1 00:00:15,276 --> 00:00:22,596 Speaker 1: Pushkin. Well, I'm going to take you back to two 2 00:00:22,636 --> 00:00:26,116 Speaker 1: thousand and five, the winter November December of two thousand 3 00:00:26,156 --> 00:00:29,076 Speaker 1: and five. In two thousand and five, the team was 4 00:00:29,196 --> 00:00:33,636 Speaker 1: just assembled to begin writing the National Plan. This is 5 00:00:33,676 --> 00:00:36,756 Speaker 1: a doctor named Carter Metscher. The team he's talking about 6 00:00:36,916 --> 00:00:39,516 Speaker 1: was in the Bush White House, and the national plan 7 00:00:40,276 --> 00:00:43,196 Speaker 1: was for what to do in a pandemic. Back in 8 00:00:43,236 --> 00:00:45,116 Speaker 1: two thousand and five, the White House asked Carter to 9 00:00:45,116 --> 00:00:49,196 Speaker 1: help answer a big question, how do you minimize disease 10 00:00:49,276 --> 00:00:52,596 Speaker 1: and death between the time a new virus starts to 11 00:00:52,676 --> 00:00:55,796 Speaker 1: spread and the time we might have a vaccine for it. 12 00:01:00,556 --> 00:01:03,716 Speaker 1: You know, an analogy we use is fire and thinking 13 00:01:03,796 --> 00:01:06,756 Speaker 1: of the exponential growth in a fire. Just like an 14 00:01:06,756 --> 00:01:11,796 Speaker 1: epidemic grows exponentially, a fire, grow exponentially. Slow a fire 15 00:01:11,876 --> 00:01:13,916 Speaker 1: and you give yourself a chance to put it out. 16 00:01:14,516 --> 00:01:17,916 Speaker 1: Slow a pathogen, and you buy time to save lives 17 00:01:18,556 --> 00:01:21,876 Speaker 1: because you can stock up on medical supplies, learn about 18 00:01:21,876 --> 00:01:24,476 Speaker 1: the new disease and how to treat it before lots 19 00:01:24,476 --> 00:01:28,436 Speaker 1: of people get sick. Carter Metcher really wasn't a political guy. 20 00:01:28,876 --> 00:01:31,716 Speaker 1: He was just an ICU doctor with a reputation for 21 00:01:31,796 --> 00:01:35,956 Speaker 1: thinking about problems in unusual ways. The White House had 22 00:01:35,996 --> 00:01:38,956 Speaker 1: called him up totally out of the blue, and at 23 00:01:38,956 --> 00:01:41,596 Speaker 1: that moment it was not at all obvious how to 24 00:01:41,596 --> 00:01:44,756 Speaker 1: slow a new virus, especially a virus that can spread 25 00:01:44,756 --> 00:01:51,316 Speaker 1: through people without symptoms. Public health experts still had the 26 00:01:51,396 --> 00:01:55,756 Speaker 1: year nineteen eighteen in mind. Back in nineteen eighteen, different 27 00:01:55,756 --> 00:01:58,356 Speaker 1: American cities that tried various ways to slow down the 28 00:01:58,396 --> 00:02:03,756 Speaker 1: Spanish flu. They close saloons and churches and ban large gatherings. 29 00:02:04,476 --> 00:02:10,156 Speaker 1: None of it seemed to make any difference. Carter teamed 30 00:02:10,236 --> 00:02:12,316 Speaker 1: up with another doctor brought in by the White House, 31 00:02:12,956 --> 00:02:17,556 Speaker 1: an oncologist named Richard Hatchett. Together, Carter and Richard went 32 00:02:17,596 --> 00:02:20,316 Speaker 1: back and looked more closely at what had actually happened 33 00:02:20,596 --> 00:02:26,796 Speaker 1: back in nineteen eighteen. And so what we began doing 34 00:02:26,996 --> 00:02:31,716 Speaker 1: was pulling daily newspaper accounts from a number of cities 35 00:02:32,356 --> 00:02:36,116 Speaker 1: to be able to identify when they were reporting their 36 00:02:36,156 --> 00:02:42,076 Speaker 1: first cases, when interventions were being implemented. The two doctors 37 00:02:42,076 --> 00:02:46,876 Speaker 1: were looking for what they called NPIs, or non pharmaceutical interventions, 38 00:02:47,836 --> 00:02:50,596 Speaker 1: the various things that cities had done to distance people. 39 00:02:51,796 --> 00:02:55,436 Speaker 1: They were also looking for death tolls. Back in nineteen eighteen, 40 00:02:55,596 --> 00:02:59,436 Speaker 1: Philadelphia had been an outlier in this regard. People died 41 00:02:59,476 --> 00:03:01,756 Speaker 1: at a greater rate in Philadelphia than in almost any 42 00:03:01,836 --> 00:03:06,676 Speaker 1: American city. And as we looked at Philadelphia closer, what 43 00:03:06,756 --> 00:03:10,116 Speaker 1: we realized is that they did eventually implement you know, 44 00:03:10,156 --> 00:03:13,916 Speaker 1: the the NPIs trying to slow transmission, but they implemented 45 00:03:13,916 --> 00:03:17,996 Speaker 1: those measures awfully late in the course of the disease outbreak. 46 00:03:19,076 --> 00:03:21,716 Speaker 1: After looking at Philadelphia, the doctors moved on to other 47 00:03:21,756 --> 00:03:26,596 Speaker 1: cities and found some really weird stuff. In Saint Louis, 48 00:03:26,596 --> 00:03:29,836 Speaker 1: for example, the death rate there was less than half 49 00:03:29,836 --> 00:03:33,956 Speaker 1: of Philadelphia's. What we found was that the cities that 50 00:03:34,196 --> 00:03:39,116 Speaker 1: implemented these interventions earlier had a lower mortality rate than 51 00:03:39,356 --> 00:03:43,316 Speaker 1: the cities that implemented these measures later. And no one 52 00:03:43,356 --> 00:03:46,916 Speaker 1: had done this before. You know, I don't, and I don't. 53 00:03:47,036 --> 00:03:51,316 Speaker 1: I'm not aware of anyone doing this before. Back when 54 00:03:51,356 --> 00:03:53,756 Speaker 1: I first heard about these White House doctors, I got 55 00:03:53,796 --> 00:03:55,636 Speaker 1: so interested that I set out to write a whole 56 00:03:55,636 --> 00:03:58,916 Speaker 1: book about them, called The Premonition. I just thought the 57 00:03:58,916 --> 00:04:02,316 Speaker 1: whole situation was wild. You had these two doctors, both 58 00:04:02,316 --> 00:04:04,556 Speaker 1: sort of shocked to find themselves in the White House, 59 00:04:04,916 --> 00:04:07,836 Speaker 1: trying to figure out how to save lives during a pandemic. 60 00:04:08,596 --> 00:04:12,596 Speaker 1: Then they earn themselves into amateur historians and find things 61 00:04:12,636 --> 00:04:16,316 Speaker 1: that no historian has ever really noticed. They also know 62 00:04:16,436 --> 00:04:19,516 Speaker 1: that their way out of their depth, so they recruit 63 00:04:19,556 --> 00:04:26,436 Speaker 1: a proper professor, a world class Harvard epidemiologist named Mark Lipsitch. Lipsitch, 64 00:04:26,676 --> 00:04:29,756 Speaker 1: Carter Mesher, and Richard Hatchett publish a paper that will 65 00:04:29,796 --> 00:04:33,276 Speaker 1: one day become famous because it shows in a rigorous, 66 00:04:33,316 --> 00:04:36,916 Speaker 1: academically respectable way that the death rates in the United 67 00:04:36,956 --> 00:04:40,716 Speaker 1: States back in nineteen eighteen had actually been very different 68 00:04:40,796 --> 00:04:44,396 Speaker 1: from city to city how many people died depending on 69 00:04:44,436 --> 00:04:48,276 Speaker 1: how quickly each city had done its social distancing. The 70 00:04:48,356 --> 00:04:51,236 Speaker 1: trick was to intervene before it was obvious that the 71 00:04:51,316 --> 00:04:54,836 Speaker 1: disease was present. Once you see the disease killing people, 72 00:04:54,996 --> 00:04:58,476 Speaker 1: it's too late. There's a lag after people get infected, 73 00:04:58,516 --> 00:05:01,676 Speaker 1: before they get sick, and another lag between the time 74 00:05:01,716 --> 00:05:05,236 Speaker 1: they get sick and the time they die. So MPIs 75 00:05:05,276 --> 00:05:08,876 Speaker 1: were sort of like a fire extinguisher, less useful after 76 00:05:08,876 --> 00:05:12,356 Speaker 1: the fire has reached the roof. If you're trying to 77 00:05:12,356 --> 00:05:15,636 Speaker 1: put out a fire or control a fire, it's much 78 00:05:15,676 --> 00:05:18,436 Speaker 1: easier to do that when the fire is contained to 79 00:05:18,796 --> 00:05:21,836 Speaker 1: for example, your stove you've got a pan with oil 80 00:05:21,876 --> 00:05:24,636 Speaker 1: in it that starts on fire, It's much easier to 81 00:05:24,756 --> 00:05:28,236 Speaker 1: contain or to suppress that fire if you act. Then 82 00:05:28,796 --> 00:05:30,836 Speaker 1: then if you wait for the entire kitchen to be 83 00:05:30,916 --> 00:05:33,676 Speaker 1: engulfed or half the house to be engulfed by the 84 00:05:33,756 --> 00:05:37,956 Speaker 1: time your kitchen's fully ablaze or half your house is ablaze, 85 00:05:38,196 --> 00:05:40,756 Speaker 1: that fire extinguisher is not going to be very effective. 