1
00:00:00,080 --> 00:00:01,599
Speaker 1: Hey guys, Saga and Crystal here.

2
00:00:01,680 --> 00:00:05,240
Speaker 2: Independent media just played a truly massive role in this election,

3
00:00:05,360 --> 00:00:07,840
Speaker 2: and we are so excited about what that means for

4
00:00:07,880 --> 00:00:08,720
Speaker 2: the future of the show.

5
00:00:08,880 --> 00:00:10,760
Speaker 1: This is the only place where you can find honest

6
00:00:10,760 --> 00:00:13,280
Speaker 1: perspectives from the left and the right that simply does

7
00:00:13,320 --> 00:00:14,680
Speaker 1: not exist anywhere else.

8
00:00:14,760 --> 00:00:17,080
Speaker 2: So if that is something that's important to you, please

9
00:00:17,120 --> 00:00:19,599
Speaker 2: go to Breakingpoints dot com. Become a member today and

10
00:00:19,640 --> 00:00:22,800
Speaker 2: you'll get access to our full shows, unedited, ad free,

11
00:00:22,800 --> 00:00:25,600
Speaker 2: and all put together for you every morning in your inbox.

12
00:00:25,680 --> 00:00:27,560
Speaker 1: We need your help to build the future of independent

13
00:00:27,560 --> 00:00:29,920
Speaker 1: news media and we hope to see you at Breakingpoints

14
00:00:29,960 --> 00:00:35,800
Speaker 1: dot com. Good morning, everybody, Happy Monday. Have an amazing

15
00:00:35,840 --> 00:00:37,360
Speaker 1: show for everybody today. What do we have, Crystal?

16
00:00:37,520 --> 00:00:39,519
Speaker 2: Indeed, we do a lot of interesting things happening in

17
00:00:39,520 --> 00:00:43,239
Speaker 2: the world. So Trump fires the statistician who gave him

18
00:00:43,240 --> 00:00:45,600
Speaker 2: in jobs report numbers that he was not too happy about.

19
00:00:45,640 --> 00:00:47,959
Speaker 2: So we'll talk about those numbers and also the fallout

20
00:00:48,000 --> 00:00:52,640
Speaker 2: from that firing. Trump is also contemplating a Galaine Maxwell pardon,

21
00:00:53,120 --> 00:00:55,600
Speaker 2: and she has been moved to a cushy club fed

22
00:00:55,680 --> 00:00:56,600
Speaker 2: in Sager's hometown.

23
00:00:56,720 --> 00:00:58,920
Speaker 1: That's right town that I was born in I've got

24
00:00:58,960 --> 00:01:01,120
Speaker 1: all the lowdown on that. I'll try I'll try to

25
00:01:01,120 --> 00:01:03,840
Speaker 1: get some inside scoop on her and Elizabeth Holmes relationship.

26
00:01:04,240 --> 00:01:05,240
Speaker 1: I'll try to report back.

27
00:01:05,560 --> 00:01:07,959
Speaker 2: In addition, bunch of updates with regard to Israel. But

28
00:01:08,040 --> 00:01:11,360
Speaker 2: we had Mike Huckabee, Steve Whitcoff, and Mike Johnson all

29
00:01:11,520 --> 00:01:13,840
Speaker 2: visiting Israel, so we have some updates there. They are

30
00:01:13,880 --> 00:01:19,080
Speaker 2: issuing an ultimatum to Hamas as the starvation continues, TikTok

31
00:01:19,360 --> 00:01:24,200
Speaker 2: is installing a former IDF sensor on their platform. As

32
00:01:24,280 --> 00:01:27,360
Speaker 2: the all out assault on campus free speech continues from

33
00:01:27,400 --> 00:01:31,440
Speaker 2: the Trump administration, Tim Dillon making some pretty uh I

34
00:01:31,440 --> 00:01:34,559
Speaker 2: would say, very incisive comments about Bary Weiss's free press

35
00:01:34,680 --> 00:01:38,240
Speaker 2: and the reason that she is garnering the valuation for

36
00:01:38,319 --> 00:01:39,720
Speaker 2: her company that she has been able.

37
00:01:39,560 --> 00:01:40,679
Speaker 1: To order billion dollars.

38
00:01:40,880 --> 00:01:42,800
Speaker 3: Yeah, okay, We've got a lot to say about that.

39
00:01:42,840 --> 00:01:45,240
Speaker 2: One having a little bit of knowledge about media companies

40
00:01:45,280 --> 00:01:47,440
Speaker 2: and what their actual value is, so we'll dig into that.

41
00:01:47,800 --> 00:01:50,880
Speaker 2: And we also have a fantastic gas Jasper Nathaniel who

42
00:01:50,920 --> 00:01:53,720
Speaker 2: has been relentlessly focusing on what is going on in

43
00:01:53,760 --> 00:01:55,560
Speaker 2: the West Bank. Of course, while all rise have been

44
00:01:55,560 --> 00:01:59,240
Speaker 2: on Gaza understandably, the project of annexation, he says, in

45
00:01:59,280 --> 00:02:01,800
Speaker 2: the West Bank basically complete. And you'll call this was

46
00:02:01,840 --> 00:02:06,160
Speaker 2: a key campaign promise from Trump directly to Maria Maddilson,

47
00:02:06,200 --> 00:02:08,680
Speaker 2: who gave him one hundred million dollars, and they were

48
00:02:08,720 --> 00:02:10,680
Speaker 2: making good on that pledge to her.

49
00:02:10,880 --> 00:02:12,680
Speaker 3: So a lot to get to there.

50
00:02:13,160 --> 00:02:15,239
Speaker 2: But we also considered just blowing up the whole show

51
00:02:15,280 --> 00:02:18,320
Speaker 2: so Sager could express his extreme discuss for two hours

52
00:02:18,360 --> 00:02:21,400
Speaker 2: at the Rose Garden. I want the potential in the

53
00:02:21,440 --> 00:02:23,040
Speaker 2: Gilded ballroom also, that's fine.

54
00:02:23,040 --> 00:02:26,919
Speaker 1: I desperately wanted to go off one day, perhaps that

55
00:02:27,000 --> 00:02:28,760
Speaker 1: I will have a guest on from the White House

56
00:02:28,840 --> 00:02:32,079
Speaker 1: Historical Association or something. These are things that absolutely zero

57
00:02:32,280 --> 00:02:35,280
Speaker 1: buddy a zero other people care about, but are deep

58
00:02:35,400 --> 00:02:37,680
Speaker 1: are right about that, I hope, so listen. I would

59
00:02:37,680 --> 00:02:41,560
Speaker 1: hope that Americans have a veneration for aesthetics, for history,

60
00:02:41,960 --> 00:02:43,920
Speaker 1: for the way that our White House should look to

61
00:02:44,040 --> 00:02:46,840
Speaker 1: the world, and to not turn it into a mar

62
00:02:46,880 --> 00:02:50,240
Speaker 1: A Lago or Saudi Arabian style of state. But you know,

63
00:02:50,360 --> 00:02:52,600
Speaker 1: I don't know. I've started to give up in terms

64
00:02:52,639 --> 00:02:55,119
Speaker 1: of that, But perhaps you're right, Perhaps, yeah, maybe.

65
00:02:54,880 --> 00:02:58,440
Speaker 3: We can negative Garden is just like it just is grot.

66
00:02:58,639 --> 00:03:01,560
Speaker 1: Maybe we can negatively polarized liberals into caring about some

67
00:03:01,600 --> 00:03:04,680
Speaker 1: of these things. I'm fine, you know, it's a big tent.

68
00:03:05,040 --> 00:03:07,680
Speaker 1: It's a big tent, and welcome anybody else in there.

69
00:03:07,680 --> 00:03:09,840
Speaker 1: Before we get to that, we do have the show

70
00:03:09,919 --> 00:03:12,640
Speaker 1: available on Facebook now. Apparently that was something some people

71
00:03:12,639 --> 00:03:15,079
Speaker 1: are asking, so we've got links down in the description.

72
00:03:15,160 --> 00:03:16,880
Speaker 1: You want to share it there on Facebook, if you're

73
00:03:16,919 --> 00:03:19,680
Speaker 1: active on the platform, go for it. It is available there.

74
00:03:19,960 --> 00:03:22,880
Speaker 1: And before you know as well, we have the show

75
00:03:22,880 --> 00:03:25,359
Speaker 1: of course Breakingpoints dot com where you can support us

76
00:03:25,560 --> 00:03:29,359
Speaker 1: monthly and yearly. Memberships has been tremendously helpful. You guys

77
00:03:29,360 --> 00:03:32,680
Speaker 1: saw our interview with Alyssa Slotkin massively viral. It's one

78
00:03:32,720 --> 00:03:34,640
Speaker 1: of the most viral things that we've done in quite

79
00:03:34,639 --> 00:03:37,000
Speaker 1: some time. Our interview, you know, just followed later on

80
00:03:37,080 --> 00:03:42,320
Speaker 1: by the Gasla Humanitarian Foundation, the whistleblower, the former Green Beret,

81
00:03:42,400 --> 00:03:47,000
Speaker 1: and Aguilar. Similarly, I've received a ton of feedback, you know,

82
00:03:47,080 --> 00:03:49,360
Speaker 1: and just inbound in terms of people who have had that.

83
00:03:49,400 --> 00:03:51,960
Speaker 1: So our ability to do these newsmaking interviews is entirely

84
00:03:52,240 --> 00:03:54,960
Speaker 1: propped up because of our premium members So Breakingpoints dot

85
00:03:55,000 --> 00:03:56,960
Speaker 1: com if you are able to help us out. If

86
00:03:56,960 --> 00:03:59,680
Speaker 1: you can't do that, no worries. Just remember subscribe to

87
00:03:59,680 --> 00:04:02,040
Speaker 1: this huge video and or if you listen to a podcast,

88
00:04:02,200 --> 00:04:04,360
Speaker 1: take a favorite episode, send it to a friend. It's

89
00:04:04,400 --> 00:04:07,280
Speaker 1: tremendously helpful to us with that list. With the economy,

90
00:04:07,280 --> 00:04:09,880
Speaker 1: as Crystal said, so Donald Trump, the president has now

91
00:04:09,920 --> 00:04:12,760
Speaker 1: fired the head of the Bureau of Labor Statistics, go

92
00:04:12,800 --> 00:04:15,600
Speaker 1: and put this up there on the screen, removing the

93
00:04:15,640 --> 00:04:20,240
Speaker 1: official quote overseeing jobs data after a dismal employment report.

94
00:04:20,440 --> 00:04:24,120
Speaker 1: So you may ask, what the hell is going on here?

95
00:04:24,160 --> 00:04:27,600
Speaker 1: And so all of it stems back to report on Friday,

96
00:04:28,080 --> 00:04:32,599
Speaker 1: after a new BLS report on jobs, which showed some

97
00:04:32,760 --> 00:04:37,800
Speaker 1: precarity in the US economy as well as some previous revisions.

98
00:04:37,839 --> 00:04:40,279
Speaker 1: And so this is a big question. It showed just

99
00:04:40,320 --> 00:04:44,080
Speaker 1: seventy three thousand jobs added last month, two hundred and

100
00:04:44,080 --> 00:04:46,320
Speaker 1: fifty eight thousand fewer jobs were created in May and

101
00:04:46,400 --> 00:04:49,560
Speaker 1: June than had previously an estimated. Quote. The report then

102
00:04:49,640 --> 00:04:53,520
Speaker 1: suggested the economy had sharply weakened during Trump's tenure, a

103
00:04:53,560 --> 00:04:56,760
Speaker 1: pattern consistent slowed down with economic growth during the first

104
00:04:56,760 --> 00:04:58,960
Speaker 1: half of the year, and an increase in inflation during

105
00:04:59,040 --> 00:05:02,160
Speaker 1: June that appeared to reflect the price pressures created by

106
00:05:02,240 --> 00:05:05,400
Speaker 1: the President's tariff. So who was this person? She was

107
00:05:05,480 --> 00:05:08,880
Speaker 1: nominated by Biden in twenty twenty three, became the commissioner

108
00:05:08,920 --> 00:05:11,320
Speaker 1: of the BLS in twenty twenty four January of twenty

109
00:05:11,360 --> 00:05:14,240
Speaker 1: twenty four. They usually serve four year terms. The Senate

110
00:05:14,279 --> 00:05:18,240
Speaker 1: confirmed her eighty six to eight. Jd Vance did vote yes.

111
00:05:18,400 --> 00:05:21,839
Speaker 1: In case anybody is wondering, much of this is about

112
00:05:21,880 --> 00:05:25,080
Speaker 1: these revisions, and you know, just to explain a little

113
00:05:25,080 --> 00:05:27,919
Speaker 1: bit behind this, because I think it's actually fair for

114
00:05:28,000 --> 00:05:30,960
Speaker 1: people to be like, how does this all happen? The BLS,

115
00:05:31,000 --> 00:05:33,680
Speaker 1: the Census and all of this, they're not the most

116
00:05:33,680 --> 00:05:37,240
Speaker 1: efficient agencies, and in some ways it's almost difficult to

117
00:05:37,240 --> 00:05:40,880
Speaker 1: blame them. You are trying to get a full survey

118
00:05:40,960 --> 00:05:44,640
Speaker 1: of the US economy using statistical sampling and all of

119
00:05:44,680 --> 00:05:48,040
Speaker 1: these very outmoded ways. Now, the Census in particular is

120
00:05:48,080 --> 00:05:50,960
Speaker 1: the most outmoded because it's constitutionally mandated the way that

121
00:05:51,000 --> 00:05:53,200
Speaker 1: they do it, and in fact, there was an effort

122
00:05:53,440 --> 00:05:56,640
Speaker 1: to revitalize and change the way that the BLS conducts

123
00:05:56,680 --> 00:05:58,919
Speaker 1: its employment data. Would anyone like to tell me what

124
00:05:58,960 --> 00:06:01,160
Speaker 1: happened to that agency in that effort. Oh right, it

125
00:06:01,200 --> 00:06:03,120
Speaker 1: was fired by Hard Lutnk in his first week while

126
00:06:03,160 --> 00:06:05,440
Speaker 1: he was in office. And so that's what happened over

127
00:06:05,480 --> 00:06:08,719
Speaker 1: at the BLS. Just in case anybody's wondering, the ability

128
00:06:08,760 --> 00:06:11,239
Speaker 1: to actually modernize the data was dozed within the first

129
00:06:11,480 --> 00:06:15,120
Speaker 1: week of the Trump administration. Yeah, it's very efficient, so

130
00:06:15,160 --> 00:06:17,200
Speaker 1: that now we have inefficient data. But people are like,

131
00:06:17,240 --> 00:06:20,200
Speaker 1: how does this continue happening? And again it's largely because

132
00:06:20,320 --> 00:06:24,039
Speaker 1: the snapshot that they get after that month, after more

133
00:06:24,160 --> 00:06:27,640
Speaker 1: accurate payroll data and other comes in causes revisions. This

134
00:06:27,720 --> 00:06:30,080
Speaker 1: happened during the Biden administration, It's now happened during the

135
00:06:30,120 --> 00:06:33,320
Speaker 1: Trump administration. The wild swings are actually more reflective of

136
00:06:33,320 --> 00:06:36,640
Speaker 1: the wilding changer nature of our economy. I'm not defending

137
00:06:36,680 --> 00:06:39,359
Speaker 1: the numbers themselves. I agree it's ridiculous that we don't

138
00:06:39,360 --> 00:06:42,279
Speaker 1: necessarily have such a quick snapshot at the same time.

