1
00:00:02,680 --> 00:00:05,080
Speaker 1: Welcome back to a numbers game with Ryan Gardowski. Thank

2
00:00:05,080 --> 00:00:07,160
Speaker 1: you guys for being here. We have an amazing show

3
00:00:07,200 --> 00:00:09,119
Speaker 1: up for you guys today, a big guest coming up,

4
00:00:09,119 --> 00:00:10,800
Speaker 1: and a lot of data to break, so let's really

5
00:00:10,800 --> 00:00:13,520
Speaker 1: get into it. On Monday's episode, I talked a lot

6
00:00:13,520 --> 00:00:17,040
Speaker 1: about affordability. It's the buzzword right now, especially since Mandani

7
00:00:17,120 --> 00:00:19,720
Speaker 1: won the New York City's mayoral election. I received a

8
00:00:19,760 --> 00:00:22,599
Speaker 1: lot of feedback about young people, especially how they fill

9
00:00:22,680 --> 00:00:24,880
Speaker 1: the economy, the worries of their ability to live and

10
00:00:24,920 --> 00:00:26,920
Speaker 1: buy a home, and the worry that AI is going

11
00:00:27,000 --> 00:00:30,440
Speaker 1: to wreck have it on their opportunities going forward. And

12
00:00:30,720 --> 00:00:32,280
Speaker 1: I have data on it, but I think that's a

13
00:00:32,320 --> 00:00:34,440
Speaker 1: really good thing going to hone into and think about

14
00:00:34,520 --> 00:00:36,680
Speaker 1: right now. And I want to use a pop culture

15
00:00:36,760 --> 00:00:40,080
Speaker 1: reference to really a misunderstanding of how young people are

16
00:00:40,120 --> 00:00:43,800
Speaker 1: doing financially in this moment. Let's go all the way

17
00:00:43,800 --> 00:00:46,040
Speaker 1: back to January twenty twenty five. Right the world was

18
00:00:46,080 --> 00:00:49,120
Speaker 1: shaken and it wasn't about the upcoming midterms or about

19
00:00:49,280 --> 00:00:51,959
Speaker 1: the Iraq War. No, it was an announcement that came

20
00:00:52,000 --> 00:00:55,680
Speaker 1: out that America's sweethearts, Brad Pitt and Jennifer Anison were

21
00:00:55,760 --> 00:00:58,480
Speaker 1: breaking up because he had cheated on her with Angelina

22
00:00:58,520 --> 00:01:03,200
Speaker 1: jo Lee then up entered the American Zeitchist afterwards, known

23
00:01:03,200 --> 00:01:06,240
Speaker 1: as Sad Jennifer. Right, Jennifer Anison has been two decades

24
00:01:06,240 --> 00:01:08,600
Speaker 1: since she broke up with her then husband Brad Pitt,

25
00:01:08,959 --> 00:01:11,240
Speaker 1: but that she's sad and it lingers in the air

26
00:01:11,280 --> 00:01:13,679
Speaker 1: to the state follows her, that Jennifer Aniston is sad

27
00:01:13,680 --> 00:01:15,320
Speaker 1: because her marriage is gonna work and she didn't get

28
00:01:15,319 --> 00:01:17,800
Speaker 1: to have children. And whether it's true or not true,

29
00:01:17,800 --> 00:01:20,640
Speaker 1: I don't know, Jennifer Aniston, but it doesn't make a

30
00:01:20,680 --> 00:01:24,640
Speaker 1: difference because the narrative prevails. Jennifer Aniston is sad and

31
00:01:24,680 --> 00:01:29,800
Speaker 1: she'll never really find happiness. That same prevailing narrative was

32
00:01:29,840 --> 00:01:32,840
Speaker 1: created just a few years later, around the global financial

33
00:01:32,920 --> 00:01:37,320
Speaker 1: crisis about millennials, the poor millennial like sad Jennifer, poor millennial.

34
00:01:37,319 --> 00:01:40,840
Speaker 1: The words just kind of always fit together. Millennials enter

35
00:01:40,880 --> 00:01:42,880
Speaker 1: the workforce at a really bad time, there's kind of

36
00:01:42,880 --> 00:01:45,399
Speaker 1: no way to shake it. They'll never be able to

37
00:01:45,480 --> 00:01:49,360
Speaker 1: get ahead, is what the pervasive idea was after that happened.

38
00:01:49,400 --> 00:01:50,840
Speaker 1: That they're always going to spend their money on like

39
00:01:50,920 --> 00:01:53,000
Speaker 1: avocado toast, that they're just going to live in dead

40
00:01:53,040 --> 00:01:56,200
Speaker 1: and in their mom's basement, and it's just gonna linger

41
00:01:56,280 --> 00:02:00,960
Speaker 1: with them forever. That prevailing narrative somehow harry though into

42
00:02:01,040 --> 00:02:04,720
Speaker 1: Generation Z, the generation afterwards who were just children when

43
00:02:04,720 --> 00:02:07,560
Speaker 1: the global financial crisis happened, that didn't have to look

44
00:02:07,600 --> 00:02:10,440
Speaker 1: for a job when unemployment was heading into the double digits.

45
00:02:10,639 --> 00:02:12,480
Speaker 1: I don't know how they got lumped into it, but

46
00:02:12,520 --> 00:02:15,280
Speaker 1: they've been lumped into it. And while I think it's

47
00:02:15,320 --> 00:02:18,840
Speaker 1: true that millennials like myself definitely got a rough hand

48
00:02:18,880 --> 00:02:21,080
Speaker 1: as when they were going to sit there and join

49
00:02:21,160 --> 00:02:25,960
Speaker 1: the economy and become adults, millennial men actually earn the

50
00:02:26,000 --> 00:02:29,600
Speaker 1: most amount of money of any generation adjusted for inflation

51
00:02:29,720 --> 00:02:32,440
Speaker 1: at this point in their lives. In their late thirties.

52
00:02:32,840 --> 00:02:36,440
Speaker 1: Millennial women lag behind men, but they still make more

53
00:02:36,480 --> 00:02:39,920
Speaker 1: money on average than most other generation's women, right, not

54
00:02:40,000 --> 00:02:42,880
Speaker 1: Gen X they're the one exception, but Baby Boomer women

55
00:02:43,080 --> 00:02:45,400
Speaker 1: for sure, though there were less of them working. I'll

56
00:02:45,440 --> 00:02:49,160
Speaker 1: put that caveat in there. But anyways, besides just that

57
00:02:49,240 --> 00:02:52,240
Speaker 1: point that millennial men make all this money, there has

58
00:02:52,280 --> 00:02:56,400
Speaker 1: been massive rebounds in general and wealth creation since the

59
00:02:56,880 --> 00:03:00,639
Speaker 1: Since the COVID pandemic. According to the Federal Reserve, Generation

60
00:03:00,880 --> 00:03:04,320
Speaker 1: Z is set to outpace millennials, so you actually make

61
00:03:04,400 --> 00:03:06,560
Speaker 1: more money than them. This is even true when you

62
00:03:06,600 --> 00:03:11,960
Speaker 1: compare across different economic distributions. Right, So poor millennials make

63
00:03:12,080 --> 00:03:15,160
Speaker 1: more money at this point in life than poor baby boomers.

64
00:03:15,520 --> 00:03:18,840
Speaker 1: Wealthy millennials make more money than wealthy baby boomers at

65
00:03:18,840 --> 00:03:21,240
Speaker 1: this point in their life on average. Now, I'm not

66
00:03:21,280 --> 00:03:24,520
Speaker 1: trying to paint an incredibly rosy picture. I'm not saying

67
00:03:24,560 --> 00:03:27,440
Speaker 1: we haven't had high inflation. We definitely have had We've

68
00:03:27,440 --> 00:03:30,919
Speaker 1: had some rough years, right, But things aren't as bad

69
00:03:31,000 --> 00:03:34,120
Speaker 1: for millennials and Gen Z as some people in the

70
00:03:34,160 --> 00:03:37,400
Speaker 1: media suggest. We're not in the same situation that young

71
00:03:37,440 --> 00:03:40,280
Speaker 1: people were, let's say, one hundred years ago, growing up

72
00:03:40,360 --> 00:03:44,200
Speaker 1: during the Great Depression. The one key area of wealth

73
00:03:44,240 --> 00:03:48,000
Speaker 1: generation that hasn't kept up for millennials and Gen Z, though,

74
00:03:48,360 --> 00:03:51,360
Speaker 1: is housing. Data from the Federal Reserve of Saint Louis

75
00:03:51,480 --> 00:03:54,560
Speaker 1: is the national median sales price on a house in

76
00:03:54,560 --> 00:03:58,160
Speaker 1: the US reached four hundred and ten thousand dollars in

77
00:03:58,240 --> 00:04:02,200
Speaker 1: Q two, twenty twenty five. Additionally, Zillo, the website Zillo

78
00:04:02,200 --> 00:04:04,480
Speaker 1: of draw that I'm up at four o'clock in the

79
00:04:04,480 --> 00:04:07,560
Speaker 1: morning searching for houses that I can't afford. That website

80
00:04:07,880 --> 00:04:11,040
Speaker 1: put that home values have increased by forty five percent

81
00:04:11,120 --> 00:04:14,800
Speaker 1: since twenty twenty, obviously unsustainable people trying to buy their

82
00:04:14,840 --> 00:04:18,840
Speaker 1: first house. Zoning laws are often blamed for this right

83
00:04:18,880 --> 00:04:21,279
Speaker 1: you can't build in a number of places like San Francisco,

84
00:04:21,279 --> 00:04:24,000
Speaker 1: which is one hundred percent true. But a big driver

85
00:04:24,240 --> 00:04:27,080
Speaker 1: for that population growth is also a mass immigration. We

86
00:04:27,160 --> 00:04:31,400
Speaker 1: are increasing the population at an unnaturally faster rate. If

87
00:04:31,440 --> 00:04:34,680
Speaker 1: you just had births versus deaths, our population would be increasing,

88
00:04:35,040 --> 00:04:38,080
Speaker 1: but bringing in more than one point one million legal

