1
00:00:00,080 --> 00:00:03,880
Speaker 1: Hello the Internet, and welcome to trend in the clown.

2
00:00:05,480 --> 00:00:11,480
Speaker 1: I'm Jack O'Brien. That's Miles Gray. It is Monday afternoon,

3
00:00:11,920 --> 00:00:17,080
Speaker 1: and this is what's trending. Oh yeah, hey, Miles. Bernie

4
00:00:17,160 --> 00:00:22,640
Speaker 1: endorsed Biden. Baby, no way, he never saw that coming.

5
00:00:22,920 --> 00:00:25,280
Speaker 1: He just did it last time when he got baked

6
00:00:25,320 --> 00:00:30,080
Speaker 1: by the establishment. Pick uh yeah, yeah, he took He

7
00:00:30,120 --> 00:00:33,160
Speaker 1: took a little while longer, right last time. Yeah, yeah, yeah.

8
00:00:33,280 --> 00:00:37,680
Speaker 1: I'm curious to know what's going on there. I mean, obviously,

9
00:00:37,760 --> 00:00:41,440
Speaker 1: like at this point, the bottom line for most people

10
00:00:41,520 --> 00:00:44,040
Speaker 1: is just like, okay, well then now we have to

11
00:00:44,200 --> 00:00:50,159
Speaker 1: defeat big orange monster. Fuck. But now, like I'm curious

12
00:00:50,200 --> 00:00:52,519
Speaker 1: to know if there if there was something behind the

13
00:00:52,560 --> 00:00:55,200
Speaker 1: scenes there that Biden might add to his platform that

14
00:00:55,320 --> 00:00:58,840
Speaker 1: might make it more palatable for people on the left

15
00:00:58,920 --> 00:01:02,520
Speaker 1: left too, like want to vote for him without fully

16
00:01:02,560 --> 00:01:06,800
Speaker 1: pinching their nose off at the polls. Um, but yeah,

17
00:01:07,000 --> 00:01:11,959
Speaker 1: that would be nice. Some Berney supporters are like, yeah, okay,

18
00:01:12,000 --> 00:01:15,759
Speaker 1: that's kind of what we expected. Some feel betrayed. I mean, yeah,

19
00:01:15,959 --> 00:01:20,240
Speaker 1: it depends on how disgusting you find the establishment and

20
00:01:20,319 --> 00:01:23,600
Speaker 1: the level of business as usual in this country. Because

21
00:01:23,600 --> 00:01:26,400
Speaker 1: I know, yeah, there's definitely a small group of people

22
00:01:26,440 --> 00:01:30,280
Speaker 1: who are, like, you know, completely not with Joe Biden.

23
00:01:30,600 --> 00:01:33,000
Speaker 1: I don't think that's a huge amount of people, because

24
00:01:33,000 --> 00:01:36,840
Speaker 1: I think most people, despite you know, disagreeing with a

25
00:01:36,880 --> 00:01:41,920
Speaker 1: lot of the platform and policies of like a Joe Biden, know,

26
00:01:42,040 --> 00:01:43,640
Speaker 1: at the end of the day, like we're going to

27
00:01:43,760 --> 00:01:46,080
Speaker 1: have to live in a world with one of these

28
00:01:46,120 --> 00:01:49,880
Speaker 1: people as president. Um. And I mean for some people

29
00:01:49,880 --> 00:01:53,160
Speaker 1: it's truly like, which person who has been accused of

30
00:01:53,200 --> 00:01:55,639
Speaker 1: sexual assault am I going to vote for? Is I

31
00:01:55,720 --> 00:01:58,000
Speaker 1: hear people say that? And now that story is actually

32
00:01:58,040 --> 00:02:00,640
Speaker 1: starting to pick up more. Yeah, So do we know

33
00:02:00,680 --> 00:02:03,520
Speaker 1: what changed? Because that's the second thing on our list

34
00:02:03,560 --> 00:02:07,160
Speaker 1: of trending topics is a terror read who is the

35
00:02:07,200 --> 00:02:10,960
Speaker 1: former Biden staffer who has accused him of sexually assaulting

36
00:02:10,960 --> 00:02:15,320
Speaker 1: her in the nineties. Yeah, Like, I think in what

37
00:02:15,440 --> 00:02:18,600
Speaker 1: it looks like is just you know, I think the

38
00:02:18,639 --> 00:02:21,839
Speaker 1: New York Times there's one Yahoo article saying that there