139
00:06:42,560 --> 00:06:45,400
Speaker 1: So you know, three hundred and thirty million people fifty

140
00:06:45,400 --> 00:06:48,440
Speaker 1: different states. It's the biggest economy in the history of

141
00:06:48,480 --> 00:06:51,080
Speaker 1: the world. It's not Liechtenstein. It's not so easy to

142
00:06:51,080 --> 00:06:52,880
Speaker 1: just be like, oh, how much money do we make today?

143
00:06:52,960 --> 00:06:55,640
Speaker 1: Or something right, right, So this is a very inefficient system.

144
00:06:55,680 --> 00:06:57,760
Speaker 1: But part of the thing is and that it has

145
00:06:57,800 --> 00:07:00,560
Speaker 1: been deviled presidents since all time. But I am laying

146
00:07:00,600 --> 00:07:02,760
Speaker 1: it out. I'm trying to as fairly as possible to

147
00:07:02,760 --> 00:07:06,400
Speaker 1: say the revisions themselves wild, wild and crazy are not

148
00:07:06,680 --> 00:07:10,440
Speaker 1: necessarily inconsistent with the wild and changing nature of all

149
00:07:10,480 --> 00:07:13,000
Speaker 1: of our economic policy over the last two years, if.

150
00:07:12,920 --> 00:07:13,800
Speaker 3: I could build on that.

151
00:07:14,040 --> 00:07:16,520
Speaker 2: So in addition, in the same way that there is

152
00:07:16,560 --> 00:07:20,040
Speaker 2: a lower response rate to polls making it more difficult

153
00:07:20,080 --> 00:07:23,160
Speaker 2: to pull what they rely upon for the core of

154
00:07:23,200 --> 00:07:26,160
Speaker 2: the data that they collect to compile these jobs numbers

155
00:07:26,360 --> 00:07:29,720
Speaker 2: is surveys that go out to businesses and also you know,

156
00:07:29,760 --> 00:07:33,880
Speaker 2: public institutions, et cetera. And so the response rate has

157
00:07:34,000 --> 00:07:37,920
Speaker 2: gotten lower over time. And you know, so initially when

158
00:07:37,920 --> 00:07:40,960
Speaker 2: they report those numbers out, it's not like they've gotten

159
00:07:41,000 --> 00:07:44,360
Speaker 2: all of their survey data back. So revisions they always

160
00:07:44,360 --> 00:07:47,560
Speaker 2: do revisions, that's nothing new. The extent the size of

161
00:07:47,600 --> 00:07:51,040
Speaker 2: these revisions is no doubt notable. And I think the

162
00:07:51,600 --> 00:07:54,080
Speaker 2: things that I saw pointed to are number one, this

163
00:07:54,200 --> 00:07:55,960
Speaker 2: is just sort of like, you know, the quality of

164
00:07:55,960 --> 00:07:57,480
Speaker 2: the data is degrading over time.

165
00:07:57,920 --> 00:07:58,680
Speaker 3: That's number one.

166
00:07:58,960 --> 00:08:02,440
Speaker 2: Number two was pointing out a lot of actually the

167
00:08:02,520 --> 00:08:05,840
Speaker 2: later survey results to come in were from public institution,

168
00:08:06,000 --> 00:08:11,160
Speaker 2: public sector institutions, and they had taken a job hit

169
00:08:11,240 --> 00:08:13,240
Speaker 2: because of DOGE and because of all the cuts and

170
00:08:13,280 --> 00:08:16,560
Speaker 2: government spending, et cetera. So that may have added to it.

171
00:08:16,600 --> 00:08:19,120
Speaker 2: And then number three just the fact that the economy

172
00:08:19,200 --> 00:08:20,200
Speaker 2: is so topsy.

173
00:08:19,880 --> 00:08:20,800
Speaker 3: Turvy right now.

174
00:08:21,320 --> 00:08:24,480
Speaker 2: All of the late survey data that came in after

175
00:08:24,520 --> 00:08:28,120
Speaker 2: the original numbers all pointed in a more negative direction,

176
00:08:28,200 --> 00:08:31,160
Speaker 2: whereas typically it'll be sort of mixed. You know, what's

177
00:08:31,160 --> 00:08:33,240
Speaker 2: coming in after the fact, you know, some of it

178
00:08:33,280 --> 00:08:35,040
Speaker 2: will be more up, some of it will be more down,

179
00:08:35,080 --> 00:08:38,000
Speaker 2: so more or less even out. So that's from my

180
00:08:38,120 --> 00:08:40,640
Speaker 2: understanding of reading into it why there was such a

181
00:08:40,679 --> 00:08:44,920
Speaker 2: significant revision on these particular jobs numbers. And I mean,

182
00:08:44,960 --> 00:08:47,160
Speaker 2: it does just kind of make a logical sense when

183
00:08:47,160 --> 00:08:51,040
Speaker 2: you think of how all over the place Trump has

184
00:08:51,040 --> 00:08:54,120
Speaker 2: been with the tariff policy in particular, that that would

185
00:08:54,160 --> 00:08:57,120
Speaker 2: make it more difficult to be able to ascertain exactly

186
00:08:57,120 --> 00:08:59,880
Speaker 2: what the impacts are and why businesses would perhaps drag

187
00:09:00,160 --> 00:09:03,800
Speaker 2: feet in sending in their own information to the government.

188
00:09:03,840 --> 00:09:06,319
Speaker 1: So I feel cool understanding because we were selected as

189
00:09:06,360 --> 00:09:08,840
Speaker 1: part of the business survey, and I completed it promptly,

190
00:09:08,920 --> 00:09:11,160
Speaker 1: even though it was a pain in the ass. Yeah,

191
00:09:11,160 --> 00:09:13,040
Speaker 1: that's I told you that. Yeah, we were part of

192
00:09:13,040 --> 00:09:15,959
Speaker 1: the I think we were a part of the annual survey,

193
00:09:16,080 --> 00:09:17,959
Speaker 1: and yeah, it was. Honestly, it was a huge pain.

194
00:09:18,040 --> 00:09:20,439
Speaker 1: So I understand why business owners out there don't want

195
00:09:20,480 --> 00:09:22,839
Speaker 1: to do it. But thank you for podcast representation to

196
00:09:22,880 --> 00:09:26,600
Speaker 1: the United States government for including breaking points in your

197
00:09:26,960 --> 00:09:29,559
Speaker 1: in your survey. But with all of that being said,

198
00:09:29,640 --> 00:09:32,439
Speaker 1: here is Donald Trump for his explanation for why he

199
00:09:32,480 --> 00:09:34,920
Speaker 1: wanted to fire the BLS. Let's take a listen.

200
00:09:35,080 --> 00:09:36,240
Speaker 4: Why don't you fire the head.

201
00:09:36,080 --> 00:09:38,280
Speaker 5: Of the Bureau of Labor Statistics because I think her

202
00:09:38,360 --> 00:09:41,679
Speaker 5: numbers were wrong, just like I thought her numbers were

203
00:09:41,679 --> 00:09:44,040
Speaker 5: wrong before the election, right on.

204
00:09:44,120 --> 00:09:48,400
Speaker 1: The monthly drive before going forward. Why should anyone.

205
00:09:48,160 --> 00:09:53,880
Speaker 5: Trust the numbers? You're right, No, you're right. Why should

206
00:09:53,960 --> 00:09:58,000
Speaker 5: anybody thrust numbers? You go back to election election day,

207
00:09:58,360 --> 00:10:01,200
Speaker 5: Look what happened two or three days before with massive

208
00:10:01,240 --> 00:10:05,760
Speaker 5: wonderful job numbers trying to get him elected or her elected,

209
00:10:05,840 --> 00:10:08,760
Speaker 5: trying to get whoever the hell was running. Because you

210
00:10:08,840 --> 00:10:12,040
Speaker 5: go back and they came out with numbers. They were

211
00:10:12,160 --> 00:10:16,400
Speaker 5: very favorable Da Kamala, Okay, they're trying to get him elected,

212
00:10:16,640 --> 00:10:19,800
Speaker 5: trying to get her elected. And then on the fifteenth

213
00:10:19,880 --> 00:10:23,160
Speaker 5: of November or thereabouts, they had an eight or nine

214
00:10:23,280 --> 00:10:29,720
Speaker 5: hundred thousand overstatement reductions right after the election. It didn't

215
00:10:29,760 --> 00:10:31,960
Speaker 5: work because you know who won, John I won.

216
00:10:33,080 --> 00:10:36,040
Speaker 1: So that's what the new Trump administration line. Here here

217
00:10:36,240 --> 00:10:39,839
Speaker 1: is here's White House Economic Council Director Kevin Hassett kind

218
00:10:39,840 --> 00:10:42,240
Speaker 1: of talking more about that that eight hundred thousand revision

219
00:10:42,520 --> 00:10:44,520
Speaker 1: that came after Joe Biden withdrew from the race, but

220
00:10:44,559 --> 00:10:46,600
Speaker 1: it was before the election. Actually, let's take a listen.

221
00:10:46,880 --> 00:10:50,920
Speaker 6: There was an eight hundred thousand and eighteen eight hundred

222
00:10:50,920 --> 00:10:54,800
Speaker 6: and eighteen thousand revision making the Joe Biden job record

223
00:10:54,840 --> 00:10:58,160
Speaker 6: a lot worse that came out after he withdrew from.

224
00:10:58,120 --> 00:10:59,439
Speaker 4: The presidential campaign.

225
00:11:00,000 --> 00:11:01,960
Speaker 6: A bunch of patterns that could make people wonder. And

226
00:11:02,000 --> 00:11:04,280
Speaker 6: I think the most important thing for people to know

227
00:11:04,400 --> 00:11:07,080
Speaker 6: is that it's the president's highest priority that the data

228
00:11:07,160 --> 00:11:09,720
Speaker 6: be trusted and that people get to the bottom of

229
00:11:09,720 --> 00:11:12,120
Speaker 6: why these revisions are so unreliable.

230
00:11:12,720 --> 00:11:15,040
Speaker 1: So again, I mean much of this as you so

231
00:11:15,120 --> 00:11:17,559
Speaker 1: you laid it all out. It is also apparently COVID

232
00:11:17,760 --> 00:11:21,920
Speaker 1: destroyed the response record. And I don't want to sit

233
00:11:21,920 --> 00:11:25,079
Speaker 1: here and defend BLS or any of this data, CPI data,

234
00:11:25,160 --> 00:11:27,959
Speaker 1: all of it. It is very spotty. People have accurately

235
00:11:28,200 --> 00:11:31,040
Speaker 1: criticized it for years, but it's kind of what we have.

236
00:11:31,280 --> 00:11:34,120
Speaker 1: And so from that, of course, it has tremendous impact

237
00:11:34,360 --> 00:11:38,840
Speaker 1: on the overall US economy, projections for business, investment, for

238
00:11:38,880 --> 00:11:41,400
Speaker 1: the Federal Reserve, for the way of course that people make,

239
00:11:41,440 --> 00:11:43,640
Speaker 1: you know, investment decisions. The S and P five hundred

240
00:11:43,640 --> 00:11:46,120
Speaker 1: stock market. The stock market, by the way, reacted terribly

241
00:11:46,520 --> 00:11:49,680
Speaker 1: to those jobs numbers. Possibly why you see why President

242
00:11:49,679 --> 00:11:51,480
Speaker 1: Trump fired that person. But let's go and put this

243
00:11:51,559 --> 00:11:54,520
Speaker 1: up there on the screen and just shows here the

244
00:11:54,559 --> 00:11:59,199
Speaker 1: actual revision for where it looked from the original numbers

245
00:11:59,440 --> 00:12:02,720
Speaker 1: and where they came down to. Now, obviously, if you're

246
00:12:02,760 --> 00:12:06,240
Speaker 1: the president and let's say in April, you have a

247
00:12:06,240 --> 00:12:08,080
Speaker 1: pretty decent number, as you can see there, over one

248
00:12:08,120 --> 00:12:12,400
Speaker 1: hundred thousand, and then you see a revision down dramatically

249
00:12:12,760 --> 00:12:16,400
Speaker 1: after so called Liberation Day, may not be so good

250
00:12:16,440 --> 00:12:19,600
Speaker 1: for you. Actually, And in fact, those two months after

251
00:12:19,640 --> 00:12:22,480
Speaker 1: Liberation Day, which were the most volatile during tariffs of

252
00:12:22,520 --> 00:12:25,640
Speaker 1: course now show a revision dramatically down, and even a

253
00:12:25,679 --> 00:12:29,360
Speaker 1: modest increase from that point forward is probably more because

254
00:12:29,520 --> 00:12:31,840
Speaker 1: of the you know, the pause that we saw in

255
00:12:31,920 --> 00:12:34,560
Speaker 1: the majority of the tariff policy. Of course August first,

256
00:12:34,600 --> 00:12:36,439
Speaker 1: just a couple of days ago, a number of other

257
00:12:36,520 --> 00:12:40,280
Speaker 1: terroriffs did go into effect. It is remaining unclear. But

258
00:12:40,320 --> 00:12:42,520
Speaker 1: the point actually that I want to make is that

259
00:12:42,600 --> 00:12:46,240
Speaker 1: beneath the surface, this was all happening no matter what's

260
00:12:46,320 --> 00:12:49,120
Speaker 1: going on with all of the bls. There's all kinds

261
00:12:49,160 --> 00:12:51,480
Speaker 1: of other data out there. It's also hilarious to me

262
00:12:51,559 --> 00:12:53,959
Speaker 1: watching Republicans be like, see the GDP numbers are good.