89
00:04:38,120 --> 00:04:42,000
Speaker 1: immigrants per year is making the situation much harder. It's

90
00:04:42,120 --> 00:04:45,360
Speaker 1: not just a supply issue when it comes to housing,

91
00:04:45,480 --> 00:04:48,520
Speaker 1: is also a demand issue. The demand grows as the

92
00:04:48,600 --> 00:04:53,279
Speaker 1: population grows, and it grows primarily by immigration, not to

93
00:04:53,320 --> 00:04:58,160
Speaker 1: mention corporations and foreigners buying up housing. So young people

94
00:04:58,760 --> 00:05:00,960
Speaker 1: long and short of it, have made big advances in

95
00:05:00,960 --> 00:05:02,839
Speaker 1: the last twenty years. And when I say young people,

96
00:05:03,040 --> 00:05:05,680
Speaker 1: people under forty or around forty from millennials, we are

97
00:05:06,080 --> 00:05:08,400
Speaker 1: middle aged guys. Sorry to break it to you, it's

98
00:05:08,400 --> 00:05:10,400
Speaker 1: hard for me to deal with sometimes too. I've actually

99
00:05:10,440 --> 00:05:12,120
Speaker 1: been saying I'm in middle age for a while now,

100
00:05:12,160 --> 00:05:14,040
Speaker 1: so that way, when I'm like real thick in the

101
00:05:14,080 --> 00:05:15,919
Speaker 1: middle Agian, noess, it won't affect me because I've been

102
00:05:15,920 --> 00:05:19,719
Speaker 1: saying something like thirty three. Anyway, we have made big

103
00:05:19,760 --> 00:05:22,479
Speaker 1: gains as a generation since the Great Recession, which is good.

104
00:05:22,760 --> 00:05:26,159
Speaker 1: There are growing concerns though, that those gains are going

105
00:05:26,160 --> 00:05:29,560
Speaker 1: to slide because of AI. We're gonna be in an

106
00:05:29,640 --> 00:05:33,240
Speaker 1: error soon where AI, you know, may take millions or

107
00:05:33,320 --> 00:05:37,000
Speaker 1: tens of millions of jobs and bring unemployment too much higher.

108
00:05:37,360 --> 00:05:41,880
Speaker 1: Big corporations are investing heavily into into AI. Research shows

109
00:05:41,880 --> 00:05:46,159
Speaker 1: that research into AI and generative AI spending, the amount

110
00:05:46,160 --> 00:05:49,120
Speaker 1: of spending is spent into those initiatives is probably the

111
00:05:49,160 --> 00:05:52,039
Speaker 1: one thing keeping the US out of a recession. They spend.

112
00:05:52,080 --> 00:05:55,799
Speaker 1: Three companies spent three hundred and seventy five billion dollars

113
00:05:55,839 --> 00:05:59,039
Speaker 1: on AI research in twenty twenty five, and it's expected

114
00:05:59,080 --> 00:06:01,760
Speaker 1: to hit half a trick billion dollars in twenty twenty six.

115
00:06:02,440 --> 00:06:06,200
Speaker 1: Here's how Dan Iver's managing director of web Bush Securities

116
00:06:06,240 --> 00:06:09,400
Speaker 1: put it, quote, it's the start of trillions being spent

117
00:06:09,480 --> 00:06:12,640
Speaker 1: in this in this build out for a fourth industrial revolution.

118
00:06:13,320 --> 00:06:15,599
Speaker 1: Big tech right now is doing the equivalent of building

119
00:06:15,680 --> 00:06:17,440
Speaker 1: up Vegas in the nineteen fifties when there was just

120
00:06:17,560 --> 00:06:20,600
Speaker 1: sand or Dubai thirty years ago. That's what's happening. With

121
00:06:20,640 --> 00:06:24,159
Speaker 1: the AI infrastructure built out, from chips to the data

122
00:06:24,240 --> 00:06:27,240
Speaker 1: centers to the grid, you're really building out the future

123
00:06:27,240 --> 00:06:32,200
Speaker 1: economy for consumers and enterprises. He added, Well, that's great

124
00:06:32,240 --> 00:06:36,039
Speaker 1: for the GDP overall, right, But is it great for workers?

125
00:06:36,240 --> 00:06:38,599
Speaker 1: Is it great for employees? Is it great for the

126
00:06:38,640 --> 00:06:41,920
Speaker 1: person trying to make a life for themselves and either

127
00:06:42,000 --> 00:06:44,920
Speaker 1: build just you know, enough capital to buy a house

128
00:06:45,160 --> 00:06:48,200
Speaker 1: and a car and sustain a family, or is it

129
00:06:48,279 --> 00:06:50,840
Speaker 1: you know, or people trying to build generational wealth, something

130
00:06:50,880 --> 00:06:54,080
Speaker 1: that you know a lot of Americans aspire to. Proponents

131
00:06:54,080 --> 00:06:56,960
Speaker 1: of AI say that whenever new technology comes around, some

132
00:06:57,120 --> 00:06:59,760
Speaker 1: old jobs are wiped out, but there's new opportunities that

133
00:06:59,760 --> 00:07:02,800
Speaker 1: we have yet to foresee, things we can't possibly imagine.

134
00:07:03,120 --> 00:07:06,799
Speaker 1: A study from Stanford University researchers back in August concluded

135
00:07:06,880 --> 00:07:10,080
Speaker 1: there's been a substantial decline and employment for early career

136
00:07:10,120 --> 00:07:13,920
Speaker 1: workers in occupations most exposed to AI, such as software

137
00:07:13,960 --> 00:07:17,520
Speaker 1: development and customer service, but job growth has continued. Entry

138
00:07:17,600 --> 00:07:21,520
Speaker 1: level employment has declined and applications of AI and automated work,

139
00:07:21,680 --> 00:07:24,400
Speaker 1: but those effects are kind of muted. When they're augmented

140
00:07:24,400 --> 00:07:26,280
Speaker 1: form and people learn how to work with AI, the

141
00:07:26,360 --> 00:07:29,720
Speaker 1: job losses aren't as severe as people who are not

142
00:07:29,760 --> 00:07:30,320
Speaker 1: trained at all.

143
00:07:30,360 --> 00:07:30,640
Speaker 2: AI.

144
00:07:31,040 --> 00:07:35,000
Speaker 1: Studies from MIT and the Rand Organization found that the

145
00:07:35,040 --> 00:07:37,800
Speaker 1: most of the investments in AI that companies have had

146
00:07:37,920 --> 00:07:40,960
Speaker 1: have shown very little to no returns, and they're slow

147
00:07:41,040 --> 00:07:45,600
Speaker 1: to integrate AI into their businesses. So maybe the fears

148
00:07:45,680 --> 00:07:49,720
Speaker 1: of the sky is falling aren't as founded as possible

149
00:07:49,760 --> 00:07:52,680
Speaker 1: because corporations are being very slow to implement this that

150
00:07:52,720 --> 00:07:55,160
Speaker 1: they haven't worked out. Ninety five percent of this investment

151
00:07:55,200 --> 00:07:56,800
Speaker 1: hasn't worked out, and there is the fear of the

152
00:07:57,440 --> 00:08:00,520
Speaker 1: economic bubble of AI will that take things back a

153
00:08:00,560 --> 00:08:04,920
Speaker 1: few more years. While AI is slower than some people

154
00:08:05,000 --> 00:08:08,360
Speaker 1: had expected, it's clearly going in one direction though, right

155
00:08:08,400 --> 00:08:10,040
Speaker 1: It's like the Internet. The Internet had a bubble, but

156
00:08:10,120 --> 00:08:13,720
Speaker 1: the Internet continued, and we're going to affect the way

157
00:08:13,760 --> 00:08:15,960
Speaker 1: we work in our economy. But it's also going to

158
00:08:16,000 --> 00:08:19,720
Speaker 1: affect our politics, which is where my brain always goes

159
00:08:19,760 --> 00:08:24,840
Speaker 1: in what happens when there's a permanent unemployment of let's

160
00:08:24,840 --> 00:08:27,280
Speaker 1: say ten to twenty percent of the workforce, and what

161
00:08:27,400 --> 00:08:30,800
Speaker 1: happens when millions of people can't find especially young people,

162
00:08:30,880 --> 00:08:34,760
Speaker 1: cannot find work and thus can't afford to have a life.

163
00:08:34,760 --> 00:08:36,680
Speaker 1: They can't afford a family, they can't afford a house,

164
00:08:36,679 --> 00:08:39,400
Speaker 1: they can't afford a car, they can't afford that avocado

165
00:08:39,440 --> 00:08:41,640
Speaker 1: toast that millennials has been eating for thirty years. According

166
00:08:41,640 --> 00:08:45,760
Speaker 1: to some baby boomers, their politics will shift to become

167
00:08:45,760 --> 00:08:48,920
Speaker 1: more progressive or more radical in any kind of direction.

168
00:08:49,080 --> 00:08:49,280
Speaker 2: Right.

169
00:08:49,520 --> 00:08:52,040
Speaker 1: That's not to say that money can't be created because

170
00:08:52,040 --> 00:08:55,720
Speaker 1: it's AI revolution. It just may be too concentrated among

171
00:08:55,760 --> 00:08:58,880
Speaker 1: the very wealthy, and those left behind who can't get

172
00:08:58,960 --> 00:09:02,800
Speaker 1: jobs will not be angry by it. The Trump administration

173
00:09:02,960 --> 00:09:05,520
Speaker 1: is really having an accelerationist view on AI.

174
00:09:05,640 --> 00:09:06,480
Speaker 2: Right, go all in.

175
00:09:06,600 --> 00:09:08,840
Speaker 1: We have to be China in this race against China,

176
00:09:09,440 --> 00:09:12,040
Speaker 1: and a lot of voters, including in private polling that

177
00:09:12,080 --> 00:09:15,120
Speaker 1: I've seen at or asking really, why why does it matter?

