1 00:00:02,720 --> 00:00:07,560 Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News. 2 00:00:09,320 --> 00:00:12,400 Speaker 2: Previously on We don't know exactly what happened yet, but 3 00:00:12,480 --> 00:00:15,680 Speaker 2: I think we suspect and I would bet dollars to 4 00:00:15,800 --> 00:00:18,239 Speaker 2: dimes that the story is very similar to a case 5 00:00:18,239 --> 00:00:20,680 Speaker 2: we had in La recently where a young woman was 6 00:00:20,720 --> 00:00:24,440 Speaker 2: basically stabbed for no reason by a psychotic homeless person. 7 00:00:24,600 --> 00:00:28,520 Speaker 3: San Francisco police arrested thirty eight year old Nima Monmeni. 8 00:00:28,840 --> 00:00:32,400 Speaker 4: Suspect is another tech executive who knew lye. 9 00:00:32,680 --> 00:00:35,199 Speaker 3: While we're not going to release any additional facts at 10 00:00:35,200 --> 00:00:38,800 Speaker 3: this time, I must point out that reckless and irresponsible 11 00:00:38,840 --> 00:00:43,839 Speaker 3: statements like those contained in mister Musk's tweet that assumed 12 00:00:43,880 --> 00:00:48,000 Speaker 3: incorrect circumstances about mister Lee's death served to mislead the 13 00:00:48,000 --> 00:00:52,320 Speaker 3: world in their perceptions of San Francisco and also negatively 14 00:00:52,360 --> 00:00:55,800 Speaker 3: impact the pursuit of justice for victims of crime. 15 00:00:55,960 --> 00:00:59,120 Speaker 2: A jury has just found Nimo Momenti guilty of second 16 00:00:59,160 --> 00:01:02,160 Speaker 2: degree murder in killing a cash app founder Bob Lee. 17 00:01:02,440 --> 00:01:04,480 Speaker 2: Didn't turn out to be a first degree murder guilty. 18 00:01:04,520 --> 00:01:05,800 Speaker 4: It is second degree murder. 19 00:01:08,560 --> 00:01:11,160 Speaker 1: When you spend a year reporting out a murder story, 20 00:01:11,560 --> 00:01:13,880 Speaker 1: you accidentally learn a thing or two about how to 21 00:01:13,880 --> 00:01:17,039 Speaker 1: commit crimes. For example, if. 22 00:01:16,880 --> 00:01:19,800 Speaker 5: You commit a serious crime in San Francisco, do not 23 00:01:20,000 --> 00:01:24,080 Speaker 5: cross a bridge. My god, there's bridge tolls, there's surveillance cameras, 24 00:01:24,120 --> 00:01:27,160 Speaker 5: there's plate readers. If you commit a crime and you 25 00:01:27,200 --> 00:01:29,920 Speaker 5: cross a bridge in any direction, you're getting picked up. 26 00:01:30,840 --> 00:01:33,679 Speaker 1: This is my source from the police department. He has 27 00:01:33,800 --> 00:01:36,840 Speaker 1: deep knowledge of the Bob Lee murder case, and he 28 00:01:36,920 --> 00:01:40,479 Speaker 1: wants to remain anonymous. We're having a voice actor read 29 00:01:40,520 --> 00:01:44,920 Speaker 1: what he told us, and he said something surprising that 30 00:01:45,040 --> 00:01:47,880 Speaker 1: the police figured out that the main suspect was Nima 31 00:01:47,880 --> 00:01:49,800 Speaker 1: mong Many almost immediately. 32 00:01:51,120 --> 00:01:53,080 Speaker 5: I believe that his vehicle was picked up on a 33 00:01:53,080 --> 00:01:56,800 Speaker 5: building surveillance camera first, and at that point, okay, we 34 00:01:56,880 --> 00:01:58,840 Speaker 5: have a vehicle near a homicide scene. 35 00:01:59,040 --> 00:02:00,960 Speaker 4: Let's see where a vehicle is traveling. 36 00:02:02,000 --> 00:02:04,960 Speaker 1: Once police had a photo of the car, they pulled 37 00:02:04,960 --> 00:02:09,360 Speaker 1: a DMV record, and then they had just about everything else. 38 00:02:09,760 --> 00:02:12,720 Speaker 5: We have a suspect, we run their criminal histories, we 39 00:02:12,840 --> 00:02:15,880 Speaker 5: run their driving history, we run their vehicles that are 40 00:02:15,880 --> 00:02:18,000 Speaker 5: linked to them, and any firearms as well. 41 00:02:19,480 --> 00:02:22,160 Speaker 1: The department had put together a full profile sheet on 42 00:02:22,240 --> 00:02:26,040 Speaker 1: Nimo MOMENTI just over forty eight hours after Bobley was stabbed, 43 00:02:26,520 --> 00:02:30,000 Speaker 1: complete with pictures of Nima smiling into the camera pulled 44 00:02:30,000 --> 00:02:34,920 Speaker 1: from his social media profiles. The information was kept tightly 45 00:02:35,000 --> 00:02:39,440 Speaker 1: under wraps. Only a few specialized units within San Francisco 46 00:02:39,480 --> 00:02:43,959 Speaker 1: Police knew his identity. Even many cops themselves weren't sure 47 00:02:43,960 --> 00:02:44,560 Speaker 1: what to believe. 48 00:02:45,160 --> 00:02:49,280 Speaker 5: When there's a lack of information put out through official channels, Cops, 49 00:02:49,360 --> 00:02:52,000 Speaker 5: like anybody else, liked to gossip and try to come 50 00:02:52,080 --> 00:02:53,239 Speaker 5: up with their own answers. 51 00:02:53,680 --> 00:02:55,639 Speaker 4: There was all kinds of speculation. 52 00:02:56,560 --> 00:02:58,720 Speaker 5: There are plenty of cops here who jumped on the 53 00:02:58,720 --> 00:03:01,560 Speaker 5: bandwagon of it was a homeless guy that stabbed him, 54 00:03:01,760 --> 00:03:04,880 Speaker 5: because that's what was tweeted out by somebody I know. 55 00:03:04,960 --> 00:03:07,720 Speaker 5: I heard somebody else bring up it was probably a 56 00:03:07,720 --> 00:03:11,040 Speaker 5: B girl thing. That's from the California penal code six 57 00:03:11,200 --> 00:03:13,160 Speaker 5: forty seven B, which is prostitution. 58 00:03:13,720 --> 00:03:17,320 Speaker 4: We call them b girls. Rich guy, super late at night. 59 00:03:17,760 --> 00:03:19,560 Speaker 5: It's not like it could have been a robbery. But 60 00:03:19,760 --> 00:03:21,520 Speaker 5: why was he down there in the first place. 61 00:03:26,680 --> 00:03:31,239 Speaker 1: During this time, misinformation was running wild, and city officials 62 00:03:31,240 --> 00:03:33,480 Speaker 1: were getting it from all directions. 63 00:03:33,720 --> 00:03:34,600 Speaker 4: It was a tough time. 64 00:03:34,840 --> 00:03:36,280 Speaker 3: It was a tough time for all of us. 65 00:03:36,560 --> 00:03:39,680 Speaker 1: That's Brooke Jenkins, the District Attorney of San Francisco. 66 00:03:40,080 --> 00:03:42,240 Speaker 3: There was a lot of pressure on the police who 67 00:03:43,040 --> 00:03:46,840 Speaker 3: not only saw that case, but to investigate it in 68 00:03:46,880 --> 00:03:50,560 Speaker 3: a way that made it fool proof. Multiple of us 69 00:03:50,640 --> 00:03:53,240 Speaker 3: were up for reelection very soon. 70 00:03:53,960 --> 00:03:57,000 Speaker 1: The next year, the mayor d A. Jenkins, and many 71 00:03:57,000 --> 00:04:00,320 Speaker 1: other city leaders would all be up for reelection. So 72 00:04:00,400 --> 00:04:04,240 Speaker 1: they wanted to squash this narrative that San Francisco was 73 00:04:04,240 --> 00:04:07,880 Speaker 1: a failed city led by buffoons who were so useless 74 00:04:08,040 --> 00:04:10,680 Speaker 1: and the neft that they allowed this great tech guy 75 00:04:10,760 --> 00:04:13,840 Speaker 1: to get killed. But also, this was not the kind 76 00:04:13,880 --> 00:04:17,400 Speaker 1: of case you could lose. You needed a conviction, so 77 00:04:17,440 --> 00:04:18,640 Speaker 1: they needed to be thorough. 78 00:04:19,200 --> 00:04:22,640 Speaker 3: You're trying to collect evidence before it's disposed of, before 79 00:04:23,000 --> 00:04:25,600 Speaker 3: somebody knows that you're on their heels. 