | wasn't the death rate, it was the 40 to 50 times

00:30:13.300 | more infections than cases.

00:30:14.820 | It was the 2 1/2% or 3% or 4% prevalence rate

00:30:19.580 | that we identified of the antibodies in the population.

00:30:23.060 | If that number is right, it's too late.

00:30:25.820 | The virus is not going to go to zero.

00:30:28.120 | And no matter how much we test and trace and isolate,

00:30:30.280 | we're not going to get the viral level down to zero.

00:30:32.880 | - So we're gonna have to let the virus go through

00:30:36.880 | the entire population in some way or--

00:30:39.120 | - No, we can talk about that in a bit.

00:30:41.160 | That's the Great Barrington Declaration.

00:30:42.440 | You don't have to let the virus go through the population.

00:30:44.240 | You can shield preferentially.

00:30:45.860 | The policy we chose was to shield preferentially

00:30:50.080 | the laptop class, the set of people who could work

00:30:53.480 | from home without losing their job.

00:30:56.440 | - Yeah. - And we did a very good job

00:30:58.280 | at protecting them.

00:30:59.420 | - Well, let me take a small tangent.

00:31:04.080 | We're gonna jump around in time,

00:31:06.620 | which I think will be the best way to tell the story.

00:31:09.260 | So that was the beginning.

00:31:10.580 | - Yeah, okay, actually, can I go back one more thing

00:31:13.620 | for that, 'cause that's really important,

00:31:14.800 | and I should have started with this.

00:31:16.600 | What led me to do those studies was a paper

00:31:22.000 | that I had remembered seeing from the H1N1 flu epidemic

00:31:25.640 | in 2009.

00:31:26.480 | This is where I had been much less active

00:31:28.340 | in writing about that.

00:31:29.180 | I had written a paper or two about that in 2009.

00:31:32.880 | There was actually this same debate over the mortality rate,

00:31:38.800 | except it unfolded over the course of three years,

00:31:41.680 | two or three years.

00:31:43.240 | The early studies of the mortality rate in H1N1

00:31:48.000 | counted the number of cases in the denominator,

00:31:52.240 | kind of the number of deaths in the numerator,

00:31:53.680 | cases meaning people identified as having H1N1,

00:31:56.640 | showing up to doctor, you know, tested to have it.

00:31:59.460 | And the early estimates of the H1N1 mortality

00:32:04.040 | were like 4%, 3%, really, really high.

00:32:07.980 | Over the course of a couple of more years,

00:32:11.280 | a whole bunch of seroprevalence studies,

00:32:12.920 | seroprevalence studies of H1N1 flu came out,

00:32:15.820 | and it turned out that there were 100 or more times

00:32:20.620 | people infected per case.

00:32:22.200 | And so the mortality rate was actually something like .02%

00:32:27.540 | for H1N1, not the three, like 100-fold difference.

00:32:32.080 | - So this made you think, okay, it took us a couple of,

00:32:35.380 | two or three years to discover the truth

00:32:37.260 | behind the actual infections for H1N1,

00:32:41.800 | and then what's the truth here,

00:32:43.940 | and can we get there faster?

00:32:45.060 | - Yeah, and it spreads in a similar way

00:32:47.620 | as the H1N1 flu did.

00:32:49.180 | I mean, it spreads via aerosolization,

00:32:51.640 | via, you know, so person-to-person breathing,

00:32:53.600 | kind of contact up.

00:32:55.480 | It may be some by fomites,

00:32:57.160 | but it seems like that's less likely now.

00:32:59.220 | In any case, it seemed really important to me

00:33:01.740 | to speed up the process

00:33:03.540 | of having those seroprevalence studies

00:33:05.900 | so that we can better understand who was at risk

00:33:09.060 | and what the right strategy ought to be.

00:33:12.260 | - This might be a good place to kind of,

00:33:15.140 | compare influenza, the flu, and COVID

00:33:19.620 | in the context of the discussion we just had,

00:33:21.740 | which is how deadly is COVID?

00:33:24.380 | So you mentioned COVID is a very particular

00:33:26.620 | kind of steepness, where the X-axis is age.

00:33:31.620 | So in that context, could you maybe compare influenza

00:33:36.260 | and COVID, because a lot of people

00:33:38.500 | outside of the folks who suggested the lizards

00:33:43.700 | who run the world have completely fabricated,

00:33:46.580 | invented COVID.

00:33:48.420 | Outside of those folks, kind of the natural process

00:33:51.860 | by which you dismiss the threat of COVID is say,

00:33:54.620 | well, it's just like the flu.

00:33:55.860 | The flu is a very serious thing, actually.

00:33:58.740 | So in that comparison, where does COVID stand?

00:34:03.620 | - Yeah, the flu is a very serious thing.

00:34:04.940 | It kills, you know, 50, 60,000 people a year,

00:34:07.960 | something like that, or depending on the particular strain

00:34:10.540 | that goes around, that's in the United States.

00:34:12.760 | The primary difference to me,

00:34:15.060 | there's lots of differences,

00:34:15.900 | but one of the most salient differences

00:34:17.340 | is the age gradient and mortality risk for the flu.

00:34:20.120 | So the flu is more deadly for two children than COVID is.

00:34:26.420 | There's no controversy about that.

00:34:29.300 | Children, thank God, have much less severe reactions

00:34:34.300 | to COVID infection than they do to flu infections.

00:34:39.980 | - And rate of fatalities and stuff like that.

00:34:41.820 | - And fatality, all of that.

00:34:42.860 | - I think you mentioned, I mean,

00:34:45.020 | it's interesting to maybe also comment on,

00:34:46.940 | I think in another conversation you mentioned

00:34:48.800 | there's a U shape to the flu curve.

00:34:53.800 | So meaning like there's actually quite a large number

00:34:56.940 | of kids that die from flu.

00:34:59.220 | - Yeah, I mean, the 1918 flu, the H1N1 flu,

00:35:02.460 | the Spanish flu in the US killed millions of younger people.

00:35:09.500 | And that is not the case with COVID.

00:35:12.380 | More than, I'm gonna get the number wrong,

00:35:16.820 | but something like 70, 80% of the deaths

00:35:19.280 | are people over the age of 60.

00:35:21.440 | - Well, we've talking about the fear the whole time, really.

00:35:24.940 | But my interaction with folks,

00:35:27.960 | now I wanna have a family, I wanna have kids,

00:35:30.260 | but I don't have that real firsthand experience.

00:35:32.820 | But my interaction with folks is at the core of fear

00:35:35.860 | that folks had is for their children.

00:35:39.900 | Like that somehow, I don't wanna get infected

00:35:44.900 | because of the kids.

00:35:47.900 | 'Cause God forbid something happens to the kids.

00:35:50.780 | And I think that obviously that makes a lot of sense,

00:35:54.540 | this kind of the kids come first, no matter what,

00:35:57.480 | that's the number one priority.

00:35:58.660 | But for this particular virus,

00:36:02.000 | that reasoning was not grounded in data.

00:36:05.420 | It seems like, or that emotion and feeling

00:36:07.980 | was not grounded in data. - It wasn't.

00:36:09.460 | But at the same time, this is way more deadly

00:36:11.540 | than the flu just overall, and especially to older people.

00:36:15.540 | - Yes.

00:36:16.500 | - Right, so-- - The numbers,

00:36:17.860 | when the story's all said and done,

00:36:21.260 | COVID would take many more lives.

00:36:24.060 | - Yeah, so I mean, 0.2 sounds like a small number,

00:36:27.380 | but it's not a small number worldwide.

00:36:29.600 | - What do you think that number will be by the,

00:36:32.580 | that's not like, but would we cross,

00:36:35.420 | I think it's in the United States,

00:36:37.260 | it's the way the deaths are currently reported,

00:36:40.700 | it's like 800,000, something like that.

00:36:42.700 | Do you think we'll cross a million?

00:36:44.440 | - Seems likely, yeah.

00:36:46.660 | - Do you think it's something that might continue

00:36:49.060 | with different variants?

00:36:50.340 | What-- - Well, I think,

00:36:51.880 | so we can talk about the end state of COVID.

00:36:53.460 | The end state of COVID is it's here forever.

00:36:56.020 | I think that there is good evidence

00:36:59.260 | of immunity after infection,

00:37:02.800 | such that you're protected both against reinfection

00:37:07.340 | and also against severe disease upon reinfection.

00:37:11.340 | So the second time you get it,

00:37:12.900 | it's not true for everyone, but for many people,

00:37:14.700 | the second time you get it will be milder,

00:37:16.380 | much milder than the first time you get it.

00:37:18.620 | - Would the long tail, like that lasts for a long time?

00:37:22.740 | - Yeah, so just there are studies

00:37:24.300 | that follow a course of people who are infected for a year,

00:37:28.340 | and the reinfection rate is something like

00:37:30.420 | somewhere between 0.3 and 1%.

00:37:33.860 | And like a pretty fantastic study out of Italy

00:37:36.300 | found that, there's one in Sweden, I think.

00:37:38.820 | There's a few studies that found similar things.

00:37:41.820 | And the reinfections tend to produce much milder disease,

00:37:46.820 | much less likely to end up in the hospital,

00:37:48.440 | much less likely to die.

00:37:50.100 | So what the end state of COVID is,

00:37:52.540 | it's circulating in the population forever,

00:37:54.580 | and you get it multiple times.

00:37:56.500 | - Yeah, and then there's, I think, studies and discussions,

00:38:00.980 | like the best protection would be to get it,

00:38:04.300 | and then also to get vaccinated.

00:38:06.220 | And then a lot of people push back against that

00:38:08.780 | for the obvious reasons from both sides,

00:38:10.720 | because somehow this discourse has become

00:38:13.060 | less scientific and more political.

00:38:14.820 | - Well, I think you wanna,

00:38:16.560 | the first time you meet it

00:38:18.100 | is gonna be the most deadly for you.

00:38:20.300 | And so the first time you meet it,

00:38:21.580 | it's just wise to be vaccinated.

00:38:22.780 | The vaccine reduces severe disease.

00:38:25.420 | - Yeah, we'll talk about the vaccine,

00:38:27.700 | 'cause I wanna make sure I address it carefully

00:38:30.100 | and properly and in full context.

00:38:32.720 | But yes, sort of to add to the context,

00:38:38.020 | a lot of the fascinating discussions we're having

00:38:40.360 | is in the early days of COVID,

00:38:43.060 | and now for people who are unvaccinated.

00:38:46.460 | That's where the interesting story is,

00:38:49.060 | the policy story, the sociological story, and so on.

00:38:52.720 | But let me go to something really fascinating,

00:38:55.920 | just because of the people involved,

00:38:57.920 | the human beings involved,

00:38:59.240 | and because of how deeply I care about science

00:39:03.000 | and also kindness, respect, and love, and human things.

00:39:07.720 | Francis Collins wrote a letter in October, 2020

00:39:12.360 | to Anthony Fauci, I think somebody else.

00:39:14.960 | I have the letter, oh, it's not a letter, email,

00:39:20.800 | I apologize.

00:39:22.560 | Hi, Tony and Cliff, cgbdeclaration.org.

00:39:27.560 | This proposal, this is the Great Barrington Declaration

00:39:32.940 | that you're a co-author on.

00:39:34.940 | This proposal from the three fringe epidemiologists

00:39:38.640 | who met with the secretary

00:39:40.120 | seemed to be getting a lot of attention,

00:39:42.200 | and even a co-signature from Nobel Prize winner,

00:39:45.920 | Mike Levitt at Stanford.