178
00:09:15,160 --> 00:09:18,040
Speaker 1: I don't understand there's more fear out of AI than

179
00:09:18,080 --> 00:09:22,000
Speaker 1: I think there is optimism, whether that is good founded

180
00:09:22,080 --> 00:09:24,480
Speaker 1: or unfounded or bad or whatnot. I'm just telling you

181
00:09:24,520 --> 00:09:28,000
Speaker 1: the data as it is, and I think that progressives

182
00:09:28,360 --> 00:09:31,320
Speaker 1: are starting to look at the temperature in the room

183
00:09:31,360 --> 00:09:33,840
Speaker 1: and saying we need to have an answer to that

184
00:09:34,000 --> 00:09:37,280
Speaker 1: call to the AI revolution, to what's happening to jobs,

185
00:09:37,800 --> 00:09:41,360
Speaker 1: and I think that they're really already testing the waters

186
00:09:41,400 --> 00:09:44,400
Speaker 1: on it. With me on today's episode is Democratic Congressman

187
00:09:44,480 --> 00:09:47,480
Speaker 1: Roe Kanna, and he has a progressive message on AI.

188
00:09:47,679 --> 00:09:50,720
Speaker 1: His conversation, I hope will bring insight to how Democrats,

189
00:09:50,800 --> 00:09:55,240
Speaker 1: especially left wing Democrats, progressive Democrats, will approach this policy

190
00:09:55,280 --> 00:09:57,920
Speaker 1: going forward, and what it could mean for our politics

191
00:09:57,920 --> 00:10:02,120
Speaker 1: in general, and who is really answering people's anxiety. That's

192
00:10:02,160 --> 00:10:08,160
Speaker 1: coming up next congress and ro Kana has represented a

193
00:10:08,200 --> 00:10:11,200
Speaker 1: California seventeen districts since twenty seventeen. It's known as the

194
00:10:11,240 --> 00:10:14,800
Speaker 1: trillion Dollar District as it's home to so many tech companies. Congressman,

195
00:10:14,840 --> 00:10:15,680
Speaker 1: thank you for being here.

196
00:10:16,400 --> 00:10:18,199
Speaker 2: Well, thank you, I appreciate it.

197
00:10:18,360 --> 00:10:20,720
Speaker 1: Well, first, I want to ask you about how you

198
00:10:20,720 --> 00:10:23,839
Speaker 1: think AI is currently affecting the economy, especially when it

199
00:10:23,840 --> 00:10:25,720
Speaker 1: comes to wages and job growth, because there's a big

200
00:10:25,720 --> 00:10:29,880
Speaker 1: difference between GDP as a whole and jobs and wages specifically.

201
00:10:31,080 --> 00:10:34,720
Speaker 3: Well, I think it's had a bifurcated effect. On the

202
00:10:34,760 --> 00:10:37,960
Speaker 3: one hand, you've had obviously a data center build out,

203
00:10:38,040 --> 00:10:42,720
Speaker 3: a huge CAPEX expenditure. That's created some jobs for electricians,

204
00:10:42,760 --> 00:10:45,520
Speaker 3: for unions, for people who are building these centers, and

205
00:10:45,559 --> 00:10:48,920
Speaker 3: there's also been huge wealth generation for those who have

206
00:10:49,679 --> 00:10:54,440
Speaker 3: invested in AI, who are developing AI, and who're in

207
00:10:54,520 --> 00:10:58,000
Speaker 3: the capitol class. On the other hand, it is already

208
00:10:58,000 --> 00:11:01,720
Speaker 3: starting to have an automation impact, making it harder for

209
00:11:01,880 --> 00:11:06,600
Speaker 3: entry level jobs, particularly for kids graduating from school, making

210
00:11:06,640 --> 00:11:10,880
Speaker 3: it harder for people who are being displaced, and.

211
00:11:12,200 --> 00:11:15,520
Speaker 2: It is creating anxiety for many.

212
00:11:15,360 --> 00:11:19,360
Speaker 3: People about whether they'll have good paying jobs in the future.

213
00:11:19,679 --> 00:11:22,800
Speaker 1: Yeah, you've tweeted this something called the new deal for AI,

214
00:11:22,880 --> 00:11:25,040
Speaker 1: and what you said was this, you call for an

215
00:11:25,080 --> 00:11:28,679
Speaker 1: AI jobs new deal, future workforce, administration hires for young

216
00:11:28,760 --> 00:11:33,120
Speaker 1: graduates and displaced workers in care jobs, government services, national

217
00:11:33,120 --> 00:11:39,080
Speaker 1: security laws, protect drivers from automation, provoking immunity for algorithmic content,

218
00:11:39,200 --> 00:11:42,199
Speaker 1: and emphasizing humanities in schools. Can you go into a

219
00:11:42,200 --> 00:11:45,040
Speaker 1: little detail what that actually means and how be executed.

220
00:11:45,679 --> 00:11:48,920
Speaker 2: Sure, if you're a college graduate or you're a graduate.

221
00:11:48,559 --> 00:11:50,959
Speaker 3: Of and you're not getting a job, then the federal

222
00:11:51,000 --> 00:11:52,960
Speaker 3: government would say, Okay, we're either going to help create

223
00:11:53,000 --> 00:11:55,640
Speaker 3: an apprenticeship and subsidizing it at a private company, or

224
00:11:55,679 --> 00:11:58,720
Speaker 3: we're going to help you in your local community do

225
00:11:58,960 --> 00:12:02,160
Speaker 3: some care job, whether it's healthcare, childcare, eldercare, or in

226
00:12:02,200 --> 00:12:05,520
Speaker 3: your local community, work on some project for that community,

227
00:12:05,679 --> 00:12:08,520
Speaker 3: whether it's an infrastructure project or whether it's a project

228
00:12:08,559 --> 00:12:10,960
Speaker 3: of delivering services. Or we could come into the federal

229
00:12:11,040 --> 00:12:14,880
Speaker 3: government to help make government more efficient, either in cybersecurity

230
00:12:14,960 --> 00:12:17,920
Speaker 3: or digital services, or on some national security projects.

231
00:12:17,920 --> 00:12:20,480
Speaker 2: And this way you're going to get experienced, and then

232
00:12:20,520 --> 00:12:20,920
Speaker 2: you can.

233
00:12:20,800 --> 00:12:23,240
Speaker 3: Go back out into the private sector, because the private

234
00:12:23,240 --> 00:12:25,600
Speaker 3: sector may not be hiring those entry level jobs. It

235
00:12:25,600 --> 00:12:27,480
Speaker 3: can be for people also right out of high school

236
00:12:27,480 --> 00:12:30,079
Speaker 3: and trade schools, though I think the trade schools ironically

237
00:12:30,080 --> 00:12:32,319
Speaker 3: will be less automated and there may be less of

238
00:12:32,400 --> 00:12:36,120
Speaker 3: a need that for them. The unemployment rate is lower

239
00:12:36,120 --> 00:12:39,720
Speaker 3: for them actually initially than those out of college. And

240
00:12:39,760 --> 00:12:42,160
Speaker 3: I also would say that it should include people then

241
00:12:42,160 --> 00:12:45,360
Speaker 3: who are being displaced based on automation, and the people

242
00:12:45,360 --> 00:12:48,200
Speaker 3: who should be paying for that, or the companies that

243
00:12:48,240 --> 00:12:51,520
Speaker 3: are automating should be having some kind of a tax

244
00:12:51,600 --> 00:12:53,520
Speaker 3: on that so that they can help pay for this

245
00:12:53,720 --> 00:12:57,360
Speaker 3: workforce administration in many ways. We should do this in

246
00:12:57,559 --> 00:13:00,920
Speaker 3: a way that's correct, as opposed to globalization, where we

247
00:13:01,120 --> 00:13:05,440
Speaker 3: basically allowed wealth to accumulate and hollowed out communities and

248
00:13:05,480 --> 00:13:08,400
Speaker 3: de industrialized and promised training programs that never came.

249
00:13:09,080 --> 00:13:11,719
Speaker 1: So, and what is revoke algorithmic community mean?

250
00:13:12,360 --> 00:13:12,440
Speaker 2: So?

251
00:13:12,640 --> 00:13:19,600
Speaker 3: Right now, if you are Facebook or X and you

252
00:13:20,320 --> 00:13:27,480
Speaker 3: amplifies things that are libelists or slander or provoking violence,

253
00:13:27,480 --> 00:13:30,240
Speaker 3: you don't have any immunity under section two thirty, I

254
00:13:30,240 --> 00:13:31,360
Speaker 3: mean you don't have any liability.

255
00:13:31,400 --> 00:13:34,560
Speaker 2: Intersection two thirty, you should have liability. What would that mean?

256
00:13:34,840 --> 00:13:38,160
Speaker 3: It would mean that these these companies may move more

257
00:13:38,200 --> 00:13:41,320
Speaker 3: to a subscription model than an algorithmic model, more to

258
00:13:41,679 --> 00:13:44,840
Speaker 3: presenting content chronologically, and I just think it would be

259
00:13:45,080 --> 00:13:47,640
Speaker 3: less of an outrage driven democracy.

260
00:13:48,280 --> 00:13:50,679
Speaker 1: Now, do you know have any estimate that sounds like

261
00:13:51,200 --> 00:13:55,240
Speaker 1: it sounds very it sounds very promising. But do you

262
00:13:55,240 --> 00:13:57,520
Speaker 1: know what an effort like that would cost by any chance?

263
00:13:58,160 --> 00:13:59,559
Speaker 3: It's hard to say because we don't know what the

264
00:13:59,600 --> 00:14:04,240
Speaker 3: display effect of AI it will be. So it would

265
00:14:04,240 --> 00:14:06,959
Speaker 3: be certainly in the billions of dollars a year, but

266
00:14:07,080 --> 00:14:09,360
Speaker 3: it depends on how much the displacement is, but it

267
00:14:09,400 --> 00:14:11,280
Speaker 3: needs to be coupled, in my view, with a comprehensive

268
00:14:11,320 --> 00:14:14,480
Speaker 3: economic development strategy, what I call a Marshall Plan for America,

269
00:14:14,720 --> 00:14:17,800
Speaker 3: build a thousand new trade schools, have a National Industrial

270
00:14:17,840 --> 00:14:21,360
Speaker 3: Development Bank that's actually funding new factories or preventing providing

271
00:14:21,400 --> 00:14:24,320
Speaker 3: financing for a factory not to close if there was

272
00:14:24,360 --> 00:14:27,360
Speaker 3: a market failure the lack of financing, or making sure

273
00:14:27,360 --> 00:14:30,600
Speaker 3: that we are investing in care jobs in communities, making

274
00:14:30,640 --> 00:14:33,560
Speaker 3: sure that we're investing in AI tech institute so people

275
00:14:33,600 --> 00:14:36,160
Speaker 3: can actually develop an AI literacy for these new jobs.

