1
00:00:04,800 --> 00:00:08,680
Speaker 1: On this episode of Each World. On February ninth, Matt

2
00:00:08,720 --> 00:00:12,840
Speaker 1: Schumer published an essay on Acts entitled Something Big Is Happening.

3
00:00:13,760 --> 00:00:16,840
Speaker 1: In it, he online the future where AI would take

4
00:00:16,880 --> 00:00:20,800
Speaker 1: over most computer based white collar jobs and encourage those

5
00:00:20,840 --> 00:00:24,720
Speaker 1: in the workforce to start using the latest AI tools. Now,

6
00:00:25,440 --> 00:00:28,560
Speaker 1: the essay was significant and that it went viral with

7
00:00:28,680 --> 00:00:32,240
Speaker 1: over eighty million views, and it raises the whole question

8
00:00:32,760 --> 00:00:36,640
Speaker 1: about the emergence of artificial intelligence and the way in

9
00:00:36,680 --> 00:00:40,120
Speaker 1: which that will affect the entire employment system. My guest

10
00:00:40,200 --> 00:00:45,600
Speaker 1: today authored a piece entitled AI Transaction Costs and a

11
00:00:45,720 --> 00:00:50,199
Speaker 1: Quiet Shift towards self Employment, in which she details the

12
00:00:50,320 --> 00:00:54,960
Speaker 1: quote what gets far less attention is whether AI changes

13
00:00:55,000 --> 00:00:58,600
Speaker 1: how work is organized in the first place. Here to

14
00:00:58,640 --> 00:01:02,760
Speaker 1: discuss AI is in impact on jobs. I'm really pleased

15
00:01:03,080 --> 00:01:07,880
Speaker 1: to welcome my guests, Leah Polagashpheeli. She is a senior

16
00:01:08,000 --> 00:01:12,520
Speaker 1: Research Fellow and director of the Labor Policy Project at

17
00:01:12,520 --> 00:01:17,560
Speaker 1: the Mercadas Center at George Mason University. Her research focuses

18
00:01:17,600 --> 00:01:22,080
Speaker 1: on labor regulations, the gig economy, and the changing nature

19
00:01:22,160 --> 00:01:40,120
Speaker 1: of work. Cleah, welcome and thank you for joining me

20
00:01:40,160 --> 00:01:40,840
Speaker 1: on Next World.

21
00:01:41,560 --> 00:01:43,440
Speaker 2: Thank you, speaker, Good rich for having me.

22
00:01:44,319 --> 00:01:46,840
Speaker 1: I have to ask you, what led you to study this?

23
00:01:47,880 --> 00:01:48,200
Speaker 2: Well?

24
00:01:48,360 --> 00:01:50,960
Speaker 3: I took an economics class in high school, and it

25
00:01:51,040 --> 00:01:52,880
Speaker 3: all started in high school, and I fell in love

26
00:01:52,920 --> 00:01:57,160
Speaker 3: with economics. So from then on forward, I studied economics,

27
00:01:57,200 --> 00:02:02,760
Speaker 3: got really interested in labor markets, and stuttered studying labor regulations,

28
00:02:03,120 --> 00:02:04,600
Speaker 3: get my PhD in economics.

29
00:02:04,640 --> 00:02:06,240
Speaker 2: What used to be a professor of economics?

30
00:02:06,280 --> 00:02:10,960
Speaker 3: Actually, so, I think economics really helps you understand the world,

31
00:02:11,160 --> 00:02:14,520
Speaker 3: and helps you understand labor markets in particular in ways

32
00:02:14,600 --> 00:02:18,120
Speaker 3: that most people don't really look at. And so I

33
00:02:18,120 --> 00:02:20,360
Speaker 3: think and it's an incredible valuable tool.

34
00:02:21,040 --> 00:02:23,560
Speaker 1: There are a lot of people who feel that artificial

35
00:02:23,600 --> 00:02:27,760
Speaker 1: intelligence is something which is happening to them, that it's

36
00:02:27,800 --> 00:02:31,400
Speaker 1: almost inevitable they can't do anything about it. There's a

37
00:02:31,400 --> 00:02:35,960
Speaker 1: real interesting question here about how can ordinary people understand

38
00:02:36,000 --> 00:02:39,840
Speaker 1: the scale of change we're going through and can they

39
00:02:39,880 --> 00:02:44,320
Speaker 1: infect influence either the direction the technology takes or the

40
00:02:44,360 --> 00:02:47,720
Speaker 1: direction that they take in responding to the technology.

41
00:02:48,600 --> 00:02:50,880
Speaker 2: Yeah, so there's a lot going on with AI.

42
00:02:51,080 --> 00:02:55,480
Speaker 3: From a purely research perspective, we see that AI is

43
00:02:55,680 --> 00:03:00,400
Speaker 3: reorganizing tasks, firms are using it, workers, are you using it?

44
00:03:00,480 --> 00:03:03,360
Speaker 3: And the tasks that workers are doing at their jobs

45
00:03:03,440 --> 00:03:06,760
Speaker 3: is changing, and I think AI can actually be really

46
00:03:06,760 --> 00:03:10,200
Speaker 3: empowering for workers because instead of having to do a

47
00:03:10,240 --> 00:03:12,560
Speaker 3: lot of mundane tasks that you had to do before.

48
00:03:12,720 --> 00:03:14,519
Speaker 3: I have a research assistant who had to do a

49
00:03:14,560 --> 00:03:17,840
Speaker 3: lot of coding and other kind of mundane tasks. She

50
00:03:18,280 --> 00:03:21,640
Speaker 3: was able to give those tasks to some AI tools

51
00:03:21,880 --> 00:03:24,480
Speaker 3: and then focus on things that she actually wanted to

52
00:03:24,560 --> 00:03:28,320
Speaker 3: build on. So she wanted to get better at communication

53
00:03:28,639 --> 00:03:32,960
Speaker 3: and writing and speaking in conferences and public appearances, and

54
00:03:33,000 --> 00:03:34,960
Speaker 3: she was able to focus on that. So I think

55
00:03:35,160 --> 00:03:38,480
Speaker 3: it's not just AI is happening to us, it's also

56
00:03:38,560 --> 00:03:40,960
Speaker 3: about how are we using the tools to change the

57
00:03:41,000 --> 00:03:43,960
Speaker 3: work that we're doing, and is it making us more valuable,

58
00:03:44,040 --> 00:03:46,000
Speaker 3: more productive, and allowing us to do the work that

59
00:03:46,040 --> 00:03:48,400
Speaker 3: we want to do. As my research assistant was like,