80 00:04:26,960 --> 00:04:29,400 Speaker 1: What she told me was that some city officials wanted 81 00:04:29,400 --> 00:04:32,520 Speaker 1: to announce, hey, we know who the killer is much 82 00:04:32,600 --> 00:04:34,840 Speaker 1: more quickly, and this became an argument. 83 00:04:35,640 --> 00:04:39,520 Speaker 3: Those were tough conversations, right when you're getting calls from 84 00:04:39,520 --> 00:04:43,760 Speaker 3: city hall that want to counter what we believe is 85 00:04:43,800 --> 00:04:46,440 Speaker 3: the right thing to do for the case. It's hard 86 00:04:46,680 --> 00:04:48,960 Speaker 3: because we all, you know, as public officials, have the 87 00:04:49,000 --> 00:04:52,640 Speaker 3: same interest in wanting San Francisco's reputation to be what 88 00:04:52,680 --> 00:04:56,200 Speaker 3: it should be right to correct this false narrative, and 89 00:04:56,279 --> 00:04:59,080 Speaker 3: we all knew that during that timeframe, however long it took, 90 00:04:59,120 --> 00:05:03,240 Speaker 3: which ended up being a few days, that this story 91 00:05:03,240 --> 00:05:06,080 Speaker 3: and this narrative was going to continue and we were 92 00:05:06,120 --> 00:05:09,120 Speaker 3: going to be under fire. And so that was a 93 00:05:09,160 --> 00:05:09,839 Speaker 3: tough time. 94 00:05:12,080 --> 00:05:14,799 Speaker 1: When I finally had some distance from Bob Lee's death 95 00:05:15,040 --> 00:05:18,320 Speaker 1: and all the media and arguing surrounding it, it felt 96 00:05:18,360 --> 00:05:20,839 Speaker 1: like I was waking up after a wild night out. 97 00:05:21,480 --> 00:05:24,880 Speaker 1: I kept wondering, Wait, how did things get so out 98 00:05:24,920 --> 00:05:29,960 Speaker 1: of control? Why did Bob's death receive so much scrutiny. 99 00:05:30,120 --> 00:05:33,280 Speaker 1: He was beloved and respected among people who knew him, sure, 100 00:05:33,839 --> 00:05:37,919 Speaker 1: but not exactly a household name. So why did this 101 00:05:38,040 --> 00:05:41,479 Speaker 1: blow up the way it did. The two things I 102 00:05:41,560 --> 00:05:45,000 Speaker 1: kept coming back to were time and place. The same 103 00:05:45,000 --> 00:05:46,719 Speaker 1: way that people would say that Bob had been in 104 00:05:46,760 --> 00:05:48,800 Speaker 1: the wrong place at the wrong time at the end 105 00:05:48,800 --> 00:05:52,479 Speaker 1: of his life, it seemed possible this whole story took 106 00:05:52,520 --> 00:05:55,919 Speaker 1: off at the worst possible time, in the most explosive 107 00:05:55,960 --> 00:05:59,120 Speaker 1: place it could have happened. I'm talking twenty twenty three 108 00:05:59,839 --> 00:06:03,120 Speaker 1: of the tail end of COVID in San Francisco, a 109 00:06:03,160 --> 00:06:05,560 Speaker 1: place people like to think of as the most liberal 110 00:06:05,600 --> 00:06:09,599 Speaker 1: city in America. It seemed tailor made to touch on 111 00:06:09,640 --> 00:06:13,960 Speaker 1: America's most sensitive nerves. So that's what we're going to 112 00:06:14,040 --> 00:06:19,120 Speaker 1: look at today. In the final episode of this series, 113 00:06:19,440 --> 00:06:22,279 Speaker 1: We're going to take a step backwards, rub our eyes clear, 114 00:06:22,760 --> 00:06:25,040 Speaker 1: and try to make sense of the time and place 115 00:06:25,200 --> 00:06:29,640 Speaker 1: surrounding Bobley's murder, not thinking about it as an isolated event, 116 00:06:30,240 --> 00:06:33,760 Speaker 1: but as part of a continuum, one moment in a 117 00:06:33,800 --> 00:06:36,440 Speaker 1: long string of developments that led us to where we 118 00:06:36,520 --> 00:06:42,919 Speaker 1: are today. I'm sean when this is foundering the killing 119 00:06:42,960 --> 00:06:55,159 Speaker 1: of Bobley. When Bobley died, there was one name that 120 00:06:55,279 --> 00:06:58,360 Speaker 1: kept coming up, a name I hadn't thought about much 121 00:06:58,400 --> 00:07:00,560 Speaker 1: the previous year, Boudin. 122 00:07:01,240 --> 00:07:05,200 Speaker 6: I started getting threats, including death threats, related to his death. 123 00:07:05,640 --> 00:07:09,440 Speaker 1: Boudin was the San Franciscodier from January twenty twenty until 124 00:07:09,520 --> 00:07:13,600 Speaker 1: July twenty twenty two. He was ousted nine months before 125 00:07:13,640 --> 00:07:18,800 Speaker 1: Bob's murder in a contentious citywide recall vote. He had 126 00:07:18,840 --> 00:07:23,320 Speaker 1: been elected on a very progressive platform of police reform, decarceration, 127 00:07:23,640 --> 00:07:27,240 Speaker 1: and eliminating cash bail. He came into power during a 128 00:07:27,280 --> 00:07:30,960 Speaker 1: moment when Black Lives Matter was gaining momentum. The phrase 129 00:07:31,040 --> 00:07:35,640 Speaker 1: defund the police was a common rallying cry, and Boudin 130 00:07:35,760 --> 00:07:38,320 Speaker 1: wasn't alone. He was part of a wave of progressive 131 00:07:38,320 --> 00:07:42,440 Speaker 1: prosecutors elected across the country, but he did sort of 132 00:07:42,480 --> 00:07:46,040 Speaker 1: become the representative of the movement. His parents were in 133 00:07:46,080 --> 00:07:50,480 Speaker 1: the weather underground and incarcerated for decades for murder. He 134 00:07:50,600 --> 00:07:54,200 Speaker 1: was raised by other notable leftists, and then he became 135 00:07:54,240 --> 00:07:58,120 Speaker 1: a Yale law graduate, a Rhodes scholar, and a judicial clerk. 136 00:07:58,880 --> 00:08:03,080 Speaker 1: His background was so intriguing and his credentials were so fancy. 137 00:08:03,600 --> 00:08:06,600 Speaker 1: He was perfect for idolization or villainization. 138 00:08:07,280 --> 00:08:10,680 Speaker 6: I was used to being blamed for everything during my 139 00:08:10,680 --> 00:08:14,600 Speaker 6: time in office. Walgreens clothes, my fault, a coyote kill 140 00:08:14,680 --> 00:08:18,440 Speaker 6: someone's dog in Golden Gate Park, my fault. Jokes on 141 00:08:18,480 --> 00:08:21,800 Speaker 6: Twitter abounded. I mean, I think there were signs literally 142 00:08:21,800 --> 00:08:24,840 Speaker 6: put up around San Francisco saying that I had Overdoe 143 00:08:24,840 --> 00:08:29,280 Speaker 6: Library books. On Twitter, I think they suggested renaming the 144 00:08:29,320 --> 00:08:32,200 Speaker 6: San Andreas Fault Line chase of Boudin's. 145 00:08:31,760 --> 00:08:39,080 Speaker 1: Fault, something that hadn't occurred to me in the moment, 146 00:08:39,320 --> 00:08:43,480 Speaker 1: but seems clearer now after time was how clearly Chase A. 147 00:08:43,520 --> 00:08:47,800 Speaker 1: Boudin's eventual recall fed directly into the response to Bob 148 00:08:47,840 --> 00:08:52,960 Speaker 1: Lee's murder. Let me explain. Boudin took office in January 149 00:08:53,000 --> 00:08:56,480 Speaker 1: twenty twenty, which I think is fair to say was 150 00:08:56,520 --> 00:08:59,240 Speaker 1: a strange time to take over law enforcement in the 151 00:08:59,280 --> 00:09:00,320 Speaker 1: major American city. 152 00:09:00,920 --> 00:09:02,959 Speaker 6: I was sworn in just too much before COVID hit, 153 00:09:03,280 --> 00:09:06,600 Speaker 6: and people started to associate me and my administration with 154 00:09:06,920 --> 00:09:09,160 Speaker 6: a lot of the changes that were driven by COVID. 