263
00:12:53,960 --> 00:12:56,840
Speaker 1: I'm like, hey, the gd numbers were good under Biden.

264
00:12:56,880 --> 00:12:59,320
Speaker 1: Does anybody think the economy was good? We're talking about

265
00:12:59,400 --> 00:13:01,200
Speaker 1: the S and P. The S and P was at

266
00:13:01,200 --> 00:13:03,360
Speaker 1: all time high as of a couple of days ago,

267
00:13:03,679 --> 00:13:06,520
Speaker 1: and you know, the S and P had two record

268
00:13:06,600 --> 00:13:09,920
Speaker 1: years under Biden. Did the economy get better under Biden?

269
00:13:10,240 --> 00:13:10,400
Speaker 3: No?

270
00:13:10,640 --> 00:13:13,080
Speaker 1: I mean, you know, and this is where the disconnect

271
00:13:13,360 --> 00:13:15,920
Speaker 1: between everyday living conditions. We're going to do this in

272
00:13:15,920 --> 00:13:18,120
Speaker 1: a little bit. We're going to talk about poor job

273
00:13:18,160 --> 00:13:21,800
Speaker 1: growth and others that come to the you know, so

274
00:13:21,920 --> 00:13:25,000
Speaker 1: called blockbuster parts of the economy. And one of the

275
00:13:25,000 --> 00:13:29,760
Speaker 1: major insights is that AI spending in particular is responsible

276
00:13:29,800 --> 00:13:33,400
Speaker 1: for so much of our current GDP growth. But that

277
00:13:33,559 --> 00:13:38,040
Speaker 1: again is not the US economy. That's not manufacturing, that

278
00:13:38,160 --> 00:13:41,760
Speaker 1: is not jobs or people who are working at lower wages.

279
00:13:41,800 --> 00:13:45,840
Speaker 1: That has no impact whatsoever on your ability to purchase

280
00:13:45,840 --> 00:13:48,120
Speaker 1: a house. There's a big chart going viral right now

281
00:13:48,120 --> 00:13:50,040
Speaker 1: on the right about you know, the average thirty year

282
00:13:50,080 --> 00:13:52,240
Speaker 1: old and the ability to buy a house and all that.

283
00:13:52,280 --> 00:13:54,440
Speaker 1: It still remains one of the most fundamental questions, like

284
00:13:54,520 --> 00:13:56,320
Speaker 1: really of our time. But the point not.

285
00:13:56,480 --> 00:13:58,079
Speaker 2: Only on the right that's going viral. By the way,

286
00:13:58,120 --> 00:14:00,079
Speaker 2: Oh good, yeah, I'm glad very much of my time.

287
00:14:00,200 --> 00:14:02,280
Speaker 1: I wanted to go everywhere. Everyone should be aware of it,

288
00:14:02,320 --> 00:14:04,199
Speaker 1: everyone should say, hey, what the hell is going on here?

289
00:14:04,240 --> 00:14:06,560
Speaker 1: I was actually just looking at a current forecast. You know,

290
00:14:06,760 --> 00:14:09,079
Speaker 1: even with all of this too late pal stuff, even

291
00:14:09,120 --> 00:14:11,880
Speaker 1: with the federal reserve cut, the forecast or twenty twenty

292
00:14:11,920 --> 00:14:14,000
Speaker 1: five interest rate is like six point two five percent.

293
00:14:14,040 --> 00:14:16,160
Speaker 1: That's still double what it was just five years ago.

294
00:14:16,360 --> 00:14:18,480
Speaker 1: Are we ever going back to three? Probably not, but

295
00:14:18,600 --> 00:14:21,080
Speaker 1: you know four or five would be nice. Certainly can

296
00:14:21,120 --> 00:14:24,120
Speaker 1: save people thousands and thousands of dollars. Yeah, whenever they're

297
00:14:24,120 --> 00:14:25,640
Speaker 1: trying to do that. So you can just look at

298
00:14:25,640 --> 00:14:28,720
Speaker 1: the carrying costs they buy, the purchasing price. Home price

299
00:14:28,760 --> 00:14:31,320
Speaker 1: has still remain very very high. Market is a disaster.

300
00:14:31,720 --> 00:14:34,080
Speaker 1: Interest rates and all of that are problem. That's the

301
00:14:34,160 --> 00:14:36,640
Speaker 1: numbers to me that matter as along with wage growth.

302
00:14:36,720 --> 00:14:40,840
Speaker 1: The S and P doesn't matter at all.

303
00:14:40,960 --> 00:14:43,440
Speaker 2: Let's just stick on the firing of the BLS commissioner,

304
00:14:43,560 --> 00:14:46,600
Speaker 2: like it's deeply disturbing. Obviously they had no issue with

305
00:14:46,600 --> 00:14:48,440
Speaker 2: this lady when she was when the jobs numbers that

306
00:14:48,480 --> 00:14:50,720
Speaker 2: were coming out they thought benefited them. And in fact,

307
00:14:50,880 --> 00:14:53,760
Speaker 2: part of why Trump was so hard hit by these

308
00:14:53,800 --> 00:14:57,040
Speaker 2: revisions is because, first of all, it really.

309
00:14:56,840 --> 00:14:58,080
Speaker 3: Does complete the picture.

310
00:14:58,200 --> 00:14:59,840
Speaker 2: I mean, there was a lot of weirdness in the

311
00:15:00,080 --> 00:15:02,600
Speaker 2: economy of like, okay, debt levels are at consumer debt

312
00:15:02,680 --> 00:15:05,400
Speaker 2: levels are at an all time high, inflation ticking up,

313
00:15:05,800 --> 00:15:08,480
Speaker 2: you see, consumer confidence is quite low. Like you see

314
00:15:08,520 --> 00:15:11,200
Speaker 2: all these negative signs, but then the jobs numbers are

315
00:15:11,200 --> 00:15:13,640
Speaker 2: hanging in there. It's like, how is that happening? And

316
00:15:13,680 --> 00:15:16,280
Speaker 2: now it's like okay, now it kind of all makes sense. Yes,

317
00:15:16,360 --> 00:15:18,440
Speaker 2: the economy is just as bad as people feel that

318
00:15:18,520 --> 00:15:21,680
Speaker 2: it is. This fills in the blanks. So prior to

319
00:15:21,720 --> 00:15:25,240
Speaker 2: this moment when these negative jobs numbers comes out, Trump

320
00:15:25,280 --> 00:15:26,120
Speaker 2: had no issue with her.

321
00:15:26,400 --> 00:15:27,880
Speaker 3: So, I mean, it just.

322
00:15:27,960 --> 00:15:30,640
Speaker 2: Is really clear he didn't like the numbers. So he's

323
00:15:30,680 --> 00:15:33,400
Speaker 2: firing the lady who put out the numbers. Now, just

324
00:15:33,440 --> 00:15:35,760
Speaker 2: so people understand, and maybe this is a little comforting,

325
00:15:36,200 --> 00:15:38,480
Speaker 2: he makes it seem like she just gets out like

326
00:15:38,520 --> 00:15:40,120
Speaker 2: a pen and pad and is like, here you go,

327
00:15:40,240 --> 00:15:42,520
Speaker 2: this is what she actually doesn't even know what they

328
00:15:42,600 --> 00:15:46,840
Speaker 2: are before the announcement happens. Now there are and it's

329
00:15:46,880 --> 00:15:49,360
Speaker 2: not like just a one plus one. There's all sorts

330
00:15:49,360 --> 00:15:52,640
Speaker 2: of statistical modeling and analysis that goes into this. And

331
00:15:52,680 --> 00:15:54,320
Speaker 2: what I was reading is that, you know, there are

332
00:15:54,360 --> 00:15:58,000
Speaker 2: all these little micro decisions that you could sort of

333
00:15:58,040 --> 00:16:00,680
Speaker 2: push in one way or the other that over time

334
00:16:00,760 --> 00:16:04,440
Speaker 2: could influence the job numbers one way or another. So

335
00:16:04,680 --> 00:16:06,680
Speaker 2: the next person that they put in who was this

336
00:16:06,800 --> 00:16:08,960
Speaker 2: lady's deputy by the way, so this is another like

337
00:16:09,080 --> 00:16:11,640
Speaker 2: career official. This is not someone who you know, they

338
00:16:11,640 --> 00:16:14,320
Speaker 2: didn't like, put Janine Piro in there or something like that.

339
00:16:14,520 --> 00:16:16,760
Speaker 3: She's busy. Now she has been confirmed for her other job.

340
00:16:17,000 --> 00:16:20,000
Speaker 2: But in any case, this is a you know, career statistician,

341
00:16:20,160 --> 00:16:24,040
Speaker 2: another like longtime career bureaucrat who's been put into this position.

342
00:16:24,520 --> 00:16:27,160
Speaker 2: But if you're her or him, I'm not sure, male

343
00:16:27,240 --> 00:16:30,440
Speaker 2: or female, if you're anyone else throughunt the government, you've

344
00:16:30,480 --> 00:16:32,440
Speaker 2: got this in your head now, like if I give

345
00:16:32,480 --> 00:16:35,080
Speaker 2: the president some news that he doesn't like, my ass

346
00:16:35,160 --> 00:16:37,800
Speaker 2: is grassed, Like I'm going to get fired. So does

347
00:16:37,880 --> 00:16:40,240
Speaker 2: that impact the way that you go about your job?

348
00:16:40,360 --> 00:16:43,120
Speaker 2: Does it impact those little micro decisions that get made

349
00:16:43,120 --> 00:16:46,240
Speaker 2: about how the statistical model is created. I think that

350
00:16:46,520 --> 00:16:49,960
Speaker 2: the comment Trump made there where he said why should

351
00:16:50,000 --> 00:16:55,120
Speaker 2: anyone trust numbers? Was very revealing. And you know, I

352
00:16:55,120 --> 00:16:57,440
Speaker 2: mean it harkens back to stop the steal. It harkens

353
00:16:57,440 --> 00:17:04,600
Speaker 2: back to his fundamental ability and desire to reshape reality

354
00:17:04,680 --> 00:17:08,120
Speaker 2: to his liking and to destabilize reality. I mean, really

355
00:17:08,240 --> 00:17:11,680
Speaker 2: is a very postmodernist presidency where it's like, what even

356
00:17:11,840 --> 00:17:13,160
Speaker 2: are facts in reality?

357
00:17:13,680 --> 00:17:17,320
Speaker 3: And so this is just sort of the logical.

358
00:17:17,320 --> 00:17:21,639
Speaker 2: Endpoint of a man in an ideology that really doesn't

359
00:17:21,680 --> 00:17:24,040
Speaker 2: actually care about what the facts and the reality are.

360
00:17:24,160 --> 00:17:27,080
Speaker 2: He cares about what's going to serve his personal interests.

361
00:17:27,160 --> 00:17:30,040
Speaker 2: At any given time. So you know, this is sort

362
00:17:30,080 --> 00:17:34,440
Speaker 2: of like a classic I guess like cult leader technique

363
00:17:34,600 --> 00:17:37,040
Speaker 2: or tactic. The more that you can just sort of

364
00:17:37,080 --> 00:17:39,960
Speaker 2: create and assert your own reality, obviously, the more power

365
00:17:39,960 --> 00:17:42,320
Speaker 2: you're going to have over your followers, the people that

366
00:17:42,440 --> 00:17:45,840
Speaker 2: believe you. So you know, I think it's also significant

367
00:17:45,920 --> 00:17:48,320
Speaker 2: in terms of Trump's psychology and the way that he

368
00:17:48,359 --> 00:17:49,240
Speaker 2: approaches power.

369
00:17:49,760 --> 00:17:52,800
Speaker 1: And we have a supercut here of Trump previously talking

370
00:17:52,840 --> 00:17:55,040
Speaker 1: about how much he liked the BLS numbers. Let's take

371
00:17:55,080 --> 00:17:55,440
Speaker 1: a listen.

372
00:17:55,680 --> 00:17:59,240
Speaker 7: The numbers were much better, as you know, than projected

373
00:17:59,320 --> 00:18:01,680
Speaker 7: by the media.

374
00:18:02,000 --> 00:18:04,720
Speaker 4: In three months, we have created three hundred.

375
00:18:04,400 --> 00:18:05,760
Speaker 1: And fifty thousand jobs.

376
00:18:05,760 --> 00:18:08,680
Speaker 7: Think of that, a bun of jobs that we created.

377
00:18:09,119 --> 00:18:10,760
Speaker 7: That's what happened this morning.

378
00:18:11,480 --> 00:18:14,159
Speaker 1: That was before the revisions. But now let's go and

379
00:18:14,160 --> 00:18:15,720
Speaker 1: put the next one up there on the screen. A

380
00:18:15,880 --> 00:18:19,320
Speaker 1: six journal actually did a very good job here, and

381
00:18:19,400 --> 00:18:21,920
Speaker 1: I'll just read directly from them. Job market data gets

382
00:18:21,960 --> 00:18:24,320
Speaker 1: revised every month, but rarely are the revisions as negative

383
00:18:24,520 --> 00:18:27,040
Speaker 1: as the ones in Friday. Now, what they say is

384
00:18:27,040 --> 00:18:28,919
Speaker 1: that the last time there was such a large revision

385
00:18:28,920 --> 00:18:31,000
Speaker 1: to the change over employment was in April of twenty

386
00:18:31,040 --> 00:18:34,000
Speaker 1: twenty the height of the pandemic. Quote. Worryingly, revisions have

387
00:18:34,040 --> 00:18:36,480
Speaker 1: been consistently negative in twenty twenty five. The lower the

388
00:18:36,560 --> 00:18:39,640
Speaker 1: Labor Department now lowering its initially reported job count every

389
00:18:39,680 --> 00:18:43,600
Speaker 1: month through June. So what happened monthly payroll came from

390
00:18:43,600 --> 00:18:47,080
Speaker 1: the Labor Department's BLS statistics. The BLS has a voluntary

391
00:18:47,119 --> 00:18:49,520
Speaker 1: monthly survey of one hundred and twenty one thousand businesses

392
00:18:49,520 --> 00:18:52,240
Speaker 1: and government agencies that employ twenty six percent of all

393
00:18:52,280 --> 00:18:56,240
Speaker 1: non farm employees. It extrapolates the responses to produce estimates

394
00:18:56,280 --> 00:18:58,760
Speaker 1: for the whole workforce. In a typical month, they will

395
00:18:58,760 --> 00:19:01,600
Speaker 1: hear back from sixty percent. However, an economist said that

396
00:19:01,640 --> 00:19:04,600
Speaker 1: the collection rate in June was just fifty nine point

397
00:19:04,600 --> 00:19:08,080
Speaker 1: five percent, so that's part of the problem. Government agencies

398
00:19:08,080 --> 00:19:11,560
Speaker 1: were then overrepresented amongst us payrolls to the bs pylS

399
00:19:11,640 --> 00:19:14,399
Speaker 1: styb from survey. Then the revision to May and June

400
00:19:14,600 --> 00:19:17,440
Speaker 1: were due to public schools, which employed some one hundred

401
00:19:17,440 --> 00:19:20,080
Speaker 1: and ten thousand fewer people in June than BLS believed

402
00:19:20,119 --> 00:19:22,400
Speaker 1: at the time. But quote the late responses from other

403
00:19:22,440 --> 00:19:26,439
Speaker 1: industries all stewed negative. After incorporation all of these weaker numbers.