60
00:03:48,440 --> 00:03:50,440
Speaker 3: I don't want to do this data work anymore. I

61
00:03:50,480 --> 00:03:52,480
Speaker 3: give that to AI, but I focus on the things

62
00:03:52,800 --> 00:03:55,840
Speaker 3: that I really want to do, which is speaking, communicating, writing,

63
00:03:55,920 --> 00:03:59,480
Speaker 3: and so forth. And I think another thing that we're missing,

64
00:04:00,040 --> 00:04:01,680
Speaker 3: and this gets to the posts that I wrote on

65
00:04:02,240 --> 00:04:06,120
Speaker 3: is again there's this fixation on AI and work debates

66
00:04:06,160 --> 00:04:10,440
Speaker 3: about whether AI is replacing workers in existing jobs. But

67
00:04:10,520 --> 00:04:13,320
Speaker 3: I think there's a deeper economic question about does AI

68
00:04:13,480 --> 00:04:17,159
Speaker 3: change how work is organized in the first place. I

69
00:04:17,200 --> 00:04:20,560
Speaker 3: think about an example of a consultant. So ten years ago,

70
00:04:20,640 --> 00:04:25,320
Speaker 3: producing a client ready report might have required whole companies,

71
00:04:25,520 --> 00:04:29,840
Speaker 3: internal research staff, editing support, data visualization teams and so forth.

72
00:04:30,120 --> 00:04:33,799
Speaker 3: Today there are AI tools that can help with drafting, coding,

73
00:04:33,920 --> 00:04:39,120
Speaker 3: summarizing data, even formatting presentations, and that lowers what's called

74
00:04:39,120 --> 00:04:42,760
Speaker 3: the minimum scale needed to operate independently. And what that

75
00:04:42,800 --> 00:04:46,440
Speaker 3: means is that consultant can now become self employed and

76
00:04:46,480 --> 00:04:50,520
Speaker 3: be an independent consultant and doesn't have to work inside

77
00:04:50,560 --> 00:04:53,880
Speaker 3: of the firm. And the job isn't gone in this example,

78
00:04:54,160 --> 00:04:57,160
Speaker 3: but the organizational structure around it can change. And I

79
00:04:57,160 --> 00:05:00,120
Speaker 3: think that's an important aspect that we're missing, that I

80
00:05:00,160 --> 00:05:04,000
Speaker 3: can actually lead to more work being self employed and

81
00:05:04,040 --> 00:05:08,880
Speaker 3: not necessarily inside of a firm structure as traditional employees.

82
00:05:09,600 --> 00:05:15,000
Speaker 1: Do you expect that people will in fact adjust and

83
00:05:15,120 --> 00:05:18,560
Speaker 1: redefine the nature of work and redefine the way they

84
00:05:18,680 --> 00:05:25,919
Speaker 1: organize work in response to the gradual evolution of artificial intelligence.

85
00:05:26,480 --> 00:05:27,640
Speaker 2: That's a really great question.

86
00:05:27,839 --> 00:05:31,600
Speaker 3: I think what might be happening and I can't predict

87
00:05:31,680 --> 00:05:34,840
Speaker 3: things into the future very well, but I should say

88
00:05:34,839 --> 00:05:38,919
Speaker 3: economists are bad at predicting things and recessions and so forth.

89
00:05:38,960 --> 00:05:41,520
Speaker 3: But I want to give you a really clear example

90
00:05:41,560 --> 00:05:45,320
Speaker 3: of what I see happening. AI is creating what's called

91
00:05:45,440 --> 00:05:50,159
Speaker 3: a transaction cost technology shock. And transaction costs are the

92
00:05:50,200 --> 00:05:53,440
Speaker 3: real world frictions of using markets, so that means finding

93
00:05:53,440 --> 00:05:58,240
Speaker 3: the right person, negotiating terms, verifying quality, ensuring you get

94
00:05:58,240 --> 00:06:03,000
Speaker 3: what you paid for. Exists partly because it's sometimes cheaper

95
00:06:03,040 --> 00:06:06,160
Speaker 3: to hire and manage people internally than to keep contracting

96
00:06:06,200 --> 00:06:10,760
Speaker 3: everything out. However, with an AI shock and technology shock,

97
00:06:10,880 --> 00:06:13,880
Speaker 3: it makes it a lot cheaper and easier to just

98
00:06:14,240 --> 00:06:17,320
Speaker 3: contract out work. And so in those terms, I think

99
00:06:17,400 --> 00:06:20,359
Speaker 3: it is going to change how work is defined and

100
00:06:20,400 --> 00:06:22,920
Speaker 3: how it is organized. And just to give you one

101
00:06:22,960 --> 00:06:26,000
Speaker 3: clear example of how powerful this is, if you think

102
00:06:26,040 --> 00:06:29,800
Speaker 3: about ride services twenty years ago, if you wanted to

103
00:06:29,839 --> 00:06:32,680
Speaker 3: find a driver outside of a traditional kind of limo

104
00:06:32,920 --> 00:06:37,359
Speaker 3: or taxi company, you might have used Craiglist, But the

105
00:06:37,400 --> 00:06:40,080
Speaker 3: transaction costs were huge, right because you didn't know who

106
00:06:40,120 --> 00:06:44,000
Speaker 3: the driver was, you couldn't track them, payments were clunky,

107
00:06:44,080 --> 00:06:46,400
Speaker 3: and there was no reputation system. So as a result,

108
00:06:46,839 --> 00:06:50,560
Speaker 3: not many people used Craigslist to find drivers. But then

109
00:06:50,560 --> 00:06:52,720
Speaker 3: you had Uber come in. And what Uber did. They

110
00:06:52,800 --> 00:06:57,280
Speaker 3: didn't just create an app. It combined GPS, digital payments,

111
00:06:57,520 --> 00:07:01,560
Speaker 3: two sided rating systems to dramatically lower those coordination and

112
00:07:01,640 --> 00:07:04,760
Speaker 3: trust costs, and suddenly it was easy and seamless to

113
00:07:04,800 --> 00:07:07,200
Speaker 3: match someone who wanted to ride with someone willing to

114
00:07:07,240 --> 00:07:10,760
Speaker 3: provide one. And that drop in transaction costs is what

115
00:07:11,000 --> 00:07:14,480
Speaker 3: enabled the expansion of the gig economy over the past decade,

116
00:07:14,920 --> 00:07:19,400
Speaker 3: not because people suddenly changed preferences, but because technology made

117
00:07:19,480 --> 00:07:22,080
Speaker 3: coordination much easier. And I think we're going to see

118
00:07:22,080 --> 00:07:24,400
Speaker 3: the same thing with AI and transaction costs are a

119
00:07:24,400 --> 00:07:27,720
Speaker 3: similar thing, but applied more to knowledge work, where we're

120
00:07:27,720 --> 00:07:31,000
Speaker 3: going to see it be a lot easier to contract

121
00:07:31,120 --> 00:07:35,280
Speaker 3: out to do project on project basis. And for a consultant,

122
00:07:35,760 --> 00:07:39,160
Speaker 3: a researcher who was previously in the firm, they now

123
00:07:39,720 --> 00:07:43,240
Speaker 3: are able to do that same job outside of the

124
00:07:43,280 --> 00:07:45,920
Speaker 3: firm with all of these AI tools, so you don't

125
00:07:45,960 --> 00:07:47,720
Speaker 3: need the entire internal structure.