276
00:14:36,360 --> 00:14:38,560
Speaker 3: So I think that what I'm calling game about with

277
00:14:38,600 --> 00:14:41,600
Speaker 3: the future workforce administration is sort of defensive. If you're

278
00:14:41,600 --> 00:14:44,680
Speaker 3: not finding a job or if you're unemployed, what do

279
00:14:44,720 --> 00:14:48,160
Speaker 3: you do? But we also need an offensive strategy, which is,

280
00:14:48,160 --> 00:14:50,560
Speaker 3: how do we have an economic development strategy for job

281
00:14:50,640 --> 00:14:53,920
Speaker 3: creation across this country so that we mitigate the need

282
00:14:54,400 --> 00:14:55,640
Speaker 3: for people being displaced.

283
00:14:56,120 --> 00:15:00,480
Speaker 1: Your career, you've been a very vocal support order of

284
00:15:01,320 --> 00:15:03,720
Speaker 1: people's personal rights and people's person protection. You've been a

285
00:15:03,720 --> 00:15:06,880
Speaker 1: civil libertarian and many facets, especially when the War on

286
00:15:07,000 --> 00:15:10,640
Speaker 1: Terror was really ramping up. Are you afraid of what's

287
00:15:10,640 --> 00:15:14,160
Speaker 1: going on people's civil liberties and AI about people's data

288
00:15:14,200 --> 00:15:15,560
Speaker 1: being really tracked and sold.

289
00:15:17,600 --> 00:15:19,560
Speaker 2: I am, I'm very concerned about it.

290
00:15:20,160 --> 00:15:22,920
Speaker 3: The first rule should be you own your data, and

291
00:15:23,120 --> 00:15:25,360
Speaker 3: we should have a data dividend in this country. It

292
00:15:25,400 --> 00:15:28,480
Speaker 3: wouldn't be life changing, but you would probably get about

293
00:15:28,480 --> 00:15:31,960
Speaker 3: five hundred bucks a year just for the exploitation or

294
00:15:31,960 --> 00:15:34,400
Speaker 3: the use of your data by these companies. And five

295
00:15:34,480 --> 00:15:36,560
Speaker 3: hundred bucks is a fair amount for folks. I mean

296
00:15:36,600 --> 00:15:39,960
Speaker 3: we're paying grossery, billy, I mean, I'll take it. So

297
00:15:40,040 --> 00:15:43,040
Speaker 3: that there should be a data dividend. There also should

298
00:15:43,080 --> 00:15:45,840
Speaker 3: be a right that you have to your own data.

299
00:15:45,880 --> 00:15:48,040
Speaker 3: That they don't have a federal government building a social

300
00:15:48,120 --> 00:15:49,320
Speaker 3: media profile on you.

301
00:15:49,360 --> 00:15:50,720
Speaker 2: Can you imagine how scary that is.

302
00:15:51,440 --> 00:15:54,480
Speaker 3: There shouldn't be companies that are able to feed you

303
00:15:55,680 --> 00:15:59,960
Speaker 3: information by building profiles on you. So I think we'd

304
00:16:00,120 --> 00:16:02,520
Speaker 3: need I called for this in twenty seventeen, an Internet

305
00:16:02,560 --> 00:16:03,600
Speaker 3: Bill of Wrights, and.

306
00:16:03,440 --> 00:16:05,360
Speaker 2: We still haven't had it past. You know.

307
00:16:05,400 --> 00:16:07,240
Speaker 3: One of the problems is, just bluntly, we've had three

308
00:16:07,280 --> 00:16:09,920
Speaker 3: eighty year old presidents in a row, and we need

309
00:16:10,280 --> 00:16:13,640
Speaker 3: leadership in this country that understands technology and understands how

310
00:16:13,680 --> 00:16:15,040
Speaker 3: to respond to that technology.

311
00:16:15,200 --> 00:16:18,360
Speaker 1: So whenever you have any criticisms towards tech, one of

312
00:16:18,400 --> 00:16:20,200
Speaker 1: the bigger things that people say is we need this

313
00:16:20,320 --> 00:16:22,880
Speaker 1: race against China. We need to be China, right, any

314
00:16:22,920 --> 00:16:26,600
Speaker 1: criticisms to our sectas you can't have anything any restrictions whatsoever.

315
00:16:27,200 --> 00:16:30,680
Speaker 1: What do you make of this race against China when

316
00:16:30,720 --> 00:16:34,520
Speaker 1: it comes to AI, and would restrictions actually promote the

317
00:16:34,600 --> 00:16:38,440
Speaker 1: Chinese Communist Party and promote like have their ability to

318
00:16:38,480 --> 00:16:40,520
Speaker 1: beat us in Ai whatever that really needs?

319
00:16:40,760 --> 00:16:43,400
Speaker 2: Right? Well, sometimes people make this have not even been

320
00:16:43,440 --> 00:16:43,920
Speaker 2: to China.

321
00:16:43,960 --> 00:16:46,560
Speaker 3: I just was in China and met with Premier Chiang,

322
00:16:46,720 --> 00:16:50,720
Speaker 3: met with the Foreign Minister Wangi, with the mayors of

323
00:16:50,720 --> 00:16:54,320
Speaker 3: Beijing and Shanghai, and had extensive conversations. A lot of

324
00:16:54,360 --> 00:16:56,400
Speaker 3: people saying this don't know that there's twenty percent youth

325
00:16:56,480 --> 00:17:00,480
Speaker 3: unemployment in China, twenty percent because they have over capacity

326
00:17:00,480 --> 00:17:03,320
Speaker 3: in their factories and they have undercapacity in software development.

327
00:17:03,880 --> 00:17:07,080
Speaker 3: They don't know the lack of economic diversification in China.

328
00:17:07,359 --> 00:17:09,680
Speaker 3: They don't know how much local governments are suffering from

329
00:17:09,720 --> 00:17:12,879
Speaker 3: debt in China. The concern on the AI races, we

330
00:17:12,920 --> 00:17:15,560
Speaker 3: should have talent here. I'm considered that we're banning foreign

331
00:17:15,640 --> 00:17:20,280
Speaker 3: students from coming here and pursuing their education. But we

332
00:17:20,359 --> 00:17:24,200
Speaker 3: can develop as long as we are open to smart

333
00:17:24,240 --> 00:17:28,000
Speaker 3: immigration policy like we were with allowing Einstein to come

334
00:17:28,040 --> 00:17:29,800
Speaker 3: and so many of the German scientists to come and

335
00:17:29,880 --> 00:17:32,600
Speaker 3: Russian scientists to come. If we continue to do that,

336
00:17:33,240 --> 00:17:35,280
Speaker 3: I am fully comforted that our model is better to

337
00:17:35,359 --> 00:17:39,240
Speaker 3: lead and we can do that while having sensible regulations

338
00:17:39,440 --> 00:17:43,800
Speaker 3: so that you aren't compromising people's jobs, having massive inequality,

339
00:17:44,000 --> 00:17:45,760
Speaker 3: or violating people's CIMIL liberties.

340
00:17:46,160 --> 00:17:49,800
Speaker 1: Well, you mentioned immigration, so you've tweeted about corporate layoffs

341
00:17:49,800 --> 00:17:51,320
Speaker 1: when it comes to AI, and that's a big part

342
00:17:51,359 --> 00:17:53,840
Speaker 1: of your new deal. AI think is that corporations have

343
00:17:53,920 --> 00:17:57,560
Speaker 1: laid off tens of thousands of American workers. Do you

344
00:17:57,600 --> 00:17:59,640
Speaker 1: have and does it give you any pause that they've

345
00:17:59,640 --> 00:18:01,600
Speaker 1: also brought in H one B workers at the same

346
00:18:01,640 --> 00:18:03,360
Speaker 1: time they're laying off Americans.

347
00:18:03,640 --> 00:18:06,880
Speaker 3: Yes, I think the H one B program should be reformed.

348
00:18:06,880 --> 00:18:08,880
Speaker 3: I've said this and today I got into Congress. It's

349
00:18:08,880 --> 00:18:11,840
Speaker 3: been abused by a lot of companies that are paying

350
00:18:11,920 --> 00:18:15,680
Speaker 3: undermarket wages for jobs that aren't really high skilled, and

351
00:18:15,920 --> 00:18:17,959
Speaker 3: there's no doubt that it needs to be reformed. Now,

352
00:18:17,960 --> 00:18:20,520
Speaker 3: I don't agree, just put on one hundred thousand dollars

353
00:18:20,520 --> 00:18:22,560
Speaker 3: fee is the reform. What I would say is you

354
00:18:22,600 --> 00:18:25,760
Speaker 3: do need people who have real skills. The best is

355
00:18:25,800 --> 00:18:27,399
Speaker 3: by the way, give them a green card, don't have

356
00:18:27,480 --> 00:18:30,000
Speaker 3: them in underwage positions like H one where they're locked

357
00:18:30,040 --> 00:18:32,840
Speaker 3: into an employer, but make sure that you're getting market

358
00:18:32,880 --> 00:18:35,159
Speaker 3: wages and make sure they're really high skilled jobs, not

359
00:18:35,320 --> 00:18:38,520
Speaker 3: just jobs to undercut American workers. But if the question

360
00:18:38,640 --> 00:18:41,280
Speaker 3: is that has the H one BVS a program led

361
00:18:41,320 --> 00:18:44,840
Speaker 3: to American layoffs that you'd have to be in a

362
00:18:44,840 --> 00:18:48,320
Speaker 3: cocoon to say to say anything, but yes it has.