155 00:09:09,640 --> 00:09:12,280 Speaker 1: Like we mentioned earlier in the series, in the first 156 00:09:12,280 --> 00:09:15,679 Speaker 1: couple years of the pandemic, crime in San Francisco was 157 00:09:15,720 --> 00:09:21,119 Speaker 1: trending down overall according to SFPD data, but the exceptions 158 00:09:21,120 --> 00:09:26,280 Speaker 1: that stand out included Carthuff's burglaries and homicides, all of 159 00:09:26,360 --> 00:09:27,439 Speaker 1: which increased a little. 160 00:09:28,040 --> 00:09:34,240 Speaker 7: SFPA protesters gathered in San Francisco's Chinatown hoping District Attorney 161 00:09:34,280 --> 00:09:34,480 Speaker 7: Chase A. 162 00:09:34,520 --> 00:09:36,000 Speaker 4: Boudin himself would show up to it. 163 00:09:36,120 --> 00:09:39,040 Speaker 1: And while you can argue over the scope and amount 164 00:09:39,120 --> 00:09:42,560 Speaker 1: of coverage and whether it was appropriate, the reality is 165 00:09:42,600 --> 00:09:45,319 Speaker 1: that this small uptick in crime did get a lot 166 00:09:45,360 --> 00:09:45,840 Speaker 1: of attention. 167 00:09:46,520 --> 00:09:49,360 Speaker 8: I think that the most important one is his catch 168 00:09:49,400 --> 00:09:52,640 Speaker 8: and release policies is soft on crime that allows the 169 00:09:52,679 --> 00:09:55,280 Speaker 8: criminals to come out and coverment more and more crime. 170 00:09:55,720 --> 00:09:58,000 Speaker 7: Around that time, Like a lot of people I knew 171 00:09:58,200 --> 00:09:59,959 Speaker 7: in Silicon Valley were like buying gun. 172 00:10:00,800 --> 00:10:03,600 Speaker 1: This is Max Chafkin, my colleague and a reporter here 173 00:10:03,640 --> 00:10:07,840 Speaker 1: at Bloomberg. He covers the intersection of tech and politics. 174 00:10:08,160 --> 00:10:11,200 Speaker 7: We're so worried about violent crime and so worried about 175 00:10:11,200 --> 00:10:14,480 Speaker 7: their own safety, rightly or wrongly, that they were like 176 00:10:14,760 --> 00:10:15,640 Speaker 7: arming themselves. 177 00:10:17,720 --> 00:10:20,160 Speaker 1: The pandemic was a time when there was just a 178 00:10:20,240 --> 00:10:24,920 Speaker 1: lot of fear, obviously fear of disease, but then there 179 00:10:24,960 --> 00:10:28,520 Speaker 1: was also fear for personal safety and a broader fear 180 00:10:28,760 --> 00:10:32,360 Speaker 1: among some tech leaders of a meddling left leaning government. 181 00:10:32,920 --> 00:10:35,240 Speaker 7: A lot of the folks in the tech industry had 182 00:10:35,280 --> 00:10:38,400 Speaker 7: come to the conclusion that like the government was out 183 00:10:38,440 --> 00:10:41,400 Speaker 7: to get them, that the government was going to not 184 00:10:42,040 --> 00:10:43,559 Speaker 7: on in the context of like violent crime, but the 185 00:10:43,640 --> 00:10:45,480 Speaker 7: government was trying to like either put them in jail, 186 00:10:45,559 --> 00:10:47,880 Speaker 7: or put them out of business or bankrupt them. 187 00:10:48,440 --> 00:10:51,840 Speaker 1: Which in the case of San Francisco wasn't a totally 188 00:10:51,920 --> 00:10:56,400 Speaker 1: wild conclusion. Chase A. Boudin and the d'ae's office did 189 00:10:56,480 --> 00:11:00,360 Speaker 1: go after tech companies. Six months into office, he sued 190 00:11:00,440 --> 00:11:02,640 Speaker 1: door Dash for misclassifying workers. 191 00:11:03,080 --> 00:11:07,520 Speaker 6: When you prosecute tech companies for stealing wages from their employees, 192 00:11:08,280 --> 00:11:13,199 Speaker 6: when you prosecute politicians for corruption, we prospect police or 193 00:11:13,320 --> 00:11:16,320 Speaker 6: tested force, all of which we did. You make our lettomies. 194 00:11:16,840 --> 00:11:21,080 Speaker 1: So a progressive da a city on lockdown with a 195 00:11:21,240 --> 00:11:25,320 Speaker 1: small uptick in some forms of crime. There was almost 196 00:11:25,480 --> 00:11:31,559 Speaker 1: this vinegar and baking soda effect. Things felt combustible on edge. 197 00:11:32,360 --> 00:11:36,640 Speaker 1: And there's one more factor at this particular time that 198 00:11:36,760 --> 00:11:39,760 Speaker 1: I do think contributed to how Boble's death would play 199 00:11:39,760 --> 00:11:44,240 Speaker 1: out in the media. When the pandemic started, many tech 200 00:11:44,280 --> 00:11:47,280 Speaker 1: workers were locked away at home on their computers all 201 00:11:47,320 --> 00:11:51,480 Speaker 1: the time, and some of these people started new podcasts. 202 00:11:51,559 --> 00:11:55,439 Speaker 7: All right, everybody, welcome to another edition of the All 203 00:11:55,480 --> 00:11:57,760 Speaker 7: In Podcasts. We'll call this episode one. 204 00:11:58,080 --> 00:12:00,400 Speaker 1: This is a clip from the first episode of the 205 00:12:00,440 --> 00:12:05,200 Speaker 1: All Limb podcast, launched on March nineteenth, twenty twenty. It's 206 00:12:05,200 --> 00:12:08,640 Speaker 1: hosted by four tech executives, the most prominent of which 207 00:12:08,720 --> 00:12:11,320 Speaker 1: is David Sachs, a co founder of PayPal. 208 00:12:11,440 --> 00:12:13,839 Speaker 2: So hold on, So, for example, okay, let's take the 209 00:12:13,960 --> 00:12:16,240 Speaker 2: Let's take the drug addict who commits a petty theft. 210 00:12:16,480 --> 00:12:19,040 Speaker 2: We all agree that person should go to treatment, not 211 00:12:19,160 --> 00:12:22,839 Speaker 2: to jail or prison, right a nonviolent, of course offender. Okay, 212 00:12:23,240 --> 00:12:24,800 Speaker 2: but how are you going to get that person to 213 00:12:24,800 --> 00:12:25,439 Speaker 2: go to treatment? 214 00:12:26,360 --> 00:12:30,360 Speaker 1: The podcast covers all sorts of things, AI, trade policy, 215 00:12:30,640 --> 00:12:36,280 Speaker 1: tech news, and very quickly it became massively popular, particularly 216 00:12:36,320 --> 00:12:40,000 Speaker 1: in wealthy tech circles. In its first few days, more 217 00:12:40,040 --> 00:12:43,679 Speaker 1: than one hundred thousand people had downloaded it, which by 218 00:12:43,720 --> 00:12:48,480 Speaker 1: podcast standards is genuinely pretty great. The politics on the 219 00:12:48,480 --> 00:12:52,640 Speaker 1: show leaned right. There's constant frustration with the progressive policies 220 00:12:52,679 --> 00:12:54,440 Speaker 1: in any number of spheres. 221 00:12:59,679 --> 00:13:02,520 Speaker 7: Like these guys were really mad about stuff that had 222 00:13:02,559 --> 00:13:05,040 Speaker 7: nothing to do with San Francisco, but San Francisco is 223 00:13:05,080 --> 00:13:09,520 Speaker 7: just sitting there as this symbol of everything wrong with 224 00:13:09,600 --> 00:13:15,160 Speaker 7: the United States. The forces of cultural progressivism, like black 225 00:13:15,160 --> 00:13:19,640 Speaker 7: Lives Matter or views on diversity, equity and inclusion become 226 00:13:19,760 --> 00:13:22,800 Speaker 7: kind of coded to them as very anti tech. And 227 00:13:22,880 --> 00:13:25,280 Speaker 7: so when you talk about like Chasa or these other 228 00:13:25,640 --> 00:13:28,080 Speaker 7: San Francisco liberals, they could be talking about something that 229 00:13:28,120 --> 00:13:30,240 Speaker 7: sounds like it has absolutely nothing to do with tech, 230 00:13:30,600 --> 00:13:33,920 Speaker 7: but like it's processed as part of this whole society 231 00:13:34,040 --> 00:13:36,160 Speaker 7: is trying to slow us down. They're going to make rules, 232 00:13:36,160 --> 00:13:38,520 Speaker 7: They're not gonna let us build, and it becomes part 233 00:13:38,520 --> 00:13:39,720 Speaker 7: of this cultural battle. 