126
00:07:48,800 --> 00:07:52,920
Speaker 1: There's this general fear that technology kills jobs with the

127
00:07:52,960 --> 00:07:57,880
Speaker 1: fact is you go back to eighteen hundred ninety seven

128
00:07:57,960 --> 00:08:02,520
Speaker 1: percent of the workforce was in agriculture. As recently as

129
00:08:02,600 --> 00:08:07,360
Speaker 1: nineteen hundred, forty one percent of the workforce was in agriculture.

130
00:08:07,840 --> 00:08:12,080
Speaker 1: Today it's around two percent. Yeah, we produce more food,

131
00:08:12,600 --> 00:08:16,480
Speaker 1: greater range of choices, and the people who stayed in

132
00:08:16,480 --> 00:08:19,800
Speaker 1: farming overall have done pretty well. But the people who

133
00:08:19,840 --> 00:08:23,600
Speaker 1: love farming have also done pretty well, but are radically

134
00:08:23,640 --> 00:08:27,120
Speaker 1: different things. Do you think that we're likely to see

135
00:08:27,640 --> 00:08:30,480
Speaker 1: a similar pattern with artificial intelligence?

136
00:08:31,720 --> 00:08:37,200
Speaker 3: Yes, every tech shock in the past on NET has

137
00:08:37,240 --> 00:08:42,079
Speaker 3: created more jobs. Now, it's destroyed some jobs in some areas,

138
00:08:42,760 --> 00:08:48,040
Speaker 3: but it's created new markets and new occupations that never

139
00:08:48,080 --> 00:08:50,360
Speaker 3: existed before. So if you think about in the nineteen hundred,

140
00:08:50,400 --> 00:08:53,559
Speaker 3: there were not many airplane pilots. Right now, that's a

141
00:08:53,600 --> 00:08:57,280
Speaker 3: whole industry and occupation that never existed over one hundred

142
00:08:57,360 --> 00:09:00,520
Speaker 3: years ago, and now it's an entire industry. And there

143
00:09:00,520 --> 00:09:03,120
Speaker 3: are all of these examples of jobs that exist in

144
00:09:03,120 --> 00:09:06,960
Speaker 3: the nineteen hundreds that don't exist today. Now, those jobs

145
00:09:06,960 --> 00:09:10,880
Speaker 3: were destroyed by technological changes, but that didn't mean on

146
00:09:11,080 --> 00:09:12,199
Speaker 3: NET that.

147
00:09:12,240 --> 00:09:13,040
Speaker 2: Jobs were lost.

148
00:09:13,160 --> 00:09:16,600
Speaker 3: On NET actually jobs grew and the economy expanded, and

149
00:09:16,720 --> 00:09:20,280
Speaker 3: markets grew and new industries emerged, and I believe, based

150
00:09:20,320 --> 00:09:22,839
Speaker 3: on the evidence that we've seen so far from past

151
00:09:22,840 --> 00:09:27,480
Speaker 3: technological changes and from research on AI today, that's going

152
00:09:27,520 --> 00:09:30,360
Speaker 3: to continue to happen. We're going to have new jobs

153
00:09:30,360 --> 00:09:33,720
Speaker 3: and markets created that we can't even imagine what those

154
00:09:33,760 --> 00:09:36,120
Speaker 3: are right now, Because if you ask someone in the

155
00:09:36,160 --> 00:09:39,120
Speaker 3: nineteen hundreds what new jobs will be there, you know,

156
00:09:39,200 --> 00:09:42,360
Speaker 3: in the year twenty twenty six, not many people would

157
00:09:42,360 --> 00:09:45,960
Speaker 3: have responded a bunch of airplane posits, right, and a

158
00:09:46,000 --> 00:09:49,640
Speaker 3: bunch of companies working on AI and workers doing those

159
00:09:49,720 --> 00:09:52,040
Speaker 3: jobs in AI. So I think we really have to

160
00:09:52,040 --> 00:09:54,840
Speaker 3: be a little bit more humble about like, oh, you know,

161
00:09:54,840 --> 00:09:57,160
Speaker 3: what are the new jobs? Well, we don't know, because

162
00:09:57,480 --> 00:10:01,160
Speaker 3: every tech shock creates new market, gets, new industries and

163
00:10:01,160 --> 00:10:04,360
Speaker 3: new opportunities, and on net we're going to see more

164
00:10:04,400 --> 00:10:05,520
Speaker 3: job growth in my opinion.

165
00:10:06,240 --> 00:10:08,439
Speaker 1: I actually know one of the guys who helped create

166
00:10:08,800 --> 00:10:12,600
Speaker 1: automatic tailler machines, and it was a study that said

167
00:10:12,960 --> 00:10:18,560
Speaker 1: that ATMs didn't eliminate tellers. In fact, employment rose for

168
00:10:18,720 --> 00:10:23,679
Speaker 1: tellers from nineteen eighty twenty ten because as the operating

169
00:10:23,760 --> 00:10:27,080
Speaker 1: cost for the branches went down, there were more local

170
00:10:27,120 --> 00:10:30,800
Speaker 1: branches requiring more tellers. Even if the branch had an

171
00:10:30,840 --> 00:10:35,040
Speaker 1: ATM outside. It's just interesting how it doesn't quite work

172
00:10:35,600 --> 00:10:36,880
Speaker 1: the way you expected to.

173
00:10:37,520 --> 00:10:38,319
Speaker 2: That's exactly right.