39
00:02:21,960 --> 00:02:26,000
Speaker 1: was a New York Times investigation, um, but they said

40
00:02:26,080 --> 00:02:29,520
Speaker 1: they found no corroboration outside of read when they when

41
00:02:29,520 --> 00:02:32,240
Speaker 1: they looked into thousand and eight. Some were just sort

42
00:02:32,280 --> 00:02:34,760
Speaker 1: of like, well, the New York Times didn't really make

43
00:02:34,880 --> 00:02:38,880
Speaker 1: much of this um and then some other depending on

44
00:02:39,000 --> 00:02:43,160
Speaker 1: like how progressive the sites are, like AlterNet is, you know,

45
00:02:43,280 --> 00:02:46,120
Speaker 1: full on just talking about like what the allegations are

46
00:02:46,120 --> 00:02:50,520
Speaker 1: and things like that. ABC is describing it as that

47
00:02:50,520 --> 00:02:55,359
Speaker 1: there's someone made allegations, and that's a yeah. I mean,

48
00:02:55,360 --> 00:02:58,280
Speaker 1: that's more than what was happening weeks ago when this

49
00:02:58,440 --> 00:03:01,640
Speaker 1: story first broke, right. I mean we were specifically asking

50
00:03:01,639 --> 00:03:04,760
Speaker 1: the question, why is nobody talking about this last week

51
00:03:04,919 --> 00:03:07,520
Speaker 1: when like it was I mean last week, it was

52
00:03:07,680 --> 00:03:11,000
Speaker 1: a week week and a half after people had begun

53
00:03:11,080 --> 00:03:14,040
Speaker 1: talking about it. But I don't know. I mean, so

54
00:03:14,120 --> 00:03:18,280
Speaker 1: the New York Times on Sunday published an article examining

55
00:03:18,280 --> 00:03:22,640
Speaker 1: tear Reid's sexual assault allegations against Joe Biden. UM. So

56
00:03:22,720 --> 00:03:25,240
Speaker 1: I think it's more of like a history of the

57
00:03:25,240 --> 00:03:27,560
Speaker 1: fact checking process, which is something that I was like

58
00:03:27,840 --> 00:03:32,080
Speaker 1: wondering when they were going to release. UM. But I

59
00:03:32,080 --> 00:03:35,920
Speaker 1: don't know necessarily the details of that story, so we'll

60
00:03:35,920 --> 00:03:38,040
Speaker 1: have to come back to it. I know that they

61
00:03:38,200 --> 00:03:41,320
Speaker 1: edited that Times article like really quickly after it was

62
00:03:41,360 --> 00:03:43,080
Speaker 1: published on Sunday because I was a lot of other

63
00:03:43,120 --> 00:03:45,720
Speaker 1: talk about it. Because there when you look on the

64
00:03:45,720 --> 00:03:48,920
Speaker 1: Internet archive of like what how the article was originally

65
00:03:48,960 --> 00:03:51,840
Speaker 1: published in the Times, there was this paragraph in there

66
00:03:51,880 --> 00:03:55,120
Speaker 1: that said, quote no other allegations about sexual assault surfaced

67
00:03:55,360 --> 00:03:58,400
Speaker 1: in the course of reporting, nor did any former Biden

68
00:03:58,520 --> 00:04:02,440
Speaker 1: staff members corroborate and details of misreads allegations. The Times

69
00:04:02,440 --> 00:04:05,600
Speaker 1: found no pattern of sexual misconduct by Mr Biden beyond

70
00:04:05,640 --> 00:04:09,440
Speaker 1: the hugs, kisses and touching that women previously said made

71
00:04:09,480 --> 00:04:15,000
Speaker 1: them uncomfortable. Oh, got it, and now they took out

72
00:04:15,240 --> 00:04:17,360
Speaker 1: they were just sort of like they took out that

73
00:04:17,480 --> 00:04:19,800
Speaker 1: sentence and like shortened it a bit, because yeah, like

74
00:04:19,880 --> 00:04:22,520
Speaker 1: when they even say it like that. No, nothing odd

75
00:04:22,560 --> 00:04:26,400
Speaker 1: except the history of people saying he's like constantly violating

76
00:04:26,400 --> 00:04:31,000
Speaker 1: their personal space and making them feel uncomfortable. Yeah, it's

77
00:04:31,040 --> 00:04:34,320
Speaker 1: just weird that it took this long. It's almost like