00:39:48.680 | There needs to be a quick and devastating

00:39:51.160 | published takedown of its premises.

00:39:54.760 | I don't see anything like that online yet.

00:39:57.960 | Is it underway?

00:39:59.000 | Question mark, Francis.

00:40:00.440 | Francis Collins, director of the NIH,

00:40:03.280 | somebody I talked to on this podcast recently.

00:40:06.120 | Okay, a million questions I wanna ask,

00:40:09.640 | but first, how did that make you feel

00:40:12.600 | when you first saw this email come to light?

00:40:17.040 | When did it come to light?

00:40:20.920 | - This week, actually, I think, or last week.

00:40:22.880 | - Okay, so this is because of freedom of information.

00:40:25.600 | - Yeah.

00:40:26.440 | - Which, by the way, sort of,

00:40:28.560 | maybe 'cause I do wanna add positive stuff

00:40:32.440 | on the side of Francis here.

00:40:34.100 | Boy, when I see stuff like that,

00:40:37.840 | I wonder if all my emails leaked.

00:40:40.600 | (laughing)

00:40:41.440 | How much embarrassing stuff.

00:40:43.000 | Like, I think I'm a good person,

00:40:45.120 | but I don't, I haven't read my old emails.

00:40:49.320 | Maybe, I'm pretty sure sometimes I can be an asshole.

00:40:53.280 | - Well, I mean, look, he's a Christian,

00:40:55.000 | and I'm a Christian, I'm supposed to forgive, right?

00:40:57.040 | I mean, I think he was looking at this

00:41:00.080 | Great Barrington Declaration as a political problem

00:41:03.780 | to be solved, as opposed to a serious alternative approach

00:41:08.520 | to the epidemic.

00:41:10.000 | - So, maybe we'll talk about it in more detail,

00:41:12.280 | but just in case people are not familiar,

00:41:15.000 | Great Barrington Declaration was a document

00:41:18.840 | that you co-authored that basically argues

00:41:22.280 | against this idea of lockdown as a solution to COVID,

00:41:26.200 | and you proposed another solution that we'll talk about.

00:41:29.400 | But the point is, it's not that dramatic of a document.

00:41:34.400 | It is just a document that criticizes

00:41:36.920 | one policy solution that was proposed.

00:41:38.920 | - But it was the policy solution

00:41:40.240 | that had been put forward by Dr. Collins

00:41:43.080 | and by Tony Fauci and a few other scientists.

00:41:47.120 | I mean, I think a relatively small number

00:41:48.920 | of scientists and epidemiologists

00:41:50.760 | in charge of the advice given to governments worldwide.

00:41:55.760 | And it was a challenge to that policy

00:42:00.040 | that said that, look, there's an alternate path,

00:42:02.960 | that the path we've chosen, this path of lockdown

00:42:06.080 | with an aim to suppress the virus to zero effectively,

00:42:09.000 | I mean, that was unstated, cannot work

00:42:12.400 | and is causing catastrophic harm

00:42:14.640 | to large numbers of poor and vulnerable people worldwide.

00:42:17.860 | We put this out in October 4th, I think, of 2020,

00:42:23.520 | and it went viral.

00:42:25.720 | I mean, I've never actually been involved

00:42:27.160 | with anything like this,

00:42:28.960 | where I just put the document on the web

00:42:31.600 | and tens of thousands of doctors signed on,

00:42:34.280 | hundreds of thousands of regular people signed on.

00:42:37.220 | It really struck a chord of people,

00:42:40.200 | 'cause I think even by October of 2020,

00:42:41.920 | people had this sense that there was something

00:42:43.600 | really wrong with the COVID policy that we'd been following.

00:42:47.080 | And they were looking for reasonable people

00:42:52.120 | to give an alternative.

00:42:52.960 | I mean, we're not arguing that COVID isn't a serious thing.

00:42:56.120 | I mean, it is a very serious thing.

00:42:57.600 | This is why we had a policy that aimed at addressing it.

00:43:01.160 | We were, but we were saying that the policy

00:43:04.920 | we're following is not the right one.

00:43:06.960 | So how does a democratic government

00:43:11.040 | deal with that challenge?

00:43:13.560 | So to me, that, you asked me how I felt.

00:43:15.920 | I was actually, frankly, just,

00:43:17.680 | I suspected there'd been some email exchanges like that,

00:43:20.900 | not necessarily from Francis Collins,

00:43:22.960 | around the government around this time.

00:43:26.000 | I mean, I felt the full brunt of a propaganda campaign

00:43:29.000 | almost immediately after we published it,

00:43:32.360 | where newspapers mischaracterized it

00:43:35.480 | in the same way over and over and over again,

00:43:37.980 | and sought to characterize me as sort of

00:43:42.880 | a marginal fringe figure or whatnot.

00:43:45.880 | Sunetra gooped Martin Kulldorff

00:43:47.640 | or the tens of thousands of other people that signed it.

00:43:50.360 | I felt the brunt of that all year long.

00:43:53.560 | So to see this in black and white,

00:43:56.240 | in the handwriting, essentially,

00:43:58.680 | I mean, the metaphorical handwriting of Francis Collins

00:44:01.040 | was actually, frankly, a disappointment,

00:44:02.320 | 'cause I've looked up to him for years.

00:44:05.320 | - Yeah, I've looked up to him as well.

00:44:09.520 | I mean, I look for the best in people,

00:44:12.160 | and I still look up to him.

00:44:15.320 | What troubles me is several things.

00:44:17.300 | The reason I said about the asshole emails

00:44:23.180 | I send late at night is,

00:44:25.740 | I can understand this email.

00:44:28.580 | It's fear, it's panic, not being sure.

00:44:34.120 | The fringe, three fringe epidemiologists.

00:44:37.440 | - Unless Mike Leavitt, who won a Nobel Prize.

00:44:39.760 | I mean.

00:44:40.600 | - But using fringe, maybe in my private thoughts,

00:44:45.400 | I have said things like that about others,

00:44:47.880 | like a little bit too unkind,

00:44:50.320 | like you don't really mean it.

00:44:52.280 | Now, add to that, he recently, this week or whatever,

00:44:56.860 | doubled down on the fringe.

00:45:00.240 | This is really troubling to me.

00:45:01.960 | I can excuse this email,

00:45:04.920 | but the arrogance there,

00:45:07.840 | Francis honestly broke my heart a little bit there.

00:45:12.720 | This was an opportunity to, especially at this stage,

00:45:16.100 | to say, just like I told him,

00:45:18.860 | to say I was wrong to use those words in that email.

00:45:23.720 | I was wrong to not be open to ideas.

00:45:27.320 | I still believe that this is not,

00:45:29.600 | like actually argue with the policy,

00:45:34.600 | with the proposed solution.

00:45:36.280 | Also, the devastating published,

00:45:41.120 | devastating takedown, devastating takedown.

00:45:45.880 | As you say, somebody who's sitting on billions of dollars

00:45:50.880 | that they're giving to scientists,

00:45:54.640 | some of whom are often not their best human beings

00:45:58.080 | because they're fighting with each other over money.

00:46:00.640 | Not being cognizant of the fact that you're,

00:46:03.940 | challenging the integrity.

00:46:06.580 | You're corrupting the integrity of scientists

00:46:08.780 | by allocating them money.

00:46:10.780 | You're now playing with that

00:46:13.460 | by saying devastating takedown.

00:46:15.940 | Where do you think the published takedown will come from?

00:46:19.580 | It will come from those scientists

00:46:21.560 | to whom you're giving money.

00:46:23.300 | What kind of example would they give

00:46:25.420 | to the academic community that thrives on freedom?

00:46:28.760 | Like this is, I believe Francis Collins.

00:46:33.060 | He's a great man.

00:46:34.580 | One of the things I was troubled by

00:46:36.700 | is the negative response to him

00:46:38.920 | from people that don't understand the positive impact

00:46:41.820 | that NIH has had on society.

00:46:44.060 | How many people it's helped.

00:46:45.860 | But this is exactly the,

00:46:48.060 | so he's not just a scientist.

00:46:50.020 | He's not just a bureaucrat who distributes money.

00:46:53.460 | He's also scientific leader

00:46:55.700 | that in difficult times we live in

00:46:58.780 | is supposed to inspire us with trust,

00:47:01.380 | with love, with the freedom of thought.

00:47:04.860 | He's supposed to, you know those fringe epidemiologists?

00:47:08.660 | Those are the heroes of science.

00:47:10.860 | When you look at the long arc of history,

00:47:13.380 | we love those people.

00:47:15.540 | We love ideas even when they get proven wrong.

00:47:18.420 | - That's what always attracted me to science.

00:47:20.240 | Like somebody, the lone voice saying,

00:47:23.780 | oh no, the moon of Jupiter does move.

00:47:26.980 | I mean, but the funny thing is,

00:47:30.740 | Galileo was saying something truly revolutionary.

00:47:32.780 | We were saying that what we proposed

00:47:34.940 | in the Great Barrier Reef Declaration

00:47:35.860 | was actually just the old pandemic plan.

00:47:38.900 | It wasn't anything really fundamentally novel.

00:47:42.160 | In fact, there were plans like this

00:47:46.340 | that lockdown scientists had written

00:47:49.700 | in late February, early March of 2020.

00:47:52.740 | So we were not saying anything radical.

00:47:54.420 | We were just calling for a debate effectively

00:47:57.180 | over the existing lockdown policy.

00:48:00.440 | And this is a disappointment,

00:48:02.680 | a really, truly a big disappointment

00:48:04.760 | because by doing this, you were absolutely right, Lex.

00:48:08.400 | He sent a signal to so many other scientists

00:48:12.280 | to just stay silent even if you had reservations.

00:48:15.180 | - Yeah, devastating takedown that people,

00:48:18.360 | you know how many people wrote to me privately?

00:48:21.280 | Like Stanford, MIT, how amazing the conversation

00:48:26.280 | with Francis Collins was.

00:48:28.100 | There's a kind of admiration because,

00:48:31.840 | okay, how do I put it?

00:48:33.440 | A lot of people get into science

00:48:38.480 | 'cause they wanna help the world.

00:48:40.480 | They get excited by the ideas

00:48:42.320 | and they really are working hard to help

00:48:46.240 | in whatever the discipline is.

00:48:48.000 | And then there is sources of funding

00:48:50.720 | which help you do help at a larger scale.

00:48:53.360 | So you admire the people that are distributing

00:48:57.840 | the money because they're often,

00:48:59.780 | at least on the surface, are really also good people.

00:49:02.800 | Oftentimes they're great scientists.

00:49:04.780 | So it's amazing.

00:49:06.820 | That's why I'm sort of,

00:49:08.460 | like sometimes people from outside think academia

00:49:12.920 | is broken some kind of way.

00:49:14.520 | No, it's a beautiful thing.

00:49:16.320 | It really is a beautiful thing.

00:49:17.960 | And that's why it's so deeply heartbreaking

00:49:19.840 | where this person is, I don't think this is malevolence.

00:49:26.440 | I think he's just incompetence at communication twice.

00:49:31.160 | - I think there's also arrogance at the bottom of it too.

00:49:33.640 | - Yes, but all of us have arrogance at the bottom.

00:49:35.800 | - Yeah, but there's a particular kind of arrogance.

00:49:38.000 | So here it's of the same kind of arrogance

00:49:40.800 | that you see when Tony Fauci gets on TV

00:49:43.280 | and says that if you criticize me,

00:49:46.560 | you're not simply criticizing a man,

00:49:48.440 | you're criticizing science itself.