174
00:10:38,360 --> 00:10:42,040
Speaker 3: We actually highlighted that ATM study on our substack as

175
00:10:42,080 --> 00:10:45,160
Speaker 3: well in a different post about AI, because that is

176
00:10:45,200 --> 00:10:48,920
Speaker 3: a great example because what happened there is that more

177
00:10:48,960 --> 00:10:52,040
Speaker 3: workers started to go into the relational roles at the

178
00:10:52,040 --> 00:10:55,480
Speaker 3: banks instead of at the ATM, and so I think

179
00:10:55,520 --> 00:10:59,199
Speaker 3: that's a great example of how AI can actually augment

180
00:10:59,360 --> 00:11:03,160
Speaker 3: workers and change the tasks and jobs that are done,

181
00:11:03,280 --> 00:11:05,360
Speaker 3: not necessarily replace workers.

182
00:11:05,400 --> 00:11:07,480
Speaker 2: And I think that's the key thing.

183
00:11:08,160 --> 00:11:11,640
Speaker 3: But we need to understand with AI and labor markets

184
00:11:12,000 --> 00:11:14,720
Speaker 3: is that the jobs. We can't say which jobs for

185
00:11:14,800 --> 00:11:18,360
Speaker 3: sure will stay or which tasks will stay. But what

186
00:11:18,480 --> 00:11:20,760
Speaker 3: we can say is likely the jobs and the task

187
00:11:20,800 --> 00:11:23,200
Speaker 3: will change and there will be more jobs for workers.

188
00:11:23,200 --> 00:11:25,440
Speaker 3: We just don't know what it'll look like and where

189
00:11:25,440 --> 00:11:27,120
Speaker 3: it'll be and what those workers will do.

190
00:11:43,960 --> 00:11:46,800
Speaker 1: As you look at these studies, I was kind of

191
00:11:46,800 --> 00:11:50,120
Speaker 1: surprised to learn that in fact, the people who seem

192
00:11:50,160 --> 00:11:54,120
Speaker 1: to gain the most productivity are the least skilled workers,

193
00:11:54,679 --> 00:11:57,959
Speaker 1: while the best workers, the top performing workers had the

194
00:11:58,080 --> 00:12:02,920
Speaker 1: smallest increase in productivity. AI for some reason, really helps

195
00:12:03,040 --> 00:12:07,120
Speaker 1: train and empower the least skilled workers, But the people

196
00:12:07,120 --> 00:12:10,160
Speaker 1: at the very top already have those skills, so they

197
00:12:10,200 --> 00:12:13,319
Speaker 1: don't gain as big an advantage as actually the lower

198
00:12:13,360 --> 00:12:14,120
Speaker 1: skilled workers.

199
00:12:14,720 --> 00:12:15,720
Speaker 2: That's a great point.

200
00:12:16,000 --> 00:12:18,520
Speaker 3: The evidence does show that when AI is used to

201
00:12:18,720 --> 00:12:21,880
Speaker 3: augment human workers, helping them be more productive, rather than

202
00:12:21,960 --> 00:12:25,800
Speaker 3: at right replacing them, it can actually revive some middle

203
00:12:25,800 --> 00:12:29,079
Speaker 3: class jobs and make workers more valuable in the labor market.

204
00:12:29,320 --> 00:12:32,920
Speaker 3: So these lower skilled or less experienced workers often see,

205
00:12:33,160 --> 00:12:37,120
Speaker 3: as you highlighted, huge productivity gains when I can support them.

206
00:12:37,600 --> 00:12:39,720
Speaker 3: And I think that comes to a question that we've

207
00:12:39,760 --> 00:12:43,079
Speaker 3: been hearing before too in these discussions about what will

208
00:12:43,080 --> 00:12:45,800
Speaker 3: happen to middle class jobs or what will happen to

209
00:12:45,800 --> 00:12:48,720
Speaker 3: the middle class. But speaker Genrich, the example you just

210
00:12:48,840 --> 00:12:50,800
Speaker 3: use is a great one to highlight as actually it

211
00:12:50,880 --> 00:12:53,840
Speaker 3: might help revive middle skilled jobs and make workers more

212
00:12:53,920 --> 00:12:57,480
Speaker 3: valuable in the labor market. It's a really fascinating change.

213
00:12:57,480 --> 00:13:00,000
Speaker 3: And I should say that a lot of the higher

214
00:13:00,200 --> 00:13:02,160
Speaker 3: skilled workers that I know are a little bit more

215
00:13:02,200 --> 00:13:05,920
Speaker 3: concerned about AI versus lower skilled workers are like this

216
00:13:05,960 --> 00:13:08,960
Speaker 3: is great, Like I'm being much more productive, I'm learning

217
00:13:09,000 --> 00:13:12,120
Speaker 3: so much so I really think it's an interesting dynamic

218
00:13:12,200 --> 00:13:14,360
Speaker 3: that's happening here, and I think it could speak a

219
00:13:14,400 --> 00:13:17,480
Speaker 3: lot to this question of what will happen to middle

220
00:13:17,480 --> 00:13:20,760
Speaker 3: skilled jobs, because in the past we've seen some technological

221
00:13:20,840 --> 00:13:22,840
Speaker 3: changes kind of hollow out.

222
00:13:22,679 --> 00:13:23,480
Speaker 2: Some of those jobs.

223
00:13:23,520 --> 00:13:27,080
Speaker 3: But I think AI has the potential to revive those

224
00:13:27,120 --> 00:13:29,720
Speaker 3: middle skilled jobs, especially because of what we just talked

225
00:13:29,720 --> 00:13:32,840
Speaker 3: about that lower skilled or less experience workers often see

226
00:13:32,840 --> 00:13:35,960
Speaker 3: the biggest productivity gains. And this research, by the way,

227
00:13:36,160 --> 00:13:39,560
Speaker 3: is really fascinating. It's done by economists from Stanford and

228
00:13:39,600 --> 00:13:42,760
Speaker 3: it uses some of the gold standard research methods, so

229
00:13:42,840 --> 00:13:46,840
Speaker 3: it really showcases that we do see huge productivity gains,

230
00:13:46,920 --> 00:13:49,720
Speaker 3: especially from lower skilled or least experience workers.

231
00:13:50,360 --> 00:13:53,480
Speaker 1: This no shee of the lower skilled workers. If you

232
00:13:53,520 --> 00:13:56,000
Speaker 1: think about it, A lot of schools will say, you know,

233
00:13:56,000 --> 00:13:59,200
Speaker 1: if you had seventy percent right, you get a See. Well,

234
00:13:59,320 --> 00:14:01,719
Speaker 1: if you go to make Donald's and on the very

235
00:14:01,720 --> 00:14:04,560
Speaker 1: first day you're the cashier and you get a seventy

236
00:14:04,559 --> 00:14:07,839
Speaker 1: percent right, you get fired. You know, I McDonald's actually

237
00:14:07,880 --> 00:14:10,880
Speaker 1: wants you to be one hundred percent right from day one.