86 00:05:42,436 --> 00:05:45,196 Speaker 1: Carter Metscher and Richard Hatchett wrote the Pandemic Plan for 87 00:05:45,236 --> 00:05:49,236 Speaker 1: the United States back in two thousand and six. Actually, 88 00:05:49,276 --> 00:05:51,956 Speaker 1: this part of the plan was officially written by the CDC, 89 00:05:52,156 --> 00:05:54,876 Speaker 1: but Carter and Richard basically created it with the help 90 00:05:54,916 --> 00:05:58,756 Speaker 1: of a mid level CDC employee named Lisa Coonin the 91 00:05:58,876 --> 00:06:01,836 Speaker 1: plan stress the importance of distancing people at the very 92 00:06:01,876 --> 00:06:05,956 Speaker 1: start of the pandemic to slow a virus down. And 93 00:06:06,036 --> 00:06:10,596 Speaker 1: so when a pandemic actually happened in two t Carter 94 00:06:10,716 --> 00:06:13,196 Speaker 1: Metcher was more shocked than just about anyone that the 95 00:06:13,316 --> 00:06:16,636 Speaker 1: United States ended up playing the role of Philadelphia and 96 00:06:16,756 --> 00:06:19,276 Speaker 1: all these other countries wound up playing the role of 97 00:06:19,316 --> 00:06:23,196 Speaker 1: Saint Louis because a lot of those countries had learned 98 00:06:23,236 --> 00:06:29,476 Speaker 1: pandemic strategy from US. I'm Michael Lewis and This is 99 00:06:29,476 --> 00:06:33,316 Speaker 1: Against the Rules, a show that explores unfairness in American 100 00:06:33,436 --> 00:06:36,956 Speaker 1: life by looking at what's happening to various characters in 101 00:06:36,996 --> 00:06:41,716 Speaker 1: American life. This season has been all about experts. Mostly, 102 00:06:41,756 --> 00:06:43,476 Speaker 1: it's been about how it's all our fault that we 103 00:06:43,516 --> 00:06:47,116 Speaker 1: don't use expertise better than we do. But this episode 104 00:06:47,196 --> 00:06:50,196 Speaker 1: is a bit different. It's about how much trouble experts 105 00:06:50,236 --> 00:06:52,636 Speaker 1: can cause when they exploit our desire for them to 106 00:06:52,676 --> 00:07:10,076 Speaker 1: tell us what we want to hear. I don't know 107 00:07:10,116 --> 00:07:11,796 Speaker 1: about you, but I feel as if I spent the 108 00:07:11,876 --> 00:07:15,276 Speaker 1: last two years living inside a casino. I've lost all 109 00:07:15,276 --> 00:07:18,196 Speaker 1: sense of time. March of twenty twenty feels to me 110 00:07:18,276 --> 00:07:21,676 Speaker 1: like about ten years ago. Back in March of twenty twenty, 111 00:07:21,916 --> 00:07:25,356 Speaker 1: only a handful of Americans had died of COVID, but 112 00:07:25,436 --> 00:07:29,156 Speaker 1: a surprising number of the early cases occurred in California's 113 00:07:29,156 --> 00:07:33,996 Speaker 1: Santa Clara County. On March the sixteenth, that county's health officer, 114 00:07:34,116 --> 00:07:37,636 Speaker 1: Sarah Cody, issued the country's first shelter in place order. 115 00:07:38,396 --> 00:07:41,596 Speaker 1: She closed schools and banned gatherings of more than fifty 116 00:07:41,596 --> 00:07:46,316 Speaker 1: people and so on. These new orders direct all individuals 117 00:07:46,396 --> 00:07:50,236 Speaker 1: to shelter at their place of residence and maintained social 118 00:07:50,276 --> 00:07:53,196 Speaker 1: distancing of at least six feet from any other person 119 00:07:53,236 --> 00:07:56,556 Speaker 1: when outside their residence. Doctor Cody was basically just following 120 00:07:56,556 --> 00:07:58,676 Speaker 1: the plan that the doctors had conceived in the Bush 121 00:07:58,676 --> 00:08:02,236 Speaker 1: White House, but she found herself way out on a limb. 122 00:08:02,836 --> 00:08:05,716 Speaker 1: At that moment, only two people in Santa Clara County 123 00:08:05,716 --> 00:08:09,716 Speaker 1: had died, and no one knew anything for sure, not 124 00:08:09,796 --> 00:08:12,996 Speaker 1: how many Americans were likely to get COVID, not how 125 00:08:12,996 --> 00:08:16,916 Speaker 1: many of those were likely to die. In March, I 126 00:08:17,036 --> 00:08:20,316 Speaker 1: heard about a study that was happening at Stanford where 127 00:08:20,316 --> 00:08:22,596 Speaker 1: we were going to try to measure the amount of 128 00:08:22,596 --> 00:08:27,236 Speaker 1: antibodies in our community. Malory Harris was a twenty three 129 00:08:27,316 --> 00:08:30,836 Speaker 1: year old first year graduate student in biology at Stanford University, 130 00:08:31,396 --> 00:08:34,876 Speaker 1: which happens to be in Santa Clara County. This study 131 00:08:34,916 --> 00:08:36,756 Speaker 1: that Stanford was about to do was going to try 132 00:08:36,796 --> 00:08:39,276 Speaker 1: to learn the most important thing to learn about COVID. 133 00:08:39,876 --> 00:08:44,836 Speaker 1: Malory jumped into help, and this would allow us to 134 00:08:44,876 --> 00:08:49,556 Speaker 1: figure out these important quantities about how the disease was spreading. 135 00:08:50,036 --> 00:08:52,876 Speaker 1: You need to know how transmissible and how lethal it is. 136 00:08:53,276 --> 00:08:58,756 Speaker 1: Right exactly, the Stanford study would wind up having seventeen authors. 137 00:08:59,316 --> 00:09:01,236 Speaker 1: A few of the names were known in the medical 138 00:09:01,276 --> 00:09:06,756 Speaker 1: research world, especially Jay Baticharia and Johnny Unidis. Eunidas was 139 00:09:06,796 --> 00:09:10,916 Speaker 1: a really big deal. His name. The study instant credibility. 140 00:09:12,076 --> 00:09:16,996 Speaker 1: So I had like this tiny volunteer thing that I did. 141 00:09:17,116 --> 00:09:20,716 Speaker 1: I like handed people their Amazon gift cards after they 142 00:09:20,756 --> 00:09:25,196 Speaker 1: got tested. And at the time, like everyone I knew 143 00:09:25,316 --> 00:09:28,836 Speaker 1: was volunteering on this study, like because it was that important, right, 144 00:09:28,876 --> 00:09:32,636 Speaker 1: Like we all wanted this information. And specifically the information 145 00:09:32,756 --> 00:09:35,556 Speaker 1: is how many people in Santa Clara County have been 146 00:09:35,596 --> 00:09:39,316 Speaker 1: infected with COVID, right exactly, because if you know that, 147 00:09:39,356 --> 00:09:41,756 Speaker 1: then you know how many people have died, so you 148 00:09:41,796 --> 00:09:44,316 Speaker 1: can start to guess what the fatality rate is of 149 00:09:44,356 --> 00:09:48,076 Speaker 1: this disease. Right, So there's that question. There are also 150 00:09:48,236 --> 00:09:52,396 Speaker 1: questions about, like how many people who get sick are 151 00:09:52,476 --> 00:09:56,236 Speaker 1: actually going to have symptoms at all and get detected 152 00:09:56,276 --> 00:09:59,676 Speaker 1: as cases. You know, that number would be helpful for 153 00:09:59,796 --> 00:10:04,596 Speaker 1: us in figuring out how transmissible is this virus? So 154 00:10:04,716 --> 00:10:08,036 Speaker 1: you were excited. I said that this was probably one 155 00:10:08,036 --> 00:10:10,596 Speaker 1: of the most important studies that I would ever be 156 00:10:10,636 --> 00:10:15,036 Speaker 1: a part of. The Stanford students had gathered blood samples 157 00:10:15,036 --> 00:10:18,916 Speaker 1: from thirty three hundred Santa Clara County residents. The Stanford 158 00:10:18,956 --> 00:10:23,756 Speaker 1: professors then tested the samples for COVID antibodies. The results 159 00:10:23,876 --> 00:10:28,556 Speaker 1: were sensational. Between fifty and eighty five times more people 160 00:10:28,596 --> 00:10:31,996 Speaker 1: in Santa Clara County than previously thought had been infected 161 00:10:31,996 --> 00:10:36,916 Speaker 1: with COVID. Thousands of people had apparently survived COVID without 162 00:10:36,916 --> 00:10:40,156 Speaker 1: ever knowing they had it, yet only two had died, 163 00:10:41,076 --> 00:10:49,996 Speaker 1: which suggested that the virus wasn't all that lethal. Welcome 164 00:10:49,996 --> 00:10:53,276 Speaker 1: back America. We have a tremendous guest, doctor John Ionidas. 