404
00:19:26,520 --> 00:19:29,480
Speaker 1: Weakness was then spread back through May due to statistical

405
00:19:29,480 --> 00:19:33,159
Speaker 1: methodology called concurrent seasonal adjustment, and most of the revision

406
00:19:33,160 --> 00:19:35,919
Speaker 1: in May is due to the quote routine recalculation of

407
00:19:36,000 --> 00:19:38,959
Speaker 1: seasonal factors. Again, I do not want to, you know,

408
00:19:39,280 --> 00:19:41,959
Speaker 1: CAPE for this insane system. I am just telling you

409
00:19:42,200 --> 00:19:44,199
Speaker 1: that is the way it has always worked. And you

410
00:19:44,200 --> 00:19:46,880
Speaker 1: should always, of course believe to take some a grain

411
00:19:46,920 --> 00:19:50,199
Speaker 1: of salt for any of these things, because snapshot statistics

412
00:19:50,200 --> 00:19:52,960
Speaker 1: are always wildly inaccurate, at least in my opinion, from

413
00:19:53,000 --> 00:19:56,159
Speaker 1: the overall economy. Even the GDP numbers, you know, you

414
00:19:56,240 --> 00:19:58,480
Speaker 1: really have to dig deep on them, and they even

415
00:19:58,600 --> 00:20:01,120
Speaker 1: they get revised all the time as well. The last

416
00:20:01,119 --> 00:20:02,720
Speaker 1: part here, let's put this up there on the screen.

417
00:20:02,840 --> 00:20:07,720
Speaker 1: This was an interesting statement from the previous commissioner of

418
00:20:07,760 --> 00:20:10,320
Speaker 1: the Bureau of Labor Statistics, who actually was nominated and

419
00:20:10,400 --> 00:20:13,920
Speaker 1: served under Donald Trump in twenty seventeen. He says, quote,

420
00:20:13,920 --> 00:20:16,280
Speaker 1: today President Trump called into question the integrity of the

421
00:20:16,320 --> 00:20:19,520
Speaker 1: employment Situation report that the BLS re pleased. He accused

422
00:20:19,560 --> 00:20:22,240
Speaker 1: the commissioner of deliberate reporting false numbers to reflect poorly

423
00:20:22,240 --> 00:20:25,639
Speaker 1: on the administration. Quote this baseless, damaging claim undermines a

424
00:20:25,720 --> 00:20:28,199
Speaker 1: valuable work and dedication of BLS staff who produced the

425
00:20:28,240 --> 00:20:31,320
Speaker 1: reports each month. This escalates the unprecedent attack on the

426
00:20:31,320 --> 00:20:34,840
Speaker 1: independence and integrity of the federal statistical system. The presiden

427
00:20:34,880 --> 00:20:37,920
Speaker 1: seems to blame someone else for unwelcome economic news. The

428
00:20:37,960 --> 00:20:40,280
Speaker 1: commissioner does not determine what the numbers are, but simply

429
00:20:40,320 --> 00:20:42,960
Speaker 1: reports what the data shows. The process of obtaining them

430
00:20:43,080 --> 00:20:46,520
Speaker 1: is decentralized by design to avoid opportunities for interference, and

431
00:20:46,560 --> 00:20:49,879
Speaker 1: they use the same proven, transparent, reliable process to produce

432
00:20:50,000 --> 00:20:52,720
Speaker 1: estimates every month. Every month, the BLS revised prior two

433
00:20:52,720 --> 00:20:56,440
Speaker 1: months employment estimates to reflect slower arriving more accurate information.

434
00:20:56,600 --> 00:20:59,040
Speaker 1: So that's what we have from somebody who's literally nominated

435
00:20:59,040 --> 00:21:01,639
Speaker 1: and worked from Trump previously. By the way, can I

436
00:21:01,680 --> 00:21:06,160
Speaker 1: just say this, mister Lutnik, please undoze the commission to

437
00:21:06,200 --> 00:21:09,560
Speaker 1: modernize the BLS statistics and let's do that. Except the

438
00:21:09,560 --> 00:21:12,119
Speaker 1: problem is now you're going to be tainted by the

439
00:21:12,119 --> 00:21:15,080
Speaker 1: fact that they fired the current BLS commissioner, and then

440
00:21:15,119 --> 00:21:17,960
Speaker 1: the business community and others are going to say, I'm

441
00:21:18,000 --> 00:21:20,320
Speaker 1: not so sure about this. You know, who really knows

442
00:21:20,359 --> 00:21:22,879
Speaker 1: what the numbers are? And in fact, perhaps that's parted

443
00:21:22,880 --> 00:21:25,919
Speaker 1: by design, is if you have uncertainty within the numbers,

444
00:21:25,960 --> 00:21:29,080
Speaker 1: then nobody can firmly trade or make decisions based on them.

445
00:21:29,119 --> 00:21:31,320
Speaker 1: But to be honest, you know, we're more worse off

446
00:21:31,600 --> 00:21:34,520
Speaker 1: for it because lack of confidence in those numbers. I

447
00:21:34,560 --> 00:21:36,399
Speaker 1: know this all sounds very squishy, but we are the

448
00:21:36,440 --> 00:21:39,880
Speaker 1: capital of the global empire, the world's reserve currency, our

449
00:21:39,920 --> 00:21:43,880
Speaker 1: federal reserve another, I mean literally all of our monetary

450
00:21:43,920 --> 00:21:46,040
Speaker 1: policy decides the fate of the globe. And I don't

451
00:21:46,040 --> 00:21:49,200
Speaker 1: even think that that's really an exaggeration. Well, that's the

452
00:21:49,200 --> 00:21:51,280
Speaker 1: type of stuff they make their decisions on. I'm not

453
00:21:51,280 --> 00:21:52,879
Speaker 1: saying it should work that way, but it does. It

454
00:21:52,880 --> 00:21:56,840
Speaker 1: has immense impact for our economy, stock market, firing, etc.

455
00:21:57,200 --> 00:21:59,520
Speaker 1: And so lack of confidence within that it's just one

456
00:21:59,560 --> 00:22:02,520
Speaker 1: more step down, in my opinion, in a batter action.

457
00:22:02,640 --> 00:22:04,280
Speaker 1: And so with that, I don't know, Yeah, I don't

458
00:22:04,280 --> 00:22:05,760
Speaker 1: know how it was a workout. Maybe it could be fined,

459
00:22:05,840 --> 00:22:07,640
Speaker 1: but you know, it doesn't look good.

460
00:22:07,840 --> 00:22:10,040
Speaker 2: I mean, it does harken back to like, you know,

461
00:22:10,600 --> 00:22:13,000
Speaker 2: the Soviet Union, where it was like they didn't want

462
00:22:13,040 --> 00:22:16,880
Speaker 2: to convey negative results to the leadership, so they would

463
00:22:16,920 --> 00:22:19,640
Speaker 2: just make stuff up and then you're unable to like, actually,

464
00:22:19,840 --> 00:22:22,280
Speaker 2: you know, plan, And there was also a sense of like, oh,

465
00:22:22,280 --> 00:22:24,600
Speaker 2: we don't were in this competition with the US. We

466
00:22:24,600 --> 00:22:26,560
Speaker 2: don't want to expose any of our dirty laundry, so

467
00:22:26,600 --> 00:22:29,160
Speaker 2: we want our like the public facing numbers to look

468
00:22:29,200 --> 00:22:32,720
Speaker 2: really good. And yeah, what happens is then no one

469
00:22:32,720 --> 00:22:35,280
Speaker 2: can make good decisions, no one can plan, there's no

470
00:22:35,359 --> 00:22:38,080
Speaker 2: sense of trust in anything that's coming out of the government,

471
00:22:38,160 --> 00:22:38,800
Speaker 2: and it's not a.

472
00:22:38,760 --> 00:22:39,480
Speaker 3: Good place to beate.

473
00:22:39,640 --> 00:22:42,040
Speaker 2: So, you know, this is part and parcel not only

474
00:22:42,080 --> 00:22:45,959
Speaker 2: with Trump's reality distortion, it's also with the way that

475
00:22:46,040 --> 00:22:51,119
Speaker 2: he has made the even the baseline assumption that the

476
00:22:51,119 --> 00:22:54,800
Speaker 2: government should be a sort of neutral entity. He has

477
00:22:54,880 --> 00:22:57,040
Speaker 2: thrown that out the window. And that might be one

478
00:22:57,080 --> 00:22:59,720
Speaker 2: of the most revolutionary things that he has done. And

479
00:22:59,760 --> 00:23:03,080
Speaker 2: I'm that in a negative way in this context since

480
00:23:03,119 --> 00:23:05,239
Speaker 2: coming back into power. And that's not to say that

481
00:23:05,280 --> 00:23:08,919
Speaker 2: there weren't government agencies that were politicized in the past,

482
00:23:09,200 --> 00:23:12,680
Speaker 2: but those were considered scandals. And one offs with the

483
00:23:12,920 --> 00:23:16,280
Speaker 2: you know, the assumption that when treating, you know, tax

484
00:23:16,400 --> 00:23:20,840
Speaker 2: designations and certainly BLS jobs numbers, that these things were

485
00:23:20,840 --> 00:23:24,320
Speaker 2: being crafted by bureaucrats who were meant to be approaching

486
00:23:24,359 --> 00:23:27,000
Speaker 2: it in a politically neutral manner. This will come up

487
00:23:27,040 --> 00:23:29,280
Speaker 2: again when we do our free speech conversation about the

488
00:23:29,400 --> 00:23:32,040
Speaker 2: all an assault on the university system over quote unquote

489
00:23:32,080 --> 00:23:36,000
Speaker 2: anti semitism. Trump has decided to use all of the

490
00:23:36,000 --> 00:23:41,359
Speaker 2: federal government as a political weapon and explicitly politicize every

491
00:23:41,400 --> 00:23:44,520
Speaker 2: single agency as much as he possibly can. And so

492
00:23:44,680 --> 00:23:47,480
Speaker 2: this also fits in, you know, that direction where he

493
00:23:47,560 --> 00:23:48,800
Speaker 2: has taken the federal government.

494
00:23:51,400 --> 00:23:54,119
Speaker 1: So let's get to a bigger economic story. This is

495
00:23:54,240 --> 00:23:58,280
Speaker 1: one again part of the reason why the numbers, or

496
00:23:58,320 --> 00:24:01,680
Speaker 1: at least other numbers that we can look to, are painting,

497
00:24:01,960 --> 00:24:04,880
Speaker 1: you know, kind of a dire picture. And everyone's you know,

498
00:24:04,920 --> 00:24:08,399
Speaker 1: on the tariff thing. Initially, the Trump and the MAGA

499
00:24:08,400 --> 00:24:11,520
Speaker 1: response was, look at all of these pannikins, they were wrong.

500
00:24:11,680 --> 00:24:14,840
Speaker 1: The jobs numbers are fine, The GDP numbers are fine.

501
00:24:14,880 --> 00:24:17,760
Speaker 1: And then the job numbers get revised and everybody's very silent.

502
00:24:17,880 --> 00:24:21,720
Speaker 1: Right now, everybody's actually a BLS, a BLS scholar in

503
00:24:21,800 --> 00:24:24,960
Speaker 1: terms of the way Chimath I'm looking at you. Let's

504
00:24:25,000 --> 00:24:26,480
Speaker 1: go and put this one up there on the screen

505
00:24:26,480 --> 00:24:29,200
Speaker 1: from the Financial Times. Well, this is Federal Reserve Bank

506
00:24:29,280 --> 00:24:33,040
Speaker 1: data from Atlanta says quote wage growth for the lowest

507
00:24:33,040 --> 00:24:36,480
Speaker 1: paid quartile of workers, people earning roughly less than eight

508
00:24:36,560 --> 00:24:39,240
Speaker 1: hundred and six dollars a week, slowed to an annual

509
00:24:39,320 --> 00:24:42,080
Speaker 1: rate of three point seven percent in June, down from

510
00:24:42,080 --> 00:24:44,359
Speaker 1: a peak of seven point five percent in late twenty

511
00:24:44,359 --> 00:24:47,560
Speaker 1: twenty two, when post pandemic labor shortage in industries such

512
00:24:47,560 --> 00:24:51,479
Speaker 1: as hospitality were most acute. Wage growth has also slowed

513
00:24:51,480 --> 00:24:54,320
Speaker 1: for higher earners, but to a lesser extent. Pay for

514
00:24:54,359 --> 00:24:57,320
Speaker 1: the top twenty five percent is up four percent four

515
00:24:57,359 --> 00:24:59,480
Speaker 1: point seven percent in the year to June, and for

516
00:24:59,560 --> 00:25:02,080
Speaker 1: overall workforce by four point three percent. Those in the

517
00:25:02,160 --> 00:25:05,000
Speaker 1: highest quartal earn more than eighteen hundred dollars per week.