78
00:04:35,400 --> 00:04:37,279
Speaker 1: this is the thing you would have expected to be

79
00:04:37,320 --> 00:04:42,120
Speaker 1: coming out the day after the allegations started servicing again,

80
00:04:42,640 --> 00:04:49,520
Speaker 1: Um so I don't know, I guess if there's a guess. Yeah,

81
00:04:49,560 --> 00:04:52,040
Speaker 1: but I mean also to just in like the timeline

82
00:04:52,040 --> 00:04:54,360
Speaker 1: of the nomination and things like that, it's like, well,

83
00:04:54,480 --> 00:04:56,600
Speaker 1: it's not like this is gonna harm Biden to get

84
00:04:56,600 --> 00:05:02,120
Speaker 1: the nomination and whatever then, because it seems like on

85
00:05:02,160 --> 00:05:05,360
Speaker 1: like Twitter, you see like you know, big d Democrats

86
00:05:05,400 --> 00:05:08,400
Speaker 1: and Republicans going into the same bag to launch assaults

87
00:05:08,440 --> 00:05:11,120
Speaker 1: at each other, being like, yeah, well you're guy is

88
00:05:11,200 --> 00:05:17,039
Speaker 1: also accused. Huh yeah, okay, maybe it'll cancel each other out.

89
00:05:17,160 --> 00:05:19,240
Speaker 1: I don't, I don't. I'm not sure how how they

90
00:05:19,240 --> 00:05:24,400
Speaker 1: think they can approach this, like sincerely, because this is

91
00:05:24,400 --> 00:05:27,400
Speaker 1: it's it's really it's a story that any other time,

92
00:05:27,440 --> 00:05:29,240
Speaker 1: if this this is the thing, like and if this

93
00:05:29,279 --> 00:05:32,159
Speaker 1: were any candidate on the right, the left would be screaming,

94
00:05:32,240 --> 00:05:34,440
Speaker 1: or at least the Democrats in Congress would be like,

95
00:05:34,560 --> 00:05:38,039
Speaker 1: we need a real thorough investigation to vet these allegations.

96
00:05:38,520 --> 00:05:41,880
Speaker 1: And we're seeing just sort of that selective outrage play

97
00:05:41,880 --> 00:05:44,320
Speaker 1: out on the left now, not on the left, on

98
00:05:44,360 --> 00:05:49,240
Speaker 1: the Democrats, on the Democrats rather. George Stephanopolis is trending.

99
00:05:49,520 --> 00:05:54,840
Speaker 1: He tested positive for COVID nineteen. He said he's basically

100
00:05:54,880 --> 00:05:59,799
Speaker 1: asymptomatic thus far, so another famous person who got tested

101
00:05:59,800 --> 00:06:03,200
Speaker 1: to by not having any symptoms, whereas a lot of

102
00:06:03,200 --> 00:06:07,800
Speaker 1: people around the country who are really struggling with the

103
00:06:07,839 --> 00:06:11,040
Speaker 1: illness can't get tested. I wonder if you can go

104
00:06:11,240 --> 00:06:14,160
Speaker 1: to a clinic and say I'm verified on Twitter? Does

105
00:06:14,200 --> 00:06:17,520
Speaker 1: that change anything for me to get tested? Now? I

106
00:06:17,520 --> 00:06:19,760
Speaker 1: mean I'm not even verified, But if that will help,

107
00:06:19,880 --> 00:06:23,400
Speaker 1: then I guess that's at incentive. Yeah, it seems like it.

108
00:06:24,120 --> 00:06:27,400
Speaker 1: I'm trying to figure out if there are more news

109
00:06:27,480 --> 00:06:30,080
Speaker 1: anchors who are getting it because they continue to work

110
00:06:30,120 --> 00:06:33,880
Speaker 1: and and they're working in New York for the most part,

111
00:06:34,600 --> 00:06:38,839
Speaker 1: or if they're just so many news anchors that they're

112
00:06:38,880 --> 00:06:41,520
Speaker 1: just the people we've heard of. There's so they're like

113
00:06:41,560 --> 00:06:46,000
Speaker 1: the most populous of types of famous people who are

114
00:06:46,200 --> 00:06:49,279
Speaker 1: over a certain age. But I don't know, I don't know.