165 00:10:54,196 --> 00:10:56,836 Speaker 1: At that moment, it really did feel possible that anyone 166 00:10:56,916 --> 00:11:02,076 Speaker 1: reacting to COVID was overreacting. Welcome You are a co 167 00:11:02,236 --> 00:11:06,756 Speaker 1: director the Meta Research Innovation Center at Stanford University. It's 168 00:11:06,796 --> 00:11:10,756 Speaker 1: now April nineteenth, two and twenty. Doctor Unidas is a 169 00:11:10,756 --> 00:11:14,076 Speaker 1: guest of Mark Levine, host of Life, Liberty and Levine 170 00:11:14,316 --> 00:11:17,156 Speaker 1: on Fox News. Tell me what was in your mind 171 00:11:17,196 --> 00:11:20,076 Speaker 1: when you wrote this piece and tell me why you 172 00:11:20,076 --> 00:11:23,356 Speaker 1: were right. I'm a person who's working with data, and 173 00:11:23,396 --> 00:11:26,796 Speaker 1: I'm also trained in infection diseases, so it was natural 174 00:11:26,876 --> 00:11:32,036 Speaker 1: that when the COVID nineteen pandemic evolved, it became a 175 00:11:32,076 --> 00:11:34,356 Speaker 1: top priority for me to understand what was going on. 176 00:11:35,396 --> 00:11:38,396 Speaker 1: Unidus is wearing a white sports jacket. He has the 177 00:11:38,396 --> 00:11:41,756 Speaker 1: air of a man who's descended onto TV. He's a doctor, 178 00:11:41,996 --> 00:11:45,156 Speaker 1: though he doesn't see patients. He's made his name exposing 179 00:11:45,156 --> 00:11:48,396 Speaker 1: the shoddiness of a lot of medical research. It became 180 00:11:48,516 --> 00:11:51,356 Speaker 1: very obvious to me that the evidence that we had 181 00:11:51,636 --> 00:11:55,436 Speaker 1: in the early phases of the pandemic was utterly unreliable. 182 00:11:56,796 --> 00:11:59,956 Speaker 1: Up until now, Unidas's published work has been kind of fun, 183 00:12:00,916 --> 00:12:03,916 Speaker 1: almost crowd pleasing. He once wrote a paper where he 184 00:12:03,956 --> 00:12:07,796 Speaker 1: took the first fifty ingredients in a cookbook and investigated 185 00:12:07,836 --> 00:12:11,236 Speaker 1: with medical researchers that about them. Half of the ingredients 186 00:12:11,236 --> 00:12:15,556 Speaker 1: supposedly cured cancer, half of them supposedly cause cancer, and 187 00:12:15,676 --> 00:12:18,996 Speaker 1: a bunch supposedly did both. I wanted to talk to 188 00:12:19,076 --> 00:12:22,316 Speaker 1: him for this episode, but he didn't return our email anyway. 189 00:12:22,596 --> 00:12:25,996 Speaker 1: In early April twenty twenty, Johnny Unidis might be the 190 00:12:25,996 --> 00:12:29,676 Speaker 1: most dangerous scientist alive to any medical researcher who uses 191 00:12:29,716 --> 00:12:34,436 Speaker 1: weak data to make some sensational claim, but he himself 192 00:12:34,556 --> 00:12:37,636 Speaker 1: is about to make a sensational claim that the world's 193 00:12:37,756 --> 00:12:41,436 Speaker 1: experts in communicable disease, have no idea what they're talking about. 194 00:12:41,996 --> 00:12:46,196 Speaker 1: It is just an astronomical error. And over the last 195 00:12:46,436 --> 00:12:50,516 Speaker 1: several weeks, we have started accumulating data that show that 196 00:12:50,676 --> 00:12:53,476 Speaker 1: there's far more people who are infected with this virus. 197 00:12:53,836 --> 00:12:57,156 Speaker 1: The vast majority of them don't even realize that they 198 00:12:57,156 --> 00:13:00,196 Speaker 1: have been infected. They are asymptomatic, they have no symptoms, 199 00:13:00,596 --> 00:13:02,996 Speaker 1: or they have very mild symptoms that they would not 200 00:13:03,076 --> 00:13:08,196 Speaker 1: even bother to do anything about. This was such an 201 00:13:08,196 --> 00:13:11,956 Speaker 1: important moment. No one could be totally certain about COVID, 202 00:13:12,316 --> 00:13:14,596 Speaker 1: how many Americans would get it or how many of 203 00:13:14,636 --> 00:13:19,356 Speaker 1: those might die. But Professor Johnny Unidas of Stanford University 204 00:13:19,796 --> 00:13:24,676 Speaker 1: sounded certain in the big picture, the risk is much 205 00:13:24,796 --> 00:13:27,476 Speaker 1: much much lower compared to what we thought before. I 206 00:13:27,516 --> 00:13:32,356 Speaker 1: think you and your team have developed a growing consensus. 207 00:13:32,396 --> 00:13:36,036 Speaker 1: I think among experts and certainly among the people. Take 208 00:13:36,076 --> 00:13:38,836 Speaker 1: care of the vulnerable, but let the rest of us 209 00:13:38,876 --> 00:13:44,556 Speaker 1: go free, that we have lives to live. No more 210 00:13:44,596 --> 00:13:48,116 Speaker 1: than ten thousand Americans will ever die of COVID. That's 211 00:13:48,116 --> 00:13:51,076 Speaker 1: the claim unit is put in print. He became a 212 00:13:51,116 --> 00:13:54,196 Speaker 1: regular on Fox News, a go to expert for other 213 00:13:54,276 --> 00:13:58,436 Speaker 1: media outlets. He and his co author Ja Bodicharia offered 214 00:13:58,476 --> 00:14:01,396 Speaker 1: their views to the Trump White House, and the Trump 215 00:14:01,396 --> 00:14:05,196 Speaker 1: White House listened to them. Scott Atlas, who ran Trump's 216 00:14:05,196 --> 00:14:08,076 Speaker 1: COVID response, wound up talking to Johnny Unidis and Jay 217 00:14:08,116 --> 00:14:14,676 Speaker 1: Bodicharia nearly every day for a year. America had a 218 00:14:14,716 --> 00:14:18,196 Speaker 1: pandemic plan, much of the rest of the world was 219 00:14:18,196 --> 00:14:22,716 Speaker 1: effectively using it, but we hesitated. And here was a 220 00:14:22,716 --> 00:14:27,236 Speaker 1: big reason why this Stanford study and these important experts 221 00:14:27,556 --> 00:14:30,356 Speaker 1: shouting to anyone who would listen that we didn't need 222 00:14:30,396 --> 00:14:34,556 Speaker 1: a plan. Why socially distance anyone? We were looking at 223 00:14:34,596 --> 00:14:40,836 Speaker 1: ten thousand American deaths max And just then in mid 224 00:14:40,836 --> 00:14:45,076 Speaker 1: April twenty twenty, that statement felt plausible. I remember talking 225 00:14:45,076 --> 00:14:46,916 Speaker 1: to a scientist friend of mine about it, who was 226 00:14:46,996 --> 00:14:51,236 Speaker 1: also sure that this Stanford professor had proven that COVID 227 00:14:51,316 --> 00:14:54,636 Speaker 1: was just way overblown and we were all way overreacting. 228 00:14:55,316 --> 00:14:57,836 Speaker 1: We were both totally pissed off that our kids schools 229 00:14:57,836 --> 00:15:01,636 Speaker 1: had closed. What neither of us knew was what other 230 00:15:01,676 --> 00:15:05,476 Speaker 1: people were making of the Stanford study. Here it is. 231 00:15:05,836 --> 00:15:09,956 Speaker 1: Here's my original posts from April nineteenth, twenty twenty. Someone 232 00:15:10,476 --> 00:15:14,556 Speaker 1: emailed me Andrew Gellman's a professor at Columbia University and 233 00:15:14,636 --> 00:15:19,476 Speaker 1: one of the country's leading statisticians. What I focused on 234 00:15:19,516 --> 00:15:25,116 Speaker 1: in that my first analysis was accounting for uncertainty in 235 00:15:25,196 --> 00:15:28,316 Speaker 1: the false positive and false negative rates of the test itself, 236 00:15:29,476 --> 00:15:35,036 Speaker 1: and there I concluded that their data are consistent with 237 00:15:35,196 --> 00:15:41,476 Speaker 1: a zero rate, in which everything is false. In plainer English, 238 00:15:41,676 --> 00:15:45,316 Speaker 1: the antibody test used by the Stanford professors was unreliable. 239 00:15:46,076 --> 00:15:49,156 Speaker 1: So unreliable that you could have just as easily concluded 240 00:15:49,196 --> 00:15:54,396 Speaker 1: that zero people tested by Stanford had actually had COVID. Therefore, 241 00:15:54,476 --> 00:15:57,636 Speaker 1: all the confirmed cases of COVID were in dead people. 242 00:15:58,636 --> 00:16:00,996 Speaker 1: You could take the same study and argue that COVID 243 00:16:01,116 --> 00:16:05,316 Speaker 1: was actually extremely lethal. Basically, Andrew Gellman showed that there 244 00:16:05,396 --> 00:16:08,236 Speaker 1: was no useful new evidence in anything in the Stanford 245 00:16:08,276 --> 00:16:14,756 Speaker 1: study was total garbage. I said that they owe an apology, 246 00:16:14,836 --> 00:16:18,196 Speaker 1: not just to us, but to Stamford. Stamford has a 247 00:16:18,196 --> 00:16:23,476 Speaker 1: world leading statistics department, and they could have easily got 248 00:16:23,516 --> 00:16:26,236 Speaker 1: on the phone with these people. They also have some 249 00:16:26,276 --> 00:16:31,036 Speaker 1: great epidemiologists at Stamford. Everyone makes mistakes. I don't think 250 00:16:31,036 --> 00:16:33,596 Speaker 1: they should apologize just because they screwed up. I think 251 00:16:33,636 --> 00:16:37,236 Speaker 1: they need to apologize because these were avoidable screwups. These 252 00:16:37,276 --> 00:16:39,636 Speaker 1: are the kind of screwups that happen if you want 253 00:16:39,676 --> 00:16:42,316 Speaker 1: to leap out with an exciting finding and you don't 254 00:16:42,316 --> 00:16:45,156 Speaker 1: look too carefully at what you might have done wrong. 