363
00:18:48,680 --> 00:18:48,960
Speaker 2: Well.

364
00:18:49,359 --> 00:18:51,720
Speaker 1: One last question of the AI and how it's affecting things,

365
00:18:51,760 --> 00:18:54,840
Speaker 1: I think you mentioned section two thirty. There hasn't been

366
00:18:54,840 --> 00:18:58,000
Speaker 1: any real big reforms. There's been a few on child protections,

367
00:18:58,040 --> 00:19:02,640
Speaker 1: but not massive reforms around internet since since Section two

368
00:19:02,640 --> 00:19:05,160
Speaker 1: thirty was created when the Internet was in its infancy.

369
00:19:05,920 --> 00:19:08,000
Speaker 1: There's a lot of losses going on around OPENINGI and

370
00:19:08,080 --> 00:19:12,800
Speaker 1: chat GPT about how these programs have promoted, you know,

371
00:19:13,000 --> 00:19:15,679
Speaker 1: people ending their own lives and basically said go do

372
00:19:15,800 --> 00:19:20,119
Speaker 1: it when when people have been very depressed. Could companies

373
00:19:20,160 --> 00:19:24,760
Speaker 1: be culpable for their programs being anti human Do you

374
00:19:24,800 --> 00:19:27,439
Speaker 1: know what I'm saying? So what I'm saying, well, I.

375
00:19:27,880 --> 00:19:29,920
Speaker 3: Think they could be culpable for the liability of someone

376
00:19:29,920 --> 00:19:33,880
Speaker 3: committing suicide, or someone going into depression, or someone having

377
00:19:33,920 --> 00:19:36,639
Speaker 3: suicidal thoughts. And I do think that there should be

378
00:19:36,720 --> 00:19:40,480
Speaker 3: liability for causing mental health at harm or causing a

379
00:19:40,480 --> 00:19:41,280
Speaker 3: harm to children.

380
00:19:42,160 --> 00:19:45,880
Speaker 1: Do you has the Democratic Party embraced what you're saying?

381
00:19:46,000 --> 00:19:49,920
Speaker 1: Because I think that the AI is clearly the future,

382
00:19:50,280 --> 00:19:53,920
Speaker 1: and it's lacking in the overall national conversation that we're having.

383
00:19:53,920 --> 00:19:55,760
Speaker 1: We're getting lost in a lot of things that aren't

384
00:19:56,080 --> 00:19:58,360
Speaker 1: the big picture, and you're one of the few politicians

385
00:19:58,400 --> 00:20:01,000
Speaker 1: really talking about this in a very forward looking way.

386
00:20:01,320 --> 00:20:04,600
Speaker 1: Have other Democratic politicians really jumped onto your messaging?

387
00:20:05,560 --> 00:20:07,359
Speaker 2: I think people are still figuring it out. Look.

388
00:20:07,400 --> 00:20:10,320
Speaker 3: I believe that that we need political leaders who understand AI,

389
00:20:10,320 --> 00:20:13,399
Speaker 3: who understand technology, understand what the future wealth generation is

390
00:20:13,440 --> 00:20:15,119
Speaker 3: going to be, what the future jobs are going to be,

391
00:20:15,400 --> 00:20:17,800
Speaker 3: and have a concrete vision of making sure that AI

392
00:20:17,880 --> 00:20:20,160
Speaker 3: is for all of us and not for them. If

393
00:20:20,160 --> 00:20:22,640
Speaker 3: we just continue down the current path, what you will

394
00:20:22,640 --> 00:20:26,480
Speaker 3: have is tech billionaires, tech trillionaires, and everyone else will

395
00:20:26,480 --> 00:20:29,960
Speaker 3: sort of be left to fortunes whims, and I don't

396
00:20:29,960 --> 00:20:31,879
Speaker 3: believe in that. I believe that we need as a

397
00:20:31,880 --> 00:20:36,639
Speaker 3: democratic society to direct the development of AI so that

398
00:20:36,680 --> 00:20:40,280
Speaker 3: it increases jobs in communities, increases wealth in communities, and

399
00:20:40,560 --> 00:20:43,520
Speaker 3: doesn't degrade our democracy. The problem is we have a

400
00:20:43,520 --> 00:20:46,359
Speaker 3: lot of people who don't understand the technology, who don't

401
00:20:46,359 --> 00:20:48,600
Speaker 3: have the confidence to go back and forth as I

402
00:20:48,760 --> 00:20:50,679
Speaker 3: recently did in a Twitter thread with some of my

403
00:20:51,000 --> 00:20:53,280
Speaker 3: tech friends who didn't like what I said, and have

404
00:20:53,359 --> 00:20:55,960
Speaker 3: the confidence to stick up for their views. And I

405
00:20:56,000 --> 00:20:58,679
Speaker 3: think this country needs to elect people who understand technology,

406
00:20:58,760 --> 00:21:02,440
Speaker 3: understand AI, and have the moral courage to stand up

407
00:21:02,600 --> 00:21:05,440
Speaker 3: to some of the corporate leaders who saying, no, we

408
00:21:05,720 --> 00:21:07,880
Speaker 3: need you to make sure that this is in America's interest,

409
00:21:07,960 --> 00:21:08,879
Speaker 3: in communities interest.

410
00:21:09,440 --> 00:21:12,000
Speaker 2: Their job is to maximize well for the shareholders. I'm

411
00:21:12,040 --> 00:21:12,840
Speaker 2: not imputing them.

412
00:21:12,920 --> 00:21:17,200
Speaker 3: But politicians' job is to look out for the interests

413
00:21:17,200 --> 00:21:17,800
Speaker 3: of the country.

414
00:21:18,720 --> 00:21:20,639
Speaker 1: And now you represent a district, I think I think

415
00:21:20,680 --> 00:21:23,560
Speaker 1: your district's worth I think are twelve or thirteen trillion dollars,

416
00:21:23,560 --> 00:21:24,400
Speaker 1: which is like insant.

417
00:21:24,560 --> 00:21:25,080
Speaker 2: That's what I say.

418
00:21:25,080 --> 00:21:26,879
Speaker 3: It was up to eighteen, I don't know, maybe it

419
00:21:27,000 --> 00:21:32,639
Speaker 3: was over billion dollars at Apple, Google, Nvidia, Broadcom, and Tesla.

420
00:21:32,680 --> 00:21:34,439
Speaker 1: So what do those CEOs say to you when you

421
00:21:34,480 --> 00:21:35,080
Speaker 1: bring this up?

422
00:21:35,119 --> 00:21:35,840
Speaker 2: They say, you.

423
00:21:35,800 --> 00:21:38,760
Speaker 1: Know, representive Connor, You're going to destroy our industry. Or

424
00:21:38,760 --> 00:21:41,080
Speaker 1: do they understand that reforms have to be made.

425
00:21:41,200 --> 00:21:43,520
Speaker 3: They understand that performers have permitted They don't. They may

426
00:21:43,520 --> 00:21:45,359
Speaker 3: not agree with all of mine, but I say to them,

427
00:21:45,400 --> 00:21:47,080
Speaker 3: you can't have an island.

428
00:21:46,760 --> 00:21:49,800
Speaker 2: Of prosperity and a sea of despair.

429
00:21:50,320 --> 00:21:53,919
Speaker 3: America can't survive half prosperous and half in decline. I

430
00:21:53,960 --> 00:21:56,520
Speaker 3: say call it the anti revolution tax, you know the

431
00:21:57,359 --> 00:22:00,719
Speaker 3: when I bring up some of these policies. But the

432
00:22:00,760 --> 00:22:05,200
Speaker 3: reality is I am not a luddite. I'm a technology optimist.

433
00:22:05,240 --> 00:22:07,560
Speaker 3: I believe AI will help us with medicine. I believe

434
00:22:07,600 --> 00:22:10,520
Speaker 3: AI will help us in times of education. A I

435
00:22:10,520 --> 00:22:14,560
Speaker 3: should be, like Steve Jobs said about computers, a bicycle

436
00:22:14,600 --> 00:22:17,520
Speaker 3: for the mind. It should be about enhancing human capability,

437
00:22:17,720 --> 00:22:22,560
Speaker 3: not about excessive elimination of jobs and excessive accumulation of

438
00:22:22,600 --> 00:22:26,000
Speaker 3: profits in the hands of a few. And yeah, that's

439
00:22:26,040 --> 00:22:27,280
Speaker 3: the balance we need to strike.

440
00:22:27,480 --> 00:22:29,400
Speaker 1: And my last question to you is do you have

441
00:22:29,680 --> 00:22:32,560
Speaker 1: a fear then that if let's say we do have this.

442
00:22:32,720 --> 00:22:34,520
Speaker 1: I mean, we already have a massive consentration of wealth,

443
00:22:34,560 --> 00:22:39,240
Speaker 1: but it is exacerbated one hundredfold one thousandfold and a

444
00:22:39,280 --> 00:22:41,520
Speaker 1: massive job losses where you have a permanent underclass of

445
00:22:41,520 --> 00:22:43,520
Speaker 1: ten to twenty percent of the population who can't get

446
00:22:43,560 --> 00:22:45,800
Speaker 1: fine work because their work has all been automated and

447
00:22:45,800 --> 00:22:48,919
Speaker 1: they weren't trained for jobs. Do you fear of a

448
00:22:49,040 --> 00:22:52,800
Speaker 1: real technological backlash by the voters to sit there and say, no,

449
00:22:52,920 --> 00:22:56,439
Speaker 1: we need something pretty strong against tech companies and in

450
00:22:56,480 --> 00:22:57,919
Speaker 1: the most excessive ways.

451
00:22:58,240 --> 00:23:00,960
Speaker 3: Yeah, I fear not only a backlash technology. I fair

452
00:23:01,040 --> 00:23:05,080
Speaker 3: backlash potential against multi racial democracy. I fear backlash against

453
00:23:05,080 --> 00:23:08,000
Speaker 3: America being a leader in the world, A fair backlash

454
00:23:08,040 --> 00:23:10,440
Speaker 3: against any engagement.