00:49:51.080 | That is at the heart also of this email.

00:49:55.000 | The certainty that the policies that they were recommending,

00:49:59.480 | Collins and Fauci were recommending

00:50:01.080 | to the President of the United States were right.

00:50:03.480 | Not just right, but right so far right

00:50:06.480 | that any challenge whatsoever to it is dangerous.

00:50:09.500 | And I think that is really the heart of that email.

00:50:13.680 | It's this idea that my position is unchallengeable.

00:50:18.680 | Now to be as charitable as I can be to this,

00:50:24.600 | I believe they thought that.

00:50:25.720 | I believe some of them still think that,

00:50:27.760 | that there was only one true policy possible

00:50:31.840 | in response to COVID.

00:50:32.680 | Every other policy was immoral.

00:50:35.140 | And if you come from that position,

00:50:37.840 | then you write an email like that.

00:50:39.180 | You go on TV, you say effectively,

00:50:41.200 | (speaking in foreign language)

00:50:42.320 | Right, I mean, that is what happens

00:50:44.580 | when you have this sort of unchallengeable arrogance

00:50:47.720 | that the policy you're following is correct.

00:50:50.040 | I mean, when we wrote the Great Bank Declaration,

00:50:52.240 | what I was hoping for was a discussion

00:50:55.720 | about how to protect the vulnerable.

00:50:57.720 | I mean, that was the key idea to me in the whole thing

00:51:00.000 | was better protection of the older population

00:51:02.960 | who were really at really serious risk

00:51:04.900 | if infected with COVID.

00:51:06.280 | And we had been doing a very poor job, I thought,

00:51:08.640 | to date in many places in protecting the vulnerable.

00:51:11.240 | And what I wanted was a discussion by local public health

00:51:16.560 | about better methods, better policies

00:51:18.800 | to protect the vulnerable.

00:51:21.480 | So when we were met with instead a series

00:51:26.120 | of essentially propagandist lies about it.

00:51:28.400 | So for instance, I kept hearing from reporters

00:51:31.560 | in those days, why do you want to let the virus rip?

00:51:34.460 | Let it rip, let it rip.

00:51:35.520 | The words let it rip does not appear

00:51:38.960 | in the Great Bank Declaration.

00:51:41.320 | The goal isn't to let the virus rip.

00:51:43.720 | The goal is to protect the vulnerable,

00:51:46.080 | to let society go as open schools and do other things

00:51:50.400 | that it functions best it can

00:51:52.200 | in the midst of a terrible pandemic, yes,

00:51:54.880 | but not let the virus rip

00:51:56.920 | where the most vulnerable aren't protected.

00:51:59.560 | The goal was to protect the vulnerable.

00:52:01.240 | So why let it rip?

00:52:02.480 | Because it was a propaganda term

00:52:04.600 | to hit the fear centers of people's brains.

00:52:07.600 | Oh, these people are immoral.

00:52:09.060 | They just want to let the virus go through society

00:52:10.720 | and hurt everybody.

00:52:11.980 | That was the idea.

00:52:14.040 | It was a way to preclude a discussion

00:52:16.360 | and preclude a debate about the existing policy.

00:52:19.960 | So I have this app called Clubhouse.

00:52:23.740 | I've gone back on it recently to practice Russian,

00:52:29.440 | unrelated for a few big Russian conversations coming up.

00:52:33.080 | Anyway, it's a great way to talk

00:52:34.440 | to regular people in Russian.

00:52:35.980 | But I also, there was a, I was nervous.

00:52:38.400 | I was preparing for a Pfizer CEO conversation

00:52:40.760 | and there was a vaccine room.

00:52:42.520 | And so I joined it.

00:52:43.580 | And it was a pro-science room.

00:52:49.000 | These are like scientists

00:52:50.080 | that were calling each other pro-science.

00:52:52.720 | The whole thing was like theater to me.

00:52:55.400 | I mean, I haven't thoroughly researched,

00:52:57.200 | but looking at the resume,

00:52:58.240 | they were like pretty solid researchers and doctors.

00:53:03.240 | And they were mocking everybody who was at all,

00:53:09.080 | I mean, it doesn't matter what they stood for,

00:53:11.840 | but they were just mocking people.

00:53:13.240 | And the arrogance was overwhelming.

00:53:15.880 | I had to shut off

00:53:17.760 | 'cause I couldn't handle that human beings

00:53:20.040 | can be like this to each other.

00:53:21.440 | And then I went back just to double check,

00:53:24.480 | is this really happening?

00:53:25.480 | How many people are here?

00:53:26.920 | Is this theater?

00:53:28.440 | And then I asked to come on stage on Clubhouse

00:53:31.520 | to make a couple of comments.

00:53:32.680 | And then as I opened my mouth and say,

00:53:34.600 | "Thank you so much.

00:53:35.680 | "This is a great room."

00:53:37.960 | Sort of the usual civil politeness,

00:53:39.920 | all that kind of stuff.

00:53:41.720 | And I said, "I'm worried that the kind of arrogance

00:53:47.680 | "with which things are being discussed here

00:53:51.440 | "will further divide us, not unite us."

00:53:55.400 | And before I said even the unite us, further divide us,

00:54:00.400 | I was thrown off stage.

00:54:02.280 | Now, this isn't why I mentioned platform,

00:54:04.320 | but I am like Lex Friedman, MIT,

00:54:08.400 | which is something those people

00:54:11.720 | seem to sometimes care about,

00:54:13.440 | followers and stuff like that.

00:54:16.320 | Did you just do that?

00:54:18.160 | And then they said, "Enough of that nonsense.

00:54:21.000 | "Enough of that nonsense."

00:54:22.720 | They said to me, "Enough of that nonsense."

00:54:27.400 | Somebody who is obviously interviewed, Francis Collins,

00:54:31.520 | is the Pfizer CEO.

00:54:35.040 | - You're bringing on French epidemiologists also.

00:54:37.760 | - Yeah, exactly.

00:54:38.760 | But this broke my heart, the arrogance.

00:54:40.720 | And this is, echoes of that arrogance

00:54:43.120 | is something you see in this email.

00:54:44.560 | And I really would love to,

00:54:46.160 | we have a million things to talk about

00:54:47.440 | to try to figure out how can we find a path forward.

00:54:50.400 | - I think a lot of the problems we've seen

00:54:54.240 | in the discussion over COVID,

00:54:57.040 | especially in the scientific community,

00:54:59.560 | there's two ways to look at science, I think,

00:55:02.360 | that have been competing with each other for a while now.

00:55:05.200 | One way, and this is the way that I view science

00:55:08.800 | and why I've always found it so attractive,

00:55:10.920 | is an invitation to a structured discussion

00:55:14.200 | where the discussion is tempered by evidence,

00:55:18.080 | by data, by reasoning and logic.

00:55:22.040 | So it's a dialectical process where if I believe A

00:55:25.240 | and you believe B, well, we talk about it,

00:55:29.560 | we come up with an experiment

00:55:30.760 | that distinguishes between the two.

00:55:32.640 | And while B turns out to be right,

00:55:34.920 | I'm all frustrated, but I buy you dinner.

00:55:37.080 | And I say, "No, no, no, C."

00:55:38.480 | And then we go on from there.

00:55:40.440 | That's what science is at its best.

00:55:43.320 | It's this process of using data in discussion.

00:55:46.400 | It's a human activity, right?

00:55:48.720 | To learn, to have the truth unfold itself before us.

00:55:53.720 | On the other hand, there's another way

00:55:57.400 | that people have used science or thought about science

00:55:59.800 | as truth in and of itself, right?

00:56:03.400 | This like, if it's science,

00:56:04.880 | therefore it's true automatically.

00:56:06.720 | What does the science say to do?

00:56:10.280 | Well, the science never says to do anything.

00:56:12.240 | The science says, "Here's what's true."

00:56:13.720 | And then we have to apply our human values to say,

00:56:17.720 | "Okay, well, if we do this,

00:56:19.920 | "well, then this is likely to happen."

00:56:21.840 | That's what the science says.

00:56:23.000 | "If we do that, then that is likely to happen.

00:56:24.960 | "Well, we'd rather have this than that, right?"

00:56:27.480 | But science doesn't tell us

00:56:29.360 | that we'd rather have this than that.

00:56:30.200 | It's our human values that tell us

00:56:31.640 | that we'd rather have this than that.

00:56:32.520 | Science plays a role, but it's not the only thing.

00:56:35.080 | It's not the only role.

00:56:36.880 | It's like, it helps us understand the constraints we face,

00:56:40.080 | but it doesn't tell us what to do

00:56:41.560 | in face of those constraints.

00:56:43.240 | - But underneath it, at the individual level,

00:56:45.600 | at the institutional level,

00:56:46.800 | it seems like arrogance is really destructive.

00:56:51.800 | So the flip side of that, the productive thing is humility.

00:56:55.760 | So sort of always not being sure that you're right.

00:57:00.760 | This is actually kind of,

00:57:04.240 | Stuart Russell talks about this for AI research.

00:57:07.680 | "How do you make sure that AI, super intelligent AI,

00:57:10.360 | "doesn't destroy us?"

00:57:11.360 | You built in a sort of module within it

00:57:15.400 | that it always doubts its actions.

00:57:18.560 | Like it's not sure.

00:57:20.160 | Like I know it says I'm supposed to destroy all humans,

00:57:23.200 | but maybe I'm wrong,

00:57:24.800 | and that maybe I'm wrong is essential for progress,

00:57:27.860 | for actually doing in the long arc of history,

00:57:30.880 | not the perfect thing,

00:57:31.760 | but better and better and better and better.

00:57:33.480 | I mean, the question I have here for you is,

00:57:36.560 | this email so clearly captures some,

00:57:41.160 | maybe echo, but maybe a core to the problem.

00:57:44.240 | Do you put responsibility of this email,

00:57:47.040 | of the shortcomings and failures

00:57:49.920 | on individuals or institutions?

00:57:52.080 | Is this Francis Collins-Antonis?

00:57:53.680 | - No, this is an institutional failure, right?

00:57:55.320 | So the NIH, so I've had two decades of NIH funding.

00:57:59.200 | I've sat on NIH review panels.

00:58:01.200 | The purpose of the NIH is what you said earlier, Lex.

00:58:03.800 | The purpose of the NIH is to support the work of scientists.

00:58:08.080 | To some extent, it's also to help scientists,

00:58:10.600 | to direct scientists to work on things

00:58:12.760 | that are very important for public health,

00:58:14.520 | or for the health of the public.

00:58:16.120 | So, and the way you do that is you say,

00:58:18.720 | okay, we're gonna put $50 million on the research

00:58:22.840 | in Alzheimer's disease this year,

00:58:24.480 | or $70 million on HIV, or whatever it is, right?

00:58:27.240 | And that pot of money,

00:58:29.020 | then scientists compete with each other

00:58:31.340 | for the best ideas to use it to address that problem.

00:58:36.140 | So it's essentially an endeavor

00:58:38.380 | to support the work of scientists.

00:58:40.320 | It is not in and of itself a policy organ.

00:58:45.060 | It doesn't say what public health policy should be.

00:58:48.220 | For that, you have the CDC.

00:58:50.580 | And what happened during the pandemic

00:58:55.280 | is that people in the NIH were called upon

00:59:00.300 | to contribute to public health policymaking.