255 00:16:45,996 --> 00:16:50,196 Speaker 1: But the authors of the Stanford study didn't apologize, at 256 00:16:50,276 --> 00:16:54,356 Speaker 1: least not the famous ones. They did the opposite. They 257 00:16:54,436 --> 00:16:57,556 Speaker 1: just kept doing these media appearances, going on podcast, etc. 258 00:16:58,196 --> 00:17:02,556 Speaker 1: Even though scientists were just so vehemently a gas at 259 00:17:02,556 --> 00:17:08,156 Speaker 1: what they've done. That's definitely science. Reporter at BuzzFeed she 260 00:17:08,276 --> 00:17:12,116 Speaker 1: covered the expert reaction to the Stanford study, which just 261 00:17:12,156 --> 00:17:17,316 Speaker 1: got louder and louder. It was the response that you 262 00:17:17,356 --> 00:17:20,396 Speaker 1: were detecting on Twitter really unusual for an academic paper. 263 00:17:20,756 --> 00:17:24,596 Speaker 1: It was unusual in that it was it was so heated, 264 00:17:24,636 --> 00:17:28,676 Speaker 1: and it came from just so many people all at once. 265 00:17:29,076 --> 00:17:31,956 Speaker 1: Help me understand. Is it unusual for a paper that 266 00:17:32,036 --> 00:17:37,196 Speaker 1: has seventeen authors to have these kind of problems? People 267 00:17:37,236 --> 00:17:41,196 Speaker 1: were struck by. Yes, the big number of co authors 268 00:17:41,196 --> 00:17:45,236 Speaker 1: on it, the sort of like caliber of the people 269 00:17:45,236 --> 00:17:48,156 Speaker 1: on its Johnny need is his name, you know, being 270 00:17:48,196 --> 00:17:51,076 Speaker 1: on there definitely made people take a second look. Does 271 00:17:51,116 --> 00:17:54,476 Speaker 1: it not strike you as strange that this person then 272 00:17:54,556 --> 00:17:58,956 Speaker 1: proceeds to produce a paper that's statistically shoddy and amplifies 273 00:17:58,996 --> 00:18:02,356 Speaker 1: its message in spite of criticism about its findings. I mean, 274 00:18:02,356 --> 00:18:04,356 Speaker 1: he seems like he's just done in this paper. Would 275 00:18:04,356 --> 00:18:07,556 Speaker 1: he's accused everybody else of doing for the previous fifteen years. 276 00:18:08,276 --> 00:18:11,036 Speaker 1: That is the APPS salute they on the head. Tragic 277 00:18:11,076 --> 00:18:15,436 Speaker 1: irony of this whole situation is this person who became 278 00:18:15,636 --> 00:18:20,796 Speaker 1: famous for calling out problems of scientific research is now 279 00:18:21,196 --> 00:18:25,876 Speaker 1: seemingly perpetuating those very same problems and not realizing the 280 00:18:26,036 --> 00:18:31,156 Speaker 1: disconnect between those two things. If I sound fixated on 281 00:18:31,196 --> 00:18:34,236 Speaker 1: this one person and on this one moment in time, 282 00:18:34,796 --> 00:18:37,836 Speaker 1: it's because I am right. Then the country had the 283 00:18:37,916 --> 00:18:42,796 Speaker 1: chance to agree on something important, exactly how dangerous COVID was, 284 00:18:43,636 --> 00:18:46,596 Speaker 1: and the answer was about to be available, thanks in 285 00:18:46,676 --> 00:18:50,436 Speaker 1: part to the work of two Australian PhD students, Leah 286 00:18:50,476 --> 00:18:55,076 Speaker 1: Moroney and Gideon Meyerwitz cats I saw all these people 287 00:18:55,396 --> 00:18:59,396 Speaker 1: saying wildly different things about the fatality rate of COVID. 288 00:19:00,236 --> 00:19:03,196 Speaker 1: That's Gideon. He and a colleague got the bright idea 289 00:19:03,196 --> 00:19:06,196 Speaker 1: of taking the dozens of studies made of COVID's lethality 290 00:19:06,276 --> 00:19:10,276 Speaker 1: all over the world and analyzing them to get as 291 00:19:10,316 --> 00:19:12,916 Speaker 1: a group. These studies suggested that COVID's death rate was 292 00:19:12,956 --> 00:19:16,836 Speaker 1: somewhere between half a percent and one percent. So, for example, 293 00:19:17,156 --> 00:19:20,916 Speaker 1: if half the American population caught COVID, somewhere between eight 294 00:19:20,996 --> 00:19:23,596 Speaker 1: hundred and seventy five thousand and one and a half 295 00:19:23,636 --> 00:19:27,116 Speaker 1: million people would die. They released the preprint of their 296 00:19:27,116 --> 00:19:31,236 Speaker 1: study May six, two twenty. We were cited by the 297 00:19:31,276 --> 00:19:35,076 Speaker 1: CDC mid last year in their planning scenarios. We've been 298 00:19:35,076 --> 00:19:38,276 Speaker 1: cited by the WHO and the EU, I think as well. 299 00:19:38,956 --> 00:19:41,556 Speaker 1: Lots of people looked at their work and said, yip, 300 00:19:41,836 --> 00:19:45,156 Speaker 1: looks about right, and it was about right. But right 301 00:19:45,196 --> 00:19:48,836 Speaker 1: away they came under attack from this Stanford professor named 302 00:19:48,916 --> 00:19:58,076 Speaker 1: Johnny Unidis who said that our paper had I think 303 00:19:58,396 --> 00:20:01,716 Speaker 1: the precise words were it was overtly wrong, and that 304 00:20:01,756 --> 00:20:05,516 Speaker 1: perhaps this was because we were PhD students. Right. How 305 00:20:05,596 --> 00:20:08,876 Speaker 1: common is the argument that you shouldn't listen to those 306 00:20:09,156 --> 00:20:13,596 Speaker 1: book because they're still working on their PhDs. I've never 307 00:20:13,636 --> 00:20:19,356 Speaker 1: heard it before. I guess people do PhDs at all 308 00:20:19,396 --> 00:20:21,436 Speaker 1: points in their life. Sometimes they have had very long 309 00:20:21,516 --> 00:20:24,756 Speaker 1: careers before their PhDs. So to say that someone is 310 00:20:25,196 --> 00:20:28,796 Speaker 1: only a student is a bit reductive. I don't want 311 00:20:28,796 --> 00:20:31,116 Speaker 1: to be reductive either, and I have found instances of 312 00:20:31,236 --> 00:20:34,636 Speaker 1: Unitas being gracious online with his critics. For example, in 313 00:20:34,716 --> 00:20:37,636 Speaker 1: a comment on the science blog Absolutely Maybe, he wrote, 314 00:20:37,916 --> 00:20:41,316 Speaker 1: and I quote, we need nuance and some distance to 315 00:20:41,436 --> 00:20:44,036 Speaker 1: understand the strong and weak points of the science that 316 00:20:44,076 --> 00:20:48,556 Speaker 1: we and our colleagues produce. This takes time, patience, and goodwill. 317 00:20:49,356 --> 00:20:51,476 Speaker 1: In the meanwhile, I consider my critics to be my 318 00:20:51,556 --> 00:20:55,956 Speaker 1: greatest benefactors. I am always grateful to them end quote. 319 00:20:56,716 --> 00:20:58,476 Speaker 1: Which is nice and all, but it was still weird 320 00:20:58,516 --> 00:21:01,676 Speaker 1: to Gideon. The unit is set out and attacked PhDs 321 00:21:01,676 --> 00:21:06,076 Speaker 1: in general. They are literally the leading experts in a 322 00:21:06,196 --> 00:21:10,076 Speaker 1: certain thing, and they're doing their PhD to uncovenue evidence 323 00:21:10,116 --> 00:21:16,876 Speaker 1: in that specific thing. These important Stanford professors were clinging 324 00:21:16,876 --> 00:21:20,156 Speaker 1: to the meaningless results of their screwed up study. Instead 325 00:21:20,156 --> 00:21:23,276 Speaker 1: of admitting they've been wrong, they tried to discredit those 326 00:21:23,316 --> 00:21:28,116 Speaker 1: who were right by comparing their academic degrees. You might 327 00:21:28,156 --> 00:21:30,156 Speaker 1: think that these Stanford guys would write about now be 328 00:21:30,236 --> 00:21:33,316 Speaker 1: laughed at by every respectable human being on the planet 329 00:21:33,356 --> 00:21:51,676 Speaker 1: and slink away in shame, But you'd be wrong. Okay, 330 00:21:51,876 --> 00:21:55,316 Speaker 1: let's pretend it's still early April twenty, and no one's 331 00:21:55,356 --> 00:21:58,516 Speaker 1: totally sure how lethal COVID will be. And for a 332 00:21:58,716 --> 00:22:01,516 Speaker 1: very brief period at the start of the pandemic, that 333 00:22:01,596 --> 00:22:04,356 Speaker 1: crucial moment when we needed to act, we were hobbled 334 00:22:04,396 --> 00:22:08,196 Speaker 1: by arguments and doubt. But one thing was becoming clear. 335 00:22:09,276 --> 00:22:14,356 Speaker 1: Lots of Americans were suddenly dying all at once, most 336 00:22:14,356 --> 00:22:18,836 Speaker 1: obviously in New York City. It was kind of an 337 00:22:18,836 --> 00:22:21,876 Speaker 1: eerie place. There were two people in rooms meant for 338 00:22:21,956 --> 00:22:25,156 Speaker 1: one person, and they were just motionless in debated bodies. 339 00:22:26,196 --> 00:22:29,796 Speaker 1: Jonathan Howard is a neurologist and psychologist at Bellevue Hospital 340 00:22:29,836 --> 00:22:32,836 Speaker 1: in New York City, but because he was a medical 341 00:22:32,876 --> 00:22:35,796 Speaker 1: doctor living in New York in late April twenty twenty, 342 00:22:36,076 --> 00:22:37,916 Speaker 1: he felt he had no choice but to try to 343 00:22:37,956 --> 00:22:41,196 Speaker 1: save these COVID patients. So you had a kind of 344 00:22:41,236 --> 00:22:45,076 Speaker 1: a ringside seat to the first kind of wave of carnage. 