455
00:23:09,920 --> 00:23:10,440
Speaker 2: With the world.

456
00:23:10,560 --> 00:23:13,760
Speaker 3: I fear that you would have an ugly nationalism that

457
00:23:13,760 --> 00:23:16,840
Speaker 3: could emerge. I call for an economic patriotism, that's an

458
00:23:16,840 --> 00:23:20,080
Speaker 3: inclusive patriotism that has a role for immigrants and has

459
00:23:20,080 --> 00:23:22,720
Speaker 3: a role for innovation. But I think you would have

460
00:23:22,760 --> 00:23:25,560
Speaker 3: a very ugly movement. I mean Andy Grove, who was

461
00:23:25,600 --> 00:23:29,560
Speaker 3: an Intel CEO and a great founders and escaped from

462
00:23:29,560 --> 00:23:32,879
Speaker 3: both Nazi Germany and a Soviet Communism, said well, the

463
00:23:32,920 --> 00:23:34,000
Speaker 3: biggest danger is.

464
00:23:34,080 --> 00:23:36,640
Speaker 2: Long unemployment lines. You haven't seen them.

465
00:23:36,680 --> 00:23:39,199
Speaker 3: I have, and so I think that's why he was

466
00:23:39,200 --> 00:23:42,840
Speaker 3: so forwardlooking in America, needing an economic development policy outside

467
00:23:43,119 --> 00:23:45,400
Speaker 3: Silicon Valley. I have one question for you, Ryan, because

468
00:23:45,400 --> 00:23:46,879
Speaker 3: I see some of your Twitter posts and things in

469
00:23:46,920 --> 00:23:47,880
Speaker 3: your always engaged.

470
00:23:48,119 --> 00:23:51,320
Speaker 2: I don't even know your politics. Are you more left right?

471
00:23:51,440 --> 00:23:53,479
Speaker 1: So I'm a pretty right wing person.

472
00:23:53,560 --> 00:23:54,040
Speaker 2: I just.

473
00:23:55,960 --> 00:23:58,639
Speaker 1: My belief is this is that I came from a

474
00:23:58,640 --> 00:24:01,199
Speaker 1: working class family. I underst and working class people, and

475
00:24:01,240 --> 00:24:03,959
Speaker 1: I always want what's best for them. That doesn't have

476
00:24:04,080 --> 00:24:06,919
Speaker 1: clear ideological lines. So you have to sit there and

477
00:24:06,960 --> 00:24:09,680
Speaker 1: be willing, and if they are upset, they will move

478
00:24:09,720 --> 00:24:13,080
Speaker 1: to policies that are extreme, and I don't. And I

479
00:24:13,119 --> 00:24:16,480
Speaker 1: believe everyone has the everyone with the ability should have

480
00:24:16,600 --> 00:24:20,080
Speaker 1: the right to at least pursue an American dream that's,

481
00:24:20,119 --> 00:24:23,600
Speaker 1: you know, one of not just comfortable living, but respectable livings.

482
00:24:24,359 --> 00:24:25,240
Speaker 2: So when I say a.

483
00:24:25,400 --> 00:24:28,120
Speaker 3: New deal hypothetically as I define it, and you can

484
00:24:28,119 --> 00:24:30,800
Speaker 3: be blind, that doesn't scare you. No.

485
00:24:30,920 --> 00:24:33,280
Speaker 1: I mean, you say a new deal because it's easy

486
00:24:33,320 --> 00:24:36,959
Speaker 1: branding FDR new deal. Everyone understands it, right, I get that.

487
00:24:37,000 --> 00:24:38,120
Speaker 1: So I work in politics.

488
00:24:38,160 --> 00:24:38,680
Speaker 2: I understand.

489
00:24:38,680 --> 00:24:41,919
Speaker 1: I'm like, Okay, that's clear when you talk about redistributive wealth.

490
00:24:42,359 --> 00:24:45,880
Speaker 1: It's not something I am naturally in opposition to because

491
00:24:45,920 --> 00:24:48,080
Speaker 1: I believe we should tax data. I don't know why

492
00:24:48,200 --> 00:24:51,080
Speaker 1: data is in tax considering it's clearly more valuable than

493
00:24:51,119 --> 00:24:54,320
Speaker 1: oil and gold is everyone's data. What I and I'm

494
00:24:54,320 --> 00:24:56,800
Speaker 1: not opposed to, and I do think that the tech

495
00:24:56,840 --> 00:25:00,720
Speaker 1: companies have gone very long without any regular at all.

496
00:25:00,920 --> 00:25:04,440
Speaker 1: What I get a little anxiety written about is how

497
00:25:04,520 --> 00:25:08,879
Speaker 1: do you in a country that is in continuation of

498
00:25:09,040 --> 00:25:13,040
Speaker 1: maximizing wealth for very few while importing one point one

499
00:25:13,040 --> 00:25:16,199
Speaker 1: million legal immgreds per year who are overwhelmingly part of

500
00:25:16,240 --> 00:25:18,960
Speaker 1: the population that would be underserved by this tech revolution.

501
00:25:19,359 --> 00:25:22,840
Speaker 1: How do you keep that prosperity going. That's my big

502
00:25:22,920 --> 00:25:26,840
Speaker 1: fear is that we're importing future poverty unless we're all

503
00:25:26,840 --> 00:25:31,320
Speaker 1: bringing in brilliant tech workers and that has a process

504
00:25:31,359 --> 00:25:34,840
Speaker 1: of running out. There will not be unless you're unless

505
00:25:34,840 --> 00:25:37,080
Speaker 1: you're going to exhaust that or people or capital belief.

506
00:25:37,320 --> 00:25:40,640
Speaker 1: But as far as nearly capitalistic, no, I'm not opposed

507
00:25:40,640 --> 00:25:42,320
Speaker 1: to taxes. I lived in New York City my almost

508
00:25:42,359 --> 00:25:44,960
Speaker 1: my entire life. I'm not opposed to taxes. I am

509
00:25:45,200 --> 00:25:47,040
Speaker 1: opposed to story the immigration issue.

510
00:25:47,040 --> 00:25:48,800
Speaker 3: Well what did you make a President Trump's point with

511
00:25:48,880 --> 00:25:51,800
Speaker 3: Laura Ingram and then where he said, look, you need

512
00:25:52,359 --> 00:25:54,320
Speaker 3: you know, for z has.

513
00:25:54,200 --> 00:25:56,320
Speaker 2: A shortage of five thousand workers. Yes, we need a

514
00:25:56,400 --> 00:25:58,480
Speaker 2: market workers. We need to be investigating market workers. I

515
00:25:58,520 --> 00:26:01,359
Speaker 2: disagreed with President Trump that American workers aren't talented. I

516
00:26:01,400 --> 00:26:02,800
Speaker 2: think they're incredibly talented.

517
00:26:02,920 --> 00:26:06,000
Speaker 3: But he said we also need immigrants to help. But

518
00:26:06,080 --> 00:26:08,000
Speaker 3: I guess were you more on Laura ingram side than

519
00:26:08,000 --> 00:26:08,600
Speaker 3: his side on that?

520
00:26:08,760 --> 00:26:11,679
Speaker 1: I think that President Trump is very easily influenced them

521
00:26:11,680 --> 00:26:13,359
Speaker 1: as by the last person who spoke to him, And

522
00:26:13,400 --> 00:26:15,280
Speaker 1: I want to know. I want to know who the

523
00:26:15,359 --> 00:26:17,840
Speaker 1: last person who spoke to him was and if it

524
00:26:17,920 --> 00:26:20,280
Speaker 1: was our commerce secretary who's been giving him tons of

525
00:26:20,320 --> 00:26:24,160
Speaker 1: bad advice. I believe that there are listen, there's an

526
00:26:24,200 --> 00:26:26,920
Speaker 1: issue where people I think have the assumption of what

527
00:26:27,000 --> 00:26:28,680
Speaker 1: they what they have a right to. And I also

528
00:26:28,720 --> 00:26:31,359
Speaker 1: think there's a big education problem. Right we have millions

529
00:26:31,400 --> 00:26:35,440
Speaker 1: of people who are not equipped for jobs that I agree,

530
00:26:35,800 --> 00:26:38,640
Speaker 1: and the graduation rates are a complete smoke screen because

531
00:26:38,640 --> 00:26:41,600
Speaker 1: they're graduating schools with no ability to read or write

532
00:26:41,600 --> 00:26:45,440
Speaker 1: and it's just graduation numbers are there to promote ways

533
00:26:45,600 --> 00:26:48,840
Speaker 1: of getting funding for the schools. You know, Mississippi, Louisiana's

534
00:26:48,880 --> 00:26:51,280
Speaker 1: done amazing jobs of jumping out that. But we need

535
00:26:51,320 --> 00:26:54,360
Speaker 1: to really look at education and how to sit there

536
00:26:54,400 --> 00:26:57,560
Speaker 1: and get these get school great would you before?

537
00:26:58,000 --> 00:26:58,600
Speaker 2: And absolutely?

538
00:26:58,600 --> 00:27:00,560
Speaker 1: And I think that the German model which worked for

539
00:27:00,560 --> 00:27:02,119
Speaker 1: a long time, it's not really working right now, but

540
00:27:02,160 --> 00:27:04,080
Speaker 1: it did work for a long time of getting these

541
00:27:04,080 --> 00:27:07,640
Speaker 1: employers into the high schools and satying this person's brilliant,

542
00:27:07,840 --> 00:27:11,000
Speaker 1: just skips college and let's have a method where you

543
00:27:11,040 --> 00:27:13,919
Speaker 1: work for a corporation again for twenty years or thirty years.

544
00:27:14,119 --> 00:27:18,040
Speaker 1: If Ford were to find a kid from Mississippi who's

545
00:27:18,119 --> 00:27:22,120
Speaker 1: not brilliant at I don't know, some I don't know writing,

546
00:27:22,480 --> 00:27:24,399
Speaker 1: can't write a novel, but he can fix the car,

547
00:27:24,400 --> 00:27:26,360
Speaker 1: why shouldn't he pick him up at seventeen years old.