00:59:03.740 | And that created the conflict of interest

00:59:06.900 | you see in that email, right?

00:59:09.060 | So now you have the head of the NIH

00:59:12.900 | in effect saying to all scientists,

00:59:15.060 | you must agree with me in the policy that I've recommended,

00:59:19.120 | or else you're a fringe.

00:59:22.160 | That is a deep conflict of interest.

00:59:25.340 | It's deep because first, he's conflicted.

00:59:27.460 | He has this dual role as the head of the NIH,

00:59:31.620 | supporter of scientific funding,

00:59:33.100 | and then also inappropriately called

00:59:35.940 | to set or help set pandemic policy.

00:59:39.460 | That should never have happened.

00:59:40.900 | There should be a bright line between those two roles.

00:59:44.360 | - Let me ask you about just Francis Collins.

00:59:47.180 | I don't know if you,

00:59:48.020 | I had a chance to talk to him on a podcast.

00:59:49.820 | I don't know if you maybe by chance

00:59:51.140 | gotten a chance to hear a few words.

00:59:52.900 | - I heard some of it, yeah.

00:59:54.420 | - Well, I have a kind of a question to that

00:59:56.740 | because a lot of people wrote to me quite negative things

01:00:01.380 | about Francis Collins.

01:00:02.580 | And like I said, I still believe he's a great man,

01:00:05.780 | a great scientist.

01:00:06.940 | One of the things when I talked to him off mic

01:00:13.420 | about the vaccine,

01:00:14.780 | the excitement he had about when we were recollecting

01:00:21.240 | when they first gotten an inkling

01:00:23.540 | that it's actually going to be possible to get a vaccine.

01:00:26.900 | Just he wasn't messaging,

01:00:28.900 | just in the private or of our own conversation,

01:00:31.260 | he was really excited.

01:00:32.760 | And why was he excited?

01:00:34.180 | Because he gets to help a lot of people.

01:00:36.500 | This is a man that really wants to help people.

01:00:40.560 | And there could be some institutional self-delusion,

01:00:43.660 | the arrogance, all those kinds of things

01:00:45.680 | that lead to this kind of email.

01:00:47.200 | But ultimately the goal is this,

01:00:49.340 | I don't think people quite realize this.

01:00:51.780 | The reason he would call you a fringe epidemiologist,

01:00:55.060 | the reason there needs to be a devastating

01:00:57.660 | published takedown, he, I believe, really believes

01:01:01.860 | that this could be very dangerous.

01:01:05.380 | And it's a lot of burden to carry on his shoulders

01:01:09.360 | because like you said, in his role

01:01:11.140 | where he defines some of the public policy,

01:01:13.440 | like depending on how he thinks about the world,

01:01:19.020 | millions of people could die because of one decision

01:01:21.660 | he make.

01:01:22.820 | And that's a lot of burden to walk with.

01:01:24.700 | - Yeah, no, I think that's right.

01:01:25.940 | I don't think that he has bad intentions.

01:01:29.420 | I think that he was basically put,

01:01:31.820 | he was put or maybe put himself in a position

01:01:34.380 | where this kind of conflict of interest

01:01:38.060 | was going to create this kind of reaction.

01:01:41.700 | The kind of humility that you're calling for

01:01:44.540 | is almost impossible when you have that dual role

01:01:48.100 | that you shouldn't have as funder of science

01:01:51.360 | and also setter of scientific policy.

01:01:53.460 | - I agree with everything you just said

01:01:54.500 | except the last part.

01:01:55.700 | The humility is almost impossible.

01:01:58.100 | Humility is always difficult.

01:02:01.660 | I think there's a huge incentive

01:02:04.780 | for humility in that position.

01:02:06.700 | Now look at history.

01:02:08.520 | Great leaders that have humility are popular as hell.

01:02:13.520 | So if you like being popular,

01:02:16.760 | if you like having impact, legacy,

01:02:19.660 | these descendants of apes seem to care about legacy,

01:02:22.240 | especially as they get older in these high positions.

01:02:25.980 | I think the incentive for humility is pretty high.

01:02:28.380 | - Well, the thing is there's a lot

01:02:30.360 | that he has to be proud of in his career.

01:02:33.040 | The Human Genome Project wouldn't have happened without him.

01:02:36.040 | And he is a great man and a great scientist.

01:02:39.480 | So it is tragic to me that his career

01:02:41.680 | has ended in this particular way.

01:02:43.320 | - Can I ask you a question

01:02:46.080 | about my podcast conversation with him?

01:02:50.700 | By way of advice or maybe criticism,

01:02:53.240 | there's a lot of people that wrote to me

01:02:56.260 | kind words of support

01:02:58.220 | and a lot of people that wrote to me

01:03:00.740 | respectful, constructive criticism.

01:03:03.340 | How would you suggest to have conversations

01:03:06.180 | with folks like that?

01:03:07.840 | And maybe, I mean,

01:03:11.020 | 'cause I have other conversations like this,

01:03:12.900 | including I was debating whether to talk

01:03:14.900 | to Anthony Fauci, he wanted to talk.

01:03:19.220 | And so what kind of conversation do you have?

01:03:22.820 | And sorry to take us on a tangent,

01:03:24.620 | but almost from an interview perspective

01:03:27.300 | of how to inspire humility and inspire trust in science

01:03:31.780 | or maybe give hope that we know what the heck we're doing

01:03:34.460 | and we're gonna figure this out.

01:03:35.700 | - I mean, I think I've been now interviewed by many people.

01:03:40.500 | I think the style you have really works well, Lex.

01:03:44.460 | You have to, 'cause I don't think you're gonna be ever

01:03:47.540 | an attack dog trying to go after somebody

01:03:50.540 | and force them to like,

01:03:52.940 | you know, submit that they were wrong

01:03:54.460 | or whatever about them.

01:03:55.820 | I mean, I also actually find that form of journalism

01:03:58.740 | and podcasting really off-putting.

01:04:00.500 | It's hard to watch.

01:04:01.940 | - Also, it's a whole other tangent.

01:04:04.100 | Is that actually effective?

01:04:05.380 | - I don't think so.

01:04:06.220 | - Do you wanna ask Hitler,

01:04:09.140 | and I think about this a lot, actually interviewing Hitler.

01:04:11.060 | I've been studying a lot about the rise and fall

01:04:13.580 | of the Third Reich.

01:04:15.140 | I think about interviewing Stalin.

01:04:16.660 | Like I put myself in that mindset,

01:04:18.520 | like how do you have conversations with people

01:04:22.700 | to understand who they are,

01:04:24.300 | so that not so you can sit there and yell at them,

01:04:28.220 | but to understand who they are

01:04:29.340 | so that you can inspire a very large number of people

01:04:32.420 | to be the best version of themselves

01:04:34.380 | and to avoid the mistakes of the past.

01:04:36.220 | - I believe that everyone that's involved in this debate

01:04:39.700 | has good intentions.

01:04:41.180 | They're coming at it from their points of view.

01:04:43.920 | They have their weaknesses,

01:04:48.180 | and if you can paint a picture in your questioning,

01:04:50.820 | by sympathetic questioning,

01:04:52.820 | of those strengths and weaknesses

01:04:55.180 | and their point of view, you've done a service.

01:04:57.340 | That's really all I personally like to see

01:05:00.180 | in those kinds of interviews.

01:05:02.100 | I don't think a gotcha moment is really the key thing there.

01:05:06.340 | The key thing is understanding where they're coming from,

01:05:09.340 | understanding their thinking,

01:05:10.660 | understanding the constraints they faced

01:05:12.340 | and how did they manage them.

01:05:14.140 | That's gonna provide a much,

01:05:15.720 | I mean, to me, that's what I look for

01:05:17.100 | when I listen to podcasts like yours,

01:05:19.860 | is an understanding of that person and the moment

01:05:24.640 | and how they dealt with it.

01:05:26.260 | - I mean, I guess the hope is to discover

01:05:29.220 | in a sympathetic way a flaw in a person's thinking together.

01:05:33.820 | Like as opposed to discovering the positive thing together,

01:05:37.140 | you discover the thing,

01:05:39.500 | where I didn't really think about that.

01:05:40.860 | - Yeah, I mean, that's how science is, right?

01:05:42.940 | That's why we find it so attractive,

01:05:45.220 | is this, I like it when a student shows me

01:05:49.780 | I'm thinking incorrectly, right?

01:05:51.780 | I'm really grateful to that student

01:05:53.960 | because now I have an opportunity to change my mind about it

01:05:57.300 | and then start thinking even more correctly.

01:05:58.980 | I mean, and there are moments when,

01:06:02.540 | I mean, like this is probably a good time to say

01:06:05.020 | like what I think I got wrong during the pandemic, right?

01:06:07.900 | So like, for instance, you said Francis Collins

01:06:10.060 | had a moment when he learned that there was quite possible

01:06:14.780 | to get a vaccine going.

01:06:16.660 | He must've learned that quite early.

01:06:19.100 | And I didn't learn that early.

01:06:21.540 | I mean, I didn't know in March of 2020,

01:06:25.060 | in my experience with vaccine development,

01:06:28.140 | it would have take, I thought it would take a decade

01:06:30.380 | or more to get a vaccine.

01:06:31.720 | That was wrong, right?

01:06:34.220 | I didn't, and I was so happy

01:06:38.020 | when I started to see the preliminary numbers

01:06:40.420 | in the Pfizer trial that strongly suggested

01:06:43.940 | it was going to work.

01:06:44.980 | - Yeah.

01:06:47.180 | - And I was, I can't, I mean, like very few times

01:06:49.420 | in my life I've been so happy to be wrong.

01:06:51.960 | - And it changes kind of,

01:06:54.340 | I think I've heard you mention that a lockdown

01:06:56.420 | is still a bad idea unless the vaccine comes out

01:06:59.980 | in like tomorrow.

01:07:01.800 | There's still like suffering and economic pain,

01:07:05.300 | all kinds of pain can still happen

01:07:07.100 | in even just a scale of weeks versus months.

01:07:12.100 | - Yeah.

01:07:14.100 | - Well, let's talk about the vaccine.

01:07:16.320 | What are your thoughts on the safety and efficacy

01:07:18.940 | of COVID vaccines at the individual and the societal level?

01:07:22.900 | - So for the vaccine safety data,

01:07:26.260 | it's actually challenging to convey to the public

01:07:30.140 | how this is normally done.

01:07:31.500 | Like normally you would do this in the context of the trial.

01:07:34.420 | You'd have a long trial with large numbers,

01:07:37.020 | relatively large numbers of people.

01:07:38.700 | You'd follow them over a long time

01:07:40.100 | and the trial will give you some indication

01:07:42.380 | of the safety of the vaccine.

01:07:43.740 | And it did.

01:07:45.140 | I mean, but the trial, the way it was constructed,

01:07:49.620 | when it was came out that it was protective against COVID,

01:07:52.780 | it was no longer ethical to have a placebo arm.

01:07:55.140 | And so that placebo arm was vaccinated,

01:07:57.940 | what large part of it.

01:08:00.140 | And so that meant that from the trial,

01:08:02.300 | you are not going to be able to get data

01:08:05.220 | on the long-term safety profiles of the vaccine.

01:08:07.720 | And also the other thing about trials,

01:08:11.220 | there's tens of thousands of people enrolled.

01:08:13.020 | That's still not enough to get,

01:08:14.500 | when you deploy a vaccine at population scale,

01:08:18.980 | you're gonna see things that weren't in the trial,

01:08:21.260 | guaranteed.