345 00:22:45,556 --> 00:22:48,676 Speaker 1: You saw how serious it was. Absolutely, I mean it 346 00:22:48,756 --> 00:22:51,516 Speaker 1: was you know, everyone in New York did. The sirens 347 00:22:51,516 --> 00:22:55,116 Speaker 1: were wailing throughout empty streets all of the time. You know, 348 00:22:55,156 --> 00:22:57,716 Speaker 1: most people didn't see it. To look at a hospital, 349 00:22:57,916 --> 00:23:02,676 Speaker 1: there was nothing special going on, but the entire hospital 350 00:23:02,876 --> 00:23:07,036 Speaker 1: was COVID and the turnover was massive. I would leave, 351 00:23:07,676 --> 00:23:10,996 Speaker 1: you know, at five or six in the evening. I'd 352 00:23:10,996 --> 00:23:12,836 Speaker 1: come back at you know, seven in the morning, and 353 00:23:13,156 --> 00:23:15,876 Speaker 1: half of my patients had been transferred to the ICU overnight, 354 00:23:15,956 --> 00:23:21,676 Speaker 1: and half of them, you know, were replaced, replaced because 355 00:23:21,676 --> 00:23:24,796 Speaker 1: they were dead. Had you ever seen anything like it? 356 00:23:25,076 --> 00:23:27,276 Speaker 1: There was nothing like it. Every five you know, the 357 00:23:27,436 --> 00:23:29,476 Speaker 1: sounds are going to stick with me as much as anything. 358 00:23:29,836 --> 00:23:32,716 Speaker 1: Every five minutes, you know, cold blue Airway team to 359 00:23:32,796 --> 00:23:35,276 Speaker 1: this bed. Five minutes later, airway team to this bed. 360 00:23:36,436 --> 00:23:39,356 Speaker 1: Inside of a month, more than ten thousand New Yorkers 361 00:23:39,356 --> 00:23:43,156 Speaker 1: had died. They hadn't just caught COVID. They caught COVID 362 00:23:43,316 --> 00:23:45,836 Speaker 1: early before doctors had a chance to learn how to 363 00:23:45,836 --> 00:23:49,276 Speaker 1: treat it. Here's a shocking fact. If you went into 364 00:23:49,276 --> 00:23:52,116 Speaker 1: an American hospital with severe COVID symptoms in March of 365 00:23:52,116 --> 00:23:55,796 Speaker 1: twenty twenty, you had a twenty five percent chance of dying. 366 00:23:56,916 --> 00:24:00,076 Speaker 1: Three months later, that number had fallen to five percent. 367 00:24:00,956 --> 00:24:03,436 Speaker 1: In the course of three months, your chance of surviving 368 00:24:03,476 --> 00:24:07,836 Speaker 1: severe COVID had gone way up because medicine had figured 369 00:24:07,836 --> 00:24:11,636 Speaker 1: stuff out. But in March of twenty twenty, medicine still 370 00:24:11,636 --> 00:24:17,476 Speaker 1: needed time to figure stuff out. Jonathan Howard wasn't watching 371 00:24:17,476 --> 00:24:21,036 Speaker 1: Fox News. He was watching Americans die of COVID. But 372 00:24:21,116 --> 00:24:22,916 Speaker 1: if it had more time on his hands, he could 373 00:24:22,916 --> 00:24:26,676 Speaker 1: have watched Johnny Unidas staying on message even though the 374 00:24:26,716 --> 00:24:29,556 Speaker 1: American death toll was already twice what he had forecasted 375 00:24:29,556 --> 00:24:32,916 Speaker 1: it would, ever be. The totality of the evidence points 376 00:24:32,916 --> 00:24:36,316 Speaker 1: to an infection that is very common, that typically is 377 00:24:36,396 --> 00:24:39,316 Speaker 1: very mild. Most people have no symptoms, they don't recognize 378 00:24:39,316 --> 00:24:44,196 Speaker 1: that they have the infection. I really respected him prior 379 00:24:44,236 --> 00:24:48,036 Speaker 1: to the pandemic. He is a big proponent of avidence 380 00:24:48,076 --> 00:24:52,916 Speaker 1: space medicine. Jonathan Howard had once considered Johnny unitas something 381 00:24:52,916 --> 00:24:55,636 Speaker 1: of a hero. They shared an interest in the weirdly 382 00:24:55,676 --> 00:25:00,076 Speaker 1: unscientific things that doctors didn't said. Jonathan had actually written 383 00:25:00,116 --> 00:25:04,636 Speaker 1: a book on medical misinformation, cognitive era and diagnostic mistakes. 384 00:25:04,676 --> 00:25:07,236 Speaker 1: It's called which sounds boring, but it grew out of 385 00:25:07,276 --> 00:25:11,316 Speaker 1: stuff that Jonathan saw doctors. So there's a doctor who 386 00:25:11,316 --> 00:25:13,436 Speaker 1: I trained with and knew pretty well and was very 387 00:25:13,476 --> 00:25:17,636 Speaker 1: friendly with who has since gone onto infamy as one 388 00:25:17,676 --> 00:25:21,556 Speaker 1: of the disinformation doesn't These are the twelve people most 389 00:25:21,556 --> 00:25:25,356 Speaker 1: responsible for spreading vaccine misinformation on Facebook. This is someone 390 00:25:25,436 --> 00:25:30,116 Speaker 1: you studied with. Yes, yes, we were professional colleagues and friendly, 391 00:25:30,516 --> 00:25:35,636 Speaker 1: and shortly after she graduated our residency programmed together. You 392 00:25:35,636 --> 00:25:38,236 Speaker 1: know again, she started posting this sort of stuff to Facebook, 393 00:25:38,316 --> 00:25:41,676 Speaker 1: and I became very fascinated after that point, as many 394 00:25:41,676 --> 00:25:45,636 Speaker 1: other people are, about why smart people can believe such 395 00:25:45,676 --> 00:25:48,836 Speaker 1: weird and wrong things. Because she's not stupid. She went 396 00:25:48,836 --> 00:25:51,716 Speaker 1: to Cornell, she went to mt she went to NYU residency. 397 00:25:52,076 --> 00:25:54,956 Speaker 1: She's smart. But how is it that she believes viruses 398 00:25:54,956 --> 00:25:57,436 Speaker 1: don't cause disease? Our coffee enemas. I'm not making that 399 00:25:57,556 --> 00:26:02,316 Speaker 1: up cure mental illness? Jonathan thought of the disinformation doesn't 400 00:26:02,316 --> 00:26:07,116 Speaker 1: as a type people inside medicine who basically rejected science. 401 00:26:08,196 --> 00:26:11,956 Speaker 1: Science could protect itself from them. These Stanford professors, though, 402 00:26:12,996 --> 00:26:16,156 Speaker 1: they were a totally different beast. They can speak in 403 00:26:16,516 --> 00:26:19,636 Speaker 1: great scientific jargons. And there's something about this, like there's 404 00:26:19,676 --> 00:26:22,796 Speaker 1: something that I find and I have to sort of 405 00:26:22,996 --> 00:26:25,916 Speaker 1: talk to my psychiatrists about this. But but personally offensive 406 00:26:26,516 --> 00:26:30,396 Speaker 1: about doctors who I feel spread misinformation and you think 407 00:26:30,436 --> 00:26:33,476 Speaker 1: they've had real effect. Oh? Absolutely. They are on the 408 00:26:33,516 --> 00:26:35,996 Speaker 1: Wall Street Journal, they are on Fox News. They have 409 00:26:36,076 --> 00:26:41,396 Speaker 1: testified in courts they have massive platforms. Ironically, whenever anyone 410 00:26:41,436 --> 00:26:45,756 Speaker 1: criticizes them, they say they have been silenced. After the 411 00:26:45,796 --> 00:26:49,036 Speaker 1: first wave of deaths in New York, Johnny Unidas raised 412 00:26:49,036 --> 00:26:52,996 Speaker 1: his loose forecast for American deaths from ten thousand to 413 00:26:53,156 --> 00:26:56,876 Speaker 1: forty thousand. But he never said he'd been wrong, and 414 00:26:56,996 --> 00:26:59,356 Speaker 1: at no point did he grapple with the new evidence. 415 00:27:00,196 --> 00:27:02,396 Speaker 1: When the evidence started to make him look like a fool, 416 00:27:03,076 --> 00:27:06,756 Speaker 1: he just began to attack the evidence. Same things, like 417 00:27:07,276 --> 00:27:11,516 Speaker 1: people didn't die of COVID, died with COVID. It sounds 418 00:27:11,756 --> 00:27:14,076 Speaker 1: incomprehensible as those words are coming out of my mouth. 419 00:27:14,316 --> 00:27:18,076 Speaker 1: But this idea that death certificates can't be trusted, and 420 00:27:18,236 --> 00:27:20,636 Speaker 1: even he implied in one article that doctors have a 421 00:27:20,636 --> 00:27:24,396 Speaker 1: financial incentive to put COVID on death certificates. I can 422 00:27:24,476 --> 00:27:27,556 Speaker 1: remember hearing too that people weren't really dying of COVID. 423 00:27:27,716 --> 00:27:30,036 Speaker 1: The people who were dying were going to die anyway. 