518
00:25:05,200 --> 00:25:08,080
Speaker 1: But the figures come after the president Office course sacked

519
00:25:08,080 --> 00:25:11,479
Speaker 1: the head of the country's labor statistics agency. But this data,

520
00:25:11,520 --> 00:25:14,720
Speaker 1: which links again to the Federal Reserve Bank of Atlanta,

521
00:25:14,880 --> 00:25:17,720
Speaker 1: just says that the lowest paid workers are suffering a

522
00:25:17,760 --> 00:25:22,320
Speaker 1: sharper slowdown in wage growth than their richer peers. And obviously,

523
00:25:22,400 --> 00:25:24,600
Speaker 1: not only is this an inequality problem, but really I

524
00:25:24,640 --> 00:25:28,040
Speaker 1: think it is a story of where you know you

525
00:25:28,119 --> 00:25:34,160
Speaker 1: can always see both patterns of political like basically, economic

526
00:25:34,200 --> 00:25:38,640
Speaker 1: precarity always leads to some sort of political radicalism. Much

527
00:25:38,680 --> 00:25:41,160
Speaker 1: of that has been MAGA in the past. Don't forget

528
00:25:41,280 --> 00:25:43,359
Speaker 1: you know, many of these people actually did vote for

529
00:25:43,400 --> 00:25:45,200
Speaker 1: Donald Trump. I'm not going to say the vast majority,

530
00:25:45,240 --> 00:25:47,960
Speaker 1: but lower quintile. You know, it's not an insignificant number

531
00:25:47,960 --> 00:25:50,080
Speaker 1: of people who actually voted for Trump. We're making less

532
00:25:50,080 --> 00:25:53,400
Speaker 1: than one hundred thousand dollars per year, and obviously, when

533
00:25:53,400 --> 00:25:56,200
Speaker 1: we broadly look at kind of where that is going

534
00:25:56,240 --> 00:25:58,480
Speaker 1: for wage growth, what you want to see is wage

535
00:25:58,480 --> 00:26:01,439
Speaker 1: growth continue to actually go down, So you want the

536
00:26:01,520 --> 00:26:05,800
Speaker 1: percentage increased to always be at the lowest quintile and

537
00:26:05,960 --> 00:26:08,400
Speaker 1: up from there to the middle class. So the problem

538
00:26:08,640 --> 00:26:11,320
Speaker 1: that I see here is that it's like poor people

539
00:26:11,560 --> 00:26:16,280
Speaker 1: are getting poorer compared to their richer compatriots. And also

540
00:26:16,680 --> 00:26:19,159
Speaker 1: that you see the way that the economy is now

541
00:26:19,240 --> 00:26:21,120
Speaker 1: currently structured, which we'll get to in a little bit

542
00:26:21,359 --> 00:26:24,800
Speaker 1: around AI and others, is vastly accruing to the top

543
00:26:24,960 --> 00:26:27,760
Speaker 1: five to ten percent of the public. It's an American

544
00:26:27,760 --> 00:26:30,240
Speaker 1: story now of what the last twenty five years, but

545
00:26:30,600 --> 00:26:32,200
Speaker 1: it is just getting worse. And that's the.

546
00:26:32,160 --> 00:26:35,040
Speaker 2: Problem I would say even I mean, the AI boom

547
00:26:35,200 --> 00:26:38,640
Speaker 2: is really I mean top one percent, top point one percent.

548
00:26:38,760 --> 00:26:42,199
Speaker 2: I mean, the consolidation of wealth that is likely to

549
00:26:42,200 --> 00:26:44,720
Speaker 2: be achieved through the AI revolution is going to be

550
00:26:45,280 --> 00:26:48,640
Speaker 2: more uneven than I think anything we've ever seen in history.

551
00:26:48,760 --> 00:26:51,800
Speaker 2: And so when you have this poor wage growth at

552
00:26:51,800 --> 00:26:56,160
Speaker 2: the bottom bottom quintile here, it means that in many

553
00:26:56,200 --> 00:26:59,000
Speaker 2: instances they're not keeping up with inflation, so it continues

554
00:26:59,040 --> 00:27:01,679
Speaker 2: falling further and forth they're behind. And then when you

555
00:27:01,680 --> 00:27:04,560
Speaker 2: think of how much housing costs go up, education costs

556
00:27:04,600 --> 00:27:06,960
Speaker 2: go up, healthcare costs go up. You know, if you

557
00:27:07,040 --> 00:27:10,320
Speaker 2: look specifically at those baskets of goods that really make

558
00:27:10,400 --> 00:27:12,680
Speaker 2: up that sort of stable middle class life, you can

559
00:27:12,720 --> 00:27:16,040
Speaker 2: see how people are just screwed and helpless. And yeah,

560
00:27:16,080 --> 00:27:18,639
Speaker 2: it is going to fuel political radicalization. I mean, I

561
00:27:18,640 --> 00:27:22,280
Speaker 2: think probably nothing has been more destabilizing to this country

562
00:27:22,280 --> 00:27:25,960
Speaker 2: than the vast inequality, level of inequality that has exploded,

563
00:27:26,040 --> 00:27:29,439
Speaker 2: especially since like you know, eighties nineties timeframe when we

564
00:27:29,560 --> 00:27:34,680
Speaker 2: had the the embrace of neoliberalism by both parties, and

565
00:27:34,760 --> 00:27:38,239
Speaker 2: so you know, we're we're living through the consequences of that,

566
00:27:38,440 --> 00:27:40,879
Speaker 2: and the really dire warning here is that it is

567
00:27:40,920 --> 00:27:43,280
Speaker 2: about to very likely get much much worse.

568
00:27:43,400 --> 00:27:45,360
Speaker 1: Yeah, and that fits actually with this next one. Let's

569
00:27:45,400 --> 00:27:47,040
Speaker 1: come put the next one please up on the screen.

570
00:27:47,440 --> 00:27:50,160
Speaker 1: This is actually probably even more troubling. This is about

571
00:27:50,160 --> 00:27:54,160
Speaker 1: the unemployed Americans enduring longer job searches in a cooling market.

572
00:27:54,240 --> 00:27:56,520
Speaker 1: And what they say is that quote number of people

573
00:27:56,640 --> 00:27:59,440
Speaker 1: unemployed for at least twenty seven weeks now tops one

574
00:27:59,480 --> 00:28:02,960
Speaker 1: point eight million, with one person saying quote, I'm considering

575
00:28:02,960 --> 00:28:07,240
Speaker 1: myself semi retired at this point, and this fits with

576
00:28:07,320 --> 00:28:09,840
Speaker 1: some of those job numbers earlier. Job seekers are out

577
00:28:09,840 --> 00:28:12,440
Speaker 1: in the cold this summer, especially ones who've been hunting

578
00:28:12,480 --> 00:28:15,560
Speaker 1: for a while. Behind headline grabbing top line numbers in

579
00:28:15,600 --> 00:28:17,919
Speaker 1: the Jobs report is a striking piece of data. The

580
00:28:18,040 --> 00:28:21,119
Speaker 1: number of people unemployed for at least twenty seven weeks

581
00:28:21,160 --> 00:28:25,160
Speaker 1: is now nearly two million, the highest level since twenty seventeen.

582
00:28:25,240 --> 00:28:29,000
Speaker 1: Not counting the pandemic unemployment surge, The median length of

583
00:28:29,080 --> 00:28:31,720
Speaker 1: unemployment in the US has ticked up from a seasonally

584
00:28:31,760 --> 00:28:34,960
Speaker 1: adjusted nine point five weeks to ten point two just

585
00:28:35,119 --> 00:28:39,000
Speaker 1: last month. Job hunting highlights a significant undercurrent in labor market,

586
00:28:39,080 --> 00:28:42,240
Speaker 1: jolted by tariffs and cautious businesses, and the latest numbers

587
00:28:42,280 --> 00:28:44,760
Speaker 1: showed job growth has been sluggish for months, while the

588
00:28:44,800 --> 00:28:47,479
Speaker 1: unemployment rate at four point two percent remains low by

589
00:28:47,560 --> 00:28:50,680
Speaker 1: historical standards. This is always the issue with unemployment rate.

590
00:28:50,720 --> 00:28:54,160
Speaker 1: I remember this during twenty twelve when Obama would be like, look,

591
00:28:54,200 --> 00:28:56,680
Speaker 1: we drop the unemployment rate. And actually one of the

592
00:28:56,680 --> 00:28:59,080
Speaker 1: ways that Robney was correct. He's like, yeah, but that's

593
00:28:59,080 --> 00:29:00,880
Speaker 1: because a ton of people are dropped out of the

594
00:29:00,960 --> 00:29:04,280
Speaker 1: labor force and are just not working anymore. That's bad, right,

595
00:29:04,720 --> 00:29:06,320
Speaker 1: especially if they don't have any money and they're descending

596
00:29:06,320 --> 00:29:09,120
Speaker 1: into poverty or losing their house. Same problem that we

597
00:29:09,160 --> 00:29:11,280
Speaker 1: see here. So the more people that drop out of

598
00:29:11,280 --> 00:29:14,200
Speaker 1: the labor force or are not seeking work, or are

599
00:29:14,360 --> 00:29:19,280
Speaker 1: seasonally adjusted for longer periods in unemployment considering themselves like

600
00:29:19,400 --> 00:29:21,080
Speaker 1: semi retired. I mean one of the people we're talking

601
00:29:21,080 --> 00:29:22,800
Speaker 1: about it, he's like a forty six year old person,

602
00:29:23,120 --> 00:29:25,160
Speaker 1: years ahead if they want to and if they don't

603
00:29:25,160 --> 00:29:28,040
Speaker 1: have the finances to continue work. I would also say

604
00:29:28,120 --> 00:29:30,920
Speaker 1: that you're going to continue seeing this for colleges because

605
00:29:31,160 --> 00:29:34,960
Speaker 1: we've had a full summer now of graduation, and from

606
00:29:34,960 --> 00:29:36,840
Speaker 1: what I've heard and some of the data backs this

607
00:29:37,000 --> 00:29:39,040
Speaker 1: up as well. New graduates are having a very tough

608
00:29:39,080 --> 00:29:42,880
Speaker 1: time mostly because a lot of bose you have cautious businesses,

609
00:29:43,040 --> 00:29:46,040
Speaker 1: And second, a lot of entry level work is bullshit,

610
00:29:46,360 --> 00:29:49,560
Speaker 1: and you know what is really good at bullshit AI? Like,

611
00:29:49,600 --> 00:29:51,600
Speaker 1: in fact, I was talking with somebody who works in

612
00:29:51,640 --> 00:29:54,160
Speaker 1: consulting and they were like, you know, a lot of

613
00:29:54,160 --> 00:29:56,720
Speaker 1: the early grut work is just familiarization and you take

614
00:29:56,800 --> 00:29:59,200
Speaker 1: minutes of meetings and you do stuff like that and

615
00:29:59,240 --> 00:30:02,280
Speaker 1: you schedule, et cetera. Guess what Microsoft teams with open

616
00:30:02,320 --> 00:30:04,400
Speaker 1: AI is really good at. Yeah, you know, they don't

617
00:30:04,440 --> 00:30:06,320
Speaker 1: need you anymore and they don't necessarily want need to

618
00:30:06,320 --> 00:30:09,560
Speaker 1: add somebody pay healthcare benefits. So you have those two

619
00:30:09,600 --> 00:30:12,560
Speaker 1: things that come together over summer, and it's a big

620
00:30:12,640 --> 00:30:14,280
Speaker 1: question for a lot of these new graduates. What are

621
00:30:14,320 --> 00:30:15,640
Speaker 1: you going to do and how's that going to work?

622
00:30:15,720 --> 00:30:15,960
Speaker 3: Yeah?

623
00:30:16,120 --> 00:30:17,920
Speaker 2: No, I mean I used to work in government consulting

624
00:30:17,960 --> 00:30:20,560
Speaker 2: when I first came out on college, and the first

625
00:30:20,560 --> 00:30:22,160
Speaker 2: thing I did was work on like a help desk

626
00:30:22,440 --> 00:30:25,160
Speaker 2: for you know, it was the US federal courts when

627
00:30:25,160 --> 00:30:28,160
Speaker 2: they would have issue with their enterprise software. And the

628
00:30:28,360 --> 00:30:30,400
Speaker 2: other thing I would do is just is like yeah,

629
00:30:30,440 --> 00:30:33,280
Speaker 2: spreadsheet jockey like you know, analysis of this or that

630
00:30:33,360 --> 00:30:36,440
Speaker 2: and compiling in the data and create a presentation like

631
00:30:36,480 --> 00:30:38,880
Speaker 2: That's exactly the sort of thing AI can do. And

632
00:30:38,920 --> 00:30:41,840
Speaker 2: for many of these companies, even if they haven't fully

633
00:30:41,880 --> 00:30:46,480
Speaker 2: implemented AI, they're looking at the landscape and they're figuring

634
00:30:46,520 --> 00:30:50,360
Speaker 2: out how so they're reluctant to bring in those new hires,

635
00:30:50,360 --> 00:30:54,240
Speaker 2: those entry level individuals, not only because of the insanity

636
00:30:54,240 --> 00:30:56,320
Speaker 2: of the you know, terra regime all over the place,

637
00:30:56,520 --> 00:30:59,400
Speaker 2: but also because they're trying to figure out, Okay, how

638
00:30:59,400 --> 00:31:01,920
Speaker 2: can I use AI to render these people irrelevant? And

639
00:31:01,960 --> 00:31:04,360
Speaker 2: you have CEOs out there who say they're very excited

640
00:31:04,360 --> 00:31:06,080
Speaker 2: to be able to lay people off. They're very excited

641
00:31:06,080 --> 00:31:08,320
Speaker 2: to work with robots insteads of human beings because they

642
00:31:08,320 --> 00:31:10,800
Speaker 2: don't take sick days, they don't require vacation, they don't

643
00:31:10,840 --> 00:31:14,360
Speaker 2: organize labor unions. So that's very you know, I think,

644
00:31:14,880 --> 00:31:17,600
Speaker 2: I think those factors are really going to play into

645
00:31:17,760 --> 00:31:21,760
Speaker 2: the unemployment rate among new college graduates. We're also at

646
00:31:21,800 --> 00:31:23,560
Speaker 2: a point now with these jobs numbers where we can

647
00:31:23,560 --> 00:31:27,360
Speaker 2: start to assess, okay, the stated goals of the tariff

648
00:31:27,560 --> 00:31:32,400
Speaker 2: regime versus the results, and one of the stated goals,

649
00:31:32,400 --> 00:31:34,400
Speaker 2: there were a variety of stated goals that has always

650
00:31:34,440 --> 00:31:35,960
Speaker 2: been sort of all over the place, but one of

651
00:31:35,960 --> 00:31:38,960
Speaker 2: the stated goals was to bring back manufacturing jobs. And

652
00:31:39,200 --> 00:31:42,480
Speaker 2: over the past in the past three months, every single month,

653
00:31:42,520 --> 00:31:43,960
Speaker 2: we have lost manufacturing jobs.