01:08:22.100 | Populations of people that weren't represented well

01:08:24.820 | in the trial are gonna be given the vaccine

01:08:27.900 | and then they're gonna have things that happen to them

01:08:30.020 | that you didn't anticipate.

01:08:32.300 | So I wasn't surprised when people were a little bit skeptical

01:08:36.300 | when the trial was done about the safety profile,

01:08:38.620 | just the way the nature of the thing was gonna make it

01:08:40.740 | so that it was gonna be hard to get a complete picture

01:08:43.700 | from the trials itself.

01:08:45.140 | And the trial showed they were pretty safe

01:08:47.560 | and quite effective at preventing both you

01:08:51.300 | from getting COVID.

01:08:52.580 | I think the main end point of the trial itself

01:08:54.900 | was a symptomatic COVID.

01:08:57.580 | Right, so that was, I mean, it was really,

01:09:02.580 | to me, like it was about as amazing achievement as anything,

01:09:06.620 | organizing a trial of that scale and running it so quickly.

01:09:10.740 | - And the final results being so surprisingly high.

01:09:14.420 | - So good, right?

01:09:15.540 | But the problem then was normally it would take a long time.

01:09:22.820 | The FDA would tell Pfizer to go back

01:09:25.460 | and try it in this subgroup.

01:09:27.140 | They'd work more on dosing.

01:09:28.660 | They do all these kinds of things that kind of didn't,

01:09:31.980 | we really didn't have time for

01:09:33.340 | in the middle of the pandemic, right?

01:09:34.800 | So you have a basis for approval that it's less full

01:09:39.800 | than normally you would have for a population scale vaccine.

01:09:44.120 | But the results were good.

01:09:46.380 | The results looked really good.

01:09:47.540 | And actually I should say for the most part,

01:09:49.980 | that's been borne out when we've given the vaccine at scale

01:09:52.980 | in terms of protection against severe disease.

01:09:55.960 | - Yeah.

01:09:56.800 | - So people who have got the vaccine for a very long time

01:10:00.300 | after they've had the full vaccination

01:10:02.660 | have had great protection against going,

01:10:05.740 | being hospitalized and dying if they get COVID.

01:10:08.460 | - Let's separate, 'cause this seems to be,

01:10:11.440 | there's critics of both categories, but different.

01:10:16.020 | Kids and kids, not older people,

01:10:20.940 | like let's say five years old and above or something like,

01:10:24.300 | or 13 years old and above.

01:10:26.060 | So for those, it seems like the reduction

01:10:31.060 | of the rate of fatalities and serious illness

01:10:35.920 | seems to be something like 10X.

01:10:38.280 | - I mean, for older people, it is a godsend, this vaccine.

01:10:42.320 | It transforms the problem of focus protection

01:10:47.020 | from something that's quite challenging,

01:10:49.220 | possible, I believe, but quite challenging

01:10:50.820 | to something that's much, much more manageable.

01:10:53.160 | Because the vaccine in and of itself,

01:10:54.960 | when deployed in older populations,

01:10:57.240 | is a form of focus protection.

01:10:58.800 | - Yes, by the way, we'll talk about the focus protection

01:11:03.320 | in one segment, 'cause it's such a brilliant idea

01:11:05.720 | for this pandemic of future pandemics.

01:11:08.480 | I thought the sociological, psychological discussion

01:11:11.760 | about the letter from Francis Collins is,

01:11:14.020 | because it was so recent, it's been so troubling to me,

01:11:17.380 | so I'm glad we talked about that first.

01:11:20.200 | But so there seems to be, the vaccines work

01:11:24.920 | to reduce deaths, and that has especially

01:11:29.320 | the most transformative effects for the older folks.

01:11:33.280 | - I've told you one thing that I got wrong in the pandemic.

01:11:35.080 | Let me tell you the second thing I got wrong,

01:11:36.520 | for sure, in the pandemic.

01:11:38.240 | In January of this year, 2021,

01:11:43.040 | I thought that the vaccines would stop infection.

01:11:46.640 | - Yes.

01:11:48.920 | - It would make it so that you were much less likely

01:11:51.640 | to be infected at all, because the antibodies

01:11:55.280 | that were produced by the vaccines looked like

01:11:56.800 | they were neutralizing antibodies that would

01:11:58.880 | essentially block you from being infected at all.

01:12:01.320 | That turned out to be wrong.

01:12:05.280 | I think, and it became clear as data came out from Israel,

01:12:11.260 | which vaccinated very early, that they were seeing

01:12:13.280 | surges of infection, even in a very highly

01:12:15.860 | vaccinated population, that the vaccine

01:12:19.980 | does not stop infection.

01:12:21.780 | - So you're a used car salesman,

01:12:24.220 | and you were selling the vaccine,

01:12:26.180 | and the features you thought a vaccine would have,

01:12:28.460 | I mean, I have a similar kind of sense

01:12:30.100 | when the vaccine came out.

01:12:31.660 | Vaccine would reduce, if you somehow were able to get it,

01:12:36.660 | it would reduce rate of death and all those kinds of things,

01:12:40.340 | but it would also reduce the chance of you getting it,

01:12:44.260 | and if you do get it, the chance of you transmitting it

01:12:47.100 | to somebody else.

01:12:48.880 | And it turns out that those latter two things

01:12:52.180 | are not as definitive, or in fact,

01:12:55.820 | I mean, I don't know to what degree they're not there at all.

01:12:57.860 | - I think it's a little complicated,

01:12:59.260 | 'cause I think the first two or three months

01:13:01.560 | after you're fully vaccinated, after the second dose,

01:13:04.300 | you have 60, 70% efficacy peak against infection.

01:13:09.300 | So, which is pretty good, right?

01:13:12.700 | But by six, seven, eight months, that drops to 20%.

01:13:16.740 | Some places, some studies, like there's a study

01:13:19.140 | out of Sweden that suggested it might even drop to zero.

01:13:21.500 | - But, and then you're also infectious

01:13:23.380 | for some period of time, if you do get it,

01:13:25.700 | even though you're vaccinated.

01:13:26.980 | - Correct.

01:13:27.820 | - Although there seems to be loosely dated

01:13:29.820 | that the period of time you're infectious is shorter.

01:13:31.860 | - Is shorter, but the infectivity per day is about as high.

01:13:36.500 | So you're still, the point is that the vaccine

01:13:39.340 | might reduce some risk of infecting others,

01:13:41.820 | but it's not a categorical difference.

01:13:44.820 | So, it's not safe to be in the presence

01:13:49.660 | of just vaccinated people.

01:13:51.220 | You can still get infected.

01:13:52.660 | - Right, so, I mean, there's a million things

01:13:56.180 | I wanna ask here, but is there in some sense,

01:13:59.700 | because the vaccine really helps on the worst part

01:14:04.700 | of this pandemic, which is killing people.

01:14:08.300 | - Yes.

01:14:09.980 | - Doesn't that mean, where does the vaccine hesitancy

01:14:14.340 | come from in terms of, it seems like,

01:14:17.660 | obviously a vaccine is a powerful solution

01:14:20.580 | to let us open this thing up?

01:14:22.580 | - Yeah, so I wrote a Wall Street Journal op-ed

01:14:24.580 | with Sunetra Gupta in December of last year,

01:14:27.540 | a very, with a very naive title, which says,

01:14:30.260 | "We can end the lockdowns in a month."

01:14:32.260 | And the idea is very simple.

01:14:33.900 | Vaccinate all vulnerable people,

01:14:39.300 | and then open up.

01:14:40.700 | - Open up.

01:14:41.540 | - Right, and the idea was that the lockdown harms,

01:14:45.180 | this is directly related to the Great Barrington Declaration.

01:14:47.100 | The Great Barrington Declaration said,

01:14:48.420 | "The lockdown harms are devastating

01:14:51.100 | "to the population at large.

01:14:53.160 | "There's this considerable segment of people

01:14:55.880 | "that are vulnerable, protect them."

01:14:58.340 | Well, with the vaccine, we have a perfect tool

01:15:00.300 | to protect the vulnerable, which is, I still believe,

01:15:02.740 | I mean, it's true, right?

01:15:04.300 | You vaccinate the vulnerable, the older population,

01:15:07.180 | and as you said, it's a tenfold decrease

01:15:09.380 | in the mortality risk from getting infected,

01:15:13.180 | which is, I mean, amazing.

01:15:14.980 | So that was the strategy we outlined.

01:15:17.220 | What happened is that the vaccine debate got transformed.

01:15:20.460 | So first, so you're asking about vaccine hesitancy.

01:15:22.860 | I think there's, first, there's the inherent limitations

01:15:27.000 | of how to measure vaccine safety, right?

01:15:29.440 | So we talked a little bit about it in the trial,

01:15:31.620 | but also after the trial, there's a mechanism,

01:15:35.140 | and this is the work I've been involved with before COVID,

01:15:37.940 | on tracking and identifying and checking

01:15:42.440 | whether the vaccines actually are safe,

01:15:43.980 | and the central challenge is one of causality.

01:15:46.380 | So you no longer have the randomized trial,

01:15:49.380 | but you wanna know, is the vaccine,

01:15:52.780 | when it's deployed at scale, causing adverse events?

01:15:56.840 | Well, you can't just look at people who are vaccinated

01:16:00.100 | and see what adverse events happen,

01:16:02.100 | 'cause you don't know what would have happened

01:16:04.020 | if the person had not been vaccinated.

01:16:06.080 | So you have to have some control group.

01:16:09.820 | Now, what happened is there's several systems

01:16:12.100 | to check this that the CDC uses.

01:16:14.700 | One very, very commonly known one now is called VAERS,

01:16:18.220 | the Vaccine Adverse Event Reporting System.

01:16:20.580 | There, anyone who has an adverse event,

01:16:22.700 | either a regular person or a doctor, can just go report,

01:16:26.740 | "Look, I had the vaccine, and two days later,

01:16:28.300 | "I had a headache," or whatever it is.

01:16:30.700 | The person died a day after they had the vaccine, right?

01:16:34.260 | Now, the vaccine was rolled out to older people first,

01:16:37.640 | and older people die sometimes, with or without the vaccine.

01:16:42.140 | So sometimes you'll see someone's vaccinated,

01:16:44.700 | and a few days later, they die.

01:16:46.660 | Did the vaccine cause it or something else cause it?

01:16:48.500 | It's really difficult to tell.

01:16:50.300 | In order to tell, you need a control group.

01:16:52.460 | For that, there are other systems the FDA and CDC have.

01:16:58.540 | Like, there's one called VSD, Vaccine Safety Data Link.

01:17:02.340 | There's another system called BEST.

01:17:04.100 | I forget what the acronym is.

01:17:06.860 | To essentially to track cohorts of people,

01:17:11.460 | vaccinated versus unvaccinated,

01:17:13.900 | with as careful of a matching as you can do.

01:17:15.660 | It's not randomized, and see if you have safety signals

01:17:20.660 | that pop up in the vaccinated

01:17:23.660 | relative to the control group unvaccinated.

01:17:27.060 | And so that's, for instance, how the myocarditis risk

01:17:31.020 | was picked up in young, especially young men.

01:17:34.220 | It's also how the higher risk of blood clots

01:17:37.980 | in middle-aged and older women,

01:17:41.020 | with the J&J vaccine was picked up.

01:17:42.820 | There, what you have are situations where

01:17:46.980 | the baseline risk of these outcomes are so low

01:17:50.420 | that if you see them in the vaccinated arm at all,

01:17:54.060 | then it's not hard to understand

01:17:55.460 | that the vaccine did this, right?