548
00:27:26,600 --> 00:27:28,920
Speaker 1: I do think that those things should be working better

549
00:27:29,000 --> 00:27:30,840
Speaker 1: than they are. And I think that a lot of

550
00:27:30,880 --> 00:27:34,080
Speaker 1: the backlash the Republicans both in the last election are

551
00:27:34,200 --> 00:27:38,400
Speaker 1: mostly around the economy, affordability, and fear of the economy.

552
00:27:38,480 --> 00:27:39,879
Speaker 1: I think those are the number one drivers. I think

553
00:27:39,920 --> 00:27:42,199
Speaker 1: people thought that Trump had a magic wand and we

554
00:27:42,200 --> 00:27:44,159
Speaker 1: can go to twenty nineteen again. But I don't think

555
00:27:44,200 --> 00:27:45,960
Speaker 1: we're ever going to be back to twenty nineteen. So

556
00:27:46,119 --> 00:27:48,879
Speaker 1: I'm not anti AI on its face. I'm not anti

557
00:27:49,040 --> 00:27:52,480
Speaker 1: tax on tech comp corporations on its face. But I

558
00:27:52,480 --> 00:27:55,639
Speaker 1: think we really need to have intelligent conversations over policies

559
00:27:55,680 --> 00:27:59,919
Speaker 1: and less conversations over personalities than flamethrowing to get Internet clicks.

560
00:28:00,000 --> 00:28:01,560
Speaker 1: I find that that's why I try to I hope

561
00:28:01,600 --> 00:28:03,800
Speaker 1: you like this podcast interview. I hope I was bringing

562
00:28:04,240 --> 00:28:04,960
Speaker 1: very very awful.

563
00:28:05,040 --> 00:28:05,200
Speaker 3: Yeah.

564
00:28:05,440 --> 00:28:06,879
Speaker 2: In fact, I to the point that I didn't know

565
00:28:06,920 --> 00:28:07,520
Speaker 2: your politics.

566
00:28:07,760 --> 00:28:10,320
Speaker 1: I appreciate that very much, and I've liked a lot

567
00:28:10,359 --> 00:28:12,360
Speaker 1: of what you've done on civil liberties for a long time,

568
00:28:12,400 --> 00:28:14,240
Speaker 1: so I've always thought of you as a very thoughtful

569
00:28:14,480 --> 00:28:15,960
Speaker 1: leader in the Democrat Party.

570
00:28:16,280 --> 00:28:19,080
Speaker 3: I appreciate it well. To be continued. I look forward

571
00:28:19,080 --> 00:28:21,359
Speaker 3: to coming back, and I wish we could have more

572
00:28:21,400 --> 00:28:24,080
Speaker 3: of this. I think we desperately need more of this

573
00:28:24,320 --> 00:28:26,960
Speaker 3: in the country. And while you were kind of I'm

574
00:28:26,960 --> 00:28:28,840
Speaker 3: not to ask me or restrained, and I'm not to

575
00:28:28,880 --> 00:28:30,480
Speaker 3: ask me a single question about e Fstein, which is

576
00:28:30,640 --> 00:28:31,440
Speaker 3: what I've been probably.

577
00:28:31,200 --> 00:28:33,360
Speaker 1: About you know, you know what, there's only so much.

578
00:28:33,440 --> 00:28:36,280
Speaker 3: I mean, the email, I will say, the one thing

579
00:28:36,320 --> 00:28:38,520
Speaker 3: about it is that it showed sort of the value

580
00:28:38,520 --> 00:28:42,960
Speaker 3: of stability and conversation with people who you may not

581
00:28:43,200 --> 00:28:45,360
Speaker 3: you may think of as caricatures. And I just think

582
00:28:45,360 --> 00:28:48,760
Speaker 3: that that kind of dialogue and seeing the humanity of

583
00:28:48,800 --> 00:28:51,360
Speaker 3: people and seeing where there may be common granted where

584
00:28:51,360 --> 00:28:54,200
Speaker 3: there's disagreement. We've got to get back to that in

585
00:28:54,200 --> 00:28:57,080
Speaker 3: our politics. Not out of a polyannishness, but if you

586
00:28:57,120 --> 00:28:59,920
Speaker 3: listened to like the Nixon Kennedy debates and the substance

587
00:29:00,120 --> 00:29:02,880
Speaker 3: behind them, we've lost a lot of the just the

588
00:29:03,000 --> 00:29:06,240
Speaker 3: robustness of an exchange of ideas. I mean, it's hurting

589
00:29:06,240 --> 00:29:07,600
Speaker 3: our democracy.

590
00:29:07,160 --> 00:29:10,920
Speaker 1: Right, even radical quote unquote radicals back then, who like

591
00:29:11,560 --> 00:29:13,160
Speaker 1: you know, they talk a lot about pap Uchanan, who

592
00:29:13,200 --> 00:29:15,120
Speaker 1: I was lucky enough to know throughout my life, but

593
00:29:15,120 --> 00:29:16,680
Speaker 1: he was very smart and very thoughtful. A lot of

594
00:29:16,680 --> 00:29:18,680
Speaker 1: things he talked about in policy ideas, And I think

595
00:29:18,680 --> 00:29:20,480
Speaker 1: that in this platform that I have, I tried to

596
00:29:20,640 --> 00:29:23,520
Speaker 1: just raise the conversation where it's not all tell me

597
00:29:23,560 --> 00:29:26,440
Speaker 1: who you hate the most. I mean, that's fun. But

598
00:29:27,040 --> 00:29:29,680
Speaker 1: I left high school and I don't I'm not trying

599
00:29:29,680 --> 00:29:32,640
Speaker 1: to return to it so but Tokersman, thank you for

600
00:29:32,640 --> 00:29:38,440
Speaker 1: coming this podcast. I genuinely appreciate it. Now it's time

601
00:29:38,480 --> 00:29:40,080
Speaker 1: for the Ask Me Anything segment. If you want a

602
00:29:40,200 --> 00:29:42,400
Speaker 1: part of the Ask Me Anything segment, email me ryanant

603
00:29:42,520 --> 00:29:46,360
Speaker 1: Numbers Gamepodcast dot Com. That's Ryan at Numbers Plural Numbers

604
00:29:46,360 --> 00:29:49,400
Speaker 1: Gamepodcast dot Com. Love your questions, guys. This one comes

605
00:29:49,480 --> 00:29:51,880
Speaker 1: from and you email me last week and I haven't

606
00:29:51,920 --> 00:29:54,480
Speaker 1: learned how to pronounce your name yet her Dwall. I

607
00:29:54,480 --> 00:29:57,160
Speaker 1: believe it is from Florida. Thank you for emailing me again.

608
00:29:57,280 --> 00:29:59,680
Speaker 1: Thank you for listening, She writes, Hey, Ryan, huge fan.

609
00:29:59,720 --> 00:30:01,800
Speaker 1: I'm an immigrant and currently a permanent residence, so I've

610
00:30:01,800 --> 00:30:05,520
Speaker 1: been around legal immigrants that right to talk about. I

611
00:30:05,640 --> 00:30:09,320
Speaker 1: am from the twenty sixteen Batchel's students here, and anecdotally

612
00:30:09,360 --> 00:30:11,240
Speaker 1: I can speak for my group. Not one single of

613
00:30:11,320 --> 00:30:13,360
Speaker 1: the people in my batch are right wing, are going

614
00:30:13,400 --> 00:30:15,840
Speaker 1: to vote for Republicans in their lifetime. They are probably

615
00:30:15,840 --> 00:30:18,520
Speaker 1: a couple who are probably moderate and agree on social

616
00:30:18,600 --> 00:30:21,720
Speaker 1: conservative stances here and there, but none of them would

617
00:30:21,720 --> 00:30:25,080
Speaker 1: ever actually vote for quote unquote racist Republicans. Would you

618
00:30:25,080 --> 00:30:28,080
Speaker 1: say economics would change it? Immigration policy would change it

619
00:30:28,200 --> 00:30:31,760
Speaker 1: or would I say, nothing will make them vote right,

620
00:30:31,840 --> 00:30:34,640
Speaker 1: because everyone cares about how to get your wife her

621
00:30:34,680 --> 00:30:37,160
Speaker 1: parents here, how to get easy money, and nobody gives

622
00:30:37,160 --> 00:30:39,840
Speaker 1: a damn about their host country and improving it. Given this,

623
00:30:40,040 --> 00:30:45,600
Speaker 1: why why even support any immigration, legal or not. I

624
00:30:45,720 --> 00:30:48,680
Speaker 1: can go on how legal visas are abused and gained,

625
00:30:48,720 --> 00:30:51,000
Speaker 1: but that would be your whole show. Thank you, Pardol,

626
00:30:51,240 --> 00:30:53,440
Speaker 1: Thank you if that's how you pronounce your name, Pardol.

627
00:30:53,520 --> 00:30:56,360
Speaker 1: Thank you for listening to this show. Amazing question. So

628
00:30:57,360 --> 00:31:01,400
Speaker 1: would changing immigration change if people are willing to vote

629
00:31:01,400 --> 00:31:03,440
Speaker 1: for Republicans. The answer is no, and it's been proven

630
00:31:03,520 --> 00:31:08,080
Speaker 1: no because we've ran this experiment. Ronald Reagan received lost

631
00:31:08,240 --> 00:31:10,920
Speaker 1: one everything except for the immigrant vote.

632
00:31:11,120 --> 00:31:11,320
Speaker 2: Right.

633
00:31:11,360 --> 00:31:13,720
Speaker 1: He lost immigrant, immigrant, and black vote, But he lost

634
00:31:13,720 --> 00:31:16,800
Speaker 1: immigrant vote pretty substantially back in nineteen eighty four when

635
00:31:16,840 --> 00:31:20,160
Speaker 1: everyone voted for Republicans. In nineteen eighty eight, when his

636
00:31:20,400 --> 00:31:22,920
Speaker 1: vice president, George H. W. Bush was running for president,

637
00:31:22,960 --> 00:31:27,400
Speaker 1: and remember HV was super liberal immigration. He invented many

638
00:31:27,600 --> 00:31:30,520
Speaker 1: of the forms of immigration that we have today that

639
00:31:30,560 --> 00:31:33,640
Speaker 1: are plaguing our country to all the different visa categories.