238
00:14:11,240 --> 00:14:14,160
Speaker 1: And so the standard of the real world as opposed

239
00:14:14,160 --> 00:14:17,760
Speaker 1: to what we've tolerated is really different, and I've been intrigued.

240
00:14:17,800 --> 00:14:22,200
Speaker 1: We've visited now several factories where you have people who

241
00:14:22,280 --> 00:14:24,960
Speaker 1: come in some as early as fourteen, learn how to

242
00:14:25,000 --> 00:14:28,840
Speaker 1: be apprentices, get a job, and it's seen as an

243
00:14:28,920 --> 00:14:32,680
Speaker 1: investment in job training. We have met the CEO of

244
00:14:32,680 --> 00:14:37,400
Speaker 1: a billion dollar corporation who entered the corporation as an apprentice,

245
00:14:37,520 --> 00:14:39,640
Speaker 1: but they came all the way up. And my guess

246
00:14:39,720 --> 00:14:44,680
Speaker 1: is that if you integrated artificial intelligence into that kind

247
00:14:44,720 --> 00:14:48,680
Speaker 1: of a system, you would be sort of dramatically empowering

248
00:14:49,080 --> 00:14:52,840
Speaker 1: the starting apprentice and they would learn very fast and

249
00:14:53,000 --> 00:14:55,960
Speaker 1: rise very quickly. But it's a very different way of

250
00:14:57,000 --> 00:15:03,320
Speaker 1: really reshaping the relationship between information and actual work and

251
00:15:03,400 --> 00:15:05,920
Speaker 1: being part of a group. The other part, which you've

252
00:15:05,960 --> 00:15:11,000
Speaker 1: written about is that we're going to lower dramatically the

253
00:15:11,000 --> 00:15:15,560
Speaker 1: cost of being independent. Your artificial intelligence partner is going

254
00:15:15,600 --> 00:15:18,320
Speaker 1: to be able to do so many things that today

255
00:15:18,640 --> 00:15:20,640
Speaker 1: you need to be part of a team to get

256
00:15:20,680 --> 00:15:22,680
Speaker 1: that done. You end up with a large number of

257
00:15:22,680 --> 00:15:27,880
Speaker 1: people being independent entrepreneurs. Isn't that going to require rethinking

258
00:15:27,960 --> 00:15:33,040
Speaker 1: things around health, insurance, labor law, et cetera. To shift

259
00:15:33,080 --> 00:15:38,040
Speaker 1: from a group setting as the norm to individual self

260
00:15:38,040 --> 00:15:39,200
Speaker 1: employed entrepreneurs.

261
00:15:39,920 --> 00:15:41,880
Speaker 3: Speaker Gingrich, you hit the nail on the head. And

262
00:15:41,960 --> 00:15:44,920
Speaker 3: I've been writing a lot on exactly this point, because

263
00:15:45,320 --> 00:15:48,400
Speaker 3: even if this shift is gradual to more people becoming

264
00:15:48,440 --> 00:15:54,280
Speaker 3: independent entrepreneur, self employed in independent work contacts, it matters

265
00:15:54,640 --> 00:15:58,680
Speaker 3: a lot because our safety, ned and benefit systems all

266
00:15:58,720 --> 00:16:02,040
Speaker 3: assume a stable wis to employment, and so if more

267
00:16:02,080 --> 00:16:06,320
Speaker 3: people are an income across multiple clients or projects, employer

268
00:16:06,480 --> 00:16:10,040
Speaker 3: tied benefits and systems make less sense.

269
00:16:09,760 --> 00:16:10,360
Speaker 2: In that world.

270
00:16:11,040 --> 00:16:14,360
Speaker 3: And I wanted to highlight that we're seeing a momentum

271
00:16:14,400 --> 00:16:19,080
Speaker 3: today for what's called portable benefit systems, and this is

272
00:16:19,080 --> 00:16:22,400
Speaker 3: happening both at the state and federal level. It's an

273
00:16:22,480 --> 00:16:27,160
Speaker 3: institutional update to match a more flexible and fluid workforce.

274
00:16:27,520 --> 00:16:31,520
Speaker 3: And the key idea there is think like HSA's but

275
00:16:31,560 --> 00:16:36,560
Speaker 3: for everything or those sort of systems where benefits are

276
00:16:36,960 --> 00:16:41,000
Speaker 3: worker owned, they travel with the worker, they're not tied

277
00:16:41,040 --> 00:16:44,600
Speaker 3: to a particular job or company, and they move with

278
00:16:44,760 --> 00:16:47,880
Speaker 3: the worker as they go from client to client or

279
00:16:47,960 --> 00:16:51,160
Speaker 3: job to job. And I think that is something that

280
00:16:51,200 --> 00:16:53,600
Speaker 3: we really need to be thinking about for the twenty

281
00:16:53,640 --> 00:16:59,640
Speaker 3: first century, for how to modernize this institutional system that's

282
00:16:59,640 --> 00:17:02,480
Speaker 3: been around for a while, and that is based on

283
00:17:02,800 --> 00:17:07,520
Speaker 3: a nineteen thirties design of the workplace. Right, So in

284
00:17:07,560 --> 00:17:10,800
Speaker 3: that setting, you have one person who's a W two

285
00:17:10,880 --> 00:17:14,080
Speaker 3: employee says that one company or one career for their

286
00:17:14,119 --> 00:17:18,400
Speaker 3: whole lives into a much more fluid and flexible workforce

287
00:17:18,400 --> 00:17:20,960
Speaker 3: that we see today. And I wanted to highlight even

288
00:17:21,000 --> 00:17:23,640
Speaker 3: with young people today. So post COVID, we're looking at

289
00:17:23,640 --> 00:17:27,760
Speaker 3: surveys of young people entering into the job market. A

290
00:17:27,840 --> 00:17:31,000
Speaker 3: lot more people today than ten years ago are going

291
00:17:31,040 --> 00:17:34,080
Speaker 3: into independent work. And I don't mean kind of uber

292
00:17:34,240 --> 00:17:36,640
Speaker 3: and lyft sort of thing. I mean like they say

293
00:17:36,720 --> 00:17:38,280
Speaker 3: I'm going to be an entrepreneur and start my own

294
00:17:38,280 --> 00:17:41,000
Speaker 3: YouTube channel and that's my first job out of college.