234 00:13:41,360 --> 00:13:43,760 Speaker 1: Within the first six months, the hosts had turned their 235 00:13:43,800 --> 00:13:46,840 Speaker 1: attention to what would become an ongoing target for them, 236 00:13:47,400 --> 00:13:49,720 Speaker 1: Chase a Boudin and crime in San Francisco. 237 00:13:50,120 --> 00:13:53,480 Speaker 2: His agenda is decarceration. It's like a fire chief who 238 00:13:53,520 --> 00:13:56,360 Speaker 2: doesn't believe in using water, yes, and. 239 00:13:57,160 --> 00:13:59,280 Speaker 7: He is part of the burn it all down party. 240 00:14:00,480 --> 00:14:03,280 Speaker 1: Within the year, they were challenging him to debates and 241 00:14:03,400 --> 00:14:07,960 Speaker 1: raising money for his recall. One of the hosts, Jason Calacanis, 242 00:14:08,360 --> 00:14:11,880 Speaker 1: even started to gofund me in twenty twenty one. It 243 00:14:12,000 --> 00:14:14,720 Speaker 1: raised more than fifty thousand dollars to pay for an 244 00:14:14,760 --> 00:14:18,599 Speaker 1: investigative journalist to write about victims of crime in San Francisco. 245 00:14:19,480 --> 00:14:23,320 Speaker 1: Here's David Sachs speaking to conservative commentator Megan Kelly on 246 00:14:23,360 --> 00:14:26,160 Speaker 1: her podcast, which also launched in twenty twenty. 247 00:14:26,440 --> 00:14:28,800 Speaker 2: During COVID, he released forty percent of the jail population. 248 00:14:29,480 --> 00:14:32,240 Speaker 2: And the crazy thing is he wrote an op ed 249 00:14:32,280 --> 00:14:35,760 Speaker 2: in the La Time saying I'm making San Francisco safer 250 00:14:35,960 --> 00:14:39,320 Speaker 2: by emptying out the jails. That was literally the headline 251 00:14:39,600 --> 00:14:42,440 Speaker 2: of the piece that he wrote in the La Times. 252 00:14:42,480 --> 00:14:45,000 Speaker 4: So he has this warp view that somehow he's going 253 00:14:45,040 --> 00:14:45,480 Speaker 4: to make all. 254 00:14:45,520 --> 00:14:49,720 Speaker 2: Us safer by emptying out the jails and not prosecuting anybody. 255 00:14:51,160 --> 00:14:54,560 Speaker 1: The campaign to recall Chase A. Boudin started in twenty 256 00:14:54,680 --> 00:14:57,360 Speaker 1: twenty one, when he had only been in office for 257 00:14:57,400 --> 00:15:01,160 Speaker 1: a year. Many of Boudin's sharp his critics were rich 258 00:15:01,280 --> 00:15:06,600 Speaker 1: tech moguls, including venture capitalists David Sachs, Gary Tan and 259 00:15:06,720 --> 00:15:11,120 Speaker 1: Ron Conway. The recall campaign raised more than double what 260 00:15:11,120 --> 00:15:15,520 Speaker 1: Boudin's supporters were able to raise. Now, I want to 261 00:15:15,560 --> 00:15:18,400 Speaker 1: take a moment to say that the people criticizing Boudin 262 00:15:18,640 --> 00:15:23,440 Speaker 1: were not all venture capitalists and keyboard warriors. He also 263 00:15:23,480 --> 00:15:27,760 Speaker 1: faced criticism from victims of crime, and in cases where 264 00:15:27,800 --> 00:15:31,560 Speaker 1: the victims were dead, often their surviving family members cast 265 00:15:31,640 --> 00:15:37,200 Speaker 1: blame on him. There are many examples, including cases where 266 00:15:37,240 --> 00:15:43,400 Speaker 1: the perpetrators were repeat offenders, and so Boudin's detractors could say, see, 267 00:15:43,440 --> 00:15:45,560 Speaker 1: he wanted to let these criminals out of jail, and 268 00:15:45,600 --> 00:15:51,960 Speaker 1: now this happened. Also, San Francisco's large Chinese population rallied 269 00:15:51,960 --> 00:15:55,560 Speaker 1: against Boudin. During this time, there was a string of 270 00:15:55,560 --> 00:15:59,680 Speaker 1: attacks on Asian Americans all over the country actually, but 271 00:16:00,040 --> 00:16:03,880 Speaker 1: also in San Francisco, and this is something I feel 272 00:16:03,880 --> 00:16:09,680 Speaker 1: really sensitive about because I'm Asian American. The stories were 273 00:16:09,680 --> 00:16:13,760 Speaker 1: really gruesome and especially upsetting because many of the victims 274 00:16:13,800 --> 00:16:17,200 Speaker 1: were elderly. An eighty four year old man who was 275 00:16:17,240 --> 00:16:20,880 Speaker 1: pushed to the ground and died, another eighty four year 276 00:16:20,880 --> 00:16:22,800 Speaker 1: old man who was kicked out of his walker at 277 00:16:22,840 --> 00:16:26,440 Speaker 1: a bus stop, two women sixty three and eighty five 278 00:16:26,760 --> 00:16:30,520 Speaker 1: stabbed at a bus stop. There are many more examples. 279 00:16:31,880 --> 00:16:35,160 Speaker 1: Victim data released by the San Francisco Police showed that 280 00:16:35,240 --> 00:16:39,000 Speaker 1: crime against Asian Americans in the city increased five hundred 281 00:16:39,000 --> 00:16:45,360 Speaker 1: percent in Boudin's first year in office. This was all 282 00:16:45,400 --> 00:16:48,240 Speaker 1: a few years ago. I think that now that the 283 00:16:48,280 --> 00:16:51,680 Speaker 1: fervor of the recall has passed, we can all acknowledge 284 00:16:51,720 --> 00:16:54,160 Speaker 1: that Chase A. Boudin didn't shove and kill an eighty 285 00:16:54,200 --> 00:16:57,200 Speaker 1: four year old man, no more than Brook Jenkins killed 286 00:16:57,200 --> 00:17:01,960 Speaker 1: Bob Lee. But at the time, these were fierce arguments 287 00:17:02,000 --> 00:17:05,960 Speaker 1: that people were having, including very rich, very powerful people. 288 00:17:10,760 --> 00:17:14,000 Speaker 1: Boudin lost the recall, and he left office in July 289 00:17:14,080 --> 00:17:17,800 Speaker 1: of twenty twenty two, nine months before Bobble was killed. 290 00:17:18,560 --> 00:17:21,879 Speaker 7: I mean this was especially that recall was a really 291 00:17:21,920 --> 00:17:26,040 Speaker 7: important example of these right wing tech guys setting a 292 00:17:26,080 --> 00:17:28,880 Speaker 7: target and then achieving it, you know, like using their 293 00:17:28,920 --> 00:17:30,600 Speaker 7: power for like a political purpose. 294 00:17:31,040 --> 00:17:34,439 Speaker 1: From my colleague Max Chafkin, the Boudin recall opened up 295 00:17:34,440 --> 00:17:37,720 Speaker 1: a new sphere of influence for tech leaders, one that 296 00:17:37,720 --> 00:17:40,240 Speaker 1: would become key in how the story of Bobbley would 297 00:17:40,280 --> 00:17:40,760 Speaker 1: play out. 298 00:17:41,320 --> 00:17:44,159 Speaker 7: You could get really involved in San Francisco politics. You 299 00:17:44,200 --> 00:17:48,119 Speaker 7: could decide that, you know, the San Francisco school board 300 00:17:48,520 --> 00:17:50,760 Speaker 7: was the thing that had to be changed, or like 301 00:17:51,040 --> 00:17:54,960 Speaker 7: San Francisco housing policy or San Francisco crime or whatever. 