654
00:31:44,120 --> 00:31:45,000
Speaker 3: So there you go.

655
00:31:45,440 --> 00:31:47,520
Speaker 2: Most of the job growth that did occur was actually

656
00:31:47,560 --> 00:31:50,400
Speaker 2: in healthcare because we have a population that's getting older,

657
00:31:50,760 --> 00:31:53,080
Speaker 2: so that's where the you know, most of the job

658
00:31:53,120 --> 00:31:56,600
Speaker 2: growth came. And effectively in every other sector you've seen

659
00:31:56,760 --> 00:31:59,920
Speaker 2: job losses over the past three months. So you know,

660
00:32:00,160 --> 00:32:03,239
Speaker 2: that's not exactly what was sold as the promise of

661
00:32:03,280 --> 00:32:06,360
Speaker 2: the new Golden Era, the new Golden age for America

662
00:32:06,400 --> 00:32:09,600
Speaker 2: and the rebirth of American manufacturing and reality we're going

663
00:32:09,600 --> 00:32:12,080
Speaker 2: in the polar opposite direction, and that is actually different

664
00:32:12,080 --> 00:32:15,080
Speaker 2: from under Biden. There was there was an improvement in

665
00:32:15,120 --> 00:32:19,000
Speaker 2: manufacturing jobs gains through the industrial policy of the Infrastructure

666
00:32:19,000 --> 00:32:24,160
Speaker 2: Act and the Chips Act. So not a good sign

667
00:32:24,200 --> 00:32:25,440
Speaker 2: there for the direction that we're heading in.

668
00:32:25,560 --> 00:32:28,600
Speaker 1: Yeah, exactly. And last piece, here, can we put please

669
00:32:28,800 --> 00:32:31,479
Speaker 1: a eleven on the screen. This is kind of the

670
00:32:31,520 --> 00:32:35,120
Speaker 1: thing I was flagging about GDP and AHI, so you

671
00:32:35,120 --> 00:32:37,440
Speaker 1: can look here from Derek Thompson. I love this graph.

672
00:32:37,760 --> 00:32:42,000
Speaker 1: AI capex as in capital expenditure now accounts for a

673
00:32:42,080 --> 00:32:46,920
Speaker 1: larger share of GDP than basically any technology since the

674
00:32:47,120 --> 00:32:52,040
Speaker 1: railroad in the eighteen hundreds. Basically it's a mini wartime economy,

675
00:32:52,040 --> 00:32:54,040
Speaker 1: he says. But the guns are chips and the tanks

676
00:32:54,040 --> 00:32:57,600
Speaker 1: are databases. Yeah, one point two percent of all infrastructure

677
00:32:57,640 --> 00:33:00,800
Speaker 1: capex as a percent of US GDP is now literally

678
00:33:00,880 --> 00:33:04,880
Speaker 1: AI data center. Also, there are some crazy stories across

679
00:33:04,920 --> 00:33:07,600
Speaker 1: the country right now that I encourage everybody to tune into.

680
00:33:07,760 --> 00:33:11,520
Speaker 1: I'm talking about people who live near meta data processing

681
00:33:11,600 --> 00:33:15,200
Speaker 1: centers that can't get water anymore because of cooling, or

682
00:33:15,280 --> 00:33:18,280
Speaker 1: people who live near data centers are seeing massive spikes

683
00:33:18,480 --> 00:33:22,840
Speaker 1: in electricity prices. I mean, it's crazy that. Let alone

684
00:33:23,080 --> 00:33:25,920
Speaker 1: the fact that farmland and all these other things all

685
00:33:25,920 --> 00:33:30,080
Speaker 1: of a sudden have wildly different valuations because people are

686
00:33:30,080 --> 00:33:31,920
Speaker 1: willing to come in and they're like, oh, no zoning

687
00:33:32,000 --> 00:33:33,600
Speaker 1: rags or any of that. I can build whatever the

688
00:33:33,600 --> 00:33:36,440
Speaker 1: hell I want. Here. We're talking about massive, big data

689
00:33:36,480 --> 00:33:39,600
Speaker 1: centers on top of that. So look, if we don't

690
00:33:39,640 --> 00:33:43,760
Speaker 1: get some cheap and abundant energy aka nuclear anytime soon,

691
00:33:43,880 --> 00:33:46,520
Speaker 1: this is going to be a serious problem. And of

692
00:33:46,560 --> 00:33:49,120
Speaker 1: course what's the investment timeline on that. It takes ten

693
00:33:49,240 --> 00:33:51,520
Speaker 1: years you know to get a reactor online. I don't

694
00:33:51,560 --> 00:33:54,200
Speaker 1: see a single progress, by the way, despite some promises

695
00:33:54,480 --> 00:33:56,680
Speaker 1: on that. And so in the interim, Oh my god,

696
00:33:56,720 --> 00:33:58,680
Speaker 1: if you're living in Texas and you're on that Soul

697
00:33:58,760 --> 00:34:02,240
Speaker 1: power grid, good luck folk. Yeah, because there is you're

698
00:34:02,280 --> 00:34:05,240
Speaker 1: going to see some on top of the deregulated market

699
00:34:05,280 --> 00:34:07,840
Speaker 1: down there, plus all those data centers. It's going to

700
00:34:07,880 --> 00:34:09,719
Speaker 1: be brewed, not on top of the one hundred and

701
00:34:09,760 --> 00:34:12,479
Speaker 1: ten degree heat you know during the summer it's bad.

702
00:34:12,880 --> 00:34:15,680
Speaker 2: I mean between with the water and the power. You know,

703
00:34:15,719 --> 00:34:16,960
Speaker 2: who's going to get prioritized.

704
00:34:17,000 --> 00:34:18,239
Speaker 1: Yeah, of course it's not going to be you.

705
00:34:18,600 --> 00:34:20,120
Speaker 3: It's going to be these AI data.

706
00:34:19,920 --> 00:34:23,160
Speaker 2: Centers, you know, with these multi billion dollar investments, and

707
00:34:23,440 --> 00:34:26,839
Speaker 2: you know, they're their bet. We talked about this last

708
00:34:26,880 --> 00:34:30,280
Speaker 2: week with that Samultman I view Von interview. Their bet

709
00:34:30,719 --> 00:34:33,280
Speaker 2: is that they don't completely stuck up all the resources

710
00:34:33,320 --> 00:34:38,320
Speaker 2: and the electric you know, the energy before AI figures

711
00:34:38,360 --> 00:34:41,120
Speaker 2: out how to do like fission energy or something. That's

712
00:34:41,160 --> 00:34:43,720
Speaker 2: basically their bet is that they can build these things

713
00:34:43,760 --> 00:34:46,480
Speaker 2: out fast enough and develop the AI quick enough that

714
00:34:46,560 --> 00:34:49,480
Speaker 2: it's going to just miraculously solve all of these problems

715
00:34:49,560 --> 00:34:51,480
Speaker 2: for us. I mean it feels like more of a

716
00:34:51,520 --> 00:34:53,880
Speaker 2: religious faith than a science, to be honest with you.

717
00:34:54,000 --> 00:34:59,480
Speaker 1: Yeah, it's it's dangerous stuff out there. All right, let's

718
00:34:59,480 --> 00:35:03,759
Speaker 1: get too the Epstein story. Trump continues to keep the

719
00:35:03,840 --> 00:35:07,799
Speaker 1: door open for his Galleen Maxwell pardon. He says, I'm

720
00:35:07,840 --> 00:35:09,759
Speaker 1: allowed to do it. We know you're allowed to do it.

721
00:35:09,800 --> 00:35:11,440
Speaker 1: The question is are you gonna do it. Let's take

722
00:35:11,480 --> 00:35:14,239
Speaker 1: a listen. Is clemency on the table for her in

723
00:35:14,320 --> 00:35:15,480
Speaker 1: exchange for testimony.

724
00:35:15,640 --> 00:35:18,680
Speaker 7: I'm allowed to do it, but nobody's asked me to

725
00:35:18,760 --> 00:35:20,960
Speaker 7: do it. I know nothing about it. I don't know

726
00:35:20,960 --> 00:35:24,160
Speaker 7: anything about the case, but I know I have the

727
00:35:24,239 --> 00:35:26,080
Speaker 7: right to do it. I have the right to get pardons.

728
00:35:26,080 --> 00:35:29,560
Speaker 7: I've given pardons to people before. But nobody's even asked

729
00:35:29,600 --> 00:35:30,200
Speaker 7: me to do it.

730
00:35:30,600 --> 00:35:33,000
Speaker 1: Nobody's questioning that. It's not true. Nobody's asked you to

731
00:35:33,000 --> 00:35:35,160
Speaker 1: do it, Glleen's Maxwell's team has certainly asked you to

732
00:35:35,200 --> 00:35:36,560
Speaker 1: do it. We're going to get to that in a second,

733
00:35:36,600 --> 00:35:38,319
Speaker 1: but before we do, we of course have to play

734
00:35:38,360 --> 00:35:42,000
Speaker 1: this clip of Trump, will you pardon Diddy and listen

735
00:35:42,080 --> 00:35:44,560
Speaker 1: to the justification he gives. Let's take a lesson Sean

736
00:35:44,600 --> 00:35:47,960
Speaker 1: Diddy Combs, right, would you consider pardoning him?

737
00:35:48,080 --> 00:35:51,200
Speaker 7: Well, he was essentially, I guess, sort of half innocent.

738
00:35:51,719 --> 00:35:52,480
Speaker 4: I don't know what they do.

739
00:35:52,719 --> 00:35:56,640
Speaker 7: He's still in jail or something, but he was celebrating

740
00:35:56,640 --> 00:35:58,560
Speaker 7: a victory, but he seems I guess it wasn't as

741
00:35:58,560 --> 00:36:05,239
Speaker 7: good as a victory. Probably. You know, I was very

742
00:36:05,280 --> 00:36:08,759
Speaker 7: friendly with him. I got along with him great, and

743
00:36:09,320 --> 00:36:11,520
Speaker 7: it seemed like a nice guy. I didn't know him well,

744
00:36:12,600 --> 00:36:15,200
Speaker 7: but when I ran for office, he was very hostile.

745
00:36:15,520 --> 00:36:17,120
Speaker 3: He said some nice things about him.

746
00:36:17,160 --> 00:36:21,560
Speaker 7: Yeah, and it's hard, you know, like you were human

747
00:36:21,640 --> 00:36:25,879
Speaker 7: beings and we don't like to have things cloud our judgment, right,

748
00:36:26,680 --> 00:36:30,480
Speaker 7: But when you knew someone and you were fine, and

749
00:36:30,520 --> 00:36:34,520
Speaker 7: then you run for office, and he made some terrible statements.

750
00:36:34,560 --> 00:36:37,279
Speaker 7: So I don't know, it's it's more difficult. It makes

751
00:36:37,320 --> 00:36:39,960
Speaker 7: it more I'm being honest, it makes it more difficult

752
00:36:40,000 --> 00:36:40,279
Speaker 7: to do.

753
00:36:40,600 --> 00:36:44,560
Speaker 1: But more likely a no for Combs would say, okay,

754
00:36:45,600 --> 00:36:47,879
Speaker 1: got it. And some of it is about nasty things

755
00:36:47,920 --> 00:36:49,120
Speaker 1: for Trump, that's the put.

756
00:36:49,520 --> 00:36:51,960
Speaker 2: I mean, it's just wild that he just out and

757
00:36:52,000 --> 00:36:54,200
Speaker 2: out and it's like, well, he didn't support me, so

758
00:36:54,440 --> 00:36:56,600
Speaker 2: I don't think I'll probably give him more. Like it

759
00:36:56,760 --> 00:36:59,160
Speaker 2: just is it's so naked. He doesn't even pretend that,

760
00:36:59,360 --> 00:37:01,720
Speaker 2: oh he was on usly prosecutor to know. It's like, well,

761
00:37:02,080 --> 00:37:04,480
Speaker 2: you know, we used to be friends, but then he

762
00:37:04,640 --> 00:37:06,920
Speaker 2: was said some nasty things about me. So I don't know,

763
00:37:07,280 --> 00:37:08,560
Speaker 2: I'm not really feeling that way.

764
00:37:08,600 --> 00:37:10,320
Speaker 3: It's just it's just insane.

765
00:37:10,320 --> 00:37:11,920
Speaker 2: And there's a bunch of new reporting too for The

766
00:37:11,920 --> 00:37:14,759
Speaker 2: New York Times about just like the brazen corruption, including

767
00:37:14,760 --> 00:37:17,920
Speaker 2: the payoffs, the bribes in order to secure these pardons,

768
00:37:18,160 --> 00:37:20,400
Speaker 2: and people around Diddy are trying to work the system

769
00:37:20,480 --> 00:37:24,120
Speaker 2: right now, trying to get in with various MAGA figures

770
00:37:24,160 --> 00:37:26,640
Speaker 2: and like payoff whoever they need to effectively to try

771
00:37:26,680 --> 00:37:28,200
Speaker 2: to secure this part. And I mean, this is just

772
00:37:28,239 --> 00:37:31,160
Speaker 2: the way this White House functions. It's it's crazy to see.

773
00:37:31,200 --> 00:37:33,839
Speaker 1: So with that and the Glaine story, let's continue. Let's

774
00:37:33,880 --> 00:37:36,759
Speaker 1: put this up there on the screen. Gallaine Maxwell has

775
00:37:36,800 --> 00:37:39,640
Speaker 1: now been moved moved to quote a cushy new club

776
00:37:39,840 --> 00:37:42,520
Speaker 1: fed prison as she pushes for a deal to tell

777
00:37:42,560 --> 00:37:45,759
Speaker 1: all on Epstein that prison is in Brian, Texas, the

778
00:37:45,800 --> 00:37:48,399
Speaker 1: town I was literally born in and it is a

779
00:37:48,440 --> 00:37:52,160
Speaker 1: minimum security prison camp. For those of you not familiar,

780
00:37:52,239 --> 00:37:55,399
Speaker 1: these prison camps are like the lowest level security in

781
00:37:55,440 --> 00:37:58,680
Speaker 1: the federal system. They barely even have like fences or

782
00:37:58,719 --> 00:38:02,680
Speaker 1: security measures. Like there's photos of Elizabeth Holmes running around

783
00:38:02,719 --> 00:38:05,560
Speaker 1: the track just just to show you. People like photographers

784
00:38:05,600 --> 00:38:08,439
Speaker 1: can get close enough to take pictures of Elizabeth Holmes,

785
00:38:08,480 --> 00:38:11,200
Speaker 1: a therenose woman who is in the facility. Now there's

786
00:38:11,200 --> 00:38:13,919
Speaker 1: a lot of actually open questions about how she even

787
00:38:13,960 --> 00:38:16,080
Speaker 1: got this number one. She wasn't supposed to be transferred.