01:17:57.380 | Young men should not be having myocarditis.

01:18:00.500 | Middle-aged women should not be having

01:18:02.260 | huge blood clots in the brain, right?

01:18:04.380 | So when you see that, you can say it's linked.

01:18:05.860 | Now, the rates are low.

01:18:07.340 | So young men, maybe one in 5,000,

01:18:09.500 | one in 10,000 of the vaccine-related myocarditis,

01:18:13.220 | pericarditis.

01:18:14.100 | Young women, middle-aged women, I don't know.

01:18:18.420 | I'm not sure what the right number might be,

01:18:19.660 | but I'd say it's like one in hundreds of thousands,

01:18:23.660 | something like that.

01:18:25.660 | So these are rare outcomes,

01:18:27.900 | but they are vaccine-linked outcomes.

01:18:30.980 | How do you deal with that as a messaging thing?

01:18:33.780 | I think you just tell people.

01:18:35.380 | You tell people here are the risks.

01:18:36.820 | You transparently tell them.

01:18:37.940 | So they're not getting into something that they don't know.

01:18:41.180 | - Yeah, and don't treat people like they're children

01:18:45.500 | and need to be told lies because they won't understand

01:18:48.700 | the full complexity of the truth.

01:18:50.980 | People, I think, are pretty good at,

01:18:54.220 | or actually, people with time

01:18:58.260 | are good at understanding data,

01:18:59.660 | but better than anything,

01:19:01.500 | they're extremely good at detecting arrogance and bullshit.

01:19:08.420 | And you give them either one of those.

01:19:10.780 | - I mean, I'll give you one

01:19:11.700 | that's where I think it's greatly undermined vaccine has,

01:19:15.100 | greatly undermined the demand for the vaccine

01:19:16.620 | is this weird denial that if you recover from COVID,

01:19:20.460 | you have extremely good immunity,

01:19:24.100 | both against infection and access to the vaccine.

01:19:27.100 | And that denial leads to people distrusting the message

01:19:31.860 | given by the CDC director, for instance,

01:19:33.900 | in favor of the vaccine.

01:19:35.780 | Why would you deny a thing that's such an obvious fact?

01:19:39.740 | You can look at the data,

01:19:41.180 | and it just pops out at you

01:19:43.700 | that people that are COVID recovered

01:19:45.860 | are not getting infected again at very high rates,

01:19:48.760 | much lower rates.

01:19:50.340 | - After these kinds of conversations,

01:19:52.220 | I'm sure after this very conversation,

01:19:55.580 | I often get a number of messages from Joe, Joe Rogan,

01:19:59.460 | and from Sam Harris, who to me are people I admire.

01:20:03.620 | I think are really intelligent, thoughtful human beings.

01:20:06.580 | They also have a platform.

01:20:08.440 | And I believe, at least in my mind,

01:20:11.140 | about this COVID set of topics,

01:20:14.140 | they represent a group of people.

01:20:19.880 | Each group has smart, thoughtful,

01:20:24.420 | well-intentioned human beings.

01:20:27.940 | And I don't know who is right,

01:20:30.340 | but I do know that they're kind of tribal a little bit,

01:20:35.340 | those groups.

01:20:37.220 | And so the question I wanna ask is like,

01:20:41.080 | what do you think about these two groups

01:20:45.060 | and this kind of tension over the vaccine

01:20:49.840 | that sometimes it just keeps finding different topics

01:20:54.040 | on which to focus on,

01:20:55.360 | like whether kids should get vaccinated or not,

01:20:57.840 | whether there should be vaccine mandates or not,

01:21:00.400 | which seem to be often very kind of specific policy

01:21:03.440 | kinds of questions that miss the bigger picture.

01:21:06.160 | - I think it's a symptom of the distrust

01:21:08.320 | that people have in public health.

01:21:10.460 | I think this kind of schism over the vaccine

01:21:13.880 | does not happen in places where the public health authorities

01:21:16.600 | have been much more trustworthy.

01:21:18.880 | So you don't see this vaccine,

01:21:20.080 | hasn't seen Sweden, for instance.

01:21:21.880 | What's happened in the United States

01:21:25.620 | is the vaccine has become,

01:21:27.340 | first because of politics,

01:21:30.560 | but then also because of the scientific arrogance,

01:21:33.020 | this sort of touchstone issue,

01:21:35.100 | and people line up on both sides of it.

01:21:37.200 | And the different language you're hearing

01:21:39.560 | is structured around that.

01:21:40.820 | So before the election, for instance,

01:21:42.680 | I did a testimony in the house

01:21:46.560 | on measurement of vaccine safety.

01:21:49.760 | And I was invited by the Republicans.

01:21:52.520 | There were, I think, four other experts

01:21:54.440 | invited by the Democrats,

01:21:55.280 | or three other experts invited by Democrats,

01:21:57.600 | each of whom had a lot of experience

01:21:59.220 | in measuring vaccine safety.

01:22:00.600 | I was really surprised to hear them each doubt

01:22:04.720 | whether the FDA would do a reasonable job

01:22:06.840 | in assessing vaccine safety,

01:22:08.760 | including by people who have long records

01:22:11.140 | of working with the FDA.

01:22:12.760 | I mean, these are professionals,

01:22:15.680 | great scientists, whose main goal in life

01:22:19.400 | is to make sure that unsafe vaccines

01:22:22.480 | don't get released into the world.

01:22:24.080 | And if they are, they get pulled.

01:22:26.400 | And they were casting doubt on the vaccine,

01:22:28.520 | the ability to track vaccine safety before the election.

01:22:32.120 | And then after the election,

01:22:35.140 | the rhetoric switched on a dime, right?

01:22:39.440 | All of a sudden, it's Republicans that are cast

01:22:41.640 | as if they're vaccine-hesitant.

01:22:43.760 | That kind of political shift, the public notices.

01:22:47.800 | If all it takes is an election to change

01:22:50.420 | how people talk about the safety of the vaccine,

01:22:52.400 | well, we're not talking science anymore,

01:22:54.280 | many people think, right?

01:22:55.720 | I think that created its hesitancy.

01:22:58.120 | The other thing I think,

01:23:00.520 | the hesitancy, some politicians viewed it

01:23:05.920 | as a political, as sort of like a political opportunity

01:23:09.840 | to sort of demonize people who are hesitant.

01:23:14.040 | And that itself fueled hesitancy, right?

01:23:16.840 | Like if you're telling me I'm a rube

01:23:18.640 | that just doesn't want the vaccine

01:23:19.920 | 'cause I want everyone to die,

01:23:20.880 | well, I'm gonna react really negatively.

01:23:23.520 | And if you're talking down to me

01:23:27.560 | about my legitimate sort of concerns

01:23:32.560 | about whether this vaccine's safe to take,

01:23:34.280 | I mean, I've heard from women

01:23:36.560 | who are thinking about getting pregnant,

01:23:37.760 | should I take the vaccine, I don't know.

01:23:39.320 | I mean, there are all kinds of questions,

01:23:41.240 | legitimate questions that I think

01:23:44.240 | should have good data to answer

01:23:45.900 | that we don't necessarily have good data to answer.

01:23:47.960 | So what do you do in the face of that?

01:23:50.160 | Well, one reaction is to pretend

01:23:52.960 | like we know for a fact that it's safe

01:23:55.480 | when we don't have the data to know for a fact

01:23:57.680 | in that particular group

01:23:58.560 | with that particular set of clinical circumstances you know.

01:24:01.880 | And that I think breeds hesitancy.

01:24:03.800 | People can detect that bullshit.

01:24:05.580 | Whereas if you just tell people, you know, I don't know.

01:24:09.320 | - Yeah, leave with humility.

01:24:10.680 | - Yeah, you'll end up with a better result.

01:24:13.360 | - Let me ask you about,

01:24:15.920 | I've recently had a conversation with a Pfizer CEO.

01:24:19.160 | This is part therapy session, part advice.

01:24:24.160 | 'Cause again, I really want us to get through this together

01:24:29.520 | and it feels like the division is a thing

01:24:31.640 | that prevents us from getting through this together.

01:24:35.280 | And once again, just like with Francis Collins,

01:24:38.640 | a lot of people wrote to me words of support

01:24:43.640 | and a lot of people wrote to me words of criticism.

01:24:47.200 | I'm trying to understand the nature of the criticism.

01:24:53.120 | So some of the criticism had to do with against the vaccine

01:24:57.520 | and those kinds of things.

01:24:58.960 | That I have a better understanding of.

01:25:01.000 | But some kind of deep distrust of Pfizer.

01:25:07.440 | So actually looking at Big Pharma broadly,

01:25:12.440 | I'm trying to understand,

01:25:16.560 | am I so naive that I just don't see it?

01:25:21.500 | Because yes, there's corrupt people and they're greedy,

01:25:26.400 | they're flawed in all walks of life.

01:25:28.960 | But companies do quite an incredible job

01:25:35.760 | of taking a good idea at the scale

01:25:37.800 | and making some money with that idea.

01:25:39.600 | But they are the ones that achieve scale on a good idea.

01:25:42.880 | It's not obvious to me,

01:25:46.360 | I don't see where the manipulation is.

01:25:49.800 | So the fear that people have,

01:25:51.340 | and I talked to Joe about this quite a bit,

01:25:55.000 | I think this is a legitimate fear

01:25:57.520 | and a fear you should often have,

01:26:00.320 | that money has influence, disproportional influence,

01:26:03.240 | especially in politics.

01:26:04.920 | So the fear is that the policy of the vaccine

01:26:09.920 | was connected to the fact that lots of money could be made

01:26:14.760 | by manufacturing the vaccine.

01:26:16.520 | And I understand that.

01:26:19.320 | And it's actually quite a heck of a difficult task

01:26:22.040 | to alleviate that concern.

01:26:24.560 | Like you really have to be a great man or woman or leader

01:26:27.880 | to convince people that you're not full of shit,

01:26:30.480 | that you're not just playing a game on them.

01:26:32.720 | I don't know, it's a difficult task.

01:26:35.280 | But at the same time,

01:26:36.280 | I really don't like the natural distrust every billionaire,

01:26:41.280 | distrust everybody who's trying to make money.

01:26:44.160 | Because it feels like, under a capitalistic system at least,

01:26:47.240 | the way to do a lot of good,

01:26:50.400 | like to do good at scale in the world

01:26:53.240 | is by being at least in part motivated by profit.

01:26:57.960 | - I mean, I share your ambivalence, right?

01:26:59.360 | So on the one hand, you have a fantastic achievement,

01:27:02.920 | the discovery of the vaccine

01:27:06.720 | and then the manufacturing at scale,

01:27:08.880 | so that billions of people can take the vaccine

01:27:12.680 | in a relatively short time.

01:27:14.080 | That is a remarkable achievement that could not have happened

01:27:17.240 | without companies like Pfizer.

01:27:18.960 | And on the other hand,

01:27:21.560 | there is this sort of corrupting influence of that money.

01:27:24.780 | Just to give you one example,

01:27:27.400 | there's an enormous controversy over whether

01:27:30.280 | relatively inexpensive repurposed drugs

01:27:32.800 | can be used to treat the disease.

01:27:35.720 | No company like Pfizer has any interest whatsoever

01:27:41.880 | in evaluating it.

01:27:43.040 | Even Merck, I think, what was Merck,

01:27:45.520 | that had the patent on ivermectin now expired,

01:27:50.440 | has no interest at all in checking to see if it works.