302 00:17:55,240 --> 00:17:58,840 Speaker 7: I think that the recall and then and then the 303 00:17:58,920 --> 00:18:02,600 Speaker 7: murder of Bobb Lee came a gateway for a lot 304 00:18:02,600 --> 00:18:06,680 Speaker 7: of these guys who maybe had started to like express 305 00:18:06,800 --> 00:18:10,159 Speaker 7: some of these feelings but hadn't considered the role that 306 00:18:10,160 --> 00:18:13,080 Speaker 7: they could play and maybe even the power that they had. 307 00:18:13,840 --> 00:18:16,879 Speaker 1: And the fervor around crime in San Francisco was still 308 00:18:16,960 --> 00:18:20,840 Speaker 1: so strong. The anti Boudin sentiment was still recent enough 309 00:18:21,280 --> 00:18:23,919 Speaker 1: that almost a year after he had been out of office, 310 00:18:24,320 --> 00:18:27,520 Speaker 1: the former DA still had people tweeting at him things 311 00:18:27,640 --> 00:18:30,879 Speaker 1: like Chase A Boudin and the criminal loving city council 312 00:18:30,960 --> 00:18:34,240 Speaker 1: that enabled him and the lawless SF for years have 313 00:18:34,359 --> 00:18:38,320 Speaker 1: Bob's literal blood on their hands. Do you see a 314 00:18:38,320 --> 00:18:43,920 Speaker 1: connection between Sacks and Musk both targeting you and spreading 315 00:18:44,280 --> 00:18:45,800 Speaker 1: misinformation about Bob Lee? 316 00:18:46,160 --> 00:18:48,439 Speaker 6: Of course, it's obvious. I mean, I'm not sure what 317 00:18:48,520 --> 00:18:49,040 Speaker 6: the question is. 318 00:18:49,440 --> 00:18:51,640 Speaker 1: I wonder if you could draw a line between those 319 00:18:51,680 --> 00:18:53,000 Speaker 1: two for a listener. 320 00:18:53,359 --> 00:18:57,439 Speaker 6: Well, Sax and Musk both spread this information about public 321 00:18:57,440 --> 00:19:01,080 Speaker 6: safety in San Francisco in general, about criminal justice performed 322 00:19:01,119 --> 00:19:06,800 Speaker 6: in general, and pushed a far right narrative about more 323 00:19:06,840 --> 00:19:10,480 Speaker 6: police and longer incarceration being an effective way to promote safety. 324 00:19:11,119 --> 00:19:16,760 Speaker 6: They also share an unshakable belief that they're right even 325 00:19:16,960 --> 00:19:20,000 Speaker 6: when they've been proved wrong, and an unwillingness to admit 326 00:19:20,119 --> 00:19:23,639 Speaker 6: error or apologize when they spread misinformation. So all of 327 00:19:23,640 --> 00:19:26,159 Speaker 6: that is entirely consistent, both with the way the recall 328 00:19:26,359 --> 00:19:29,639 Speaker 6: targeted me and my policies and my supporters, and with 329 00:19:29,720 --> 00:19:33,159 Speaker 6: the way that they handled the messaging around the Bobbly murder. 330 00:19:33,720 --> 00:19:40,080 Speaker 6: This is quintessential cherry picking of facts or of anecdotes, 331 00:19:40,119 --> 00:19:41,960 Speaker 6: not even facts, because in this case, all we knew 332 00:19:42,000 --> 00:19:47,600 Speaker 6: was that he'd been killed, and seizing a situation or development, 333 00:19:47,920 --> 00:19:50,480 Speaker 6: and out of whole cloth, inventing facts around it to 334 00:19:50,480 --> 00:19:53,800 Speaker 6: fit a narrative, and once the whole cloth starts to 335 00:19:53,920 --> 00:19:59,119 Speaker 6: unravel because it's not based in any reality, they just 336 00:19:59,160 --> 00:20:01,159 Speaker 6: move on and find something else that fits their narrative 337 00:20:01,400 --> 00:20:04,040 Speaker 6: and don't make any effort to apologize or to grapple 338 00:20:04,119 --> 00:20:06,920 Speaker 6: with the reality of what happened. And in many ways, 339 00:20:07,480 --> 00:20:10,080 Speaker 6: my recall was sort of a Canarian the coal mine 340 00:20:10,080 --> 00:20:12,880 Speaker 6: for the direction that the tech elite were moving politically 341 00:20:12,920 --> 00:20:15,400 Speaker 6: on a wide range of issues, not just criminal justice, 342 00:20:15,400 --> 00:20:20,320 Speaker 6: but racial justice and gender equality, and party politics, taxes, 343 00:20:21,800 --> 00:20:26,040 Speaker 6: foreign aid, I mean, higher education. The list goes on 344 00:20:26,080 --> 00:20:26,400 Speaker 6: and on. 345 00:20:28,400 --> 00:20:34,560 Speaker 1: Musk didn't respond to our interview request. After Bobley's death, 346 00:20:35,160 --> 00:20:39,120 Speaker 1: and after the truth of what happened surfaced, very few 347 00:20:39,119 --> 00:20:43,760 Speaker 1: of the folks who spread misinformation recanted publicly. In fact, 348 00:20:44,240 --> 00:20:47,840 Speaker 1: all in host David Sachs became even more involved in 349 00:20:47,880 --> 00:20:52,600 Speaker 1: conservative politics. He became a major supporter and fundraiser for 350 00:20:52,680 --> 00:20:56,439 Speaker 1: Donald Trump and later became the White House AI At 351 00:20:56,480 --> 00:21:00,840 Speaker 1: cryptos Are. He's still on the podcast. He continues to 352 00:21:00,880 --> 00:21:04,639 Speaker 1: talk about crime in cities with Democratic leaders and calls 353 00:21:04,680 --> 00:21:09,880 Speaker 1: for expanded police and military presence in those cities. He 354 00:21:09,960 --> 00:21:12,359 Speaker 1: and the other all In hosts declined to speak to 355 00:21:12,440 --> 00:21:18,960 Speaker 1: us for this podcast. After the break, we end where 356 00:21:19,000 --> 00:21:23,880 Speaker 1: this all began in San Francisco. Why the city itself 357 00:21:23,920 --> 00:21:27,199 Speaker 1: and its long history with the tech elite primed it 358 00:21:27,240 --> 00:21:48,680 Speaker 1: for the powder kead moment of Bob Lee's murder. When 359 00:21:48,720 --> 00:21:51,920 Speaker 1: Bob Lee moved to San Francisco in two thousand and four, 360 00:21:52,480 --> 00:21:54,919 Speaker 1: both he and the city were in the midst of 361 00:21:55,040 --> 00:21:59,120 Speaker 1: massive changes. On the one hand, you have Bob coming 362 00:21:59,119 --> 00:22:03,040 Speaker 1: from Saint Louis, from being a small time programmer, he 363 00:22:03,040 --> 00:22:07,040 Speaker 1: helps develop Google's ad platform, having a direct hand in 364 00:22:07,080 --> 00:22:09,520 Speaker 1: turning it into one of the richest companies in the world. 365 00:22:10,920 --> 00:22:13,919 Speaker 1: On the other hand, you have San Francisco itself. It 366 00:22:14,000 --> 00:22:16,720 Speaker 1: was at the epicenter of the era's most pressing tech 367 00:22:16,840 --> 00:22:22,399 Speaker 1: and cultural issues. That year, San Francisco issued the first 368 00:22:22,480 --> 00:22:26,520 Speaker 1: marriage licenses to same sex couples. In the course of 369 00:22:26,520 --> 00:22:31,879 Speaker 1: a month, more than four thousand couples were married. This 370 00:22:32,080 --> 00:22:35,120 Speaker 1: was during the time when then President George W. Bush 371 00:22:35,320 --> 00:22:38,200 Speaker 1: was calling for a federal amendment to define marriage as 372 00:22:38,240 --> 00:22:42,879 Speaker 1: a union between a man and a woman. Then, in 373 00:22:42,920 --> 00:22:45,399 Speaker 1: two thousand and seven, when Bob was a young dad 374 00:22:45,440 --> 00:22:50,959 Speaker 1: and expecting his second child. The iPhone debuted that, along 375 00:22:50,960 --> 00:22:54,080 Speaker 1: with the Android operating system which Bob was helping to build, 376 00:22:54,720 --> 00:22:57,960 Speaker 1: ushered in an entire category of companies that would hire 377 00:22:58,040 --> 00:23:03,120 Speaker 1: many thousands of workers in San Francis, young excited engineers 378 00:23:03,640 --> 00:23:08,480 Speaker 1: working at the likes of Uber, Instagram, and Airbnb. I 379 00:23:08,560 --> 00:23:10,960 Speaker 1: bring all of this up to say, in order to 380 00:23:11,040 --> 00:23:13,960 Speaker 1: understand why the story of Bob Lee exploded the way 381 00:23:14,000 --> 00:23:16,879 Speaker 1: it did, we need to reflect on the ways that 382 00:23:16,960 --> 00:23:20,000 Speaker 1: Bob and the city itself were mirrors for each other. 