115
00:06:49,360 --> 00:06:52,520
Speaker 1: I think it's just like anything we you know, without

116
00:06:52,560 --> 00:06:55,320
Speaker 1: these tests, we just don't know really do. We just

117
00:06:55,600 --> 00:06:58,520
Speaker 1: don't get it how how prevalent it really is. I

118
00:06:58,520 --> 00:07:00,640
Speaker 1: mean because we were even you're ar that article in

119
00:07:00,640 --> 00:07:03,560
Speaker 1: our text set about how like in California, like experts

120
00:07:03,600 --> 00:07:05,960
Speaker 1: suspect that it was COVID nineteen was in the state

121
00:07:06,000 --> 00:07:10,480
Speaker 1: like months ago? Yeah yeah, like before Uh any like

122
00:07:10,520 --> 00:07:13,760
Speaker 1: I think they're saying back in December it was already spreading.

123
00:07:14,400 --> 00:07:20,600
Speaker 1: Um is the suspicion, and uh, you know they're they're Yeah,

124
00:07:20,600 --> 00:07:24,040
Speaker 1: we just don't know I've known people who were sick

125
00:07:24,080 --> 00:07:27,400
Speaker 1: in the months before it supposedly got here, but they

126
00:07:27,480 --> 00:07:29,920
Speaker 1: seemed to have a lot of the symptoms that you

127
00:07:29,960 --> 00:07:34,560
Speaker 1: would associate with COVID nineteen. And it'll be interesting when

128
00:07:34,600 --> 00:07:38,240
Speaker 1: we were able to do the blood serum testing. But

129
00:07:38,680 --> 00:07:40,680
Speaker 1: you know, like I said on tomorrow's episode, I think

130
00:07:41,160 --> 00:07:45,480
Speaker 1: that's going to come down to again, the George Stephanopoli

131
00:07:45,560 --> 00:07:48,760
Speaker 1: and uh Rudy Goberts of the world are going to

132
00:07:49,160 --> 00:07:53,600
Speaker 1: have access to those tests, probably before the rest of

133
00:07:53,640 --> 00:07:55,240
Speaker 1: the world. Oh no, I heard they were going to

134
00:07:55,360 --> 00:07:59,440
Speaker 1: give it to the most vulnerable and uh poverty stricken

135
00:07:59,560 --> 00:08:04,760
Speaker 1: community these totally, that's that's what in a fever team.

136
00:08:04,800 --> 00:08:10,520
Speaker 1: I mean, honestly, a birthdate based a birthdate based lottery

137
00:08:10,960 --> 00:08:14,520
Speaker 1: makes more sense than what we're doing currently, which is

138
00:08:14,600 --> 00:08:18,280
Speaker 1: just income based giving it out. Yeah, it's income based.

139
00:08:18,320 --> 00:08:20,720
Speaker 1: It's based on like, yeah, it might not be like

140
00:08:21,000 --> 00:08:22,920
Speaker 1: I'll pay you a thousand dollars to the test, but

141
00:08:23,000 --> 00:08:26,360
Speaker 1: it's like this rich person has a rich person doctor

142
00:08:26,480 --> 00:08:29,960
Speaker 1: who's able to get them access to things, and like,

143
00:08:30,320 --> 00:08:33,920
Speaker 1: you know, there there's just ways in America. It's not

144
00:08:34,040 --> 00:08:39,000
Speaker 1: it's not it's the opposite of a meritocracy and finally,

145
00:08:39,679 --> 00:08:44,320
Speaker 1: prayers for cat is trending. Oh for Karl Anthony Towns

146
00:08:44,760 --> 00:08:48,640
Speaker 1: Um because his mother passed away from COVID nineteen. It

147
00:08:48,679 --> 00:08:52,760
Speaker 1: was like a huge thing weeks ago because he remember

148
00:08:53,200 --> 00:08:55,720
Speaker 1: he was, yeah, when his mom had contracted it. He

149
00:08:55,800 --> 00:08:59,760
Speaker 1: was really distraught and trying to really you know, in

150
00:09:00,000 --> 00:09:03,160
Speaker 1: courage people to take this seriously and social distancing seriously

151
00:09:03,160 --> 00:09:06,760
Speaker 1: and know that this this has real effects. Um. Yeah,

152
00:09:06,760 --> 00:09:10,439
Speaker 1: and unfortunately, Yeah, his mother had passed away, and people

153
00:09:10,440 --> 00:09:13,360
Speaker 1: are sort of rallying around him and get showing their

154
00:09:13,400 --> 00:09:17,000
Speaker 1: support and things like that, because you know, like anyone

155
00:09:17,040 --> 00:09:23,080
Speaker 1: you for athletes to like mothers are typically they're always

156
00:09:23,080 --> 00:09:24,840
Speaker 1: the ones saying that they're the real m v P.