01:27:54.480 | - Not only do they not have interest,

01:27:57.600 | they have a way of talking about people

01:28:01.560 | who might have a little bit of interest.

01:28:04.560 | That's again-- - Fringe.

01:28:06.680 | - Full of arrogance. - Yeah.

01:28:10.360 | - And that is what troubles me.

01:28:12.620 | It's back to the play of science.

01:28:16.000 | They're not a bit of curiosity.

01:28:17.680 | One, okay, one, the natural curiosity of a human being,

01:28:20.760 | they should always be there and an open-mindedness.

01:28:23.320 | And second, in the case of ivermectin

01:28:25.680 | and other things like that,

01:28:27.240 | you have to acknowledge that there's a very large number

01:28:30.380 | of people who care about this topic

01:28:33.600 | and this is a way to inspire them

01:28:36.520 | to also play in the space of science,

01:28:38.840 | to inspire them with science.

01:28:39.840 | You can't just dismiss everybody.

01:28:42.660 | You can't just dismiss people, period.

01:28:45.880 | - Yeah, well, I mean, I think, here, take ivermectin.

01:28:48.360 | There's actually a study funded by the NIH,

01:28:51.680 | by Tony Fauci's NIAID and the NIH,

01:28:55.660 | called ACTIV-6, that's a randomized trial of ivermectin.

01:29:00.660 | It's due to be completed in March 2023.

01:29:05.500 | So normally, when you have private actors

01:29:10.940 | like these big drug companies that have no interest

01:29:13.720 | in conducting some kind of scientific experiment

01:29:16.640 | that would have some public benefit,

01:29:18.760 | it's the job of the government,

01:29:20.880 | and in this case, the NIH, to fund that kind of work.

01:29:24.500 | The NIH has been incredibly slow

01:29:27.680 | in its evaluations of these repurposed drugs

01:29:32.920 | and it's been left to lots of other private activities

01:29:37.800 | of uneven quality and hence,

01:29:40.120 | that's why you have these big fights.

01:29:42.240 | Because the data are not solid,

01:29:44.240 | you're gonna have these big fights.

01:29:45.960 | - Yeah, but also, okay, forget the process of science here,

01:29:50.120 | the studies, not enough effort being put into the studies,

01:29:53.600 | just the way it's being communicated.

01:29:55.220 | - Yeah, no, like, horse-paced, I mean, come on.

01:29:57.540 | The FDA put a tweet out telling people who are like,

01:30:01.820 | they're taking ivermectin because they've heard

01:30:03.460 | good things about it and they're sick

01:30:04.740 | and they're desperate, and to call it horse-paced

01:30:07.580 | was just, that was terrible.

01:30:09.740 | - That was deeply irresponsible.

01:30:11.220 | My hope is grounded in the fact that young people

01:30:14.560 | see the bullshit of this, young PhD students,

01:30:17.780 | graduate students, young students in college,

01:30:20.200 | they see the less-than-stellar way

01:30:25.200 | that our scientific leaders

01:30:27.660 | and our political leaders are behaving

01:30:29.380 | and then the new generation will not repeat the mistakes

01:30:32.340 | of the past, that is my hope.

01:30:34.140 | 'Cause that's the cool thing I see about young people

01:30:37.460 | is they're good at detecting bullshit

01:30:40.260 | and they don't wanna be part of that.

01:30:42.380 | That's my hope in the space of science.

01:30:46.780 | Let me return to this idea

01:30:48.460 | of the Great Barrington Declaration,

01:30:50.600 | return to the beginning.

01:30:52.680 | So what are the basics?

01:30:54.760 | Can you describe what the Great Barrington Declaration is?

01:30:57.280 | What are some of the ideas in it?

01:30:58.640 | You mentioned focused protection.

01:31:00.880 | What are your concerns about lockdowns?

01:31:04.440 | Just paint the picture of this early proposal.

01:31:07.360 | - Sure, so the Great Barrington Declaration,

01:31:09.240 | first, why is it called Great Barrington Declaration?

01:31:11.320 | - It's such a great name.

01:31:13.300 | I mean, it's such an epic name,

01:31:16.820 | but the reason why it's called that is way less than epic.

01:31:19.880 | - It was because the conference,

01:31:23.920 | which is organized by Martin Kulldorff,

01:31:25.560 | who was a professor at Harvard University,

01:31:27.800 | biostatistician, he actually designed the safety system,

01:31:32.800 | the statistical system that the FDA uses

01:31:36.340 | for tracking vaccine safety.

01:31:38.960 | He and I had met previously just the summer before,

01:31:43.120 | that summer, and he invited me to come

01:31:46.460 | to this small conference where he was inviting me

01:31:48.760 | and Sunetra Gupta, who is a professor

01:31:51.600 | of theoretical epidemiology at Harvard,

01:31:53.400 | sorry, at Oxford University.

01:31:54.900 | And I mean, I jumped at the chance

01:31:58.920 | because I knew that Martin and Sunetra

01:32:01.040 | were both smarter than me, and it would be fun

01:32:03.280 | to talk about what the right strategy would be.

01:32:07.280 | On the drive in, I didn't know what the name

01:32:09.600 | of the town was, and I asked.

01:32:12.080 | They said it was Great Barrington.

01:32:13.600 | I had it in the back of my head.

01:32:16.040 | Martin and I arrived a little early,

01:32:17.640 | and we were writing an op-ed about some of the ideas,

01:32:20.220 | I hope we'll get to talk about very soon,

01:32:22.380 | about focus protection and the right strategy.

01:32:25.460 | And when Sunetra arrived, we realized

01:32:28.200 | we'd actually come basically to the same place

01:32:30.180 | about the right way to deal with the epidemic.

01:32:33.000 | And I thought, well, why don't we write something

01:32:37.980 | like the Port Huron Statement,

01:32:39.300 | is what I had in the back of my head.

01:32:41.500 | And I'm like, well, what's the name of this town again?

01:32:43.620 | It was Great Barrington.

01:32:44.740 | - Yeah, so it's not Barrington, it's Great Barrington.

01:32:47.980 | - Which is fantastic, right?

01:32:50.080 | - It's so over the top that it's perfect.

01:32:52.820 | It's literally like the Big Bang.

01:32:55.780 | There's something about these over-the-top,

01:32:57.500 | fun titles that just really deliver the power.

01:33:01.140 | - That's my main contribution,

01:33:02.500 | was the title, the name Great Barrington.

01:33:04.820 | But yeah, so it was kind of a,

01:33:07.380 | so the idea is actually, well, the title is great,

01:33:12.080 | and I think that it was written in a very stylish way.

01:33:15.220 | Like it's less than a page,

01:33:16.980 | you can go look online and read it.

01:33:18.740 | It's written for, not for scientists,

01:33:21.820 | but for the general public,

01:33:23.180 | so that people can understand the ideas really simply.

01:33:25.740 | But it is not actually a radical set of ideas.

01:33:29.500 | It actually represents the old pandemic plans

01:33:32.820 | that we've used for a century,

01:33:35.180 | dealing with other similar pandemics.

01:33:37.260 | And it's twofold.

01:33:40.460 | First, let me talk about the science it rests on,

01:33:43.420 | and then I'll talk about the plan.

01:33:45.100 | The science, actually,

01:33:45.940 | some of it we already talked about.

01:33:47.700 | There's this massive age gradient

01:33:49.640 | in the risk of COVID infection.

01:33:52.000 | Older people face much higher risk than younger people.

01:33:54.740 | The second bit of science is all,

01:33:57.420 | that's not controversial, right?

01:33:58.820 | Even if you think the IFR is 0.7 or 0.2,

01:34:02.020 | no matter what, everyone agrees on this age gradient.

01:34:05.180 | The second bit of science is also not controversial.

01:34:10.260 | The lockdown-focused policies that we followed

01:34:13.380 | have absolutely devastating consequences

01:34:16.380 | on the health of the population.

01:34:18.600 | Let me just give you some examples.

01:34:21.980 | And this was known in October of 2020 when we wrote it.

01:34:24.700 | So the UN was sounding alarms

01:34:28.060 | that there would be tens of millions of people

01:34:31.940 | who would starve as a consequence

01:34:34.120 | of the economic dislocation caused by the lockdowns.

01:34:37.420 | And that's come to pass.

01:34:38.740 | Hundreds of thousands of children

01:34:40.820 | in places like South Asia dead from starvation

01:34:43.420 | as a consequence of lockdowns.

01:34:46.840 | The priorities like the treatment of patients

01:34:53.860 | with tuberculosis in poor countries

01:34:57.900 | stopped because of lockdowns.

01:35:00.640 | Childhood vaccination of measles, mumps, rubella,

01:35:05.700 | DPT, diphtheria, so on, pertussis, tetanus,

01:35:09.380 | all those standard vaccination campaigns stopped.

01:35:13.300 | Tens of millions of children skipping these doses

01:35:16.820 | for diseases that are actually deadly for them.

01:35:19.420 | - Is there, just on a small tangent,

01:35:23.540 | is it well understood to you,

01:35:25.700 | what are the mechanisms that stop all those things

01:35:28.100 | because of lockdowns?

01:35:29.260 | Is it some aspect of supply chains?

01:35:31.300 | Is it just literally because hospital doors are closed?

01:35:35.140 | Is it because there's a disincentive to go outside

01:35:38.620 | by people even when they deeply need help?

01:35:41.100 | - It's all of the above.

01:35:42.660 | But a lot of those efforts,

01:35:44.340 | like especially those vaccination efforts,

01:35:45.860 | are funded and run by Western efforts.

01:35:49.900 | Like Gavi is a, I think it's a Gates-funded thing actually,

01:35:53.420 | that provides vaccines for millions of kids worldwide.

01:35:58.180 | And those efforts were scaled back.

01:36:01.220 | Malaria prevention efforts.

01:36:02.900 | So in the developing world,

01:36:04.780 | it was a devastating effect, these lockdowns.

01:36:08.220 | There was also direct effects.

01:36:09.300 | Like in India, the lockdowns, when they first instituted,

01:36:13.420 | there was an order that 10 million migrant workers

01:36:17.340 | who live in big cities, and they live hand to mouth,

01:36:20.180 | they buy coconuts, they sell the coconuts.

01:36:23.140 | With the money, they buy food for themselves

01:36:24.980 | and coconuts for the next day to sell,

01:36:26.980 | walk back to their villages or go back to their villages

01:36:32.620 | overnight.

01:36:33.580 | So 10 million people walking back to their villages

01:36:35.900 | or taking a train back.

01:36:36.900 | A thousand died en route.

01:36:39.100 | Overcrowded trains, dying essentially on the side

01:36:41.620 | of the road.

01:36:42.460 | I mean, it was absolutely inhumane policy.

01:36:44.700 | And the lockdowns there,

01:36:48.820 | it's actually, it's kind of like what's happened

01:36:52.420 | in the West as well, but it was so severe.

01:36:55.380 | There was a seroprevalence study done in Mumbai

01:36:58.540 | by a friend of mine at the University of Chicago.

01:37:00.180 | What he found was that in the slums of Mumbai,

01:37:03.500 | there were 70% seroprevalence in July or August of 2020,

01:37:08.500 | whereas in the rest of Mumbai, it was 20%.

01:37:11.340 | - Yeah. - Right?

01:37:12.300 | So it was incredibly unequal.

01:37:13.740 | The lockdowns protected the relatively well off

01:37:17.660 | and spread the disease among the poor.