383 00:23:22,640 --> 00:23:25,760 Speaker 7: Keep in mind that Silicon Valley in San Francisco are 384 00:23:25,760 --> 00:23:26,360 Speaker 7: not the same thing. 385 00:23:26,800 --> 00:23:29,320 Speaker 1: Max Chafkin, again, my colleague at Bloomberg. 386 00:23:29,440 --> 00:23:33,639 Speaker 7: Silicon Valley originally are these peninsula suburbs south of the 387 00:23:33,640 --> 00:23:37,600 Speaker 7: Bay Area, basically halfway between San Francisco and San Jose. 388 00:23:37,760 --> 00:23:41,080 Speaker 1: About an hour south of San Francisco by car. It 389 00:23:41,119 --> 00:23:43,240 Speaker 1: really came into its own as a tech hub in 390 00:23:43,280 --> 00:23:44,960 Speaker 1: the seventies and eighties. 391 00:23:45,119 --> 00:23:49,640 Speaker 7: And the politics of Silicon Valley were kind of the 392 00:23:49,680 --> 00:23:54,600 Speaker 7: politics of conservative, suburban, upper middle class you're kind of 393 00:23:54,680 --> 00:23:57,720 Speaker 7: like typical, like Ronald Reagan voters and so on. San 394 00:23:57,760 --> 00:24:01,440 Speaker 7: Francisco was not as big a part of the tech 395 00:24:01,480 --> 00:24:03,480 Speaker 7: industry know un till more recently. 396 00:24:04,000 --> 00:24:06,640 Speaker 1: Right around the time Bob Lee moved to San Francisco, 397 00:24:07,720 --> 00:24:11,240 Speaker 1: America was living through a wave of urban revival. People 398 00:24:11,280 --> 00:24:14,560 Speaker 1: wanted to live in cities again where there were more restaurants, clubs, 399 00:24:14,600 --> 00:24:15,320 Speaker 1: and museums. 400 00:24:15,800 --> 00:24:18,920 Speaker 7: Suddenly you had all these engineers who wanted to live 401 00:24:19,040 --> 00:24:23,920 Speaker 7: in San Francisco, companies wanting to recruit there, and San 402 00:24:23,920 --> 00:24:29,000 Speaker 7: Francisco itself wanting to accommodate these companies and giving them 403 00:24:29,040 --> 00:24:32,120 Speaker 7: tax breaks, trying to find ways to lure them north. 404 00:24:33,240 --> 00:24:37,679 Speaker 1: But San Francisco is not Sunny Vail. The politics are different. 405 00:24:38,400 --> 00:24:41,800 Speaker 1: It's a storied, progressive city, home to New Age thinking, 406 00:24:42,200 --> 00:24:46,639 Speaker 1: the Black Panthers and Harvey Milk. This influx of wealthy 407 00:24:46,720 --> 00:24:49,400 Speaker 1: tech entrepreneurs made for strange bedfellows. 408 00:24:50,280 --> 00:24:55,440 Speaker 7: San Francisco itself has kind of complicated feelings about growth 409 00:24:55,560 --> 00:25:01,280 Speaker 7: and industry, and there always is local opposition to development 410 00:25:01,520 --> 00:25:04,639 Speaker 7: as well as like local opposition to change of any kind. 411 00:25:06,040 --> 00:25:10,320 Speaker 1: Rents went up, local businesses shut down. In twenty thirteen, 412 00:25:10,920 --> 00:25:14,040 Speaker 1: the same year that Bob Lee helped launch his legacy project, 413 00:25:14,359 --> 00:25:18,679 Speaker 1: cash App, there were large protests stopping Google workers from 414 00:25:18,760 --> 00:25:21,280 Speaker 1: commuting outside the city on company buses. 415 00:25:21,720 --> 00:25:27,159 Speaker 7: I'm talking about like protests against tech companies or politicians 416 00:25:27,160 --> 00:25:31,520 Speaker 7: making comments about tech workers or tech executives as they 417 00:25:31,600 --> 00:25:37,880 Speaker 7: saw the villainization of what, in their minds was an 418 00:25:37,920 --> 00:25:44,160 Speaker 7: industry that was like fundamentally valuable, like creating jobs, bringing 419 00:25:44,160 --> 00:25:47,840 Speaker 7: in revenue, and also good. 420 00:25:48,160 --> 00:25:51,359 Speaker 1: And for me. There's one incident that really seemed to 421 00:25:51,440 --> 00:25:56,480 Speaker 1: sum up this tense dynamic. In twenty fourteen, a woman 422 00:25:56,600 --> 00:26:00,000 Speaker 1: was at a bar wearing an early prototype of smart 423 00:26:00,040 --> 00:26:02,480 Speaker 1: glasses called Google Glass. 424 00:26:03,160 --> 00:26:04,000 Speaker 4: It's some video now. 425 00:26:04,960 --> 00:26:08,439 Speaker 1: She was recording bar patrons, which they didn't like, and 426 00:26:08,520 --> 00:26:11,840 Speaker 1: so they snatched them off her face, and people started 427 00:26:11,920 --> 00:26:13,359 Speaker 1: using the phrase glass fire. 428 00:26:15,520 --> 00:26:17,239 Speaker 7: Think about that from the from the point of view 429 00:26:17,240 --> 00:26:20,720 Speaker 7: of the tech industry. We have this like quirky cool product, 430 00:26:20,800 --> 00:26:24,480 Speaker 7: Google Glass, Like it's so great, right, and you know 431 00:26:24,600 --> 00:26:26,119 Speaker 7: you're gonna be able to check your email. 432 00:26:26,119 --> 00:26:27,000 Speaker 4: It's so awesome. 433 00:26:27,320 --> 00:26:29,720 Speaker 7: People aren't even probably gonna use it, but maybe they will, 434 00:26:29,760 --> 00:26:32,399 Speaker 7: and if they do, it'll be exciting. And like a person, 435 00:26:32,720 --> 00:26:35,680 Speaker 7: a normal person in San Francisco is just like, why 436 00:26:35,720 --> 00:26:38,200 Speaker 7: is there a camera pointed at me? And also why 437 00:26:38,280 --> 00:26:40,959 Speaker 7: is my rent gone up by like forty or fifty 438 00:26:40,960 --> 00:26:44,080 Speaker 7: percent or more, and so you can understand why there's 439 00:26:44,080 --> 00:26:46,240 Speaker 7: a confrontation. But from the tech people, it's just this 440 00:26:46,320 --> 00:26:51,560 Speaker 7: like completely unwarranted aggression, like they expected to be celebrated 441 00:26:51,920 --> 00:26:55,520 Speaker 7: and instead, you know, they were greeted with the kind 442 00:26:55,520 --> 00:27:02,760 Speaker 7: of usual skepticism or even hostility. And basically the tech 443 00:27:02,800 --> 00:27:08,720 Speaker 7: industry didn't like that, and the tech industry found it offensive. 444 00:27:13,600 --> 00:27:16,400 Speaker 1: I moved here in twenty fourteen during a moment when 445 00:27:16,440 --> 00:27:20,399 Speaker 1: San Francisco was rocked by gentrification and the perception that 446 00:27:20,440 --> 00:27:23,399 Speaker 1: the city's artists and weirdos were being pushed out by 447 00:27:23,440 --> 00:27:27,240 Speaker 1: a wave of tech workers. At the time, I had 448 00:27:27,240 --> 00:27:30,119 Speaker 1: never heard of uber or lyft. I caught up with 449 00:27:30,119 --> 00:27:32,359 Speaker 1: an old friend and he told me to download these 450 00:27:32,400 --> 00:27:36,439 Speaker 1: apps on my phone, and I remember how totally novel 451 00:27:36,480 --> 00:27:39,359 Speaker 1: it felt, this idea that you could press a button 452 00:27:39,440 --> 00:27:42,080 Speaker 1: on your phone and then the person shows up to 453 00:27:42,119 --> 00:27:47,520 Speaker 1: do something for you at an absolute bargain price. It 454 00:27:47,560 --> 00:27:51,560 Speaker 1: was a genuine clash of cultures. Newly wealthy tech entrepreneurs 455 00:27:52,200 --> 00:27:55,600 Speaker 1: and their well compensated workers who believed they were building 456 00:27:55,600 --> 00:27:59,359 Speaker 1: the future versus a city that did not like that 457 00:27:59,440 --> 00:28:00,520 Speaker 1: vision for the future. 