424 00:27:30,436 --> 00:27:33,716 Speaker 1: That's a claim that still exists today. Most people I 425 00:27:33,756 --> 00:27:38,036 Speaker 1: saw who died were older and unhealthy, but they were 426 00:27:38,076 --> 00:27:41,676 Speaker 1: sixty year olds with diabetes. Sixty year olds with diabetes. Sure, 427 00:27:41,716 --> 00:27:43,796 Speaker 1: it's not unheard of that they die of a heart attack, 428 00:27:43,836 --> 00:27:46,716 Speaker 1: but they don't die in mass in large numbers. At 429 00:27:46,716 --> 00:27:49,396 Speaker 1: the same time, I saw thirty year olds diet. The 430 00:27:49,476 --> 00:27:51,436 Speaker 1: youngest eyes personally saw die at that time was a 431 00:27:51,516 --> 00:27:54,236 Speaker 1: twenty three year old, So it was very clear that 432 00:27:54,276 --> 00:27:58,036 Speaker 1: something different was happening. We were at the start of 433 00:27:58,036 --> 00:28:02,836 Speaker 1: an ugly new war, not just on COVID, on reality 434 00:28:03,436 --> 00:28:06,556 Speaker 1: and in this war, Doctor John you needus acted like 435 00:28:06,596 --> 00:28:10,116 Speaker 1: the general of one of the armies. He's aimed frontline 436 00:28:10,156 --> 00:28:14,076 Speaker 1: doctors for killing their patients. On a podcast, he said 437 00:28:14,516 --> 00:28:16,836 Speaker 1: a lot of lives were lost at the very beginning 438 00:28:17,316 --> 00:28:20,476 Speaker 1: because of doctors not knowing how to use mechanical ventilation, 439 00:28:21,276 --> 00:28:25,436 Speaker 1: just going crazy and relating people who too early did 440 00:28:25,476 --> 00:28:27,996 Speaker 1: not have to be intubated. So probably we lost a 441 00:28:27,996 --> 00:28:31,316 Speaker 1: lot of lives, if you know, we were sort of 442 00:28:31,316 --> 00:28:34,636 Speaker 1: too aggressive early on, so be it. Let the statistics 443 00:28:34,636 --> 00:28:37,836 Speaker 1: say that. But the multiple studies have shown that that's 444 00:28:37,836 --> 00:28:40,916 Speaker 1: not to be the case. So rather than sort of say, 445 00:28:41,196 --> 00:28:43,996 Speaker 1: you know what, I underestimated COVID, I got it wrong. 446 00:28:44,116 --> 00:28:46,356 Speaker 1: Let me try to do better from here out, he 447 00:28:46,676 --> 00:28:50,876 Speaker 1: kind of threw frontline doctors under the bus. In a 448 00:28:50,956 --> 00:28:54,036 Speaker 1: previous episode this season, we heard about a COVID patient 449 00:28:54,076 --> 00:28:56,116 Speaker 1: whose family tried to refuse to allow him to be 450 00:28:56,116 --> 00:29:00,236 Speaker 1: treated because they all knew that doctors were killing patients 451 00:29:00,356 --> 00:29:03,436 Speaker 1: by intubating them. Those people didn't just pluck that bit 452 00:29:03,476 --> 00:29:08,356 Speaker 1: of misinformation from some conspiracy theorists. It had the endorsement 453 00:29:08,436 --> 00:29:12,916 Speaker 1: of a professor or of medicine at Stanford University. You 454 00:29:12,996 --> 00:29:17,116 Speaker 1: know this paper, very ironically entitled Forecasting for COVID nineteen 455 00:29:17,196 --> 00:29:22,076 Speaker 1: has failed, in which he spoke about empty hospital wards, 456 00:29:23,116 --> 00:29:27,236 Speaker 1: that most hospital awards were empty, expecting a tsunami of 457 00:29:27,276 --> 00:29:30,876 Speaker 1: disease that never came, writing as if the pandemic was 458 00:29:30,996 --> 00:29:35,116 Speaker 1: over at that point. Essentially, we begin tonight with that 459 00:29:35,316 --> 00:29:37,916 Speaker 1: grim new milestone, as the nation tries to stop the 460 00:29:37,956 --> 00:29:41,876 Speaker 1: spread of the coronavirus. Total US cases have now topped 461 00:29:41,916 --> 00:29:45,636 Speaker 1: five million since this pandemic began, a grueling eight months ago, 462 00:29:45,916 --> 00:29:49,036 Speaker 1: in which nearly one hundred and sixty three thousand Americans 463 00:29:49,036 --> 00:29:51,596 Speaker 1: have died and our way of life has been altered. 464 00:29:53,156 --> 00:29:55,716 Speaker 1: By August twenty twenty, the picture of COVID was a 465 00:29:55,716 --> 00:29:59,356 Speaker 1: lot clearer, and it obviously didn't look anything like the 466 00:29:59,396 --> 00:30:01,916 Speaker 1: picture painted by the experts who claimed to know more 467 00:30:01,996 --> 00:30:14,556 Speaker 1: than what we could see right under our noses. There's 468 00:30:14,596 --> 00:30:18,476 Speaker 1: now this dreadful league table for COVID. It just came 469 00:30:18,476 --> 00:30:21,036 Speaker 1: out in the Lancet as I was reporting this episode 470 00:30:21,756 --> 00:30:27,316 Speaker 1: Pandemic Preparedness and COVID nineteen. The studies authors ranked countries 471 00:30:27,356 --> 00:30:30,556 Speaker 1: by their ability to prevent COVID infections. In the rich 472 00:30:30,596 --> 00:30:34,396 Speaker 1: country division. The United States ranked second to last, just 473 00:30:34,516 --> 00:30:38,676 Speaker 1: above Argentina. In the lower weight class division. Here are 474 00:30:38,716 --> 00:30:42,436 Speaker 1: some of the countries that have outperformed the United States. Yemen, 475 00:30:43,156 --> 00:30:48,276 Speaker 1: Saudi Arabia, Zimbabwe. The deaths are bad enough, but they 476 00:30:48,316 --> 00:30:51,596 Speaker 1: speak to a bigger problem. I once had this realization 477 00:30:51,636 --> 00:30:53,836 Speaker 1: because I was riding in a taxi to the airport 478 00:30:53,996 --> 00:30:56,276 Speaker 1: and the taxi driver is listening to one of those 479 00:30:56,316 --> 00:31:01,196 Speaker 1: holy radio stations. That's Andrew Gelman, again, professor of Statistics 480 00:31:01,196 --> 00:31:04,356 Speaker 1: and political science at Columbia. The voice on the Holy 481 00:31:04,476 --> 00:31:08,196 Speaker 1: roller radio station started telling him how we're all sinners 482 00:31:08,756 --> 00:31:12,956 Speaker 1: and we have to accept that added the human rights, 483 00:31:13,476 --> 00:31:16,716 Speaker 1: and it really resonated with me that we are all 484 00:31:16,796 --> 00:31:21,156 Speaker 1: sinners in that sense that we make mistakes and accepting 485 00:31:21,196 --> 00:31:24,116 Speaker 1: that you are as sinner, and like, that's kind of 486 00:31:24,116 --> 00:31:27,596 Speaker 1: the first step. And then the next step is is 487 00:31:27,676 --> 00:31:32,716 Speaker 1: that you say, like how can I learn from my mistakes? 488 00:31:33,116 --> 00:31:37,036 Speaker 1: I find it very frustrating at all levels when people 489 00:31:37,236 --> 00:31:40,636 Speaker 1: don't admit their mistakes. It just makes me want to scream. 490 00:31:41,556 --> 00:31:44,316 Speaker 1: I have a hard time imagine you screaming, but I 491 00:31:44,356 --> 00:31:50,716 Speaker 1: believe you the ice cream. I've been known to scream. 492 00:31:50,916 --> 00:31:54,356 Speaker 1: That's what's its steak, our ability to learn so we 493 00:31:54,396 --> 00:31:57,436 Speaker 1: don't wind up going through this all over again. And 494 00:31:57,516 --> 00:32:00,756 Speaker 1: there's one very obvious thing that we might have learned 495 00:32:00,996 --> 00:32:05,876 Speaker 1: from a pandemic. It's now killed one million Americans. It's 496 00:32:05,876 --> 00:32:08,676 Speaker 1: what they didn't learn back in nineteen eighteen, and with 497 00:32:08,836 --> 00:32:13,956 Speaker 1: card measure helped to figure out what saved people and 498 00:32:13,996 --> 00:32:25,476 Speaker 1: what killed them. Right now, the city of Miami has 499 00:32:25,516 --> 00:32:29,836 Speaker 1: more than triple the death rate of San Francisco. Why 500 00:32:30,196 --> 00:32:32,276 Speaker 1: the red counties in California that followed the lead of 501 00:32:32,276 --> 00:32:35,396 Speaker 1: the Stanford professors and revolted against public health rules early 502 00:32:35,436 --> 00:32:38,476 Speaker 1: in the pandemic, Well now they have double and triple 503 00:32:38,556 --> 00:32:40,876 Speaker 1: the death rates of the blue counties that more or 504 00:32:40,956 --> 00:32:44,316 Speaker 1: less complied with the rules. The United States had a 505 00:32:44,356 --> 00:32:47,076 Speaker 1: pandemic plan that advised city and county health officials to 506 00:32:47,156 --> 00:32:50,516 Speaker 1: intervene early and distance people when a new pathogen started 507 00:32:50,556 --> 00:32:53,676 Speaker 1: to move through the population. Did the cities and counties 508 00:32:53,796 --> 00:32:57,076 Speaker 1: that more or less did this actually save lives? I mean, 509 00:32:57,076 --> 00:32:58,716 Speaker 1: it looks to me like they did, But what do 510 00:32:58,836 --> 00:33:01,996 Speaker 1: I know? The trouble is that no one seems to know. 511 00:33:03,476 --> 00:33:06,156 Speaker 1: Do you think we've learned in this country how to 512 00:33:06,236 --> 00:33:09,956 Speaker 1: better respond to the next one? I think, if anything, 513 00:33:10,356 --> 00:33:15,036 Speaker 1: you know, we probably have regressed. Carter Mesher again, who 514 00:33:15,076 --> 00:33:20,156 Speaker 1: created the pandemic strategy, We didn't really use that. People 515 00:33:20,556 --> 00:33:23,436 Speaker 1: haven't looked at what's happening and said, oh, that