788
00:38:16,440 --> 00:38:19,759
Speaker 1: Glane was previously housed at a Federal Correctional Institute low

789
00:38:19,840 --> 00:38:22,600
Speaker 1: security prison, which is traditionally where you're supposed to do

790
00:38:22,680 --> 00:38:24,719
Speaker 1: with sex offender. Sex venders are not supposed to be

791
00:38:24,719 --> 00:38:27,239
Speaker 1: moved to federal prison camps because it's a privilege to

792
00:38:27,239 --> 00:38:29,040
Speaker 1: be able to be housed there. So it's an open

793
00:38:29,120 --> 00:38:31,879
Speaker 1: question for why the BOP felt the need to trans

794
00:38:32,080 --> 00:38:34,279
Speaker 1: an open question. I'm just saying, though, I mean, listen,

795
00:38:34,440 --> 00:38:37,479
Speaker 1: all right TOOYA guys, by the way, FOYA people out there,

796
00:38:37,600 --> 00:38:39,640
Speaker 1: let's get some records. This is all out you know,

797
00:38:39,680 --> 00:38:42,279
Speaker 1: should be there in the open. Who ordered it, you know,

798
00:38:42,480 --> 00:38:45,560
Speaker 1: was it the head of the BOP the Department of Justice,

799
00:38:45,640 --> 00:38:48,680
Speaker 1: in exchange for what Because this all gets to what

800
00:38:48,880 --> 00:38:51,880
Speaker 1: is the deal being struck here between Glaine and the

801
00:38:51,920 --> 00:38:56,360
Speaker 1: Trump administration in these extraordinary proffer sessions or whatever where

802
00:38:56,600 --> 00:39:00,200
Speaker 1: she's naming some one hundred individuals. Who are those individuals?

803
00:39:00,320 --> 00:39:02,799
Speaker 1: To what end are you naming these people? The Glen

804
00:39:02,960 --> 00:39:06,520
Speaker 1: Maxwell team obviously is saying, you know, we want our

805
00:39:06,520 --> 00:39:08,960
Speaker 1: whole conviction thrown out. They're trying to appeal to the

806
00:39:09,000 --> 00:39:11,960
Speaker 1: Supreme Court. Well that Justice Department fight her on that.

807
00:39:11,960 --> 00:39:14,799
Speaker 1: That's still an open question. So listen, I mean, this

808
00:39:14,880 --> 00:39:18,399
Speaker 1: is all like very sketchy behavior because if the door

809
00:39:18,480 --> 00:39:21,080
Speaker 1: is now open to a pardon, and let's say she

810
00:39:21,160 --> 00:39:22,920
Speaker 1: comes out and she has a lot to say, but

811
00:39:22,960 --> 00:39:25,800
Speaker 1: none of it is about Donald Trump, can anybody really

812
00:39:25,840 --> 00:39:28,879
Speaker 1: trust you know that this is legitimate? And see that's

813
00:39:28,920 --> 00:39:31,960
Speaker 1: the problem. This is why you need a independent special prosecutor,

814
00:39:32,000 --> 00:39:35,799
Speaker 1: somebody with no skin, like actual political skin in the

815
00:39:35,840 --> 00:39:38,160
Speaker 1: game to ensure that this stands up to some sort

816
00:39:38,160 --> 00:39:40,440
Speaker 1: of legal scrutiny, because if this lady walks free, it

817
00:39:40,480 --> 00:39:42,520
Speaker 1: will be one of the craziest things I've ever seen

818
00:39:42,560 --> 00:39:42,960
Speaker 1: in mid.

819
00:39:42,920 --> 00:39:44,360
Speaker 3: Yeah, it's disgusting. She's a monster.

820
00:39:44,520 --> 00:39:45,600
Speaker 1: She shouldn't even be in this prison.

821
00:39:45,680 --> 00:39:47,600
Speaker 3: No, not at all. I mean she shouldn't be there.

822
00:39:48,680 --> 00:39:51,120
Speaker 2: By the way, typically these sorts of transfers are done

823
00:39:51,160 --> 00:39:54,240
Speaker 2: by US marshals. It was done directly by your prisons

824
00:39:54,320 --> 00:39:59,000
Speaker 2: officials instead. So just you know, another anomaly here. But no,

825
00:39:59,200 --> 00:40:01,880
Speaker 2: I mean we can I see what's happening. It's happening

826
00:40:01,920 --> 00:40:03,480
Speaker 2: right in front of our eyes. They had that meeting,

827
00:40:04,360 --> 00:40:06,120
Speaker 2: you know, there were those leaks to the Wall Street

828
00:40:06,160 --> 00:40:08,120
Speaker 2: Journal which may have come from Glaine and her team

829
00:40:08,120 --> 00:40:12,120
Speaker 2: about the birthday book and whatever. Then immediately the Todd

830
00:40:12,120 --> 00:40:15,680
Speaker 2: Blanche meets with Glaine and lo and behold, next thing,

831
00:40:15,719 --> 00:40:18,720
Speaker 2: you know, she's getting moved to this cushy like I mean, listen,

832
00:40:18,719 --> 00:40:20,880
Speaker 2: I don't think being in federal prisons ever like really

833
00:40:21,000 --> 00:40:23,640
Speaker 2: some vacation the way it's portrayed. But the places that

834
00:40:23,680 --> 00:40:26,879
Speaker 2: they portray as like club fed this is the type

835
00:40:26,880 --> 00:40:29,200
Speaker 2: of place, right, It's the best you can get within

836
00:40:29,239 --> 00:40:31,799
Speaker 2: the federal prison system. Not saying that that's like a

837
00:40:31,840 --> 00:40:36,640
Speaker 2: great experience for anyone anyway, But you know, that session,

838
00:40:36,920 --> 00:40:39,960
Speaker 2: that proffer session was not just about Okay, what do

839
00:40:40,000 --> 00:40:41,919
Speaker 2: you have on other people? Because I don't think Trump

840
00:40:42,000 --> 00:40:44,000
Speaker 2: cares about what do you have on other people? What

841
00:40:44,080 --> 00:40:45,759
Speaker 2: he cares about is what do you have on me?

842
00:40:46,360 --> 00:40:49,000
Speaker 2: What do you have on me? And the fact that

843
00:40:49,040 --> 00:40:52,640
Speaker 2: she had enough to apparently at least secure this little

844
00:40:52,680 --> 00:40:57,520
Speaker 2: nice transfer for herself over the you know, the protocols

845
00:40:57,880 --> 00:41:00,000
Speaker 2: like she should not have been transferred. She was not

846
00:41:00,120 --> 00:41:02,600
Speaker 2: actually eligible to be transferred. They had to make an

847
00:41:02,640 --> 00:41:06,160
Speaker 2: exception for her to get this transfer. Means she had

848
00:41:06,200 --> 00:41:08,719
Speaker 2: something that made him nervous enough that like, okay, well,

849
00:41:08,760 --> 00:41:11,320
Speaker 2: let's at least go this far and then we'll see

850
00:41:11,360 --> 00:41:14,040
Speaker 2: if she actually gets the pardon, if the federal government

851
00:41:14,040 --> 00:41:17,719
Speaker 2: stops fighting her appeal at the Supreme Court. But you know,

852
00:41:17,880 --> 00:41:20,480
Speaker 2: I think we can all see what's happening, like what's

853
00:41:20,480 --> 00:41:23,200
Speaker 2: happening on the surface level, and interpret what is going

854
00:41:23,239 --> 00:41:24,000
Speaker 2: on behind the scenes.

855
00:41:24,160 --> 00:41:27,040
Speaker 1: It's just more sketchy than anything else, especially whenever you

856
00:41:27,160 --> 00:41:30,560
Speaker 1: have Trump continuing to open the door for a pardon.

857
00:41:30,719 --> 00:41:35,040
Speaker 1: So yeah, that's where we are all currently at. Galaine

858
00:41:35,080 --> 00:41:39,239
Speaker 1: is sitting pretty in the federal prison camp system and

859
00:41:39,320 --> 00:41:42,279
Speaker 1: the so called club FED. Let's go ahead and go

860
00:41:42,320 --> 00:41:44,000
Speaker 1: to the next one. Please, let's put it up there

861
00:41:44,080 --> 00:41:47,359
Speaker 1: on the screen. The FBI has redacted Trump's name now

862
00:41:47,400 --> 00:41:51,080
Speaker 1: in the Epstein files. This is from Jason Leopold. Jason Leopold,

863
00:41:51,360 --> 00:41:53,759
Speaker 1: just so people don't know, is really one of the

864
00:41:53,760 --> 00:41:57,239
Speaker 1: pre eminent kind of FEYA reporters of our time. If

865
00:41:57,280 --> 00:42:00,680
Speaker 1: anybody recalls on this Epstein story, I previously referenced the

866
00:42:00,719 --> 00:42:04,040
Speaker 1: CIA Inspector General who was covering up, you know, sex

867
00:42:04,080 --> 00:42:07,719
Speaker 1: crimes and pedophilia. That was all because really of Leopold's work,

868
00:42:07,800 --> 00:42:10,439
Speaker 1: through a tremendous amount of FOYA victory that he had

869
00:42:10,480 --> 00:42:13,000
Speaker 1: actually over the CIA and eventually getting his hands on

870
00:42:13,040 --> 00:42:15,480
Speaker 1: those documents. So what he was able to find in

871
00:42:15,520 --> 00:42:20,399
Speaker 1: the report is basically how they were redacting Trump's name

872
00:42:20,600 --> 00:42:23,319
Speaker 1: along with by the way, several other names. Would be

873
00:42:23,400 --> 00:42:25,880
Speaker 1: nice to know who some of those people were, but

874
00:42:26,480 --> 00:42:30,279
Speaker 1: officially it's the redaction that is from he was able

875
00:42:30,320 --> 00:42:34,560
Speaker 1: to learn through FOYA. Not only that, but the order

876
00:42:34,640 --> 00:42:39,000
Speaker 1: itself came down from this current administration. And it's one

877
00:42:39,080 --> 00:42:41,960
Speaker 1: too where in a certain way I think they made

878
00:42:42,200 --> 00:42:45,400
Speaker 1: he was his own They were really their own worst enemy,

879
00:42:45,440 --> 00:42:48,799
Speaker 1: because what happened prior, based on everything I've read now

880
00:42:48,840 --> 00:42:51,040
Speaker 1: so far in the open record, is these one hundred

881
00:42:51,040 --> 00:42:53,640
Speaker 1: thousand documents were scattered all over the government. Nobody had

882
00:42:53,640 --> 00:42:56,480
Speaker 1: actually compiled them together. So Cash, Patel and these other

883
00:42:56,480 --> 00:42:58,360
Speaker 1: people were like, all right, put it all together. And

884
00:42:58,400 --> 00:43:01,000
Speaker 1: then slowly but surely they're like, yeah, you know, Trump

885
00:43:01,200 --> 00:43:04,200
Speaker 1: is in here. Just so it starts to float up

886
00:43:04,200 --> 00:43:06,080
Speaker 1: to the White House as of two months before the

887
00:43:06,120 --> 00:43:08,800
Speaker 1: memo comes in, and then the order at some point,

888
00:43:08,880 --> 00:43:11,279
Speaker 1: you know, gets it. Hey, let's go and miss you, redactions,

889
00:43:11,320 --> 00:43:15,200
Speaker 1: et cetera. And somewhere around that time of redaction and

890
00:43:15,280 --> 00:43:18,000
Speaker 1: knowledge by Trump that his name is in there, that

891
00:43:18,239 --> 00:43:21,319
Speaker 1: is when the infamous memo comes out from the administration

892
00:43:21,480 --> 00:43:23,759
Speaker 1: of nothing to see here, killed himself. We will do

893
00:43:23,840 --> 00:43:26,560
Speaker 1: no more further releases. And look, you can draw your

894
00:43:26,560 --> 00:43:29,560
Speaker 1: own conclusions. And again, part of the reason why I

895
00:43:29,560 --> 00:43:32,880
Speaker 1: think this is so foolish decision by the administration is

896
00:43:32,920 --> 00:43:35,759
Speaker 1: that what's happening now are all of these leaks from

897
00:43:35,840 --> 00:43:39,200
Speaker 1: inside of the DOJ. The birthday book, it only came

898
00:43:39,239 --> 00:43:42,160
Speaker 1: from two places. Came from the FBI, or came from Galane.

899
00:43:42,320 --> 00:43:45,279
Speaker 1: I still think it came from the FBI because they

900
00:43:45,280 --> 00:43:47,279
Speaker 1: haven't released the letter. And the only reason not to

901
00:43:47,320 --> 00:43:50,080
Speaker 1: release the letter is because it's scanned, and they have

902
00:43:50,160 --> 00:43:53,200
Speaker 1: me even acknowledging the story down in the fiftieth paragraph

903
00:43:53,280 --> 00:43:56,000
Speaker 1: or so, that the scans of the book are available

904
00:43:56,040 --> 00:43:58,319
Speaker 1: in the Epstein files, and that's one of the ways

905
00:43:58,360 --> 00:44:01,160
Speaker 1: that you'd be able to get it. But similarly with

906
00:44:01,200 --> 00:44:03,560
Speaker 1: some of the other letters that they didn't release of

907
00:44:03,600 --> 00:44:07,040
Speaker 1: Bill Clinton and others, same thing, they referenced scans being

908
00:44:07,040 --> 00:44:08,680
Speaker 1: made of that. Of course it could be available to

909
00:44:08,760 --> 00:44:11,760
Speaker 1: Gleane's legal defense team, so you know that's certainly possible

910
00:44:11,800 --> 00:44:14,080
Speaker 1: as well. But if you look at other stories, like

911
00:44:14,120 --> 00:44:16,000
Speaker 1: at the New York Times, the scans of the book

912
00:44:16,200 --> 00:44:19,120
Speaker 1: where Trump wrote to Epstein in nineteen ninety seven, same thing.

913
00:44:19,200 --> 00:44:21,359
Speaker 1: It's a scan, it's not a picture of the actual book.