295
00:17:41,480 --> 00:17:43,920
Speaker 3: And so that is all changing now and we really

296
00:17:43,960 --> 00:17:48,240
Speaker 3: need to rethink kind of the institutional framework that we

297
00:17:48,359 --> 00:17:51,800
Speaker 3: have where everything is tied to a wtwo job. And

298
00:17:51,840 --> 00:17:55,240
Speaker 3: I think that's why portal benefits reforms and systems bills

299
00:17:55,280 --> 00:17:58,600
Speaker 3: introduced at the state and federal level today are helping

300
00:17:58,680 --> 00:18:02,520
Speaker 3: to create an update to match this modern workforce.

301
00:18:03,520 --> 00:18:08,679
Speaker 1: There are streaming channels now in which people make amazing

302
00:18:08,720 --> 00:18:09,880
Speaker 1: amounts of money.

303
00:18:10,000 --> 00:18:11,960
Speaker 2: A lot more than an entry level job.

304
00:18:13,520 --> 00:18:18,359
Speaker 1: Let's studying and more talking and posturing maybe the key

305
00:18:18,560 --> 00:18:19,800
Speaker 1: to a higher income level.

306
00:18:20,160 --> 00:18:21,720
Speaker 3: But I think you're right, and I just want to

307
00:18:21,760 --> 00:18:24,280
Speaker 3: say the data and the research one hundred percent supports

308
00:18:24,320 --> 00:18:27,000
Speaker 3: that we are seeing a lot more people at younger

309
00:18:27,040 --> 00:18:32,120
Speaker 3: ages go into these independent work jobs, influencers, YouTube channels,

310
00:18:32,119 --> 00:18:34,000
Speaker 3: and so forth. Now I don't know if that's a

311
00:18:34,000 --> 00:18:36,639
Speaker 3: good thing or a bad thing and what it means,

312
00:18:36,640 --> 00:18:40,399
Speaker 3: but I do think it is forecasting what's going to

313
00:18:40,440 --> 00:18:43,280
Speaker 3: happen in the future because AI is going to take

314
00:18:43,359 --> 00:18:47,200
Speaker 3: that and multiply it by ten. According to my analysis

315
00:18:47,240 --> 00:18:50,280
Speaker 3: on the ways that AI will change how work is organized.

316
00:18:50,600 --> 00:18:54,000
Speaker 3: And that's why it's really important to get the policies

317
00:18:54,200 --> 00:18:59,000
Speaker 3: and the institutional framework right for a workforce ten fifteen

318
00:18:59,080 --> 00:19:01,760
Speaker 3: years from now where a lot more people are in

319
00:19:01,840 --> 00:19:06,680
Speaker 3: these independent work or influencer or other self employed work arrangements.

320
00:19:07,240 --> 00:19:12,160
Speaker 3: Because again, our institutional framework is designed around W two jobs,

321
00:19:12,560 --> 00:19:15,560
Speaker 3: an employee employer relationship. Great a lot of things are

322
00:19:15,560 --> 00:19:17,600
Speaker 3: designed around that. So we really need to be talking

323
00:19:17,600 --> 00:19:19,240
Speaker 3: about the infrastructure today.

324
00:19:19,920 --> 00:19:22,439
Speaker 1: It makes me think that Congress ought to be holding

325
00:19:22,520 --> 00:19:31,320
Speaker 1: hearings on the rise of entrepreneurial systems that are individual structures.

326
00:19:31,920 --> 00:19:35,760
Speaker 1: You know, what's the health insurance, what's the taxes, what

327
00:19:35,920 --> 00:19:39,080
Speaker 1: kind of records do you need, et cetera, which will

328
00:19:39,119 --> 00:19:42,960
Speaker 1: be totally different, and most of that will be mediated

329
00:19:43,359 --> 00:19:47,440
Speaker 1: by artificial intelligence. So you actually will have an ability

330
00:19:47,760 --> 00:19:51,399
Speaker 1: to be your own bookkeeper on a scale that would

331
00:19:51,440 --> 00:19:52,760
Speaker 1: historically have been impossible.

332
00:19:53,560 --> 00:19:56,080
Speaker 3: Yeah, that's right, and I couldn't agree more with you,

333
00:19:56,119 --> 00:19:58,520
Speaker 3: Speaker and riche on. Congress needs to be talking about

334
00:19:58,560 --> 00:20:02,119
Speaker 3: these and actually they've started a little bit with these hearings.

335
00:20:02,160 --> 00:20:05,560
Speaker 3: I've actually testified at four different hearings trying to unpack

336
00:20:05,680 --> 00:20:09,840
Speaker 3: independent workforce and portable benefits. Sometimes the problem that you

337
00:20:09,880 --> 00:20:13,560
Speaker 3: get is this turns into an independent contractor issue, with

338
00:20:13,800 --> 00:20:16,040
Speaker 3: people bringing up ideas of like, well, this is about

339
00:20:16,119 --> 00:20:20,119
Speaker 3: employer misclassification of workers, and then we're sort of missing.

340
00:20:19,800 --> 00:20:21,320
Speaker 2: The boat at that point.

341
00:20:21,400 --> 00:20:24,920
Speaker 3: We're like, no, we're talking about independent entrepreneurs and real

342
00:20:25,000 --> 00:20:27,960
Speaker 3: freelancers and self employed workers, and so we need to

343
00:20:28,000 --> 00:20:31,040
Speaker 3: be thinking about those people, not just about oh, well,

344
00:20:31,119 --> 00:20:33,879
Speaker 3: this is an independent contractor in a vulnerable position and

345
00:20:33,960 --> 00:20:37,720
Speaker 3: the company is exploiting them or misclassifying them.

346
00:20:37,760 --> 00:20:40,560
Speaker 2: So unfortunately, those conversations.

347
00:20:39,960 --> 00:20:43,360
Speaker 3: Some of them have started but they keep being dragged

348
00:20:43,400 --> 00:20:47,600
Speaker 3: into some of these older debates about independent contracting rather

349
00:20:47,640 --> 00:20:51,280
Speaker 3: than thinking forward about the new type of jobs that

350
00:20:51,320 --> 00:20:53,480
Speaker 3: are emerging today and tomorrow.

351
00:21:09,880 --> 00:21:13,600
Speaker 1: Wi't there be a great deal of tension and confusion

352
00:21:14,200 --> 00:21:18,240
Speaker 1: about the rise of these brand new sort of self supported,

353
00:21:18,600 --> 00:21:20,640
Speaker 1: independently functioning entrepreneurs.