01:37:20.700 | So that's in the developing world.

01:37:25.500 | In the developed world, the health effects of lockdowns

01:37:27.900 | were also quite bad, right?

01:37:31.100 | So we've talked already about isolation and depression.

01:37:34.160 | There was a study done in July of 2020

01:37:37.660 | that found that one in four young adults

01:37:41.180 | seriously considered suicide.

01:37:43.440 | Now, suicide rates haven't spiked up so much,

01:37:47.940 | but the depths of despair that would lead somebody

01:37:51.420 | to seriously consider suicide itself

01:37:53.420 | should be a source of great concern in public health.

01:37:57.820 | - Yeah, this is one of the troubling things

01:38:00.100 | about measuring wellbeing is we're okay

01:38:03.780 | at measuring death and suicide.

01:38:06.220 | We're not so good at measuring suffering.

01:38:09.020 | It's like people talk about maybe even Holodomor

01:38:14.020 | in under Stalin or the concentration camps with Hitler.

01:38:19.460 | We talk about deaths, but we don't talk about the suffering

01:38:23.940 | over periods of years by people living in fear,

01:38:27.740 | by people starving, psychological trauma

01:38:30.980 | that lasts a lifetime, all of those things.

01:38:33.900 | - I mean, and just to get back to that point,

01:38:36.460 | we closed schools, especially in blue states,

01:38:38.540 | we closed schools.

01:38:40.120 | Now, richer parents could send their kids

01:38:42.300 | to private schools, many of which stayed open

01:38:44.100 | even in the blue states.

01:38:45.080 | They could get pods, they could get tutors,

01:38:47.060 | but that's not true for poor and middle-class parents.

01:38:50.580 | And as a result, what we did is we took away

01:38:54.540 | life opportunities for kids.

01:38:56.060 | We tried to teach five-year-olds to read via Zoom

01:38:59.420 | in kindergarten, right?

01:39:01.740 | And the consequence actually, you think, okay,

01:39:05.240 | we can just make it up, but it's really difficult

01:39:07.260 | to make that up.

01:39:09.020 | There's a literature in health economics

01:39:11.840 | that shows that even relatively small disruptions

01:39:16.740 | in schooling can have lifelong consequences,

01:39:20.260 | negative consequences for kids, right?

01:39:22.500 | So they end up growing up poorer,

01:39:25.380 | they lead shorter lives and less healthy lives

01:39:28.860 | as a consequence, and that's what the literature now shows

01:39:31.620 | is likely to happen with the interruptions of schooling

01:39:34.940 | that we had in the United States.

01:39:36.700 | Many European countries actually managed to avoid this.

01:39:39.160 | There were, in the early days of the epidemic,

01:39:40.860 | great indications that children, first,

01:39:43.260 | were not very severely at risk from COVID itself,

01:39:46.660 | nor are they super spreaders.

01:39:48.120 | Schools were not the source of community spread,

01:39:51.620 | community spread spread the disease to schools,

01:39:54.860 | not the other way around.

01:39:56.100 | And if we can talk about the scientific base of that

01:39:59.300 | if you'd like, but that was pretty well known

01:40:01.720 | even in October.

01:40:02.780 | We closed hospitals in order to keep them

01:40:06.660 | available to COVID patients, but as a result,

01:40:10.980 | women skipped breast cancer screening.

01:40:14.140 | As a result, they are showing up with late-stage

01:40:16.980 | breast cancer that should have been picked up last year.

01:40:19.680 | Men and women skipped colon cancer screening,

01:40:21.900 | again, with later-stage disease that should have been

01:40:24.200 | picked up last year with earlier stage.

01:40:26.200 | For patients with diabetes, it's very important

01:40:29.840 | to have regular screening for blood sugar levels

01:40:33.200 | and sort of counseling for lifestyle improvement,

01:40:36.760 | and we skipped that.

01:40:38.160 | People stayed home with heart attacks

01:40:39.720 | and died at home with heart attacks.

01:40:41.540 | So you had this sort of wide range of medical

01:40:47.400 | and psychological harms that were being utterly ignored

01:40:51.920 | as a result of the lockdowns.

01:40:54.000 | - Plus there's the economic pain.

01:40:55.900 | So like you said, whatever is a good term

01:41:00.440 | for the non-laptop class, people would lose their jobs.

01:41:05.040 | Yes, there might be in the Western world

01:41:07.200 | support for them financially, but the big loss there

01:41:11.440 | that is perhaps correlated with the depression

01:41:14.360 | and suicide is loss of meaning, loss of hope for the future,

01:41:19.360 | loss of kind of a sense of stability,

01:41:23.000 | all the pride you have in being able to make money

01:41:26.000 | that allows you to pave your own way in the world,

01:41:32.800 | and yes, just having less money than you're used to,

01:41:35.600 | so your family, your kids are suffering,

01:41:37.520 | all those kinds of things.

01:41:39.120 | - There's again an economics literature on this,

01:41:41.320 | on deaths of despair it was called.

01:41:43.600 | 2009, there was the Great Recession,

01:41:45.920 | it led to an enormous uptick in overdose from drugs,

01:41:50.840 | suicidality, depression, as a result of the job losses

01:41:55.080 | that happened during the Great Recession.

01:41:57.560 | Well, that's happening again,

01:41:59.160 | like an enormous increase in drug overdoses.

01:42:02.700 | That's not an accident, that's a lockdown harm, right?

01:42:07.920 | Same thing with the job losses.

01:42:10.480 | The job losses, by the way, it's so interesting

01:42:12.920 | because the states that stayed open

01:42:15.640 | have had much, much lower unemployment

01:42:18.440 | than the states that stayed closed.

01:42:20.740 | The labor force participation rates declined by 3%,

01:42:23.520 | it's women that separated

01:42:25.440 | because they stayed home with their kids.

01:42:28.540 | We've reversed a generation of women,

01:42:31.440 | improving women's participation in the labor force.

01:42:37.120 | - Do you think it has to do with institutions

01:42:41.140 | that we mentioned that there was so much priority given

01:42:44.480 | or so much power given to maybe NIH

01:42:48.240 | versus other civilian leaders,

01:42:51.680 | or do people just not care about the economic pain?

01:42:54.420 | The leaders, I mean, 'cause to me it was obvious.

01:42:59.380 | I mean, probably it's just studying history.

01:43:03.520 | Whenever I listen to people on Twitter,

01:43:06.560 | on mainstream news, or just anything,

01:43:09.820 | I realize that's the very kind of top.

01:43:14.380 | The people that have a voice

01:43:17.240 | represent a tiny selection of people,

01:43:19.640 | and so whenever there's hard times,

01:43:21.760 | I always kind of think about the quiet, the voiceless,

01:43:26.760 | the quiet suffering of the tens of millions,

01:43:30.000 | of the hundreds of millions.

01:43:32.400 | Do political leaders not just give a damn?

01:43:36.400 | - I mean, I think it was actually a very odd ethical thing

01:43:39.560 | at the beginning of the pandemic,

01:43:41.280 | where if you brought up economic harms at all,

01:43:44.320 | you were seen as callous.

01:43:45.980 | Right, so I had a reporter call me up

01:43:50.080 | almost at the very beginning of the epidemic

01:43:51.600 | asking me about a very particular phenomenon.

01:43:56.600 | So he was anticipating a rise in child abuse

01:44:00.680 | because children were gonna be staying at home,

01:44:02.320 | child abuse is generally picked up at school.

01:44:05.000 | And that actually happened.

01:44:06.080 | So the report of child abuse dropped,

01:44:08.520 | but actual child abuse increased.

01:44:10.420 | 'Cause normally you pick up the child abuse at school

01:44:14.120 | and then you have the intervention, right?

01:44:16.120 | So yeah, so I was talking about,

01:44:17.880 | well, there's gonna be some economic harms

01:44:19.120 | and they're gonna have health consequences,

01:44:20.200 | but the economic harms matter.

01:44:21.760 | But he counseled me, and I think he had my best interest

01:44:26.760 | at heart, like if we were to put that in the story,

01:44:29.520 | I would be, I'd essentially be canceled.

01:44:31.760 | 'Cause what the narrative that arose in March of 2020

01:44:36.360 | is if you care about money at all,

01:44:39.740 | you're evil and crass, you must only care about lives.

01:44:43.760 | The problem with that narrative is that that money,

01:44:46.360 | which we're talking about, is actually lives of poor people.

01:44:51.280 | Right, when you throw 100 million people around the world

01:44:54.680 | into poverty, you're going to see enormous harm

01:44:58.100 | to their health, enormous increases in mortality.

01:45:01.300 | It is not immoral to think about that and worry about that

01:45:05.000 | in the context of this pandemic response.

01:45:07.320 | Our mind focused so much on COVID that it forgot

01:45:11.040 | that there are so many other public health priorities

01:45:13.160 | as well that need our attention desperately.

01:45:15.500 | - And this is the thing I sensed about San Francisco

01:45:21.240 | when I visited, I was thinking of moving there for a startup.

01:45:24.600 | This is the thing I'm really afraid of,

01:45:26.580 | especially if I have any effect on the world

01:45:30.400 | through a startup, is losing touch in this kind of way.

01:45:34.300 | That you mentioned the laptop class,

01:45:36.980 | living in this world where you're only concerned

01:45:40.280 | about this particular class of people.

01:45:44.400 | And also, perhaps early on in the pandemic,

01:45:48.920 | amongst the laptop class, there was a legitimate concern

01:45:51.480 | for health, like you're not sure how deadly this virus is.

01:45:55.960 | You're not sure who to listen to,

01:45:57.260 | so there's a real concern.

01:45:58.900 | And then at a certain point when the data starts coming in,

01:46:02.060 | you start becoming more and more detached from the data.

01:46:05.120 | You start caring less and less,

01:46:07.280 | and you start just swimming in the space of narratives,

01:46:10.740 | like existing in the space of narratives.

01:46:12.420 | And you have this narrative in San Francisco

01:46:15.420 | in the laptop class that you just are very proud

01:46:19.660 | that you know the truth, you're the sole possessors

01:46:22.360 | of the truth, you congratulate yourself on it,

01:46:25.140 | and you don't care what actually gigantic,

01:46:28.040 | detrimental effect it has on society,

01:46:29.820 | 'cause you're mostly fine.

01:46:33.100 | I'm so terrified of that.

01:46:36.860 | - Well, I think the antidote to that is just to remember.

01:46:39.740 | - You remember.

01:46:40.580 | - Yeah. - Yeah.

01:46:41.900 | - I don't think, you know, remember where you came from,

01:46:44.020 | and remember who you're doing this for.

01:46:46.380 | At the back of your head should always be,

01:46:47.980 | what's the purpose?

01:46:49.520 | Like, why am I here?

01:46:50.780 | What's the purpose of this?

01:46:52.540 | If the purpose is simply self-aggrandizement,

01:46:56.540 | then you should rethink,

01:46:57.660 | 'cause it'll just end up being a hollow life.

01:46:59.960 | - All of us will be forgotten in the end.

01:47:03.220 | Focus protection, the idea, the policy,

01:47:08.580 | what is focus protection?

01:47:09.980 | - Right, so I was saying that there's two scientific bases.

01:47:13.140 | So one is this steep age gradient,

01:47:15.180 | and the second is the existence of locked arms.

01:47:17.100 | Again, I think there's very little disagreement,

Jay Bhattacharya: The Case Against Lockdowns | Lex Fridman Podcast #254

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