157
00:09:24,960 --> 00:09:28,200
Speaker 1: And I can only imagine, uh, you know, anyone who

158
00:09:28,240 --> 00:09:31,240
Speaker 1: has a functioning relationship with their mothers absolutely awful to

159
00:09:31,240 --> 00:09:34,360
Speaker 1: go through something like that. So yeah, that's that's why

160
00:09:34,360 --> 00:09:36,640
Speaker 1: that's trending. But a lot of people are, you know,

161
00:09:36,840 --> 00:09:40,360
Speaker 1: sharing kind words and things like that. Yeah, I remember

162
00:09:40,400 --> 00:09:43,160
Speaker 1: he was one of the first kind of famous people

163
00:09:43,240 --> 00:09:47,560
Speaker 1: who really seemed to have a i don't know, like

164
00:09:47,640 --> 00:09:52,000
Speaker 1: a public appearance that was like that that's fully relatable

165
00:09:52,040 --> 00:09:57,120
Speaker 1: and also like yeah, super effect or um yeah, ship,

166
00:09:57,480 --> 00:10:00,760
Speaker 1: and it was before people were fully taking it seriously,

167
00:10:00,800 --> 00:10:05,160
Speaker 1: I feel like, yeah, exactly, And I think that's why

168
00:10:05,200 --> 00:10:07,839
Speaker 1: it's like, you know, even though you know, I'm sure

169
00:10:07,880 --> 00:10:10,440
Speaker 1: at this point most people in their lives have heard

170
00:10:10,520 --> 00:10:14,520
Speaker 1: of or know someone who has possibly contracted COVID nineteen

171
00:10:14,640 --> 00:10:18,560
Speaker 1: or is dealing with that, and yeah, just it you

172
00:10:19,200 --> 00:10:22,320
Speaker 1: the humanity of it becomes very real to everyone. You know.

173
00:10:23,320 --> 00:10:25,720
Speaker 1: You hope that we can continue to like really look

174
00:10:25,760 --> 00:10:28,640
Speaker 1: at things like this to motivate us, to to put

175
00:10:28,640 --> 00:10:33,600
Speaker 1: our needs of normalcy aside, because ultimately, like you know,

176
00:10:33,800 --> 00:10:37,200
Speaker 1: really really cutting down on our ability or the possibility

177
00:10:37,240 --> 00:10:40,319
Speaker 1: of spreading any kind of infection you know, can lead

178
00:10:40,360 --> 00:10:45,480
Speaker 1: to the outcomes like this. So yeah, tough, tough, tough time. Yeah.

179
00:10:45,520 --> 00:10:48,480
Speaker 1: I really said, we'll shout out to him, shout out

180
00:10:48,520 --> 00:10:52,120
Speaker 1: to his whole family and uh, all the the people

181
00:10:52,200 --> 00:10:59,000
Speaker 1: of the Minnesota Timberwolves organization. That's really yeah. And and

182
00:10:59,280 --> 00:11:02,880
Speaker 1: just to remind people, you know, Karl Anthony Towns has

183
00:11:02,920 --> 00:11:05,719
Speaker 1: a big microphone, big megaphone to be able to let

184
00:11:05,760 --> 00:11:08,960
Speaker 1: people know what has happened in his life, but there

185
00:11:09,000 --> 00:11:14,200
Speaker 1: are thousands, tens of thousands of people who are also

186
00:11:14,360 --> 00:11:18,199
Speaker 1: touched in an identical way, who need just as much

187
00:11:18,200 --> 00:11:21,160
Speaker 1: support and people and people who need just as what's

188
00:11:21,160 --> 00:11:23,880
Speaker 1: support getting back to what normalcy is for them after this.

189
00:11:24,000 --> 00:11:28,680
Speaker 1: So you know, we gotta gotta keep everybody in our thoughts,

190
00:11:28,920 --> 00:11:32,040
Speaker 1: be there for each other. Yep. Alright, guys, be well,

191
00:11:32,400 --> 00:11:38,480
Speaker 1: be safe, uh and uh stay home. We will be

192
00:11:38,520 --> 00:11:42,319
Speaker 1: back tomorrow with a great episode of the podcast, and

193
00:11:42,480 --> 00:11:44,839
Speaker 1: we'll talk to you guys them by later