worked 516 00:33:23,436 --> 00:33:26,436 Speaker 1: and that didn't and in a sensible way that enables 517 00:33:26,516 --> 00:33:31,756 Speaker 1: us to move forward more intelligently. They haven't done that. No, 518 00:33:31,916 --> 00:33:34,676 Speaker 1: I you know, I don't think. I don't think we have. 519 00:33:35,436 --> 00:33:38,796 Speaker 1: It's not just an opinion this. There's data to support 520 00:33:38,836 --> 00:33:42,116 Speaker 1: the point. For it turns out that not only did 521 00:33:42,116 --> 00:33:44,356 Speaker 1: the United States do a worse job than other countries 522 00:33:44,396 --> 00:33:47,716 Speaker 1: at preventing disease at the start of the pandemic compared 523 00:33:47,756 --> 00:33:52,396 Speaker 1: with other countries, we're actually doing even worse now despite 524 00:33:52,436 --> 00:33:55,516 Speaker 1: having better access to vaccines than just about everyone else. 525 00:33:56,476 --> 00:34:00,036 Speaker 1: So you look from July first, twenty twenty one to 526 00:34:00,036 --> 00:34:03,356 Speaker 1: today and what happened in those countries, Well, in the 527 00:34:03,436 --> 00:34:06,556 Speaker 1: United States, we've seen twelve hundred dusts per million. If 528 00:34:06,556 --> 00:34:08,796 Speaker 1: you take a look at the UK, they're at about 529 00:34:08,836 --> 00:34:11,556 Speaker 1: six hundred deaths per million. They're half the death rate 530 00:34:11,596 --> 00:34:14,396 Speaker 1: in the UK during that period of time across those 531 00:34:14,436 --> 00:34:17,276 Speaker 1: waves is half the death rate of the US. If 532 00:34:17,276 --> 00:34:19,916 Speaker 1: we look at Canada, Canada had a death rate of 533 00:34:19,916 --> 00:34:22,796 Speaker 1: about three hundred deaths per million, so about a quarter 534 00:34:22,956 --> 00:34:25,516 Speaker 1: of what the United States has. If we take a 535 00:34:25,596 --> 00:34:29,796 Speaker 1: look at Japan, they had about one hundred deaths per 536 00:34:29,836 --> 00:34:32,276 Speaker 1: million over that same period of time that the United 537 00:34:32,276 --> 00:34:37,796 Speaker 1: States had twelve hundred deaths per million. By now, it 538 00:34:37,876 --> 00:34:40,116 Speaker 1: just sounds like numbers. So let's do something to make 539 00:34:40,156 --> 00:34:43,916 Speaker 1: it sound like something else. Think of one person you 540 00:34:44,076 --> 00:34:49,836 Speaker 1: loved who's died, a single person, take a moment. I'll 541 00:34:49,876 --> 00:34:58,796 Speaker 1: do the same. Now, multiply that feeling by hundreds of thousands. 542 00:35:00,036 --> 00:35:02,996 Speaker 1: Our society is still failing its people in ways that 543 00:35:03,036 --> 00:35:07,196 Speaker 1: other societies are not, and there's a reason for that failure. 544 00:35:07,996 --> 00:35:25,356 Speaker 1: These other societies or learning we're not. When you all 545 00:35:25,396 --> 00:35:28,156 Speaker 1: were looking back at nineteen eighteen, it seems kind of, oh, 546 00:35:28,196 --> 00:35:31,556 Speaker 1: it's quaint that they didn't understand. They didn't understand what happened, 547 00:35:31,796 --> 00:35:33,876 Speaker 1: and now you can come in and you can understand 548 00:35:33,956 --> 00:35:36,476 Speaker 1: for them what they didn't understand at the time. And 549 00:35:36,596 --> 00:35:40,316 Speaker 1: we would never do such a thing. And we're exactly, 550 00:35:40,596 --> 00:35:44,316 Speaker 1: you know, we're exactly where they were that we don't 551 00:35:44,516 --> 00:35:47,316 Speaker 1: This stuff is happening and no one's learned anything. And 552 00:35:47,476 --> 00:35:49,396 Speaker 1: I just don't, you know, I can understand what their 553 00:35:49,476 --> 00:35:53,796 Speaker 1: excuse was. I don't know what our excuse is. Yeah, 554 00:35:53,836 --> 00:35:59,276 Speaker 1: I don't don't. I don't either. I don't either, I 555 00:35:59,316 --> 00:36:02,876 Speaker 1: don't know. One of my takeaways from Carter Mesher and 556 00:36:02,956 --> 00:36:07,556 Speaker 1: this entire season is the importance of I don't know, 557 00:36:08,476 --> 00:36:12,396 Speaker 1: and how it's a sign of true expertise, but how 558 00:36:12,436 --> 00:36:16,516 Speaker 1: hard for an expert it is to say, especially as 559 00:36:16,556 --> 00:36:19,356 Speaker 1: they age and grow used to being viewed as the 560 00:36:19,436 --> 00:36:25,796 Speaker 1: person who knows. Which brings us back to Malory Harris, 561 00:36:26,356 --> 00:36:28,516 Speaker 1: who had come to Stanford as a twenty three year 562 00:36:28,556 --> 00:36:32,636 Speaker 1: old graduate student. She'd arrived thinking she understood the rules 563 00:36:32,636 --> 00:36:37,116 Speaker 1: of expertise. Her job, she thought, was to accumulate evidence, 564 00:36:37,636 --> 00:36:40,316 Speaker 1: to ask questions of it, but to let the answers 565 00:36:40,356 --> 00:36:44,116 Speaker 1: fall where they may. In March of twenty twenty, I 566 00:36:44,156 --> 00:36:47,156 Speaker 1: started to see that that's not actually how this works. 567 00:36:48,316 --> 00:36:51,676 Speaker 1: She watched her superiors, John Unidas and Jay Batachario and 568 00:36:51,756 --> 00:36:54,596 Speaker 1: a handful of other Stanford scientists how they went on 569 00:36:54,676 --> 00:36:58,316 Speaker 1: TV and sounded certain about things that they either could 570 00:36:58,316 --> 00:37:02,756 Speaker 1: not know or were entirely wrong about. So I had 571 00:37:02,796 --> 00:37:07,756 Speaker 1: it explained to me by a more senior academic that 572 00:37:08,276 --> 00:37:11,716 Speaker 1: at the beginning of COVID, journalists were looking for people 573 00:37:11,916 --> 00:37:15,196 Speaker 1: who would say either that this is the flu or 574 00:37:15,196 --> 00:37:19,356 Speaker 1: that it's ebola, and those were the only scientists who 575 00:37:19,436 --> 00:37:22,796 Speaker 1: you would hear from, even though the majority of the 576 00:37:22,836 --> 00:37:26,036 Speaker 1: scientific community was like, it's somewhere in this range and 577 00:37:26,076 --> 00:37:28,916 Speaker 1: there's a lot of uncertainty here. But the people who 578 00:37:28,956 --> 00:37:31,916 Speaker 1: would get platformed were the people who were making kind 579 00:37:31,956 --> 00:37:36,716 Speaker 1: of the most sensational and the most certain claims. Malory 580 00:37:36,796 --> 00:37:39,676 Speaker 1: was meant to be studying biology with these guys. Now 581 00:37:39,676 --> 00:37:45,916 Speaker 1: she was just studying these guys. And I read about, 582 00:37:45,996 --> 00:37:49,916 Speaker 1: you know, what happened with tobacco, and who were the 583 00:37:49,956 --> 00:37:54,036 Speaker 1: scientists who were attacking the link between smoking and cancer 584 00:37:54,436 --> 00:37:57,356 Speaker 1: and what happened with climate change? And why, like, even 585 00:37:57,356 --> 00:37:59,836 Speaker 1: though we knew for decades before I was even born, 586 00:37:59,996 --> 00:38:02,196 Speaker 1: that this was going to be a problem, why weren't 587 00:38:02,236 --> 00:38:04,556 Speaker 1: we seeing action, and who were the scientists who were 588 00:38:04,596 --> 00:38:06,916 Speaker 1: helping to delay that right? And like what happened with 589 00:38:06,956 --> 00:38:09,956 Speaker 1: AIDS denialism. Why did you have the small group of 590 00:38:09,956 --> 00:38:13,396 Speaker 1: people who were saying that HIV doesn't cause AIDS who 591 00:38:13,556 --> 00:38:16,836 Speaker 1: kept getting platformed even when they didn't really have solid 592 00:38:16,836 --> 00:38:20,676 Speaker 1: evidence for that. Did you see any patterns? Oh? Yeah. 593 00:38:21,436 --> 00:38:23,836 Speaker 1: One thing that had happened a lot was scientists claiming 594 00:38:23,876 --> 00:38:26,436 Speaker 1: they were experts when in fact they'd wandered pretty far 595 00:38:26,596 --> 00:38:30,236 Speaker 1: from their area of genuine expertise. And how hard it 596 00:38:30,316 --> 00:38:33,516 Speaker 1: was for the general public to see the difference. For example, 597 00:38:33,556 --> 00:38:36,476 Speaker 1: you might have made your name debunking bad medical research, 598 00:38:36,876 --> 00:38:38,876 Speaker 1: but it didn't mean you had the first clue about 599 00:38:38,956 --> 00:38:42,916 Speaker 1: virology or disease control. Another pattern was the way people 600 00:38:42,956 --> 00:38:47,356 Speaker 1: seemingly devoted to reason became wedded to positions, That is, 601 00:38:47,636 --> 00:38:51,396 Speaker 1: they didn't change their minds with the evidence. One day, 602 00:38:51,436 --> 00:38:54,716 Speaker 1: Malory looked up and saw that her Stanford