914
00:44:21,560 --> 00:44:24,160
Speaker 1: And they say again, if you read further, scans of

915
00:44:24,200 --> 00:44:26,759
Speaker 1: the book are available in the Epstein file. So it's

916
00:44:26,800 --> 00:44:29,520
Speaker 1: just open season in terms of leaks on the administration.

917
00:44:29,840 --> 00:44:31,880
Speaker 1: And he's currently the only story on it, and it

918
00:44:31,920 --> 00:44:34,160
Speaker 1: could be one hundred and fifty other stories. But you know,

919
00:44:34,200 --> 00:44:36,640
Speaker 1: I guess he's scared of whatever that story is, or

920
00:44:36,880 --> 00:44:39,440
Speaker 1: you know, is just doing a stupid job at covering up.

921
00:44:39,600 --> 00:44:42,280
Speaker 1: You can answer that problem at any time by actually

922
00:44:42,320 --> 00:44:44,640
Speaker 1: releasing it. And killing this entire story. But that's what

923
00:44:44,680 --> 00:44:45,839
Speaker 1: they've chosen not to do.

924
00:44:46,000 --> 00:44:48,000
Speaker 2: Yeah, well, and at this point it's really too late because,

925
00:44:48,040 --> 00:44:49,719
Speaker 2: like you said, you could never trust what came out

926
00:44:49,719 --> 00:44:52,120
Speaker 2: of them from them at this point, like, there's no

927
00:44:52,200 --> 00:44:53,920
Speaker 2: way that you're going to be able to get some

928
00:44:53,920 --> 00:44:56,640
Speaker 2: sort of full accounting from these people because they've invested

929
00:44:56,640 --> 00:44:58,440
Speaker 2: a whole lot in a cover up, and we're going

930
00:44:58,480 --> 00:45:02,560
Speaker 2: to cover the also some video irregularities too, and that

931
00:45:02,560 --> 00:45:04,759
Speaker 2: that video that they really because remember when they put

932
00:45:04,760 --> 00:45:07,600
Speaker 2: out the memo the he killed himself, nothing to see here,

933
00:45:07,880 --> 00:45:11,360
Speaker 2: we're moving on case clothes, Prince Andrew, you're vindicated. When

934
00:45:11,400 --> 00:45:14,720
Speaker 2: that memo went out, the only proof that they offered

935
00:45:15,360 --> 00:45:18,480
Speaker 2: was this video evidence, which was the post to demonstrate

936
00:45:18,520 --> 00:45:21,160
Speaker 2: conclusively to all of us that no, there there was

937
00:45:21,239 --> 00:45:24,240
Speaker 2: no funny business on the night that Jeffrey Epstein was killed.

938
00:45:24,239 --> 00:45:24,759
Speaker 5: It was just.

939
00:45:24,760 --> 00:45:28,680
Speaker 2: A you know, series of unfortunate incidents where the cameras

940
00:45:28,719 --> 00:45:32,680
Speaker 2: were not functioning and the guards were asleep, and you know,

941
00:45:32,760 --> 00:45:34,560
Speaker 2: here's the video and let's all move on.

942
00:45:34,719 --> 00:45:36,960
Speaker 1: Right, Okay, let's get to that then. So CBS News

943
00:45:37,000 --> 00:45:39,240
Speaker 1: actually did a great job. They did almost ten minute

944
00:45:39,480 --> 00:45:42,279
Speaker 1: investigation into this and they compiled some of the stuff

945
00:45:42,280 --> 00:45:43,800
Speaker 1: that we've talked about here about the camera angles and

946
00:45:43,800 --> 00:45:45,120
Speaker 1: all this, but they did a very good job of

947
00:45:45,160 --> 00:45:47,400
Speaker 1: showing some of the graphics, and they brought in some

948
00:45:47,440 --> 00:45:51,200
Speaker 1: more metadata analyzes to show both a third person that's

949
00:45:51,239 --> 00:45:55,040
Speaker 1: available and visible in the video actually down in the background,

950
00:45:55,120 --> 00:45:57,799
Speaker 1: and just a debunk colt preposterous. The one released by

951
00:45:57,800 --> 00:45:59,359
Speaker 1: the DJ is let's take a listen.

952
00:45:59,600 --> 00:46:02,239
Speaker 4: CBS News has reviewed the tape and this is what

953
00:46:02,320 --> 00:46:06,279
Speaker 4: it shows. The primary entrance is here. The pathway from

954
00:46:06,320 --> 00:46:08,719
Speaker 4: the entrance to the stairs is completely out of view

955
00:46:08,760 --> 00:46:12,360
Speaker 4: of the camera, leaving ample room for someone to enter unseen.

956
00:46:12,880 --> 00:46:15,520
Speaker 4: This is the last time Epstein is believed to have

957
00:46:15,520 --> 00:46:17,799
Speaker 4: been seen on camera, but we don't see him walking

958
00:46:17,880 --> 00:46:21,120
Speaker 4: up the stairs to his cell. Bongino also said none

959
00:46:21,200 --> 00:46:24,040
Speaker 4: of this video was doctored. It was the original, raw

960
00:46:24,080 --> 00:46:25,080
Speaker 4: footage from the camera.

961
00:46:25,200 --> 00:46:26,879
Speaker 7: We're going to give the original so you don't think

962
00:46:27,080 --> 00:46:28,200
Speaker 7: there are any shenanigans.

963
00:46:28,200 --> 00:46:31,320
Speaker 4: But a review by multiple video forensic experts found several

964
00:46:31,360 --> 00:46:35,319
Speaker 4: indications this may not be raw footage. For example, at

965
00:46:35,360 --> 00:46:38,960
Speaker 4: eleven twenty one PM, a cursor appears briefly indicating the

966
00:46:39,040 --> 00:46:42,520
Speaker 4: video may have actually been a recording of a computer screen.

967
00:46:42,680 --> 00:46:46,279
Speaker 4: Experts told CBS News the videos metadata, which tracks how

968
00:46:46,280 --> 00:46:49,279
Speaker 4: a file is created and changed, shows it's made from

969
00:46:49,360 --> 00:46:53,360
Speaker 4: two clips edited together. Then at eleven fifty eight PM,

970
00:46:53,440 --> 00:46:57,320
Speaker 4: the video suddenly jumps to midnight and the aspect ratio

971
00:46:57,520 --> 00:47:00,880
Speaker 4: shifts and every night should have the same missing. So

972
00:47:00,920 --> 00:47:03,439
Speaker 4: we're looking for that video to release that as well,

973
00:47:03,560 --> 00:47:05,680
Speaker 4: showing that a minute is missing every night. But a

974
00:47:05,719 --> 00:47:08,680
Speaker 4: government source familiar with the probe told CBS News the

975
00:47:08,760 --> 00:47:11,520
Speaker 4: FBI and other agencies are in possession of a copy

976
00:47:11,560 --> 00:47:14,880
Speaker 4: of the video that records with no interruption.

977
00:47:15,520 --> 00:47:17,719
Speaker 1: Well, what do you got? All right? I mean, it's

978
00:47:17,719 --> 00:47:20,120
Speaker 1: pretty convincing to me. Do you think funny how you.

979
00:47:20,280 --> 00:47:23,239
Speaker 2: Never released that video showing, oh, every night, the same

980
00:47:23,280 --> 00:47:24,160
Speaker 2: minute is missing.

981
00:47:24,160 --> 00:47:25,120
Speaker 3: We're going to get that to you.

982
00:47:25,719 --> 00:47:27,560
Speaker 1: So what do we talk about? We talked about it.

983
00:47:27,560 --> 00:47:30,840
Speaker 1: It's like, it is theoretically possible that some nineteen nineties

984
00:47:30,880 --> 00:47:33,080
Speaker 1: EERO system does that, But then you're going to have

985
00:47:33,080 --> 00:47:35,720
Speaker 1: to release a lot of evidence. And as I mean again,

986
00:47:35,760 --> 00:47:38,160
Speaker 1: the metadata there doesn't lie, as they show they also

987
00:47:38,200 --> 00:47:40,720
Speaker 1: have this recording, It's like, what are we doing potato

988
00:47:40,840 --> 00:47:43,080
Speaker 1: quality now from the government where we're going to record

989
00:47:43,120 --> 00:47:45,279
Speaker 1: our photos. He's like, what are we doing here?

990
00:47:45,320 --> 00:47:47,319
Speaker 3: You know, It's just.

991
00:47:47,440 --> 00:47:48,920
Speaker 1: That's like, that's the Boomerius move.

992
00:47:48,960 --> 00:47:52,040
Speaker 2: I've ever heard the degradation in this government to not

993
00:47:52,200 --> 00:47:54,920
Speaker 2: even be able to do the basis of an effective

994
00:47:54,920 --> 00:47:57,560
Speaker 2: cover up soccer. I thought about thee what have we

995
00:47:57,600 --> 00:47:57,880
Speaker 2: come to?

996
00:47:58,239 --> 00:47:58,399
Speaker 3: Thought?

997
00:47:58,440 --> 00:48:00,160
Speaker 1: What we come? I kept thinking about that in the

998
00:48:00,440 --> 00:48:03,200
Speaker 1: whole land Guido thing, I'm like, what, we can't even

999
00:48:03,360 --> 00:48:03,600
Speaker 1: do it.

1000
00:48:03,719 --> 00:48:05,680
Speaker 2: We can't even do it coup. It's our own best

1001
00:48:05,719 --> 00:48:07,800
Speaker 2: sell American coup, and I want to just want.

1002
00:48:07,640 --> 00:48:09,080
Speaker 1: To kill him and put the guy on the thing.

1003
00:48:09,120 --> 00:48:11,520
Speaker 1: We don't need to recognize some fake government orright, let's

1004
00:48:11,560 --> 00:48:12,840
Speaker 1: let's all just do it properly.

1005
00:48:13,160 --> 00:48:14,759
Speaker 3: I don't know if you saw this as well.

1006
00:48:14,800 --> 00:48:16,600
Speaker 2: I think it was from CBS too, because they did

1007
00:48:16,640 --> 00:48:19,480
Speaker 2: the most thorough analysis. There's also a part where you

1008
00:48:19,560 --> 00:48:22,200
Speaker 2: see a like mystery figure.

1009
00:48:21,920 --> 00:48:24,440
Speaker 3: In orange exactly yes, which is also.

1010
00:48:24,239 --> 00:48:26,080
Speaker 2: Really significant because they claim, oh, there's no one in

1011
00:48:26,080 --> 00:48:29,600
Speaker 2: this video, and then there is that appears at a

1012
00:48:29,600 --> 00:48:33,520
Speaker 2: certain point, and I think another outlet had reported sources

1013
00:48:33,600 --> 00:48:36,320
Speaker 2: indicating that no, this whole like, oh there's always a

1014
00:48:36,360 --> 00:48:37,080
Speaker 2: minute missing thing.

1015
00:48:37,120 --> 00:48:39,080
Speaker 3: It's just bullshit. That's just not true.

1016
00:48:39,480 --> 00:48:44,000
Speaker 2: So and not to mention this camera angle, and this

1017
00:48:44,080 --> 00:48:46,120
Speaker 2: is part of what CBS is pointing out, doesn't even

1018
00:48:46,160 --> 00:48:48,239
Speaker 2: show what they purport to show. Like they claim that

1019
00:48:48,239 --> 00:48:50,279
Speaker 2: this if you know, if you're you have your eyes

1020
00:48:50,320 --> 00:48:52,640
Speaker 2: on this camera, this means no one could go in

1021
00:48:52,800 --> 00:48:55,439
Speaker 2: or come out of this cell without you seeing it there,

1022
00:48:55,480 --> 00:48:57,200
Speaker 2: And that's also just not true.

1023
00:48:57,640 --> 00:48:59,320
Speaker 3: So the way they portrayed.

1024
00:48:58,920 --> 00:49:03,640
Speaker 2: The video was a lie. The you know, the explanation

1025
00:49:03,680 --> 00:49:05,840
Speaker 2: that this is raw footage and this hasn't been altered,

1026
00:49:05,960 --> 00:49:07,880
Speaker 2: that's a lie. The idea there was no other figures in,

1027
00:49:08,239 --> 00:49:10,560
Speaker 2: that's a lie. The missing minute thing, that's a lie.

1028
00:49:10,600 --> 00:49:13,000
Speaker 2: So you're like, okay, well, so what are you hiding here?

1029
00:49:13,440 --> 00:49:15,960
Speaker 2: Given that this is the only piece of evidence, Like,

1030
00:49:16,000 --> 00:49:18,960
Speaker 2: it's completely legitimate for people to parse this minute by minute,

1031
00:49:19,080 --> 00:49:22,200
Speaker 2: since the only piece of evidence that the government has

1032
00:49:22,239 --> 00:49:25,000
Speaker 2: put out to back up their claim that there was

1033
00:49:25,040 --> 00:49:27,680
Speaker 2: nothing to see there, nothing untoward happened on the night

1034
00:49:27,719 --> 00:49:30,040
Speaker 2: that Jeffrey Epstein quote unquote killed himself.

1035
00:49:30,400 --> 00:49:33,320
Speaker 3: And lo and behold, we've now got.

1036
00:49:32,920 --> 00:49:37,480
Speaker 2: You know, increasingly a lengthy list of inconsistencies and question

1037
00:49:37,600 --> 00:49:40,360
Speaker 2: marks and lies about this one video they put out.

1038
00:49:40,360 --> 00:49:44,000
Speaker 1: Yes, that's exactly right. And finally P. Six please, what

1039
00:49:44,080 --> 00:49:47,040
Speaker 1: do we have here from the Maxwell Prison? The new

1040
00:49:47,080 --> 00:49:51,120
Speaker 1: prison in Texas quietly that she's been transferred to prohibits

1041
00:49:51,160 --> 00:49:54,600
Speaker 1: cameras and recording devices, very few surveillance and footage, no

1042
00:49:54,719 --> 00:49:58,920
Speaker 1: public visibility. They moved her into facility where transparency is

1043
00:49:59,000 --> 00:50:01,879
Speaker 1: actually signific only reduced quote banned by design, right as

1044
00:50:01,880 --> 00:50:04,600
Speaker 1: he starts cooperating with the d o J. So let's

1045
00:50:04,600 --> 00:50:08,360
Speaker 1: see you know, good luck are Let's keep our eyes open,

1046
00:50:08,560 --> 00:50:13,840
Speaker 1: shall we? Down in my hometown of Brian, Texas. Okay,