640
00:31:34,000 --> 00:31:38,040
Speaker 1: And Reagan had the amnesty between eighty four and eighty eight,

641
00:31:38,800 --> 00:31:42,520
Speaker 1: HW Bush received a smaller share of the immigrant vote

642
00:31:42,640 --> 00:31:45,600
Speaker 1: than Ronald Reagan did. Then in nineteen ninety two, we

643
00:31:45,640 --> 00:31:49,480
Speaker 1: had Bob Dole, sorry run nineteen to two was HW's reelection.

644
00:31:49,640 --> 00:31:53,720
Speaker 1: In HW's reelection, after he had done another several smaller

645
00:31:53,760 --> 00:31:57,040
Speaker 1: amnesties and created brand new visa categories to bring in

646
00:31:57,360 --> 00:32:00,880
Speaker 1: millions of more immigrants. During his presidency, he got an

647
00:32:00,880 --> 00:32:03,920
Speaker 1: even smaller share of the immigrant vote than there was

648
00:32:03,960 --> 00:32:08,040
Speaker 1: Bob Dole. Bob Dole very I mean, not super left

649
00:32:08,080 --> 00:32:10,760
Speaker 1: wing on immigration, but much more left wing immigration than

650
00:32:10,800 --> 00:32:14,120
Speaker 1: Pap Buchanan and his chief Republican rival. And he got

651
00:32:14,160 --> 00:32:16,440
Speaker 1: a smaller share of the imigrant vote than George H. W.

652
00:32:16,520 --> 00:32:19,720
Speaker 1: Bush did. George W. Bush got the I think he

653
00:32:19,760 --> 00:32:21,400
Speaker 1: got in the UND thirty nine percent. There are some

654
00:32:21,520 --> 00:32:23,239
Speaker 1: numbers to say forty four, but I think they've been,

655
00:32:23,480 --> 00:32:25,680
Speaker 1: you know, taken it down. It's really thirty nine percent,

656
00:32:25,880 --> 00:32:28,960
Speaker 1: thirty nine percent of the immigrant voat, which was considered

657
00:32:29,000 --> 00:32:32,120
Speaker 1: the high watermark until Donald Trump in twenty twenty four.

658
00:32:32,160 --> 00:32:35,400
Speaker 1: And Trump did not run left wing on immigration. He

659
00:32:35,480 --> 00:32:38,200
Speaker 1: said mass deportations that were signs at his rally. It

660
00:32:38,360 --> 00:32:42,160
Speaker 1: wasn't a hidden agenda point, and people didn't care. I

661
00:32:42,200 --> 00:32:44,640
Speaker 1: think in part because of the economy, they were willing

662
00:32:44,640 --> 00:32:47,280
Speaker 1: to give Trump, you know, a chance. And I think

663
00:32:47,280 --> 00:32:49,960
Speaker 1: that a lot of immigrants legal immigrants fell at the

664
00:32:50,000 --> 00:32:56,360
Speaker 1: border was unfair. Fairness is a American trait that people

665
00:32:56,400 --> 00:32:58,240
Speaker 1: talk about rugged individualism and all the rest of it,

666
00:32:58,280 --> 00:33:01,240
Speaker 1: limited government, but fairness is a component of being an American.

667
00:33:01,280 --> 00:33:04,320
Speaker 1: They don't like the idea that they have to work

668
00:33:04,360 --> 00:33:06,960
Speaker 1: for something and then foreigners get to sit there and

669
00:33:07,040 --> 00:33:10,320
Speaker 1: abuse the system and take advantage. It's a really intricate

670
00:33:10,360 --> 00:33:12,720
Speaker 1: part of who we are as a people. So I

671
00:33:12,720 --> 00:33:15,040
Speaker 1: think that that is definitely something that is to cheer

672
00:33:15,120 --> 00:33:18,200
Speaker 1: one right. Looser immigration laws supporting amnesties will not make

673
00:33:18,240 --> 00:33:20,600
Speaker 1: immigrants want to vote for you. And I think that

674
00:33:20,600 --> 00:33:23,239
Speaker 1: there's also an issue where immigrants come from and what

675
00:33:23,320 --> 00:33:26,720
Speaker 1: their beliefs are. Right immigrants, whether they have different opinions

676
00:33:26,760 --> 00:33:29,920
Speaker 1: over Christianity. A lot of countries from Eastern Asia don't have,

677
00:33:30,240 --> 00:33:33,120
Speaker 1: and then at least rather don't also have high opinions

678
00:33:33,120 --> 00:33:36,600
Speaker 1: of Christianity, on gun ownership, on free speech, on welfare,

679
00:33:36,600 --> 00:33:38,840
Speaker 1: on the size of government. Some of these immigrant views

680
00:33:38,840 --> 00:33:41,920
Speaker 1: are incompatible if we want to continue a country that

681
00:33:42,000 --> 00:33:44,080
Speaker 1: has been going on for over two hundred and fifty

682
00:33:44,120 --> 00:33:45,440
Speaker 1: years or two hundred few years as.

683
00:33:45,400 --> 00:33:45,920
Speaker 2: Of next year.

684
00:33:46,600 --> 00:33:49,920
Speaker 1: That's the thing to rationalize and say, Okay, when we

685
00:33:49,960 --> 00:33:52,200
Speaker 1: talk about immigrants, it's not like a bag of oranges.

686
00:33:52,200 --> 00:33:55,160
Speaker 1: We don't have to bring in every single person that

687
00:33:55,320 --> 00:33:58,240
Speaker 1: just categorize as an immigrant. Do they share our values?

688
00:33:58,280 --> 00:34:01,040
Speaker 1: Do they provide benefits to our economy? Are they going

689
00:34:01,080 --> 00:34:03,600
Speaker 1: to commick crime these are are they going to belong

690
00:34:03,640 --> 00:34:06,320
Speaker 1: to gangs? Are they going to assimilate? And assimilation is

691
00:34:06,400 --> 00:34:09,680
Speaker 1: really an interesting category as well, because a lot of

692
00:34:09,719 --> 00:34:13,759
Speaker 1: immigrants and their children who do assimilate kind of assimilate

693
00:34:13,840 --> 00:34:16,560
Speaker 1: to shit lib culture. There was I'm gonna guess because

694
00:34:16,560 --> 00:34:20,040
Speaker 1: your name is Perdual, you're Indian or Southeast Asian of

695
00:34:20,080 --> 00:34:23,680
Speaker 1: some type. There was a show on Netflix a couple

696
00:34:23,719 --> 00:34:25,680
Speaker 1: of years ago called Never Have I Ever, which I

697
00:34:25,760 --> 00:34:27,799
Speaker 1: really liked. It was a very funny show and it

698
00:34:27,840 --> 00:34:31,000
Speaker 1: was about an Indian girl and she was very clearly

699
00:34:31,040 --> 00:34:32,400
Speaker 1: assimilated into American culture.

700
00:34:32,440 --> 00:34:32,560
Speaker 2: Right.

701
00:34:32,600 --> 00:34:36,120
Speaker 1: She ate meat, she wanted a white boyfriend. She was

702
00:34:37,120 --> 00:34:39,719
Speaker 1: but she was she was a leftist. She talked about

703
00:34:39,760 --> 00:34:44,360
Speaker 1: Ruth Biter Ginsberg and and and idealizing liberal values and abortion.

704
00:34:44,920 --> 00:34:46,680
Speaker 1: And obviously it was just like it was a TV show.

705
00:34:46,680 --> 00:34:48,160
Speaker 1: It wasn't a reality show. It was it was it

706
00:34:48,200 --> 00:34:49,480
Speaker 1: was it was written, it was a narra, it was

707
00:34:49,480 --> 00:34:52,239
Speaker 1: a scripted show. But I think that that's true for

708
00:34:52,320 --> 00:34:56,080
Speaker 1: a lot of immigrant second generation immigrants, especially immigrants I

709
00:34:56,120 --> 00:35:00,480
Speaker 1: find from Asia who do assimilate rather quickly to America culture,

710
00:35:00,520 --> 00:35:04,480
Speaker 1: but assimilate to the leftist American culture, the predominant culture

711
00:35:04,480 --> 00:35:07,400
Speaker 1: on college campuses, and I think that's something really to

712
00:35:07,640 --> 00:35:10,560
Speaker 1: chew on. So should we take in immigrants, Yeah, there's

713
00:35:10,640 --> 00:35:13,239
Speaker 1: immigrants worthy of taking in the world that are going

714
00:35:13,280 --> 00:35:16,360
Speaker 1: to be great Americans, but one point one million legal

715
00:35:16,360 --> 00:35:18,759
Speaker 1: immigrants per year and any of their family members that

716
00:35:18,800 --> 00:35:23,200
Speaker 1: they can bring over and endless birthright citizenship or illegal aliens,

717
00:35:23,719 --> 00:35:26,359
Speaker 1: we probably should clamp down on that. But Pirdaal, I'm

718
00:35:26,400 --> 00:35:28,640
Speaker 1: happy you're here and I'm happy you're a listener. So

719
00:35:28,680 --> 00:35:30,600
Speaker 1: thank you so much for your question. I really appreciate it.

720
00:35:30,840 --> 00:35:33,680
Speaker 1: If you like this podcast, please press like and subscribe

721
00:35:33,680 --> 00:35:36,600
Speaker 1: on the iHeartRadio app, Apple Podcasts, wherever you get your podcasts,

722
00:35:36,640 --> 00:35:40,040
Speaker 1: and on YouTube now can't forget about YouTube. Thank you guys.

723
00:35:40,120 --> 00:35:40,560
Speaker 2: So much.

724
00:35:40,600 --> 00:35:42,400
Speaker 1: I will see you guys on Monday,