professors were 603 00:38:54,756 --> 00:38:58,716 Speaker 1: advising a governor to do things like keep schools from 604 00:38:58,836 --> 00:39:04,116 Speaker 1: enforcing mass mandates. We're talking about Florida now seeing record 605 00:39:04,236 --> 00:39:08,796 Speaker 1: numbers of new cases. Despite that the governor, Rhonda Santacis, 606 00:39:08,916 --> 00:39:12,596 Speaker 1: is fighting to ban mass in schools, and that fight 607 00:39:12,796 --> 00:39:18,876 Speaker 1: is escalating very dramatically today. Malory's family lived in Florida, 608 00:39:18,956 --> 00:39:22,796 Speaker 1: and her own professors were now threatening their lives. She 609 00:39:22,916 --> 00:39:25,636 Speaker 1: finally broke and wrote an open letter in the Stanford 610 00:39:25,676 --> 00:39:31,236 Speaker 1: School newspaper in which she called out her superiors. For 611 00:39:31,316 --> 00:39:34,356 Speaker 1: the past year and a half, I have carefully followed 612 00:39:34,396 --> 00:39:39,116 Speaker 1: public health recommendations and university guidelines to protect those around me, 613 00:39:39,516 --> 00:39:43,396 Speaker 1: including the people mentioned here, even as they work to 614 00:39:43,596 --> 00:39:48,916 Speaker 1: undo these protections for others. That's what bravery sounds like. 615 00:39:50,716 --> 00:39:55,596 Speaker 1: I'm like a shy person actually, so you know, being 616 00:39:55,756 --> 00:40:01,636 Speaker 1: public is really like intimidating for me. I didn't know 617 00:40:03,796 --> 00:40:08,476 Speaker 1: if it could harm me professionally. I just really felt 618 00:40:08,516 --> 00:40:12,876 Speaker 1: like I needed to say something. There's a question hidden 619 00:40:12,916 --> 00:40:17,756 Speaker 1: in her words, why me let me take a moment here, 620 00:40:17,836 --> 00:40:20,876 Speaker 1: because this is our final episode and it echoes a 621 00:40:20,876 --> 00:40:24,556 Speaker 1: lot of our previous episodes. The experts who happened to 622 00:40:24,596 --> 00:40:27,236 Speaker 1: have been quickest to see just how deadly COVID was 623 00:40:28,076 --> 00:40:31,316 Speaker 1: had no talent for self promotion. They didn't go on 624 00:40:31,396 --> 00:40:34,716 Speaker 1: TV like the Stanford professors. We sort of got to 625 00:40:34,756 --> 00:40:38,396 Speaker 1: this problem in episode two. The Stanford professors were actually 626 00:40:38,396 --> 00:40:40,876 Speaker 1: a lot like the old baseball people that Bill James 627 00:40:40,916 --> 00:40:44,876 Speaker 1: talked about in episode three. They stopped thinking because they 628 00:40:44,876 --> 00:40:48,196 Speaker 1: thought they knew something that they didn't. But this episode 629 00:40:48,276 --> 00:40:50,676 Speaker 1: is also returned to the first episode of the series, 630 00:40:50,676 --> 00:40:54,676 Speaker 1: our episode about the L six These PhD students are 631 00:40:54,716 --> 00:40:58,396 Speaker 1: the L sixes of academic life. We've arrived at the 632 00:40:58,396 --> 00:41:01,516 Speaker 1: point where we need them so badly that they're stepping 633 00:41:01,596 --> 00:41:04,076 Speaker 1: up and putting their careers on the line to save 634 00:41:04,196 --> 00:41:07,636 Speaker 1: us from ourselves. But why, where the hell are the 635 00:41:07,756 --> 00:41:11,156 Speaker 1: l ones? Why do we now require that our young 636 00:41:11,236 --> 00:41:18,916 Speaker 1: PhD students be brave? What forces are you worried about 637 00:41:18,996 --> 00:41:21,396 Speaker 1: being corrupted by? When you get to be an old, 638 00:41:21,516 --> 00:41:26,276 Speaker 1: established academic, Being a scientist isn't a glamorous job. I 639 00:41:26,316 --> 00:41:31,996 Speaker 1: think that getting public attention can be really exciting to scientists, 640 00:41:32,316 --> 00:41:37,916 Speaker 1: and you know, making compromises in how you communicate so 641 00:41:37,996 --> 00:41:41,956 Speaker 1: that you can get that public attention. We all carry 642 00:41:41,956 --> 00:41:44,316 Speaker 1: with us our own values, our own ways let we 643 00:41:44,356 --> 00:41:49,116 Speaker 1: think that the world should work. The sticking point would 644 00:41:49,156 --> 00:41:55,196 Speaker 1: be if those values were to impact the quality of 645 00:41:55,236 --> 00:41:58,076 Speaker 1: my science and the ricor with which I approach my science. 646 00:41:58,876 --> 00:42:01,956 Speaker 1: Your willingness to actually live in the world of evidence, right, 647 00:42:05,676 --> 00:42:07,956 Speaker 1: I don't have some grand point on which to end 648 00:42:07,996 --> 00:42:13,156 Speaker 1: this season, just a small one. Life eventually humbles us all. 649 00:42:15,196 --> 00:42:17,996 Speaker 1: What I love about experts, the best of them anyway, 650 00:42:18,556 --> 00:42:22,236 Speaker 1: is that they get to their humility early. They have to. 651 00:42:23,476 --> 00:42:27,556 Speaker 1: It's part of who they are, it's necessary for what 652 00:42:27,596 --> 00:42:31,156 Speaker 1: they're doing. They set out to get to the bottom 653 00:42:31,156 --> 00:42:36,236 Speaker 1: of something that has no bottom, and so they're reminded 654 00:42:36,756 --> 00:42:41,836 Speaker 1: constantly of what they don't know. They move through the 655 00:42:41,836 --> 00:42:45,116 Speaker 1: world focused not on what they know, but on what 656 00:42:45,156 --> 00:43:13,436 Speaker 1: they might find out. Against the Rules is written and 657 00:43:13,516 --> 00:43:16,756 Speaker 1: hosted by me Michael Lewis and produced by Katherine Girardeau 658 00:43:16,956 --> 00:43:20,356 Speaker 1: and Lydia Jean Cott. Julia Barton is our editor, with 659 00:43:20,396 --> 00:43:24,396 Speaker 1: additional editing by Audrey Dilling. Beth Johnson is our fact checker, 660 00:43:24,676 --> 00:43:31,116 Speaker 1: and mil o'bell executive produces. Our music is created by 661 00:43:31,236 --> 00:43:35,636 Speaker 1: John Evans and Matthias Bossi, a stellwagen steinfanette. We record 662 00:43:35,676 --> 00:43:39,676 Speaker 1: our show at Berkeley Advanced Media Studios, expertly helmed by 663 00:43:39,756 --> 00:43:43,756 Speaker 1: Cofa Ruth, Thanks also to Jacob Weisberg, Heather Fame, John 664 00:43:43,796 --> 00:43:48,676 Speaker 1: SNAr Is, Carly mcglory, Christina Sullivan, Eric Sandler, Maggie Taylor, 665 00:43:49,156 --> 00:43:55,796 Speaker 1: Morgan Rattner, Nicole Morano, Royston Preserve, Daniella Lakhan, Mary Beth Smith, 666 00:43:56,076 --> 00:44:00,076 Speaker 1: and Jason Gambrel. Against the Rules is a production of 667 00:44:00,116 --> 00:44:03,356 Speaker 1: Pushkin Industries. If you love this show and others from 668 00:44:03,356 --> 00:44:08,076 Speaker 1: Pushkin Industries, consider subscribing the Pushkin Plus. Pushkin Plus is 669 00:44:08,116 --> 00:44:12,676 Speaker 1: a podcast subscribe that offers bonus content and uninterrupted listening 670 00:44:12,716 --> 00:44:15,716 Speaker 1: for four dollars and ninety nine cents a month. Look 671 00:44:15,756 --> 00:44:19,876 Speaker 1: for Pushkin Plus on Apple Podcasts subscriptions. Keep in touch, 672 00:44:20,236 --> 00:44:23,356 Speaker 1: sign up for Pushkin's newsletter at pushkin dot Fm, or 673 00:44:23,396 --> 00:44:28,116 Speaker 1: follow at pushkin pots Define more Pushkin podcasts, listen on 674 00:44:28,156 --> 00:44:32,956 Speaker 1: the iHeartRadio app, Apple Podcasts, or wherever you listen to podcasts. 675 00:44:36,956 --> 00:44:38,796 Speaker 1: You really want people to go away with that. It's 676 00:44:38,796 --> 00:44:42,556 Speaker 1: like start trusting people who say they don't know. That's 677 00:44:42,636 --> 00:44:45,196 Speaker 1: the main takeaway if they don't take anything else. So 678 00:44:45,316 --> 00:44:48,876 Speaker 1: where from the season, hopefully they'll take that away. Yeah, 679 00:44:48,876 --> 00:44:50,916 Speaker 1: we're gonna there's gonna be spring up in the wake 680 00:44:50,996 --> 00:44:53,756 Speaker 1: of our podcast, a new cable news channel where every 681 00:44:53,836 --> 00:44:59,236 Speaker 1: day nobody knows anything. Everybody's you might be right, that'll 682 00:44:59,276 --> 00:45:01,756 Speaker 1: be every show will be that I might be wrong. 683 00:45:01,756 --> 00:45:08,476 Speaker 1: We could get a bunch of women, right. I know, 684 00:45:09,396 --> 00:45:12,156 Speaker 1: I know I'm probably wrong. I'm really not sure, but 685 00:45:12,516 --> 00:45:15,636 Speaker 1: I mean you might be right, probably wrong. I mean 686 00:45:15,636 --> 00:45:18,276 Speaker 1: I don't want to step on any toils. I can't, yeh, 687 00:45:18,396 --> 00:45:21,196 Speaker 1: followed by our new show, Am I an impostor?