458 00:28:03,960 --> 00:28:06,840 Speaker 9: Can you tell me about how that turns into this 459 00:28:06,960 --> 00:28:11,639 Speaker 9: idea that the wealthy white tech guy becomes the most 460 00:28:11,840 --> 00:28:14,159 Speaker 9: disenfranchised in San Francisco. 461 00:28:15,119 --> 00:28:18,680 Speaker 7: I mean, there's so many things going on that leads 462 00:28:18,760 --> 00:28:24,200 Speaker 7: to this kind of like victim narrative where tech guys 463 00:28:24,240 --> 00:28:29,119 Speaker 7: feel like they are being persecuted by politicians. 464 00:28:30,760 --> 00:28:34,560 Speaker 1: Black Lives Matter and Me Too were powerful movements that 465 00:28:34,600 --> 00:28:38,600 Speaker 1: shook things up in San Francisco, even on the government level. 466 00:28:39,520 --> 00:28:43,160 Speaker 1: The Mayor's office released a plan for police reforms, the 467 00:28:43,160 --> 00:28:47,920 Speaker 1: city created a dedicated office to handle sexual harassment, and 468 00:28:48,280 --> 00:28:50,480 Speaker 1: this is where you start to see a split between 469 00:28:50,520 --> 00:28:54,040 Speaker 1: powerful tech companies and the people who work for them. 470 00:28:54,520 --> 00:28:58,440 Speaker 1: Many tech workers engaged meaningfully with these social justice movements. 471 00:28:59,200 --> 00:29:03,920 Speaker 1: In some cases, they organized against their own employers. At Google, 472 00:29:04,160 --> 00:29:08,160 Speaker 1: in twenty eighteen, employees staged a walkout over sexual harassment. 473 00:29:09,120 --> 00:29:12,440 Speaker 1: Facebook employees staged a virtual walkout in the wake of 474 00:29:12,480 --> 00:29:16,719 Speaker 1: George Floyd's death. Coinbase employees pushed their management to make 475 00:29:16,760 --> 00:29:21,480 Speaker 1: a statement supporting Black Lives Matter, and when management pushed back, 476 00:29:22,000 --> 00:29:28,360 Speaker 1: many workers left to the company in protest. I think 477 00:29:28,480 --> 00:29:32,280 Speaker 1: sometimes there's a tendency to conflate white collar tech employees 478 00:29:32,680 --> 00:29:35,560 Speaker 1: with the companies that they work for. As if they're 479 00:29:35,560 --> 00:29:38,280 Speaker 1: happy to cash their large paychecks and turn a blind 480 00:29:38,280 --> 00:29:42,280 Speaker 1: eye to their employer's actions. But there are many examples 481 00:29:42,280 --> 00:29:46,480 Speaker 1: of employees risking their livelihoods to push these companies to 482 00:29:46,560 --> 00:29:51,600 Speaker 1: do better. To me, it feels like the city's radical 483 00:29:51,680 --> 00:29:55,080 Speaker 1: history was having an effect on tech workers, even if 484 00:29:55,080 --> 00:29:59,040 Speaker 1: it didn't reach the executives. This was also around the 485 00:29:59,080 --> 00:30:02,400 Speaker 1: time of the so called TEXADUS, when the number of 486 00:30:02,480 --> 00:30:05,680 Speaker 1: companies left San Francisco and shut down their offices in 487 00:30:05,720 --> 00:30:10,800 Speaker 1: the city, including Coinbase, Stripe, PayPal, and Twitter, which had 488 00:30:10,840 --> 00:30:12,000 Speaker 1: been rebranded as. 489 00:30:12,160 --> 00:30:16,120 Speaker 7: X COVID lockdowns. A lot of these are, of course, 490 00:30:16,240 --> 00:30:19,200 Speaker 7: white collar type workplaces, but there are some factories sworled 491 00:30:19,200 --> 00:30:21,560 Speaker 7: in there, and you know, factory owners were no fan 492 00:30:21,840 --> 00:30:25,080 Speaker 7: of government shutdowns. It's a whole bunch of things. 493 00:30:26,280 --> 00:30:29,480 Speaker 1: I can't help but think of Bob Lee in this context, 494 00:30:30,040 --> 00:30:34,360 Speaker 1: in this particular time and place. By the time he died, 495 00:30:34,520 --> 00:30:37,320 Speaker 1: he no longer lived in San Francisco. He had moved 496 00:30:37,320 --> 00:30:39,960 Speaker 1: to Miami. A lot of his friends were there. It 497 00:30:40,040 --> 00:30:43,360 Speaker 1: had lacks or COVID restrictions, and it was also becoming 498 00:30:43,400 --> 00:30:47,200 Speaker 1: a center for cryptocurrency startups, which he was involved in. 499 00:30:48,520 --> 00:30:51,360 Speaker 1: That said, he was still flying back to San Francisco 500 00:30:51,480 --> 00:30:55,400 Speaker 1: all the time for work, to see his kids, to party, 501 00:30:56,200 --> 00:30:58,440 Speaker 1: and sure San Francisco was not the same place he 502 00:30:58,440 --> 00:31:02,120 Speaker 1: had moved to in his twenties, defined by that explosion 503 00:31:02,240 --> 00:31:06,800 Speaker 1: of new startups. But Bob, forever a curious person, was 504 00:31:06,840 --> 00:31:09,720 Speaker 1: still attracted to the things that made the city strange 505 00:31:10,320 --> 00:31:14,000 Speaker 1: Edgy a good place to have a good time. I 506 00:31:14,080 --> 00:31:18,320 Speaker 1: asked my colleague Max about this, about why Bob's peers 507 00:31:18,320 --> 00:31:21,720 Speaker 1: may have spread false rumors about the city that Bob, 508 00:31:21,840 --> 00:31:26,240 Speaker 1: by all accounts, still loved. For his part, Max was 509 00:31:26,240 --> 00:31:28,440 Speaker 1: willing to give the benefit of the doubt to some 510 00:31:28,480 --> 00:31:31,240 Speaker 1: of the people who pushed false narratives about Bob's death, 511 00:31:31,520 --> 00:31:36,240 Speaker 1: who connected it to crumbling liberal cities and progressive Das. 512 00:31:36,000 --> 00:31:39,640 Speaker 7: I hear a political thing, I also hear an honest 513 00:31:39,640 --> 00:31:42,160 Speaker 7: reaction to a very upsetting thing. This kind of thing 514 00:31:42,200 --> 00:31:46,800 Speaker 7: repeats itself all across the country, where like something unspeakable happens, 515 00:31:47,120 --> 00:31:50,200 Speaker 7: and in making sense of it, we end up swirling 516 00:31:50,240 --> 00:31:52,440 Speaker 7: in our politics or whatever we think is wrong. I've 517 00:31:52,440 --> 00:31:56,840 Speaker 7: had people in my own life react almost the exact 518 00:31:56,840 --> 00:31:59,640 Speaker 7: same way to violent crime, like where I live in 519 00:31:59,640 --> 00:32:04,560 Speaker 7: New York or whatever and like blame poor city governance 520 00:32:04,920 --> 00:32:08,000 Speaker 7: or bad national policy, or some combination of those two things. 521 00:32:08,120 --> 00:32:10,360 Speaker 1: It seems to me that people wanted to hold up 522 00:32:10,480 --> 00:32:14,240 Speaker 1: Boble's murder as revealing something about the city and decline 523 00:32:14,320 --> 00:32:17,720 Speaker 1: or the country and decline due to homelessness and crime. 524 00:32:18,320 --> 00:32:20,880 Speaker 1: What do you think that this death ultimately exposed? 