354
00:21:21,920 --> 00:21:24,720
Speaker 3: Yeah, that's a great question, and we're actually seeing it today.

355
00:21:25,040 --> 00:21:27,320
Speaker 3: Their bills will introduced at the federal level and at

356
00:21:27,320 --> 00:21:32,400
Speaker 3: the state level that enable portable benefits systems or try

357
00:21:32,440 --> 00:21:37,440
Speaker 3: to modernize labor laws and health insurance systems to create

358
00:21:37,560 --> 00:21:41,280
Speaker 3: benefits that are more portable. We are seeing unions today

359
00:21:41,400 --> 00:21:45,960
Speaker 3: come speak against those and their argument is that, look,

360
00:21:46,160 --> 00:21:49,400
Speaker 3: most of these workers are misclassified height or they're exploited,

361
00:21:49,440 --> 00:21:52,159
Speaker 3: and again they keep going back to this issue of

362
00:21:52,240 --> 00:21:56,000
Speaker 3: independent contractors and so forth. I understand where they're coming from,

363
00:21:56,320 --> 00:22:01,320
Speaker 3: But the real question is that ten years from now,

364
00:22:01,640 --> 00:22:04,000
Speaker 3: a lot more workers are going to be entrepreneurs and

365
00:22:04,000 --> 00:22:07,480
Speaker 3: self employed, and how are are unions going to operate

366
00:22:07,520 --> 00:22:10,320
Speaker 3: in that framework in that modern economy. And I think

367
00:22:10,640 --> 00:22:13,800
Speaker 3: it's really important to try to think about how can

368
00:22:14,080 --> 00:22:18,800
Speaker 3: the infrastructure around labor unions also be reformed so that

369
00:22:19,440 --> 00:22:21,400
Speaker 3: they're not kind of missing the boat and still living

370
00:22:21,440 --> 00:22:24,960
Speaker 3: in the twentieth century with the twenty first century economy.

371
00:22:25,160 --> 00:22:28,600
Speaker 3: I've written a little bit about how we can separate

372
00:22:29,240 --> 00:22:32,760
Speaker 3: the good aspects of unions, which is worker voice, which

373
00:22:32,800 --> 00:22:38,000
Speaker 3: is essential, and separate that from our labor structures around

374
00:22:38,080 --> 00:22:41,640
Speaker 3: unions that create monopoly kind of bargaining structures and so forth.

375
00:22:41,760 --> 00:22:44,440
Speaker 2: So kind of monopoly bad, worker voice good.

376
00:22:44,880 --> 00:22:48,359
Speaker 3: How can we create a system that allows for worker

377
00:22:48,480 --> 00:22:51,640
Speaker 3: voice aspect to thrive? But how do we move away

378
00:22:51,760 --> 00:22:56,160
Speaker 3: from the monopoly bargaining structures that kind of bring things down?

379
00:22:56,880 --> 00:22:58,800
Speaker 1: When you look at all this, it's a huge level

380
00:22:58,800 --> 00:23:03,000
Speaker 1: of change. If AI is the future, what's the role

381
00:23:03,040 --> 00:23:06,800
Speaker 1: of education and all that, and should we be rethinking

382
00:23:07,359 --> 00:23:10,080
Speaker 1: how children get educated if they're going to be living

383
00:23:10,119 --> 00:23:11,719
Speaker 1: in a world of artificial intelligence.

384
00:23:13,040 --> 00:23:14,359
Speaker 2: You could go two ways with this.

385
00:23:14,520 --> 00:23:19,600
Speaker 3: So it's interesting that China is taking an approach towards

386
00:23:19,640 --> 00:23:25,320
Speaker 3: AI where they're basically formally requiring educational systems to start

387
00:23:25,359 --> 00:23:28,960
Speaker 3: training kids as young as six years old to start

388
00:23:29,080 --> 00:23:33,720
Speaker 3: using AI, because I think they're predicting a future where

389
00:23:33,880 --> 00:23:36,919
Speaker 3: this is the most valuable skill and tool that you

390
00:23:36,960 --> 00:23:39,680
Speaker 3: can have. So there's an argument for the China model.

391
00:23:40,200 --> 00:23:42,960
Speaker 3: Some of the research and literature I've been reading for

392
00:23:43,119 --> 00:23:45,600
Speaker 3: the other side of the model is that, no, we

393
00:23:45,640 --> 00:23:49,720
Speaker 3: should not introduce AI to children this young because they

394
00:23:49,760 --> 00:23:54,000
Speaker 3: need to be able to develop these critical thinking skills

395
00:23:54,119 --> 00:23:57,680
Speaker 3: and other foundational skills early on so that they can

396
00:23:57,920 --> 00:24:01,160
Speaker 3: later be able to assess AI's work, if.

397
00:24:01,080 --> 00:24:01,720
Speaker 2: That makes sense.

398
00:24:02,160 --> 00:24:05,000
Speaker 3: And so there are these two different models I've seen

399
00:24:05,080 --> 00:24:07,600
Speaker 3: being discussed about educational systems with AI.

400
00:24:08,040 --> 00:24:09,920
Speaker 2: And again, the second model.

401
00:24:09,720 --> 00:24:13,959
Speaker 3: Is more like, don't introduce AI early, let them build

402
00:24:14,000 --> 00:24:18,160
Speaker 3: the skills independently as humans to critically assess and so forth,

403
00:24:18,480 --> 00:24:23,000
Speaker 3: and then the AI can augment those skills. So I

404
00:24:23,080 --> 00:24:25,160
Speaker 3: can't tell you which one is the right one off

405
00:24:25,160 --> 00:24:27,280
Speaker 3: the top of my head. I have two young children,

406
00:24:27,359 --> 00:24:30,119
Speaker 3: two and four years old, and I'm thinking about this

407
00:24:30,200 --> 00:24:32,240
Speaker 3: as well, and I was like, I would like them

408
00:24:32,480 --> 00:24:35,679
Speaker 3: not to use AI early on and then train on

409
00:24:35,760 --> 00:24:36,480
Speaker 3: it much later.

410
00:24:36,560 --> 00:24:38,520
Speaker 2: And the way that a lot of us I.

411
00:24:38,480 --> 00:24:41,680
Speaker 3: Grew up going to school in nineteen nineties and so forth,

412
00:24:41,720 --> 00:24:45,920
Speaker 3: and I think we started to take on computer skills slowly,

413
00:24:46,640 --> 00:24:47,920
Speaker 3: but it was still really helpful.

414
00:24:48,280 --> 00:24:48,800
Speaker 2: It was great.