525 00:32:23,280 --> 00:32:27,080 Speaker 7: You know, I'm just like really resistant to, like, especially 526 00:32:27,080 --> 00:32:32,360 Speaker 7: after hearing these guys spout off and like turn this 527 00:32:32,440 --> 00:32:35,840 Speaker 7: man's death into like a symbol of something. I'm like 528 00:32:35,920 --> 00:32:38,479 Speaker 7: really resistant to the idea that it meant something except 529 00:32:38,520 --> 00:32:42,920 Speaker 7: that it was horrible. It's just staggering to think about 530 00:32:43,640 --> 00:32:47,560 Speaker 7: how strong that political narrative was and how wrong it 531 00:32:47,720 --> 00:32:51,560 Speaker 7: was on a pure, like factual basis in this one case. 532 00:32:51,760 --> 00:32:54,600 Speaker 1: And how quickly it evaporated as soon as there was 533 00:32:54,640 --> 00:33:03,600 Speaker 1: a suspect arrested. Yeah, for Nina's part, as of this recording, 534 00:33:03,840 --> 00:33:08,920 Speaker 1: he's incarcerated and awaiting sentencing. He's hired new attorneys who 535 00:33:08,920 --> 00:33:10,840 Speaker 1: are going to make a motion for a new trial. 536 00:33:11,600 --> 00:33:16,840 Speaker 1: He's also filed civil suits against several media outlets. Bob's 537 00:33:16,880 --> 00:33:20,720 Speaker 1: family has filed a civil suit against multiple parties, including 538 00:33:20,800 --> 00:33:25,000 Speaker 1: Nima's family, alleging they knew about Nima's actions before he 539 00:33:25,080 --> 00:33:31,080 Speaker 1: was arrested and helped him hide and destroy evidence. If 540 00:33:31,120 --> 00:33:34,160 Speaker 1: the story of Bob Lee is anything I think it's 541 00:33:34,200 --> 00:33:39,320 Speaker 1: a cautionary tale about jumping to conclusions. Nima jumped to 542 00:33:39,320 --> 00:33:43,120 Speaker 1: conclusions about what happened to his sister, people on Twitter 543 00:33:43,200 --> 00:33:46,040 Speaker 1: jumped to conclusions about what happened to Bob Lee and 544 00:33:46,080 --> 00:33:49,760 Speaker 1: what it said about San Francisco. The media jumped to 545 00:33:49,800 --> 00:33:54,000 Speaker 1: conclusions about Bob's social life, and at every step it 546 00:33:54,040 --> 00:33:59,479 Speaker 1: only led to bad outcomes. A couple months ago, I 547 00:33:59,520 --> 00:34:02,920 Speaker 1: called up Bob's former wife, Krista, and I asked her 548 00:34:03,040 --> 00:34:06,320 Speaker 1: if she harbored any resentment for those who used Bob's 549 00:34:06,360 --> 00:34:10,439 Speaker 1: death to further a political narrative, and her answer was no, 550 00:34:10,640 --> 00:34:11,160 Speaker 1: not really. 551 00:34:12,719 --> 00:34:16,520 Speaker 8: Anger should be reserved for those that actually deserve it. 552 00:34:17,280 --> 00:34:19,719 Speaker 8: And it sucks, especially when you get hurt by the 553 00:34:19,719 --> 00:34:23,280 Speaker 8: people around you, But there's no reason to stay angry 554 00:34:23,280 --> 00:34:26,400 Speaker 8: at them. Obviously, the people involved in the murder of 555 00:34:26,440 --> 00:34:29,920 Speaker 8: Bob I will forever be angry with. But you know, 556 00:34:30,080 --> 00:34:35,320 Speaker 8: there is a lot of people that in the beginning, 557 00:34:35,360 --> 00:34:37,720 Speaker 8: it was a mystery, it was misguided, it was everyone 558 00:34:37,840 --> 00:34:41,080 Speaker 8: was kind of throwing theories out there, and there's still people. 559 00:34:41,280 --> 00:34:43,640 Speaker 8: There's still a lot of people throwing theories out there, 560 00:34:43,719 --> 00:34:47,680 Speaker 8: So yeah, you can't get mad at that. They were 561 00:34:47,719 --> 00:34:49,680 Speaker 8: just trying to help. They just wanted to talk, So 562 00:34:49,719 --> 00:34:56,880 Speaker 8: it's okay. 563 00:34:58,680 --> 00:35:02,880 Speaker 1: In death, Bob a symbol drifting further and further away 564 00:35:02,920 --> 00:35:06,719 Speaker 1: from the actual person. First he was a symbol for 565 00:35:06,800 --> 00:35:10,160 Speaker 1: the San Francisco doom loop. Then he became a symbol 566 00:35:10,160 --> 00:35:14,120 Speaker 1: for the excesses of the tech elite. In the midst 567 00:35:14,120 --> 00:35:16,359 Speaker 1: of all this, his friends kept telling me that they 568 00:35:16,400 --> 00:35:20,359 Speaker 1: didn't recognize Bob in any of the coverage. I think 569 00:35:20,400 --> 00:35:23,360 Speaker 1: this trend is bigger than Bob Lee. We have this 570 00:35:23,520 --> 00:35:27,040 Speaker 1: impulse to turn individual tragedies into collective I told you 571 00:35:27,160 --> 00:35:32,000 Speaker 1: sos to turn people into martyrs. Sometimes their death comes 572 00:35:32,040 --> 00:35:34,960 Speaker 1: to represent something that they maybe would have wanted nothing 573 00:35:35,000 --> 00:35:38,560 Speaker 1: to do with, and so I thought I would end 574 00:35:38,680 --> 00:35:42,640 Speaker 1: on a few concrete memories of him. One thing that 575 00:35:42,680 --> 00:35:45,920 Speaker 1: came up often was how much Bob loved facetiming people 576 00:35:46,680 --> 00:35:49,399 Speaker 1: at twelve that night while they're in bed. At six 577 00:35:49,440 --> 00:35:52,520 Speaker 1: in the morning, when they've just woken up, people would 578 00:35:52,520 --> 00:35:55,640 Speaker 1: hear their phone ring, think who the hell is facetiming 579 00:35:55,680 --> 00:35:59,879 Speaker 1: me right now and then see it was Bob. Another 580 00:36:00,080 --> 00:36:03,240 Speaker 1: friend mentioned to me that Bob kept on his phone 581 00:36:03,719 --> 00:36:06,160 Speaker 1: a list of people who were close to him so 582 00:36:06,200 --> 00:36:08,080 Speaker 1: that he could be reminded to check in on them 583 00:36:08,080 --> 00:36:12,640 Speaker 1: from time to time. One of Bob's friends from his 584 00:36:12,680 --> 00:36:16,799 Speaker 1: college fraternity hosted on an online memorial thanking Bob for 585 00:36:16,920 --> 00:36:20,160 Speaker 1: quote programming a website for me that was due the 586 00:36:20,160 --> 00:36:23,759 Speaker 1: next day. It was an utter mess. Bob finished the 587 00:36:23,760 --> 00:36:27,239 Speaker 1: website program in fifteen minutes, just so we could go 588 00:36:27,320 --> 00:36:30,120 Speaker 1: out and get a beer. He always did things like that. 589 00:36:34,320 --> 00:36:36,799 Speaker 1: In the last year of his life, Bob moved to 590 00:36:36,840 --> 00:36:40,640 Speaker 1: Miami with his elderly father as his roommate. It was 591 00:36:40,680 --> 00:36:43,719 Speaker 1: a long search for an apartment because Bob was bent 592 00:36:43,840 --> 00:36:47,399 Speaker 1: on finding a place that had two master bedrooms, one 593 00:36:47,440 --> 00:36:52,040 Speaker 1: for him and one for his dad. His dad didn't 594 00:36:52,080 --> 00:36:55,600 Speaker 1: know about this until after Bob had died. The real 595 00:36:55,760 --> 00:36:58,960 Speaker 1: estate agent mentioned it to him as he was moving out. 596 00:36:59,600 --> 00:37:01,840 Speaker 1: Bob and his dad only got to live in that 597 00:37:01,920 --> 00:37:38,880 Speaker 1: department for six months. Foundering is reported hosted and executive 598 00:37:38,920 --> 00:37:43,560 Speaker 1: produced by me Sean Wen Eric Mesiti's mental produced our show. 599 00:37:44,080 --> 00:37:47,680 Speaker 1: Bart Warshaw is our audio engineer Our story editors are 600 00:37:47,760 --> 00:37:52,120 Speaker 1: Joshua Brustein, Tom Giles, Anne Vander May, and Nicole Beamster Bower. 601 00:37:52,640 --> 00:37:56,880 Speaker 1: Voice acting by Mark Laydwarf. Be sure to subscribe and 602 00:37:56,920 --> 00:38:00,000 Speaker 1: if you like our show, leave a review. Most important, 603 00:38:00,520 --> 00:38:08,200 Speaker 1: tell your friends told to blend to fl