415
00:24:48,840 --> 00:24:51,800
Speaker 3: I remember being introduced in middle school. But I think

416
00:24:51,840 --> 00:24:54,520
Speaker 3: AI is a whole nother ballgame, because it can make

417
00:24:54,560 --> 00:24:58,680
Speaker 3: you maybe think in a more lazy way, because you're saying, oh,

418
00:24:58,720 --> 00:25:02,760
Speaker 3: I'm just gonna give this AI versus if you can't

419
00:25:02,840 --> 00:25:05,439
Speaker 3: use AI, it might force you to kind of unlock

420
00:25:05,480 --> 00:25:09,159
Speaker 3: those critical thinking skills and dig deeper. So it'll be

421
00:25:09,240 --> 00:25:12,720
Speaker 3: really interesting to see how this unfolds and the educational debates.

422
00:25:13,080 --> 00:25:17,000
Speaker 1: I notice going to restaurants, you'll have four or five

423
00:25:17,040 --> 00:25:21,639
Speaker 1: six year old kids have a phone and they're playing games.

424
00:25:22,240 --> 00:25:24,960
Speaker 1: It's a different world. You are going to be very

425
00:25:25,000 --> 00:25:29,480
Speaker 1: busy because you're a serious student of an area that

426
00:25:29,560 --> 00:25:33,520
Speaker 1: is going to be changing so dramatically and in so

427
00:25:33,640 --> 00:25:37,399
Speaker 1: many different, unpredictable ways that just keeping up with it

428
00:25:37,440 --> 00:25:39,320
Speaker 1: is going to keep you pretty busy, don't you think.

429
00:25:40,320 --> 00:25:43,240
Speaker 3: Yeah, I think labor markets are the most important things

430
00:25:43,560 --> 00:25:47,680
Speaker 3: right now in terms of economics and policy. We're also

431
00:25:47,720 --> 00:25:51,720
Speaker 3: at a really important moment in American labor. As you

432
00:25:51,760 --> 00:25:55,400
Speaker 3: can see, there's this new sort of coalition on pro

433
00:25:55,440 --> 00:26:00,440
Speaker 3: worker that includes a lot of Republicans and Democrats, and

434
00:26:00,720 --> 00:26:04,719
Speaker 3: they're pushing on this well pro worker now rather than

435
00:26:04,760 --> 00:26:07,800
Speaker 3: pro business, and so it's a really fascinating time to

436
00:26:07,840 --> 00:26:11,200
Speaker 3: be working on this because it's harder to say. Maybe

437
00:26:11,280 --> 00:26:13,480
Speaker 3: twenty years ago or ten years ago, you might say like, oh,

438
00:26:13,480 --> 00:26:17,080
Speaker 3: the Republicans won't support unions. But now that dynamic is

439
00:26:17,160 --> 00:26:19,679
Speaker 3: changing a bit because there's this coalition of we're all

440
00:26:19,720 --> 00:26:22,080
Speaker 3: pro worker, we don't want to be pro business anymore,

441
00:26:22,440 --> 00:26:24,560
Speaker 3: and so forth. So it's a really interesting time. But

442
00:26:24,640 --> 00:26:27,840
Speaker 3: what doesn't change, I should say, is trade offs. Right,

443
00:26:28,160 --> 00:26:31,000
Speaker 3: so the same trade offfs will be there. If you

444
00:26:31,040 --> 00:26:35,560
Speaker 3: have significant wage gain, increases new rigid rules in a

445
00:26:35,600 --> 00:26:39,160
Speaker 3: way that is not offset by increases in productivity, you're

446
00:26:39,160 --> 00:26:41,800
Speaker 3: going to get the same dynamics in the future, which

447
00:26:41,840 --> 00:26:47,520
Speaker 3: is slower employment growth, layoffs, more pushes towards automation. We've

448
00:26:47,520 --> 00:26:49,880
Speaker 3: done research to review one hundred and forty seven economic

449
00:26:49,920 --> 00:26:54,280
Speaker 3: studies looking at union monopoly structures and bargaining power, and

450
00:26:54,320 --> 00:26:58,000
Speaker 3: what I can report consistently is that you get these

451
00:26:58,000 --> 00:27:03,000
Speaker 3: short term gains, mostly on wages and benefits, but you

452
00:27:03,080 --> 00:27:08,800
Speaker 3: also get long term costs slower employment growth, more downsizing,

453
00:27:09,480 --> 00:27:12,560
Speaker 3: more closures, and so forth. And people don't think about

454
00:27:12,600 --> 00:27:15,160
Speaker 3: those long term costs. They just want to think about

455
00:27:15,200 --> 00:27:16,520
Speaker 3: the short term wage games.

456
00:27:16,960 --> 00:27:19,720
Speaker 1: Oh, thank you for joining me. You're in a field

457
00:27:19,720 --> 00:27:22,040
Speaker 1: that is going to change constantly and it is going

458
00:27:22,119 --> 00:27:24,960
Speaker 1: to be very important and our listeners can learn more

459
00:27:25,000 --> 00:27:28,720
Speaker 1: about the work you're doing if they'll visit Mercados dot org.

460
00:27:28,840 --> 00:27:30,400
Speaker 1: So thank you very very.

461
00:27:30,359 --> 00:27:32,440
Speaker 2: Much, Thank you so much for your time.

462
00:27:34,720 --> 00:27:38,440
Speaker 1: Thank you to my yest Lea Policasheli. Newworld is produced

463
00:27:38,440 --> 00:27:42,399
Speaker 1: by Gager three sixty and iHeartMedia. Our executive producer is

464
00:27:42,400 --> 00:27:46,520
Speaker 1: Guarnsey Sloan. Our researcher is Rachel Peterson. The artwork for

465
00:27:46,560 --> 00:27:50,440
Speaker 1: the show who's created by Steve Penley. Special thanks to

466
00:27:50,440 --> 00:27:53,640
Speaker 1: the team of Gingrash three sixty. If you've been enjoying Newsworld,

467
00:27:54,000 --> 00:27:56,520
Speaker 1: I hope you'll go to Apple podcast and both rate

468
00:27:56,600 --> 00:28:00,199
Speaker 1: us with five stars and give us a review. This

469
00:28:00,280 --> 00:28:03,280
Speaker 1: can learn what it's all about. Join me on substat

470
00:28:03,600 --> 00:28:07,320
Speaker 1: at Gingrish three sixty dot net. I'm Newt Gingrich. This

471
00:28:07,520 --> 00:28:08,240
Speaker 1: is Newtsworld.